Prefabricated field layout planning modeling method and system based on digital aided design

Abstract

The invention relates to the technical field of field layout modeling processing, in particular to a prefabricated field layout planning modeling method and system based on digital aided design. The method comprises the following steps: screening out a plurality of construction feature points of each three-dimensional point cloud model; obtaining a plurality of model areas at a real-time moment; according to the number of construction feature points in each model area at the real-time moment, the position distribution of different points and the initial voxel size, the adjustment voxel size of each model area at the real-time moment is obtained, and a three-dimensional adjustment point cloud model at the real-time moment is obtained; obtaining a construction offset direction vector and a construction offset of each unregistered point in the initial three-dimensional point cloud model, and obtaining an unregistered point area model of the initial three-dimensional point cloud model; and splicing the three-dimensional adjustment point cloud model at the real-time moment and the non-registered point region model to obtain a prefabricated field layout model at the next moment. According to the method, the accuracy of planning and modeling is improved by obtaining the accurate deviation trend in the construction process.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of field layout modeling processing, and particularly relates to a prefabrication field layout planning modeling method and system based on digital auxiliary design. BACKGROUND

[0002] The prefabrication field layout planning modeling is to reasonably arrange production lines and optimize logistics paths in a limited field through modeling technology, so as to realize efficient, safe and low-cost prefabricated component production. In the prior art, when the digital technology is used for planning modeling, a corresponding three-dimensional model is usually constructed based on BIM technology, so as to guide the specific construction process. However, in the actual construction process, due to the influence of the surrounding environment, the installation coordinates in the prefabrication stage and the model deviation are enlarged due to the difference in the segmented settlement of the super-long linear structure in the construction, and the difference in the construction sequence leads to the difference from the initial modeling model, and the demand for model accuracy is different in different construction stages, so that the accuracy of the existing construction planning modeling is poor. SUMMARY

[0003] In order to solve the technical problem that the accuracy of the existing construction planning modeling is poor due to the influence of the environment and the demand for model accuracy being different in different construction stages, the purpose of the present application is to provide a prefabrication field layout planning modeling method and system based on digital auxiliary design, and the technical solution adopted is as follows: The present application provides a prefabrication field layout planning modeling method based on digital auxiliary design, which comprises the following steps: obtaining binocular depth images containing a construction area at each time for different monitoring positions in a prefabrication field, and an initial three-dimensional point cloud model; obtaining three-dimensional point cloud models corresponding to the binocular depth images at each time for all monitoring positions; screening a plurality of construction feature points according to the position distribution characteristics of different points in each three-dimensional point cloud model; registering different three-dimensional point cloud models based on the construction feature points, and obtaining a plurality of model regions at a real-time time according to the position change characteristics of the registered points in the three-dimensional point cloud models between different adjacent times; obtaining the adjustment voxel size of each model region at the real-time time according to the number of construction feature points, the position distribution of different points and the initial voxel size in each model region at the real-time time, and obtaining a three-dimensional adjustment point cloud model at the real-time time; obtaining the construction offset direction vector and the construction offset amount of each unregistered point in the initial three-dimensional point cloud model according to the position distribution characteristics of different points between the three-dimensional adjustment point cloud model at the real-time time and the initial three-dimensional point cloud model, and obtaining an unregistered point region model of the initial three-dimensional point cloud model; splicing the three-dimensional adjustment point cloud model at the real-time time and the unregistered point region model to obtain a prefabrication field layout model at the next time.

[0004] Further, the construction feature point acquisition method comprises: According to the position distribution characteristics of different points in each three-dimensional point cloud model, the construction feature contribution degree of each point is obtained; If the construction feature contribution degree of a point is greater than a preset contribution threshold, the corresponding point is taken as a construction feature point.

