River dredging method and system based on silt identification and grading treatment

Abstract

This invention relates to the field of industrial data analysis technology, specifically to a method and system for river dredging based on silt identification and grading. The method includes the following steps: real-time scanning of the work area within a predetermined range in front of the dredging equipment to obtain dynamic silt disturbance terrain data generated by the dredging equipment's operation; based on the dynamic silt disturbance terrain data, outputting the expected amount of silt disturbance in front of the dredging equipment due to disturbance through a built-in dynamic disturbance model; obtaining the static target dredging thickness at the location based on the real-time position coordinates and working elevation of the dredging equipment; fusing the static target dredging thickness with the expected silt disturbance amount to generate a target working elevation; and generating a control signal based on the target working elevation. This method achieves a fundamental shift from passive response to active prediction, solves the control lag problem, and improves the system's adaptability and reliability under different operating conditions.

Description

Technical Field

[0001] This invention relates to the field of industrial data analysis technology, and more specifically, to a method and system for river dredging based on silt identification and grading. Background Technology

[0002] Traditional river dredging methods rely primarily on pre-set static construction drawings and the experience of operators. During the dredging process, the agitation and suction of dredging equipment (such as cutter suction dredgers) violently disturb the bottom silt, causing dynamic changes such as "silt uplift" or "erosion depression" in front of the equipment. This disturbance effect makes the actual working terrain significantly different from the static drawings, and existing technologies cannot detect and predict this change in real time. This leads to problems such as over-dredging, under-dredging, low operational accuracy, low efficiency, and high safety risks to equipment operation.

[0003] While some existing dredging control schemes have introduced some sensors, they are mostly limited to monitoring the status of the equipment itself or lagging measurements of the already operated area, and the data collected has not been used to build effective predictive models.

[0004] These methods lack the ability to perform forward-looking industrial analysis and intelligent prediction of unused areas in front of the equipment. Their control logic is mostly static or simple feedback adjustment, which cannot cope with the complex and time-varying dynamic disturbances during the dredging process.

[0005] Due to the lack of advanced data fusion and analysis mechanisms, existing technologies are poorly adaptable to different soil types and working conditions, resulting in unstable control effects and an inability to achieve closed-loop optimization control based on real-time industrial data analysis.

[0006] Therefore, a method and system for river dredging based on silt identification and grading are provided. Summary of the Invention

[0007] The purpose of this invention is to provide a method and system for river dredging based on silt identification and grading, so as to solve the problems mentioned in the background art.

[0008] To achieve the above objectives, the present invention aims to provide a river dredging method based on silt identification and grading, comprising the following steps:

[0009] S1. Obtain the riverbed topography data and silt layer geological data of the target river channel, and generate a three-dimensional dredging prescription map through data fusion and rule setting;

[0010] S2. During the dredging operation, the location and operating parameters of the dredging equipment are obtained in real time. At the same time, the work area within a predetermined range in front of the dredging equipment is scanned in real time to obtain dynamic silt disturbance terrain data caused by the operation of the dredging equipment.

[0011] S3. Based on dynamic silt disturbance terrain data, the expected amount of silt disturbance in front of the dredging equipment is output through the built-in dynamic disturbance model.

[0012] S4. Based on the real-time location coordinates and working elevation of the dredging equipment, query the three-dimensional dredging prescription map to obtain the static target dredging thickness at that location. Integrate the static target dredging thickness with the expected sludge disturbance amount to generate the target working elevation, and generate control signals based on the target working elevation.

[0013] As a further improvement to this technical solution, in step S1, the riverbed topographic data includes the riverbed reference elevation. Geological data of the silt layer includes the static target dredging thickness. Physical and mechanical properties of silt; these include silt density. Cohesion with silt .

[0014] As a further improvement to this technical solution, the real-time operating parameters of the dredging device include the lateral movement speed. auger speed and suction port vacuum .

[0015] As a further improvement to this technical solution, in S2, the work area within a predetermined range in front of the dredging equipment is scanned in real time. This is achieved by installing a high-frequency multibeam sonar in front of the dredging equipment. The high-frequency multibeam sonar emits sound waves in a fan-shaped area in front and below at a high frequency and receives echoes, thereby generating three-dimensional point cloud data of the riverbed surface in the area not directly contacted in front of the dredging equipment in real time.

