CN120279198A - Digital twin body construction method and system applied to customs supervision - Google Patents
Digital twin body construction method and system applied to customs supervision Download PDFInfo
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Abstract
本申请公开了应用于海关监管的数字孪生体构建方法及系统，涉及数据处理技术领域。该应用于海关监管的数字孪生体构建方法包括：获取海关监管的多个历史数字孪生体三维模型；基于各个历史数字孪生体三维模型，将三维模型划分为多个区域块；基于各个区域块，获取多个关键度值；基于各个关键度值，配置对应区域块的细节层次；基于各个区域块的细节层次，渲染生成当前数字孪生体三维模型。本申请通过识别数字孪生体三维模型中不同区域的货物运输的频繁程度大小，进而将三维模型划分为多个区域块，再确定各个区域块中的货物堆放的密集程度大小，为货物堆放密集程度高的区域配置高细节层次，为货物堆放密集程度低的区域配置低细节层次。
The present application discloses a method and system for constructing a digital twin for customs supervision, and relates to the field of data processing technology. The method for constructing a digital twin for customs supervision includes: obtaining multiple historical digital twin three-dimensional models of customs supervision; dividing the three-dimensional model into multiple regional blocks based on each historical digital twin three-dimensional model; obtaining multiple criticality values based on each regional block; configuring the detail level of the corresponding regional block based on each criticality value; rendering and generating the current digital twin three-dimensional model based on the detail level of each regional block. The present application identifies the frequency of cargo transportation in different areas of the digital twin three-dimensional model, and then divides the three-dimensional model into multiple regional blocks, and then determines the density of cargo stacking in each regional block, configures a high level of detail for areas with high cargo stacking density, and configures a low level of detail for areas with low cargo stacking density.
Description
Technical Field
The application relates to the technical field of data processing, in particular to a digital twin body construction method and a digital twin body construction system applied to customs supervision.
Background
The digital twin body of customs supervision is a technology of cloning physical entities in a digital space and realizing virtual-real intercommunication so as to improve customs supervision efficiency, optimize management flow and improve decision making capability. That is, the digital twin body of the customs supervision realizes comprehensive monitoring and optimizing management of the customs logistics process through technical means such as three-dimensional modeling, dynamic simulation, multidimensional visualization, cross-department cooperation and the like, thereby not only improving the efficiency and safety of the customs supervision, but also laying a foundation for the development of future intelligent customs.
In the process Of constructing a digital twin under customs supervision, different areas Of the three-dimensional model often need to be configured with different Levels Of Detail (LOD). If the detail level configuration is inaccurate, the non-key area is easy to use a high detail level, so that the calculation resource is wasted, or the key area is used a low detail level, so that the quality of the digital twin three-dimensional model is reduced.
Disclosure of Invention
The application aims to provide a digital twin body construction method and a digital twin body construction system for customs supervision, which are used for solving the technical problem that accurate configuration detail layers for different regional blocks are difficult to be realized in the digital twin body construction process of customs supervision.
In order to achieve the above purpose, the present application provides the following technical solutions:
In a first aspect, the present application proposes a digital twin construction method applied to customs supervision, the digital twin construction method applied to customs supervision comprising:
acquiring a plurality of historical digital twin three-dimensional models of customs supervision;
dividing the three-dimensional model into a plurality of region blocks based on each historical digital twin three-dimensional model;
acquiring a plurality of key degree values based on each regional block, wherein the regional blocks are in one-to-one correspondence with the key degree values, and the key degree values are at least used for representing the density degree of stacking cargoes in the corresponding regional blocks;
Configuring the detail level of the corresponding region block based on each criticality value;
And rendering and generating a current digital twin three-dimensional model based on the detail level of each region block.
As a specific solution in the technical solution of the present application, the dividing the three-dimensional model into a plurality of region blocks based on each historical digital twin three-dimensional model includes:
Acquiring a first area plane based on the three-dimensional model, wherein the first area plane is an area plane formed by projection of the three-dimensional model along the z-axis direction, and the z-axis of the three-dimensional model is parallel to the vertical direction in reality;
Acquiring a first coordinate point based on the first area plane, wherein the first coordinate point is any coordinate point in the first area plane;
Each sequence segment is at least used for representing whether the first coordinate point in the corresponding historical digital twin three-dimensional model has goods stored or taken out;
acquiring a transportation frequency value based on each sequence segment, wherein the transportation frequency value is at least used for representing the frequency of cargo transportation in the first coordinate point;
and dividing the three-dimensional model into a plurality of regional blocks based on the transportation frequency value.
