An underwater dredging robot and a dredging method

Abstract

The embodiment of the present specification provides a water and underwater dredging robot and a dredging method. The water and underwater dredging robot comprises a floating body, a crane, a slurry pump and an electric control device. The dredging method is controlled and executed by the electric control device of the water and underwater dredging robot, and the dredging method comprises: moving the floating body to a set position on the water surface, controlling the slurry pump to dive to a set depth along a guide rod by the crane, pumping the silt at the bottom of the water by the slurry pump, and discharging the silt by a sewage pipe.

Description

TECHNICAL FIELD

[0001] The present specification relates to the technical field of environmental protection equipment, in particular to a water and underwater dredging robot and a dredging method. BACKGROUND

[0002] For most of the current water bodies, the main dredging method is to build a cofferdam, drain the water in the water body, and then use an excavator to dredge after the sediment in the water body is deposited for a certain period of time. This method requires diversion, has large engineering quantity, long construction period, low work efficiency, and high labor intensity. Some water bodies cannot use large engineering machinery for dredging due to geographical location, unhardened riverbed, etc.

[0003] Therefore, it is necessary to provide a water and underwater dredging robot and a dredging method which are convenient to operate and have high efficiency. SUMMARY

[0004] One of the embodiments of the present specification provides a water and underwater dredging robot, comprising: a floating body, the floating body comprising a device platform, the device platform being connected with a float; a water pump connected with the float, a hydraulic station for providing power, and a positioning system for positioning and navigation are arranged on the device platform; a crane is arranged on the device platform; a slurry pump is arranged below the device platform, the device platform is connected with a guide rod, the slurry pump is movably arranged on the guide rod, and the slurry pump is connected with the crane; the outlet of the slurry pump is connected with a sewage pipe, the other end of the sewage pipe has a sludge outlet for sending out sludge; an electric control device is in communication connection with the floating body, the crane and the slurry pump respectively.

[0005] In some embodiments, the electric control device comprises a first controller and a second controller; the first controller is used for: acquiring underwater sludge parameters and operation area related parameters; determining working parameters of the slurry pump and the crane based on the underwater sludge parameters and the operation area related parameters; sending control instructions to the slurry pump and the crane based on the working parameters of the slurry pump and the crane; wherein the underwater sludge parameters comprise sludge thickness and sludge strength; the operation area related parameters comprise water flow velocity of the operation area; the second controller is used for: receiving user input working instructions, and sending the working instructions to at least one of the floating body, the crane and the slurry pump after processing the working instructions.

[0006] In some embodiments, the underwater silt parameters and the work area related parameters are used to determine the working parameters of the slurry pump and the crane, including: determining a matching vector based on the underwater silt parameters and the work area related parameters; and determining the working parameters of the slurry pump and the crane based on a search result of the matching vector in a vector database.

[0007] In some embodiments, the above-mentioned water and underwater dredging robot further comprises a plurality of pressure sensors arranged below the equipment platform; and the electronic control device is further configured to: acquire pressure values measured by each pressure sensor in real time; determine the impurity condition in the silt based on the pressure values measured by each pressure sensor; and adjust the working parameters of the slurry pump and the crane based on the impurity condition.

[0008] In some embodiments, the electronic control device is further configured to: when the impurity condition is that the silt contains hard impurities with a size greater than a threshold value, send a stop working instruction and a position moving instruction to the water and underwater dredging robot.

[0009] One of the embodiments of the present specification provides a dredging method performed by any of the above-mentioned water and underwater dredging robots, and the dredging method is controlled by the electronic control device of the water and underwater dredging robot; the dredging method comprises: moving the floating body to a set position on the water surface, controlling the crane to submerge the slurry pump along the guide rod to a set depth, and pumping the silt at the bottom of the water by the slurry pump, and the silt is discharged by the sewage pipe.

[0010] In some embodiments, the electronic control device comprises a first controller and a second controller; the first controller is configured to: acquire underwater silt parameters and work area related parameters; determine the working parameters of the slurry pump and the crane based on the underwater silt parameters and the work area related parameters; and send control instructions to the slurry pump and the crane based on the working parameters of the slurry pump and the crane; wherein the underwater silt parameters include silt thickness and silt strength; and the work area related parameters include water flow velocity of the work area; and the second controller is configured to: receive a working instruction input by a user, and send the working instruction to at least one of the floating body, the crane and the slurry pump after processing.

[0011] In some embodiments, the underwater silt parameters and the work area related parameters are used to determine the working parameters of the slurry pump and the crane, including: determining a matching vector based on the underwater silt parameters and the work area related parameters; and determining the working parameters of the slurry pump and the crane based on a search result of the matching vector in a vector database.

[0012] In some embodiments, the electronic control device is further configured to: acquire pressure values ​​measured by each of the pressure sensors in real time; determine the impurity status in the sludge based on the pressure values ​​measured by each of the pressure sensors; and adjust the operating parameters of the slurry pump and the crane based on the impurity status.

[0013] In some embodiments, the electronic control device is further configured to: when the impurity condition is hard impurities with a hardness greater than a threshold, send a stop operation command and / or a position movement command to the underwater dredging robot. Attached Figure Description

[0014] This specification will be further described by way of exemplary embodiments, which will be described in detail with reference to the accompanying drawings. These embodiments are not limiting; in these embodiments, the same reference numerals denote the same structures, wherein:

[0015] Figure 1 This is a structural schematic diagram of an underwater dredging robot according to some embodiments of this specification;

[0016] Figure 2 This is a schematic diagram of a dredging method according to some embodiments of this specification;

[0017] Figure 3 This is a schematic diagram of the working process of the electronic control device according to some embodiments of this specification;

[0018] Figure 4 This is an exemplary flowchart illustrating the determination of operating parameters according to some embodiments of this specification;

[0019] Figure 5 This is another schematic diagram of the working process of the electronic control device shown in some embodiments of this specification;

[0020] Figure 6 This is a model diagram of the impurity judgment model shown in some embodiments of this specification. Detailed Implementation

[0021] To more clearly illustrate the technical solutions of the embodiments in this specification, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are merely some examples or embodiments of this specification. For those skilled in the art, these drawings can be applied to other similar scenarios without creative effort. Unless obvious from the context or otherwise specified, the same reference numerals in the drawings represent the same structures or operations.