[0005] Further, the construction feature contribution degree acquisition method comprises: For each three-dimensional point cloud model, the depth value of each point is obtained; The local surface fitting of all points in the neighborhood range of each point in the three-dimensional point cloud model is performed to obtain the maximum principal curvature and normal vector of each point; According to the similarity of the normal vector between each point and all other points in the neighborhood range, the difference in the depth value, and the maximum principal curvature of each point, the construction feature contribution degree of each point is obtained, the difference in the depth value and the maximum principal curvature are positively correlated with the construction feature contribution degree, and the similarity of the normal vector is negatively correlated with the construction feature contribution degree.

[0006] Further, the model region acquisition method comprises: The relative distance of the registration points in the three-dimensional point cloud model between each time and the next time is obtained, and the average of the relative distances of the corresponding registration points between all times is taken as the matching difference degree of the registration points; The ratio of the matching difference degree between each point and other points in the neighborhood range of each point is obtained, if the ratio is greater than a preset matching threshold, the corresponding point is taken as a same region point; all same region points form a model region.

[0007] Further, the adjusted voxel size acquisition method specifically comprises: According to the number of construction feature points in each model region at the real-time time, and the position distribution of different points, the high-precision demand degree of each model region is obtained; The high-precision demand degree is negatively correlated mapped, and the gain adjustment of the initial voxel size is performed according to the negatively correlated mapping result to obtain the adjusted voxel size of each model region.

[0008] Further, the high-precision demand degree acquisition method comprises: The number of construction feature points in each model region at the real-time time is normalized, and the product of the normalized result and the average of the matching difference degrees in all model regions is taken as the high-precision demand degree of each model region.

[0009] Further, the adjusted voxel size acquisition method comprises: The high-precision requirement degree is negatively correlated mapped, the sum of positive integer 1 and the negative correlation mapping result is taken as the weight, the product of the initial voxel size and the weight is taken as the adjusted voxel size.

[0010] Further, the construction offset direction vector and the construction offset amount obtaining method comprises: According to the position distribution characteristics of different points between the three-dimensional adjustment point cloud model at the real-time moment and the initial three-dimensional point cloud model, and the number of construction feature points in each model region, the single adjustment amplitude coefficient of each unregistered point in the initial three-dimensional point cloud model is obtained. The sum of the coordinate offset vectors of all registered points between the three-dimensional adjustment point cloud model and the initial three-dimensional point cloud model at the real-time moment is taken as the construction offset direction vector. The difference between the positive integer 1 and the single adjustment amplitude coefficient is obtained, and the product between the difference and the modulus value of the construction offset direction vector is taken as the construction offset amount.

[0011] Further, the single adjustment amplitude coefficient obtaining method comprises: The position coordinate difference of each pair of registered points between the three-dimensional adjustment point cloud model at the real-time moment and the initial three-dimensional point cloud model is obtained, and the coordinate offset vector is constructed. For the unregistered points in the initial three-dimensional point cloud model, according to the relative distance between the center of the construction area where each unregistered point is located and the real-time construction position, the high-precision requirement degree of all model regions, and the modulus value of the coordinate offset vector of all registered points in the three-dimensional adjustment point cloud model at the real-time moment, the single adjustment amplitude coefficient of each unregistered point is obtained, the relative distance and the high-precision requirement degree are positively correlated with the single adjustment amplitude coefficient, and the modulus value of the coordinate offset vector is negatively correlated with the single adjustment amplitude coefficient.

[0012] The application further provides a prefabrication field layout planning modeling system based on digital auxiliary design, which comprises a memory, a processor, and a computer program stored in the memory and capable of running on the processor.

