A method and system for rapid detection of the load limit of a warehouse rack

Abstract

The application relates to a kind of warehouse shelf carrying limit quick detection method and system, and to warehouse shelf technical field, it includes: collection shelf specification and shelf detection image;According to the shelf specification, determine the shelf reference size and shelf reference image;Match by the shelf reference size to obtain size recommendation category;According to the comparison and identification of shelf reference image and shelf detection image, determine installation abnormal condition;Determine the carrying detection recommendation category in combination with installation abnormal condition and size recommendation category;Based on carrying detection recommendation category, output to the preset detection display terminal, and collect carrying placement detection image;Determine the carrying change parameter by comparing carrying placement detection image and shelf detection image;Determine the carrying limit detection result according to carrying change parameter and shelf specification, and output the carrying limit detection result to the preset detection display terminal.The application has the effect of conveniently obtaining the household warehouse shelf carrying limit detection result quickly and accurately.

Description

Technical Field

[0001] This invention relates to the field of warehouse racking technology, and in particular to a rapid detection method and system for the load-bearing limit of warehouse racking. Background Technology

[0002] Storage racks are three-dimensional storage devices used for storing goods in layers and categories, achieving efficient space utilization and standardized operations. Storage racks typically consist of uprights, beams, and shelves, and are assembled from these components.

[0003] In the current use of home-use storage racks, in order to determine the true load-bearing capacity of the racks and prevent situations such as rack collapse, falling objects causing injury or damage to goods due to blind overloading, it is generally necessary to conduct load-bearing limit testing on the storage racks. Users typically assemble and level the storage racks, first pre-loading them with light objects to check overall stability, then evenly placing heavy objects according to the nominal load-bearing capacity, gradually loading them, and observing the denting of shelves, bending of beams, tilting of uprights, and deformation of connectors. They also listen for any abnormal noises. The absence of obvious permanent deformation, structural sway, and damage or cracks is used as the basis for determining the safety limit, thereby ensuring the safe use of the storage racks.

[0004] Currently, when testing the load-bearing limits of household storage racks, the operators rely solely on visual observation and auditory judgment, resulting in highly subjective results that are not easy to obtain accurately. Summary of the Invention

[0005] To facilitate users in quickly and accurately obtaining test results for the load-bearing limits of home storage racks, this invention provides a rapid test method and system for the load-bearing limits of storage racks.

[0006] In a first aspect, the present invention provides a rapid detection method for the load-bearing limit of warehouse racks, employing the following technical solution: A rapid method for detecting the load-bearing limit of warehouse racking includes: Collect the specifications and inspection images of household storage racks; Determine the basic dimensions and image of the shelving based on its specifications. The shelf span, overall height, and number of layers are extracted from the shelf baseline dimensions to obtain recommended sizes; By comparing and identifying the rack reference image with the rack inspection image, installation abnormalities can be determined. The recommended types of load-bearing tests should be determined based on the installation anomalies and the recommended types of dimensions. Based on the recommended types of load-bearing detection, the data is output to the preset detection display terminal, and load-bearing placement detection images are acquired; By comparing the load placement detection images with the shelf detection images, the load variation parameters are determined; The load-bearing limit test results are determined based on the load-bearing variation parameters and the rack specifications, and the load-bearing limit test results are output to the preset test display terminal; Methods for determining the recommended types of load-bearing tests include: Retrieve the type and location of the installation anomaly; Based on the shelf specifications, retrieve the installation location and shelf placement method; Calculate the vector distance between the installation location and the abnormal location and use it as the abnormal distance vector value; Determine the impact value of abnormal locations by combining the shelf placement method with the abnormal distance vector value; The impact value of each anomaly is determined by matching the anomaly types with a pre-defined anomaly level library. Calculate the sum of the impact values ​​of the abnormal location and the type of abnormality, and use it as the comprehensive impact value of the abnormality. Based on the comprehensive impact value of the anomaly, the recommended size types are selected to obtain the location selection types, and the location selection types are used as the recommended types for load-bearing detection.

[0007] By adopting the above technical solution, the system collects shelf specifications and inspection images to determine the shelf reference dimensions, reference images, and recommended size categories. It then identifies installation anomalies by comparing images and determines the recommended load-bearing capacity testing categories accordingly. Furthermore, it acquires load-bearing placement inspection images to obtain load variation parameters. Finally, based on these parameters and shelf specifications, it derives the load-bearing limit test results. This allows users to quickly test the load-bearing limits of home storage shelves, significantly improving testing efficiency while ensuring accuracy. It adapts to the simplified and rapid testing needs of home scenarios, enabling users to quickly and accurately obtain load-bearing limit test results for home storage shelves. Moreover, by retrieving anomaly types, anomaly locations, installation locations, and shelf placement methods, it calculates anomaly distance vector values, determines the impact values ​​of anomaly locations and types, and obtains the comprehensive impact value of anomalies. Based on this, it selects locations and types as recommended load-bearing capacity testing categories. This makes the determination of recommended load-bearing capacity testing categories more targeted and reasonable, based on the degree of impact of installation anomalies on the testing plan.

[0008] Optional methods for determining the impact value of abnormal locations include: Determine whether the shelf placement method is consistent with the preset floor placement method; If so, the ground distance influence value is determined based on the abnormal distance vector value, and the ground distance influence value is used as the abnormal location influence value; If not, retrieve the hanging position point, bottom position point, and top position point based on the shelf specifications; Calculate the vector distance between the suspension point and the bottom point and use it as the bottom distance vector value; Calculate the vector distance between the suspension point and the top point and use it as the top distance vector value; The hanging distance influence value is determined by combining the bottom distance vector value, the top distance vector value, and the abnormal distance vector value, and the hanging distance influence value is used as the abnormal position influence value.

[0009] By adopting the above technical solution, the ground and hanging scenarios are distinguished by the shelf placement method, and the influence value of ground distance or hanging distance is determined as the influence value of abnormal position. This can adapt to shelves with different installation and placement methods, accurately calculate the influence value of abnormal position, and improve the scenario adaptability and calculation accuracy of abnormal impact assessment.

[0010] Optionally, methods for determining the influence value of suspension distance include: The top anomaly distance value and the top anomaly angle value are determined by combining the anomaly distance vector value and the top distance vector value. The bottom anomaly distance value and the bottom anomaly angle value are determined by combining the anomaly distance vector value and the bottom distance vector value. Calculate the distance between the bottom position point and the top position point and use it as the top-bottom distance value; Determine whether the abnormal angle value at the bottom is less than the abnormal angle value at the top; If so, calculate the ratio between the bottom anomaly distance value and the top-bottom distance value and use it as the bottom anomaly ratio value; The impact value of the bottom anomaly is determined by combining the bottom anomaly ratio value and the bottom anomaly angle value, and this impact value is used as the impact value of the suspension distance. If not, calculate the ratio between the top anomaly distance value and the top-bottom distance value and use it as the top anomaly ratio value; The impact value of the top anomaly is determined by combining the top anomaly ratio value and the top anomaly angle value, and this impact value is used as the impact value of the suspension distance.

