A sampling device for anionic synthetic detergents for tableware

Abstract

A sampling device for anionic synthetic detergents used in tableware relates to the field of tableware sample liquid sampling technology. Its main structure includes: a sample liquid collection box placed inside a base, with tableware placed within the box; rinsing nozzles connected to the outlet of a peristaltic pump via pipes; a local unit receiving point cloud data from a 3D vision camera and a laser contour scanner, processing it, completing 3D modeling, calculating the inner surface area of ​​the tableware, calculating the required running time of the peristaltic pump and sending the calculations to a PLC controller, and generating a rinsing path plan; the PLC controller controlling the peristaltic pump to start and stop according to the running time, and controlling the first robotic arm to move according to the rinsing path plan, driving the rinsing nozzles to complete the rinsing of the tableware. This invention solves the problems existing in the measurement of the inner surface area of ​​tableware and sample liquid preparation in the prior art, improving the accuracy and efficiency of sample liquid preparation for the detection of anionic synthetic detergents in tableware.

Description

Technical Field

[0001] This invention relates to the field of tableware sample liquid sampling technology, and in particular to a tableware anionic synthetic detergent sampling device. Background Technology

[0002] Anionic synthetic detergents are the main components of commonly used detergents such as laundry powder, dish soap, and laundry detergent liquid. Their main component is sodium dodecylbenzenesulfonate, a low-toxicity chemical substance widely used due to its ease of use, easy solubility, good stability, and low cost. However, if tableware is not thoroughly cleaned, detergent residue can remain, potentially harming human health.

[0003] In the field of food safety, there are strict hygiene standards for disinfected tableware. Appendix A of GB14934-2016, the National Food Safety Standard for Disinfected Tableware, clearly stipulates that 100 mL of distilled water should be used per 100 cm² of surface area for testing the cleaning and disinfection effect. This standard aims to ensure the cleanliness and disinfection effect of tableware surfaces, thereby protecting consumers' food safety. However, traditional sample preparation methods often rely on manual measurement and estimation, which is not only time-consuming and labor-intensive but also difficult to guarantee the accuracy of measurements. Due to the diverse shapes and complex internal surface areas of tableware, manual measurement often has significant errors, leading to inaccurate sample preparation ratios and affecting the reliability of test results. Furthermore, existing technologies lack automated control methods in the sample preparation process, making the preparation process cumbersome and inefficient.

[0004] For example, the existing patent CN222481926U, "A sample preparation device for detecting anionic synthetic detergents for tableware," provides a sample preparation device for detecting anionic synthetic detergents for tableware that can automatically extract distilled water and repeatedly rinse the inner surface of the tableware. However, since it does not have the function of measuring the inner surface area of ​​the tableware, it cannot achieve automated and high-precision preparation of sample solutions for detecting anionic synthetic detergents for tableware. Summary of the Invention

[0005] The purpose of this invention is to provide an innovative sampling device for anionic synthetic detergents used in tableware, which can solve the problems existing in the measurement of the inner surface area of ​​tableware and the preparation of sample solutions in the prior art, and improve the accuracy and efficiency of sample solution preparation for the detection of anionic synthetic detergents used in tableware.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is a sampling device for anionic synthetic detergents used in tableware, comprising:

[0007] The base has a slot for placing the sample collection box.

[0008] The peristaltic pump is fixedly installed on the base, and its inlet is connected to an external water source.

[0009] The flushing nozzle is fixedly installed on the first robotic arm and connected to the outlet of the peristaltic pump through a pipe;

[0010] A 3D vision camera is fixedly mounted on the second robotic arm to capture global images and acquire point cloud data of the inner surface of the tableware.

[0011] A laser contour scanner, fixedly mounted on the third robotic arm, is used to scan the blind spot of the 3D vision camera and acquire point cloud data;

[0012] The sample collection box has a sample rack for placing tableware installed inside.

[0013] The local unit is used to receive point cloud data acquired by the 3D vision camera and laser contour scanner, process and complete 3D modeling, use point cloud segmentation algorithm to extract geometric features of the inner surface of tableware, calculate the inner surface area of ​​tableware, calculate the volume of distilled water required for this sampling, calculate the running time required for the peristaltic pump and send it to the PLC controller, and also generate spiral progressive rinsing path planning and send it to the PLC controller.

[0014] The PLC controller is used to control the peristaltic pump to start and stop according to the running time, and also to control the first robotic arm to move according to the rinsing path plan, so as to drive the rinsing nozzle to complete the rinsing of the tableware.

[0015] Furthermore, the local machine's 3D modeling is based on a deep learning model with the ResNet-50 architecture.

[0016] Furthermore, the sampling device also includes a touch screen, which is fixedly mounted on the base and communicates with the local unit and the PLC controller.

[0017] Furthermore, the first robotic arm, the second robotic arm, and the third robotic arm are all fixedly mounted on the base.

