Springback detection auxiliary method and system

Abstract

The invention provides a springback detection auxiliary method and system, and the method comprises the following steps: projecting an initial detection region grid pattern to the surface of a to-be-detected concrete member, obtaining an image of the surface of the member, and recognizing the positioning state of the detection region grid pattern on the surface of the member based on a pre-stored detection region arrangement rule, s3, generating measurement area distribution characteristic data, acquiring an identification result of the positioning state and the measurement area distribution characteristic data, and judging whether the identification result and the measurement area distribution characteristic data conform to the measurement area arrangement rule or not, if not, adjusting the measurement area grid pattern, and repeating the steps S2 to S3 until the positioning state and the measurement area distribution characteristic data conform to the measurement area arrangement rule, and if yes, executing the step S3; and if not, locking the final measuring area grid pattern to complete measuring area selection. According to the method, the measuring area delimiting precision is improved, and the labor and time cost is saved.

Description

Technical Field

[0001] This invention belongs to the field of building construction quality control technology, and specifically relates to an auxiliary method and system for rebound detection. Background Technology

[0002] Rebound hammer testing is a commonly used method for assessing the strength of concrete structures. The "Technical Specification for Testing the Compressive Strength of Concrete by Rebound Method" (JGJ / T 23-2011) specifies strict requirements for the number, spacing, and area of ​​test zones and points. However, traditional rebound hammer testing is highly repetitive, cumbersome, and subject to numerous limitations, heavily relying on the experience of on-site operators, which to some extent affects testing efficiency and the consistency of results. Summary of the Invention

[0003] This invention provides a rebound detection auxiliary method and system, which improves the accuracy of test area delineation and saves labor and time costs.

[0004] The technical solution of the present invention is as follows:

[0005] A rebound detection auxiliary method includes the following steps:

[0006] S1: Project the initial test area grid pattern onto the surface of the concrete component to be tested;

[0007] S2: Acquire an image of the component surface, and based on the pre-stored measurement area layout rules, identify the positioning status of the measurement area grid pattern on the component surface, and generate measurement area distribution characteristic data;

[0008] S3: Obtain the identification result of the positioning status and the distribution characteristic data of the measurement area, and determine whether it conforms to the measurement area layout rules. If it does not conform, adjust the measurement area grid pattern and repeat steps S2-S3 until the positioning status and the distribution characteristic data of the measurement area conform to the measurement area layout rules. If it does conform, lock the final measurement area grid pattern and complete the measurement area selection.

[0009] Furthermore, in the rebound detection auxiliary method, step S2 employs the YOLO machine vision algorithm to identify and analyze the localization status of the test area grid pattern in the image; and / or,

[0010] The pre-stored survey area layout rules in step S2 are pre-set in the rule base based on the JGJ / T 23-2011 standard.

[0011] Furthermore, in the rebound detection auxiliary method, step S3 utilizes an adaptive survey area planning algorithm to perform a compliance check on the positioning state. When the positioning state does not conform to the survey area layout rules, a correction calculation is automatically performed to generate instructions for adjusting the position or layout of the survey area grid graphic; and / or,

[0012] In step S1, an initial test area grid pattern is projected onto the surface of the concrete component to be tested using a laser projection module. In step S3, the adjustment command includes a command to refocus the laser projection module.

[0013] Furthermore, the rebound detection auxiliary method further includes:

[0014] S4: Perform detection and obtain rebound values ​​within the finally locked test area grid, and associate the rebound values ​​with the corresponding test area locations; and / or,

[0015] S5: For a single test area that has completed rebound testing, automatically change the projection color for marking; and / or,

[0016] S6: After all test areas have been completed, a missed detection judgment is performed. If any missed detection areas are found, an alarm is issued; and / or,

[0017] S7: Saves the detected test area distribution information and test data as an independent historical task, and supports subsequent retrospective viewing and report generation.