[0011] By adopting the above technical solution, by calculating the abnormal distance value at the top, the abnormal angle value at the top, the abnormal distance value at the bottom, the abnormal angle value at the bottom, and the distance value between the top and bottom, and determining the abnormal impact value at the bottom or top based on the angle size as the impact value of the suspension distance, the difference in the impact of abnormalities at the upper and lower parts of the suspended rack can be accurately distinguished, the quantitative evaluation of abnormal locations can be refined, and the impact value of the suspension distance can be made more consistent with the actual stress of the structure.

[0012] Optionally, the selection methods for location selection types include: Determine the shelving material based on the shelving specifications; By combining the shelf material and the shelf's standard dimensions, the standard weight range of the shelf can be calculated. Retrieve the weight range of recommended categories based on size; The recommended category weight range is adjusted based on the comprehensive impact value of the anomalies to obtain the category weight adjustment range; Based on the overlap between the shelf baseline weight range and the category weight adjustment range, recommended categories by size are selected to obtain categories that meet the weight requirements, and these categories that meet the weight requirements are used as the categories for location selection.

[0013] By adopting the above technical solution, the reference weight range of the shelving is determined by combining the shelving material and the shelving reference dimensions, and the recommended type weight range is adjusted according to the comprehensive impact value of abnormalities to obtain the type weight adjustment range. The weight is selected to meet the requirements of the type based on the overlap of the weight ranges. This can combine the shelving's own load-bearing capacity with the impact of installation abnormalities, so that the type of location selection conforms to the actual load-bearing potential of the shelving.

[0014] Optional methods for determining the weight requirement include: Select recommended product types whose weight adjustment range falls completely within the shelf's baseline weight range and use them as the types that fall completely within the range. Retrieve the values ​​of the falling categories based on the complete falling category; Determine the number of exceptions required by combining the exception type and the exception location; Determine if the number of categories falling into the category is greater than the number of abnormal requirements; If so, then it will fall completely into the category as the weight to satisfy the category; If not, calculate the difference between the number of categories falling into the category and the number of abnormal requirements, and use it as the number of requirement deviations. The size recommendation categories that do not fall completely into the category are taken as the remaining recommendation categories, and the degree of overlap between the intervals is determined based on the remaining recommendation categories; The remaining recommendation categories are selected by combining the interval overlap and the demand deviation values, and these categories are used as the remaining supplementary categories. The remaining supplementary types are combined with the types that fall completely and used as weight-satisfied types.

[0015] By adopting the above technical solution, the types that completely fall into the category are screened, the number of types that fall into the category is compared with the number of abnormal requirements, and the remaining supplementary types are selected based on the degree of overlap in the interval when the number is insufficient. Finally, the weight-satisfied types are obtained by combining them, so that the weight-satisfied types can meet the detection requirements.

[0016] Optional methods for determining the number of exceptional requirements include: Determine additional values ​​based on the abnormal location points; Determine the severity level of the anomaly based on its type; Select anomaly types whose severity level is greater than the preset severity baseline level and use them as severity types; Retrieve the number of severity values ​​based on severity category; Calculate the ratio between the number of critical cases and the number of abnormal supplementary cases, and use this ratio as the severity ratio. The severity adjustment factor is determined based on the severity ratio value; Calculate the product between the abnormal supplement value and the severity adjustment factor, and use it as the abnormal requirement value.

[0017] By adopting the above technical solution, the abnormality supplementary value is determined by the abnormal location point, the severity type and severity value are determined according to the type and severity level of the abnormality, the severity ratio value and severity adjustment coefficient are calculated, and then the abnormality demand value is obtained. This can quantify the demand for the number of detection types based on the severity of the abnormality, so that the configuration of the detection solution matches the installation abnormality risk level and improves the detection targeting.

[0018] Optionally, methods for determining the load-bearing variation parameters include: Based on image recognition of placement, the type and size of the placed items are identified. Determine the density value of the items based on the type of items placed; The load-bearing capacity of the placed item is calculated based on the item's density value and the item's dimensions. By combining the load-bearing placement detection images with the shelf detection images, the placement variation parameters are determined; The load-bearing capacity is determined by combining the placement variation parameters and the load-bearing capacity of the placed items.

[0019] By adopting the above technical solution, the load-bearing value of the placed items is determined by identifying the type and size of the placed items, and the placement change parameters are obtained by combining image comparison. These parameters are then combined with the load-bearing value of the placed items to form the load-bearing change parameters. This allows the external load to be correlated with the structural deformation, enabling a quantitative expression of load-bearing changes and providing reliable data support for subsequent limit determination.

[0020] Optionally, methods for determining the placement of variable parameters include: Core detection points are marked based on shelf inspection images; The image of the load placement detection image is registered with the image of the shelf detection image, and the core detection points are extracted from the image of the load placement detection image as the load change points. Determine the node change by combining the core detection points and the points where load changes; Determine the standard variation based on the shelf specifications; Select the load-bearing change points corresponding to node changes that are greater than the specification baseline changes, and designate them as the points of severe change; The image of the severe change was obtained by combining the image of the severely changed location with the image of the load placement detection. Determine the type of severe change based on the image showing severe changes; The severity of the change is combined with the amount of change at each node and used as a parameter for placing the change.

[0021] By adopting the above technical solution, the load-bearing change points are obtained by marking the core detection points and performing image registration. The node change amount is calculated and the points with severe changes are screened. The types of severe changes are determined by combining the images of severe changes, and finally the placement change parameters are formed. This enables precise location of key parts of the shelf structure deformation, quantification of the degree and type of deformation, and makes the load-bearing change evaluation more structured and accurate.

[0022] Secondly, the present invention provides a rapid detection system for the load-bearing limit of warehouse racks, employing the following technical solution: A rapid detection system for the load-bearing limit of warehouse racking includes: The data acquisition module is used to acquire shelving specifications, shelving inspection images, and load-bearing placement inspection images. The memory stores a program for implementing a rapid detection method for the load-bearing limit of a warehouse rack as described in any one of the first aspects; The processor loads and executes programs stored in memory.

[0023] In summary, the present invention has at least one of the following beneficial technical effects: 1. By collecting shelf specifications and shelf inspection images, the system determines the shelf reference dimensions, shelf reference images, and recommended size types. Combined with image comparison, it identifies installation anomalies and determines the recommended load-bearing test types accordingly. Then, by collecting load-bearing placement inspection images, it obtains load change parameters. Finally, based on the load change parameters and shelf specifications, it derives the load-bearing limit test results. This allows users to quickly test the load-bearing limits of home storage shelves, significantly improving testing efficiency while ensuring accuracy. It adapts to the simplified and rapid testing needs of home scenarios, enabling users to quickly and accurately obtain the load-bearing limit test results for home storage shelves. 2. By differentiating between ground-based and suspended scenarios based on shelf placement methods, the influence value of ground distance or suspension distance can be determined separately as the influence value of abnormal locations. This approach can adapt to shelves with different installation and placement methods, accurately calculate the influence value of abnormal locations, and improve the scenario adaptability and calculation accuracy of abnormal impact assessment. 3. By identifying the type and size of the placed items, the load-bearing capacity of the placed items is determined. Combined with image comparison, the placement change parameters are obtained. These parameters are then combined with the load-bearing capacity of the placed items to form the load-bearing change parameters. This allows the external load to be correlated with the structural deformation, enabling a quantitative expression of load-bearing changes and providing reliable data support for subsequent limit determination. Attached Figure Description

[0024] Figure 1 This is a flowchart of a method for quickly testing the load-bearing limit of warehouse racking. Detailed Implementation

[0025] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments.