[0022] It should be understood that the terms “system,” “device,” “unit,” and / or “module” used herein are one way to distinguish different components, elements, parts, sections, or assemblies at different levels. However, if other terms can achieve the same purpose, they may be replaced by other expressions.

[0023] As indicated in this specification and claims, unless the context clearly indicates otherwise, the words "a," "an," "an," and / or "the" do not specifically refer to the singular and may also include the plural. Generally speaking, the terms "comprising" and "including" only indicate the inclusion of expressly identified steps and elements, which do not constitute an exclusive list, and the method or apparatus may also include other steps or elements.

[0024] Flowcharts are used in this specification to illustrate the operations performed by the system according to embodiments of this specification. It should be understood that the preceding or following operations are not necessarily performed in exact order. Instead, the steps can be processed in reverse order or simultaneously. Furthermore, other operations can be added to these processes, or one or more steps can be removed from them.

[0025] The accumulation of decaying matter and sediment from aquatic plants can create black and foul-smelling water bodies. Eliminating this matter and sediment can remove the black and foul odor. Currently, this can be addressed by cleaning the silt from the bottom of the water body. Mechanical dredging methods include using excavators, cutter suction dredgers, surface dredging devices, and underwater dredging devices. In some embodiments, the dredging device can be installed on a floating platform, allowing for dredging without draining the water body, offering convenient operation and high efficiency.

[0026] Figure 1 This is a structural schematic diagram of an underwater dredging robot 100 according to some embodiments of this specification.

[0027] like Figure 1 As shown, in some embodiments, the underwater dredging robot 100 may include a float, a crane 160, a slurry pump 170, and an electrical control device 190.

[0028] A float refers to the main body of a device that can float on the surface of water. In some embodiments, the float may be a ship-like structure, and the float may include various functional devices related to dredging.

[0029] In some embodiments, the float may include a device platform 110, which can be used to mount various devices. For example, the device platform 110 may be connected to a float 120, which is used to keep the device platform 110 afloat on the water surface so that the devices mounted on the device platform 110 do not come into contact with the water. In some embodiments, the float 120 is filled with air and water so that both the float 120 and the device platform 110 connected to the float 120 can float on the water surface. In some embodiments, the float 120 includes an air valve 121, which can inject or extract air into the float 120 to adjust the amount of water in the float 120 used to maintain the balance of the float.

[0030] In some embodiments, the device platform 110 may be connected to at least one pontoon 120. In one specific embodiment, the device platform 110 may be connected to two pontoons 120, which are located on opposite sides of the device platform 110, to correct buoyancy and maintain the balance of the device platform 110.

[0031] In some embodiments, the equipment platform 110 may also be equipped with a water pump 140 connected to the float 120. The water pump 140 can be used to inject water into the float 120, thereby adjusting the buoyancy of the float by regulating the air-to-water ratio within the float 120. In some embodiments, the water pump 140 can draw water from the water body where the equipment platform 110 is located to inject water into the float 120. In other embodiments, the water pump 140 may be connected to a water storage device, which may be located on the equipment platform 110 and store water for injection into the float 120. The water in the water storage device may be pre-filled with highly clean water to maintain the cleanliness of the inside of the float 120 during use. In some embodiments, by adjusting the air-to-water ratio within two or more floats 120 according to the water conditions and the weight of the float, the balance of the equipment platform 110 can be ensured, preventing the equipment platform 110 from tipping over and causing equipment damage.

[0032] In some embodiments, the equipment platform 110 may also be equipped with a hydraulic station 130 for providing power to the underwater dredging robot 100. The hydraulic station 130 may include multiple components, such as power elements, actuators, control elements, auxiliary elements, and hydraulic oil. The hydraulic station 130 is capable of transmitting power and motion and enabling the output of the hydraulic station 130 to meet specific performance requirements.

[0033] In some embodiments, the device platform 110 may also be equipped with a positioning system 150 for positioning and navigation. In some embodiments, the positioning system 150 may include, but is not limited to, a GPS positioning system, which receives radio signals to provide accurate geographical location, movement speed and time information of the underwater dredging robot 100, so that staff can clearly understand the location of the underwater dredging robot 100 and facilitate further control operations.

[0034] In some embodiments, the underwater dredging robot 100 may include a crane 160, which can be used to vertically lift items (e.g., a slurry pump 170) within a certain range. In some embodiments, the crane 160 may be mounted on an equipment platform 110.

[0035] In some embodiments, the underwater dredging robot 100 may include a slurry pump 170. The slurry pump 170 is a machine that increases the energy of a solid-liquid mixture by using centrifugal force (impeller rotation). Under centrifugal force, the liquid is thrown from the center of the impeller to the outer edge and gains energy, exiting the impeller at high speed and entering the pump casing. In the pump casing, the liquid decelerates due to the gradual expansion of the flow channel, converting some of its kinetic energy into static pressure energy, and finally flows out at a higher pressure to the required location. In some embodiments, the slurry pump 170 may be positioned below the equipment platform 110 for pumping sludge from the bottom of the water body.

[0036] In some embodiments, the equipment platform 110 is connected to a guide rod 180. In one specific embodiment, the guide rod 180 may be vertically arranged, extending from the equipment platform 110 to below the equipment platform 110. In some embodiments, a slurry pump 170 may be movably disposed on the guide rod 180, so that the slurry pump 170 can move along the guide rod 180. In some embodiments, the slurry pump 170 may be connected to a crane 160, which can drive the slurry pump 170 to move along the guide rod 180.

[0037] In some embodiments, the outlet of the slurry pump 170 may be connected to a sewage pipe 171, the other end of which may have a sludge outlet 172 for discharging sludge. In some embodiments, the sludge outlet 172 may be located above the equipment platform 110. In other embodiments, the sludge outlet 172 may be located on the shore or at another designated location.

[0038] In some embodiments, the underwater dredging robot 100 may include an electronic control device 190, which may be communicatively connected to a float, a crane 160 and a slurry pump 170 to control the underwater dredging robot 100 to perform dredging operations.

[0039] In some embodiments, the electronic control device 190 may include a remote control unit. In some embodiments, the remote control unit may be located on the water and on the shore respectively, so that operators can remotely control the underwater dredging robot 100 from the shore. In some embodiments, the remote control unit may include a field controller and a remote handle controller, wherein the field controller may be located in the electronic control device 190, and the remote handle controller may be located on the shore (e.g., in the operating room), so that operators can control the movement of the underwater dredging robot 100 and control the start and stop of the slurry pump 170 and the crane 160 from the shore by operating the remote handle controller.