[0018] A rebound detection auxiliary system, comprising:

[0019] A laser projection module is used to project a grid pattern of the test area onto the surface of the concrete component to be tested;

[0020] The image recognition module is used to acquire images of the component surface and, based on pre-stored measurement area layout rules, identify the positioning status of the measurement area grid pattern on the component surface.

[0021] A central control module, which is communicatively connected to both the laser projection module and the graphic recognition module, is used to acquire the recognition result of the positioning status and determine whether the positioning status conforms to the survey area layout rules. If it does not conform, the survey area grid graphic is adjusted, and the above steps of recognizing the positioning status and determining whether it conforms to the survey area layout rules are repeated until the positioning status conforms to the survey area layout rules. If it does conform, the laser projection module is controlled to lock the final survey area grid graphic, thus completing the survey area selection.

[0022] Furthermore, in the aforementioned rebound detection auxiliary system, the laser projection module is equipped with a light brightness adjustment function to automatically adjust the projection brightness according to the ambient light; and / or,

[0023] The laser projection module is equipped with a focus adjustment function, which is used to automatically refocus after detecting that the projected beam is blocked.

[0024] Furthermore, in the aforementioned rebound detection auxiliary system, the image recognition module includes:

[0025] An image acquisition unit is used to acquire surface images of components including the grid pattern of the test area;

[0026] The image analysis unit is used to analyze the image based on the layout specifications of the survey area and to identify the positioning status of the grid pattern of the survey area.

[0027] Furthermore, in the rebound detection auxiliary system, the system is an integrated terminal device, and the laser projection module, the graphic recognition module, and the central control module are all housed within the casing of the terminal device.

[0028] Furthermore, in the rebound detection auxiliary system, the system also includes a bracket, which is installed below the terminal device to provide fixed support for the terminal device.

[0029] Furthermore, in the aforementioned rebound detection auxiliary system, the central control module includes a voice broadcast unit for issuing voice prompts; and / or,

[0030] The terminal device has a power supply installed inside its casing.

[0031] The beneficial effects of this invention are as follows:

[0032] This invention provides an auxiliary method for rebound testing. By projecting test areas onto the surface of a component, visually recognizing and adaptively adjusting the test areas, this method achieves standardization in the selection of test areas on the component surface, precision in recognition and judgment, automation in adjustment and optimization, and intelligence in the testing process. It solves the problems of non-standard test areas, large errors in quantity / overlap judgment, and low efficiency in traditional manual testing, improves the accuracy, efficiency, and compliance of rebound testing, reduces the amount of manual work, and can be widely applied to rebound testing of concrete components such as walls, columns, and beams. Attached Figure Description

[0033] Figure 1 This is a flowchart of a rebound detection auxiliary method according to the present invention;

[0034] Figure 2 This is a schematic diagram of a rebound detection auxiliary system according to the present invention;

[0035] Figure 3 This is a schematic diagram of a terminal device of a rebound detection auxiliary system according to the present invention.

[0036] In the diagram: 1. Terminal equipment; 11. Laser projection module; 12. Graphic recognition module; 13. Central control module; 14. Housing; 2. Support; 3. Component surface; 4. Survey area grid pattern. Detailed Implementation

[0037] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. The advantages and features of the present invention will become clearer from the following description and claims. It should be noted that the drawings are all in a very simplified form and use non-precise proportions, and are only used to facilitate and clarify the illustration of the embodiments of the present invention.

[0038] like Figure 1 As shown, this embodiment provides a rebound detection auxiliary method, including the following steps S1-S3, and may also include S5-S8.

[0039] S1: Project the initial test area grid pattern 4 onto the surface 3 of the concrete component to be tested.

[0040] Specifically, the laser projection module 11 is aligned with the surface 3 of the concrete component to be tested. The central control module 13 controls the laser projection module 11 to start, adjusts the laser projection edge to coincide with the edge of the component, and inputs technical parameters such as the type of concrete component to be tested and the number of test areas into the built-in software of the central control module 13, so that the laser projection module 11 automatically generates the initial test area grid pattern 4 (i.e., the rebound detection test area).

