Method and system for evaluating cleaning efficiency of water surface cleaning robot

Abstract

The invention discloses a method and a system for evaluating the cleaning efficiency of a water surface cleaning robot. The method comprises the following steps: fixing a tested water surface cleaning robot in a standard test environment; acquiring flow field velocity distribution of the front edge of the inlet of the robot by using a particle image velocity measurement system; based on the flow field velocity distribution, the average suction velocity of the inlet front edge is extracted; calculating a speed reaching rate according to the average suction speed and a preset critical capture speed; and taking the speed standard-reaching rate as an evaluation index of the cleaning efficiency. Based on the fluid mechanics principle and the PIV technology, the inlet flow velocity of the water surface cleaning robot is evaluated through the inlet flow velocity obtained through an experiment, and therefore the garbage suction capacity of the water surface cleaning robot is judged. Compared with subjective visual judgment, simple testing and other modes, the scheme enables the evaluation result to have higher reliability and repeatability.

Description

A method and system for evaluating the cleaning efficiency of a water surface cleaning robot. Technical Field

[0001] This invention belongs to the technical field of water surface cleaning equipment, specifically relating to a method and system for evaluating the cleaning efficiency of a water surface cleaning robot. Background Technology

[0002] In recent years, with the development of smart home technology, swimming pool surface cleaning robots, as an automated cleaning device, have gradually replaced traditional manual cleaning methods and have been widely welcomed due to their advantages such as high efficiency, safety, and energy saving. When the machine is working, its power unit (such as a propeller) generates high-speed, high-pressure fluid at the outlet, which is transmitted to the machine inlet through the internal flow channel. This causes the fluid velocity at the machine inlet to increase and the static pressure to decrease, forming a local negative pressure zone, thereby attracting water surface debris near the machine inlet to enter the collection device inside the machine with the water flow.

[0003] The cleaning efficiency of a water surface cleaning robot is a core indicator for its transition from laboratory research to engineering applications, directly impacting the feasibility of its design. Its cleaning efficiency is closely related to the flow rate at the robot's inlet; therefore, the cleaning efficiency can be evaluated by detecting its suction speed.

[0004] However, the industry currently lacks a scientific and unified evaluation system, with existing evaluations often relying on subjective visual judgment or simple, non-standard tests. Therefore, a scientifically quantifiable experimental method is urgently needed to evaluate the cleaning efficiency of pool surface cleaning robots and provide optimization directions for production. Summary of the Invention

[0005] To address the current lack of a scientific and unified method for evaluating the cleaning efficiency of water surface cleaning robots, this invention provides a method and system for evaluating the cleaning efficiency of water surface cleaning robots.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0007] The first aspect of the present invention provides a method for evaluating the cleaning efficiency of a water surface cleaning robot, comprising:

[0008] The water surface cleaning robot under test was fixed in a standard test environment;

[0009] The velocity distribution of the flow field at the leading edge of the robot inlet is obtained using a particle image velocimetry system;

[0010] Based on the flow field velocity distribution, the average suction velocity at the inlet leading edge is extracted;

[0011] The speed achievement rate is calculated based on the average inhalation rate and the preset critical capture rate.

[0012] The rate of achieving the speed target is used as an evaluation index for cleaning efficiency.

[0013] Preferably, the particle image velocimetry system includes a laser, a high-speed camera, and tracer particles. The sheet light generated by the laser illuminates the measurement area perpendicularly, and the optical axis of the high-speed camera is perpendicular to the sheet light plane and continuously acquires tracer particle images.

[0014] Preferably, the velocity vector field is obtained by cross-correlation calculation of the acquired image sequence, and the average inhalation velocity is obtained by ensemble averaging of multiple sets of velocity vector fields.

[0015] Preferably, the critical capture velocity is determined by recording the trajectory of successfully captured particles in multiple experiments, extracting their minimum velocity, and statistically averaging the results.

[0016] Preferably, the velocity compliance rate is the ratio of the area of ​​the region in the effective inlet cross-section where the normal velocity is not less than the critical capture velocity to the total cross-sectional area.

[0017] Preferably, the method also includes synchronously recording the robot's power consumption, using the ratio of speed achievement rate to unit power consumption as an energy efficiency ratio indicator, for horizontal comparison of the comprehensive cleaning-energy consumption performance of different models.

