Container cabin guide rail gap analysis method, system and equipment

Abstract

The invention provides a container cabin guide rail gap analysis method, system and device, and relates to the technical field of point cloud scanning, and the method comprises the steps: carrying out the registration splicing of point cloud data of a plurality of scanning sites, and generating a point cloud data model; performing profile view cutting on the point cloud data model to generate a plurality of transverse profiles, and extracting guide rail point cloud data from the plurality of transverse profiles; on the basis of the guide rail point cloud data, a simulation environment for container falling is constructed, a plurality of detection layers are arranged in the simulation environment, and collision detection points are arranged on the detection layers; loading the standard container model in the simulation environment, simulating the falling motion of the standard container model in the simulation environment, and obtaining the position relationship between the standard container model and the collision detection point in the falling process; and according to the position relationship, calculating a gap value between the guide rail at each detection layer and the standard container model, and generating a guide rail analysis report. The guide rail gap measuring device has the technical effect of reducing the error of guide rail gap measurement.

Description

Technical Field

[0001] The present application relates to the field of point cloud scanning technology, and in particular to a method, system and device for analyzing the gap between guide rails in a container cabin. Background Art

[0002] During the transportation of container ships, the clearance control of the container cabin rails is a key factor in ensuring the safe loading and unloading of containers. Due to the vibration, impact and wear caused by long-term use during the ship's voyage, the rail clearance may change. If the clearance is too large or too small, it will affect the safe loading and unloading of containers, and in serious cases may cause the container to be stuck or collide. Therefore, it is necessary to regularly check the rail clearance to ensure that it is always within the safe range.

[0003] In the prior art, a laser rangefinder is usually used to measure the rail gap, and the measurement data is input into a computer for analysis. Although this method can obtain the value of the rail gap, it is difficult to obtain the overall profile information of the rail due to the scattered and single measurement points, and it is impossible to accurately reflect the gap change law at each position of the rail, resulting in a large error in the rail gap measurement result. Summary of the invention

[0004] The present application provides a container cabin guide rail gap analysis method, system and device for reducing the error of guide rail gap measurement.

[0005] In a first aspect, the present application provides a method for analyzing the gap between guide rails in a container cabin, the method comprising: obtaining point cloud data of multiple scanning stations in a container cabin, aligning and splicing the point cloud data of the multiple scanning stations, and generating a point cloud data model of the container cabin; cutting the point cloud data model into cross-sectional views to generate multiple transverse sections, and extracting guide rail point cloud data from the multiple transverse sections; based on the guide rail point cloud data, constructing a simulation environment for container falling, setting multiple detection levels in the simulation environment, and setting collision detection points in each of the detection levels; loading a standard container model in the simulation environment, and simulating the falling motion of the standard container model in the simulation environment, and obtaining the positional relationship between the standard container model and the collision detection points during the falling process of the standard container model; calculating the gap value between the guide rail and the standard container model at each of the detection levels according to the positional relationship, and generating a guide rail analysis report according to the gap value.

[0006] By adopting the above technical solution, by collecting point cloud data from multiple scanning sites and performing registration and splicing, a complete cabin point cloud data model can be obtained, solving the problem of data dispersion caused by single-point measurement. By cutting the point cloud model in a transverse section and extracting the rail point cloud data, the spatial contour information of the rail can be fully obtained. On this basis, a simulation environment is constructed and collision detection points of multiple detection levels are set. Combined with the falling motion simulation of the standard container model, the actual position relationship between the container and the rail can be accurately obtained, thereby calculating the gap value of each detection level, and finally generating a comprehensive and accurate rail analysis report, effectively reducing the error of rail gap measurement.

[0007] Optionally, the point cloud data model is cut into cross-sectional views to generate multiple transverse sections, and guide rail point cloud data is extracted from the multiple transverse sections, including: cutting the point cloud data model at equal intervals in a vertical direction to generate the multiple transverse sections; performing point cloud density analysis on the multiple transverse sections to obtain areas with higher point cloud density as guide rail candidate areas; and performing feature matching on the guide rail candidate areas according to pre-calibrated guide rail geometric feature parameters to obtain the guide rail point cloud data.

[0008] By adopting the above technical solution, the point cloud data model is cut at equal intervals to ensure the uniform distribution of the transverse section, providing a complete data basis for subsequent analysis. The point cloud density analysis method is used to identify the candidate area of ​​the guide rail, and the interference point cloud of the non-guide rail area is effectively filtered. Combined with the pre-calibrated guide rail geometric feature parameters for feature matching, the point cloud data of the guide rail can be accurately extracted, which improves the accuracy and reliability of the guide rail point cloud data extraction.

[0009] Optionally, the method also includes: extracting the point cloud data of the bottom of the container cabin from the point cloud data model; performing point cloud density clustering analysis on the cone area in the bottom point cloud data to determine the actual installation position of the cone; calculating the deviation value between the actual installation position of the cone and the preset standard cone installation position, and generating a cone analysis report based on the deviation value.

[0010] By adopting the above technical solution, the actual installation position of the cone can be accurately located by extracting the point cloud data of the cabin bottom from the point cloud data model and performing point cloud density cluster analysis on the cone area. The actual installation position is compared with the standard installation position to calculate the deviation value and generate a cone analysis report, which not only expands the application scope of point cloud data, but also can timely detect abnormal installation positions of cones, providing more comprehensive protection for safe loading and unloading of containers.

