Self-learning disorder measurement device under complex recognition condition

Abstract

The utility model provides a self-learning disordered measurement device under a complex identification condition. The device comprises a power part, a supporting column, a support, a label identification camera, a laser scanner, a telescopic fixing part and the like. Through staggered arrangement of the label identification camera and the laser scanner, the device can automatically identify measurement points on a workpiece and automatically adjust the position according to the shape and the size of the workpiece, so that efficient measurement is realized. The telescopic fixing part is connected with the power part, the position of the workpiece can be dynamically adjusted, and stability and accuracy in the measuring process are guaranteed. According to the device, manual intervention is reduced, the measurement efficiency and precision are improved, the labor intensity is reduced, and the overall automation level is improved.

Description

Technical Field

[0001] This utility model relates to a self-learning disordered measurement device under complex recognition conditions. Background Technology

[0002] With the continuous development of the automotive inspection tool industry, the need has emerged to inspect parts based on different measuring points. In the past, parts inspection work required at least one person to record the data while inspecting, using paper or ordinary electronic devices. This resulted in data that was difficult to guarantee in terms of accuracy and traceability, and it was also time-consuming and had very high labor costs.

[0003] In existing technologies, the positioning and orientation of workpieces typically require manual or pre-programmed adjustments before measurement. However, workpieces in actual production often exhibit variations in size, shape, and surface condition, making it difficult for traditional equipment to adapt to constantly changing workpiece conditions and environmental factors, resulting in measurement errors and low efficiency. Therefore, existing technologies have developed a method where identification tags are first attached to the workpiece, and a measuring camera uses these tags to confirm the location of the identification points. This method has a high tolerance for errors in the workpiece's placement and angle. However, this device still relies on manual determination of the identification tag's position before measurement, resulting in low efficiency.

[0004] Therefore, it is necessary to address the issue that determining identification tags in existing technologies requires more manpower. Utility Model Content

[0005] The purpose of this invention is to overcome the shortcomings of the prior art and provide a self-learning disordered measurement device for complex identification situations. By using staggered label recognition cameras and laser scanners, it successfully solves the problems of difficult workpiece positioning and high dependence on manual labor in traditional measurement methods. The purpose of this invention is achieved as follows:

[0006] This utility model provides a self-learning disordered measurement device for complex identification situations, including a power unit, a support column, and a bracket. The support column is fixedly installed on the power unit, and the bracket is fixedly installed on the support column. The bracket is equipped with a label recognition camera and a laser scanner. A track is telescopically installed on the power unit, and a placement platform is fixedly installed on the track. A telescopic fixing part is telescopically installed on the placement platform. There are three label recognition cameras and three laser scanners. The label recognition cameras and laser scanners are staggered and arranged around the placement platform. There are gaps between the staggered label recognition cameras and laser scanners, and the telescopic fixing parts are located on the opposite side of the gaps.

[0007] Furthermore, it includes a fixing ring, which is located on the inner side of the bracket, and the inner side of the fixing ring is movable and fixed to the track portion.

[0008] Furthermore, the track section is provided in three parts, and the interval between the track sections is 90 degrees.

[0009] Furthermore, the bracket has a support portion on its inner side, and one end of the support portion is fixedly connected to a fixing ring.

[0010] Furthermore, the telescopic fixing part is movably fixed to the placement platform, and the lower end of the telescopic fixing part is movably fixed to the power unit.

[0011] Furthermore, both the tag recognition camera and the laser scanner are horizontally positioned.

[0012] Furthermore, a pressure sensor is provided on the telescopic fixing part, and the pressure sensor is electrically connected to the power part.

[0013] Furthermore, the storage platform is circular.

[0014] Compared with the prior art, the beneficial effects of this utility model are: by setting up the label recognition camera and the laser scanner alternately, the preset measurement point position on the workpiece can be automatically identified, eliminating the process of manually determining the label position, thereby reducing manual operation and calibration time. Attached Figure Description

[0015] Figure 1 This is a top-view schematic diagram of a self-learning disordered measurement device under complex recognition conditions;

[0016] Figure 2 This is a frontal view schematic diagram of a self-learning disordered measurement device under complex recognition conditions;

[0017] In the diagram: 1. Laser scanner, 2. Storage platform, 3. Fixing ring, 4. Tag recognition camera, 5. Telescopic fixing part, 6. Track part, 7. Bracket, 8. Support part, 9. Power unit, 10. Pressure sensor, 11. Support column. Detailed Implementation

[0018] To enhance understanding of this utility model, the present utility model will be further described in detail below with reference to the embodiments and accompanying drawings. These embodiments are only used to explain the present utility model and do not constitute a limitation on the scope of protection of the present utility model.

[0019] like Figure 1-2As shown, an embodiment of this utility model provides a self-learning disordered measurement device for complex recognition situations, including a power unit 9, a support column 11, and a bracket 7. The support column 11 is fixedly mounted on the power unit 9, and the bracket 7 is fixedly mounted on the support column 11. A label recognition camera 4 and a laser scanner 1 are mounted on the bracket 7. A track 6 is telescopically mounted on the power unit 9, and a platform 2 is fixedly mounted on the track 6. A telescopic fixing part 5 is telescopically mounted on the platform 2. Three label recognition cameras 4 and three laser scanners 1 are provided, arranged alternately. Both the label recognition cameras 4 and laser scanners 1 surround the platform 2, with gaps between them. The telescopic fixing parts 5 are located on opposite sides of the gaps. In one possible application scenario, the device is located in a testing room, with the power unit 9 mounted on a table or the ground. Initially, the platform 2 is at the bottom. After a workpiece is placed on it and fixed by the telescopic fixing parts 5, the track 6 lifts the workpiece upwards, preventing damage to the cameras during transport.