As a specific scheme in the technical scheme of the application, the sequence segment comprises 00, 10 and 01, wherein 01 is used for indicating that no goods are stored in or taken out from the first coordinate point in the corresponding historical digital twin three-dimensional model, 10 is used for indicating that goods are stored in the first coordinate point in the corresponding historical digital twin three-dimensional model, and 00 is used for indicating that goods are taken out from the first coordinate point in the corresponding historical digital twin three-dimensional model.
As a specific scheme in the technical scheme of the present application, the obtaining the value of the frequency of transportation based on each sequence segment includes:
Acquiring a first sequence segment based on each sequence segment, wherein the first sequence segment is the last sequence segment in time sequence in each sequence segment;
acquiring a height value and a time sequence value based on the first sequence segment, wherein the height value is the numerical sum of the first sequence segment and a sequence segment before the time sequence of the first sequence segment, and the time sequence value is a sequence number corresponding to the first sequence segment;
and acquiring the transportation frequency value based on the height value and the time sequence value.
As a specific solution in the technical solution of the present application, the calculation formula for obtaining the transportation frequency value based on the height value and the time sequence value is as follows:
Wherein P represents a transportation frequency value, S represents a height value, n represents the number of each sequence segment; Representing the range of intervals used to map the values in brackets to [0,1 ]; representing absolute values.
As a specific solution in the technical solution of the present application, the dividing the three-dimensional model into a plurality of area blocks based on the transportation frequency value includes:
The method comprises the steps of obtaining a first feature vector based on a first coordinate point, wherein the first feature vector is a transportation frequency value corresponding to the first coordinate point and a coordinate value corresponding to a plane of a first area where the first coordinate point is located;
Based on a clustering algorithm, clustering each first coordinate point through the first feature vector to obtain a plurality of first clustering clusters;
The three-dimensional model is divided into a plurality of region blocks based on the respective first clusters.
As a specific solution in the technical solution of the present application, the obtaining a plurality of criticality values based on each region block includes:
Acquiring a first area block based on each area block, wherein the first area block is any one area block in each area block;
Acquiring a plurality of second coordinate points based on the first region block, wherein the second coordinate points are any coordinate points positioned in the first region block;
The method comprises the steps of obtaining a second feature vector corresponding to each second coordinate point based on each second coordinate point, wherein the second feature vector is a transport frequency degree difference value corresponding to each second coordinate point and a coordinate value corresponding to a plane of a first area, and the transport frequency degree difference value is a difference value between a current transport frequency degree value and a historical transport frequency degree value of the corresponding second coordinate point;
Based on a clustering algorithm, clustering each second coordinate point through a second feature vector to obtain a plurality of second clustering clusters;
and acquiring a criticality value corresponding to the first region block based on each second cluster.
As a specific solution in the technical solution of the present application, the calculation formula for obtaining the criticality value corresponding to the first area block based on each second cluster is as follows:
Wherein M represents a criticality value corresponding to the first region block; M represents the number of second cluster clusters corresponding to the first area block; representing the sum of the height values of the second coordinate points corresponding to the ith second cluster; Representing the number of second coordinate points in the ith second cluster; Representing an area corresponding to the first region block; representing the area corresponding to the first area plane; The values in brackets are shown as being mapped to the interval ranges of [0,1 ].
As a specific solution in the technical solution of the present application, the configuring the level of detail of the corresponding area block based on each criticality value includes:
If the key value corresponding to the first area block is larger than or equal to a preset value, configuring the detail level of the first area block as a high detail level, otherwise, configuring the detail level of the first area block as a low detail level.
In a second aspect, the present application proposes a digital twin construction system for customs supervision, comprising:
The reader is used for acquiring a plurality of historical digital twin three-dimensional models of customs supervision;
the server is used for dividing the three-dimensional model into a plurality of area blocks based on the three-dimensional model of each historical digital twin body;
the method comprises the steps of obtaining a plurality of key degree values based on each area block, wherein the area blocks correspond to the key degree values one by one, and the key degree values are at least used for representing the density degree of stacking of cargoes in the corresponding area blocks;
and configuring a detail level of the corresponding region block based on each criticality value;
And rendering and generating a current digital twin three-dimensional model based on the detail level of each region block.
The server is further used for acquiring a first area plane based on the three-dimensional model, wherein the first area plane is an area plane formed by projection of the three-dimensional model along the z-axis direction, and the z-axis of the three-dimensional model is parallel to the vertical direction in reality;
acquiring a first coordinate point based on the first area plane; the first coordinate point is any coordinate point in the first area plane;
Each sequence segment is at least used for representing whether the first coordinate point in the corresponding historical digital twin three-dimensional model has goods stored or taken out;
The method comprises the steps of obtaining a transportation frequency value based on each sequence segment, wherein the transportation frequency value is at least used for representing the frequency of cargo transportation in the first coordinate point;
and dividing the three-dimensional model into a plurality of region blocks based on the transportation frequency value.