[0040] In some embodiments, the electronic control device 190 may include a first controller and a second controller.

[0041] A controller can be a master control device that controls the starting, speed regulation, braking, and reversing of equipment by changing the wiring of the main circuit or control circuit and changing the resistance value in the circuit according to a predetermined sequence. A controller can consist of a program counter, instruction register, instruction decoder, timing generator, and operation controller. Controllers can include combinational logic controllers, microprogrammed controllers, etc.

[0042] In some embodiments, the first controller may be used to: acquire underwater silt parameters and relevant parameters of the work area; determine the operating parameters of the slurry pump 170 and the crane 160 based on the underwater silt parameters and the relevant parameters of the work area; and send control commands to the slurry pump 170 and the crane 160 based on the operating parameters of the slurry pump 170 and the crane 160; wherein the underwater silt parameters include: silt thickness and silt strength; and the relevant parameters of the work area include: the water flow velocity in the work area. In some embodiments, the second controller may be used to: receive operating commands input by a user, process the operating commands, and send them to at least one of the float, the crane 160, and the slurry pump 170.

[0043] For more information on the functions of the first controller, please refer to [link / reference needed]. Figure 3 The relevant descriptions will not be repeated here.

[0044] In some embodiments, determining the operating parameters of the slurry pump 170 and the crane 160 based on underwater sludge parameters and relevant parameters of the work area may include: determining a matching vector based on underwater sludge parameters and the relevant parameters of the work area; and determining the operating parameters of the slurry pump 170 and the crane 160 based on the retrieval results of the matching vector in the vector database.

[0045] For more information on determining operating parameters, please refer to [link / reference]. Figure 4 The relevant descriptions will not be repeated here.

[0046] In some embodiments, the underwater dredging robot 100 may further include multiple pressure sensors. A pressure sensor can refer to a device or apparatus that can sense pressure signals and convert them into usable output electrical signals according to a certain rule. A pressure sensor may consist of a pressure-sensitive element and a signal processing unit, etc. In some embodiments, the pressure sensor may be disposed below the equipment platform 110 to detect the pressure of silt at the bottom of the water body, thereby further determining the amount of impurities in the silt based on the pressure conditions.

[0047] In some embodiments, based on pressure sensors, the electronic control device can be further used to: acquire pressure values ​​measured by each pressure sensor in real time; determine the impurity status in the sludge based on the pressure values ​​measured by each pressure sensor; and adjust the operating parameters of the slurry pump 170 and the crane 160 based on the impurity status.

[0048] For more information on the functions of electronic control devices, please refer to [link / reference needed]. Figure 5 The relevant descriptions will not be repeated here.

[0049] In some embodiments, when the impurities include hard impurities larger than a threshold size, a stop operation command and a position relocation command are sent to the above-water and underwater dredging robot 100. More information on adjusting the operating parameters of the slurry pump 170 and the crane 160 based on pressure sensor detection can be found at [link to relevant documentation]. Figure 5 The details and related descriptions will not be repeated here.

[0050] In some embodiments, the underwater dredging robot 100 may further include a thruster 111, which can be used to drive the underwater dredging robot 100 to move. In some embodiments, the thruster 111 may be a propeller thruster, installed below the waterline, which draws water in from the intake surface of the propeller blades and discharges it from the exhaust surface by rotating the propeller, using the reaction force of the water to propel forward.

[0051] In some embodiments, the surface and underwater dredging robot 100 may include at least one thruster 111. In one specific embodiment, the surface and underwater dredging robot 100 may include two, four, six, or eight thrusters 111, which are respectively installed in multiple locations of the surface and underwater dredging robot 100 to achieve precise movement in multiple directions.

[0052] In some embodiments, the underwater dredging robot 100 may further include a detector, such as a sonar detector and / or a dual-frequency echo rangefinder, to detect, locate, and communicate with underwater targets using underwater sound waves. In some embodiments, the detector may be used to acquire data on silt thickness and impurities in the silt, and may also be used to determine the travel status, measure the distance from the shore, etc. In some embodiments, the detector may be located below the equipment platform 110.

[0053] In some embodiments, by setting up a detector, it is possible to obtain relevant information such as the location and thickness of underwater silt, as well as the location and movement status of the underwater dredging robot 100. Based on the detection results of the detector, the movement speed of the underwater dredging robot 100 and the parameters of the slurry pump 170 during the dredging process can be further determined to achieve efficient dredging.

[0054] In some embodiments, the underwater dredging robot 100 may further include a crushing device, such as a crusher, which may be located at the inlet of the slurry pump 170 to loosen the sludge below the slurry pump 170 or crush hard objects in the sludge into small particles through squeezing and bending actions. In some embodiments, a pressure sensor may be installed at the connection between the crushing device and the slurry pump 170 to monitor and acquire the pressure data experienced by the crushing device when crushing the sludge. In some embodiments, the operating parameters of the crushing device may also be determined by a first controller and further adjusted based on the impurity levels.

[0055] In some embodiments, by providing a crushing device, the sludge can be loosened or hard objects in the sludge can be crushed into small particles, preventing hard objects in the sludge from clogging the slurry pump 170 and affecting the dredging effect.

[0056] Figure 2 This is a schematic diagram of a dredging method 200 according to some embodiments of this specification.

[0057] In some embodiments, the electronic control device 190 of the surface and underwater dredging robot 100 can control the surface and underwater dredging robot 100 to perform dredging. For example... Figure 2 As shown, in some embodiments, the dredging method 200 may include the following steps:

[0058] Step 210: The float moves to the set position on the water surface.

[0059] In some embodiments, the electronic control device can control the movement of the floating body by controlling the start and stop of the thrusters. In some embodiments, the electronic control device can automatically plan the movement path of the floating body according to a pre-set target position. The electronic control device controls the start and stop of each thruster of the surface and underwater dredging robot to make the floating body move according to the preset movement path until it reaches the set position. In some embodiments, operators on shore can control each thruster of the surface and underwater dredging robot individually by operating a remote handle controller, so that the floating body can move to the pre-set position.

[0060] Step 220: The crane controls the slurry pump to descend along the guide rod to the set depth.

[0061] In some embodiments, the crane can drive the slurry pump to move up or down along the guide rod. When the float reaches the set position, the crane can drive the slurry pump to descend to a preset depth. This preset depth can be set according to the silt conditions at the bottom of the water body, which can be detected and determined by a sonar detector.