[0013] The application has the following beneficial effects: The application obtains three-dimensional point cloud models corresponding to binocular depth image pairs at each moment in all monitoring positions, eliminates single-view blind areas; according to the position distribution characteristics of different points in each three-dimensional point cloud model, a plurality of construction feature points are screened out to provide stable and significant anchor points for registration and change detection, and the registration accuracy and algorithm efficiency are improved; different three-dimensional point cloud models are registered based on the construction feature points, the position change characteristics of the registration points in the three-dimensional point cloud models between different adjacent moments are obtained, and a plurality of model regions at the real-time moment are obtained; according to the number of construction feature points, the position distribution of different points and the initial voxel size in each model region at the real-time moment, the adjustment voxel size of each model region at the real-time moment is obtained, and the three-dimensional adjustment point cloud model at the real-time moment is obtained, the accuracy and speed are balanced, and the regions with large shape changes and complex construction structures are retained; according to the position distribution characteristics of different points between the three-dimensional adjustment point cloud model at the real-time moment and the initial three-dimensional point cloud model, and the number of construction feature points in all model regions, the construction offset direction vector and the construction offset amount of each unregistered point in the initial three-dimensional point cloud model are obtained, and the unregistered point region model of the initial three-dimensional point cloud model is obtained; the three-dimensional adjustment point cloud model at the real-time moment and the unregistered point region model are spliced to obtain the precast yard layout model at the next moment. The application obtains the accurate offset trend in the construction process, and improves the accuracy of planning modeling. BRIEF DESCRIPTION OF DRAWINGS

[0014] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor based on these drawings.

[0015] Figure 1 A flowchart of a precast yard layout planning modeling method based on digital auxiliary design provided by an embodiment of the present application; Figure 2 A flowchart of a method for obtaining construction offset direction vector and construction offset amount provided by an embodiment of the present application. DETAILED DESCRIPTION

[0016] In order to further illustrate the technical means and effects adopted by the present application to achieve the predetermined invention purpose, the following will combine the drawings and the preferred embodiments to specifically describe the specific implementation, structure, features and effects of the precast yard layout planning modeling method and system based on digital auxiliary design according to the present application. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures or characteristics in one or more embodiments can be combined in any suitable form.

[0017] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0018] The application provides a prefabrication field layout planning modeling method and system based on digital auxiliary design.

[0019] Please refer to Figure 1 The prefabrication field layout planning modeling method provided by the embodiment of the application is shown in a method flowchart, and specifically comprises the following steps. Step S1: Obtain binocular depth images containing construction areas at each moment at different monitoring positions in the prefabrication field, and an initial three-dimensional point cloud model.

[0020] In the embodiment of the application, considering that different stages of construction have different requirements for the accuracy of the model, the prefabrication field model at different stages needs to be analyzed, and the accuracy of the model in different areas needs to be adjusted. First, monitoring positions are uniformly selected in the construction site, binocular cameras are installed to ensure that the entire construction site is covered, Zhang's calibration method is used to calibrate all binocular cameras, binocular images at each moment at different monitoring positions in the prefabrication field are obtained, which is conducive to semantic recognition to identify the construction area in the monocular image, and the binocular images of the construction area corresponding to the monitoring position are registered by ORB to obtain binocular depth images containing depth information. In order to better understand the accuracy of real-time construction, an initial three-dimensional point cloud model of the construction project needs to be obtained according to the initial construction plan CAD or BIM, which is conducive to comparison and adjustment. The specific semantic recognition and ORB registration are technical means familiar to those skilled in the art, and will not be described here.

[0021] It should be noted that in the embodiment of the application, the monitoring of the construction stage is performed every 24 hours as a monitoring period.

[0022] Step S2: Obtain three-dimensional point cloud models corresponding to the binocular depth images at each moment at all monitoring positions; according to the position distribution characteristics of different points in each three-dimensional point cloud model, a plurality of construction feature points are screened out; based on the construction feature points, different three-dimensional point cloud models are registered, and according to the position change characteristics of the registered points in the three-dimensional point cloud models between different adjacent moments, a plurality of model regions at real-time moments are obtained.

[0023] In order to realize high-precision, full-time-domain, and full-space-dimension digital control of the construction state, multiple monitoring positions are analyzed to eliminate single-view blind areas, and three-dimensional point cloud models corresponding to the binocular depth images at each moment at all monitoring positions are obtained.

