Control method and system of ditch clearing robot and ditch clearing robot

Abstract

The present invention provides a control method, system and dredging robot for ditch cleaning, which relates to the technical field of dredging robot control, including: dividing the dredging area into a grid matrix, collecting and calculating the mean value of silt density at multiple points in the grid, taking the mean value as the silt density in the grid, and generating a silt density distribution map of the dredging area; dynamically switching the dredging mode according to the density level of the grid, and taking corresponding dredging methods according to different dredging modes; obtaining all paths passing through each grid in the dredging area, analyzing the total length of each path, calculating the repetition coefficient on each path, and obtaining the number of dredging mode switching times and energy consumption coefficient of each path; generating a path evaluation coefficient according to the repetition coefficient, the number of dredging mode switching times and the energy consumption coefficient, and the dredging robot adopts the path with the highest path evaluation coefficient to perform the dredging task. The present invention provides an efficient and intelligent solution for ditch cleaning operations.

Description

Technical Field

[0001] The present invention relates to the technical field of dredging robot control, and in particular to a control method and system for a ditch dredging robot and the dredging robot. Background Art

[0002] In the existing ridge ditch desilting operations, the traditional method mainly relies on manual cleaning or the use of simple mechanical equipment for operation. The efficiency of this method is often affected by human factors, and it is difficult to achieve refined management when faced with complex terrain or uneven silt distribution. Manual operation is not only time-consuming and labor-intensive, but also prone to omissions or excessive cleaning due to misjudgment. In addition, although the use of simple mechanical equipment has improved efficiency to a certain extent, it is usually lacking in flexibility and difficult to adapt to the real-time changing desilting environment, especially when operating in a large range of ridge ditch areas, the efficiency problem is particularly prominent. This leads to the fact that when dealing with different silt densities and terrain changes, traditional methods often seem to be unable to cope with it, and it is difficult to efficiently complete the desilting task.

[0003] At the same time, existing technologies also have obvious deficiencies in path planning and energy consumption management. Most traditional equipment fails to integrate advanced navigation and path optimization algorithms, and usually relies on fixed routes for operation. This method fails to fully consider the path overlap problem that may occur during the dredging process, resulting in repeated dredging and waste of resources. In addition, the energy consumption problem has not been effectively solved. Traditional equipment usually lacks the ability to monitor and optimize energy consumption in real time, and cannot adjust energy consumption according to the real-time needs of dredging tasks, which not only increases operating costs, but also imposes a certain burden on environmental protection. In summary, the existing technology has obvious limitations in achieving efficient and intelligent dredging operations, and there is an urgent need for an intelligent solution that can dynamically optimize dredging paths, adjust dredging strategies in real time, and effectively manage energy consumption.

[0004] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0005] The object of the present invention is to provide a control method and system for a ditch clearing robot and a ditch clearing robot, so as to solve the problems raised in the above-mentioned background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A control method for a ditch desilting robot, the specific steps comprising:

[0008] Step 1: Divide the desilting area into a grid matrix, collect and calculate the mean value of the silt density at multiple points in the grid, use the mean value as the silt density in the grid, and generate a silt density distribution map of the desilting area;

[0009] Step 2: According to the generated silt density distribution map, the density level of each grid is determined, and the dredging mode is dynamically switched according to the density level of the grid, and the corresponding dredging method is adopted according to different dredging modes;

[0010] Step 3: Obtain all paths passing through each grid in the dredging area, analyze the total length of each path, calculate the repetition coefficient of the dredging robot on each path, and obtain the number of dredging mode switching times and energy consumption coefficient of each path;

[0011] Step 4: Generate a path evaluation coefficient based on the repetition coefficient, the number of dredging mode switching times and the energy consumption coefficient. The dredging robot adopts the path with the highest path evaluation coefficient to perform the dredging task. At the same time, the path data, sludge density changes, dredging mode switching status and equipment status information obtained during the dredging process are uploaded to the remote terminal in real time, so that users can monitor the dredging progress and equipment operation status.