[0016] Dynamic silt disturbance terrain data is extracted from 3D point cloud data, including the slope change rate of the silt surface in front. The relative elevation difference of the predetermined range ahead and the spatial gradient of the perturbation region .

[0017] As a further improvement to this technical solution, in step S3, the expected amount of silt disturbance caused by disturbance in front of the dredging execution equipment is output. The specific steps are as follows:

[0018] S31. Input the dynamic silt disturbance terrain data, the real-time operating parameters of the dredging equipment, and the physical and mechanical property parameters of the silt at the current location obtained from the three-dimensional dredging prescription map into the dynamic disturbance model;

[0019] S32, Dynamic disturbance model based on silt cohesion Theoretical shear stress is obtained from operating parameters. The expected amount of sludge disturbance is set according to the shear stress. Scope;

[0020] when At that time, the mode was determined to be shear failure-dominated, and silt uplift was expected. A setting was then established. ;

[0021] when Furthermore, when the vacuum level at the suction port exceeds a preset critical vacuum level, it is determined to be in the dominant suction mode, and erosion depressions are expected to occur. ;

[0022] S33. Based on the expected silt disturbance amount The set result outputs the expected sludge disturbance amount. The value;

[0023] ;

[0024] In the formula, It is a symbolic function; Weights for equipment operation disturbances; For real-time terrain feedback weights; The perturbation trend weight.

[0025] As a further improvement to this technical solution, the dynamic disturbance model includes an adaptive optimization module and a self-learning optimization module;

[0026] The adaptive optimization module specifically includes:

[0027] The dynamic perturbation model is based on the real-time spatial gradient. Based on the prediction error history, dynamically adjust the real-time terrain feedback weights. and perturbation trend weight The value of ;

[0028] The self-learning optimization module specifically includes:

[0029] Record the prediction at the current time point The system takes the values ​​and all input parameters as input. When the dredging equipment reaches the predicted position, the system uses a subsequent verification depth sounding device to obtain the actual operational effect at that predicted position and calculates the true disturbance amount. ;

[0030] Calculate the prediction error: ;

[0031] Dynamically adjust the operational disturbance weights of dredging equipment based on prediction error. Real-time terrain feedback weights and perturbation trend weight .

[0032] As a further improvement to this technical solution, in step S4, the static target dredging thickness is fused with the expected sludge disturbance amount to generate a target operating elevation. The specific steps for generating control signals based on the target operating elevation are as follows:

[0033] S41. Obtain the riverbed reference elevation at the current location from the 3D dredging prescription map. And static target dredging thickness The expected sludge disturbance amount was obtained from the dynamic disturbance model. ;

[0034] S42, Simultaneously introduce dynamic stability factors Based on a dual fusion decision-making approach considering both perturbation properties and stability, the target operational elevation is generated. ;

[0035] S43. Using the target operating elevation as the set point, compare it with the real-time operating elevation of the dredging equipment to generate a control signal.

[0036] As a further improvement to this technical solution, in step S42, the target operation elevation is generated. The specific steps are as follows:

[0037] S421. Based on the cohesiveness of silt The dynamic stability factor is obtained by combining the current equipment with the overall disturbance intensity. ;

[0038] S422. Dual-level fusion decision-making includes first-level decision-making and second-level decision-making.

[0039] The first level of decision-making involves determining the compensation direction based on the physical properties of the disturbance, specifically as follows:

[0040] when At that time, the compensation direction is positive compensation;

[0041] when At that time, the compensation direction is negative compensation;

[0042] when At that time, the compensation direction is zero compensation;

[0043] The second-level decision-making process involves determining the compensation coefficient based on dynamic stability. Specifically:

[0044] Preset high stability threshold and low stability threshold ;

[0045] when At that time, a conservative compensation strategy is adopted, and the compensation coefficient is:

[0046]

[0047] In the formula, This is the safety compensation coefficient;

[0048] when When a linear compensation strategy is adopted, the compensation coefficient is:

[0049]

[0050] In the formula, This is the preset maximum compensation coefficient; This is the preset minimum compensation coefficient;

[0051] when At that time, an enhanced compensation strategy is adopted, with the compensation coefficient being:

[0052]

[0053] In the formula, To enhance the compensation coefficient;

[0054] S423. Generate the target operational elevation based on the results of the dual-fusion decision. ;

[0055]

[0056] In the formula, To add a safety margin.