[0026] A rapid detection method for the load-bearing limit of storage racks is disclosed. This method involves collecting rack specifications, rack inspection images, and load-bearing placement inspection images to determine the rack's baseline dimensions, baseline images, and recommended size types. Image comparison is then used to identify installation anomalies. The comprehensive impact value of the anomaly is obtained by quantifying related parameters, and suitable recommended load-bearing inspection types are selected. Furthermore, by combining load-bearing placement inspection images and rack inspection images, the load-bearing value of placed items and placement variation parameters are determined, thus obtaining load variation parameters. Finally, the load-bearing limit detection result is determined based on the load variation parameters and rack specifications. This method facilitates rapid detection of the load-bearing limit of home storage racks, significantly improving detection efficiency while ensuring accuracy. It adapts to the simplified and rapid detection needs of home scenarios, enabling users to quickly and accurately obtain load-bearing limit detection results for home storage racks.

[0027] Reference Figure 1 This invention discloses a rapid detection method for the load-bearing limit of warehouse racking, comprising: S100: Collects images of household storage racks, including rack specifications and inspection data.

[0028] The shelving specifications refer to a set of parameters reflecting the structural and dimensional characteristics of home storage shelving, mainly including key information such as shelving length, width, height, shelf span, number of layers, material type, and nominal load-bearing capacity. The shelving specifications are obtained by scanning and reading the QR codes pre-installed on the home storage shelving using a pre-set detection and display terminal. The detection and display terminal is a device capable of scanning codes, taking photos, and displaying the data; it can be a mobile phone, tablet, or other similar device.

[0029] Shelf inspection images refer to overall and partial visual images of household storage shelves in an unloaded state. These images are obtained by capturing images of the front, sides, and connection nodes of the shelves from a fixed position under uniform lighting conditions using a pre-set inspection and display terminal.

[0030] S101: Determine the reference dimensions and reference image of the shelving based on the shelving specifications.

[0031] Among them, the shelf reference dimension refers to the theoretical reference dimension corresponding to the shelf specification. The shelf reference image refers to the ideal visual form of the shelf under conditions of no installation abnormalities, no load, and no deformation.

[0032] By inputting the shelf specifications into a preset specification database, the baseline dimensions and images of the shelves are obtained for easy subsequent use.

[0033] The specification database contains a pre-stored table of different shelf specifications and their corresponding shelf reference dimensions and images. The specification database is retrieved after the operator has pre-entered the information.

[0034] S102: Extract shelf span, overall height, and number of layers from the shelf baseline dimensions to obtain recommended size types.

[0035] The recommended size categories refer to the set of load-bearing testing types that are compatible with the shelf structure dimensions. Recommended size categories include types of boxed / bottled drinking water, books and magazines, rice / flour bags, and storage boxes fully loaded with clothing / miscellaneous items.

[0036] Shelf span refers to the distance between individual shelves in a shelving unit. Overall height refers to the total height of the shelving unit. Number of shelves refers to the number of spaces between individual shelves in a shelving unit.

[0037] By extracting key parameters such as shelf span, overall height, and number of layers from the shelf's baseline dimensions, and then inputting them into a preset size category database for matching, recommended size categories are obtained for convenient subsequent use.

[0038] The size category database pre-stores a table of different recommended size categories and their corresponding size parameters, which is obtained after the operator pre-inputs the data.

[0039] S103: Based on the comparison and identification between the shelf reference image and the shelf inspection image, determine the installation abnormality.

[0040] Installation anomalies refer to structural deviations between the actual assembled and placed home storage racks and the ideal standard state shown in the reference image. Installation anomalies include types and locations. Types of anomalies refer to the specific types of structural deviations that occur after the actual assembly and placement of the home storage racks. Locations of anomalies refer to the specific locations where structural deviations occur after the actual assembly and placement of the home storage racks.

[0041] Abnormalities include tilted uprights, skewed shelves, loose clips, excessive gaps between joints, overall asymmetry, and wobbling of the shelving.

[0042] By comparing and identifying the reference image of the shelf with the inspection image of the shelf, the differences in contour, angle, and position offset at the same location are identified. The values ​​of angle deviation, displacement offset, missing features, etc. obtained from the comparison and identification are compared with the preset installation tolerance threshold one by one. Items that exceed the threshold are judged as the corresponding installation abnormality type. Combined with the specific location point, the installation abnormality situation is obtained, which is convenient for subsequent use.

[0043] S104: Determine the recommended types of load-bearing tests based on installation anomalies and recommended dimensional types.

[0044] Among them, the recommended load-bearing test types refer to the set of load-bearing test types selected when conducting load-bearing tests that are most suitable for the current state of the household shelving.

[0045] By combining the analysis of installation anomalies with the recommended size types, the recommended types of load-bearing tests can be determined to facilitate subsequent use.

[0046] S105: Based on the recommended types of load detection, output to the preset detection display terminal and acquire load placement detection images.

[0047] Among them, the load-bearing placement detection image refers to the image of the shelf under stress after heavy objects have been placed according to the selected recommended load-bearing detection types. The load-bearing placement detection images are acquired through a preset detection display terminal.

[0048] By sending the determined load-bearing detection recommendations to the detection display terminal, the user places the items according to the recommended load-bearing detection types, and then takes a picture of the shelf under load, thereby acquiring a load-bearing placement detection image.

[0049] S106: By comparing the load placement detection image with the shelf detection image, the load change parameters are determined.

[0050] Among them, the load-bearing variation parameters refer to the set of parameters that reflect the type and degree of deformation of the rack under load.

[0051] By comparing and analyzing the images of the load placement detection and the images of the shelf detection, the load variation parameters can be determined, which will facilitate subsequent use.

[0052] S107: Determine the load limit test result based on the load variation parameters and shelf specifications, and output the load limit test result to the preset test display terminal.

[0053] Among them, the load-bearing limit test result refers to the conclusion of the judgment on the load-bearing capacity of household storage racks.

[0054] By inputting the load-bearing variation parameters and the design parameters in the rack specifications into a preset mechanical evaluation model, the load-bearing limit test results are obtained, and the load-bearing limit test results are output to the test display terminal. This allows users to quickly test the load-bearing limit of home storage racks, greatly improving testing efficiency while ensuring testing accuracy. It adapts to the simplified and rapid testing needs of home scenarios, thus enabling users to quickly and accurately obtain the load-bearing limit test results of home storage racks.