[0018] A second aspect of the present invention provides a system for evaluating the cleaning efficiency of a water surface cleaning robot, comprising:

[0019] Standard test tanks are used to provide a static water testing environment;

[0020] A fixing device is used to rigidly position the water surface cleaning robot to be tested within the test pool;

[0021] The particle image velocimetry module, which includes a laser, a high-speed camera and a tracer particle delivery unit, is used to acquire the two-dimensional flow field velocity distribution at the leading edge of the robot entrance.

[0022] The data processing unit is communicatively connected to the high-speed camera and is used to perform cross-correlation calculations on the acquired particle images and output the average inhalation velocity.

[0023] The evaluation output module is used to compare the average inhalation speed with the preset critical capture speed, calculate and display the speed compliance rate as a quantitative indicator of cleaning efficiency.

[0024] Preferably, the laser is a pulsed solid-state laser, which forms a sheet of light to illuminate the measurement area after being expanded by a cylindrical lens.

[0025] Preferably, the high-speed camera has a frame rate of ≥500fps and its optical axis is perpendicular to the light plane.

[0026] Preferably, it also includes a power consumption measurement module, which is used to synchronously collect the robot's input power and couple it with the speed achievement rate to calculate the energy efficiency ratio, which is then displayed by the evaluation output module.

[0027] By adopting the above technical solutions, the beneficial effects of this invention are:

[0028] 1. Based on fluid dynamics principles and PIV technology, the inlet flow velocity of a water surface cleaning robot is evaluated through experimentally obtained inlet flow velocity, thereby assessing its garbage suction capability. Compared to subjective visual judgment and simple tests, this method makes the evaluation results more reliable and repeatable.

[0029] 2. The visualized flow field provided by PIV technology provides a basis for diagnosing and optimizing the fluid structure design of water surface cleaning robots, and can diagnose the performance shortcomings of water surface cleaning robots, providing a clear direction for product optimization. Attached Figure Description

[0030] Figure 1 is a schematic diagram of the particle image velocimetry technology used in this application.

[0031] Figure 2 shows the layout of the flow field collection surface for the water surface cleaning robot.

[0032] Explanation of reference numerals in the attached figures: 1. Test cell; 2. Laser; 3. Cylindrical lens; 4. Laser sheet beam; 5. High-speed camera; 6. Tracer particle; 7. Measurement surface; 8. Interrogation domain; 9. Water surface; 10. First vertical acquisition surface; 11. Second vertical acquisition surface; 12. Third vertical acquisition surface; 13. Longitudinal acquisition surface; 14. Model under test. Detailed Implementation

[0033] To enable those skilled in the art to better understand the technical solutions of this invention, the technical solutions of the invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, what is described is only a part of the embodiments of this invention, and not all of them. Based on the embodiments of the invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the invention.

[0034] Debris on the water surface floats because its density is lower than that of water and can be carried by the water flow. Based on this characteristic, water surface cleaning robots use the water flow to suck the debris into the machine. By detecting the water flow speed at the robot's inlet, its suction speed can be obtained, thus evaluating its cleaning ability.

[0035] Based on this, referring to Figures 1 and 2, this application provides a water surface cleaning robot cleaning efficiency evaluation system, which includes a test pool 1, a laser 2, a cylindrical lens 3, a high-speed camera 5, and tracer particles 6.

[0036] In this embodiment, the test model 14 of the water surface cleaning robot to be evaluated is placed in the test pool 1. The laser 2 is installed on the side of the test pool 1, and the laser beam generated by it is expanded by the cylindrical lens 3 to form a laser sheet light 4, which is used to illuminate the flow field at the inlet end of the water surface cleaning robot test model 14. A total of four acquisition surfaces are set, namely the first vertical acquisition surface 10, the second vertical acquisition surface 11, the third vertical acquisition surface 12, and the longitudinal acquisition surface 13, as shown in Figure 2. Among them, the first vertical acquisition surface 10, the second vertical acquisition surface 11, and the third vertical acquisition surface 12 are evenly distributed on the axial plane at the inlet of the test model 14, and the longitudinal acquisition surface 13 is parallel to the water surface 9. The spatial position of the high-speed camera 5 (frame rate ≥ 500fps) is determined according to the setting of the laser sheet light 4, keeping the camera optical axis perpendicular to the laser sheet light 4.