[0057] As a further improvement to this technical solution, in step S43, the control signal is generated specifically based on the deviation between the real-time working elevation and the target working elevation. This generates control signals of different levels, while a first deviation threshold and a second deviation threshold are preset in advance.

[0058] when When the deviation is within the first deviation threshold range, a first control signal is generated and a first-level adjustment command is output.

[0059] when When the deviation exceeds the first deviation threshold range but does not exceed the second deviation threshold range, a second control signal is generated, a second-level adjustment command is output, and a warning message is sent to the operator interface.

[0060] when If the deviation exceeds the second deviation threshold range, a third control signal is generated, a third-level adjustment command is output, and an alarm is triggered.

[0061] On the other hand, the present invention provides a river dredging system based on silt identification and grading, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the river dredging method based on silt identification and grading as described above.

[0062] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0063] 1. The river dredging method and system based on silt identification and classification introduces the influence of the expected silt disturbance amount, and makes accurate predictions before the disturbance actually affects the operation effect, which solves the control lag problem, improves the accuracy of dredging, and enhances the adaptability and reliability of the system under different working conditions.

[0064] 2. In this river dredging method and system based on silt identification and graded treatment, the dual fusion decision-making mechanism enables the control system to accurately respond to disturbances of different physical properties and intelligently adjust the compensation intensity according to the stability level. The hierarchical architecture enhances the system's adaptability and robustness, maintains precise operation under stable conditions, and provides strong intervention under critical conditions, effectively avoiding problems such as under-dredging, over-dredging, and equipment damage. Attached Figure Description

[0065] Figure 1 This is a flowchart of the overall method of the present invention. Detailed Implementation

[0066] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0067] Example 1: Please refer to Figure 1 As shown, this embodiment provides a river dredging method based on silt identification and grading, including the following steps:

[0068] S1. Obtain the riverbed topography data and silt layer geological data of the target river channel, and generate a three-dimensional dredging prescription map through data fusion and rule setting; before the dredging operation, establish the mapping relationship between the spatial coordinate system of the dredging execution equipment and the spatial coordinate system of the three-dimensional dredging prescription map.

[0069] In S1, the riverbed topographic data includes the riverbed reference elevation. Geological data of the silt layer includes the static target dredging thickness. Physical and mechanical properties of silt; these include silt density. Cohesion with silt .

[0070] Among them, the riverbed reference elevation is a three-dimensional data field that records any point within the river channel. The elevation of the hard riverbed beneath the silt layer is the "bottom line" for dredging operations and the core basis for preventing over-excavation.

[0071] The static target dredging thickness is a three-dimensional data field that records any point within the river channel. The planned thickness of silt to be removed is derived from the total thickness surveyed minus the thickness that needs to be retained according to regulations. The physical and mechanical properties of the silt are a parameter related to spatial coordinates. The bound attribute data, silt density, is used to calculate the effects of inertial force and gravity; silt cohesion is used to determine the silt's ability to resist shear failure, and is a key parameter for predicting whether it will "bulge" or "flow".

[0072] Furthermore, the three-dimensional dredging prescription map includes coordinates of each point within the target river channel. The target thickness of static silt that needs to be removed.

[0073] S2. During the dredging operation, the location and operating parameters of the dredging equipment are obtained in real time. At the same time, the work area within a predetermined range in front of the dredging equipment is scanned in real time to obtain dynamic silt disturbance terrain data caused by the operation of the dredging equipment.

[0074] Real-time operating parameters of dredging equipment include lateral speed. auger speed and suction port vacuum .

[0075] In S2, the work area within a predetermined range in front of the dredging equipment is scanned in real time. This is achieved by installing a high-frequency multibeam sonar directly in front of the dredging equipment. The installation position is 2-5 meters in front of the dredging equipment, and the scanning frequency is 10-30Hz. The high-frequency multibeam sonar emits sound waves in a fan-shaped area in front and below at a high frequency and receives the echoes, generating three-dimensional point cloud data of the riverbed surface in the area not directly contacted in front of the dredging equipment in real time.