[0055] The mechanical assessment model retrieves the actual deformation from the load-bearing variation parameters and the allowable elastic deformation threshold from the design parameters in the rack specifications. It then compares the actual deformation with the allowable elastic deformation threshold and combines this with the theoretical failure strength of the rack material to deduce the load weight corresponding to the critical deformation, thereby calculating the load-bearing limit value. Finally, it reduces the theoretical limit value by a safety factor (usually 1.5 to 2.0) to obtain the load-bearing limit test result.

[0056] To further ensure the rationality of the recommended types of load-bearing tests, it is necessary to conduct further separate analysis and calculation on the recommended types of load-bearing tests, which will be explained in detail through the steps shown below.

[0057] The method for determining the recommended types of load-bearing tests includes the following steps: S200: Retrieve the type and location of the anomaly based on the installation anomaly.

[0058] Among them, the abnormal situation can be detected by retrieving the type and location of the abnormality, which is convenient for subsequent use.

[0059] S201: Retrieve installation location and shelf placement method based on shelf specifications.

[0060] The installation location points refer to the standard characteristic points on the shelf structure determined according to the shelf specifications, used for installation and stress analysis, such as the top and bottom ends of the uprights, the ends of the shelves, the bottom support feet, and the top frame points. Shelf placement method refers to the way the shelves are arranged and installed in the usage scenario, including ground placement and hanging installation.

[0061] S202: Calculate the vector distance between the installation location point and the abnormal location point and use it as the abnormal distance vector value.

[0062] Among them, the abnormal distance vector value refers to the parameter used to characterize the spatial distance and orientation between the installation location point and the abnormal location point.

[0063] By extracting the coordinate data of the installation location point and the abnormal location point, the distance and direction angle between the two points are calculated through the coordinate difference. The resulting distance with direction is used as the abnormal distance vector value for convenient subsequent use.

[0064] S203: Determine the impact value of abnormal location by combining the shelf placement method and the abnormal distance vector value.

[0065] Among them, the abnormal location impact value refers to the degree of impact on structural stability and load-bearing capacity testing when installation abnormalities occur in different locations of the shelf.

[0066] By combining the analysis of shelf placement methods with abnormal distance vector values, the impact value of abnormal locations can be determined, facilitating subsequent use.

[0067] S204: Determine the impact value of an anomaly by matching the anomaly type with a preset anomaly level library.

[0068] Among them, the category impact value refers to the severity of the impact of different abnormality categories on the load-bearing safety and structural stability of the shelving.

[0069] By matching the identified anomaly types with a preset anomaly level library, corresponding scores are assigned according to the degree of harm the anomaly poses to the carrier, and these scores are used as the category impact values.

[0070] The anomaly level library pre-stores a table that compares different anomaly types with their corresponding impact values. Severe anomalies such as tilted columns and loose structures are assigned higher impact values, while minor anomalies such as slight gaps and uneven appearance are assigned lower impact values. The anomaly level library is obtained after the operator has pre-entered the information.

[0071] S205: Calculate the sum of the impact values ​​of the location and the type of anomalies and use it as the comprehensive impact value of the anomaly.

[0072] Among them, the comprehensive impact value of anomalies refers to the degree of influence on structural stability and load-bearing capacity testing after considering the types and locations of anomalies.

[0073] The sum of the impact values ​​of abnormal location and type is calculated, and the result is used as the comprehensive impact value of the abnormality for convenient subsequent use.

[0074] S206: Based on the comprehensive impact value of the anomaly, the recommended size types are selected to obtain the location selection types, and the location selection types are used as the recommended types for load testing.

[0075] Among them, the location selection type refers to the load-bearing detection type that is selected based on the size recommendation type and combined with the comprehensive impact value of the anomaly, and is suitable for the current anomaly level of the shelf.

[0076] By analyzing the comprehensive impact value of anomalies to select recommended size types, the location selection types are obtained and used as recommended load-bearing detection types, thereby improving the accuracy of the obtained recommended load-bearing detection types.

[0077] To further ensure the rationality of the impact value of abnormal locations, it is necessary to perform a further separate analysis and calculation of the impact value of abnormal locations, which will be explained in detail through the following steps.

[0078] The method for determining the impact value of abnormal locations includes the following steps: S300: Determine whether the shelf placement method is consistent with the preset ground placement method. If yes, proceed to S301; if no, proceed to S302.

[0079] In this process, the system determines whether the abnormal distance vector value can be directly used to determine whether the shelf placement method is consistent with the preset ground placement method.

[0080] S301: Determine the ground distance influence value based on the abnormal distance vector value, and use the ground distance influence value as the abnormal location influence value.

[0081] Among them, the ground distance influence value refers to the degree of impact of abnormal positions on the structural stability and load-bearing capacity of the rack in the ground placement scenario.

[0082] When the shelf placement method is consistent with the preset ground placement method, it means that the abnormal distance vector value can be directly determined. Therefore, the distance value is retrieved from the abnormal distance vector value, and the product value between it and the preset distance influence coefficient is calculated as the ground distance influence value. Then, the ground distance influence value is used as the abnormal position influence value, thereby improving the accuracy of the obtained abnormal position influence value.

[0083] The distance influence coefficient is a coefficient used to convert the distance value in the abnormal distance vector into a ground distance influence value. This coefficient is preset by the operator according to requirements. A larger distance value in the abnormal distance vector indicates a greater deviation from the standard installation position, and a higher ground distance influence value.

[0084] S302: Retrieve hanging position point, bottom position point and top position point based on shelf specifications.

[0085] Among them, the suspension point refers to the force-bearing connection point connected to the suspension support structure when the shelf is installed in a suspended manner. The bottom point refers to the point corresponding to the structural features of the bottom area of ​​the shelf. The top point refers to the point corresponding to the structural features of the top area of ​​the shelf.

[0086] When the placement of the shelving is inconsistent with the preset ground placement, it means that the abnormal distance vector value cannot be directly determined. Therefore, the preset specification database is queried through the shelving specifications to retrieve the hanging position point, bottom position point and top position point for subsequent use.

[0087] The specification database pre-stores a table showing the different shelf specifications and their corresponding hanging positions, bottom positions, and top positions.

[0088] S303: Calculate the vector distance between the suspension position point and the bottom position point and use it as the bottom distance vector value.

[0089] Among them, the bottom distance vector value refers to the parameters of spatial distance and orientation between the suspension position point and the bottom position point.

[0090] By extracting the coordinate data of the suspension position point and the bottom position point, the distance and direction angle between the two points are calculated through the coordinate difference. The resulting distance with direction is used as the bottom distance vector value for convenient subsequent use.

[0091] S304: Calculate the vector distance between the suspension position point and the top position point and use it as the top distance vector value.

[0092] Among them, the top distance vector value refers to the parameters of spatial distance and orientation between the suspension position point and the top position point.

[0093] By extracting the coordinate data of the hanging position point and the top position point, the distance and direction angle between the two points are calculated through the coordinate difference. The resulting distance with direction is used as the top distance vector value for convenient subsequent use.

[0094] S305: Combine the bottom distance vector value, top distance vector value and abnormal distance vector value to determine the suspension distance influence value, and use the suspension distance influence value as the abnormal position influence value.

