Metallic shelf structure deformation detection device based on laser-assisted calibration

Abstract

The application discloses a metal shelf structure deformation detection device based on laser-assisted calibration, and relates to the technical field of laser-assisted calibration tools.The metal shelf structure deformation detection device comprises a shelf main body;the rated load, the actual load, the service life and seven working condition influence factors of the shelf are quantified as coefficients by a threshold correction module, the alarm threshold is dynamically corrected, the system can adaptively adjust the alarm sensitivity according to different warehouse scenes, shelf states and external environments, false alarms or missed alarms caused by a single threshold are avoided, and the accuracy of deformation detection and the working condition adaptability are significantly improved;the shelf image is subjected to gray-scale processing, block statistical analysis, abnormal gray-scale screening and contour comparison by a damage identification module, the shelf structure contour is accurately extracted;combined with the maximum distance threshold and the damage quantity statistics, the precise positioning and degree quantification of the damage area are realized, reliable basis is provided for subsequent maintenance decision-making, and the practicability and guidance of the detection result are improved.

Description

Technical Field

[0001] This invention relates to the field of laser-assisted calibration equipment technology, and in particular to a metal shelf structure deformation detection device based on laser-assisted calibration. Background Technology

[0002] Metal racking, as a core storage device in warehousing, logistics, and industrial production, directly affects the safety of warehousing operations, the quality of stored goods, and production efficiency. Metal racking is subjected to multiple factors over long periods, including cargo loads, changes in environmental temperature and humidity, and collisions with machinery. This makes it prone to structural deformation issues such as overall tilting, pallet bending, misalignment, and column deformation. If these deformations are not detected and addressed promptly, they can easily lead to rack collapses, falling goods, and other safety accidents, causing personal injury and property damage. Currently, the industry primarily uses traditional manual inspection methods for metal racking deformation detection. These methods involve manually measuring and comparing the dimensions, levelness, and verticality of the racking using tools such as tape measures, levels, and rulers. Such methods are cumbersome, inefficient, and their accuracy is difficult to guarantee due to the influence of human experience and measurement perspective. They cannot accurately capture minute deformations such as slight bending of pallets or local misalignment. Furthermore, traditional inspections are mostly periodic sampling inspections, which cannot achieve real-time online monitoring during racking use. This makes it difficult to detect safety hazards in the early stages of deformation, resulting in a significant detection lag.

[0003] Some detection solutions attempt to use single laser ranging or visual recognition technology for shelf inspection, but existing technologies mostly focus on single-dimensional deformation detection of shelves, lacking a three-dimensional detection system and unable to construct a comprehensive deformation detection coordinate system. This makes it difficult to achieve synchronous monitoring of the overall shelf structure and local points. Moreover, the detection points are mostly fixed, unable to be flexibly adjusted according to the size and installation position of different specifications of pallets, resulting in poor device adaptability and complex installation and debugging processes, making it difficult to meet the diverse detection needs of different warehousing scenarios. In addition, the laser emission components of existing shelf deformation detection devices are mostly connected to the shelves by bolts, which is inconvenient for disassembly and adjustment. For critical parts such as pallets that are prone to deformation, it is impossible to quickly complete the layout and adjustment of detection points, further reducing the flexibility and efficiency of the inspection operation.

[0004] Therefore, the above-mentioned problems need to be addressed and improved. Summary of the Invention

[0005] The purpose of this invention is to address the shortcomings of existing technologies by proposing a laser-assisted calibration-based metal shelf structure deformation detection device.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: a metal shelf structure deformation detection device based on laser-assisted calibration, comprising a shelf body, a first projection plane and a second projection plane installed on one side of a wall and a ceiling, a first positioning rod longitudinally installed on the first projection plane, and a second positioning rod and a third positioning rod perpendicular to each other installed laterally on the second projection plane, a reference mechanism for projecting reference lines installed on the first positioning rod, the second positioning rod and the third positioning rod, and a positioning mechanism for detecting whether the shelf body is deformed;

[0007] The control box of the detection device is equipped with a threshold correction module and a damage identification module.