[0012] Furthermore, the desilting area is divided into The area of ​​each grid is ,in: is the length of the grid, which is preset to be the width of the robot’s dredging head. is the width of the grid, which is determined by the preset desilting interval;

[0013] The grid density is calculated by averaging the density values ​​of the n sampling points in each grid. The silt density is based on the following formula:

[0014] ;

[0015] In the formula, For Grid The density of silt, is the number of sampling points in the grid, For the grid The silt density value at each sampling point is is the index of the number of sampling points, and , Grid The row and column indices of ;

[0016] Output grid density distribution map for subsequent dredging mode judgment and path planning.

[0017] Furthermore, based on the generated silt density distribution map, the density level of each grid is determined based on the following specific logic:

[0018] Set the low density threshold and high density threshold, denoted as and , according to the grid The density of silt , determine the dredging mode:

[0019] when When cleaning, select the light siltation mode, which is low-power silt removal with only the suction function turned on;

[0020] when When cleaning, select the heavy siltation mode, which is a medium-efficiency silt removal mode with both stirring and suction functions turned on;

[0021] when When cleaning, select the obstacle clearing mode, which enables high-efficiency silt removal with stirring, cutting and suction functions simultaneously.

[0022] Furthermore, the robot operating system is used to obtain all paths passing through each grid in the dredging area, and the number of grids repeatedly passed in the path and the number of times the grid is repeatedly passed in the path are collected, and the repetition coefficient of each path is calculated based on this:

[0023]

[0024] In the formula, is the repetition coefficient, is a natural constant, is the number of grids that are repeatedly passed through in the path, is the number of times the path repeats the grid. is a preset proportionality factor used to adjust the sensitivity of the repetition factor, and ;

[0025] Get the number of dredging mode switching times for each path. The number of dredging mode switching times is the total number of dredging mode switching times when the robot performs the dredging task through the path, and record the number of dredging mode switching times as ;

[0026] Based on the robot operating system, the repetition length of each path is obtained, and the repetition energy consumption of the robot on the path is calculated in combination with the average energy consumption per unit length of the robot. The ratio of the repetition energy consumption to the sum of the repetition energy consumption of all paths is taken as the energy consumption coefficient, which is recorded as .

[0027] Furthermore, the path evaluation coefficient is generated according to the repetition coefficient, the number of dredging mode switching times and the energy consumption coefficient, and the formula is as follows:

[0028] ;

[0029] In the formula, is the path evaluation coefficient, is the repetition coefficient, is the number of times the path's dredging mode is switched, is the energy consumption coefficient of the path, , and is the preset scaling factor, and ;

[0030] The dredging robot adopts the path with the highest path evaluation coefficient to perform the dredging task. At the same time, the path data, silt density changes, dredging mode switching status and equipment status information obtained during the dredging process are uploaded to the remote terminal in real time, making it convenient for users to monitor the dredging progress and equipment operation status.

[0031] The present invention further provides a control system for a ditch cleaning robot, wherein the control system for the ditch cleaning robot is used to execute the control method for the ditch cleaning robot, and comprises:

[0032] The silt density calculation module is used to divide the dredging area into a grid matrix, collect and calculate the average silt density of multiple points in the grid, use the average as the silt density in the grid, and generate a silt density distribution map of the dredging area;

[0033] The dredging mode switching module is used to determine the density level of each grid according to the generated silt density distribution map, and dynamically switch the dredging mode according to the density level of the grid, and adopt corresponding dredging methods according to different dredging modes;

[0034] The path planning module is used to obtain all the paths passing through each grid in the dredging area, analyze the total length of each path, calculate the repetition coefficient of the dredging robot on each path, and obtain the number of dredging mode switching times and energy consumption coefficient of each path;

[0035] The task execution monitoring module is used to generate a path evaluation coefficient based on the repetition coefficient, the number of dredging mode switching times and the energy consumption coefficient. The dredging robot adopts the path with the highest path evaluation coefficient to perform the dredging task. At the same time, the path data, sludge density changes, dredging mode switching status and equipment status information obtained during the dredging process are uploaded to the remote terminal in real time, which is convenient for users to monitor the dredging progress and equipment operation.