[0076] Dynamic silt disturbance terrain data is extracted from 3D point cloud data. This dynamic silt disturbance terrain data includes the rate of change of the slope of the silt surface in front. The relative elevation difference of the predetermined range ahead and the spatial gradient of the perturbation region .

[0077] Among them, the rate of change of the slope of the silt surface in front of the equipment reflects the rate of change of the inclination of the silt surface in front of the equipment, reflecting the severity of the silt being pushed; the relative elevation difference of the predetermined range in front of the equipment refers to the difference between the real-time scanning elevation and the original elevation in the static prescription map at a certain distance in front of the equipment; a positive value directly indicates the initial height of the bulge. The spatial gradient of the disturbance area describes the spatial distribution and the steepness of the change of the disturbance (such as bulge or depression).

[0078] Traditional methods focus only on static terrain. This solution captures dynamic disturbances through real-time scanning, enabling real-time monitoring of terrain changes and early identification of disturbance propagation trends during operations, thus providing real-time feedback data for prediction models.

[0079] S3. Based on dynamic silt disturbance terrain data, the expected amount of silt disturbance in front of the dredging equipment is output through the built-in dynamic disturbance model.

[0080] In S3, the expected amount of sludge disturbance caused by disturbance in front of the dredging equipment is output. The specific steps are as follows:

[0081] S31. Input the dynamic silt disturbance terrain data, the real-time operating parameters of the dredging equipment, and the physical and mechanical properties of the silt at the current location obtained from the three-dimensional dredging prescription map into the dynamic disturbance model. The model is based on silt rheology theory and fluid dynamics principles, and considers the viscoelastic properties of silt, the interaction mechanism between equipment and silt, and the spatiotemporal characteristics of disturbance propagation.

[0082] S32, Dynamic disturbance model based on silt cohesion Theoretical shear stress is obtained from operating parameters. The expected amount of sludge disturbance is set according to the shear stress. Scope;

[0083]

[0084] In the formula, This is an empirical conversion factor related to equipment type (such as cutter head shape) and size; it is obtained through bench tests and calibration based on preliminary field data; in dredging scenarios, the theoretical shear stress acting on sludge is mainly generated by the mechanical agitation of the equipment. The product of the lateral speed and the cutter head rotation speed is a key operating parameter that comprehensively characterizes the agitation power and intensity of the equipment. The faster the speed and the higher the rotation speed, the greater the shear force applied to the sludge.

[0085] when At that time, the mode was determined to be shear failure-dominated, and silt uplift was expected. A setting was then established. ;

[0086] Cohesion of silt It is its inherent ability to resist shear deformation. When the external shear stress applied by the equipment exceeds the shear strength of the silt itself, the structure of the silt will be sheared and damaged, resulting in it being stirred and pushed, thus forming a bulge in front of the cutter head.

[0087] when Furthermore, when the vacuum level at the suction port exceeds a preset critical vacuum level, it is determined to be in the dominant suction mode, and erosion depressions are expected to occur. In this embodiment, the preset critical vacuum level is -65 kPa, and different critical vacuum levels are set according to the sludge with different cohesion.

[0088] This indicates that the mechanical agitation is insufficient to directly push the silt away. In this case, the suction pump of the dredging equipment (such as a cutter suction pump) takes over. The high vacuum will generate a strong negative pressure at the suction port, which will create an upward "suction force" on the silt particles. This "suction force" will counteract the effective stress between the silt particles. When it is strong enough, it will directly "suck" the silt particles out of the riverbed, or cause local soil erosion at the edge of the suction port, forming erosion depressions.

[0089] The principle behind this judgment is based on the Mohr-Coulomb criterion.

[0090] S33. Based on the expected silt disturbance amount The set result outputs the expected sludge disturbance amount. The value;

[0091] ;

[0092] In the formula, This is the sign function, which takes +1 or -1 based on the decision result of S32; The disturbance weights for equipment operation are determined through regression analysis of disturbance tests under different operating conditions, with values ​​ranging from 0.1 to 0.5, and adjusted according to the equipment type. As the real-time terrain feedback weight, the correlation analysis based on the scan data and the actual disturbance increases with the increase of system confidence; The perturbation trend weights are determined by minimizing the time series prediction error.