[0024] It should be noted that in the embodiments of the present application, the binocular depth images at each moment are spliced for all monitoring positions to obtain a three-dimensional point cloud model at each moment, and the specific means are well known to those skilled in the art and will not be described here.

[0025] In the precast yard construction scene, the precast component is a narrow structure, and the depth of the internal points changes obviously, and the geometric features are more obvious. Therefore, according to the position distribution characteristics of different points in each three-dimensional point cloud model, a plurality of construction feature points are screened out.

[0026] Preferably, in an embodiment of the present application, the construction feature point acquisition method comprises: According to the position distribution characteristics of different points in each three-dimensional point cloud model, the construction feature contribution degree of each point is obtained. Preferably, in an embodiment of the present application, the construction feature contribution degree acquisition method comprises: For each three-dimensional point cloud model, the depth value of each point is obtained. It should be noted that the depth value is the height distance of a point in the point cloud model from the ground.

[0027] The local surface fitting of all points in the neighborhood range of each point in the three-dimensional point cloud model is performed to obtain the maximum principal curvature and normal vector of each point. According to the similarity of the normal vectors between each point and all other points in the neighborhood range, the difference of the depth value and the maximum principal curvature of each point, the construction feature contribution degree of each point is obtained, and the difference of the depth value and the maximum principal curvature are positively correlated with the construction feature contribution degree, and the similarity of the normal vectors is negatively correlated with the construction feature contribution degree.

[0028] It should be noted that in an embodiment of the present application, the similarity of the normal vectors is reflected by calculating the cosine value of the included angle between the normal vectors, the larger the included angle cosine value, the greater the similarity, the closer the direction of the normal vectors, and the smaller the construction feature contribution degree. The specific means are well known to those skilled in the art and will not be described here.

[0029] In an embodiment of the present application, the average value of the cosine of the angle between the normal vector of each point and all other points in the neighborhood range is obtained as the normal vector similarity; the average depth of all points in the neighborhood range of each point is obtained as the average depth; the difference between the depth value of each point and the average depth is obtained as the depth difference; the ratio of the maximum principal curvature of each point and the normal vector similarity is obtained, the product between the ratio result and the depth difference is calculated, and the product is normalized as the construction feature contribution degree of each point; thus, the correlation between the depth value difference, the maximum principal curvature, the normal vector similarity and the construction feature contribution degree is constructed based on the above mathematical operations, that is, the greater the depth value difference, the greater the maximum principal curvature, the smaller the normal vector similarity, the more obvious the change of the geometric feature, and the greater the construction feature contribution degree.

[0030] If the construction feature contribution degree of a certain point is greater than the preset contribution threshold, the corresponding point is taken as a construction feature point.

[0031] It should be noted that in an embodiment of the present application, the size of the preset contribution threshold is 0.5; in other embodiments of the present application, the size of the preset contribution threshold can be set according to specific circumstances, which is not limited or described herein.

[0032] In order to align different three-dimensional point cloud models to the same coordinate system for comparison, the different three-dimensional point cloud models are registered based on the construction feature points; it should be noted that in an embodiment of the present application, the three-dimensional point cloud models are all sampled under the same voxel size, and the construction feature points of the three-dimensional point cloud models are input into ICP for matching, and the specific means are well known to those skilled in the art, which is not described herein.

[0033] Considering that the state of the construction stage is different, the contribution degree is inconsistent, and the region needs to be analyzed, according to the position change characteristics of the registration points in the three-dimensional point cloud model between different adjacent time points, a plurality of model regions at the real time are obtained.

[0034] Preferably, in an embodiment of the present application, the method for obtaining the model region comprises: The relative distance of the registration points in the three-dimensional point cloud model between each time point and the next time point is obtained, and the average relative distance of the corresponding registration points between all time points is obtained as the matching difference degree of the registration points; The ratio of the matching difference degree between each point and other points in the neighborhood range of each point is obtained, if the ratio is greater than a preset matching threshold, the corresponding point is taken as a same region point; all same region points form a model region.