[0036] The present invention also provides a dredging robot, which is controlled by using the above-mentioned control method for a ditch dredging robot.

[0037] Compared with the prior art, the present invention has the following beneficial effects:

[0038] The present invention provides an efficient and intelligent control method for a ditch desilting robot by adopting the technical means of grid management and intelligent desilting mode switching. The desilting area is divided by a grid matrix and the silt density distribution is calculated, making the desilting process more targeted. Secondly, by dynamically switching the desilting mode, the desilting strategy can be flexibly adjusted according to the actual silt density, significantly improving the desilting efficiency and effect. In addition, through the calculation and optimization of the path evaluation coefficient, the robot can autonomously select a low-energy, high-efficiency path to perform tasks, reducing resource waste. The real-time data upload and monitoring function provides users with a convenient way to supervise and adjust the progress of desilting tasks, further ensuring the efficient operation of the equipment and the successful implementation of desilting tasks. These features enable the present invention to improve the intelligence level and work efficiency of the entire system while solving the limitations of traditional desilting methods. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 It is a schematic diagram of the overall method flow of the present invention;

[0040] Figure 2 It is a schematic diagram of the system module of the present invention. DETAILED DESCRIPTION

[0041] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with specific embodiments.

[0042] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the present invention should be understood by people with ordinary skills in the field to which the present invention belongs. The words "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0043] Example:

[0044] See also Figure 1 , the present invention provides a technical solution:

[0045] A control method for a ditch desilting robot, the specific steps comprising:

[0046] Step 1: Divide the desilting area into a grid matrix, collect and calculate the mean value of the silt density at multiple points in the grid, use the mean value as the silt density in the grid, and generate a silt density distribution map of the desilting area;

[0047] In this embodiment, the desilting range is divided into The area of ​​each grid is ,in: is the length of the grid, which is preset to be the width of the robot’s dredging head. is the width of the grid, which is determined by the preset desilting interval;

[0048] The grid density is calculated by averaging the density values ​​of the n sampling points in each grid. The silt density is based on the following formula:

[0049] ;

[0050] In the formula, For Grid The density of silt, is the number of sampling points in the grid, For the grid The silt density value at each sampling point is is the index of the number of sampling points, and , Grid The row and column indices of ;

[0051] Output grid density distribution map for subsequent dredging mode judgment and path planning.

[0052] The advantage of step 1 is that it refines the management of the desilting area by dividing it into a grid matrix. This gridding method allows accurate measurement and calculation of silt density, thereby generating a detailed silt density distribution map. This method improves the accuracy of data collection, making subsequent desilting mode selection and path planning more scientific and accurate. By obtaining multi-point density data in each grid and calculating its mean, the impact of local abnormal points on the overall desilting strategy can be effectively reduced.

[0053] Compared with the prior art, the beneficial effect of step 1 is that it provides a more systematic and accurate way to identify and evaluate the distribution of silt. Traditional methods may rely on data from a single point or a small number of sampling points, which are susceptible to measurement errors and random factors. The grid matrix and density mean calculation method significantly reduce this error and provide a more reliable data basis, thereby improving the efficiency and accuracy of dredging. In this solution, the use of step 1 can play a key role in promoting the overall solution. By generating a silt density distribution map, the dredging robot can select an appropriate dredging mode based on accurate density data and optimize path planning. This data-driven decision can improve dredging efficiency, reduce unnecessary energy consumption and time waste, and ultimately ensure the efficient completion of dredging tasks and the rational use of resources.