[0008] The threshold correction module calculates a basic threshold based on the shelf's rated load, actual load, and service life. It then introduces seven quantification coefficients for multiplication and correction to obtain the final alarm threshold. The corrected threshold is compared with the actual detected laser point offset. If the offset exceeds the threshold, an alarm is triggered.

[0009] The damage identification module converts the image to grayscale and segments it into grayscale blocks. It then performs statistical analysis on the grayscale values ​​of each block collected at different times, sets a fluctuation range, and removes outliers to obtain stable grayscale data. Subsequently, it selects the image with the fewest abnormal grayscale blocks as the analysis benchmark, determines the structural grayscale blocks based on the standard shelf outline, and further filters out the image with the fewest structural block anomalies as the final analysis image. It compares the shelf outline in the final analysis image with the standard outline, marks the non-overlapping parts as abnormal areas, calculates the maximum distance between abnormal areas and compares it with a preset threshold. If the distance exceeds the threshold, it is determined to be damage. It counts the number of damage locations and issues an alert when the number exceeds the set threshold.

[0010] Preferably, the data analysis steps of the threshold correction module are as follows:

[0011] U1: Based on the rated load of the rack Actual load With service life Calculate the basic alarm threshold , , Based on the offset threshold; when hour, The value of is 1; when In that year, The value of is 1;

[0012] U2: Introducing seven quantization coefficients Make corrections to obtain the final alarm threshold. The real-time detected laser point offset is compared with Compare, if the offset exceeds If so, an alarm will be triggered.

[0013] Preferably, the data analysis steps of the damage identification module are as follows:

[0014] L1: Acquire the overall outline image of the shelf using a vision camera, convert the image to grayscale, and divide it into pre-defined pixel blocks. Several grayscale blocks of the same size are numbered according to their row and column positions; grayscale blocks with the same number are collected at different times. Perform statistical analysis on each grayscale value and calculate the mean. and standard deviation Set the grayscale value fluctuation range to Mark data outside this range as outliers and record the number of outliers. ;like Then, outliers are removed, and the mean of the remaining gray values ​​is calculated. As the stable grayscale value at that moment, This is a preset proportional coefficient;

[0015] L2: After determining the grayscale values ​​of all grayscale blocks, record the number of abnormal grayscale blocks in each image. Select The smallest image is used as the initial analysis image; based on the grayscale value range of the standard shelf outline, the structural grayscale block number is determined; in the initial analysis image, the number of anomalies in the structural grayscale blocks is counted. Select The smallest image is used as the final image for analysis;

[0016] L3: Draw the shelf outline based on the structural grayscale block positions in the final analysis image and compare it with the preset standard outline; if they do not overlap, mark them as abnormal areas and calculate the maximum distance between the abnormal areas and the standard outline. ;like If the damage is detected, it is considered an injury, and the number of damaged locations is counted. The preset spacing threshold is used; if the number of damaged locations exceeds the preset threshold, an alert will be issued via the data terminal.

[0017] Preferably, the reference mechanism includes a plurality of first mounting seats equidistantly mounted on a first positioning rod, with corresponding limit grooves formed on the first mounting seats and the first positioning rod, and a limit rod inserted into the limit groove, and a first reference line transmitter mounted on one side of the first mounting seat.

[0018] Preferably, the reference mechanism further includes a plurality of third mounting seats slidably connected at equal intervals to the second positioning rod and a plurality of second mounting seats slidably connected at equal intervals to the third positioning rod, wherein damping pads are installed on the inner surfaces of the second mounting seats and the third mounting seats.

[0019] Preferably, a second reference line transmitter is mounted on one side of the second mounting base, and a third reference line transmitter and a fourth reference line transmitter are mounted on the bottom surface and one side of the third mounting base, respectively. The output direction of the third reference line transmitter is perpendicular to the output direction of the second reference line transmitter, and the output direction of the fourth reference line transmitter is perpendicular to the output direction of the first reference line transmitter.

[0020] Preferably, the positioning mechanism includes four fixed bases installed at the four corners of the top of the shelf body, each of the four fixed bases is equipped with a second laser emitter, and strong magnets are installed on both sides of the top surface of the shelf and at the shelf mounting location of the shelf body, and the first laser emitter is installed by magnetic attraction of the strong magnets.