[0035] It should be noted that in an embodiment of the present application, the relative distance is calculated by using the Euclidean distance or Manhattan distance, and the specific means are well known to those skilled in the art, which is not described herein.

[0036] It should be noted that in one embodiment of the present application, the size of the preset matching threshold is 0.6; in other embodiments of the present application, the size of the preset matching threshold can be set according to specific circumstances, which is not limited and described here.

[0037] Step S3: According to the number of construction feature points in each model region, the position distribution of different points and the initial voxel size at the real-time moment, the adjusted voxel size of each model region at the real-time moment is obtained, and the three-dimensional adjusted point cloud model at the real-time moment is obtained; according to the position distribution characteristics of different points between the three-dimensional adjusted point cloud model at the real-time moment and the initial three-dimensional point cloud model, and the number of construction feature points in all model regions, the construction offset direction vector and the construction offset amount of each unregistered point in the initial three-dimensional point cloud model are obtained, and the unregistered point region model of the initial three-dimensional point cloud model is obtained.

[0038] During construction, the regions that have been completed and have simple structures have a smaller contribution to real-time construction, and retaining high precision for the corresponding three-dimensional model will cause data redundancy, while the regions that are unstable in form and have complex structures during the current construction process should retain higher precision, so it is necessary to adjust the voxel size; the points with greater depth and geometric feature changes have more construction feature points, the more points with greater depth and geometric feature changes, the more complex the structure, the greater the contribution to construction, the more accurate modeling is needed, and the smaller the voxel size adjustment, the closer to the size with greater precision. According to the number of construction feature points in each model region, the position distribution of different points and the initial voxel size at the real-time moment, the adjusted voxel size of each model region at the real-time moment is obtained, and the three-dimensional adjusted point cloud model at the real-time moment is obtained.

[0039] Preferably, in one embodiment of the present application, the method for obtaining the adjusted voxel size comprises: According to the number of construction feature points in each model region at the real-time moment, and the position distribution of different points, the high precision requirement degree of each model region is obtained; Preferably, in one embodiment of the present application, the method for obtaining the high precision requirement degree comprises: The number of construction feature points in each model region at the real-time moment is normalized, and the product of the normalization result and the average of the matching difference degrees of all model regions is calculated as the high precision requirement degree of each model region.

[0040] It should be noted that the ratio of the number of construction feature points in each model area to the number of all points at the real-time time is calculated, that is, the number of construction feature points in each model area at the real-time time is normalized, the more the number of construction feature points, the greater the change of geometric features, and the higher the precision modeling is needed; the closer the distance between points in the area, the smaller the matching difference, the smaller the contribution of the stable region to the real-time construction, and the smaller the high precision demand degree, in other embodiments of the application, normalization can also be performed by linear normalization or normalization function, and specific means are technical means familiar to those skilled in the art, which will not be repeated here.

[0041] The high precision demand degree is negatively correlated, and the gain adjustment of the initial voxel size is performed according to the negative correlation mapping result to obtain the adjusted voxel size of each model area.

[0042] It should be noted that the greater the high precision demand degree, the more the number of construction feature points, the more obvious the geometric feature and depth change of the model area, the greater the contribution to the construction, and the more the modeling precision of the model area needs to be maintained, the smaller the degree of adjustment of the voxel size, and the closer to the initial voxel size.

[0043] It should be noted that in an embodiment of the application, the reciprocal of the high precision demand degree is negatively correlated, and in other embodiments of the application, the reciprocal of the high precision demand degree can be negatively correlated by an exponential function with a natural constant as the base The specific means are technical means familiar to those skilled in the art, which will not be limited and repeated here.

[0044] In an embodiment of the application, the sum of the positive integer 1 and the negative correlation mapping result is obtained as the weight; the product of the initial voxel size and the weight is calculated as the adjusted voxel size.

[0045] It should be noted that in the embodiments of the application, the initial voxel size is 0.03mm.