[0095] Among them, the hanging distance influence value refers to the degree of impact of abnormal positions on the structural stability and load-bearing capacity of the rack in the hanging placement scenario.

[0096] By combining and analyzing the bottom distance vector value, top distance vector value, and abnormal distance vector value, the influence value of the suspension distance is determined, and the influence value of the suspension distance is used as the influence value of the abnormal position, thereby improving the accuracy of the obtained influence value of the abnormal position.

[0097] To further ensure the rationality of the suspension distance influence value, it is necessary to perform a further separate analysis and calculation of the suspension distance influence value, which will be explained in detail through the steps shown below.

[0098] The method for determining the influence value of suspension distance includes the following steps: S400: Combine the abnormal distance vector value and the top distance vector value to calculate and determine the top abnormal distance value and the top abnormal angle value.

[0099] The top anomaly distance value refers to the straight-line deviation of the anomaly point relative to the top reference position. The top anomaly angle value refers to the degree of tilt or deflection of the anomaly point relative to the top reference direction.

[0100] By using the top distance vector value as the reference length of the top structure of the shelf and the abnormal distance vector value as the offset of the abnormality relative to the top reference point, the top abnormality distance value and the top abnormality angle value are obtained through vector decomposition and trigonometric function calculation.

[0101] S401: Combine the abnormal distance vector value and the bottom distance vector value to calculate and determine the bottom abnormal distance value and the bottom abnormal angle value.

[0102] Among them, the bottom anomaly distance value refers to the straight-line deviation distance of the anomaly point relative to the bottom reference position. The bottom anomaly angle value refers to the degree of tilt and deflection of the anomaly point relative to the bottom reference direction.

[0103] By using the bottom distance vector value as the reference length of the top structure of the shelf and the abnormal distance vector value as the offset of the abnormality relative to the bottom reference point, the bottom abnormal distance value and the bottom abnormal angle value are obtained through vector decomposition and trigonometric function calculation.

[0104] S402: Calculate the distance between the bottom position point and the top position point and use it as the top-bottom distance value.

[0105] The top-bottom distance value refers to the distance between the bottom position point and the top position point.

[0106] The distance between the bottom and top points is calculated, and the result is used as the top-bottom distance value for later use.

[0107] S403: Determine whether the bottom abnormal angle value is less than the top abnormal angle value. If yes, proceed to S404; if no, proceed to S406.

[0108] Specifically, by judging whether the abnormal angle value at the bottom is less than the abnormal angle value at the top, the difference in the degree of deflection of the abnormality in the upper and lower areas of the shelf can be distinguished.

[0109] S404: Calculate the ratio between the bottom anomaly distance value and the top-bottom distance value and use it as the bottom anomaly ratio value.

[0110] Among them, the bottom anomaly ratio value refers to the ratio between the bottom anomaly distance value and the top-bottom distance value.

[0111] When the bottom abnormal angle value is less than the top abnormal angle value, the bottom abnormal ratio value is calculated to facilitate subsequent use.

[0112] S405: Combine the bottom anomaly ratio value and the bottom anomaly angle value to calculate and determine the bottom anomaly impact value, and use the bottom anomaly impact value as the suspension distance impact value.

[0113] Among them, the bottom abnormality impact value refers to the degree of impact that the abnormal offset has relative to the bottom reference dimension of the shelf.

[0114] By weighting the bottom anomaly ratio and bottom anomaly angle values, and using the result as the bottom anomaly impact value, and then using this impact value as the suspension distance impact value, the accuracy of the obtained suspension distance impact value is improved. The specific weights for the weighted calculation are preset by the operator according to actual needs.

[0115] S406: Calculate the ratio between the top anomaly distance value and the top-bottom distance value and use it as the top anomaly ratio value.

[0116] Among them, the top anomaly ratio value refers to the ratio between the top anomaly distance value and the top-bottom distance value.

[0117] When the bottom abnormal angle value is not less than the top abnormal angle value, the top abnormal ratio value is calculated to facilitate subsequent use.

[0118] S407: Combine the top anomaly ratio value and the top anomaly angle value to calculate and determine the top anomaly impact value, and use the top anomaly impact value as the suspension distance impact value.

[0119] Among them, the top abnormality impact value refers to the degree of impact that the abnormal offset has relative to the reference dimension of the top of the shelf.

[0120] By weighting the top anomaly ratio and top anomaly angle values, and using the result as the top anomaly impact value, and then using this top anomaly impact value as the suspension distance impact value, the accuracy of the obtained suspension distance impact value is improved. The specific weights for the weighting calculation are preset by the operator according to actual needs.

[0121] To further ensure the rationality of the location selection categories, it is necessary to conduct further separate analysis and calculation on the location selection categories, which will be explained in detail through the steps shown below.

[0122] The method for selecting location types includes the following steps: S500: Determine the shelving material based on the shelving specifications.

[0123] Among them, the material of the shelving refers to the type of material that makes up the main structure of the shelving. The material of the shelving can be steel, aluminum alloy, plastic, wood, etc.

[0124] By inputting the shelf specifications into a preset specification database, the shelf material can be matched for convenient subsequent use.

[0125] The specification database contains a pre-stored table of different shelf specifications and their corresponding materials. The specification database is retrieved after the operator has pre-entered the information.

[0126] S501: Calculate the standard weight range of the shelf by combining the shelf material and the shelf reference dimensions.

[0127] The shelf reference weight range refers to the calculated theoretical weight range of the shelf itself.

[0128] The corresponding material density is obtained by retrieving the material of the shelf, and the volume of the main structure is calculated based on the shelf's baseline dimensions. The self-weight of the shelf is obtained by calculating the product between the material density and the volume of the main structure, and the upper and lower limits are adjusted to form a reasonable weight range, which is the shelf's baseline weight range.

[0129] S502: Retrieve the weight range of recommended categories based on size.

[0130] The recommended weight range refers to the standard load-bearing weight range of the recommended configuration for each size.

[0131] By inputting the recommended size type into the preset type weight database, a recommended type weight range is obtained for easy subsequent use.

[0132] The category weight database contains a pre-stored table of recommended categories of different sizes and their corresponding recommended weight ranges. The category weight database is obtained after the operator pre-inputs the data.

[0133] S503: Adjust the range of recommended category weights based on the comprehensive impact value of anomalies to obtain the category weight adjustment range.

[0134] Among them, the category weight adjustment range refers to the adjustment value corresponding to the recommended category weight range.

[0135] By matching the magnitude of the comprehensive impact value of the anomaly with the corresponding weight correction coefficient, the upper limit and range of the recommended weight range for each type are adjusted accordingly. The higher the comprehensive impact value of the anomaly, the lower the upper limit of the adjusted load-bearing range, and finally, the weight adjustment range for each type of shelf that is suitable for the abnormal state of the shelf is obtained.

[0136] S504: Based on the overlap between the shelf reference weight range and the category weight adjustment range, select the recommended categories by size to obtain the categories that meet the weight requirements, and use the categories that meet the weight requirements as the categories for location selection.