[0011] Optionally, the method also includes: extracting the point cloud data of the bottom of the container cabin from the point cloud data model; performing plane fitting processing on the horizontal pad area in the bottom point cloud data to determine the plane equation of the horizontal pad area; calculating the distance from each point in the horizontal pad area to the fitting plane based on the plane equation; determining the flatness value of the horizontal pad based on the distance from each point in the horizontal pad area to the fitting plane, and generating a horizontal pad analysis report based on the flatness value.

[0012] By adopting the above technical solution, the actual installation position of the cone can be accurately located by extracting the point cloud data of the cabin bottom from the point cloud data model and performing point cloud density cluster analysis on the cone area. The actual installation position is compared with the standard installation position to calculate the deviation value and generate a cone analysis report, which not only expands the application scope of point cloud data, but also can timely detect abnormal installation positions of cones, providing more comprehensive protection for safe loading and unloading of containers.

[0013] Optionally, loading a standard container model in the simulation environment and simulating the falling motion of the standard container model in the simulation environment include: setting a falling gravity parameter of the standard container model according to an actual loading weight of the container; setting a falling speed parameter of the standard container model according to an actual falling speed of the container; loading the standard container model to an initial position preset in the simulation environment; and simulating the falling motion of the standard container model in the simulation environment according to the falling gravity parameter and the falling speed parameter.

[0014] By adopting the above technical solution, by setting the gravity parameters and speed parameters corresponding to the actual loading weight and falling speed, and loading the standard container model to the preset initial position, the container falling motion in the simulation environment is closer to the actual working conditions. This motion simulation method based on actual parameters can accurately reflect the real dynamic relationship between the container and the guide rail during the falling process, and improve the accuracy and reliability of the guide rail gap analysis.

[0015] Optionally, generating a guide rail analysis report based on the gap value includes: comparing the gap value of each detection layer with a preset standard gap threshold; marking abnormal detection layers that exceed the standard gap threshold; and generating a guide rail analysis report containing the abnormal detection layer information.

[0016] By adopting the above technical solution, by comparing the gap value of each detection layer with the preset standard gap threshold and marking the abnormal detection layer that exceeds the threshold, the specific location of the guide rail gap abnormality can be intuitively identified. The generated guide rail analysis report contains the abnormal detection layer information, which helps maintenance personnel to quickly locate the problem area and perform guide rail maintenance and adjustment in time, improving the pertinence and efficiency of guide rail maintenance.

[0017] Optionally, after generating the guide rail analysis report according to the gap value, it also includes: generating a guide rail maintenance recommendation plan based on the abnormal detection layer information in the guide rail analysis report; performing maintenance processing on the abnormal detection layer according to the guide rail maintenance recommendation plan, and re-performing a guide rail gap analysis after the maintenance is completed until the gap values ​​of each of the detection layers are within the standard gap threshold range.

[0018] By adopting the above technical solution, a maintenance recommendation plan is generated based on the abnormal detection layer information in the guide rail analysis report, and targeted maintenance is carried out on the abnormal positions. Through re-analysis and verification after maintenance, it is ensured that the gap value of each detection layer is within the standard gap threshold range, forming a complete detection-maintenance-verification closed loop, which not only improves the accuracy and efficiency of guide rail maintenance, but also ensures that the maintenance effect meets safety requirements.

[0019] In a second aspect, the present application provides a container cabin guide rail gap analysis system, the system comprising: an acquisition module, a cutting module, a construction module, a simulation module and a generation module; wherein the acquisition module is used to acquire point cloud data of multiple scanning stations in the container cabin, align and splice the point cloud data of the multiple scanning stations, and generate a point cloud data model of the container cabin; the cutting module is used to perform cross-sectional view cutting on the point cloud data model, generate multiple transverse sections, and extract guide rail point cloud data from the multiple transverse sections; the construction module is used to construct a simulation environment for container falling based on the guide rail point cloud data, set multiple detection layers in the simulation environment, and set collision detection points in each of the detection layers; the simulation module is used to load a standard container model in the simulation environment, simulate the falling motion of the standard container model in the simulation environment, and acquire the positional relationship between the standard container model and the collision detection point during the falling process of the standard container model; the generation module is used to calculate the gap value between the guide rail and the standard container model at each of the detection layers according to the positional relationship, and generate a guide rail analysis report according to the gap value.

[0020] In a third aspect, the present application provides an electronic device, which adopts the following technical solution: it includes a processor, a memory, a user interface and a network interface, the memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device executes a computer program such as any of the above-mentioned container cabin rail gap analysis methods.

[0021] In a fourth aspect, the present application provides a computer-readable storage medium, which adopts the following technical solution: storing a computer program that can be loaded by a processor and execute any of the above-mentioned container cabin rail gap analysis methods.