[0046] It should be noted that in the embodiments of the application, the relative distance can be obtained by existing distance calculation methods such as Euclidean distance or Manhattan distance, and the specific means are technical means familiar to those skilled in the art, which will not be repeated here.

[0047] Based on the adjusted voxel size of each model area, the three-dimensional point cloud model at the real-time time is sampled to obtain a three-dimensional adjusted point cloud model at the real-time time.

[0048] In actual construction process, due to the influence of external factors, such as uneven settlement or construction vibration interference, there will be certain error between the actual construction and the preset initial three-dimensional model, and the next time the precast yard needs to be adjusted, so the unregistered area is adjusted based on the offset direction and offset degree of the constructed part. According to the position distribution characteristics of the different points between the real-time three-dimensional adjustment point cloud model and the initial three-dimensional point cloud model, and the number of construction feature points in all model areas, the construction offset direction vector and the construction offset amount of each unregistered point in the initial three-dimensional point cloud model are obtained, and the unregistered point area model of the initial three-dimensional point cloud model is obtained.

[0049] Preferably, in an embodiment of the present application, the method for obtaining the construction offset direction vector and the construction offset amount is as follows: Figure 2 A flow chart for obtaining the construction offset direction vector and the construction offset amount is shown in FIG. 1, which includes the following steps: Step S201: According to the position distribution characteristics of the different points between the real-time three-dimensional adjustment point cloud model and the initial three-dimensional point cloud model, and the number of construction feature points in each model area, the single adjustment amplitude coefficient of each unregistered point in the initial three-dimensional point cloud model is obtained.

[0050] Preferably, in an embodiment of the present application, the method for obtaining the single adjustment amplitude coefficient includes: The position coordinate difference of each pair of registered points between the real-time three-dimensional adjustment point cloud model and the initial three-dimensional point cloud model is obtained, and a coordinate offset vector is formed; For the unregistered points in the initial three-dimensional point cloud model, according to the relative distance between the center of the construction area where each unregistered point is located and the real-time construction position, the high-precision demand degree of all model areas, and the coordinate offset vector modulus value of all registered points in the three-dimensional adjustment point cloud model at the real-time moment, the single adjustment amplitude coefficient of each unregistered point is obtained. The relative distance and the high-precision demand degree are positively correlated with the single adjustment amplitude coefficient, and the coordinate offset vector modulus value is negatively correlated with the single adjustment amplitude coefficient.

[0051] It should be noted that the greater the relative distance, the less susceptible to unstable settlement interference, the greater the degree of adjustment, the greater the high-precision demand degree, the more unstable the construction area, the greater the need for adjustment, and the greater the single adjustment amplitude coefficient; the smaller the offset degree, the closer to the initial model, and the smaller the adjustment amplitude.

[0052] In an embodiment of the present application, the relative distance between the center of the construction area where each point is located and the real-time construction position is obtained; the mean value of the high-precision requirement degree of all model areas is obtained; the cumulative value of the coordinate offset vector modulus of all registration points in the three-dimensional adjusted point cloud model at the real-time time is obtained; the product between the relative distance and the mean value of the high-precision requirement degree is obtained, the ratio of the product result and the cumulative value of the coordinate offset vector modulus is calculated, and normalization is performed, which is taken as the single adjustment amplitude coefficient. Therefore, the correlation between the relative distance, the high-precision requirement degree, the cumulative value and the single amplitude coefficient is constructed based on the above basic mathematical operations, that is, the larger the relative distance, the larger the high-precision requirement degree, the smaller the cumulative value, and the larger the single amplitude coefficient.

[0053] Step S202: obtaining the sum of the coordinate offset vectors of all registration points between the three-dimensional adjusted point cloud model and the initial three-dimensional point cloud model at the real-time time as the construction offset direction vector.

[0054] By taking the mean value, the overall trend of the coordinate offset vectors of all registration points can be quantified.

[0055] Step S203: obtaining the difference between the positive integer 1 and the single adjustment amplitude coefficient, calculating the product between the difference and the modulus value of the construction offset direction vector as the construction offset amount.

