Intelligent mechanical cutter suction system based on culvert dredging

Abstract

The invention discloses an intelligent mechanical cutter suction system based on culvert dredging, and relates to the field of intelligent control, the intelligent mechanical cutter suction system comprises a mobile bearing module, the left side of the top end of the mobile bearing module is provided with a mechanical arm execution component, and the center of the top end of the mobile bearing module is provided with a main control unit, the power module is used for providing moving capacity and overall power support for all functional parts and modules through a walking mechanism and an energy system; the mechanical arm execution component is used for executing cutter suction dredging operation through a mechanical arm driven by the hydraulic driving system and a hydraulic cutter suction head at the tail end of the mechanical arm; when facing complex and changeable culvert deposits, the device always keeps the most efficient cutting and suction state, the overall dredging time is greatly shortened, the operation accuracy and consistency are improved, the mechanical structure overload and collision risks caused by rough operation or path deviation are reduced, and the service life of a core working device is prolonged.

Description

Technical Field

[0001] This invention relates to the field of intelligent control technology, specifically to an intelligent mechanical suction system for culvert dredging. Background Technology

[0002] With the acceleration of urbanization, numerous culverts, box culverts, and underground drainage networks bear critical functions of flood control, drainage, and water conveyance. These underground structures commonly face severe siltation problems due to long-term operation, particularly before and after the rainy season, leading to immense dredging pressure. This places higher demands on the intelligent operation and maintenance of drainage facilities. Developing intelligent dredging equipment capable of adapting to complex working conditions has become an inevitable technological direction for improving the modernization level of infrastructure management.

[0003] Existing equipment generally lacks the ability to identify the work object in real time, and operation relies entirely on the driver's experience. It cannot make scientific judgments on the ever-changing silt, clay, sand and gravel mixtures or layered bottom materials in culverts, resulting in inappropriate selection of operating parameters and an inability to adaptively adjust according to the differences in soil hardness and viscosity, leading to low efficiency or equipment overload damage. The lack of precise trajectory tracking and work point recording means that traditional methods are prone to leaving blind spots in dredging, making it difficult to quantify and evaluate the dredging effect. In culverts with low visibility and narrow spaces, equipment is prone to collisions, resulting in poor operational safety, large fluctuations in overall work quality and efficiency, and poor economic efficiency. Summary of the Invention

[0004] (a) Technical problems to be solved In view of the above-mentioned shortcomings of the existing technology, the present invention provides an intelligent mechanical suction system for culvert dredging, which can effectively solve the problems of the existing technology.

[0005] (II) Technical Solution To achieve the above objectives, the present invention provides the following technical solution: This invention discloses an intelligent mechanical suction system for culvert dredging, comprising a mobile support module, a robotic arm actuator mounted on the left side of the top of the mobile support module, and a main control unit mounted at the center of the top of the mobile support module, wherein: The mobile load-bearing module is used to provide mobility and overall power support for various functional components and modules through a walking mechanism and energy system; A robotic arm execution component for performing a suction and dredging operation via a robotic arm driven by a hydraulic drive system and a hydraulic suction head at its end. The main control unit has sub-modules deployed at its lower level, and the sub-modules include: The environmental perception module is used to acquire visual information of the working environment, the outline of siltation ahead, and obstacle information in real time, so as to fuse environmental data; it integrates underwater video monitoring, forward-looking sonar, and collision avoidance sonar sensors. The soil layer identification module is used to collect real-time operational data such as the rotational speed of the suction head of the robotic arm's execution component, the torque of the hydraulic drive system, and the mud suction flow rate. By matching and analyzing real-time operational data, environmental data, and feature databases, it can determine the soil layer type of the current working surface online. The auger suction control module is used to pre-store the optimal operation parameter strategy library corresponding to different soil layer types. Based on the identified soil layer results, it adjusts and outputs the target speed and target torque commands for the hydraulic auger suction head on the robotic arm execution component, so as to realize the automatic matching and optimization of operation parameters. The trajectory control module is used to receive environmental fusion data from the environmental perception module based on the PID control algorithm, and combine it with the preset dredging trajectory plan to perform motion control on the mobile carrier module and the robotic arm execution components, so as to ensure that the system operates accurately and stably along the predetermined path. The auger suction diagnostic module is used to compare the torque parameters obtained by the auger suction head of the robotic arm's actuator with the preset benchmark value of the currently identified soil layer. If the real-time value continues to deviate abnormally from the benchmark range, an early warning signal is generated indicating wear, blockage, or malfunction of the auger suction head of the robotic arm's actuator.