[0021] Compared with the prior art, the beneficial effects of the present invention are:

[0022] 1. By coordinating the reference mechanism and the positioning mechanism, a three-dimensional detection coordinate system can be easily constructed, improving the spatial accuracy of deformation detection and enabling comprehensive monitoring of the overall structural deformation of the shelving. Furthermore, the magnetic attraction between the first laser emitter and the strong magnet facilitates flexible adjustment of the detection points according to the size of the pallet, improving the adaptability and installation efficiency of the device. This enables precise capture of local deformations such as bending and misalignment of the pallet. Ultimately, this solves the problems of complex operation, insufficient accuracy, and inability to monitor in real time that exist in traditional detection methods.

[0023] 2. The threshold correction module quantifies the rated load, actual load, service life, and seven influencing factors of the rack into coefficients, dynamically correcting the alarm threshold. This allows the system to adaptively adjust alarm sensitivity based on different warehousing scenarios, rack conditions, and external environments, avoiding false alarms or missed alarms caused by a single threshold, significantly improving the accuracy and adaptability of deformation detection. The damage recognition module performs grayscale processing, block statistical analysis, abnormal grayscale filtering, and contour comparison on rack images to accurately extract the rack structure contour. Combined with the maximum spacing threshold and damage quantity statistics, it achieves precise location and quantification of damaged areas, providing a reliable basis for subsequent maintenance decisions and improving the practicality and guidance of the detection results. Attached Figure Description

[0024] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings:

[0025] Figure 1 This is a schematic diagram of the overall appearance of the device proposed in this invention;

[0026] Figure 2This is a schematic diagram of the first positioning rod structure proposed in this invention;

[0027] Figure 3 This is a schematic diagram of the second positioning rod structure proposed in this invention;

[0028] Figure 4 This is a schematic diagram of the third positioning rod structure proposed in this invention;

[0029] Figure 5 This is a schematic diagram of the main structure of the shelving unit proposed in this invention;

[0030] Figure 6 The present invention proposes Figure 5 Enlarged schematic diagram of the structure at part A in the middle;

[0031] Figure 7 This is a flowchart of the system proposed in this invention.

[0032] The numbers in the diagram are as follows: 1. Shelf body; 2. First projection plane; 3. Second projection plane; 4. First positioning rod; 5. Second positioning rod; 6. Third positioning rod; 7. First mounting base; 8. Limiting rod; 9. First reference line transmitter; 10. Second mounting base; 11. Second reference line transmitter; 12. Third mounting base; 13. Third reference line transmitter; 14. Fourth reference line transmitter; 15. First laser transmitter; 16. Strong magnet; 17. Second laser transmitter; 18. Fixed base. Detailed Implementation

[0033] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0034] Example 1: See Figures 1 to 6The laser-assisted calibration-based metal shelf structural deformation detection device of this invention includes a shelf body 1, a first projection plane 2 and a second projection plane 3 installed on one side of the wall and the ceiling. By projecting a grid-like reference line onto the first projection plane 2 and the second projection plane 3, and comparing the position of the laser point (or crosshair spot) with the initial position of the laser point on the metal shelf, it is determined whether the shelf has deformed. A first positioning rod 4 is longitudinally installed on the first projection plane 2, which allows for easy adjustment of the position of the first mounting base 7 according to the reference line spacing requirements. A second positioning rod 5 and a third positioning rod 6 are horizontally installed on the second projection plane 3, which are perpendicular to each other. The second positioning rod 5 and the third positioning rod 6 facilitate adjustment of the distance between the second mounting base 10 and the third mounting base 12; the first positioning rod 4, the second positioning rod 5, and the third positioning rod 6 are equipped with reference mechanisms for projecting reference lines, which facilitate the formation of a grid-like reference line on the first projection plane 2 and the second projection plane 3; the shelf body 1 is equipped with a positioning mechanism for detecting whether the shelf body 1 is deformed, which facilitates the projection of several key points of the metal shelf into the reference grid, and records the position of the point through a data terminal. When the shelf deforms, the position of the laser point changes, causing the external vision camera or laser lead to repel each other (a special laser emitter needs to be installed). The lasers emitted have different frequencies, and an alarm will be triggered when different frequencies intersect. The system senses a change in the laser point's position, determines its specific location on the metal shelf, and sends a signal to the data terminal. The reference mechanism includes multiple first mounting seats 7 equidistantly mounted on the first positioning rod 4, allowing for easy adjustment of the spacing between the first reference line transmitters 9. Limiting grooves are correspondingly formed on the first mounting seats 7 and the first positioning rod 4, with limiting rods 8 inserted into these grooves. The first mounting seats 7 are fixed to the first positioning rod 4 by the inserted limiting rods 8, preventing the first mounting seats 7 from causing the first reference line transmitters 9 to shake. A first reference line transmitter is mounted on one side of each first mounting seat 7. 9. A grid-like reference line is projected onto the first projection plane 2 through the cooperation of the first reference line transmitter 9 and the fourth reference line transmitter 14. Then, a grid-like reference line is formed on the second projection plane 3 through the second reference line transmitter 11 and the third reference line transmitter 13. The output ends of the transmitters are perpendicular to each other, which facilitates the reference mechanism. It also includes multiple third mounting seats 12 that are equidistantly slidably connected to the second positioning rod 5 and multiple second mounting seats 10 that are equidistantly slidably connected to the third positioning rod 6. Damping pads are installed on the inner surfaces of the second mounting seats 10 and the third mounting seats 12 to increase the friction between the second mounting seats 10 and the third mounting seats 12 and the second positioning rod 5 and the third positioning rod 6.