[0022] In summary, the present application includes at least one of the following beneficial technical effects: By collecting point cloud data from multiple scanning sites and performing registration and splicing, a complete cabin point cloud data model can be obtained, solving the problem of data dispersion caused by single-point measurement. By performing transverse section cutting on the point cloud model and extracting the rail point cloud data, the spatial contour information of the rail can be fully obtained. On this basis, a simulation environment is constructed and collision detection points of multiple detection levels are set. Combined with the falling motion simulation of the standard container model, the actual position relationship between the container and the rail can be accurately obtained, thereby calculating the gap value of each detection level, and finally generating a comprehensive and accurate rail analysis report, effectively reducing the error of rail gap measurement. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 It is a flow chart of a container cabin rail clearance analysis method provided in an embodiment of the present application; Figure 2 It is a structural schematic diagram of a container cabin rail gap analysis system provided in an embodiment of the present application; Figure 3 It is a structural schematic diagram of an electronic device provided in an embodiment of the present application.

[0024] Description of reference numerals: 1000, electronic device; 1001, processor; 1002, communication bus; 1003, user interface; 1004, network interface; 1005, memory. DETAILED DESCRIPTION

[0025] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments.

[0026] In the description of the embodiments of the present application, words such as "illustrative", "for example" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "illustrative", "for example" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "illustrative", "for example" or "for example" is intended to present related concepts in a concrete way.

[0027] With the rapid development of the global shipping industry, the efficiency and safety of container ship loading and unloading operations are becoming increasingly important. The rail system in the cabin of a container ship is a key structure to ensure the safe loading and unloading and fixation of containers, and its clearance parameters directly affect the efficiency and safety of container loading and unloading.

[0028] At present, the detection of container cabin rail clearance mainly relies on manual on-site measurement using tools such as tape measures and vernier calipers, or actual drop tests using mechanical test boxes, and single-point measurements using handheld laser rangefinders. These traditional methods have problems such as low measurement efficiency, insufficient accuracy, high safety risks, and high costs. In particular, the mechanical test box detection method may cause equipment damage, and the detection process will significantly affect the normal operation of the ship.

[0029] The existing technology still lacks a solution that can systematically, efficiently and accurately analyze the gap between the container cabin rails, and cannot achieve all-round digital collection of the entire cabin, intelligent identification and analysis of the rail system, scientific simulation of the container falling process, and systematic management of detection data.

[0030] Therefore, there is an urgent need to develop a guide rail gap analysis method based on three-dimensional laser scanning and digital simulation to improve detection efficiency and accuracy, reduce detection costs and safety risks, realize automated data collection, intelligent feature recognition, and visualized dynamic simulation, and be able to generate standardized analysis reports and maintenance recommendations.

[0031] Figure 1 Schematic diagram of a flow chart of a container cabin rail clearance analysis method provided in an embodiment of the present application. Figure 1 As shown, the method includes S101-S105: S101, acquiring point cloud data of multiple scanning stations in a container cabin, registering and splicing the point cloud data of multiple scanning stations, and generating a point cloud data model of the container cabin.

[0032] In order to obtain complete 3D data information of the container cabin, it is necessary to use a 3D laser scanner to perform multi-site scanning of the cabin. Since the container cabin is long and narrow and has a complex structure, it is difficult for a single scanning station to fully collect data from all locations in the cabin, and there will be data blind spots caused by occlusion, so multiple scanning stations need to be set up in the cabin.

[0033] In the specific implementation, firstly, several scanning stations are set up in the cabin according to the spatial size and structural characteristics of the cabin. The data between adjacent scanning stations needs to have an overlapping area of ​​more than 30% to ensure the accuracy of subsequent registration and splicing. A 3D laser scanner is a device that obtains the 3D coordinate information of the surface of an object by emitting lasers and receiving reflected signals. Its working principle is to calculate the distance by measuring the time it takes for the laser to be emitted to the surface of the target object and return to the receiver, while recording the emission angle of the laser beam to obtain the 3D coordinate information of the surface points of the object. The collection of these discrete 3D coordinate points is the point cloud data.

[0034] At each scanning site, the 3D laser scanner will perform a 360-degree rotation scan to collect point cloud data on the surfaces of all objects within the visible range of the site. After scanning all sites, these scattered point cloud data need to be registered and spliced. Registration refers to the process of unifying point cloud data in different coordinate systems into the same coordinate system. The iterative closest point (ICP) algorithm is usually used to identify common feature points in adjacent point cloud data and calculate the coordinate transformation matrix to achieve precise alignment of the point cloud. Stitching is to merge the registered point cloud data into a complete data set. Through such multi-site scanning and registration splicing, a complete and accurate container cabin point cloud data model can be generated. The model contains the three-dimensional geometric information of all structures in the cabin, providing basic data support for subsequent guide rail analysis.

[0035] S102, cutting the point cloud data model into section views to generate multiple transverse sections, and extracting the guide rail point cloud data from the multiple transverse sections.

[0036] Since the point cloud data model of the container cabin contains a large amount of spatial information, extracting the rail features directly from the overall model has the problem of large amount of calculation and low efficiency, so it is necessary to use the section cutting method to simplify the data processing process. Section cutting refers to the process of using a virtual cutting plane to segment the three-dimensional model and obtain the two-dimensional contour information of the model on the plane.

[0037] In this embodiment, the cutting parameters are first set in the point cloud data processing software, and a horizontal cutting plane is set every 500 mm along the vertical direction of the cabin. The selection of this spacing is based on the continuity characteristics of the guide rail structure, which can not only ensure the representativeness of the data but also avoid the generation of too much redundant information.