[0054] Step 2: According to the generated silt density distribution map, the density level of each grid is determined, and the dredging mode is dynamically switched according to the density level of the grid, and the corresponding dredging method is adopted according to different dredging modes;

[0055] In this embodiment, the density level of each grid is determined based on the generated sludge density distribution map, and the specific logic is as follows:

[0056] Set the low density threshold and high density threshold, denoted as and , according to the grid The density of silt , determine the dredging mode:

[0057] when When cleaning, select the light siltation mode, which is low-power silt removal with only the suction function turned on;

[0058] when When cleaning, select the heavy siltation mode, which is a medium-efficiency silt removal mode with both stirring and suction functions turned on;

[0059] when When cleaning, select the obstacle clearing mode, which enables high-efficiency silt removal with stirring, cutting and suction functions simultaneously.

[0060] The advantage of step 2 is that it can intelligently determine the density level of each grid based on the silt density distribution map and dynamically switch the silt removal mode. This process ensures the flexibility and pertinence of the silt removal method, enabling the robot to adopt the most suitable silt removal strategy according to different silt densities, such as light siltation mode, heavy siltation mode or obstacle clearing mode. This dynamic adjustment not only improves silt removal efficiency, but also effectively reduces unnecessary energy consumption and equipment wear.

[0061] Compared with the existing technology, the beneficial effect of step 2 is that it breaks through the limitations of the traditional fixed dredging mode. Traditional technology may only be able to dredge in a single mode and cannot effectively cope with the challenges brought by different density situations. By dynamically switching modes, this solution can achieve more sophisticated operations, fully utilize the equipment capabilities in high-density areas, and save resources in low-density areas. This intelligent mode selection improves the overall operation efficiency and effectiveness. In this solution, the use of step 2 can play an important role in promoting the overall solution. It ensures that the robot can respond to environmental changes in real time and select the optimal dredging strategy when performing dredging tasks. This intelligent adjustment capability not only improves the efficiency and accuracy of the dredging process, but also lays a good foundation for subsequent data analysis and path planning, ensuring the smooth progress and optimized execution of the entire dredging process.

[0062] Step 3: Obtain all paths passing through each grid in the dredging area, analyze the total length of each path, calculate the repetition coefficient of the dredging robot on each path, and obtain the number of dredging mode switching times and energy consumption coefficient of each path;

[0063] In this embodiment, the robot operating system is used to obtain all paths passing through each grid in the dredging area, and the number of grids repeatedly passed in the path and the number of times the grids are repeatedly passed in the path are collected, and the repetition coefficient of each path is calculated based on this:

[0064] ;

[0065] In the formula, is the repetition coefficient, is a natural constant, is the number of grids that are repeatedly passed through in the path, is the number of times the path repeats the grid. is a preset proportionality factor used to adjust the sensitivity of the repetition factor, and ;

[0066] The repetition coefficient is used to measure the degree of repeated grids in path planning, reflecting the efficiency and optimization level of the path. A higher repetition coefficient indicates that there is a large redundancy in path planning, which may lead to resource waste, so the repetition coefficient needs to be reduced as much as possible. This formula can quantify the degree of redundancy in path planning, which helps to optimize the path. Indicates the total number of grids that are passed through multiple times in the path. The larger the value, the more areas are passed through repeatedly, and the repetition coefficient should increase accordingly; It indicates the total number of repetitions based on repeated passing through the grid. If the number of repetitions is high, it means that the redundancy of path planning is more serious. , and There is a positive correlation.

[0067] Get the number of dredging mode switching times for each path. The number of dredging mode switching times is the total number of dredging mode switching times when the robot performs the dredging task through the path, and record the number of dredging mode switching times as ;

[0068] The robot operating system is used to obtain the repetitive length of each path. The repetitive energy consumption of the robot on the path is calculated by combining the average energy consumption per unit length of the robot. The ratio of the repetitive energy consumption to the sum of the repetitive energy consumption of all paths is taken as the energy consumption coefficient, which is recorded as .