[0093] The dynamic perturbation model includes an adaptive optimization module and a self-learning optimization module;

[0094] The adaptive optimization module specifically includes:

[0095] The dynamic perturbation model is based on the real-time spatial gradient. Based on the prediction error history, dynamically adjust the real-time terrain feedback weights. and perturbation trend weight The value of ; spatial gradient It can be used to assist in judging the development trend and stability of disturbances; a high value (A steep, raised front) indicates that the disturbance is developing rapidly and energy is concentrated. In this case, the weighting coefficients of the model can be appropriately increased. and This allows real-time scan data to have a higher weight in the calculation, making the system more responsive. A low value A gentle bulge indicates that the disturbance may have stabilized, and the model can rely more on the equipment operating parameters for prediction.

[0096] The self-learning optimization module specifically includes:

[0097] Record the prediction at the current time point The system takes the values ​​and all input parameters as input. When the dredging equipment reaches the predicted position, the system uses a subsequent verification depth sounding device to obtain the actual operational effect at that predicted position and calculates the true disturbance amount. The rear-mounted verification depth sounding device is a measuring device installed behind the dredging equipment to verify the dredging effect in real time. Specifically, it is a high-frequency multibeam sonar installed on the towed body behind the dredging equipment to perform strip-shaped topographic mapping of the work area.

[0098] Calculate the prediction error: ;

[0099] Dynamically adjust the operational disturbance weights of dredging equipment based on prediction error. Real-time terrain feedback weights and perturbation trend weight .

[0100] S4. Based on the real-time location coordinates of the dredging equipment. and working elevation The static target dredging thickness at a given location is obtained by querying the 3D dredging prescription map. This static target dredging thickness is then fused with the expected sludge disturbance amount to generate the target operating elevation. Control signals are then generated based on this target operating elevation. ;

[0101] In S4, the static target dredging thickness is fused with the expected sludge disturbance amount to generate the target operating elevation. The specific steps for generating control signals based on the target operating elevation are as follows:

[0102] S41. Obtain the riverbed reference elevation at the current location from the 3D dredging prescription map. And static target dredging thickness The expected sludge disturbance amount was obtained from the dynamic disturbance model. ;

[0103] S42, Simultaneously introduce dynamic stability factors Based on a dual fusion decision-making approach considering both perturbation properties and stability, the target operational elevation is generated. ;

[0104] In S42, the target operation elevation is generated. The specific steps are as follows:

[0105] S421. Based on the cohesiveness of silt The dynamic stability factor is obtained by combining the current equipment with the overall disturbance intensity. ;

[0106]

[0107]

[0108] In the formula, The overall disturbance intensity; It is a ratio function;

[0109] S422. Dual-level fusion decision-making includes first-level decision-making and second-level decision-making.

[0110] The first level of decision-making involves determining the compensation direction based on the physical properties of the disturbance, specifically as follows:

[0111] when When this occurs, it indicates that there is an expected silt uplift, and the compensation direction is positive compensation;

[0112] when When this occurs, it indicates the presence of anticipated erosion depressions, and the compensation direction is negative compensation;

[0113] when When this condition is met, it indicates that there is no significant expected disturbance, and the compensation direction is zero compensation;

[0114] The second-level decision-making process involves determining the compensation coefficient based on dynamic stability. Specifically:

[0115] Preset high stability threshold and low stability threshold ;

[0116] when At this time, it indicates that the current sludge has strong resistance to disturbance, the equipment operation is relatively mild, the system determines to trust the original prescription map, and adopts a conservative compensation strategy with a compensation coefficient of:

[0117]

[0118] In the formula, The safety compensation coefficient has a value range of 0.3-0.6.