As a specific scheme in the technical scheme of the application, the sequence segment comprises 00, 10 and 01, wherein 01 is used for indicating that no goods are stored in or taken out from the first coordinate point in the corresponding historical digital twin three-dimensional model, 10 is used for indicating that goods are stored in the first coordinate point in the corresponding historical digital twin three-dimensional model, and 00 is used for indicating that goods are taken out from the first coordinate point in the corresponding historical digital twin three-dimensional model.
The server is further used for acquiring a first sequence segment based on each sequence segment, wherein the first sequence segment is a sequence segment with the last time sequence in each sequence segment;
the method comprises the steps of obtaining a height value and a time sequence value based on a first sequence segment, wherein the height value is the numerical sum of the first sequence segment and a sequence segment before the time sequence of the first sequence segment, and the time sequence value is a sequence number corresponding to the first sequence segment;
And obtaining the transportation frequency value based on the height value and the time sequence value.
As a specific solution in the technical solution of the present application, the calculation formula for obtaining the transportation frequency value based on the altitude value and the time sequence value by the server is as follows:
Wherein P represents a transportation frequency value, S represents a height value, n represents the number of each sequence segment; Representing the range of intervals used to map the values in brackets to [0,1 ]; representing absolute values.
The server is further configured to obtain a first feature vector based on the first coordinate point, where the first feature vector is a transportation frequency value corresponding to the first coordinate point and a coordinate value corresponding to a plane of the first region where the first coordinate point is located;
Clustering each first coordinate point through the first feature vector based on a clustering algorithm to obtain a plurality of first clustering clusters;
And dividing the three-dimensional model into a plurality of region blocks based on the respective first clusters.
The server is further used for acquiring a first area block based on each area block, wherein the first area block is any one area block in each area block;
and acquiring a plurality of second coordinate points based on the first region block; the second coordinate point is any coordinate point located in the first area block;
the method comprises the steps of obtaining a first feature vector corresponding to each first coordinate point based on each first coordinate point, wherein the first feature vector is a transport frequency degree difference value corresponding to each first coordinate point and a coordinate value corresponding to a plane of a first area;
Clustering each second coordinate point through a second feature vector based on a clustering algorithm to obtain a plurality of second clustering clusters;
and acquiring a criticality value corresponding to the first region block based on each second cluster.
As a specific scheme in the technical scheme of the present application, the calculation formula for obtaining the criticality value corresponding to the first area block based on each second cluster by the server is as follows:
Wherein M represents a criticality value corresponding to the first region block; M represents the number of second cluster clusters corresponding to the first area block; representing the sum of the height values of the second coordinate points corresponding to the ith second cluster; Representing the number of second coordinate points in the ith second cluster; Representing an area corresponding to the first region block; representing the area corresponding to the first area plane; The values in brackets are shown as being mapped to the interval ranges of [0,1 ].
As a specific scheme in the technical scheme of the application, the server is further used for configuring the detail level of the first area block as a high detail level if the corresponding criticality value of the first area block is larger than or equal to a preset value, or configuring the detail level of the first area block as a low detail level if the corresponding criticality value of the first area block is larger than or equal to the preset value.
Compared with the prior art, the application has the beneficial effects that:
According to the method, the frequency of cargo transportation in different areas in the digital twin body three-dimensional model is identified, the three-dimensional model is further divided into a plurality of area blocks, the density of cargo stacking in each area block is determined, high detail levels are configured for areas with high cargo stacking density, and low detail levels are configured for areas with low cargo stacking density. The method realizes the use of low-detail level rendering in the area needing no high attention, can reduce the rendering burden and improve the system performance, and uses high-detail level rendering in the area needing high attention to ensure better rendering effect, thereby providing clearer visual information.