[0041] Step S1 uses laser projection to standardize the initial test area grid, replacing manual line drawing, quickly building the detection framework, providing accurate identification benchmarks, adapting to complex scenarios and reducing the operational threshold, laying an efficient and standardized foundation for subsequent intelligent compliance verification and full-process detection.

[0042] S2: Acquire an image of the component surface 3, and based on the pre-stored test area layout rules, identify the positioning status of the test area grid pattern 4 on the component surface 3, and generate test area distribution characteristic data.

[0043] Specifically, after the image acquisition unit (e.g., a high-definition industrial camera) of the image recognition module acquires an image of the component surface 3 containing the initial test area grid pattern 4, it preprocesses the image (denoising, enhancement, and grayscale conversion), and transmits the preprocessed image to the image analysis unit. The image analysis unit loads the pre-stored test area layout rules, uses the YOLO machine vision algorithm to identify and analyze the image's positioning status, and generates test area distribution characteristic data. The test area distribution characteristic data includes the number of test areas, the coordinate range of each test area, and the overlap determination result of the test area components.

[0044] Step S2 uses the YOLO machine vision algorithm to accurately identify the positioning status of the initial test area grid and generate distribution characteristic data including the number of test areas and the overlap judgment structure. This provides high-precision and automated data support for the compliance judgment of test areas, and solves problems such as errors in manual judgment of test area data, overlap omissions, and location violations.

[0045] S3: Obtain the identification result of the positioning status and the distribution characteristic data of the measurement area, and determine whether it conforms to the measurement area layout rules. If it does not conform, adjust the measurement area grid pattern 4 and repeat steps S2-S3 until the positioning status conforms to the measurement area layout rules. If it does conform, lock the final measurement area grid pattern 4 and complete the measurement area selection.

[0046] Specifically, the central control module 13 acquires the positioning status recognition result and the measurement area distribution characteristic data, and calls the adaptive measurement area planning algorithm to perform a compliance check on the positioning status. The compliance check includes whether the coordinate position of the measurement area network meets the spacing requirements, whether the measurement area covers defect areas, whether the measurement area size meets the standard, and whether the number of measurement areas meets the specifications. When the positioning status does not meet the measurement area layout rules, a correction calculation is automatically performed to generate instructions for adjusting the position or layout of the measurement area grid graphic 4 until the positioning status meets the measurement area layout rules. Finally, the final measurement area grid graphic 4 is locked, and the measurement area selection is completed.

[0047] In step S1, an initial test area grid pattern 4 is projected onto the surface 3 of the concrete component to be tested using a laser projection module 11. The adjustment commands in step S3 include one or more of the following: refocusing the laser projection module 11, adjusting the projection position, and adjusting the grid size. This setup ensures that the test area grid pattern 4 remains clear and identifiable, providing a reliable visual basis for subsequent accurate identification and compliant adjustments, and guaranteeing a smooth testing process.

[0048] Step S3 uses an adaptive survey area planning algorithm to verify the compliance of the survey area positioning status and distribution characteristics data, automatically generates and executes adjustment instructions for the survey area location, quantity, and layout, ensuring that the final survey area meets the specifications, while significantly improving the automation and efficiency of survey area selection.

[0049] The above method, through projection of the test area on the component surface 3, visual recognition, and adaptive adjustment, achieves standardization of test area selection, accuracy of recognition and judgment, automation of adjustment and optimization, and intelligence of the detection process. It solves the problems of non-standard test areas, large errors in quantity / overlap judgment, and low efficiency in traditional manual inspection, improves the accuracy, efficiency, and compliance of rebound detection, and reduces the amount of manual work. It can be widely applied to rebound detection of concrete components such as walls, columns, and beams.

[0050] As a preferred implementation, S4-S7 may also be included.