[0119] when When the silt is in a critical stable state, the system determines that appropriate compensation is needed, and a linear compensation strategy is adopted with the following compensation coefficient:

[0120]

[0121] In the formula, The preset maximum compensation coefficient is set to 1.2; The preset minimum compensation coefficient is set to 0.6;

[0122] when At that time, an enhanced compensation strategy was adopted, indicating that the silt was highly susceptible to disturbance. The system determined that active intervention was necessary, and the compensation coefficient was:

[0123]

[0124] In the formula, To enhance the compensation coefficient, the value range is 1.2-1.8;

[0125] In summary, the dual-fusion decision-making system is based on the physical characteristics of disturbance direction and intensity during dredging operations, which are both independent and interrelated. The first-level decision is based on the fundamental principles of fluid mechanics and soil mechanics: The bulging phenomenon, which represents shear stress, requires positive compensation to cut off excess silt. The depression phenomenon, which represents the dominant erosion process, requires negative compensation to follow up and clear the unstable area.

[0126] The second level of decision-making is based on system stability theory, using dynamic stability factors. Quantitatively assess the real-time balance between "sludge disturbance resistance" and "equipment disturbance intensity": when When the temperature is high, the system is stable, and conservative compensation should be used to avoid excessive intervention; when... When in the critical range, linear compensation with smooth transition is used to maintain control continuity; when At extremely low levels, the system is on the verge of instability, and proactive intervention through enhanced compensation is necessary to prevent accidents. This hierarchical decision-making mechanism aligns with the engineering decision-making logic of "qualitative first, then quantitative."

[0127] This dual-fusion decision-making mechanism achieves a balance between control precision and system security.

[0128] First, by decoupling the direction and intensity of the decision, the inherent defect of the "one-size-fits-all" compensation in the traditional method is solved, so that the control system can not only accurately respond to disturbances with different physical properties, but also intelligently adjust the compensation intensity according to the degree of stability.

[0129] Secondly, the layered architecture enhances the system's adaptability and robustness, maintaining precise operation under stable working conditions and providing strong intervention under critical working conditions, effectively avoiding problems such as under-digging, over-digging, and equipment damage.

[0130] Most importantly, this physical mechanism-based decision-making process provides clear engineering interpretability for the control system, enabling operators to understand the system's decision-making logic and build trust, thus laying a solid foundation for human-machine collaboration and engineering applications.

[0131] S423. Generate the target operational elevation based on the results of the dual-fusion decision. ;

[0132]

[0133] In the formula, To add a safety margin;

[0134] Among them, additional safety margin The calculation is as follows:

[0135] when and hour, (Additional margin based on slope change rate); This is a safety margin factor, with a value ranging from 0.1 to 0.3.

[0136] This is at the critical state of shear instability. Slope change rate. It directly reflects the acceleration of disturbance development, similar to the creep acceleration phenomenon before slope instability in soil mechanics. Larger... The value indicates that the disturbance is accumulating rapidly, only compensating for the current expected value. It is insufficient to cope with the larger disturbances that are about to occur.

[0137] when and hour, (Additional margin based on spatial gradient);

[0138] At this point, the soil is in a critical state of collapse. The spatial gradient ∇S reflects the steepness of the erosion pit's shape, indicating a steep erosion gradient. A large scale implies: a significant local flow field acceleration effect, a sharp decrease in slope stability, and the risk of cascading collapses. At the same time, this design provides deep safety redundancy, ensuring the removal of unstable slopes.

[0139] In other cases ;

[0140] when At any time, regardless How to set the value, forced setting ;

[0141] This is because the dynamic disturbance model determines that, under the current operating parameters and geological conditions, the equipment will not cause significant uplift or subsidence of the silt ahead. At this point, the most reasonable and safest operational objective is the initially planned static objective: to remove the preset silt thickness and reach the target depth. The elevation.

[0142] S43. Using the target operating elevation as a set point, compare it with the real-time operating elevation of the dredging equipment to generate a control signal;

[0143] In S43, the control signal is generated specifically based on the deviation between the real-time working elevation and the target working elevation. This generates control signals of different levels, while a first deviation threshold and a second deviation threshold are preset in advance.

[0144] when When the deviation is within the first deviation threshold range, a first control signal is generated and a first-level adjustment command is output; the equipment is fine-tuned while maintaining the current main operating parameters to smoothly track the target operating elevation.