Drawings
FIG. 1 is a schematic flow chart of a digital twin body construction method applied to customs supervision according to an embodiment of the present application;
fig. 2 is a schematic structural diagram of a digital twin body construction system applied to customs supervision according to an embodiment of the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
The terms "first", "second", and the like in the description of embodiments of the present application and in the above-described figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order, e.g., a first feature vector and a second feature vector, which are described below, and which belong to different feature vectors. It is to be understood that the names so used may be interchanged where appropriate, so that the embodiments described herein may be implemented in an order other than that illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or modules is not necessarily limited to those explicitly listed but may include other steps or modules not expressly listed or inherent to such process, method, article, or apparatus, the configuration of the modules appearing in an embodiment of the application may be merely a logical configuration, may be implemented in another configuration, such that a plurality of modules may be combined or integrated in another system, or some features may be omitted, or not implemented, and further, the coupling or direct coupling or communication connection between the illustrated or discussed modules may be through some interfaces, indirect coupling or communication connection between the modules may be in electrical or other like form, none of which is limiting in an embodiment of the application. The modules or sub-modules described as separate components may or may not be physically separate, may or may not be physical modules, or may be distributed in a plurality of circuit modules, and some or all of the modules may be selected according to actual needs to achieve the purposes of the embodiment of the present application.
Before understanding the embodiments of the present application, it should be clear that in computer graphics and three-dimensional modeling, level of Detail (LOD) is a term used to describe the Level of Detail of a model. For example, LOD0, LOD1, LOD2, etc. are different levels of detail, each level corresponding to a different number of faces or vertices, for displaying models of appropriate levels of detail for different application requirements. In an embodiment of the present application, the level of detail may be selected according to the requirements, for example, in this embodiment, the high level of detail may be LOD0, and LOD0 may be 5000 faces, that is, each container model in three-dimensional modeling is composed of approximately 5000 faces. The container model at this level of detail (i.e., LOD 0) is very detailed and is suitable for applications where a great deal of detail needs to be displayed. In this embodiment, the low level of detail may be LOD2, and LOD2 may be 500 faces, i.e., each container model in three-dimensional modeling consists of approximately 500 faces. The container model of the level has less details, is suitable for occasions needing quick rendering, and can optimize rendering performance and resource consumption while maintaining visual quality by using the models of different levels of detail. Of course, in other embodiments of the present application, high and low levels of detail different from those of the above embodiments may be set as desired.
It should be clear that the emphasis of customs port supervision is the cargo storage area, and the cargo storage area is a planned known area, so that the subsequent analysis is only performed for the planned cargo storage area in the digital twin three-dimensional model, and the non-cargo storage area (for example, the placement area of the non-movable equipment, or the road on which the vehicle is traveling, etc.) is directly configured to be rendered at a low level of detail.
In order to solve the technical problem that the digital twin body construction process under customs supervision is difficult to accurately configure the detail level for different area blocks, which is provided in the background art, the application provides a digital twin body construction method applied to customs supervision. As shown in fig. 1, the digital twin construction method applied to customs supervision includes steps S100 to S500.
And step 100, acquiring a plurality of historical digital twin three-dimensional models of customs supervision.
It should be clear that in the customs supervision process, the historical digital twin three-dimensional model needs to be updated to obtain the current digital twin three-dimensional model, and after the current digital twin three-dimensional model is obtained by updating, the historical digital twin three-dimensional model also needs to be stored (generally stored in a computer readable storage medium). The three-dimensional model of the stored historical digital twin body is obtained from a computer readable storage medium and is a mature technology, and is not described herein.
Step 200, dividing the three-dimensional model into a plurality of regional blocks based on each historical digital twin three-dimensional model.
In embodiments of the present application, the three-dimensional model may be divided into a plurality of region blocks in any reasonable manner. For example, a historical digital twin three-dimensional model may be partitioned into a plurality of regional blocks using a spatial grid. It should be clear that the division of the three-dimensional model of the historical digital twin volume into a plurality of region blocks by the spatial grid is a mature technique, and will not be described here.
It is noted that at customs ports, containers are typically unloaded from the ship onto the quay using a quay container crane, or loaded from the quay onto the ship, and transported from one side of the quay to the other using a straddle carrier, or transported from the quay to an inland freight station. For areas where containers are frequently circulated, important attention is often required to ensure the efficiency and safety of centralized transportation. Therefore, if the container transportation of a certain area is more frequent, the important attention needs to be paid to the area, and the three-dimensional model needs to be given a higher level of detail when being constructed, so that the accuracy of customs supervision is ensured. If the three-dimensional model is divided into a plurality of region blocks by using the above-described spatial grid, it is likely that the frequent transportation region and the infrequent transportation region are divided into the same region block. If a certain area block contains both frequent and infrequent transportation areas, then it is not always possible to configure an accurate level of detail for that area later.
In order to divide the three-dimensional model into a plurality of suitable region blocks and thereby facilitate subsequent configuration of the level of detail for each region block, in one embodiment of the present application, step S200, divides the three-dimensional model into a plurality of region blocks based on each historical digital twin three-dimensional model, includes steps S210 through S250.