[0051] S4: Within the finally locked survey area grid, perform detection and obtain rebound values. Associate the rebound values ​​with the corresponding survey area locations to generate structured data containing survey area coordinates and rebound values. After step S3 locks the final survey area grid, the inspection personnel perform rebound detection within the corresponding grid area and obtain rebound values. The system uses the image recognition module 12 to detect the corresponding survey area coordinates and number, automatically binding the rebound value of a single detection with the location information of the survey area to form a one-to-one corresponding data record, which is stored in the system database.

[0052] Step S4 improves the accuracy of data recording, providing reliable support for subsequent data management and report generation.

[0053] S5: For a single test area that has completed the rebound detection, the projection color is automatically changed for marking. After the system confirms that a certain test area has completed the detection through the rebound value association record (step S4), the central control module 13 sends a color switching command to the laser projection module 11 to automatically change the original projection color of the test area (such as green in the undetected state) to the preset detected marking color (such as red). The color update is only performed on a single completed test area, while the remaining undetected test areas retain their original colors.

[0054] Step S5 uses color coding to distinguish the detection status of the test area, allowing the testing personnel to intuitively identify the tested / untested areas, avoiding duplicate or missed tests and reducing the rate of operational errors.

[0055] S6: After all test areas are completed, a missed detection judgment is performed. If a missed detection area exists, an alarm is issued. The system pre-counts the total number of test areas to be locked. After all test operations are completed, the system automatically counts the number of test areas with associated rebound values. By comparing the number of tested areas with the total number of test areas, if the number of tested areas is less than the total number of test areas, it is determined that a missed detection area exists. The central control module 13 then triggers an audible and visual alarm (such as a voice announcement saying "A missed detection area exists, please check" and a highlighting of the coordinates of the missed detection area on the display screen) to prompt the testing personnel to conduct a retest.

[0056] Step S6 enables automated detection and verification, avoiding omissions or errors in manual counting, ensuring that all test areas are tested, and guaranteeing the integrity of the test data.

[0057] S7: Saves the detection area distribution information and detection data as independent historical tasks, and supports subsequent retrospective viewing and report generation. The system packages and stores all data from this detection into "independent historical tasks," including detection area distribution information (final grid coordinates, quantity, projected image, compliance verification results) and detection data (rebound value of each detection area, detection time, detection personnel, missed detection alarm records, etc.). After storage, it supports retrieval and retrospection by keywords such as task name and detection time, and has a built-in standardized report template. It can call the data of this task to automatically fill in the detection area information, detection results, etc., and generate a detection report that conforms to the JGJ / T23-2011 standard.

[0058] Step S7 enables structured and traceable storage of test data, avoiding problems such as loss of manual records and data disorder, and meeting the industry requirements for archiving and management of engineering test data.

[0059] As a preferred implementation, the pre-stored survey area layout rules in step S2 are pre-set in the rule base based on the JGJ / T23-2011 standard.

[0060] Specifically, the rule base pre-converts the core requirements for test area layout in the "Technical Specification for Testing Compressive Strength of Concrete by Rebound Method" (JGJ / T23-2011) (such as test area size of 200mm×200mm, test area spacing of not less than 200mm, avoiding component edges / defects / embedded parts, the number of test areas for beam components not less than 10, and no overlapping test areas, etc.) into structured, algorithmically identifiable parameterized rules (including numerical thresholds, logical judgment conditions, etc.). In step S2, the image analysis unit of the image recognition module 12 calls the rule base to compare the test area positioning status (coordinates, size, quantity, overlap, etc.) identified by the YOLO machine vision algorithm with the preset rules in the base one by one, completing the generation of test area distribution characteristic data and preliminary compliance judgment, providing a unified and standardized judgment basis for the adjustment decision in the subsequent step S3, realizing the standardization and automation of test area judgment, reducing the operation threshold, and ensuring the compliance of testing.