[0069] The advantage of step 3 is that it provides a systematic method to obtain and evaluate the dredging path. By analyzing the total length of the path, the repetition coefficient, the number of dredging mode switching times and the energy consumption coefficient, the dredging robot can select the most optimized path for the dredging task. This process helps to reduce repeated dredging and unnecessary energy consumption, and improve the overall dredging efficiency.

[0070] Compared with the prior art, the beneficial effect of step 3 is that it not only takes into account the length of the path, but also comprehensively analyzes the repeatability and energy consumption of the path. Traditional methods may only focus on the shortest path, while ignoring the complexity of the path and the actual energy consumption. Through comprehensive path analysis, this solution can better balance efficiency and resource use, reduce repetitive work, and improve work accuracy. In this solution, the use of step 3 can play an important role in promoting the overall solution. It provides a clear data-driven path selection mechanism for the dredging robot, ensuring that the robot can flexibly select the best path in different scenarios. This not only improves the efficiency of dredging work, but also ensures the long-term operation and stable performance of the equipment, and ultimately achieves the efficient and economical completion of the dredging task.

[0071] Step 4: Generate a path evaluation coefficient based on the repetition coefficient, the number of dredging mode switching times and the energy consumption coefficient. The dredging robot uses the path with the highest path evaluation coefficient to perform the dredging task. At the same time, the path data, silt density changes, dredging mode switching status and equipment status information obtained during the dredging process are uploaded to the remote terminal in real time, so that users can monitor the dredging progress and equipment operation status.

[0072] In this embodiment, the path evaluation coefficient is generated according to the repetition coefficient, the number of switching times of the dredging mode and the energy consumption coefficient, and the formula based on it is as follows:

[0073] ;

[0074] In the formula, is the path evaluation coefficient, is the repetition coefficient, is the number of times the path's dredging mode is switched, is the energy consumption coefficient of the path, , and is the preset scaling factor, and This is because energy consumption is the most direct and critical resource consumption indicator when the dredging robot performs tasks, which directly affects the economy and sustainability of the operation. If the energy consumption is too high, it will not only increase the energy cost, but also may cause the robot to be unable to complete long-term operations. Therefore, the weight of the energy consumption coefficient is set to the maximum; repeated dredging will lead to reduced efficiency and waste of resources, so it is necessary to strictly control the path repeatability, and the weight of the influence of the repetition coefficient is ranked second; the switching of dredging modes will have a certain impact on the stability and efficiency of the robot operation, such as increasing time costs or equipment wear. However, its impact is usually more local and limited, so the weight of the number of dredging mode switching is set to the minimum.

[0075] Path evaluation coefficient It is an indicator used to comprehensively evaluate the advantages and disadvantages of dredging paths. The higher its value, the more energy-efficient and efficient the path is in performing dredging tasks, with fewer repeated operations and more stable dredging mode switching. When it increases, it means that there are more repeated operations in the dredging path. Repeatedly passing through the same area will lead to a waste of time and energy. Decreases accordingly; when When the speed increases, it means that the dredging robot needs to switch frequently between different modes during the execution of the task, which will have a certain negative impact on the efficiency of the dredging operation and the stability of the equipment. decrease; when When it increases, it means that the energy consumption level of the dredging path increases. This usually indicates that more energy is required during the dredging task, which has a certain negative impact on the work efficiency and economy. will decrease; that is, , , and There is a negative correlation.

[0076] The dredging robot adopts the path with the highest path evaluation coefficient to perform the dredging task. At the same time, the path data, silt density changes, dredging mode switching status and equipment status information obtained during the dredging process are uploaded to the remote terminal in real time, making it convenient for users to monitor the dredging progress and equipment operation status.