[0037] Furthermore, four flow field sampling surfaces are arranged in a half-side region of the fuselage, as follows: three of the vertical sampling surfaces are perpendicular to the water flow direction, the first vertical sampling surface 10 is located at the central axis of the fuselage, the third vertical sampling surface 12 is close to the side wall of the fuselage, and the second vertical sampling surface 11 is located at the center of the two. This set of sampling surfaces can completely cover the inlet leading edge flow field from the axis to the key working area of ​​the outer wall, while the longitudinal sampling surface 13 is along the water flow direction and covers the machine inlet leading edge flow field.

[0038] This application embodiment also provides a method for evaluating the cleaning efficiency of a water surface cleaning robot: the test model 14 to be evaluated is fixed in the test pool 1 using a fixing device to prevent displacement during operation. A PIV system is deployed, which includes a laser, a high-speed camera, and tracer particles.

[0039] Furthermore, the laser 2 is positioned to the side of the water surface cleaning robot being measured, and the laser beam it generates is expanded by the cylindrical lens 3 to form a vertical laser sheet beam, which is used to illuminate the flow field in the measurement area.

[0040] Furthermore, the high-speed camera 5 is installed on the side of the water surface cleaning robot being tested, and the camera's optical axis is perpendicular to the plane of the laser sheet 4, for acquiring images of tracer particles.

[0041] Position, align and fix the laser 2, cylindrical lens 3 and high-speed camera 5 as required above. Add an appropriate amount of tracer particles 6 to the test cell 1 as the mass point for fluid observation at an instant. Start the test model and start the PIV system after it has stabilized.

[0042] Laser 2 emits pulsed sheet light 4 to illuminate the collection surface 7. The high-speed camera 5 is adjusted to focus on the plane where the laser sheet light 4 is located to ensure that the edges of the tracer particles in the area illuminated by the laser are clearly visible. Then, the shooting frame rate of the high-speed camera is adjusted, and the high-speed camera 5 continuously takes pictures to collect the sequence images of tracer ions 6 in the collection surface.

[0043] Since the test section is illuminated by a continuous laser 2, the time intervals between the series of images captured by the high-speed camera 5 are equal and known. Image preprocessing is performed on the acquired PIV images to extract the image signals of the tracer particles, obtaining the original images of the tracer particle motion in the test section. A cross-correlation algorithm is then used to solve for the displacement of each particle in the interrogation domain 8 based on two tracer particle motion images with known time intervals. The particle displacements are analyzed to obtain the velocity vector field of the acquisition surface. Ensemble averaging of the data yields the time-averaged velocity field, from which the average inlet leading edge suction velocity is extracted.

[0044] Furthermore, assuming after After a time interval, the fluid particles are at and The displacement in the direction is , The flow field image at time t is ; The flow field image at time t is ,in and For the random noise in the system, calculate and cross-correlation function From the definition of the autocorrelation function, we can obtain The autocorrelation function is:

[0045]

[0046] right , , Perform Fourier transform:

[0047]

[0048]

[0049]

[0050] Calculating the cross-correlation function using Fourier transform :

[0051]

[0052]

[0053] Extracting the water flow velocity at the inlet of the tested model:

[0054]

[0055]

[0056] In the formula: The longitudinal instantaneous flow velocity at a certain moment during the test. To obtain the vertical instantaneous velocity, the average value of the instantaneous velocity is taken over a sufficiently long time period, and the longitudinal time-averaged velocity is obtained. , representing the average flow velocity along the direction of water flow. This represents the average flow velocity perpendicular to the direction of water flow.

[0057] Since the density of surface debris is lower than that of water, its movement is mainly driven by water flow. Therefore, the critical capture velocity for surface debris to be cleaned is crucial. As a criterion, the velocity component of the water flow acting on the waste in the normal direction of the inlet must not be less than a critical value. .

[0058] In a preferred embodiment, the critical capture velocity in this application is obtained by: experimentally recording the complete motion trajectory of each successfully inhaled particle, extracting the minimum velocity in its trajectory, repeating the experiment multiple times, statistically analyzing the minimum captured velocities to form an experimental sample, and taking the average of the samples, which is defined as the critical capture velocity.

[0059] Furthermore, the normal velocity of the effective flow section at the inlet was measured using PIV technology, thus obtaining the core indicators. Speed ​​compliance rate Where S is the cross-section The area of ​​the region, where A is the cross-sectional area. A higher value means a larger effective working area for the tested model, stronger waste capture capabilities, and higher cleaning efficiency.