[0061] In a preferred implementation, step S3 uses an adaptive survey area planning algorithm to perform a compliance check on the positioning status. When the positioning status does not conform to the survey area layout rules, a correction calculation is automatically performed to generate instructions for adjusting the position or layout of the survey area grid graphic 4.

[0062] Specifically, the adaptive survey area planning algorithm should satisfy the following boundary constraints:

[0063] ① Constraints on the number and area of ​​survey areas:

[0064]

[0065] in:

[0066] N is the number of survey areas;

[0067] i is the survey area number, where i is a positive integer from 1 to N;

[0068] S i Let be the area of ​​the i-th survey area (unit: m);

[0069] a i Let be the side length of the i-th square survey area (in meters), which must meet the upper limit of the area of ​​a single survey area specified in the standard.

[0070] ② Constraints on the distance between adjacent survey areas:

[0071]

[0072] in:

[0073] i and j are the survey area numbers, where i and j are positive integers from 1 to N;

[0074] a i Let be the side length of the i-th square survey area (unit: m);

[0075] a j Let be the side length of the j-th square survey area (unit: m);

[0076] x i and y i Let i be the coordinates of the center of the i-th survey area;

[0077] x j and y j Let be the coordinates of the center of the j-th survey area.

[0078] ③ Distance constraint between the test area and the edge of the component to be tested:

[0079] If the boundary range of the component to be measured is [0,L]×[0,W], and the boundary coordinates of the measurement area i are rectangular...

[0080]

[0081] in:

[0082] i is the survey area number, where i is a positive integer from 1 to N;

[0083] x i The coordinates of the center of the survey area;

[0084] a i The side length of the square survey area (unit: m);

[0085] L is the length of the component to be measured;

[0086] W represents the width of the component to be measured.

[0087] ④ Global area constraints

[0088] The total area of ​​all survey areas shall not exceed the usable area of ​​the walls:

[0089]

[0090] in:

[0091] L eff =L-0.4, W eff =W-0.4 is the effective length / width after deducting edge buffers (minimum reserved boundary bidirectional buffer);

[0092] L is the length of the component to be measured;

[0093] W represents the width of the component to be measured.

[0094] The adaptive survey area planning algorithm first calls the parameterized rules corresponding to the JGJ / T 23-2011 standard in the rule base to perform full-dimensional compliance verification on the survey area positioning status (coordinates, size, quantity, overlap, etc.) identified in step S2. When violations such as survey area position offset, quantity discrepancy, or overlap are detected, the algorithm automatically calculates the optimal adjustment scheme through coordinate calculation, layout optimization, and other correction logic, and generates instructions to adjust the position, layout, or quantity of the survey area grid, providing a precise adjustment basis for the laser projection module 11.

[0095] like Figure 2 and Figure 3 As shown, this embodiment also provides a rebound detection auxiliary system, including a laser projection module 11, a graphic recognition module 12, and a central control module 13.

[0096] The laser projection module 11 is used to project a grid map of the test area onto the surface 3 of the concrete component to be tested. It can use a 5W high-power laser projection head with a projection resolution of 1080P, an illumination adjustment range of 10%-100%, and a focal length adjustment range of 0.5m-5m.

[0097] The laser projection module 11 is equipped with a light brightness adjustment function and a built-in light sensor to automatically adjust the projection brightness according to the ambient light, ensuring that the grid pattern 4 in the test area is clearly visible in both strong and low light environments. The laser projection module 11 is also equipped with a focus adjustment function and a built-in occlusion detection sensor to automatically refocus when the projection beam is detected to ensure that the grid pattern is projected clearly.

[0098] The image recognition module 12 is used to acquire images of the component surface 3 and, based on pre-stored measurement area layout rules, uses the YOLO machine vision algorithm to identify the positioning status of the measurement area grid pattern 4 on the component surface 3, generating measurement area distribution characteristic data (including the number of measurement areas and overlap determination results). The image recognition module 12 may include an image acquisition unit and an image analysis unit.