And S210, acquiring a first area plane based on the three-dimensional model.
In this embodiment, the first area plane is an area plane formed by projection of the three-dimensional model along the z-axis direction. The z-axis of the three-dimensional model is parallel to the vertical direction in reality. That is, the first region plane may be any plane perpendicular to the z-axis in this embodiment, for example, the first region plane may be an xoy plane (i.e., a plane having a z-axis value of 0).
And step S220, acquiring a first coordinate point based on the first area plane.
In this embodiment, the first coordinate point is an arbitrary coordinate point in the first area plane. In this embodiment, the first coordinate points may be set in the first area plane by using a grid method, and of course, each first coordinate point may also be selected from the first area plane by using a random selection method.
And step S230, acquiring each sequence segment corresponding to the first coordinate point based on each historical digital twin three-dimensional model.
In this embodiment, each sequence segment is at least used to characterize whether the first coordinate point has a cargo deposit or withdrawal in the corresponding historical digital twin three-dimensional model.
It should be clear that in the embodiment of the present application, any reasonable sequence segment may be used to record whether the first coordinate point in the corresponding historical digital twin three-dimensional model has goods stored in or taken out. For example, in one embodiment of the application, the sequence segments may include 00, 10, and 01. The 01 is used for representing that no goods are stored in or taken out from the first coordinate point in the corresponding historical digital twin three-dimensional model. The 10 is used for representing the goods stored into the first coordinate point in the corresponding historical digital twin three-dimensional model. The 00 is used for representing that goods are taken out from the first coordinate point in the corresponding historical digital twin three-dimensional model.
And step S240, acquiring a transportation frequency value based on each sequence segment.
In this embodiment, the transportation frequency value is at least used to characterize the frequency of transportation of the cargo in the first coordinate point.
It should be clear that in the embodiments of the present application, the transportation frequency value may be obtained based on each sequence segment in any reasonable manner. For example, the numerical value of each sequence segment may be combined as the transportation frequency value. As can be seen from the foregoing, if the goods at a certain coordinate point are stored more frequently, the sum of the sequence segments is greater. If the cargoes are taken out more frequently at a certain coordinate point, the sum value of each sequence segment tends to be 0. Specifically, a calculation formula (hereinafter referred to as a first calculation formula) of the numerical sums of the respective sequence segments may be as follows:
Wherein S represents the numerical sum of each sequence segment corresponding to the first coordinate point, n represents the number of each sequence segment corresponding to the first coordinate point; representing the value corresponding to the kth sequence segment. In binary, the value represented by 00 is 0, the value represented by 10 is 2, and the value represented by 01 is 1.
It should be clear that the frequency of transportation is not only related to the numerical sum (i.e. the height value hereinafter) of each sequence segment, but also to time (i.e. the time sequence value hereinafter), in order to be able to obtain an accurate transportation frequency value, in one embodiment of the present application, step S240, obtaining the transportation frequency value based on each sequence segment, includes steps S241 to S243.
Step S241, based on each sequence segment, acquiring a first sequence segment.
In this embodiment, the first sequence segment is a sequence segment with the last time sequence in each sequence segment.
And step S242, acquiring a height value and a time sequence value based on the first sequence segment.
In this embodiment, the height value is a numerical sum (calculation mode first calculation formula) of the first sequence segment and a sequence segment before the first sequence segment timing. The time sequence value is a sequence number corresponding to the first sequence segment.
Step S243, obtaining the transportation frequency value based on the height value and the time sequence value.
In an embodiment of the present application, the transportation frequency value may be obtained in any reasonable manner based on the height value and the time sequence value. For example, the transportation frequency value may be a ratio of the height value and the time sequence value. Or step S243, based on the height value and the time sequence value, obtaining a calculation formula of the transportation frequency value as follows:
Wherein P represents a transportation frequency value, S represents a height value, n represents the number of each sequence segment; Representing the range of intervals used to map the values in brackets to [0,1 ]; representing absolute values.
In the embodiment, the higher the speed of goods storage in the first coordinate point is, the higher the transportation frequency value is, the lower the transportation frequency value is, and the higher the speed of goods taking out in the first coordinate point is, the higher the transportation frequency value is, and the transportation frequency value tends to 0 when the goods are not stored or taken out in the first coordinate point for a long time.
And step S250, dividing the three-dimensional model into a plurality of regional blocks based on the transportation frequency value.
In the embodiment of the present application, each first coordinate point having a similar transportation frequency value may be divided into the same region block based on the transportation frequency value. And the characteristics of each first coordinate point in the same region block are similar, so that the subsequent accurate configuration of the detail level for the region is facilitated.