[0056] Based on this, by obtaining the construction offset direction vector and the construction offset amount of each unregistered point in the initial three-dimensional point cloud model, starting from the initial coordinates of each unregistered point in the initial three-dimensional point cloud model, moving the construction offset amount in the direction of the construction offset direction vector, the new coordinates of each unregistered point are obtained, and the unregistered point area model of the initial three-dimensional point cloud model is constructed.

[0057] Step S4: splicing the three-dimensional adjusted point cloud model at the real-time time and the unregistered point area model to obtain the precast yard layout model at the next time.

[0058] By fusing the real-time construction point cloud and the unregistered area data, the precast yard layout model is dynamically updated to reflect the real-time state of component installation, site occupation, etc., to optimize the component production, stacking and transportation path planning, and to improve the accuracy of the yard layout planning modeling.

[0059] In summary, the application screens a plurality of construction feature points of each three-dimensional point cloud model, then obtains a plurality of model regions at a real-time moment, obtains an adjusted voxel size of each model region at the real-time moment according to the number of construction feature points in each model region at the real-time moment, the position distribution of different points and the initial voxel size, and obtains a three-dimensional adjusted point cloud model at the real-time moment, obtains a construction offset direction vector and a construction offset amount of each unregistered point in the initial three-dimensional point cloud model, and obtains an unregistered point region model of the initial three-dimensional point cloud model, splices the three-dimensional adjusted point cloud model at the real-time moment and the unregistered point region model to obtain a precast yard layout model at the next moment. The application obtains an accurate offset trend in the construction process, and improves the accuracy of planning modeling.

[0060] The application further provides a precast yard layout planning modeling system based on digital auxiliary design, which comprises a memory, a processor, and a computer program stored in the memory and capable of running on the processor, and when the processor executes the computer program, the steps of any one of the precast yard layout planning modeling methods based on digital auxiliary design are implemented.

[0061] It should be noted that the above-mentioned sequence of the embodiments of the application is only for description, and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multi-task processing and parallel processing are also possible or can be advantageous.

[0062] Each of the embodiments in the specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other. Each embodiment mainly describes the differences from other embodiments.

Claims

1. A prefabrication site layout planning and modeling method based on digital-aided design, characterized in that: The method comprises: Obtain binocular depth images of the construction area at each moment from different monitoring locations in the prefabrication site, as well as the initial 3D point cloud model; Obtain the 3D point cloud model corresponding to the binocular depth image at each moment for all monitoring locations; screen multiple construction feature points based on the position distribution characteristics of different points in each 3D point cloud model; align different 3D point cloud models based on the construction feature points, and obtain multiple model areas at real time based on the position change characteristics of the registration points in the 3D point cloud model between different adjacent moments; Based on the number of construction feature points in each model area at the real time, the position distribution of different points, and the initial voxel size, the adjusted voxel size of each model area at the real time is obtained, and the three-dimensional adjusted point cloud model at the real time is obtained; based on the position distribution characteristics of different points between the three-dimensional adjusted point cloud model at the real time and the initial three-dimensional point cloud model, as well as the number of construction feature points in all model areas, the construction offset direction vector and construction offset of each unregistered point in the initial three-dimensional point cloud model are obtained, and the unregistered point area model of the initial three-dimensional point cloud model is obtained; The real-time 3D adjusted point cloud model and the unregistered point area model are spliced ​​to obtain the prefabricated field layout model at the next moment.

2. The prefabrication site layout planning and modeling method based on digital-aided design according to claim 1 is characterized in that: The method for obtaining the construction feature points includes: According to the position distribution characteristics of different points in each 3D point cloud model, the construction feature contribution of each point is obtained; If the construction feature contribution of a certain point is greater than the preset contribution threshold, the corresponding point will be regarded as a construction feature point.