[0145] when When the deviation exceeds the first deviation threshold range but does not exceed the second deviation threshold range, a second control signal is generated, a second-level adjustment command is output, and a warning message is sent to the operator interface. The second-level adjustment command includes significantly adjusting the pressure of the cutter head cylinder or the power of the suction pump to quickly eliminate the deviation. At the same time, an audio-visual warning message is sent to the operator interface, indicating that the current tracking deviation is increasing, but the system is automatically processing it.

[0146] when If the deviation exceeds the second deviation threshold, a third control signal is generated, outputting a third-level adjustment command and triggering an alarm. The third-level adjustment command outputs an emergency stop or rapid lifting command to the actuator of the dredging equipment, immediately stopping the dredging operation or quickly removing the working parts from the riverbed; at the same time, it records the current equipment status and location information, and triggers the highest-level audible and visual alarm to notify the operator to handle the emergency.

[0147] The first deviation threshold is determined based on the equipment positioning accuracy and the control system response characteristics, and is typically [value missing]. The second deviation threshold is determined based on the equipment's safe operating range and mechanical structure limits, and is typically [value missing]. .

[0148] By dividing the control signals into three levels, precise tracking control under normal operating conditions, rapid intervention and recovery under abnormal operating conditions, and safety protection under dangerous operating conditions are achieved, ensuring the optimal balance between accuracy, efficiency and safety in dredging operations.

[0149] Example 2: This example provides a river dredging system based on silt identification and grading, including a memory, a processor, and a computer program stored in the memory and executable on the processor. The processor executes the computer program to implement the river dredging method based on silt identification and grading described above.

[0150] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely preferred examples and are not intended to limit the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the claimed invention.

Claims

1. A river dredging method based on silt identification and grading, characterized in that, Includes the following steps: S1. Obtain the riverbed topography data and silt layer geological data of the target river channel, and generate a three-dimensional dredging prescription map through data fusion and rule setting; S2. During the dredging operation, the location and operating parameters of the dredging equipment are obtained in real time. At the same time, the work area within a predetermined range in front of the dredging equipment is scanned in real time to obtain dynamic silt disturbance terrain data caused by the operation of the dredging equipment. S3. Based on dynamic silt disturbance terrain data, the expected amount of silt disturbance in front of the dredging equipment is output through the built-in dynamic disturbance model. In step S3, the expected amount of sludge disturbance caused by disturbance in front of the dredging equipment is output. The specific steps are as follows: S31. Input the dynamic silt disturbance terrain data, the real-time operating parameters of the dredging equipment, and the physical and mechanical property parameters of the silt at the current location obtained from the three-dimensional dredging prescription map into the dynamic disturbance model; S32, Dynamic disturbance model based on silt cohesion Theoretical shear stress is obtained from operating parameters. The expected amount of sludge disturbance is set according to the shear stress. Scope; when At that time, the mode was determined to be shear failure-dominated, and silt uplift was expected. A setting was then established. ; when Furthermore, when the vacuum level at the suction port exceeds a preset critical vacuum level, it is determined to be in the dominant suction mode, and erosion depressions are expected to occur. ; S33. Based on the expected silt disturbance amount The set result outputs the expected sludge disturbance amount. The value; ； In the formula, It is a symbolic function; Weights for equipment operation disturbances; For real-time terrain feedback weights; To perturb the trend weight, The lateral velocity, The cutting speed is the speed of the cutter. The relative elevation difference within the predetermined area ahead. The slope change rate of the silt surface in front; S4. Based on the real-time location coordinates and working elevation of the dredging equipment, query the three-dimensional dredging prescription map to obtain the static target dredging thickness at that location. Integrate the static target dredging thickness with the expected sludge disturbance amount to generate the target working elevation, and generate control signals based on the target working elevation.

2. The river dredging method based on silt identification and grading treatment according to claim 1, characterized in that: In S1, the riverbed topographic data includes the riverbed reference elevation. ; Geological data of the silt layer includes static target dredging thickness. Physical and mechanical properties of silt; these include silt density. Cohesion with silt .

3. The river dredging method based on silt identification and grading treatment according to claim 2, characterized in that: The real-time operating parameters of the dredging equipment include the lateral movement speed. auger speed and suction port vacuum .