[0018] The beneficial effects of this invention are as follows:

[0019] This invention achieves precise measurement of the inner surface area of ​​tableware using a 3D vision camera and a laser contour scanner. Based on this surface area, the water volume is selected and pumped using a peristaltic pump. A local PLC controller precisely controls the distilled water usage, ensuring thorough rinsing without any blind spots. This process automates sample preparation, improving accuracy and efficiency while significantly reducing the workload of operators. Attached Figure Description

[0020] Figure 1This is a three-dimensional structural diagram of an embodiment of the present invention (excluding the local unit, PLC controller, and sample collection box).

[0021] Figure 2 This is a three-dimensional structural diagram of the sample collection box according to an embodiment of the present invention;

[0022] Figure 3 This is a schematic diagram of the three-dimensional structure of the sample storage rack according to an embodiment of the present invention;

[0023] Figure 4 This is a schematic diagram of the electrical connections in an embodiment of the present invention. Detailed Implementation

[0024] The present invention will now be described in further detail with reference to the accompanying drawings.

[0025] Figures 1-4 This invention illustrates a specific embodiment of the tableware anionic synthetic detergent sampling device, comprising:

[0026] The base 101 has a placement slot 101a for placing the sample collection box 301.

[0027] The peristaltic pump 102 is fixedly installed on the base 101, and its inlet is connected to an external water source.

[0028] The flushing nozzle 201 is fixedly installed on the first robotic arm 204 and connected to the outlet of the peristaltic pump 102 through a pipe.

[0029] The 3D vision camera 202 is fixedly mounted on the second robotic arm 205 and is used to capture global images and acquire point cloud data of the inner surface of the tableware.

[0030] The laser contour scanner 203 is fixedly mounted on the third robotic arm 206 and is used to scan the blind area of ​​the 3D vision camera, including the chamfer of the bottle mouth, the groove of the bottom of the cup, and the edge of the bowl mouth, and to acquire point cloud data.

[0031] The sample collection box 301 has a sample rack 302 for placing tableware fixedly installed inside it.

[0032] The local machine 401 is used to receive point cloud data acquired by the 3D vision camera 202 and the laser contour scanner 203, process it, and complete 3D modeling. It uses a point cloud segmentation algorithm to extract the geometric features of the inner surface of the tableware, calculates the inner surface area of ​​the tableware, calculates the volume of distilled water required for this sampling, calculates the running time required for the peristaltic pump 102 and sends it to the PLC controller 402, and also generates a spiral progressive rinsing path plan and sends it to the PLC controller 402. The 3D modeling of the local machine 401 is based on a deep learning model with a ResNet-50 architecture. It processes 224×224 pixel RGB images, compresses the feature map size into a global feature vector, uses it for classification tasks, and outputs the classification results via a Softmax layer. The training data for this deep learning model comes from 50,000 images of tableware of various materials (including labeled datasets), ensuring the accuracy of recognition and modeling.

[0033] The PLC controller 402 is used to control the peristaltic pump 102 to start and stop according to the running time, and also to control the first robotic arm 204 to move according to the rinsing path plan, so as to drive the rinsing nozzle 201 to complete the rinsing of the tableware.

[0034] The touch screen 103 is fixedly installed on the base 101 and is communicatively connected to the local unit 401 and the PLC controller 402.

[0035] This embodiment uses an industrial switch to complete the networking of various devices.

[0036] In this embodiment, the first robotic arm 204, the second robotic arm 205, and the third robotic arm 206 are all fixedly mounted on the base 101.

[0037] The operation steps and working process of using the device of this embodiment for tableware rinsing and sampling are as follows:

[0038] 1. Place the tableware in

[0039] The operator first places the sample collection box 301 in the placement slot 101a of the tableware anionic synthetic detergent sampling device, and then places the tableware 5 to be tested on the sample rack 302 in the sample collection box. Figure 3 As shown.

[0040] Perform system initialization.

[0041] II. Calculating the inner surface area of ​​tableware

[0042] A 3D vision camera 202 and a laser contour scanner 203 are used to scan the tableware, generating a 3D point cloud model to lay the foundation for subsequent processing. 3D Vision Camera 202 Acquisition: Global imaging is initiated to acquire the complete point cloud of the tableware's inner surface (including large areas such as the bowl body and cup walls). Laser Contour Scanner 203 Acquisition: The third robotic arm 206 drives the laser contour scanner 203 to scan line by line along the inner wall trajectory, focusing on covering camera blind spots such as the chamfer of the bottle mouth, the groove at the bottom of the cup, and the edge of the bowl rim.

[0043] The point cloud data obtained from the tracing is uploaded to the local machine 401 for processing and to complete the 3D modeling. The point cloud segmentation algorithm is used to extract the geometric features of the inner surface of the tableware and calculate the inner surface area of ​​the tableware.