[0137] Among them, the weight-satisfaction type refers to the type corresponding to the selection based on the weight.

[0138] By analyzing the overlap between the shelf baseline weight range and the category weight adjustment range, we can select the recommended categories by size to obtain the categories that meet the weight requirements, and use the categories that meet the weight requirements as the categories for location selection, thereby improving the accuracy of obtaining the categories for location selection.

[0139] To further ensure the reasonableness of the weight requirement, a more detailed analysis and calculation of the weight requirement is required, which will be explained in detail through the steps shown below.

[0140] The method for determining the weight requirement includes the following steps: S600: Select recommended sizes whose weight adjustment range falls completely within the shelf reference weight range and use them as the sizes that fall completely within the range.

[0141] Among them, "completely falling into the category" refers to the recommended categories whose weight adjustment range completely falls within the shelf reference weight range.

[0142] By selecting and defining the types that fall completely into the category, it becomes easier to use them later.

[0143] S601: Retrieve the number of values ​​for the falling categories based on the total falling categories.

[0144] Among them, the number of types that fall into the category refers to the number of types that fall into the category completely.

[0145] By calculating the number of all types that fall into the category, and using the count as the numerical value of the number of types that fall into the category, it is convenient for subsequent use.

[0146] S602: Determine the number of exceptions required by combining the exception type and the exception location.

[0147] Among them, the number of abnormal requirements refers to the intensity of demand for load-bearing testing and structural verification, as well as the number of testing points required to reflect abnormal rack installation.

[0148] By analyzing the types and locations of anomalies, the required number of anomalies can be determined, facilitating subsequent use.

[0149] S603: Determine if the number of categories falling into the error is greater than the number of abnormal requirements. If yes, proceed to S604; if no, proceed to S605.

[0150] Specifically, the system determines whether additional categories need to be added by checking if the number of categories falls into the category exceeds the number of abnormal requirements.

[0151] S604: The complete fall into the class is used as the weight to satisfy the class.

[0152] When the number of categories that fall into the category is greater than the number of abnormal requirements, it means that no additional categories are needed. Therefore, the category that falls into the category is used as the weight to satisfy the category.

[0153] S605: Calculate the difference between the number of types falling into the category and the number of abnormal demands, and use it as the demand deviation value.

[0154] Among them, the number of demand deviations refers to the difference between the number of types that fall into the category and the number of abnormal demands.

[0155] When the number of categories is not greater than the number of abnormal requirements, it means that additional categories need to be added. Therefore, the number of requirement deviations is calculated for future use.

[0156] S606: The size recommendation categories that do not fall completely into the category are taken as the remaining recommendation categories, and the interval overlap is determined based on the remaining recommendation categories.

[0157] The remaining recommended categories refer to the size recommended categories other than those that completely fall into the category.

[0158] Interval overlap refers to the proportion of overlap between the weight adjustment interval corresponding to the remaining recommended categories and the shelf baseline weight interval.

[0159] By defining the remaining recommended categories and calculating the overlap length between the category weight adjustment range and the shelf baseline weight range for each remaining recommended category, and then dividing the overlap length by the effective length of the corresponding range, the range overlap degree is obtained, which is convenient for subsequent use.

[0160] S607: Combine the interval overlap and the number of demand deviations to select the remaining recommended categories and use them as the remaining supplementary categories.

[0161] Among them, the remaining supplementary categories refer to the size recommendation categories that can be used as supplementary candidates from the remaining recommended categories, based on a comprehensive assessment of the degree of interval matching and the degree of demand deviation.

[0162] By sorting the interval overlap from largest to smallest, and selecting the remaining recommended categories corresponding to the number of preceding demand deviation values ​​as the remaining supplementary categories, it is convenient to use them later.

[0163] S608: Based on the combination of the remaining supplementary types and the completely falling types, and used as the weight-satisfied types.

[0164] In this process, by combining the remaining supplementary types with the types that completely fall into the category, a set of types is formed to serve as the weight-satisfying types, thereby improving the accuracy of the obtained weight-satisfying types.

[0165] To further ensure the rationality of the number of abnormal requests, it is necessary to conduct a further separate analysis and calculation of the number of abnormal requests, which will be explained in detail through the steps shown below.

[0166] The method for determining the number of abnormal requests includes the following steps: S700: Determine the additional values ​​for the abnormal location based on the abnormal location.

[0167] Among them, the abnormal supplementary values ​​refer to the values ​​corresponding to the abnormal location points.

[0168] By calculating the abnormal location points and using the calculation results as supplementary values ​​for the abnormal locations, it is convenient for subsequent use.

[0169] S701: Determine the severity level of the anomaly based on its type.

[0170] Among them, the severity level refers to the type of abnormal shelving installation and the severity level of the classification.

[0171] The severity level of an anomaly is obtained by inputting the anomaly type into a preset type level database, which facilitates subsequent use.

[0172] The category-level database pre-stores a table of different anomaly types and their corresponding severity levels, which is obtained after the operator has pre-entered the information.

[0173] For example, the severity level of an anomaly is high when the suspension connection point is offset, medium when the shelf beam is slightly tilted, and low when the shelf edge is slightly scratched.

[0174] S702: Select anomaly types whose severity level is greater than the preset severity baseline level and use them as severity types.

[0175] Among them, the severity baseline level refers to the pre-set severity judgment threshold, which is used to distinguish between general abnormalities and severe abnormalities that require special attention.

[0176] By selecting anomalies whose severity level is greater than the preset severity baseline level and designating them as severity categories, it is easier to use them in the future.

[0177] S703: Retrieve the number of severity values ​​based on severity type.

[0178] The number of severity levels refers to the number of severity categories.

[0179] The severity categories are counted, and the count results are used as the severity value.

[0180] S704: Calculate the ratio between the number of critical cases and the number of abnormal supplementary cases, and use it as the criticality ratio.

[0181] Among them, the severity ratio refers to the ratio between the number of severity cases and the number of abnormal supplementary cases.

[0182] Calculating the severity ratio facilitates subsequent use.

[0183] S705: Determine the severity adjustment factor based on the severity ratio value.

[0184] The severity adjustment factor is a coefficient used to adjust the abnormal supplementary values. A higher severity ratio results in a larger severity adjustment factor and a greater correction to the abnormal supplementary values. The severity ratio value is entered into a preset ratio adjustment database to obtain the severity adjustment coefficient, which is convenient for subsequent use.

[0185] The ratio adjustment database pre-stores a table of different severity ratio ranges and their corresponding severity adjustment coefficients. The ratio adjustment database is pre-set by the operator according to actual needs.

[0186] S706: Calculate the product between the abnormal supplement value and the severity adjustment factor and use it as the abnormal requirement value.

[0187] Specifically, the accuracy of the obtained abnormal demand value is improved by calculating the product between the abnormal supplement value and the severity adjustment coefficient, and using the calculation result as the abnormal demand value.

[0188] To further ensure the rationality of the load-bearing variation parameters, it is necessary to perform further separate analysis and calculation on the load-bearing variation parameters, which will be explained in detail through the steps shown below.