[0006] Furthermore, the environmental perception module constructs a three-dimensional contour model of the silt in front of it using forward-looking sonar, providing a basis for trajectory planning. It also forms a near-field protection zone around the robot using collision avoidance sonar, and monitors the distance to the surrounding culvert walls or obstacles in real time. When the distance is lower than the safety threshold, it sends an emergency obstacle avoidance or pause command to the trajectory control module.

[0007] Furthermore, the matching analysis process in the soil layer identification module is as follows: Construct and pre-store a feature database containing standard operation data features and environmental data features corresponding to various typical soil layer categories; During the operation, real-time operation data and environmental data of the current work surface are collected, and their corresponding real-time features are extracted; The extracted real-time features are matched and analyzed with the standard features of various soil layer categories pre-stored in the feature database to calculate the similarity between the real-time features and each standard feature. Based on the similarity comparison results, the soil layer category corresponding to the standard feature with the highest similarity is identified as the soil layer category of the current working face, thus completing the online identification.

[0008] Furthermore, the standard operating data features include the sluice head rotation speed, torque relationship curve features, and mud flow rate variation features; the environmental data features include the silt contour features acquired by forward-looking sonar and the texture features of underwater video images.

[0009] Furthermore, the working logic of the suction control module is as follows: when the current soil layer is identified as a high-cohesion sub-clay layer, a high torque and medium-low speed operation command is output to overcome the adhesion force; when the soil layer is identified as a loose silt layer, a medium torque and high speed command is output to improve the suction efficiency; when the soil layer is identified as a sand and gravel layer containing coarse particles, an intermittent high peak torque and low speed command is output to cope with particle impact and prevent jamming.

[0010] Furthermore, the process by which the trajectory control module controls the operation of the mobile support module and the robotic arm execution components is as follows: Based on real-time environmental data obtained by the environmental perception module, a preset dredging operation trajectory planning scheme is loaded. According to the trajectory planning scheme, combined with the real-time pose of the mobile carrier module and the real-time working position of the grouting head of the robotic arm execution component, the moving speed and steering command of the mobile carrier module's walking mechanism, as well as the motion adjustment amount of each joint of the robotic arm execution component, are dynamically calculated through the PID control algorithm. The system synchronously outputs control commands to the walking mechanism of the mobile carrier module and the hydraulic drive unit of the robotic arm execution component, enabling the mobile carrier module to move along a predetermined path and the suction head of the robotic arm execution component to perform operations along the dredging cross-section trajectory, thereby achieving coordinated motion control and trajectory tracking of the entire system.

[0011] Furthermore, the auger suction diagnostic module calculates the deviation of the real-time torque value from the median of the reference range and performs continuous time-series analysis based on the deviation: if the deviation increases instantaneously in a short period of time and then recovers, it is defined as encountering an instantaneous impact from a hard object; if the deviation shows a continuous and slow upward trend, it is defined as progressive wear of the auger suction teeth; if the deviation increases abruptly or remains at a high level when the soil layer does not change significantly, it is defined as blockage of the internal flow channel of the auger suction head or structural damage to key components.

[0012] Furthermore, the trajectory control module and the suction diagnostic module are interconnected via a wireless network with a digital recording module. This digital recording module records and stores in real time the spatial coordinates of each dredging operation point, the corresponding identified soil layer information, the applied operation parameters, and environmental perception data, generating a digital archive and operation map for the dredging operation. The digital archive supports multi-dimensional retrieval and statistical analysis by time, spatial location, or soil layer type. The operation map can visually display the operated area, the unoperated blind area, the distribution areas of different soil types, and the operation quality assessment indicators for each area.

[0013] Furthermore, the digital recording module is interconnected with an interactive monitoring module via a wireless network. The interactive monitoring module is used to match an external display device to centrally display the digital archives and operation maps of the dredging operation fed back by the digital recording module, and to provide a manual intervention interface.