[0062] Step 230: The slurry pump sucks up the silt from the bottom of the water.

[0063] In some embodiments, when the slurry pump reaches a set depth, the electronic control device can activate the slurry pump to suck up the silt at the bottom of the water body.

[0064] Step 240: The sludge is discharged through the sewage pipe.

[0065] In some embodiments, sludge at the bottom of the water body is pumped out by a slurry pump and sent out through a sewage pipe for further treatment.

[0066] In some embodiments, by determining the dredging location of the underwater dredging robot and further controlling the slurry pump to descend to the depth of the sludge by a crane, the sludge is pumped out, thus achieving sludge cleaning at a specified location and depth with high cleaning efficiency and minimal disturbance to the water body.

[0067] Figure 3 This is a schematic diagram illustrating the operation of an electronically controlled device according to some embodiments of this specification. In some embodiments, process 300 may be executed by the electronically controlled device.

[0068] like Figure 3 As shown, in some embodiments, the electronic control device may include a first controller 331 and a second controller 332.

[0069] In some embodiments, the first controller 331 can be used to acquire underwater silt parameters 310 and work area related parameters 320.

[0070] Underwater silt parameter 310 can refer to the relevant physical properties of the underwater silt to be cleaned. For example, underwater silt parameter 310 can include silt thickness and silt strength. It can also include sand content, density, and bulk density.

[0071] In some embodiments, various devices and / or equipment can be used to acquire underwater silt parameters. For example, a detector positioned at the bottom of the buoy can be used to determine relevant data in the underwater silt parameter 310. In one specific embodiment, a dual-frequency echo rangefinder can be used to determine the silt thickness by measuring the time difference between when ultrasonic waves of different frequencies emitted by the instrument reach the bottom of the silt and are received by the instrument.

[0072] In some embodiments, pressure data can be acquired using a pressure sensor to determine relevant data in underwater sludge parameter 310. In some embodiments, the pressure sensor can be installed at the connection between the guide rod and the slurry pump to monitor and acquire pressure data as the slurry pump descends. In some embodiments, the pressure sensor can be installed at the connection between the crushing device and the slurry pump to monitor and acquire pressure data experienced by the crushing device when crushing sludge. Understandably, the greater the sludge strength, the greater the pressure experienced by the crushing device when crushing sludge; based on the acquired pressure data, the sludge strength can be determined.

[0073] The operating area related parameter 320 can refer to the environmental parameters of the working area where the dredging robot is located. For example, the operating area related parameter 320 may include water flow velocity. It can also include water depth and environmental protection requirements. Understandably, when the environmental requirements of the operating area are high, the operating parameters of the relevant components of the dredging robot can be appropriately lower to avoid causing significant disturbance to the operating area, thereby causing some pollution to the environment and violating environmental protection requirements.

[0074] In some embodiments, various devices can be used to acquire relevant parameters 320 of the work area. For example, a water flow velocity meter can be used to monitor the water flow velocity in the work area and acquire relevant data. Another example is the use of a laser rangefinder to determine water depth data by measuring the time required for the laser emitted by the rangefinder to travel from its emission point to the bottom of the water and back.

[0075] In some embodiments, the first controller 331 can determine the operating parameters 370 of the slurry pump and crane based on the underwater silt parameters 310 and the relevant parameters 320 of the work area. For example, based on experience or historical data, the corresponding operating parameters 370 of the slurry pump and crane are determined according to the underwater silt parameters 310 and the relevant parameters 320 of the work area. Another example is that, based on a parameter-determined model, the underwater silt parameters 310 and the relevant parameters 320 of the work area are used as inputs to output the operating parameters 370 of the slurry pump and crane. For further explanation of the method for determining the operating parameters 370, please refer to [link to relevant documentation]. Figure 4 And its related descriptions.

[0076] Figure 4 This is an exemplary flowchart illustrating the determination of operating parameters according to some embodiments of this specification. In some embodiments, process 400 may be executed by an electronically controlled device. Figure 4 As shown, process 400 may include the following steps:

[0077] Step 410: Determine the matching vector based on underwater silt parameters and relevant parameters of the work area.

[0078] The vector to be matched can refer to a data vector that meets preset conditions, obtained by processing underwater silt parameters and relevant parameters of the working area. These preset conditions may include: arranged in a fixed order, numerical values ​​within a preset range, and / or all data being normalized.

[0079] As an example, in the underwater silt parameters, the silt strength is 18 kPa and the silt thickness is 0.36 m. In the relevant parameters of the working area, the water flow velocity is 1.2 m / s and the water depth is 1.3 m. Based on the above parameters, a matching vector A = (18, 0.36, 1.2, 1.3) can be established, where different element values ​​in the matching vector A represent different underwater silt parameter values ​​or working area relevant parameter values.

[0080] Step 420: Based on the retrieval results of the vector to be matched in the vector database, determine the operating parameters of the slurry pump and the crane.

[0081] In some embodiments, the method further includes determining the operating parameters of the crushing device.

[0082] In some embodiments, historical data can be processed to obtain data vectors that meet preset conditions, thereby constructing a vector database. The historical data may include underwater silt parameters and relevant parameters of the working area during the dredging robot's historical operations, as well as the corresponding reasonable operating parameters of the slurry pump, crane, and crushing device. The preset conditions may include: arranged in a fixed order, numerical values ​​within a preset range, and / or all data being normalized. For example, historical data can be obtained by querying manually recorded data, connecting to the internet, querying databases, and / or searching third-party platforms.

[0083] As an example only, the vector to be matched can be compared and analyzed with the vectors in the vector database. The working parameters of the slurry pump, crane and crushing device corresponding to at least one vector in the vector database that meets the preset conditions can be used as the search results. The preset conditions can be that the standard deviation between the element value in the vector to be matched and the element value of the corresponding vector in the vector database is less than a threshold.

[0084] In some embodiments, the vectors in the vector database can be clustered to obtain multiple cluster centers, each cluster center corresponding to the operating parameters of a slurry pump, crane, and crushing device; then the distance between the vector to be matched and each cluster center is calculated, and the operating parameters of the slurry pump, crane, and crushing device corresponding to the nearest at least one cluster center are used as the search results.

[0085] Based on the above search results, relevant calculations were performed to determine the working parameters of the slurry pump, crane, and crushing device in the dredging robot.