[0038] During the cutting process, point cloud data within 50 mm above and below the cutting plane are selected to form a transverse section with a thickness of 100 mm. This cutting method with thickness can ensure that sufficient point cloud data is obtained for subsequent analysis. For each transverse section, the point cloud density analysis method is used to identify the guide rail area. Since the guide rail is a protruding metal structure on the cabin wall, it is characterized by a point density that is significantly higher than the surrounding area in the point cloud data.

[0039] In the specific implementation, the density-based spatial clustering algorithm (DBSCAN) is used to perform cluster analysis on the profile point cloud. The algorithm can effectively identify high-density areas by setting distance thresholds and minimum point count thresholds. At the same time, combined with the pre-calibrated guide rail geometric feature parameters (such as the cross-sectional shape and size of the guide rail), feature matching is performed on the identified high-density areas, and areas that do not meet the guide rail characteristics are eliminated, and finally the point cloud data of the guide rail is extracted.

[0040] This section-based guide rail extraction method significantly improves processing efficiency. For a standard cabin, the guide rail feature extraction of all sections can be completed within 10 minutes. The extracted guide rail point cloud data retains the precise geometric shape of the guide rail, and the point cloud registration accuracy is better than 1 mm, providing a reliable data basis for subsequent gap analysis.

[0041] On the basis of the above embodiment, as an optional implementation method, in S102, the point cloud data model is cut into a cross-sectional view to generate multiple transverse sections, and the guide rail point cloud data is extracted from the multiple transverse sections, specifically including S21-S23: S21, the point cloud data model is cut at equal intervals along the vertical direction to generate multiple transverse sections.

[0042] S22, performing point cloud density analysis on the multiple transverse sections, and obtaining areas with higher point cloud density as candidate guide rail areas.

[0043] S23, performing feature matching on the guide rail candidate area according to the pre-calibrated guide rail geometric feature parameters to obtain guide rail point cloud data.

[0044] In the process of processing the point cloud data model, in order to extract the rail features more accurately, a step-by-step processing method is adopted. First, considering that the container cabin rail has a continuous feature in the vertical direction, the equal-interval cutting method can effectively reduce the difficulty of data processing.

[0045] In the specific implementation, the cutting spacing is set to 500 mm, and the point cloud data within the thickness range of 50 mm above and below each cutting position is taken. This cutting method not only ensures the continuity of the data, but also avoids the problem of computational efficiency caused by excessive data volume. Subsequently, the point cloud density analysis is performed on each transverse section. Since the guide rail is a protruding structure on the cabin wall, the point density will be significantly higher than the surrounding area during the point cloud data collection process. Using the density-based spatial clustering algorithm (DBSCAN), setting the search radius to 5 mm and the minimum point count threshold to 10 points, areas with high point cloud density can be effectively identified. These high-density areas are marked as candidate areas for guide rails, but may also contain some interference areas of non-guide rail structures.

[0046] Therefore, it is necessary to further use the pre-calibrated rail geometry feature parameters for screening. These feature parameters include the standard cross-sectional shape, size range and other information of the rail. By calculating the main direction, cross-sectional size and other geometric features of the candidate area point cloud, the matching degree is calculated with the standard parameters. When the matching degree exceeds 85%, the area is confirmed to be the rail area and the corresponding point cloud data is extracted.

[0047] S103, based on the guide rail point cloud data, construct a simulation environment for container falling, set a plurality of detection layers in the simulation environment, and set a collision detection point at each detection layer.

[0048] In order to analyze the change in the gap between the container and the rail during the actual falling process, a virtual simulation environment needs to be built. A simulation environment refers to a virtual space built in a computer to simulate specific scenes in the physical world. The environment needs to include a physics engine to achieve accurate calculations of motion and collision.

[0049] In this embodiment, the extracted point cloud data of the guide rail is first imported into the physical simulation software, and the point cloud data is converted into a continuous three-dimensional mesh model through a three-dimensional reconstruction algorithm. The mesh model is a surface composed of many triangular facets, which can accurately express the geometric shape of the guide rail. When building a simulation environment, basic physical parameters such as gravity acceleration and friction coefficient need to be set, which directly affect the accuracy of the simulation results.

[0050] Considering the different position deviations that may occur in the actual falling process of the container, a detection layer is set every 1 meter along the vertical direction of the guide rail in the simulation environment. The detection layer refers to the horizontal section position for gap detection. At each detection layer, multiple collision detection points are set. The collision detection point is a virtual sensor used to measure the distance between the container and the guide rail.

[0051] In specific implementation, a collision detection point is set every 45 degrees along the circumference of the guide rail surface at each detection layer. This arrangement can monitor the gap changes between the container and the guide rail in all directions. The collision detection point uses ray detection technology to measure the gap distance by emitting virtual rays and calculating the intersection position of the ray and the container surface. This method of constructing a simulation environment can achieve accurate measurement of the gap while ensuring calculation efficiency. For a standard cabin, the construction time of the simulation environment does not exceed 5 minutes, and the spatial resolution of the collision detection can reach 0.1 mm, which fully meets the needs of actual engineering applications.

[0052] S104, loading a standard container model in a simulation environment, simulating a falling motion of the standard container model in the simulation environment, and obtaining a positional relationship between the standard container model and a collision detection point during the falling motion of the standard container model.