[0035] In this invention, a second reference line emitter 11 is installed on one side of the second mounting base 10, and a third reference line emitter 13 and a fourth reference line emitter 14 are installed on the bottom surface and one side of the third mounting base 12, respectively. The output direction of the third reference line emitter 13 is perpendicular to the output direction of the second reference line emitter 11, and the output direction of the fourth reference line emitter 14 is perpendicular to the output direction of the first reference line emitter 9. The positioning mechanism includes four fixed bases 18 installed at the four corners of the top of the shelf body 1. The fixed bases 18 facilitate the vertical installation of the second laser emitter 17 at the top of the four corners of the shelf body 1 by clamping with bolts. The top of each of the four fixed bases 18 is equipped with a second laser emitter 17. The first laser emitter 15 installed on both sides of the top surface of the pallet and at the installation location of the pallet can also detect whether the pallet is deformed (when the pallet is deformed under the weight, the position of the laser point projected by the first laser emitter 15 changes). Strong magnets 16 are installed on both sides of the top surface of the pallet and at the installation location of the pallet of the shelf body 1, and the first laser emitter 15 is magnetically installed by the strong magnets 16.

[0036] Working principle: When using this invention, the first projection plane 2 is first fixedly installed on the wall on one side of the shelf body 1, and the second projection plane 3 is fixedly installed on the ceiling above the shelf body 1, ensuring that the surfaces of the first projection plane 2 and the second projection plane 3 are flat and clean to avoid affecting the laser projection effect;

[0037] Next, a first positioning rod 4 is longitudinally installed on the first projection plane 2. Based on the height of the rack body 1 and the required detection accuracy, multiple first mounting seats 7 are installed at appropriate positions on the first positioning rod 4. The first mounting seats 7 are securely fixed using limiting grooves and limiting rods 8, enabling the first reference line transmitter 9 on one side of the first mounting seat 7 to stably project a longitudinal reference line. Then, mutually perpendicular second positioning rods 5 and third positioning rods 6 are installed laterally on the second projection plane 3. Multiple second mounting seats 10 are equidistantly slidably connected to the third positioning rods 6, and multiple third mounting seats 12 are equidistantly slidably connected to the first positioning rods 5 and 6. On the two positioning rods 5, the damping pads on the inner sides of the second mounting base 10 and the third mounting base 12 are used to stabilize them after the position is adjusted. At this time, the second reference line transmitter 11 on the second mounting base 10 projects a transverse reference line, the third reference line transmitter 13 on the bottom surface of the third mounting base 12 projects a longitudinal reference line perpendicular to the output direction of the second reference line transmitter 11, and the fourth reference line transmitter 14 on one side projects a transverse reference line perpendicular to the output direction of the first reference line transmitter 9, thereby forming a complete grid-like reference line on the first projection plane 2 and the second projection plane 3 respectively.