[0020] Optionally, this device can be applied to the inspection of automotive parts. For complex parts, traditional manual inspection methods are not only time-consuming but also prone to errors. By using the label recognition camera 4 and laser scanner 1 in this device, different measurement points of the parts can be automatically identified and measured automatically through real-time scanning of the identification labels. There is no need for manual positioning of measurement points, which greatly improves production efficiency and measurement accuracy and reduces labor costs.

[0021] In one embodiment, the device further includes a fixing ring 3, which is disposed inside the support 7 and is movably fixed to the track section 6. This structural design makes the movement of the track section 6 more stable and smooth. When the track section 6 moves the platform 2 up and down, the fixing ring 3 can effectively support the movement of the track section 6, reducing the offset or instability caused by external vibration, impact, or load changes. This ensures the accurate positioning of the measuring tool during movement and the smooth operation of the track, avoiding measurement errors caused by instability. It should be noted that the connection between the track section 6 and the telescopic fixing section 5 and the power section 9 is existing technology, for example, an up-and-down piston movement structure achieved by a rack, belt, or lever, which can achieve the purpose of up-and-down movement.

[0022] Furthermore, there are three track sections 6, with a 90-degree interval between them. This design facilitates the insertion of workpieces.

[0023] Furthermore, a support portion 8 is provided on the inner side of the bracket 7, and one end of the support portion 8 is fixedly connected to the fixing ring 3. This further enhances the structural stability of the fixing ring 3 and reduces vibration.

[0024] In one embodiment, the telescopic fixing part 5 is movably fixed to the placement platform 2, and the lower end of the telescopic fixing part 5 is movably fixed to the power unit 9. While the telescopic fixing part 5 is fixedly connected to the power unit 9, it ensures the stability of the workpiece during the measurement process.

[0025] In one embodiment, both the tag recognition camera 4 and the laser scanner 1 are horizontally positioned to ensure they maintain a consistent working angle with the workpiece surface.

[0026] In one embodiment, a pressure sensor 10 is provided on the telescopic fixing part 5, and the pressure sensor 10 is electrically connected to the power unit 9. The pressure sensor 10 can monitor the pressure applied to the workpiece by the telescopic fixing part 5 in real time, ensuring that the pressure on the workpiece does not exceed the set range during the measurement process. The power unit 9 is provided with a control unit to control the force of the telescopic fixing part 5, so as to avoid damage to the workpiece or distortion of measurement data due to excessive pressure.

[0027] In one embodiment, the placement platform 2 is circular. This avoids workpiece tilting or instability caused by uneven local stress.

[0028] This invention successfully solves the problems of difficult workpiece positioning and high dependence on manual labor in traditional measurement methods by using staggered label recognition cameras 4 and laser scanners 1.

[0029] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this utility model and are not intended to limit it. Although this utility model has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of this utility model without departing from the spirit and scope of the technical solutions of this utility model, and all such modifications or substitutions should be covered within the scope of the claims of this utility model.

Claims

1. A self-learning disordered measurement device for complex recognition situations, characterized in that, The device includes a power unit, a support column, and a bracket. The support column is fixedly mounted on the power unit, and the bracket is fixedly mounted on the support column. The bracket is equipped with a tag recognition camera and a laser scanner. A track is telescopically mounted on the power unit, and a storage platform is fixedly mounted on the track. A telescopic fixing part is telescopically mounted on the storage platform. There are three tag recognition cameras and three laser scanners. The tag recognition cameras and laser scanners are arranged alternately, and they are all arranged around the storage platform. There are gaps between the staggered tag recognition cameras and laser scanners, and the telescopic fixing parts are located on the opposite side of the gaps.

2. The self-learning disordered measurement device for complex recognition situations according to claim 1, characterized in that, It includes a fixing ring, which is located on the inner side of the bracket, and the inner side of the fixing ring is movable and fixed to the track.

3. The self-learning disordered measurement device for complex recognition situations according to claim 2, characterized in that, The track section is provided in three parts, and the interval between the track sections is 90 degrees.

4. The self-learning disordered measurement device for complex recognition situations according to claim 2, characterized in that, The bracket has a support part on its inner side, and one end of the support part is fixedly connected to the fixing ring.

5. The self-learning disordered measurement device for complex recognition situations according to claim 1, characterized in that, The telescopic fixing part is movably fixed to the placement platform, and the lower end of the telescopic fixing part is movably fixed to the power unit.

6. The self-learning disordered measurement device for complex recognition situations according to claim 1, characterized in that, Both the tag recognition camera and the laser scanner are horizontally positioned.

7. The self-learning disordered measurement device for complex recognition situations according to claim 1, characterized in that, A pressure sensor is provided on the telescopic fixing part, and the pressure sensor is electrically connected to the power part.

8. The self-learning disordered measurement device for complex recognition situations according to claim 1, characterized in that, The storage platform is circular.

Images