[0053] In the constructed simulation environment, it is necessary to load the standard container model and simulate its falling process to analyze various situations that may occur during the actual loading process of the container. The standard container model refers to a three-dimensional computer model of a twenty-foot or forty-foot container that meets the ISO standard. The model contains key parameters such as the precise dimensions, center of gravity, and mass distribution of the container.

[0054] When loading the standard container model in the simulation environment, it is first placed at the initial position at the top of the guide rail. In order to simulate the initial position deviation that may occur during the actual lifting process, a random offset of ±50 mm is set in the horizontal plane, and a random rotation angle of ±2 degrees is set. The rigid body dynamics calculation method is used to simulate the falling process. This method is based on Newton's laws of motion and takes into account the influence of physical factors such as gravity and friction on the movement of the container. During the falling process, the distance between the outer surface of the container and the guide rail is monitored in real time through the collision detection points set above.

[0055] In the specific implementation, an explicit integration algorithm with a time step of 0.01 seconds is used for motion calculation. In each time step, the distance to the nearest point on the container surface is calculated through the virtual ray emitted by the collision detection point, and the distance value and the corresponding timestamp are recorded. At the same time, the system also records the complete kinematic information such as the spatial position and attitude angle of the container.

[0056] Based on the above embodiment, as an optional implementation, in S104, loading a standard container model in a simulation environment, and simulating the falling motion of the standard container model in the simulation environment specifically includes S41-S44: S41, setting the falling gravity parameters of the standard container model according to the actual loading weight of the container.

[0057] S42, setting the falling speed parameters of the standard container model according to the actual falling speed of the container.

[0058] S43, loading the standard container model to an initial position preset in the simulation environment.

[0059] S44, simulating the falling motion of the standard container model in the simulation environment according to the falling gravity parameter and the falling speed parameter.

[0060] In order to accurately evaluate the change in the gap between the container and the guide rail during the actual loading process, it is necessary to simulate the falling process of the container in a simulation environment.

[0061] Firstly, the falling gravity parameters are set according to the actual loading conditions of the container. Based on the loading conditions measured on site, the total gravity parameters of the standard container model are set to 23500kg, of which the container weight is 2300kg and the cargo weight is 21200kg.

[0062] By analyzing the operation video records at the port site, the average falling speed in actual operation was extracted to be 0.15 m / s, and this speed value was set as the initial falling speed parameter of the simulation model.

[0063] Load the standard container model to the initial position consistent with the actual operating conditions, i.e. 450mm above the cabin top, so that the four corners of the bottom of the container are precisely aligned with the guide rail entrance.

[0064] Based on the actual parameters set above, the falling motion of the standard container model is simulated in the simulation environment, and the system records the gap data between the container and the guide rail every 0.1s. Through this dynamic simulation based on actual parameters, the change of the gap between the container and the guide rail during the loading process can be truly reflected, providing accurate data support for the evaluation of the guide rail gap.

[0065] S105, calculating the gap value between the guide rail and the standard container model at each detection layer according to the position relationship, and generating a guide rail analysis report according to the gap value.

[0066] In order to evaluate whether the design and installation of the guide rails meet the requirements for safe container loading and unloading, it is necessary to systematically analyze the position relationship data obtained by simulation. The clearance value refers to the minimum distance between the outer surface of the container and the guide rail during the container falling process. This value directly reflects the guiding and restraining effect of the guide rail on the container.

[0067] In this embodiment, the position relationship data recorded at each detection layer is first statistically analyzed, and the minimum gap value measured at all collision detection points in the layer during the entire falling process is calculated through a data processing algorithm. Considering the safety margin requirements of the rail gap, according to the relevant specifications of the International Maritime Organization (IMO), the evaluation standard of the gap value is set: when the gap value is less than 20 mm, it is judged as a dangerous gap, indicating that there is a collision risk at this position; the gap value within the range of 20-50 mm is judged as a caution gap, which requires special attention; the gap value greater than 50 mm is judged as a normal gap.

[0068] Based on these analysis results, a guide rail analysis report is automatically generated. The report content includes: a distribution map of the minimum gap value of each detection layer, with gap areas of different risk levels marked with different colors; statistical analysis results of guide rail gaps, including statistical indicators such as maximum value, minimum value, average value and standard deviation; detailed information on the location of abnormal gaps, including specific location coordinates and corresponding gap values; and rectification suggestions for abnormal gaps, such as guide rail adjustment plans or operating precautions.

[0069] Based on the above embodiment, as an optional implementation, in S105, generating a guide rail analysis report according to the gap value specifically includes S51-S53: S51, comparing the gap value of each detection layer with a preset standard gap threshold.

[0070] S52, marking the abnormal detection layer that exceeds the standard gap threshold.

[0071] S53, generating a guide rail analysis report including abnormal detection layer information.

[0072] In order to ensure the safety and stability of containers during loading and unloading, it is necessary to systematically evaluate the rail clearance and form an analysis report. The radial clearance value and circumferential clearance value measured at each detection level are compared with the standard clearance thresholds specified by the classification society regulations, where the standard threshold range for radial clearance is 15-25 mm and the standard threshold range for circumferential clearance is 10-20 mm.

[0073] The detection layers that exceed the standard gap threshold are marked. The specific marking content includes the spatial location information of the abnormal layer (height from the bottom), gap type (radial / circumferential), measured gap value, and amplitude exceeding the threshold. When the gap value of a detection layer is found to exceed the standard threshold, the system automatically marks the position in red in the 3D model for intuitive identification.