[0014] Furthermore, the environmental perception module is interconnected with the soil layer identification module and the suction control module via a wireless network, the trajectory control module is interconnected with the suction control module via a wireless network, and the trajectory control module is interconnected with the suction diagnosis module via a wireless network.

[0015] (III) Beneficial Effects Compared with the known prior art, the technical solution provided by this invention has the following beneficial effects: 1. By integrating multi-dimensional environmental data in real time, the robot analyzes the operation data of the suction head of the robotic arm's execution component online, automatically identifies different soil types such as silt, sand, gravel, and hard clay, and immediately calls the optimal speed and torque parameters from the strategy library. This ensures that the robot maintains the most efficient cutting and suction state when facing complex and ever-changing culvert silt, significantly shortening the overall dredging time and improving the accuracy and consistency of the operation.

[0016] 2. Through the PID algorithm and trajectory planning of the trajectory control module, the smooth and precise movement of the robotic arm's execution components and the entire machine is ensured. This reduces the risk of mechanical overload and collision caused by rough operation or path deviation. The system can dynamically compare the actual torque of the continuously monitored suction head with the theoretical benchmark value of the current soil layer. Once a continuous abnormal torque is detected, the system can generate an accurate early warning signal in advance and intervene in time to prevent small problems from developing into serious equipment damage. This reduces unplanned downtime and high maintenance costs, and extends the service life of the core working device. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0018] Figure 1 This is a three-dimensional structural diagram of the present invention; Figure 2 This is a three-dimensional structural schematic diagram of the present invention from another angle; Figure 3 This is a schematic diagram of the overall framework of the present invention; Figure 4 This is a schematic diagram of the main control unit of the present invention.

[0019] The labels in the diagram represent: 1. Mobile bearing module; 2. Robotic arm execution component; 3. Main control unit; 31. Environmental perception module; 32. Soil layer identification module; 33. Dredging control module; 34. Trajectory control module; 35. Dredging diagnosis module; 36. Digital recording module; 37. Interactive monitoring module. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0021] The present invention will be further described below with reference to embodiments.

[0022] This embodiment uses an intelligent mechanical suction system for culvert dredging, such as... Figure 1 - Figure 4 As shown, the system includes a mobile support module 1, a robotic arm execution component 2 mounted on the left side of the top of the mobile support module 1, and a main control unit 3 mounted at the center of the top of the mobile support module 1. The mobile load-bearing module 1 is used to provide mobility and overall power support to various functional components and modules through the walking mechanism and energy system.

[0023] The robotic arm execution component 2 is used to perform a suction and dredging operation via a robotic arm driven by a hydraulic drive system and a hydraulic suction head at its end.

[0024] The main control unit 3 has sub-modules deployed at its lower level, including: The environmental perception module 31 is used to acquire visual information of the working environment, the outline of the silt in front, and information of obstacles in real time, so as to fuse environmental data; it integrates underwater video monitoring, forward-looking sonar, and collision avoidance sonar sensors; the environmental perception module 31 constructs a three-dimensional outline model of the silt in front through the forward-looking sonar to provide a basis for trajectory planning, and forms a near-field protection zone around the robot through the collision avoidance sonar, and monitors the distance to the surrounding culvert wall or obstacles in real time. When the distance is lower than the safety threshold, it sends an emergency obstacle avoidance or pause command to the trajectory control module 34.

[0025] The soil layer identification module 32 is used to collect real-time operational data such as the rotation speed, hydraulic drive system torque, and mud suction flow rate of the suction head of the robotic arm execution component 2. By matching and analyzing the real-time operational data, environmental data, and feature database, the soil layer type of the current working surface is determined online.

[0026] The suction control module 33 is used to pre-store the optimal operating parameter strategy library corresponding to different soil layer types. Based on the identified soil layer results, it adjusts and outputs the target speed and target torque commands for the hydraulic suction head on the robotic arm execution component 2, realizing automatic matching and optimization of operating parameters. The working logic of the suction control module 33 is as follows: when the current soil layer is identified as a high-cohesion sub-clay layer, it outputs high torque and medium-low speed operating commands to overcome the adhesion force; when it is identified as a loose silt layer, it outputs medium torque and high speed commands to improve suction efficiency; when it is identified as a sand and gravel layer containing coarse particles, it outputs intermittent high peak torque and low speed commands to cope with particle impact and prevent jamming.