[0086] In some embodiments, by utilizing methods such as database retrieval and cluster analysis, the operating parameters of the slurry pump, crane, and crushing device in the underwater dredging robot can be determined quickly and efficiently, and the determined parameters conform to the actual situation.

[0087] In some embodiments, a preset algorithm may be used for calculation.

[0088] For example, the operating parameters of slurry pumps, cranes, and crushing devices corresponding to vectors in the vector database that meet the distance requirements to the vector to be matched are used as initial operating parameters. The initial operating parameters are iterated at least once using a preset algorithm to determine candidate operating parameters. The candidate operating parameters that meet the preset conditions are determined as the optimal operating parameters and used as the final operating parameters.

[0089] The preset conditions may include: the number of iterations reaching a threshold and / or the evaluation value of the working parameters meeting the requirements.

[0090] In some embodiments of this specification, reasonable working parameters can be obtained relatively quickly and accurately by using a preset algorithm for iterative calculation, thereby improving calculation efficiency.

[0091] For example, we can set the number of initial working parameters to N, and the dimension of each initial working parameter to D, where D can refer to the number of parameters in the initial working parameters that need to be iteratively updated.

[0092] The vector corresponding to the i-th initial working parameter It can be represented as:

[0093]

[0094] A vector corresponding to N initial working parameters It can be represented as:

[0095]

[0096] Where 0 is an identifier (representing the 0th iteration, i.e., the initial value before the iteration has started), and i is the number of the candidate working parameter, where i≤N.

[0097] In some embodiments, at least one candidate operating parameter corresponds to at least one optimization rate. There is a one-to-one correspondence between an element in the candidate operating parameter and an incremental element in the optimization rate. The optimization rate can refer to the adjustment magnitude of the operating parameter during each iteration. Updating an incremental element in the optimization rate can include: updating the current incremental element based on the loss of the previous iteration, and using the updated incremental element as the incremental element for the next iteration. The loss of the previous iteration can be determined based on the difference between the candidate operating parameters obtained after the previous iteration and the historical optimal operating parameter. The historical optimal operating parameter can refer to the operating parameter with the best current dredging efficiency among the multiple candidate operating parameters determined during the iteration process.

[0098] For example, after the (k+1)th iteration, the updated incremental element can be calculated using the following formula (1):

[0099]

[0100] Where i represents the candidate working parameter number, i≤N; d represents the parameter number among the candidate working parameters, d≤D; k represents the iteration round number, k≥0. This represents the incremental element to be processed corresponding to the parameter numbered d obtained after the k-th iteration of the i-th candidate working parameter. Let d represent the parameter with index d in the i-th candidate working parameters obtained after the k-th iteration. ω represents the inertia weight constant. c1 represents the individual learning factor, and c2 represents the group learning factor. r1 and r2 are arbitrary values ​​in the interval [0, 1], which can be used to increase the randomness of the search. Let d be the parameter value of the i-th candidate working parameter in the optimal solution of each iteration after the k-th iteration. The optimal solution can refer to the set of parameters corresponding to a candidate working parameter when the evaluation value of a certain candidate working parameter is the maximum value among the multiple evaluation values ​​corresponding to it in each iteration after the k-th iteration. Let d be the parameter value with number d in the optimal solution of all N candidate working parameters after the k-th iteration. The optimal solution can refer to the set of parameters corresponding to the candidate working parameter with the largest evaluation value among the aforementioned candidate working parameters with the largest evaluation value in each iteration after the k-th iteration.

[0101] The aforementioned inertia weight constant ω, individual learning factor c1, group learning factor c2, and random constants r1 and r2 can be system default values, empirical values, manually preset values, or any combination thereof, set according to actual needs.

[0102] In some embodiments, the maximum absolute value of the increment element in each iteration can be v. max This represents the maximum adjustment range of the parameters in each candidate working parameter group. In the (k+1)th iteration, the multidimensional increment V, composed of the adjustment ranges of the parameters in each block of candidate working parameters, is... i It can be represented as (v i1 v i2 , ..., v iD The vector of multidimensional increments corresponding to N candidate working parameters can be represented as ((v 11 v 12 , ..., v 1D ), (v 21 v 22 , ..., v 2D ), ..., (v N1 v N2 , ..., v ND In this vector, the value of any increment element can be positive, 0, or negative, but its absolute value is no greater than v. max .

[0103] In some embodiments, each candidate working parameter can be updated based on the incremental element in the updated optimization velocity. For example, after the (k+1)th iteration, the updated candidate working parameter can be calculated using the following formula (2):

[0104]

[0105] For example, after the first iteration, the updated i-th candidate working parameter can be calculated using the following formula (3):

[0106]

[0107] The vector representation of the N updated candidate working parameters can be calculated using the following formula (4):

[0108]

[0109] In some embodiments, all candidate working parameters may be subject to constraints in each iteration.

[0110] For example, constraints can be determined based on rated power, meaning that among the candidate operating parameters, the maximum processing power of the slurry pump, crane, and crushing device must not exceed the rated power, or must not exceed the rated power multiplied by a safety factor less than 1. Alternatively, constraints can be determined based on user requirements, such as a maximum processing time of no more than one minute at each location; or, for example, a limit on the maximum processing power of the slurry pump, crane, and crushing device when the water flow velocity is too high. Furthermore, a limit on the minimum processing power should be imposed when the sludge strength is too high to prevent excessively low efficiency.

[0111] During the iteration process, the candidate working parameters should satisfy all constraints. If at least one of the candidate working parameters does not satisfy the constraints, then the at least one parameter value is initialized or assigned a value that satisfies the constraints and is close to the historical optimal solution, and the iteration continues.

[0112] In some embodiments, the parameter values ​​in each candidate operating parameter have a functional relationship with underwater silt parameters and relevant parameters of the operating area. For example, the processing power of the slurry pump, crane, and crushing device is positively correlated with silt intensity. As another example, the processing power of the slurry pump, crane, and crushing device is negatively correlated with water flow velocity. The functional relationship can be determined based on historical data. For instance, the functional relationship can be determined by processing the processing power data of the slurry pump, crane, and crushing device with silt intensity data or water flow velocity data from multiple historical datasets, such as by modeling or various data analysis algorithms, such as regression analysis and discriminant analysis.

[0113] In some embodiments, by calculating the functional relationship between candidate parameters and underwater silt parameters and relevant parameter values ​​of the working area, the calculation results of the preset algorithm can be made more consistent with the actual situation.