[0044] III. Calculation of flushing parameters (surface area and water distribution, path planning)

[0045] The local unit 401 strictly adheres to the ratio of 100 mL of distilled water per 100 cm² surface area to calculate the precise volume of distilled water required for this sampling. Simultaneously, considering the flow rate of the peristaltic pump 102, the specific operating time of the peristaltic pump 102 is calculated using the formula "Peristaltic pump running time (s) = (surface area / 100) × 100 mL ÷ flow rate (mL / s)" and then transmitted to the PLC controller 402.

[0046] The local unit 401 generates a spiral progressive rinsing path plan based on the 3D model of the tableware and sends it to the PLC controller 402.

[0047] IV. Precise Rinsing and Sample Collection

[0048] The PLC controller 402 drives the first robotic arm 204 to move the rinsing nozzle 201 to a designated position above the tableware.

[0049] The PLC controller 402 controls the peristaltic pump 102 to start. The peristaltic pump 102 draws distilled water at a set flow rate and sprays it onto the inner wall of the tableware through the nozzle 201. At the same time, the PLC controller 402 controls the first robotic arm 204 to move the rinsing nozzle 201 according to the planned spiral progressive rinsing path, dynamically adjusting the operating status to complete all-round rinsing without dead angles.

[0050] V. Sample Recovery and Temporary Storage

[0051] Remove the sample collection box 301, collect the tableware, and cover it.

[0052] The collected sample solutions are stored for testing.

[0053] VI. System Reset

[0054] After one sampling is completed and before the next sampling begins, each robotic arm returns to the standby position, and the system resets to prepare for processing the next tableware sample.

[0055] VII. Quality Monitoring and Data Management

[0056] The local unit 401 records key parameters for each operation in real time, including rinsing time and distilled water usage.

[0057] The model specifications of each device in this embodiment are shown in the table below:

[0058] Equipment Name Brand / Model Key Specifications 3D vision camera Cognex In-Sight 2800 1280×1024 resolution, supports deep learning, ±0.1mm accuracy, IP67 protection. robotic arm Universal Robots UR10e Six-axis collaboration, 12.5kg load capacity, ±0.05mm repeatability, and supports EtherCAT communication. Laser profilometer Keyence LJ-V7000 The scanning speed is 64kHz, the Z-axis resolution is 0.5μm, and it supports 3D point cloud modeling. peristaltic pump Masterflex L / SBT-100EA Flow rate range 0.001-100mL / min, accuracy ±0.5%, with PID control. PLC controller Siemens S7-1200 1215C It has 14 digital inputs and 10 digital outputs, 2 analog inputs, and supports PROFINET communication. base Custom-made stainless steel protective enclosure (SUS304 material) Dimensions: 1500×1500×1800mm Local machine Precision 7875 Tower Multi-core parallel processing of point cloud algorithms, ultra-large L3 cache to improve reconstruction efficiency, large video memory to accommodate complex meshes, and CUDA to accelerate PCL / Open3D algorithms.

Claims

1. A sampling device for anionic synthetic detergents used in tableware, characterized in that, include: The base (101) has a placement slot (101a) for placing the sample collection box (301). A peristaltic pump (102) is fixedly installed on a base (101), and its inlet is connected to an external water source; The flushing nozzle (201) is fixedly installed on the first robotic arm (204) and connected to the outlet of the peristaltic pump (102) through a pipe; A 3D vision camera (202) is fixedly mounted on the second robotic arm (205) for global imaging and acquisition of point cloud data of the inner surface of the tableware; A laser contour scanner (203) is fixedly mounted on the third robotic arm (206) and is used to scan the blind area of ​​the 3D vision camera and acquire point cloud data; The sample collection box (301) has a sample rack (302) for placing tableware fixedly installed inside it; The local machine (401) is used to receive point cloud data acquired by the 3D vision camera (202) and the laser contour scanner (203), process and complete the three-dimensional modeling, use the point cloud segmentation algorithm to extract the geometric features of the inner surface of the tableware, calculate the inner surface area of ​​the tableware, calculate the volume of distilled water required for this sampling, calculate the running time required for the peristaltic pump (102) and send it to the PLC controller (402), and also to generate a spiral progressive rinsing path plan and send it to the PLC controller (402). The PLC controller (402) is used to control the peristaltic pump (102) to start and stop according to the running time, and also to control the first robotic arm (204) to move according to the rinsing path plan, thereby driving the rinsing nozzle (201) to complete the rinsing of the tableware.

2. The sampling device for anionic synthetic detergents for tableware according to claim 1, characterized in that: The 3D modeling of the local machine (401) is based on a deep learning model with the ResNet-50 architecture.

3. The sampling device for anionic synthetic detergents for tableware according to claim 1, characterized in that: It also includes a touch screen (103), which is fixedly installed on the base (101) and communicates with the local machine (401) and the PLC controller (402).

4. The sampling device for anionic synthetic detergents for tableware according to claim 1, characterized in that: The first robotic arm (204), the second robotic arm (205), and the third robotic arm (206) are all fixedly installed on the base (101).

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