[0027] The trajectory control module 34, based on a PID control algorithm, receives environmental fusion data from the environmental perception module 31 and, combined with a preset dredging trajectory plan, performs motion control on the mobile carrier module 1 and the robotic arm execution component 2 to ensure the system operates accurately and stably along a predetermined path. The process of the trajectory control module 34 controlling the operation of the mobile carrier module 1 and the robotic arm execution component 2 is as follows: Based on the real-time environmental data obtained by the environmental perception module 31, a preset dredging operation trajectory planning scheme is loaded. Based on the trajectory planning scheme, and combining the real-time pose of the mobile carrier module 1 and the real-time working position of the suction head of the robotic arm execution component 2, the PID control algorithm is used to dynamically calculate the moving speed and steering command of the walking mechanism of the mobile carrier module 1, as well as the motion adjustment amount of each joint of the robotic arm execution component 2. The control commands are synchronously output to the walking mechanism of the mobile bearing module 1 and the hydraulic drive unit of the robotic arm execution component 2, so that the mobile bearing module 1 walks along the predetermined path and the suction head of the robotic arm execution component 2 performs operations along the dredging section trajectory, thereby realizing the coordinated motion control and trajectory tracking of the entire system.

[0028] The auger suction diagnostic module 35 is used to compare the torque parameters acquired by the auger suction head of the robotic arm execution component 2 with the preset benchmark value of the currently identified soil layer. If the real-time value continuously deviates abnormally from the benchmark range, an early warning signal for wear, blockage, or failure of the auger suction head of the robotic arm execution component 2 is generated. The auger suction diagnostic module 35 calculates the deviation of the real-time torque value from the median of the benchmark range and performs continuous time-series analysis based on the deviation: if the deviation increases instantaneously in a short period of time and then recovers, it is defined as encountering an instantaneous impact from a hard object; if the deviation shows a continuous and slow upward trend, it is defined as progressive wear of the auger suction teeth; if the deviation increases abruptly or remains at a high level when the soil layer has not changed significantly, it is defined as blockage of the internal flow channel of the auger suction head or structural damage to key components.

[0029] The environmental perception module 31 is interconnected with the soil layer identification module 32 and the suction control module 33 via a wireless network. The trajectory control module 34 is interconnected with the suction control module 33 via a wireless network. The trajectory control module 34 is interconnected with the suction diagnosis module 35 via a wireless network.

[0030] Compared with existing technologies, this method achieves online automatic identification and discrimination of soil layer types through environmental data fusion and feature database matching analysis. Based on the identified soil layer results, it adaptively retrieves and outputs target speed and torque commands for the suction head from the pre-stored optimal operation parameter strategy library, thereby realizing automatic matching and optimization of operation parameters. This significantly improves the accuracy and efficiency of dredging operations, enhances the adaptive optimization capability for complex working conditions, and effectively reduces reliance on manual operation and the risk of misjudgment. By monitoring the torque of the suction head of the robotic arm execution component 2 in real time and comparing it with the preset soil layer benchmark value, it can provide early warning of wear, blockage, or failure risks of the suction head, extending the service life of key components.

[0031] At other levels, this embodiment also provides a matching analysis process, specifically as follows: A feature database was constructed and pre-stored, containing standard operation data features and environmental data features corresponding to various typical soil layer categories. The standard operation data features include the characteristics of the cutter head rotation speed, torque relationship curve, and mud flow rate variation. The environmental data features include the contour features of silt acquired by forward-looking sonar and the texture features of underwater video images. During the operation, real-time operation data and environmental data of the current work surface are collected, and their corresponding real-time features are extracted; The extracted real-time features are matched and analyzed with the standard features of various soil layer categories pre-stored in the feature database to calculate the similarity between the real-time features and each standard feature. Based on the similarity comparison results, the soil layer category corresponding to the standard feature with the highest similarity is identified as the soil layer category of the current working face, thus completing the online identification.

[0032] Compared with existing technologies, by constructing a feature database containing multi-dimensional standard operating procedures and environmental characteristics, it is possible to perform similarity matching analysis between real-time collected operation data and environmental characteristics and pre-stored standards, thereby realizing online automatic identification of soil layer categories during operation. This breaks through the limitations of traditional reliance on manual experience or single sensor judgment and improves the accuracy of soil layer identification under complex working conditions.