[0114] In some embodiments, the evaluation value can be determined based on the power consumption and dredging time during the operation of the dredging robot. For example, the evaluation value can be determined by assigning a score to the power consumption and dredging time respectively, and then performing a weighted summation, where the lower the power consumption, the higher the score; and the shorter the dredging time, the higher the score.

[0115] In some embodiments, the evaluation values ​​are determined by assessing the working parameters based on power consumption and dredging time, so that the working parameters determined by the preset algorithm are more in line with the actual situation and meet the user's needs.

[0116] In some embodiments, a dredging time prediction model can also be used to predict the dredging time of the dredging robot under different operating parameters. The dredging time prediction model can be a machine learning model, which may include models obtained by convolutional neural networks (CNNs), deep neural networks (DNNs), or combinations thereof.

[0117] The inputs to the dredging time prediction model can include underwater silt parameters, relevant parameters of the work area, and working parameters, and its output can include the predicted dredging time.

[0118] In some embodiments, the dredging time prediction model can be acquired through training, which can be performed by an electronically controlled device. In some embodiments, the dredging time prediction model can be trained using multiple training samples. These training samples include underwater silt parameters, relevant parameters of the sample's operating area, and sample operating parameters, along with their corresponding labels. The labels characterize the dredging time corresponding to the sample. The labels can be obtained through manual annotation.

[0119] In some embodiments, by using a trained machine learning model for prediction, the dredging time of the dredging robot can be determined quickly and accurately, thereby improving work efficiency and saving costs.

[0120] In some embodiments, the input to the dredging time prediction model may also include impurity conditions. For further explanation of impurity conditions, see [link to relevant documentation]. Figure 5 and Figure 6 And related explanations. In some embodiments, by incorporating impurity information into the input of the dredging time prediction model, the model's output can be made more consistent with reality and meet user needs.

[0121] In some embodiments, the first controller 331 may send control commands 350 to the slurry pump and the crane based on the operating parameters 370 of the slurry pump and the crane.

[0122] In some embodiments, the first controller 331 may also send control commands 350 to the crushing device based on the operating parameters 370 of the crushing device.

[0123] Control command 350 can refer to commands that adjust the working status of relevant components of the dredging robot. For example, control command 350 may include: changing the processing power, changing the angle, changing the length, and starting or stopping operation.

[0124] In some embodiments, the second controller 332 may be used to receive user input of work instructions 340.

[0125] The user-inputted work instruction 340 can refer to the instruction information input by the user into the second controller 332 through human-computer interaction. For example, the user can input work instructions through voice, touch screen clicks, and / or key input. The user-inputted work instruction 340 can refer to instructions that adjust the working status of relevant components of the dredging robot. For example, in addition to the relevant content of the control instruction 350, the user-inputted work instruction 340 can also include: changing the floating body's running direction, changing the floating body's running speed, and commanding the floating body to start / stop moving, etc.

[0126] like Figure 3 As shown, the second controller 332 can send the processed work instruction 360 obtained after processing the user-input work instruction 340 to at least one of the float, crane, and slurry pump. In some embodiments, the second controller 332 can also send the processed work instruction 360 obtained after processing the user-input work instruction 340 to the crushing device.

[0127] Processing may refer to the second controller 332 converting the user-input signals into other forms of signals to facilitate information exchange between different components of the dredging robot. The user-input signals may include, but are not limited to, voice signals and / or mechanical signals. For example, processing may include: converting voice signals into electrical signals, analog signals into digital signals, mechanical signals into electrical signals, and combinations thereof.

[0128] In some embodiments, users can reasonably control the operation of the dredging robot through its own adjustment and manual adjustment. When the robot's own adjustment cannot meet the needs, manual adjustment can be performed to improve the efficiency of sludge treatment, enhance the user experience, and save costs.

[0129] Figure 5 This is another schematic diagram of the operation of an electronically controlled device according to some embodiments of this specification. In some embodiments, process 500 can be executed by the electronically controlled device. Figure 5 As shown, process 500 includes the following steps:

[0130] Step 510: Acquire the pressure values ​​measured by each pressure sensor in real time.

[0131] For example, pressure values ​​can be obtained based on pressure data output from pressure sensors installed at multiple locations on the dredging robot. For more information on acquisition methods, please refer to [link / reference needed]. Figure 3 And its related descriptions.

[0132] Step 520: Based on the pressure values ​​measured by each pressure sensor, determine the impurities in the sludge.

[0133] The condition of impurities can refer to the content and hardness of impurities in the silt. Impurities can be considered as solid substances in the silt. For example, impurities may include, but are not limited to, stones, concrete blocks, glass blocks, and wood blocks. In some embodiments, the condition of impurities can be determined through data analysis or modeling.

[0134] In some embodiments, sound data during the operation of the dredging robot can be collected by a sound acquisition device (such as a sonar) installed at the bottom of the equipment platform. The sound data and real-time pressure data obtained by a pressure sensor can be combined to determine the condition of impurities.

[0135] In some embodiments, an impurity detection model can also be used to determine the impurity status. The impurity detection model can be a machine learning model. It may include convolutional neural networks, deep neural networks, or combinations thereof.

[0136] Figure 6 This is a model diagram of the impurity judgment model shown in some embodiments of this specification. For example... Figure 6 As shown, the impurity judgment model 630 may include a sound feature extraction layer 631, a pressure parameter extraction layer 632, and a judgment layer 633. The structural types of the sound feature extraction layer 631, the pressure parameter extraction layer 632, and the judgment layer 633 may include convolutional neural networks, deep neural networks, or models obtained by combinations thereof.

[0137] In some embodiments, the input to the sound feature extraction layer 631 may include sound data 610, and its output may be a sound feature vector 640; the input to the pressure parameter extraction layer 632 may include pressure data 620, and its output may be a pressure feature vector 650; the input to the judgment layer 633 may include the sound feature vector 640 and the pressure feature vector 650, and its output may be an impurity status 660.

[0138] In some embodiments, the pressure data 620 can be a data sequence of the dredging robot from the start of its work to the present at its current location. For example, if the dredging robot works continuously at its current location for 2 minutes and collects data every 15 seconds, the pressure data obtained are 0 kPa, 0 kPa, 5 kPa, 7 kPa, 8 kPa, 11 kPa, 13 kPa, and 18 kPa, then the data sequence can be (0, 0, 5, 7, 8, 11, 13, 18).