[0060] After processing the data obtained from each collection surface, the area S of the region is calculated. i Cross-sectional area A i and the local compliance rate of a single acquisition surface The first vertical acquisition surface 10, the second vertical acquisition surface 11, and the third vertical acquisition surface 12 , as well as These respectively reflect the proportion of the effective working area in the radial region at different positions of the machine, and the longitudinal acquisition surface 13. This reflects the proportion of the effective working area along the water flow direction at the inlet leading edge. To comprehensively evaluate the overall cleaning efficiency of the tested model, the total area of ​​the four sampling surfaces is set to... Then the overall speed compliance rate of the tested model is obtained. :

[0061]

[0062] This weighted calculation method can take into account both radial and axial flow field characteristics, and can also reflect the flow field contribution of different sampling surfaces through area weight. It avoids the one-sided evaluation that may be caused by a single sampling surface, and ensures that the velocity compliance rate of the tested model can objectively and comprehensively reflect the machine's garbage collection capability.

[0063] Obviously, the above embodiments are merely illustrative examples for clear explanation and are not intended to limit the implementation. Those skilled in the art will recognize that other variations or modifications can be made based on the above description. It is neither necessary nor possible to exhaustively list all possible implementations here. However, obvious variations or modifications derived therefrom are still within the scope of protection of this invention.

Claims

1. A method for evaluating the cleaning efficiency of a water surface cleaning robot, characterized in that, include: The water surface cleaning robot under test was fixed in a standard test environment; The velocity distribution of the flow field at the leading edge of the robot inlet is obtained using a particle image velocimetry system; Based on the flow field velocity distribution, the average suction velocity at the inlet leading edge is extracted; the velocity compliance rate is calculated based on the average suction velocity and the preset critical capture velocity; the velocity compliance rate is used as an evaluation index of cleaning efficiency.

2. The method according to claim 1, characterized in that, The particle image velocimetry system includes a laser, a high-speed camera, and tracer particles. The laser generates sheet light that vertically illuminates the measurement area, and the high-speed camera's optical axis is perpendicular to the sheet light plane and continuously acquires tracer particle images.

3. The method according to claim 2, characterized in that, The velocity vector field is obtained by cross-correlation calculation of the acquired image sequence, and the average inhalation velocity is obtained by ensemble averaging of multiple sets of velocity vector fields.

4. The method according to any one of claims 1-3, characterized in that, The critical capture velocity was determined by recording the trajectory of successfully captured particles in multiple experiments, extracting their minimum velocity, and then statistically averaging the results.

5. The method according to claim 4, characterized in that, The velocity compliance rate is the ratio of the area of ​​the region in the effective inlet cross section where the normal velocity is not less than the critical capture velocity to the total area of ​​the cross section.

6. The method according to claim 1, characterized in that, It also includes synchronously recording robot power consumption, using the ratio of speed achievement rate to unit power consumption as an energy efficiency ratio indicator, which is used to compare the overall cleaning-energy consumption performance of different models.

7. A system for evaluating the cleaning efficiency of a water surface cleaning robot, characterized in that, include: A standard test pool is used to provide a static water testing environment; a fixing device is used to rigidly position the water surface cleaning robot under test within the test pool; The particle image velocimetry module includes a laser, a high-speed camera, and a tracer particle delivery unit, used to acquire the two-dimensional flow field velocity distribution at the leading edge of the robot entrance; the data processing unit is communicatively connected to the high-speed camera, used to perform cross-correlation calculations on the acquired particle images and output the average inhalation velocity; the evaluation output module is used to compare the average inhalation velocity with a preset critical capture velocity, calculate and display the velocity compliance rate as a quantitative indicator of cleaning efficiency.

8. The water surface cleaning robot cleaning efficiency evaluation system according to claim 7, characterized in that, The laser is a pulsed solid-state laser, which is expanded by a cylindrical lens to form sheet light that illuminates the measurement area.

9. The water surface cleaning robot cleaning efficiency evaluation system according to claim 8, characterized in that, The high-speed camera has a frame rate of ≥500fps and its optical axis is perpendicular to the plane of the light sheet.

10. A system for evaluating the cleaning efficiency of a water surface cleaning robot according to any one of claims 7 to 9, characterized in that, It also includes a power consumption measurement module, which is used to synchronously collect the robot's input power and couple it with the speed achievement rate to calculate the energy efficiency ratio, which is then displayed by the evaluation output module.

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