[0033] In this embodiment, the trajectory control module 34 and the suction diagnostic module 35 are interconnected via a wireless network with a digital recording module 36. The digital recording module 36 is used to record and associate the spatial coordinates of each dredging operation point, the corresponding soil layer information, the applied operation parameters, and environmental perception data in real time, generating a digital archive and operation map for the dredging operation. The digital archive for the dredging operation supports multi-dimensional retrieval and statistical analysis by time, spatial location, or soil layer type. The operation map can visually display the operated area, the unoperated blind area, the area with different soil types, and the operation quality assessment indicators for each area.

[0034] The digital recording module 36 is wirelessly connected to the interactive monitoring module 37. The interactive monitoring module 37 is used to match external display devices to centrally display the digital archives and operation maps of the dredging operation fed back by the digital recording module 36, and provides a manual intervention interface. It offers multiple operation modes, including: fully automatic mode, in which the system executes completely autonomously according to preset tasks; semi-automatic auxiliary mode, in which the operator specifies key points or areas and the system automatically completes detailed operations; and manual remote operation mode, in which the operator directly controls the actions of each actuator through devices such as joysticks, while the sensing information and early warning information provided by the system serve as auxiliary references.

[0035] In summary, this invention integrates multi-source environmental perception capabilities, enabling real-time monitoring of the working environment, siltation contours, and obstacles, ensuring the safety and visibility of the operation process. By collecting real-time operational data such as the rotational speed, torque, and mud flow rate of the suction head of the robotic arm execution component 2 and matching it with a feature database, it can accurately identify soil layer types online and automatically call the pre-stored optimal operational parameter strategy to dynamically adjust the target rotational speed and torque of the suction head, achieving precise and efficient adaptation to different soil conditions. It adopts motion control based on PID algorithm, combined with environmental data and preset dredging trajectory, which can accurately control the movement of the mobile bearing module 1 and the robotic arm execution component 2, ensuring stable autonomous operation along the predetermined path and reducing human intervention. By continuously comparing the standby torque with the soil reference value, wear, blockage or potential faults of the suction head can be identified in a timely manner, and early warnings can be given. This effectively prevents equipment damage, reduces maintenance costs and ensures the continuity of operation, thus realizing intelligent dredging operations in culverts.

[0036] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions will not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An intelligent mechanical suction system for culvert dredging, characterized in that, The mobile support module (1) is included, and a robotic arm execution component (2) is installed on the left side of the top of the mobile support module (1). A main control unit (3) is installed at the center of the top of the mobile support module (1). The mobile load-bearing module (1) is used to provide mobility and overall power support to various functional components and modules through the walking mechanism and energy system; The robotic arm execution component (2) is used to perform a dredging operation by means of a robotic arm driven by a hydraulic drive system and a hydraulic squeegee head at its end. The main control unit (3) has sub-modules deployed at its lower level, and the sub-modules include: The environmental perception module (31) is used to acquire visual information of the working environment, the outline of the silt in front and the information of obstacles in real time, so as to integrate environmental data; The soil layer identification module (32) is used to collect real-time operation data of the rotation speed, hydraulic drive system torque and mud suction flow rate of the suction head of the robotic arm execution component (2) during operation. By matching and analyzing the real-time operation data, environmental data and feature database, the soil layer type of the current working surface is determined online. The suction control module (33) is used to adjust and output the target rotation speed and target torque command for the hydraulic suction head on the robotic arm execution component (2) according to the identified soil layer results; The trajectory control module (34) is used to receive environmental fusion data from the environmental perception module (31) based on the PID control algorithm, and combine the preset dredging trajectory plan to perform motion control on the mobile carrier module (1) and the robotic arm execution component (2). The suction diagnostic module (35) is used to compare the torque parameters obtained by the suction head of the robotic arm execution component (2) with the preset benchmark value of the currently identified soil layer. If the real-time value continues to deviate abnormally from the benchmark range, a warning signal of the suction head of the robotic arm execution component (2) is generated.