[0189] The method for determining the load-bearing variation parameters includes the following steps: S800: Based on image recognition of load placement, it identifies the type and size of the placed items.

[0190] The "type of items" refers to the category to which the items belong (e.g., cardboard boxes, metal parts, turnover boxes, barrelled materials, etc.). The "size of items" refers to the length, width, and height of the items.

[0191] By denoising, correcting distortion, and scaling the image of the object to be placed, and then using a target detection model to identify the outline and category of the object in the image, the object's category label is output and used as the object's category. The smallest bounding rectangle of the object is located, the pixel size is read, and the actual physical size is converted by combining the calibration ratio to obtain the length, width, and height data, which are used as the size of the object.

[0192] S801: Determine the density value of the items based on the type of items placed.

[0193] Among them, the item density value refers to the density value corresponding to the type of item placed.

[0194] The item density value is obtained by inputting the type of item to be placed into a preset item type database, which facilitates subsequent use.

[0195] S802: Calculate the load-bearing capacity of the placed item based on the item density value and the item size.

[0196] Among them, the load-bearing capacity of the placed item refers to the weight value corresponding to the placed item.

[0197] The length, width, and height data of the placed item are retrieved and the volume of the placed item is calculated. Then, the product of the item density value and the volume of the placed item is calculated and used as the load-bearing value of the placed item for convenient subsequent use.

[0198] S803: Determine placement change parameters by combining the load-bearing placement detection image and the shelf detection image.

[0199] Among them, the placement change parameter refers to the quantitative parameter that characterizes the changes in the state of the object, such as its placement position, posture, coverage area, and relative offset.

[0200] By combining and analyzing the images of the load-bearing placement detection with the images of the shelf detection, the placement variation parameters can be determined, which will facilitate subsequent use.

[0201] S804: The load change parameter is a combination of the placement change parameter and the load capacity of the placed item.

[0202] In this method, by combining the placement change parameters and the load-bearing value of the placed items, a parameter set is obtained and used as the load-bearing change parameters, thereby improving the accuracy of the obtained load-bearing change parameters.

[0203] To further ensure the rationality of the placement variation parameters, it is necessary to perform further separate analysis and calculation on the placement variation parameters, which will be explained in detail through the steps shown below.

[0204] The method for determining the placement of variable parameters includes the following steps: S900: Marks core detection points based on shelf detection images.

[0205] Among them, the core detection points refer to the feature points marked in the shelf inspection images that play a key role in monitoring the structural strength, load-bearing stability, and deformation of the shelf.

[0206] By performing distortion correction, noise reduction, and edge enhancement on the rack inspection images, the structural outlines of rack uprights, beams, supports, and suspension points are clearly restored. Then, the system automatically identifies the core load-bearing parts in the images, such as the main load-bearing uprights, beam connection nodes, shelf support corners, suspension fixing points, and bottom support feet, and marks the corresponding positions of these parts to obtain the core inspection points for easy subsequent use.

[0207] S901: Perform image registration between the load placement detection image and the shelf detection image, and extract the core detection points from the load placement detection image as load change points.

[0208] Among them, the load change point refers to the position point of the core detection point in the load placement detection image.

[0209] By extracting stable features such as shelf structure corners and contours from two images, and through feature matching and transformation calculations, the load placement detection image is aligned to the coordinate system of the shelf detection image to achieve pixel-level accurate registration. Then, the coordinates of the core detection points marked in the shelf detection image are mapped to the load placement detection image through the registration transformation relationship. The corresponding feature points are located on the load placement detection image, and invalid points that cannot be identified due to being obscured by placed items are eliminated. The remaining valid points are the load change points.

[0210] S902: Determine the node change by combining the core detection points and the load-bearing change points.

[0211] Among them, the node change refers to the quantitative difference in displacement, offset, angular deformation, etc. of each node.

[0212] By comparing the core detection points of the shelf in the unloaded state with the points of load change after loading, and calculating the pixel-level coordinate difference, and then converting it into the actual physical offset by combining the image calibration ratio, the angular deflection of the node is calculated, and then the node change amount is formed by integrating the results.

[0213] S903: Determine the specification baseline variation based on the shelf specifications.

[0214] Among them, the specification baseline change refers to the allowable node offset baseline threshold under standard load.

[0215] By inputting the shelf specifications into a preset specification database, the specification baseline variation is obtained for easy subsequent use.

[0216] The specification database pre-stores a table of different shelf specifications and their corresponding specification baseline variations, which is retrieved after being pre-entered by the operator.

[0217] S904: Select the load-bearing change points where the node change is greater than the specification baseline change and designate them as the points with severe changes.

[0218] Among them, the points with severe changes refer to the bearing capacity change points where the change in node amount is greater than the change in specification benchmark amount.

[0219] By selecting and defining locations with significant changes, it becomes easier to use them later.

[0220] S905: Obtain the image of severe change by combining the image of the location with the image of the load placement detection.

[0221] Among them, the image with severe changes refers to the image corresponding to the point with severe changes.

[0222] By extracting the pixel coordinates of severely deformed points in the load-bearing placement detection image and outlining the structural areas around the points that exceed the deformation limit, the complete scene of the shelf and placed items is preserved, thus obtaining a severely deformed image for subsequent use.

[0223] S906: Determine the type of severe change based on the image of severe change.

[0224] Among them, the "severe change" category refers to the abnormal type of shelving that exceeds the standard for deformation or displacement after being loaded.

[0225] By identifying the rack structure to which the highlighted abnormal areas belong in images with severe changes, and distinguishing parts such as uprights, beams, hanging connection points, support legs, and shelf support corners, the system then queries a pre-set structural type database based on the rack structure to match the types of severe changes, facilitating subsequent use.

[0226] The structure type database pre-stores a table of different shelving structures and their corresponding types of significant variations. The structure type database is retrieved after the operator pre-inputs the data.

[0227] S907: The severity of the change is combined with the amount of change at the node and used as the placement change parameter.

[0228] In this method, by combining the severity of changes with the amount of node changes, a set of parameters is formed to serve as placement change parameters, thereby improving the accuracy of the obtained placement change parameters.

[0229] Based on the same inventive concept, embodiments of the present invention provide a rapid detection system for the load-bearing limit of warehouse racks, comprising: The data acquisition module is used to acquire shelving specifications, shelving inspection images, and load-bearing placement inspection images. The memory stores a program for implementing a rapid detection method for the load-bearing limit of a warehouse rack, as described above. The processor loads and executes programs stored in memory.

[0230] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional modules is used as an example. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. The specific working process of the system, device, and unit described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0231] The above description is merely a preferred embodiment of the present invention. The scope of protection of the present invention is not limited to the above embodiments. All technical solutions falling within the scope of the present invention's concept are within the scope of protection of the present invention. It should be noted that for those skilled in the art, any improvements and modifications made without departing from the principles of the present invention should also be considered within the scope of protection of the present invention.