4. The river dredging method based on silt identification and grading treatment according to claim 3, characterized in that: In S2, the work area within a predetermined range in front of the dredging equipment is scanned in real time. This is achieved by installing a high-frequency multibeam sonar in front of the dredging equipment. The high-frequency multibeam sonar emits sound waves in a fan-shaped area in front and below at a high frequency and receives the echoes, generating three-dimensional point cloud data of the riverbed surface in the area not directly contacted in front of the dredging equipment in real time. Dynamic silt disturbance terrain data is extracted from 3D point cloud data, including the slope change rate of the silt surface in front. The relative elevation difference of the predetermined range ahead and the spatial gradient of the perturbation region .

5. The river dredging method based on silt identification and grading treatment according to claim 4, characterized in that: The dynamic perturbation model includes an adaptive optimization module and a self-learning optimization module; The adaptive optimization module specifically includes: The dynamic perturbation model is based on the real-time spatial gradient. Based on the prediction error history, dynamically adjust the real-time terrain feedback weights. and perturbation trend weight The possible values ​​of ; The self-learning optimization module specifically includes: Record the prediction at the current time point The system takes the values ​​and all input parameters as input. When the dredging equipment reaches the predicted position, the system uses a subsequent verification depth sounding device to obtain the actual operational effect at the predicted position and calculates the true disturbance amount. ; Calculate the prediction error: ; Dynamically adjust the operational disturbance weights of dredging equipment based on prediction error. Real-time terrain feedback weights and perturbation trend weight .

6. The river dredging method based on silt identification and grading treatment according to claim 5, characterized in that: In step S4, the static target dredging thickness is fused with the expected sludge disturbance amount to generate the target operating elevation. The specific steps for generating the control signal based on the target operating elevation are as follows: S41. Obtain the riverbed reference elevation at the current location from the 3D dredging prescription map. And static target dredging thickness The expected sludge disturbance amount was obtained from the dynamic disturbance model. ; S42, Simultaneously introduce dynamic stability factors Based on a dual fusion decision-making approach considering both perturbation properties and stability, the target operational elevation is generated. ; S43. Using the target operating elevation as the set point, compare it with the real-time operating elevation of the dredging equipment to generate a control signal.

7. The river dredging method based on silt identification and grading treatment according to claim 6, characterized in that: In step S42, the target work elevation is generated. The specific steps are as follows: S421. Based on the cohesiveness of silt The dynamic stability factor is obtained by combining the current equipment with the overall disturbance intensity. ; S422. Dual-level fusion decision-making includes first-level decision-making and second-level decision-making. The first level of decision-making involves determining the compensation direction based on the physical properties of the disturbance, specifically as follows: when At that time, the compensation direction is positive compensation; when At that time, the compensation direction is negative compensation; when At that time, the compensation direction is zero compensation; The second-level decision-making process involves determining the compensation coefficient based on dynamic stability. Specifically: Preset high stability threshold and low stability threshold ; when At that time, a conservative compensation strategy is adopted, and the compensation coefficient is: In the formula, This is the safety compensation coefficient; when When a linear compensation strategy is adopted, the compensation coefficient is: In the formula, This is the preset maximum compensation coefficient; This is the preset minimum compensation coefficient; when At that time, an enhanced compensation strategy is adopted, with the compensation coefficient being: In the formula, To enhance the compensation coefficient; S423. Generate the target operational elevation based on the results of the dual-fusion decision. ; In the formula, To add a safety margin.

8. The river dredging method based on silt identification and grading treatment according to claim 7, characterized in that: In step S43, the control signal is generated specifically based on the deviation between the real-time working elevation and the target working elevation. This generates control signals of different levels, while a first deviation threshold and a second deviation threshold are preset in advance. when When the deviation is within the first deviation threshold range, a first control signal is generated and a first-level adjustment command is output. when When the deviation exceeds the first deviation threshold range but does not exceed the second deviation threshold range, a second control signal is generated, a second-level adjustment command is output, and a warning message is sent to the operator interface. when If the deviation exceeds the second deviation threshold range, a third control signal is generated, a third-level adjustment command is output, and an alarm is triggered.

9. A river dredging system based on silt identification and grading, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: The processor executes a computer program to implement the river dredging method based on silt identification and grading as described in any one of claims 1-8.

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