[0139] In some embodiments, by using the continuously collected pressure data sequence as input to the model, pressure change data during the operation of the dredging robot can be obtained, making the model's output judgment results more evidence-based and improving the accuracy of the judgment.

[0140] In some embodiments, the parameters of the sound feature extraction layer 631, the pressure parameter extraction layer 632, and the judgment layer 633 can be trained using multiple sets of labeled training samples to obtain the impurity judgment model 630. For example, the training samples of the sound feature extraction layer 631 may include sample sound data, and the training samples of the pressure parameter extraction layer 632 may include sample pressure data, with corresponding labels being sound feature vectors and pressure feature vectors, respectively; the samples of the judgment layer 633 may be the outputs of the sound feature extraction layer 631 and the pressure parameter extraction layer 632, with labels representing the corresponding impurity conditions.

[0141] In some embodiments, multiple training samples can be input into the sound feature extraction layer 631, the stress parameter extraction layer 632, and the judgment layer 633. Their parameters are iteratively updated through joint training until the trained sound feature extraction layer 631, stress parameter extraction layer 632, and judgment layer 633 meet preset conditions, thus obtaining the trained sound feature extraction layer 631, stress parameter extraction layer 632, and judgment layer 633, thereby obtaining the impurity judgment model 630. The preset conditions can be that the loss function is less than a threshold, convergence, or the training cycle reaches a threshold. In some embodiments, the method for iteratively updating the model parameters can include conventional model training methods such as stochastic gradient descent. For example, sample sound data can be input into the sound feature extraction layer 631, sample pressure data can be input into the pressure parameter extraction layer 632, and then the outputs of the sound feature extraction layer 631 and the pressure parameter extraction layer 632 can be used as training sample data and input into the judgment layer 633. A loss function can be constructed based on the output of the judgment layer 633 and the label. Then, the parameters of the sound feature extraction layer 631, the pressure parameter extraction layer 632 and the judgment layer 633 can be iteratively updated based on the loss function until the preset conditions are met and the training is completed.

[0142] The parameters of the impurity judgment model obtained through the above training method can, in some cases, help solve the problem of difficulty in obtaining labels when training the sound feature extraction layer and the pressure parameter extraction layer separately, thereby improving the model training efficiency.

[0143] In some embodiments, by utilizing a custom model containing multiple layers to acquire multiple data points in real time, the level of impurities during the operation of the dredging robot can be determined relatively quickly, thus meeting user needs.

[0144] Step 530: Adjust the operating parameters of the slurry pump and crane based on the impurity conditions. In some embodiments, this also includes adjusting the operating parameters of the crushing device.

[0145] As an example, when the impurities have a high hardness level, the processing power of the crushing device and slurry pump can be appropriately increased, while the drilling speed of the crushing device can be appropriately reduced. By adjusting the operating parameters based on the impurities in the sludge, the probability of malfunctions in the dredging robot can be effectively reduced, dredging efficiency can be improved, and dredging costs can be reduced.

[0146] In some embodiments, when the condition of impurities in the sludge changes, it is possible to... Figure 3 In the described preset algorithm, during multiple iterations, the optimized speed that satisfies both the constraints and the evaluation value satisfying the preset conditions is extracted. The current working parameters are then updated, resulting in multiple updated working parameters. Finally, the optimal working parameters are determined based on the evaluation values ​​corresponding to the updated working parameters. Satisfying both the constraints and the evaluation value satisfying the preset conditions means satisfying the relevant constraints and preset conditions in the preset algorithm. For more information on the preset algorithm and its constraints and preset conditions, please refer to [link to relevant documentation]. Figure 3 And its related descriptions.

[0147] In some embodiments, by updating the preset algorithm based on changes in impurities, the calculated working parameters are made more consistent with the actual situation, thereby improving the working efficiency of the dredging robot and reducing costs.

[0148] In some embodiments, when the impurities include hard impurities with a hardness greater than a threshold, the electronic control device can send a stop operation command and / or a position movement command to the underwater dredging robot.

[0149] The hardness of impurities can be determined by a detector, for example, by combining sound data collected by sonar and real-time pressure data obtained by a pressure sensor to determine the hardness value of the impurities. Impurities can be classified into multiple hardness levels based on multiple preset hardness values. Understandably, the higher the hardness level of an impurity, the harder it is. Impurities exceeding a preset threshold range can be classified as hard impurities.

[0150] In some embodiments, preset operating parameters can be adjusted based on the hardness level of the impurities. These operating parameters may include the movement data of the surface and underwater dredging robot. In some embodiments, the higher the hardness level of the impurities, the greater the adjustment range of the operating parameters of the surface and underwater dredging robot, and the greater the movement range of the robot.

[0151] For more information on sending commands, please refer to [link / reference]. Figure 3 And its related descriptions.

[0152] In some embodiments, by monitoring the hard impurities in the sludge in real time during the dredging process and making corresponding treatment plans, damage to the relevant components of the dredging robot can be effectively avoided, its wear and tear or the probability of failure can be reduced, dredging efficiency can be improved, and costs can be reduced.

[0153] The basic concepts have been described above. Obviously, for those skilled in the art, the detailed disclosure above is merely illustrative and does not constitute a limitation of this specification. Although not explicitly stated herein, those skilled in the art may make various modifications, improvements, and corrections to this specification. Such modifications, improvements, and corrections are suggested in this specification and therefore remain within the spirit and scope of the exemplary embodiments described herein.

[0154] Furthermore, this specification uses specific terms to describe embodiments thereof. For example, "an embodiment," "one embodiment," and / or "some embodiments" refer to a particular feature, structure, or characteristic associated with at least one embodiment of this specification. Therefore, it should be emphasized and noted that references to "an embodiment," "one embodiment," or "an alternative embodiment" in different locations throughout this specification do not necessarily refer to the same embodiment. Moreover, certain features, structures, or characteristics in one or more embodiments of this specification can be appropriately combined.

[0155] Furthermore, unless expressly stated in the claims, the order of processing elements and sequences, the use of numbers and letters, or other names described in this specification are not intended to limit the order of the processes and methods described herein. Although various examples have been discussed in the foregoing disclosure of embodiments that are currently considered useful, it should be understood that such details are for illustrative purposes only, and the appended claims are not limited to the disclosed embodiments; rather, the claims are intended to cover all modifications and equivalent combinations that conform to the substance and scope of the embodiments described herein. For example, while the system components described above can be implemented using hardware devices, they can also be implemented solely using software solutions, such as installing the described system on existing servers or mobile devices.