[0099] The image acquisition unit uses a high-definition industrial camera to acquire images of the component surface 3, including the grid pattern 4 of the test area. It supports real-time continuous acquisition, ensuring that the grid pattern 4 of the test area is clearly captured, providing a high-quality data source for subsequent analysis, and is suitable for the rough surface 3 of concrete components and the complex light and shadow on-site environment.

[0100] The image analysis unit incorporates a YOLO algorithm model and rule base for preprocessing acquired images. It uses the YOLO algorithm model to detect and select bounding boxes within a grid of survey areas, count the number of survey areas, determine overlap, and analyze the location status of the survey areas based on survey area layout rules, generating survey area distribution characteristic data. The YOLO algorithm model rapidly detects and selects survey areas, and the rule base automates the counting of survey areas, overlap determination, and location status analysis. This results in high accuracy and fast response time, avoiding the subjective errors and inefficiencies of manual analysis.

[0101] The central control module 13 is communicatively connected to both the laser projection module 11 and the image recognition module 12. It acquires the recognition results of the positioning status and the distribution characteristics of the measurement area, and determines whether the positioning status conforms to the measurement area layout rules. If not, it adjusts the measurement area grid pattern 4, repeating the steps of recognizing the positioning status and determining whether it conforms to the measurement area layout rules until the positioning status conforms to the rules. If it does, it controls the laser projection module 11 to lock the final measurement area grid pattern 4, completing the measurement area selection. The main control unit of the central control module 13 can employ a high-performance embedded processor to execute adaptive measurement area planning algorithms, compliance judgment logic (including quantity / overlap verification), and collaborative control of each module.

[0102] In the above structure, by setting up the laser projection module 11, the graphic recognition module 12 and the central control module 13 to work together, the measurement area projection, visual recognition and adaptive adjustment of the concrete component surface 3 are realized, which improves the accuracy, efficiency and compliance of rebound detection, reduces the amount of manual work, and can be widely used for rebound detection of concrete components such as walls, columns and beams.

[0103] like Figure 2As shown, in a preferred embodiment, the system is an integrated terminal device 1, in which the laser projection module 11, the image recognition module 12, and the central control module 13 are all housed within the casing 14 of the terminal device 1. Integrating the laser projection module 11, the image recognition module 12, and the central control module 13 within the casing 14 allows for independent operation throughout the entire process through internal collaboration, improving the device's portability and operational stability.

[0104] like Figure 2 As shown, in a preferred embodiment, the system further includes a support 2, preferably a triangular support 2. The support 2 is installed below the terminal device 1 to provide fixed support for the terminal device 1. The triangular support 2 provides stable support for the terminal device 1 and is suitable for different scenarios such as the ground and scaffolding.

[0105] In a preferred embodiment, the central control module 13 further includes a voice broadcasting unit for issuing voice prompts to facilitate prompting of testing personnel.

[0106] Furthermore, the central control module 13 also includes a storage unit for storing rule bases, historical task data, detection images, and bounce value data.

[0107] Furthermore, the central control module 13 also includes a human-machine interaction unit, including a touch screen, for displaying the measurement area image, measurement area distribution characteristic data (quantity, overlap status), and detection data, and supports manual adjustment of measurement area parameters.

[0108] Furthermore, the central control module 13 also includes a power supply unit with a built-in rechargeable lithium battery to power the entire system, supporting a battery life of ≥8 hours.

[0109] The above description is merely a description of preferred embodiments of the present invention and is not intended to limit the scope of the present invention in any way. Any changes or modifications made by those skilled in the art based on the above disclosure shall fall within the protection scope of the claims.