[0077] The advantage of step 4 is that it proposes a method for comprehensive evaluation and selection of the optimal dredging path, which generates a path evaluation coefficient by calculating the path repetition coefficient, the number of dredging mode switching times and the energy consumption coefficient. This method can not only identify the path with the highest dredging efficiency, but also effectively reduce resource consumption and equipment wear, and optimize the overall efficiency of the dredging operation.

[0078] Compared with the prior art, the beneficial effect of step 4 is reflected in its comprehensive consideration of multiple factors that affect dredging efficiency, not just the path length. Traditional technology may only focus on the physical distance of the path, while ignoring the complexity and energy consumption of the dredging operation. By integrating path repeatability, mode switching frequency and energy consumption, this method provides a more intelligent and efficient path selection strategy, which improves the economy and accuracy of dredging operations. In this scheme, the use of step 4 can play a key role in promoting the overall scheme. It provides a scientific path selection basis for the dredging robot to ensure that the optimal path is selected when performing tasks. This not only improves operating efficiency, but also reduces unnecessary repetitive operations and energy consumption, improves the transparency and monitorability of the dredging process, and provides reliable data support for users to monitor the operating status of the system.

[0079] See also Figure 2 , a control system for a ditch desilting robot, comprising:

[0080] The silt density calculation module is used to divide the dredging area into a grid matrix, collect and calculate the average silt density of multiple points in the grid, use the average as the silt density in the grid, and generate a silt density distribution map of the dredging area;

[0081] The dredging mode switching module is used to determine the density level of each grid according to the generated silt density distribution map, and dynamically switch the dredging mode according to the density level of the grid, and adopt corresponding dredging methods according to different dredging modes;

[0082] The path planning module is used to obtain all the paths passing through each grid in the dredging area, analyze the total length of each path, calculate the repetition coefficient of the dredging robot on each path, and obtain the number of dredging mode switching times and energy consumption coefficient of each path;

[0083] The task execution monitoring module is used to generate a path evaluation coefficient based on the repetition coefficient, the number of dredging mode switching times and the energy consumption coefficient. The dredging robot adopts the path with the highest path evaluation coefficient to perform the dredging task. At the same time, the path data, sludge density changes, dredging mode switching status and equipment status information obtained during the dredging process are uploaded to the remote terminal in real time, which is convenient for users to monitor the dredging progress and equipment operation.

[0084] The present invention also provides a dredging robot, which is controlled by using the above-mentioned control method for a ditch dredging robot.

[0085] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0086] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product. Those skilled in the art may appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein may be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed by hardware or software methods depends on the specific application and design constraints of the technical solution.

[0087] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, and may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0088] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. A control method for a ditch desilting robot, characterized in that: The specific steps include: Step 1: Divide the desilting area into a grid matrix, collect and calculate the mean value of the silt density at multiple points in the grid, use the mean value as the silt density in the grid, and generate a silt density distribution map of the desilting area; Step 2: According to the generated silt density distribution map, the density level of each grid is determined, and the dredging mode is dynamically switched according to the density level of the grid, and the corresponding dredging method is adopted according to different dredging modes; Step 3: Obtain all paths passing through each grid in the dredging area, analyze the total length of each path, calculate the repetition coefficient of the dredging robot on each path, and obtain the number of dredging mode switching times and energy consumption coefficient of each path; Step 4: Generate a path evaluation coefficient based on the repetition coefficient, the number of dredging mode switching times and the energy consumption coefficient. The dredging robot adopts the path with the highest path evaluation coefficient to perform the dredging task. At the same time, the path data, sludge density changes, dredging mode switching status and equipment status information obtained during the dredging process are uploaded to the remote terminal in real time, so that users can monitor the dredging progress and equipment operation status.

2. The control method of a ditch desilting robot according to claim 1, characterized in that: The desilting area is divided into The area of ​​each grid is ,in: is the length of the grid, which is preset to be the width of the robot’s dredging head. is the width of the grid, which is determined by the preset desilting interval; The grid density is calculated by averaging the density values ​​of the n sampling points in each grid. The silt density is based on the following formula: ； In the formula, For Grid The density of silt, is the number of sampling points in the grid, For the grid The silt density value at each sampling point is is the index of the number of sampling points, and , Grid The row and column indices of ; Output grid density distribution map for subsequent dredging mode judgment and path planning.