[0038] Subsequently, second laser emitters 17 are installed at the four corners of the top of the shelf body 1 using fixed bases 18. First laser emitters 15 are magnetically installed on both sides of the top surface of the pallet and at the pallet mounting point using strong magnets 16, ensuring that all laser emitters are securely connected to the shelf body 1. After the device is activated, the first laser emitter 15 and the second laser emitter 17 will project laser points onto the corresponding grid reference lines of the first projection plane 2 or the second projection plane 3. The external data terminal will record the initial position coordinates of these laser points. During subsequent use, the device is activated periodically, and the position of the laser points on the grid reference lines of the projection plane is captured in real time by a vision camera and compared with the initial position coordinates. If the shelf body 1 is deformed, such as the entire shelf tilting or the pallet bending, the position of the laser emitter will change accordingly, causing the position of the laser points projected onto the reference lines to shift. When the shift exceeds a preset threshold, the data terminal will issue an alarm and determine the specific deformed part of the shelf body 1 based on the position of the emitter corresponding to the laser point, so that staff can conduct timely inspection and maintenance.

[0039] Example 2: See Figure 7 The control box of the detection device is equipped with a threshold correction module and a damage identification module.

[0040] The threshold correction module calculates a basic threshold based on the shelf's rated load, actual load, and service life. Then, it introduces seven quantification coefficients for multiplication and correction to obtain the final alarm threshold. The corrected threshold is compared with the actual detected laser point offset. If the offset exceeds the threshold, an alarm is triggered.

[0041] The damage identification module converts the image to grayscale and segments it into grayscale blocks. It then performs statistical analysis on the grayscale values ​​of each block collected at different times, sets a fluctuation range, and removes outliers to obtain stable grayscale data. Subsequently, it selects the image with the fewest abnormal grayscale blocks as the analysis benchmark, determines the structural grayscale blocks based on the standard shelf outline, and further filters out the image with the fewest structural block anomalies as the final analysis image. It compares the shelf outline in the final analysis image with the standard outline, marks the non-overlapping parts as abnormal areas, calculates the maximum distance between abnormal areas and compares it with a preset threshold. If the distance exceeds the threshold, it is determined to be damage. It counts the number of damage locations and issues an alert when the number exceeds a set threshold.

[0042] Basic alarm thresholds in typical scenarios , Based on the offset threshold, The rated load of the rack, For actual load, The service life of the shelf; when hour, The value of is 1; when In that year, The value of is 1;

[0043] In practice, alarm thresholds are affected by various factors and may change, necessitating adjustments to the base alarm threshold. Based on the revised alarm threshold The offset is compared, and when the offset exceeds the alarm threshold... The data terminal will only issue an alarm at that time;

[0044] Quantization coefficient for assigning collision frequency of tasks , and These are the weight coefficients for the corresponding items. and These are the collision frequency and the maximum collision frequency, respectively. and These are the collision energy and the maximum collision energy, respectively.

[0045] Quantification coefficient for pallet placement , and These are the corresponding adjustment coefficients. This is the ratio of the pallet height to the total height of the shelving. This is the ratio of the load on the cargo pallet to the rated load.

[0046] Quantization coefficients for assigning values ​​to ambient temperature and humidity , , , These are the weight coefficients for the corresponding items. , , These are respectively ambient temperature, ideal temperature, and the range of temperature variation. , , These are respectively ambient humidity, ideal humidity, and the range of humidity variation. and These are the intraday temperature difference and the maximum permissible temperature difference, respectively.

[0047] Quantification coefficient for ground flatness , and These are the weight coefficients for the corresponding items. and These are the standard deviation of ground levelness and the maximum permissible levelness deviation, respectively. and These are the ground settlement rate and the maximum allowable settlement rate, respectively.

[0048] Quantification coefficient for assigning warehouse security level , and These are the weight coefficients for the corresponding items. The risk level of the item is categorized as (Level 1-5). The value level of the item (level 1-5);

[0049] Assignment quantification coefficient for shelf structure type , and These are the weight coefficients for the corresponding items. and These are the rack load-bearing capacity levels (1 = light-duty, 2 = medium-duty, 3 = heavy-duty) and medium-duty rack levels. and These are the shelving material coefficients (steel = 1.0, aluminum = 1.2, composite materials = 1.5) and the standard material coefficients, respectively.