[0156] Similarly, it should be noted that, in order to simplify the descriptions disclosed herein and thus aid in the understanding of one or more embodiments, the foregoing description of embodiments in this specification sometimes combines multiple features into a single embodiment, drawing, or description thereof. However, this method of disclosure does not imply that the subject matter of this specification requires more features than those mentioned in the claims. In fact, the embodiments contain fewer features than all the features of the single embodiments disclosed above.

[0157] In some embodiments, numbers describing the quantity of components and attributes are used. It should be understood that such numbers used in the description of embodiments are modified in some examples with the terms "approximately," "approximately," or "generally." Unless otherwise stated, "approximately," "approximately," or "generally" indicates that the numbers are allowed to vary by ±20%. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximate values, which may be changed depending on the characteristics required by individual embodiments. In some embodiments, numerical parameters should take into account specified significant digits and employ a general method of digit reservation. Although the numerical ranges and parameters used to confirm their breadth of range in some embodiments of this specification are approximate values, in specific embodiments, such values ​​are set as precisely as feasible.

[0158] For each patent, patent application, patent application publication, and other material, such as articles, books, specifications, publications, and documents, referenced in this specification, the entire contents of which are incorporated herein by reference. This excludes historical application documents that are inconsistent with or conflict with the content of this specification, as well as documents that limit the broadest scope of the claims in this specification (currently or subsequently appended to this specification). It should be noted that in the event of any inconsistency or conflict between the descriptions, definitions, and / or terminology used in the supplementary materials to this specification and the content of this specification, the descriptions, definitions, and / or terminology used in this specification shall prevail.

[0159] Finally, it should be understood that the embodiments described in this specification are merely illustrative of the principles of the embodiments described herein. Other variations may also fall within the scope of this specification. Therefore, alternative configurations of the embodiments described herein are intended to be illustrative rather than limiting, and should be considered consistent with the teachings of this specification. Accordingly, the embodiments described herein are not limited to those explicitly introduced and described herein.

Claims

1. A surface and underwater dredging robot, characterized in that, include: The floating body includes an equipment platform connected to a pontoon; the equipment platform is equipped with a water pump connected to the pontoon, a hydraulic station for providing power, and a positioning system for positioning and navigation; A crane, which is mounted on the equipment platform; A slurry pump is installed below the equipment platform. The equipment platform is connected to a guide rod, and the slurry pump is movably mounted on the guide rod. The slurry pump is connected to a crane, which can drive the slurry pump to move up or down along the guide rod. The outlet of the slurry pump is connected to a sewage pipe, and the other end of the sewage pipe has a sludge outlet for discharging sludge. An electrical control device is communicatively connected to the float, the crane, and the slurry pump, respectively. The electronic control device includes a first controller; The first controller is used for: Acquire underwater silt parameters and relevant parameters of the work area; The matching vector is determined based on the underwater silt parameters and the relevant parameters of the work area. Based on the vector to be matched, a search is performed in the vector database. The working parameters of the slurry pump and the crane corresponding to the vector in the vector database that meet the distance requirement with the vector to be matched are used as initial working parameters. The initial working parameters are iterated for at least one round using a preset algorithm to determine candidate working parameters. The candidate working parameters that meet the preset conditions are determined as the working parameters of the slurry pump and the crane. Based on the operating parameters of the slurry pump and the crane, control commands are sent to the slurry pump and the crane.

2. The underwater dredging robot according to claim 1, characterized in that, The electronic control device includes a second controller; The underwater silt parameters include: silt thickness and silt strength; the relevant parameters of the work area include: the water flow velocity in the work area; The second controller is used for: The system receives work instructions input by the user, processes the work instructions, and sends them to at least one of the floating body, the crane, and the slurry pump.

3. The underwater dredging robot according to claim 2, characterized in that, The underwater dredging robot also includes multiple pressure sensors, which are located below the equipment platform; the electronic control device is further used for: Real-time acquisition of pressure values ​​measured by each of the pressure sensors; Based on the pressure values ​​measured by each of the pressure sensors, the condition of impurities in the sludge is determined. Based on the impurities, the operating parameters of the slurry pump and the crane are adjusted.

4. The underwater dredging robot according to claim 3, characterized in that, The electronic control device is also used to: when the impurity condition is that it contains hard impurities with a size greater than a threshold, send a stop operation command and a position movement command to the underwater dredging robot.

5. A dredging method performed by the underwater dredging robot as described in claim 4, characterized in that, The underwater dredging robot is controlled by its electronic control device to perform dredging; the dredging method includes: The float moves to a set position on the water surface, and the crane controls the slurry pump to descend along the guide rod to a set depth. The slurry pump sucks up the silt from the bottom of the water, and the silt is sent out through the sewage pipe.

6. The method according to claim 5, characterized in that, The electronic control device includes a first controller and a second controller; The first controller is used for: Acquire underwater silt parameters and relevant parameters of the work area; Based on the underwater silt parameters and the relevant parameters of the work area, the operating parameters of the slurry pump and the crane are determined; Based on the operating parameters of the slurry pump and the crane, control commands are sent to the slurry pump and the crane; The underwater silt parameters include: silt thickness and silt strength; the relevant parameters of the work area include: water flow velocity in the work area; The second controller is used for: The system receives work instructions input by the user, processes the work instructions, and sends them to at least one of the floating body, the crane, and the slurry pump.

7. The method according to claim 6, characterized in that, Based on the underwater silt parameters and the relevant parameters of the work area, the operating parameters of the slurry pump and the crane are determined, including: The matching vector is determined based on the underwater silt parameters and the relevant parameters of the work area. Based on the retrieval results of the vector to be matched in the vector database, the operating parameters of the slurry pump and the crane are determined.

8. The method according to claim 6, characterized in that, The electronic control device is also used for: Real-time acquisition of pressure values ​​measured by each of the pressure sensors; Based on the pressure values ​​measured by each of the pressure sensors, the condition of impurities in the sludge is determined. Based on the impurities, the operating parameters of the slurry pump and the crane are adjusted.

9. The method according to claim 8, characterized in that, The electronic control device is also used to: when the impurities are hard impurities with a hardness greater than a threshold, send a stop operation command and / or a position movement command to the underwater dredging robot.

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