2. The intelligent mechanical suction system for culvert dredging according to claim 1, characterized in that, The environmental perception module (31) constructs a three-dimensional contour model of the silt in front of it using forward-looking sonar, forms a near-field protection zone around the robot using collision avoidance sonar, and monitors the distance to the surrounding culvert walls or obstacles in real time. When the distance is lower than the safety threshold, it sends an emergency obstacle avoidance or pause command to the trajectory control module (34).

3. The intelligent mechanical suction system for culvert dredging according to claim 1, characterized in that, The matching analysis process in the soil layer identification module (32) is as follows: Construct and pre-store a feature database containing standard operation data features and environmental data features corresponding to various typical soil layer categories; During the operation, real-time operation data and environmental data of the current work surface are collected, and their corresponding real-time features are extracted; The extracted real-time features are matched and analyzed with the standard features of various soil layer categories pre-stored in the feature database to calculate the similarity between the real-time features and each standard feature. Based on the similarity comparison results, the soil layer category corresponding to the standard feature with the highest similarity is identified as the soil layer category of the current working face, thus completing the online identification.

4. The intelligent mechanical suction system for culvert dredging according to claim 3, characterized in that, The standard operating data features include the sluice head rotation speed, torque relationship curve features, and mud flow rate variation features; the environmental data features include the silt contour features acquired by forward-looking sonar and the texture features of underwater video images.

5. The intelligent mechanical suction system for culvert dredging according to claim 1, characterized in that, The working logic of the suction control module (33) is as follows: when the current soil layer is identified as a high-cohesion sub-clay layer, it outputs a high torque and medium-low speed operation command; when it is identified as a loose silt layer, it outputs a medium torque and high speed command; when it is identified as a sand and gravel layer containing coarse particles, it outputs an intermittent high peak torque and low speed command.

6. The intelligent mechanical suction system for culvert dredging according to claim 1, characterized in that, The process by which the trajectory control module (34) controls the operation of the mobile support module (1) and the robotic arm execution component (2) is as follows: Based on the real-time environmental data obtained by the environmental perception module (31), a preset dredging operation trajectory planning scheme is loaded. According to the trajectory planning scheme, combined with the real-time pose of the mobile bearing module (1) and the real-time working position of the suction head of the robotic arm execution component (2), the moving speed and turning command of the walking mechanism of the mobile bearing module (1) and the motion adjustment amount of each joint of the robotic arm execution component (2) are dynamically calculated by the PID control algorithm. The control commands are synchronously output to the walking mechanism of the mobile bearing module (1) and the hydraulic drive unit of the robotic arm execution component (2), so that the mobile bearing module (1) walks along a predetermined path and the suction head of the robotic arm execution component (2) performs operations along the dredging section trajectory.

7. The intelligent mechanical suction system for culvert dredging according to claim 1, characterized in that, The squeegee diagnostic module (35) calculates the deviation of the real-time torque value from the median of the reference range and performs continuous time-series analysis based on the deviation: if the deviation increases instantaneously in a short period of time and then recovers, it is defined as encountering an instantaneous impact from a hard object; if the deviation shows a continuous and slow upward trend, it is defined as progressive wear of the squeegee teeth; if the deviation increases dramatically or remains at a high level when the soil layer does not change significantly, it is defined as blockage of the internal flow channel of the squeegee head or structural damage to key components.

8. The intelligent mechanical suction system for culvert dredging according to claim 1, characterized in that, The trajectory control module (34) and the suction diagnosis module (35) are connected to a digital recording module (36) via a wireless network. The digital recording module (36) is used to record and store in real time the spatial coordinates of each dredging operation point, the corresponding soil layer information, the applied operation parameters and environmental perception data, and generate a digital archive and operation map of the dredging operation.

9. The intelligent mechanical suction system for culvert dredging according to claim 8, characterized in that, The digital recording module (36) is connected to the interactive monitoring module (37) via a wireless network. The interactive monitoring module (37) is used to match an external display device to centrally display the dredging operation digital files and operation map fed back by the digital recording module (36) and to provide a manual intervention interface.

10. The intelligent mechanical suction system for culvert dredging according to claim 1, characterized in that, The environmental perception module (31) is interconnected with the soil layer identification module (32) and the suction control module (33) via a wireless network. The trajectory control module (34) is interconnected with the suction control module (33) via a wireless network. The trajectory control module (34) is interconnected with the suction diagnosis module (35) via a wireless network.

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