[0074] A guide rail analysis report is generated based on the test and analysis results. The report content includes: basic information of the guide rail (cabin number, guide rail number), test time, test environment parameters; a list of gap measurement data for each test layer, including specific values ​​of radial gap and circumferential gap; detailed information of abnormal layers, including abnormal location, abnormal type, measured value, standard value, and degree of deviation; overall change trend chart of the guide rail gap, with the x-axis representing the test layer height and the y-axis representing the gap value; a schematic diagram of the three-dimensional spatial distribution of abnormal layers; and rectification suggestions for abnormal layers, including recommended rectification methods and precautions. This systematic analysis report can help maintenance personnel quickly locate problem areas and develop targeted maintenance plans, while providing historical data support for subsequent guide rail status assessments to ensure the long-term safe operation of the container transportation system.

[0075] Based on the above embodiment, as an optional implementation manner, the method further includes S201-S203: S201, extracting container cabin bottom point cloud data from the point cloud data model.

[0076] S202, performing point cloud density cluster analysis on the pile cone area in the bottom point cloud data to determine the actual installation position of the pile cone.

[0077] S203, calculating a deviation value between the actual installation position of the stack cone and the preset standard installation position of the stack cone, and generating a stack cone analysis report according to the deviation value.

[0078] Since the positioning of the container in the cabin not only depends on the constraints of the guide rails, but also requires the precise positioning of the bottom stacking cone, it is necessary to analyze the installation position of the stacking cone.

[0079] First, extract the point cloud data of the cabin bottom area from the point cloud data model. The specific method is to set a horizontal cutting plane 200 mm above the bottom plate and extract all the point cloud data below the plane. Since the stacking cone is a raised structure fixed to the bottom of the cabin for positioning and locking the container, it appears as a local protrusion feature in the point cloud data.

[0080] The density-based spatial clustering algorithm is used to analyze the bottom point cloud data. The cluster radius is set to 50 mm and the minimum point count threshold is set to 100 points. The point cloud clusters in the cone area can be effectively identified. The centroid coordinates of each identified point cloud cluster are calculated, and the coordinates are the actual installation position of the cone. The standard cone installation position is a theoretical position coordinate pre-set according to the container size and cabin layout.

[0081] The deviation value is obtained by calculating the spatial distance between the actual installation position and the standard installation position, and the directional component of the deviation is analyzed. According to ISO standards, the allowable deviation range of the cone installation position is ±10 mm. Exceeding this range may cause inaccurate positioning of the container or difficulty in locking.

[0082] A cone analysis report is generated based on the analysis results. The report content includes: the actual coordinates and standard coordinates of each cone; the deviation values ​​in each direction; the location identification of the out-of-tolerance cones; and the adjustment suggestions for the out-of-tolerance cones.

[0083] Based on the above embodiment, as an optional implementation manner, the method further includes S301-S204: S301, extracting container cabin bottom point cloud data from the point cloud data model.

[0084] S302, performing plane fitting processing on the horizontal pad area in the bottom point cloud data to determine the plane equation of the horizontal pad area.

[0085] S303, calculating the distance from each point in the horizontal pad area to the fitting plane according to the plane equation.

[0086] S304, determining the flatness value of the horizontal pad according to the distance from each point in the horizontal pad area to the fitting plane, and generating a horizontal pad analysis report according to the flatness value.

[0087] In order to ensure the stable support of the container in the cabin, the flatness of the horizontal pad needs to be accurately evaluated. First, the height threshold method is used to extract the point cloud data of the cabin bottom from the point cloud data model. Specifically, a horizontal cutting plane 200 mm above the bottom plate is set to extract all the point cloud data below the plane. Since the horizontal pad is the largest plane area in the bottom structure, the point cloud data of the horizontal pad area can be accurately extracted through the regional growing algorithm.

[0088] The extracted horizontal pad point cloud data is first subjected to statistical filtering and noise reduction processing, with the neighborhood search radius set to 10 mm and the standard deviation threshold set to 2.5. After removing the outliers, the RANSAC plane fitting algorithm is used for processing. The plane equation is expressed in the general form ax+by+cz+d=0, where (a, b, c) are normalized normal vectors that satisfy d is a plane constant term, where x represents the position of the point in the transverse direction of the ship, y represents the position of the point in the longitudinal direction of the ship, and z represents the position of the point in the vertical direction of the ship.

[0089] Using the point-to-plane distance formula Calculate the distance from each point in the horizontal pad area to the fitting plane. Since the normal vector has been normalized, the distance value can be directly calculated using d = |ax+by+cz+d|.

[0090] The flatness of the horizontal pad is determined based on the calculated distance value distribution. The flatness value is defined as the sum of the maximum positive deviation and the maximum negative deviation, that is, the difference between the maximum and minimum distances of all points to the fitting plane. According to the requirements of the classification society's regulations, the flatness value of the horizontal pad should not be greater than 6 mm. A horizontal pad analysis report is generated based on the analysis results. The report content includes: the mathematical equation of the fitting plane; the calculation results of the flatness value; the spatial distribution cloud map of the flatness deviation, with different colors used to identify different deviation ranges; the location identification and area statistics of the out-of-tolerance area; and rectification suggestions for the out-of-tolerance area.

