CN117132508B - Digital twin data driving method and system based on GIS+BIM technology - Google Patents
Digital twin data driving method and system based on GIS+BIM technology Download PDFInfo
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Abstract
The invention relates to the technical field of data processing, in particular to a digital twin data driving method and system based on a GIS+BIM technology, comprising the following steps: the method comprises the steps of collecting three-dimensional point cloud data of an urban road in a laser scanning mode, dividing the data points into high-density data points and low-density data points, obtaining road leveling coefficients according to the discreteness and the number of the high-density data points, the density and the nearest distance of the data points, obtaining noise influence degrees according to the difference between the nearest distances of the data points, obtaining the minimum neighborhood sample number, obtaining outliers in the three-dimensional point cloud data by using a DBSCAN density clustering algorithm, obtaining new three-dimensional point cloud data, and constructing a visualized road digital twin model by using a GIS+BIM technology. According to the invention, high-quality three-dimensional point cloud data is obtained through efficient denoising, and the accuracy of road visualization, analysis and decision making of the digital twin model is ensured.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a digital twin data driving method and system based on a GIS+BIM technology.
Background
Digital twin data driving method and system based on GIS (geographic information system) and BIM (building information model) technology, which are to create and manage virtual model by utilizing data collected in the real world, are based on data analysis and prediction, and are used for optimizing decision making, evaluating scene, improving design and improving operation efficiency. The method has wide application scenes, in the aspects of urban road maintenance and maintenance, a digital twin model can be constructed by collecting point cloud data of roads and using a GIS+BIM technology, and then the data in the digital twin model are analyzed and predicted to obtain an analysis result, so that effective decision support is provided for a manager.
The collected road point cloud data may contain noise or invalid points, the existing DBSCAN density clustering algorithm is generally used for filtering noise data points in the road point cloud data, but the noise data points are affected by defects such as cracks and pits on a road, road defects with different degrees are needed to achieve a good denoising effect, so that when the parameter size is selected improperly, the denoising effect of the road point cloud data is poor, and the noise interferes with the construction of a digital twin model, so that the visualization, analysis and decision of road management are inaccurate.
Disclosure of Invention
The invention provides a digital twin data driving method and system based on a GIS+BIM technology, which are used for solving the existing problems.
The digital twin data driving method and system based on the GIS and BIM technology adopt the following technical scheme:
one embodiment of the invention provides a digital twin data driving method based on a GIS+BIM technology, which comprises the following steps:
collecting three-dimensional point cloud data of an urban road in a laser scanning mode, and marking the minimum value in Euclidean distances between each data point and other data points as the nearest distance of each data point according to the three-dimensional coordinates of the data points in the three-dimensional point cloud data; obtaining a distance threshold value by using an Ojin algorithm according to the nearest distances of all data points in the three-dimensional point cloud data; recording the data points with the nearest distance less than or equal to the distance threshold value as high-density data points; recording the data points with the nearest distances larger than the distance threshold as low-density data points;
obtaining a reference high-density data point corresponding to each high-density data point and the discreteness of the high-density data points in the three-dimensional point cloud data according to the Euclidean distance between all the high-density data points in the three-dimensional point cloud data; recording the number of the reference high-density data points corresponding to each high-density data point as the density of each high-density data point; obtaining road leveling coefficients of the three-dimensional point cloud data according to the discreteness and the number of the high-density data points in the three-dimensional point cloud data, the density of all the high-density data points and the difference of the nearest distances between the high-density data points and the low-density data points;
obtaining the noise influence degree in the three-dimensional point cloud data according to the difference between the nearest distances of the high-density data point and the low-density data point in the three-dimensional point cloud data; obtaining the minimum neighborhood sample number corresponding to the three-dimensional point cloud data according to the noise influence degree and the road leveling coefficient in the three-dimensional point cloud data;
obtaining outliers in the three-dimensional point cloud data by using a DBSCAN density clustering algorithm according to the minimum neighborhood sample number corresponding to the three-dimensional point cloud data; deleting outliers in the three-dimensional point cloud data to obtain new three-dimensional point cloud data; and constructing a visualized road digital twin model by using a GIS+BIM technology according to the new three-dimensional point cloud data.
Further, according to the euclidean distance between all the high-density data points in the three-dimensional point cloud data, the reference high-density data point corresponding to each high-density data point and the discreteness of the high-density data points in the three-dimensional point cloud data are obtained, and the method comprises the following specific steps:
calculating Euclidean distance between each high-density data point and other high-density data points in the three-dimensional point cloud data, and recording the Euclidean distance smaller than or equal to other high-density data points corresponding to a distance threshold value as reference high-density data points corresponding to each high-density data point;
and calculating Euclidean distance between any two high-density data points in the three-dimensional point cloud data, and recording the average value of Euclidean distances between all the high-density data points in the three-dimensional point cloud data as the discreteness of the high-density data points in the three-dimensional point cloud data.