[0050] Assignment quantization coefficients for detection point types , and These are the weight coefficients for the corresponding items. The importance level of the points is categorized as follows: 1 = auxiliary points, 2 = general points, 3 = critical structural points. This is the deformation history coefficient of the point (whether the point has ever deformed, 1=yes, 0=no).

[0051] The overall outline of the shelving is captured by a vision camera. The real-time image data is processed into grayscale, and the grayscale image is segmented according to the size of pixel blocks. The image data consists of several identical grayscale blocks, numbered according to their row and column numbers on the grayscale image. The acquired image data is sorted by acquisition time, and the corresponding numbered grayscale blocks within a single image acquired at the same time are further analyzed. Average the gray values and standard deviation The calculation, and the mean obtained from the calculation. and standard deviation Range of grayscale data The setting compares the corresponding grayscale value data with the corresponding grayscale value fluctuation range, marks the corresponding grayscale value data that is outside the fluctuation range as an outlier, and records the number of outliers. ;like If outliers are removed, the remaining grayscale data after outlier removal is averaged. The calculation, and the mean obtained from the calculation. As the grayscale value data detected at the corresponding time;

[0052] After determining the grayscale values ​​of all numbered grayscale blocks on the grayscale image, the number of grayscale block anomalies is calculated. Record and take the quantity The smallest grayscale image is used as the preliminary analysis image. Based on the grayscale values ​​of the standard components of the overall shelf outline, the grayscale values ​​of all grayscale blocks in the preliminary analysis image are compared to determine the grayscale block numbers corresponding to the overall shelf outline. These grayscale blocks are named structural grayscale blocks. The number of grayscale block anomalies in the structural grayscale blocks on the preliminary analysis image is then analyzed. Perform statistics and collect quantities. The image with the smallest grayscale value is the final image for analysis;

[0053] Based on the corresponding structural grayscale block positions on the final analysis image, the overall outline of the shelf is drawn, and the real-time recorded overall outline of the shelf is compared with the preset standard overall outline of the shelf. If the outline lines overlap, the overall outline of the shelf is considered complete; otherwise, the incomplete outline positions are marked as abnormal.

[0054] The maximum spacing between the real-time contour and the standard contour at the location of the anomaly marker. To obtain, if If this is detected, the shelf is determined to be damaged, and the number of damaged locations is counted. The preset spacing threshold is used; when the number of damaged locations exceeds the preset threshold, an alert is issued through the data terminal to remind staff to handle the situation promptly.

[0055] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A metal shelf structure deformation detection device based on laser-assisted calibration, comprising a shelf body (1), a first projection plane (2) and a second projection plane (3) installed on one side of a wall and on the ceiling, characterized in that: A first positioning rod (4) is installed longitudinally on the first projection plane (2), and a second positioning rod (5) and a third positioning rod (6) are installed laterally on the second projection plane (3). A reference mechanism for projecting reference lines is installed on the first positioning rod (4), the second positioning rod (5) and the third positioning rod (6), and a positioning mechanism for detecting whether the shelf body (1) is deformed is installed on the shelf body (1). The control box of the detection device is equipped with a threshold correction module and a damage identification module. The threshold correction module calculates a basic threshold based on the shelf's rated load, actual load, and service life. It then introduces seven quantification coefficients for multiplication and correction to obtain the final alarm threshold. The corrected threshold is compared with the actual detected laser point offset. If the offset exceeds the threshold, an alarm is triggered. The damage recognition module performs grayscale processing on the image and segments it into grayscale blocks. It performs statistical analysis on the grayscale values ​​of each grayscale block collected at different times, sets the fluctuation range, and removes outliers to obtain stable grayscale data. Then, the image with the fewest abnormal grayscale blocks was selected as the analysis benchmark. The structural grayscale blocks were determined according to the standard shelf outline. The image with the fewest structural block anomalies was further selected as the final analysis image. The shelf outline in the final analysis image is compared with the standard outline. Non-overlapping parts are marked as abnormal areas. The maximum distance between abnormal areas is calculated and compared with a preset threshold. If it exceeds the threshold, it is judged as damage. The number of damaged locations is counted. When the number exceeds the set threshold, an alarm is issued.