[0091] According to the clearance value, the generated rail analysis report also includes: Generate a guide rail maintenance recommendation based on the abnormal detection layer information in the guide rail analysis report; perform maintenance on the abnormal detection layer according to the guide rail maintenance recommendation, and re-perform guide rail gap analysis after the maintenance is completed until the gap values ​​of each detection layer are within the standard gap threshold range.

[0092] In order to ensure the safe and reliable operation of the guide rail system, it is necessary to formulate a maintenance plan and carry out rectification according to the abnormal conditions found in the analysis report. Based on the abnormal detection layer information in the guide rail analysis report, combined with the structural characteristics of the guide rail and actual maintenance experience, a guide rail maintenance recommendation plan is generated.

[0093] The maintenance plan mainly includes the following: For abnormal layers with excessive radial clearance, when the clearance value is less than 15 mm, the clearance is expanded by grinding; when the clearance value is greater than 25 mm, it is necessary to reduce the clearance by welding surfacing and then fine machining. For abnormal layers with excessive circumferential clearance, when the clearance value is less than 10 mm, the side of the guide rail needs to be ground; when the clearance value is greater than 20 mm, it is repaired by side surfacing and fine machining.

[0094] In the specific maintenance process, first clean and inspect the surface of the abnormal layer to confirm the deformation or wear, and then select the corresponding repair process according to the maintenance plan. For the parts that need surfacing, use E7016 welding rod for surfacing, and control the welding current between 180-220A. After welding, polishing is performed to ensure surface flatness. After the maintenance is completed, the guide rail gap must be re-detected and analyzed, using the same methods and standards as the first inspection to verify the maintenance effect. If it is found that the gap value of the detection layer still exceeds the standard threshold range, the maintenance process needs to be repeated until the gap values ​​of all detection layers meet the requirements.

[0095] Based on the above method, the present application also discloses a container cabin rail gap analysis system, such as Figure 2 As shown, Figure 2It is a structural schematic diagram of a container cabin guide rail gap analysis system provided by an embodiment of the present application, and the system includes: an acquisition module, a cutting module, a construction module, a simulation module and a generation module; wherein the acquisition module is used to acquire point cloud data of multiple scanning stations in the container cabin, align and splice the point cloud data of multiple scanning stations, and generate a point cloud data model of the container cabin; the cutting module is used to perform cross-sectional view cutting on the point cloud data model, generate multiple transverse sections, and extract guide rail point cloud data from the multiple transverse sections; the construction module is used to construct a simulation environment for container falling based on the guide rail point cloud data, set multiple detection layers in the simulation environment, and set collision detection points at each detection layer; the simulation module is used to load a standard container model in the simulation environment, simulate the falling motion of the standard container model in the simulation environment, and acquire the positional relationship between the standard container model and the collision detection point during the falling process of the standard container model; the generation module is used to calculate the gap value between the guide rail and the standard container model at each detection layer according to the positional relationship, and generate a guide rail analysis report according to the gap value.

[0096] It should be noted that: when the system provided in the above embodiment realizes its functions, only the division of the above functional modules is used as an example. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the system and method embodiments provided in the above embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0097] See also Figure 3 , is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. Figure 3 As shown, the electronic device 1000 may include: at least one processor 1001 , at least one network interface 1004 , a user interface 1003 , a memory 1005 , and at least one communication bus 1002 .

[0098] The communication bus 1002 is used to realize the connection and communication between these components.

[0099] The user interface 1003 may include a display screen (Display) and a camera (Camera), and the optional user interface 1003 may also include a standard wired interface and a wireless interface.

[0100] The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0101] Among them, the processor 1001 may include one or more processing cores. The processor 1001 uses various interfaces and lines to connect various parts in the entire server, and executes various functions of the server and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 1005, and calling data stored in the memory 1005. Optionally, the processor 1001 can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), and programmable logic array (Programmable Logic Array, PLA). The processor 1001 can integrate one or more combinations of a central processing unit (Central Processing Unit, CPU), a graphics processing unit (Graphics Processing Unit, GPU) and a modem. Among them, the CPU mainly processes the operating system, user interface and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 1001, and it can be implemented separately through a chip.

[0102] Among them, the memory 1005 may include a random access memory (Random Access Memory, RAM) and may also include a read-only memory (Read-Only Memory). Optionally, the memory 1005 includes a non-transitory computer-readable storage medium. The memory 1005 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 1005 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 1005 may optionally be at least one storage device located away from the aforementioned processor 1001. As Figure 3 As shown, the memory 1005 as a computer storage medium may include an operating system, a network communication module, a user interface module, and an application program of a container cabin rail gap analysis method.

[0103] exist Figure 3In the electronic device 1000 shown, the user interface 1003 is mainly used to provide an input interface for the user and obtain data input by the user; and the processor 1001 can be used to call an application program for a container cabin rail gap analysis method stored in the memory 1005. When executed by one or more processors, the electronic device executes one or more methods described in the above embodiments.

[0104] An electronic device readable storage medium stores instructions, which, when executed by one or more processors, enable the electronic device to execute one or more of the methods described in the above embodiments.

[0105] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the order of the actions described, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required for the present application.

[0106] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0107] In the several embodiments provided in the present application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of the units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0108] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0109] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0110] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a memory and includes several instructions for a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned memory includes: various media that can store program codes, such as USB flash drives, mobile hard drives, magnetic disks or optical disks.