3. The prefabrication site layout planning and modeling method based on digital-aided design according to claim 2 is characterized in that: The method for obtaining the construction feature contribution includes: For each 3D point cloud model, obtain the depth value of each point; Perform local surface fitting on all points in the neighborhood of each point in the 3D point cloud model to obtain the maximum principal curvature and normal vector of each point; The construction feature contribution of each point is obtained based on the similarity of the normal vector between each point and all other points in the neighborhood, the difference in depth values, and the maximum principal curvature of each point. The difference in depth values ​​and the maximum principal curvature are positively correlated with the construction feature contribution, while the similarity of the normal vector is negatively correlated with the construction feature contribution.

4. The prefabrication site layout planning and modeling method based on digital-aided design according to claim 1 is characterized in that: The method for obtaining the model area includes: Obtain the relative distance of the registration points in the 3D point cloud model between each moment and the next moment, and obtain the average of the relative distances of the corresponding registration points between all moments as the matching difference degree of the registration points; Obtain the ratio of the matching difference between each point and other points in the neighborhood of each point. If the ratio is greater than the preset matching threshold, the corresponding points are regarded as points in the same region; all points in the same region constitute a model region.

5. The method for prefabrication site layout planning and modeling based on digital-aided design according to claim 4 is characterized in that: The method for adjusting the voxel size specifically includes: According to the number of construction feature points in each model area at real time and the position distribution of different points, the high-precision requirement level of each model area is obtained; A negative correlation mapping is performed on the degree of high precision requirement, and the initial voxel size is gain-adjusted according to the negative correlation mapping result to obtain the adjusted voxel size of each model area.

6. The method for prefabrication site layout planning and modeling based on digital-aided design according to claim 5, characterized in that: The method for obtaining the high-precision requirement degree includes: The number of construction feature points in each model area at real time is normalized, and the product of the normalized result and the mean value of the matching difference in all model areas is calculated as the high-precision requirement of each model area.

7. The prefabrication site layout planning and modeling method based on digital-aided design according to claim 5 is characterized in that: The method for obtaining the adjusted voxel size includes: Perform negative correlation mapping on the degree of high-precision requirement, and obtain the sum of the positive integer 1 and the negative correlation mapping result as the weight; calculate the product of the initial voxel size and the weight as the adjusted voxel size.

8. The method for prefabrication site layout planning and modeling based on digital-aided design according to claim 1, characterized in that: The method for obtaining the construction offset direction vector and the construction offset includes: Based on the position distribution characteristics of different points between the real-time 3D adjustment point cloud model and the initial 3D point cloud model, as well as the number of construction feature points in each model area, the single adjustment amplitude coefficient of each unregistered point in the initial 3D point cloud model is obtained; Obtain the sum of the coordinate offset vectors of all registration points between the 3D adjusted point cloud model and the initial 3D point cloud model in real time as the construction offset direction vector; The difference between the positive integer 1 and the single adjustment amplitude coefficient is obtained, and the product between the difference and the modulus value of the construction offset direction vector is calculated as the construction offset.

9. The method for prefabrication site layout planning and modeling based on digital-aided design according to claim 8, characterized in that: The method for obtaining the single adjustment amplitude coefficient includes: Obtain the position coordinate difference of each pair of registration points between the real-time 3D adjusted point cloud model and the initial 3D point cloud model to form a coordinate offset vector; For the unregistered points in the initial three-dimensional point cloud model, the single adjustment amplitude coefficient of each unregistered point is obtained based on the relative distance between the center of the construction area where each unregistered point is located and the real-time construction position, the high-precision requirement level of all model areas, and the coordinate offset vector modulus of all registered points in the three-dimensional adjustment point cloud model at real time. The relative distance and the high-precision requirement level are positively correlated with the single adjustment amplitude coefficient, while the coordinate offset vector modulus is negatively correlated with the single adjustment amplitude coefficient.

10. A prefabrication site layout planning and modeling system based on digital-aided design, the system comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the prefabrication site layout planning and modeling method based on digital-aided design as described in any one of claims 1 to 9 are implemented.

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