In an embodiment of the present application, the three-dimensional model may be divided into a plurality of region blocks based on the transportation frequency value in any reasonable manner. For example, step S250, dividing the three-dimensional model into a plurality of region blocks based on the transportation frequency value, includes steps S251 to S253.
And step S251, acquiring a first characteristic vector based on the first coordinate point.
In this embodiment, the first feature vector is a transportation frequency value corresponding to the first coordinate point and a coordinate value corresponding to the first coordinate point located on the first area plane.
And step S252, clustering each first coordinate point through the first feature vector based on a clustering algorithm to obtain a plurality of first clustering clusters.
It should be clear that the clustering algorithm is an unsupervised learning method in machine learning, and its objective is to divide the samples in the dataset into several groups (or "clusters") so that the sample similarity in the same cluster is high, and the sample similarity between different clusters is low. In this embodiment, any reasonable clustering algorithm may be used to cluster each first coordinate point, for example, a K-Means algorithm or a K-Means algorithm. The K-Means algorithm or the K-Means algorithm is a mature technology and will not be described here.
Step S253, dividing the three-dimensional model into a plurality of region blocks based on the respective first clusters.
Note that, in the present embodiment, a plurality of first coordinate points in each first cluster collectively form one region block.
And step S300, acquiring a plurality of criticality values based on each region block.
In an embodiment of the present application, the region blocks are in one-to-one correspondence with the criticality values. The criticality value is at least used for representing the density of stacking cargoes in the corresponding area block.
It should be noted that at customs terminals, containers are typically stacked together in a layer-by-layer stack. As can be seen from the foregoing, if the stacking height of the containers in the area block is higher, the height value in the corresponding first coordinate point is also larger. In other words, in the embodiment of the present application, the sum of the height values in the respective first coordinate points in each region block may be taken as the criticality value of the region block.
It should be noted that, if a certain area has more stacked containers, the area needs to be focused, that is, when a three-dimensional model is formed, a higher level of detail needs to be configured for the area. Because of the different sizes of containers, it is difficult to accurately characterize the number of containers in a certain region simply by the sum of the height values in the respective first coordinate points in that region. In other words, it is also difficult to configure an accurate level of detail for a certain region based on the criticality value later only by the sum of the height values in the respective first coordinate points in the region as the criticality value.
In order to further configure accurate levels of detail for each region block, in one embodiment of the present application, step S300, a plurality of criticality values are acquired based on each region block, including steps S310 through S350.
Step S310, acquiring a first area block based on each area block.
In this embodiment, the first area block is any one area block among the area blocks. That is, in the present application, the obtaining of the criticality value of each region block may refer to the first region block, which will not be described in detail later.
And step S320, acquiring a plurality of second coordinate points based on the first area block.
In this embodiment, the second coordinate point is an arbitrary coordinate point located in the first region block.
And step S330, based on the second coordinate points, obtaining a second feature vector corresponding to each second coordinate point.
In this embodiment, the second feature vector is a transport frequency difference value corresponding to each second coordinate point and a coordinate value corresponding to the plane of the first area. And the transport frequency difference value is the difference value between the current transport frequency value and the historical transport frequency value of the corresponding second coordinate point.
It is easy to understand that if two second coordinate points are located in the same container stack, the difference of the transportation frequency of each time sequence of the two second coordinate points is the same (that is, the difference of the transportation frequency of the two coordinate points on each time sequence is changed synchronously), and the coordinate values of the two second coordinate points are similar. That is, in the present embodiment, the number of containers in the first area block can be further determined by whether or not each of the second coordinate points in the first area block is approximated.
And S340, clustering each second coordinate point through a second feature vector based on a clustering algorithm to obtain a plurality of second clustering clusters.
In this embodiment, any reasonable clustering algorithm may be used to cluster each first coordinate point, for example, a K-Means algorithm or a K-Means algorithm. The K-Means algorithm or the K-Means algorithm is a mature technology and will not be described here.
And step 350, acquiring a criticality value corresponding to the first area block based on each second cluster.
In this embodiment, the number of second cluster clusters is substantially the same as the number of stacks in the first area block. Based on this, in step S350, based on each second cluster, a calculation formula for obtaining the criticality value corresponding to the first area block is as follows:
Wherein M represents a criticality value corresponding to the first region block; M represents the number of second cluster clusters corresponding to the first area block; representing the sum of the height values of the second coordinate points corresponding to the ith second cluster; Representing the number of second coordinate points in the ith second cluster; Representing an area corresponding to the first region block; representing the area corresponding to the first area plane; The values in brackets are shown as being mapped to the interval ranges of [0,1 ].