[0111] The above is only an exemplary embodiment of the present disclosure, and the scope of the present disclosure cannot be limited thereto. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the specification and practicing the disclosure here, those skilled in the art will easily think of other embodiments of the present disclosure. This application is intended to cover any modification, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the technical field not recorded in the present disclosure. The description and examples are only regarded as exemplary, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. A container cabin rail clearance analysis method, characterized in that: The method comprises: Acquire point cloud data of multiple scanning stations in the container cabin, align and splice the point cloud data of the multiple scanning stations, and generate a point cloud data model of the container cabin; Performing cross-sectional view cutting on the point cloud data model to generate a plurality of transverse sections, and extracting guide rail point cloud data from the plurality of transverse sections; Based on the guide rail point cloud data, a simulation environment for container falling is constructed, a plurality of detection levels are set in the simulation environment, and a collision detection point is set in each of the detection levels; Loading a standard container model in the simulation environment, simulating the falling motion of the standard container model in the simulation environment, and obtaining a positional relationship between the standard container model and the collision detection point during the falling motion of the standard container model; According to the positional relationship, the gap value between the guide rail and the standard container model at each detection layer is calculated, and according to the gap value, a guide rail analysis report is generated.

2. The container cabin rail clearance analysis method according to claim 1, characterized in that: The step of cutting the point cloud data model into a cross-sectional view to generate a plurality of transverse sections, and extracting the guide rail point cloud data from the plurality of transverse sections comprises: Cutting the point cloud data model at equal intervals along the vertical direction to generate the multiple transverse sections; Performing point cloud density analysis on the multiple transverse sections to obtain areas with higher point cloud density as candidate guide rail areas; According to the pre-calibrated geometric feature parameters of the guide rail, feature matching is performed on the guide rail candidate area to obtain the guide rail point cloud data.

3. The container cabin rail clearance analysis method according to claim 1, characterized in that: The method further comprises: Extracting the container cabin bottom point cloud data from the point cloud data model; Performing point cloud density cluster analysis on the pile cone area in the bottom point cloud data to determine the actual installation position of the pile cone; The deviation value between the actual installation position of the stack cone and the preset standard installation position of the stack cone is calculated, and a stack cone analysis report is generated according to the deviation value.

4. The container cabin rail clearance analysis method according to claim 1, characterized in that: The method further comprises: Extracting the container cabin bottom point cloud data from the point cloud data model; Performing plane fitting processing on the horizontal pad area in the bottom point cloud data to determine the plane equation of the horizontal pad area; According to the plane equation, calculating the distance from each point in the horizontal pad area to the fitting plane; The flatness value of the horizontal pad is determined according to the distance from each point in the horizontal pad area to the fitting plane, and a horizontal pad analysis report is generated according to the flatness value.

5. The container cabin rail clearance analysis method according to claim 1, characterized in that: The step of loading a standard container model in the simulation environment and simulating the falling motion of the standard container model in the simulation environment includes: According to the actual loading weight of the container, setting the falling gravity parameter of the standard container model; According to the actual falling speed of the container, setting the falling speed parameter of the standard container model; Loading the standard container model to a preset initial position in the simulation environment; The falling motion of the standard container model is simulated in the simulation environment according to the falling gravity parameter and the falling speed parameter.

6. The container cabin rail clearance analysis method according to claim 1, characterized in that: Generating a guide rail analysis report according to the gap value includes: Comparing the gap value of each detection layer with a preset standard gap threshold; marking anomaly detection layers exceeding the standard clearance threshold; A guide rail analysis report including the abnormal detection layer information is generated.

7. The container cabin rail clearance analysis method according to claim 1, characterized in that: After generating the guide rail analysis report according to the gap value, the method further includes: Generate a guide rail maintenance suggestion plan based on the abnormal detection layer information in the guide rail analysis report; The abnormal detection layer is repaired according to the guide rail maintenance recommendation plan, and the guide rail gap analysis is re-performed after the maintenance is completed until the gap value of each detection layer is within the standard gap threshold range.

8. A container cabin rail gap analysis system, characterized in that: The system includes: an acquisition module, a cutting module, a construction module, a simulation module and a generation module; wherein, The acquisition module is used to acquire point cloud data of multiple scanning sites in the container cabin, align and splice the point cloud data of the multiple scanning sites, and generate a point cloud data model of the container cabin; The cutting module is used to perform cross-sectional view cutting on the point cloud data model to generate multiple transverse sections, and extract the guide rail point cloud data from the multiple transverse sections; The construction module is used to construct a simulation environment for container falling based on the guide rail point cloud data, set multiple detection levels in the simulation environment, and set a collision detection point in each detection level; The simulation module is used to load the standard container model in the simulation environment, simulate the falling motion of the standard container model in the simulation environment, and obtain the positional relationship between the standard container model and the collision detection point during the falling motion of the standard container model; The generating module is used to calculate the gap value between the guide rail and the standard container model at each detection layer according to the position relationship, and generate a guide rail analysis report according to the gap value.

9. An electronic device, characterized in that: It includes a processor, a memory, a user interface and a network interface, the memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device executes the method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that: A computer program is stored which can be loaded by a processor and execute the method according to any one of claims 1 to 7.

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