3. The control method of a ditch desilting robot according to claim 2, characterized in that: According to the generated silt density distribution map, the density level of each grid is judged based on the following specific logic: Set the low density threshold and high density threshold, denoted as and , according to the grid The density of silt , determine the dredging mode: when When cleaning, select the light siltation mode, which is low-power silt removal with only the suction function turned on; when When cleaning, select the heavy siltation mode, which is a medium-efficiency silt removal mode with both stirring and suction functions turned on; when When cleaning, select the obstacle clearing mode, which enables high-efficiency silt removal with stirring, cutting and suction functions simultaneously.

4. The control method of a ditch desilting robot according to claim 1, characterized in that: Use the robot operating system to obtain all paths passing through each grid in the dredging area, and collect the number of grids passed repeatedly in the path and the number of times the grid is passed repeatedly in the path, and use this to calculate the repetition coefficient of each path: ； In the formula, is the repetition coefficient, is a natural constant, is the number of grids that are repeatedly passed through in the path, is the number of times the path repeats the grid. is a preset proportionality factor used to adjust the sensitivity of the repetition factor, and ; The number of dredging mode switching times for each path is obtained. The number of dredging mode switching times is the total number of dredging mode switching times when the robot performs the dredging task through the path, and the number of dredging mode switching times is recorded as ; Based on the robot operating system, the repetition length of each path is obtained, and the repetition energy consumption of the robot on the path is calculated in combination with the average energy consumption per unit length of the robot. The ratio of the repetition energy consumption to the sum of the repetition energy consumption of all paths is taken as the energy consumption coefficient, which is recorded as .

5. The control method of a ditch desilting robot according to claim 1, characterized in that: The path evaluation coefficient is generated according to the repetition coefficient, the number of dredging mode switching times and the energy consumption coefficient. The formula is as follows: ； In the formula, is the path evaluation coefficient, is the repetition coefficient, is the number of times the path's dredging mode is switched, is the energy consumption coefficient of the path, , and is the preset scaling factor, and ; The dredging robot adopts the path with the highest path evaluation coefficient to perform the dredging task. At the same time, the path data, silt density changes, dredging mode switching status and equipment status information obtained during the dredging process are uploaded to the remote terminal in real time, making it convenient for users to monitor the dredging progress and equipment operation status.

6. A control system for a ditch desilting robot, characterized in that: The control system of the ditch cleaning robot is used to execute the control method of the ditch cleaning robot according to any one of claims 1 to 5, comprising: The silt density calculation module is used to divide the dredging area into a grid matrix, collect and calculate the average silt density of multiple points in the grid, use the average as the silt density in the grid, and generate a silt density distribution map of the dredging area; The dredging mode switching module is used to determine the density level of each grid according to the generated silt density distribution map, and dynamically switch the dredging mode according to the density level of the grid, and adopt corresponding dredging methods according to different dredging modes; The path planning module is used to obtain all the paths passing through each grid in the dredging area, analyze the total length of each path, calculate the repetition coefficient of the dredging robot on each path, and obtain the number of dredging mode switching times and energy consumption coefficient of each path; The task execution monitoring module is used to generate a path evaluation coefficient based on the repetition coefficient, the number of dredging mode switching times and the energy consumption coefficient. The dredging robot adopts the path with the highest path evaluation coefficient to perform the dredging task. At the same time, the path data, sludge density changes, dredging mode switching status and equipment status information obtained during the dredging process are uploaded to the remote terminal in real time, which is convenient for users to monitor the dredging progress and equipment operation.

7. A dredging robot, characterized in that: The dredging robot is controlled by using a control method for a ditch dredging robot as described in any one of claims 1-5.

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