Claims

1. A rapid detection method for the load-bearing limit of warehouse racking, characterized in that, include: Collect the specifications and inspection images of household storage racks; Determine the basic dimensions and image of the shelving based on its specifications. The shelf span, overall height, and number of layers are extracted from the shelf baseline dimensions to obtain recommended sizes; By comparing and identifying the rack reference image with the rack inspection image, installation abnormalities can be determined. The recommended types of load-bearing tests should be determined based on the installation anomalies and the recommended types of dimensions. Based on the recommended types of load-bearing detection, the data is output to the preset detection display terminal, and load-bearing placement detection images are acquired; By comparing the load placement detection images with the shelf detection images, the load variation parameters are determined; The load-bearing limit test results are determined based on the load-bearing variation parameters and the rack specifications, and the load-bearing limit test results are output to the preset test display terminal; Methods for determining the recommended types of load-bearing tests include: Retrieve the type and location of the installation anomaly; Based on the shelf specifications, retrieve the installation location and shelf placement method; Calculate the vector distance between the installation location and the abnormal location and use it as the abnormal distance vector value; Determine the impact value of abnormal locations by combining the shelf placement method with the abnormal distance vector value; The impact value of each anomaly is determined by matching the anomaly types with a pre-defined anomaly level library. Calculate the sum of the impact values ​​of the abnormal location and the type of abnormality, and use it as the comprehensive impact value of the abnormality. Based on the comprehensive impact value of the anomaly, the recommended size types are selected to obtain the location selection types, and the location selection types are used as the recommended types for load-bearing detection.

2. The rapid detection method for the load-bearing limit of a warehouse racking according to claim 1, characterized in that, Methods for determining the influence value of abnormal locations include: Determine whether the shelf placement method is consistent with the preset floor placement method; If so, the ground distance influence value is determined based on the abnormal distance vector value, and the ground distance influence value is used as the abnormal location influence value; If not, retrieve the hanging position point, bottom position point, and top position point based on the shelf specifications; Calculate the vector distance between the suspension point and the bottom point and use it as the bottom distance vector value; Calculate the vector distance between the suspension point and the top point and use it as the top distance vector value; The hanging distance influence value is determined by combining the bottom distance vector value, the top distance vector value, and the abnormal distance vector value, and the hanging distance influence value is used as the abnormal position influence value.

3. The rapid detection method for the load-bearing limit of a warehouse rack according to claim 2, characterized in that, The methods for determining the influence value of suspension distance include: The top anomaly distance value and the top anomaly angle value are determined by combining the anomaly distance vector value and the top distance vector value. The bottom anomaly distance value and the bottom anomaly angle value are determined by combining the anomaly distance vector value and the bottom distance vector value. Calculate the distance between the bottom position point and the top position point and use it as the top-bottom distance value; Determine whether the abnormal angle value at the bottom is less than the abnormal angle value at the top; If so, calculate the ratio between the bottom anomaly distance value and the top-bottom distance value and use it as the bottom anomaly ratio value; The impact value of the bottom anomaly is determined by combining the bottom anomaly ratio value and the bottom anomaly angle value, and this impact value is used as the impact value of the suspension distance. If not, calculate the ratio between the top anomaly distance value and the top-bottom distance value and use it as the top anomaly ratio value; The impact value of the top anomaly is determined by combining the top anomaly ratio value and the top anomaly angle value, and this impact value is used as the impact value of the suspension distance.

4. The rapid detection method for the load-bearing limit of a warehouse rack according to claim 1, characterized in that, The methods for selecting location types include: Determine the shelving material based on the shelving specifications; By combining the shelf material and the shelf's standard dimensions, the standard weight range of the shelf can be calculated. Retrieve the weight range of recommended categories based on size; The recommended category weight range is adjusted based on the comprehensive impact value of the anomalies to obtain the category weight adjustment range; Based on the overlap between the shelf baseline weight range and the category weight adjustment range, recommended categories by size are selected to obtain categories that meet the weight requirements, and these categories that meet the weight requirements are used as the categories for location selection.

5. The rapid detection method for the load-bearing limit of a warehouse rack according to claim 4, characterized in that, Methods for determining the weight requirement include: Select recommended product types whose weight adjustment range falls completely within the shelf's baseline weight range and use them as the types that fall completely within the range. Retrieve the values ​​of the falling categories based on the complete falling category; Determine the number of exceptions required by combining the exception type and the exception location; Determine if the number of categories falling into the category is greater than the number of abnormal requirements; If so, then it will fall completely into the category as the weight to satisfy the category; If not, calculate the difference between the number of categories falling into the category and the number of abnormal requirements, and use it as the number of requirement deviations. The size recommendation categories that do not fall completely into the category are taken as the remaining recommendation categories, and the degree of overlap between the intervals is determined based on the remaining recommendation categories; The remaining recommendation categories are selected by combining the interval overlap and the demand deviation values, and these categories are used as the remaining supplementary categories. The remaining supplementary types are combined with the types that fall completely and used as weight-satisfied types.

6. The rapid detection method for the load-bearing limit of a warehouse rack according to claim 5, characterized in that, Methods for determining the number of abnormal requests include: Determine additional values ​​based on the abnormal location points; Determine the severity level of the anomaly based on its type; Select anomaly types whose severity level is greater than the preset severity baseline level and use them as severity types; Retrieve the number of severity values ​​based on severity category; Calculate the ratio between the number of critical cases and the number of abnormal supplementary cases, and use this ratio as the severity ratio. The severity adjustment factor is determined based on the severity ratio value; Calculate the product between the abnormal supplement value and the severity adjustment factor, and use it as the abnormal requirement value.

7. The rapid detection method for the load-bearing limit of a warehouse racking according to claim 1, characterized in that, Methods for determining load-bearing variation parameters include: Based on image recognition of placement, the type and size of the placed items are identified. Determine the density value of the items based on the type of items placed; The load-bearing capacity of the placed item is calculated based on the item's density value and the item's dimensions. By combining the load-bearing placement detection images with the shelf detection images, the placement variation parameters are determined; The load-bearing capacity is determined by combining the placement variation parameters and the load-bearing capacity of the placed items.

8. The rapid detection method for the load-bearing limit of a warehouse rack according to claim 7, characterized in that, Methods for determining the placement of variable parameters include: Core detection points are marked based on shelf inspection images; The image of the load placement detection image is registered with the image of the shelf detection image, and the core detection points are extracted from the image of the load placement detection image as the load change points. Determine the node change by combining the core detection points and the points where load changes; Determine the standard variation based on the shelf specifications; Select the load-bearing change points corresponding to node changes that are greater than the specification baseline changes, and designate them as the points of severe change; The image of the severe change was obtained by combining the image of the severely changed location with the image of the load placement detection. Determine the type of severe change based on the image showing severe changes; The severity of the change is combined with the amount of change at each node and used as a parameter for placing the change.

9. A rapid detection system for the load-bearing limit of warehouse racking, characterized in that, include: The data acquisition module is used to acquire shelving specifications, shelving inspection images, and load-bearing placement inspection images. The memory stores a program for implementing a rapid detection method for the load-bearing limit of a warehouse rack as described in any one of claims 1 to 8; The processor loads and executes programs stored in memory.

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