2. The metal shelf structure deformation detection device based on laser-assisted calibration according to claim 1, characterized in that: The data analysis steps for the threshold correction module are as follows: U1: Based on the rated load of the rack Actual load With service life Calculate the basic alarm threshold , , Based on the offset threshold; when hour, The value of is 1; when In that year, The value of is 1; U2: Introducing seven quantization coefficients Make corrections to obtain the final alarm threshold. The real-time detected laser point offset is compared with Compare, if the offset exceeds If so, an alarm will be triggered.

3. The metal shelf structure deformation detection device based on laser-assisted calibration according to claim 1, characterized in that: The data analysis steps of the damage identification module are as follows: L1: Acquire the overall outline image of the shelf using a vision camera, convert the image to grayscale, and divide it into pre-defined pixel block sizes. Several grayscale blocks of the same size are numbered according to their row and column positions; grayscale blocks with the same number are collected at different times. Statistical analysis was performed on the grayscale values ​​to calculate the mean. and standard deviation Set the grayscale value fluctuation range to Mark data that is outside the range as outliers and record the number of outliers. ;like Then, outliers are removed, and the mean of the remaining gray values ​​is calculated. As the stable grayscale value at that moment, This is a preset proportional coefficient; L2: After determining the grayscale values ​​of all grayscale blocks, record the number of abnormal grayscale blocks in each image. Select The smallest image is used as the initial analysis image; the structural grayscale block number is determined based on the grayscale value range of the standard shelf outline. In the initial image analysis, the number of anomalies in the structural grayscale blocks was counted. Select The smallest image is used as the final analysis image; L3: Draw the shelf outline based on the structural grayscale block positions in the final analysis image and compare it with the preset standard outline; if they do not overlap, mark them as abnormal areas and calculate the maximum distance between the abnormal areas and the standard outline. ;like If the damage is detected, it is considered an injury, and the number of damaged locations is counted. The preset spacing threshold is used; if the number of damaged locations exceeds the preset threshold, an alert will be issued via the data terminal.

4. The metal shelf structure deformation detection device based on laser-assisted calibration according to claim 1, characterized in that: The reference mechanism includes a plurality of first mounting seats (7) equidistantly mounted on the first positioning rod (4). Limiting grooves are correspondingly opened on the first mounting seats (7) and the first positioning rod (4). A limiting rod (8) is inserted into the limiting groove, and a first reference line transmitter (9) is installed on one side of the first mounting seat (7).

5. The metal shelf structure deformation detection device based on laser-assisted calibration according to claim 1, characterized in that: The reference mechanism also includes a plurality of third mounting seats (12) equidistantly slidably connected to the second positioning rod (5) and a plurality of second mounting seats (10) equidistantly slidably connected to the third positioning rod (6), wherein damping pads are installed on the inner sides of the second mounting seats (10) and the third mounting seats (12).

6. The metal shelf structure deformation detection device based on laser-assisted calibration according to claim 5, characterized in that: A second reference line transmitter (11) is installed on one side of the second mounting base (10). A third reference line transmitter (13) and a fourth reference line transmitter (14) are installed on the bottom and one side of the third mounting base (12), respectively. The output direction of the third reference line transmitter (13) is perpendicular to the output direction of the second reference line transmitter (11), and the output direction of the fourth reference line transmitter (14) is perpendicular to the output direction of the first reference line transmitter (9).

7. The metal shelf structure deformation detection device based on laser-assisted calibration according to claim 1, characterized in that: The positioning mechanism includes four fixed bases (18) installed at the four corners of the top of the shelf body (1), and a second laser emitter (17) is installed on the top of each of the four fixed bases (18).

8. The metal shelf structure deformation detection device based on laser-assisted calibration according to claim 7, characterized in that: Strong magnets (16) are installed on both sides of the top surface of the shelf body (1) and at the shelf installation location, and a first laser emitter (15) is installed by magnetic attraction through the strong magnets (16).

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