In this embodiment, the larger the key value corresponding to the first area block is, the more the number of containers in the first area block is, the more important attention needs to be paid to the first area block, and the smaller the key value corresponding to the first area block is, the fewer the number of containers in the first area block is, so that attention to the first area block can be reduced.
Step S400, configuring the detail level of the corresponding region block based on each criticality value.
In the embodiment of the application, the detail level of the corresponding region block can be configured in any reasonable manner based on each key value. For example, if the greater the criticality value corresponding to a region block, the higher the level of detail configured for that region may be. In a specific embodiment of the present application, step S400 configures a level of detail of a corresponding area block based on each criticality value, including configuring the level of detail of the first area block to be a high level of detail if the criticality value corresponding to the first area block is greater than or equal to a preset value, and configuring the level of detail of the first area block to be a low level of detail if not.
In the embodiment of the present application, the preset value may be any suitable value, for example, the preset value may be 0.5 or 0.6.
And S500, rendering and generating a current digital twin three-dimensional model based on the detail level of each region block.
It is clear that rendering is performed on the corresponding region blocks based on the pre-configured level of detail, and the three-dimensional model is formed as a mature technology, which is not described herein.
It is clear that in the embodiment of the digital twin body construction method applied to customs supervision, the frequency of cargo transportation in different areas in the digital twin body three-dimensional model is identified, the three-dimensional model is further divided into a plurality of area blocks, the density of cargo stacking in each area block is determined, high detail levels are configured for areas with high cargo stacking density, and low detail levels are configured for areas with low cargo stacking density. The method realizes the use of low-detail level rendering in the area needing no high attention, can reduce the rendering burden and improve the system performance, and uses high-detail level rendering in the area needing high attention to ensure better rendering effect, thereby providing clearer visual information.
Having introduced the embodiment of the digital twin construction method applied to customs supervision provided by the embodiment of the application, the embodiment of the digital twin construction system applied to customs supervision provided by the application is described below. Specifically, as shown in fig. 2, the digital twin body construction system 10 applied to customs supervision includes:
A reader 11 for acquiring a plurality of historical digital twin three-dimensional models of customs supervision;
a server 12 for dividing the three-dimensional model into a plurality of region blocks based on the respective historical digital twin three-dimensional models;
the method comprises the steps of obtaining a plurality of key degree values based on each area block, wherein the area blocks correspond to the key degree values one by one, and the key degree values are at least used for representing the density degree of stacking of cargoes in the corresponding area blocks;
and configuring a detail level of the corresponding region block based on each criticality value;
And rendering and generating a current digital twin three-dimensional model based on the detail level of each region block.
As a specific embodiment of the present application, the server 12 is further configured to obtain a first area plane based on the three-dimensional model, where the first area plane is an area plane formed by a projection of the three-dimensional model along a z-axis direction, and the z-axis of the three-dimensional model is parallel to a vertical direction in reality;
acquiring a first coordinate point based on the first area plane; the first coordinate point is any coordinate point in the first area plane;
Each sequence segment is at least used for representing whether the first coordinate point in the corresponding historical digital twin three-dimensional model has goods stored or taken out;
The method comprises the steps of obtaining a transportation frequency value based on each sequence segment, wherein the transportation frequency value is at least used for representing the frequency of cargo transportation in the first coordinate point;
and dividing the three-dimensional model into a plurality of region blocks based on the transportation frequency value.
As a specific embodiment of the application, the sequence segment comprises 00, 10 and 01, wherein 01 is used for representing that no goods are stored or taken out from the first coordinate point in the corresponding historical digital twin three-dimensional model, 10 is used for representing that goods are stored into the first coordinate point in the corresponding historical digital twin three-dimensional model, and 00 is used for representing that goods are taken out from the first coordinate point in the corresponding historical digital twin three-dimensional model.
As a specific embodiment of the present application, the server 12 is further configured to obtain a first sequence segment based on each sequence segment, where the first sequence segment is a sequence segment with a last time sequence in each sequence segment;
the method comprises the steps of obtaining a height value and a time sequence value based on a first sequence segment, wherein the height value is the numerical sum of the first sequence segment and a sequence segment before the time sequence of the first sequence segment, and the time sequence value is a sequence number corresponding to the first sequence segment;
And obtaining the transportation frequency value based on the height value and the time sequence value.