Claims

1. A rebound detection auxiliary method, characterized in that, Includes the following steps: S1: Project the initial test area grid pattern (4) onto the surface (3) of the concrete component to be tested; S2: Obtain an image of the surface (3) of the component, and based on the pre-stored test area layout rules, identify the positioning status of the test area grid pattern (4) on the surface (3) of the component, and generate test area distribution characteristic data; S3: Obtain the identification result of the positioning status and the distribution characteristic data of the measurement area, and determine whether it conforms to the measurement area layout rules. If it does not conform, adjust the measurement area grid pattern (4) and repeat steps S2-S3 until the positioning status and the distribution characteristic data of the measurement area conform to the measurement area layout rules. If it does conform, lock the final measurement area grid pattern (4) and complete the measurement area selection.

2. The rebound detection auxiliary method as described in claim 1, characterized in that, In step S2, the YOLO machine vision algorithm is used to identify and analyze the location status of the grid pattern (4) in the image; and / or, The pre-stored survey area layout rules in step S2 are pre-set in the rule base based on the JGJ / T 23-2011 standard.

3. The rebound detection auxiliary method as described in claim 2, characterized in that, In step S3, an adaptive survey area planning algorithm is used to perform a compliance check on the positioning status. When the positioning status does not conform to the survey area layout rules, a correction calculation is automatically performed to generate instructions for adjusting the position or layout of the survey area grid graphic (4); and / or, In step S1, the initial test area grid pattern (4) is projected onto the surface (3) of the concrete component to be tested by the laser projection module (11). The adjustment command in step S3 includes the command to refocus the laser projection module (11).

4. The rebound detection auxiliary method as described in claim 1, characterized in that, Also includes: S4: Perform detection and obtain rebound values ​​within the finally locked test area grid, and associate the rebound values ​​with the corresponding test area locations; and / or, S5: For a single test area that has completed rebound testing, automatically change the projection color for marking; and / or, S6: After all test areas have been completed, a missed detection judgment is performed. If any missed detection areas are found, an alarm is issued; and / or, S7: Saves the detected test area distribution information and test data as an independent historical task, and supports subsequent retrospective viewing and report generation.

5. A rebound detection auxiliary system, characterized in that, include: A laser projection module (11) is used to project the grid pattern (4) of the test area onto the surface (3) of the concrete component to be tested; The image recognition module (12) is used to acquire an image of the component surface (3) and, based on the pre-stored measurement area layout rules, identify the positioning status of the measurement area grid pattern (4) on the component surface (3). The central control module (13) is communicatively connected to the laser projection module (11) and the graphic recognition module (12) to obtain the recognition result of the positioning status and determine whether the positioning status conforms to the measurement area layout rules. If it does not conform, the measurement area grid graphic (4) is adjusted, and the above steps of recognizing the positioning status and determining whether it conforms to the measurement area layout rules are repeated until the positioning status conforms to the measurement area layout rules. If it does conform, the laser projection module (11) is controlled to lock the final measurement area grid graphic (4) to complete the measurement area selection.

6. The rebound detection auxiliary system as described in claim 5, characterized in that, The laser projection module (11) is equipped with a light brightness adjustment function to automatically adjust the projection brightness according to the ambient light; and / or, The laser projection module (11) is equipped with a focus adjustment function, which is used to automatically perform a refocusing operation after detecting that the projected beam is blocked.

7. The rebound detection auxiliary system as described in claim 5, characterized in that, The image recognition module includes: Image acquisition unit, used to acquire images of the component surface (3) including the grid pattern (4) of the test area; The image analysis unit is used to analyze the image based on the layout specifications of the survey area and to identify the positioning status of the grid pattern (4) of the survey area.

8. The rebound detection auxiliary system as described in claim 5, characterized in that, The system is an integrated terminal device (1), and the laser projection module (11), the graphic recognition module (12) and the central control module (13) are all located inside the housing (14) of the terminal device (1).

9. The rebound detection auxiliary system as described in claim 8, characterized in that, The system also includes a bracket (2), which is installed below the terminal device (1) for fixing and supporting the terminal device (1).

10. The rebound detection auxiliary system as described in claim 8, characterized in that, The central control module (13) also includes a voice broadcast unit for issuing voice prompts; and / or, The central control module (13) also includes a power supply unit.

Images