As a specific embodiment of the present application, the calculation formula for obtaining the transportation frequency value based on the altitude value and the time sequence value by the server 12 is as follows:
Wherein P represents a transportation frequency value, S represents a height value, n represents the number of each sequence segment; Representing the range of intervals used to map the values in brackets to [0,1 ]; representing absolute values.
As a specific embodiment of the present application, the server 12 is further configured to obtain a first feature vector based on the first coordinate point, where the first feature vector is a transportation frequency value corresponding to the first coordinate point and a coordinate value corresponding to a plane of the first region where the first coordinate point is located;
Clustering each first coordinate point through the first feature vector based on a clustering algorithm to obtain a plurality of first clustering clusters;
And dividing the three-dimensional model into a plurality of region blocks based on the respective first clusters.
As a specific embodiment of the present application, the server 12 is further configured to obtain a first area block based on each area block, where the first area block is any one area block in each area block;
and acquiring a plurality of second coordinate points based on the first region block; the second coordinate point is any coordinate point located in the first area block;
the method comprises the steps of obtaining a first feature vector corresponding to each first coordinate point based on each first coordinate point, wherein the first feature vector is a transport frequency degree difference value corresponding to each first coordinate point and a coordinate value corresponding to a plane of a first area;
Clustering each second coordinate point through a second feature vector based on a clustering algorithm to obtain a plurality of second clustering clusters;
and acquiring a criticality value corresponding to the first region block based on each second cluster.
As a specific embodiment of the present application, the calculation formula for obtaining the criticality value corresponding to the first area block based on each second cluster by the server 12 is as follows:
Wherein M represents a criticality value corresponding to the first region block; M represents the number of second cluster clusters corresponding to the first area block; representing the sum of the height values of the second coordinate points corresponding to the ith second cluster; Representing the number of second coordinate points in the ith second cluster; Representing an area corresponding to the first region block; representing the area corresponding to the first area plane; The values in brackets are shown as being mapped to the interval ranges of [0,1 ].
As a specific embodiment of the present application, the server 12 is further configured to configure the level of detail of the first area block to be a high level of detail if the criticality value corresponding to the first area block is greater than or equal to a preset value, or configure the level of detail of the first area block to be a low level of detail if not.
It is clear that in the embodiment of the digital twin body construction system applied to customs supervision, the frequency of cargo transportation in different areas in the digital twin body three-dimensional model is identified, the three-dimensional model is further divided into a plurality of area blocks, the density of cargo stacking in each area block is determined, high detail levels are configured for areas with high cargo stacking density, and low detail levels are configured for areas with low cargo stacking density. The method realizes the use of low-detail level rendering in the area needing no high attention, can reduce the rendering burden and improve the system performance, and uses high-detail level rendering in the area needing high attention to ensure better rendering effect, thereby providing clearer visual information.
It should be apparent that computer-readable storage media of the present application, including both permanent and non-permanent, removable and non-removable media, may be used to implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media for a computer include, but are not limited to, phase change memory, static random access memory, dynamic random access memory, other types of random access memory, read only memory, electrically erasable programmable read only memory, flash memory or other memory technology, read only compact disc read only memory, digital versatile disks or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by the computing device. Computer-readable media, as defined herein, does not include transitory computer-readable media, such as modulated data signals and carrier waves.
In the foregoing embodiments, the descriptions of the embodiments are emphasized, and for parts of one embodiment that are not described in detail, reference may be made to related descriptions of other embodiments.
It will be clearly understood by those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described method, apparatus and device may refer to corresponding procedures in the foregoing method embodiments, which are not repeated herein.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus, device and method may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, and the configuration of the modules, for example, is merely one logical functional configuration, and may be implemented in other ways, for example, multiple modules or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or modules, which may be in electrical, mechanical, or other forms.
The modules described as separate components may or may not be physically separate, and components shown as modules may or may not be physical modules, i.e., may be located in one place, or may be distributed over a plurality of network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist alone physically, or two or more modules may be integrated into one module. The integrated modules may be implemented in hardware or in software functional modules. The integrated modules, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer readable storage medium.
In the above embodiments, it may be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product.
The computer program product includes one or more computer instructions. When the computer program is loaded and executed on a computer, the flow or functions according to the embodiments of the present application are fully or partially produced. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by a wired (e.g., coaxial cable, fiber optic, digital subscriber line), or wireless (e.g., infrared, wireless, microwave, etc.). The computer readable storage medium may be any available medium that can be stored by a computer or a data storage device such as a server, data center, etc. that contains an integration of one or more available media. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., digital versatile disk), or a semiconductor medium (e.g., solid state disk (Solid STATE DISK, SSD)), etc.
Although embodiments of the present application have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made hereto without departing from the principles of the present application.
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