Further, the method for obtaining the road leveling coefficient of the three-dimensional point cloud data according to the discreteness and the number of the high-density data points in the three-dimensional point cloud data, the density of all the high-density data points and the difference of the nearest distances between the high-density data points and the low-density data points comprises the following specific steps:
in the three-dimensional point cloud data, dividing the average value of the nearest distances of all high-density data points by the average value of the nearest distances of all low-density data points, and recording the average value as the relative density of the road defect;
obtaining the detail information quantity of the road defect according to the discreteness of the high-density data points in the three-dimensional point cloud data, the number of the high-density data points and the density of all the high-density data points;
and obtaining the road leveling coefficient of the three-dimensional point cloud data according to the detail information quantity of the road defect and the relative density of the road defect.
Further, the obtaining the detail information quantity of the road defect according to the discreteness of the high-density data points, the number of the high-density data points and the density of all the high-density data points in the three-dimensional point cloud data comprises the following specific steps:
marking the product of the normalized value of the number of the high-density data points in the three-dimensional point cloud data and the discreteness of the high-density data points in the three-dimensional point cloud data as the distribution characteristic of the road defect;
subtracting the minimum concentration from the maximum concentration in each high-density data point and all corresponding reference high-density data points in the three-dimensional point cloud data, and recording the obtained value as the weight of the concentration of each high-density data point;
according to the weight of the density of all the high-density data points in the three-dimensional point cloud data, marking the weighted variance of the density of all the high-density data points as the density change degree of the high-density data points in the three-dimensional point cloud data;
and (3) recording the product of the distribution characteristics of the road defects and the concentration degree change degree of the high-density data points in the three-dimensional point cloud data as the detail information quantity of the road defects.
Further, the specific calculation formula corresponding to the road leveling coefficient of the three-dimensional point cloud data is obtained according to the detail information quantity of the road defect and the relative density of the road defect, wherein the specific calculation formula comprises:
where a is the road flatness coefficient of the three-dimensional point cloud data,is the mean value of the nearest distances of all high-density data points in the three-dimensional point cloud data, +.>Is the mean of the nearest distances of all low-density data points in the three-dimensional point cloud data,for the number of high-density data points in the three-dimensional point cloud data, S is the number of data points in the three-dimensional point cloud data, C is the discreteness of the high-density data points in the three-dimensional point cloud data, and +.>For the concentration of the ith high-density data point in the three-dimensional point cloud data,is the average value of the densities of all high-density data points in the three-dimensional point cloud data, and is +.>For the maximum concentration of the ith high-density data point and all corresponding reference high-density data points in the three-dimensional point cloud data,/the method comprises the following steps of>For the minimum concentration of the ith high-density data point and all corresponding reference high-density data points in the three-dimensional point cloud data,/the method comprises the following steps of>Is an exponential function with a base of natural constant.
Further, the method for obtaining the noise influence degree in the three-dimensional point cloud data according to the difference between the nearest distances of the high-density data point and the low-density data point in the three-dimensional point cloud data comprises the following specific steps:
in the three-dimensional point cloud data, calculating the difference of the nearest distances between each high-density data point and all corresponding reference high-density data points, and recording the minimum value in the difference of the nearest distances as the noise influence degree of each high-density data point;
the average value of the noise influence degree of all the high-density data points in the three-dimensional point cloud data is recorded as the noise influence degree of the road defect area;
calculating the standard deviation of the nearest distances of all low-density data points in the three-dimensional point cloud data, and recording the product of the standard deviation and a preset first weight plus the product of the noise influence degree of the road defect area and a preset second weight as the noise influence degree in the three-dimensional point cloud data.
Further, the obtaining the minimum neighborhood sample number corresponding to the three-dimensional point cloud data according to the noise influence degree and the road leveling coefficient in the three-dimensional point cloud data comprises the following specific steps:
obtaining an adjustment coefficient of the minimum neighborhood sample number according to the noise influence degree in the three-dimensional point cloud data and the road leveling coefficient of the three-dimensional point cloud data;
and obtaining the minimum neighborhood sample number corresponding to the three-dimensional point cloud data according to the adjustment coefficient of the minimum neighborhood sample number and the preset lower limit value and upper limit value of the minimum neighborhood sample number.
Further, the specific calculation formula corresponding to the adjustment coefficient for obtaining the minimum neighborhood sample number according to the noise influence degree in the three-dimensional point cloud data and the road leveling coefficient of the three-dimensional point cloud data is as follows:
wherein R is an adjustment coefficient of the minimum neighborhood sample number, A is a road leveling coefficient of three-dimensional point cloud data,for a preset first weight, +.>For a preset second weight, V is the standard deviation of the nearest distance of all low-density data points in the three-dimensional point cloud data, +.>For the noise influence degree of the ith high-density data point in the three-dimensional point cloud data, +.>For the number of high density data points in the three-dimensional point cloud data, +.>Is an exponential function with a base of natural constant.
Further, according to the adjustment coefficient of the minimum neighborhood sample number, the preset lower limit value and the preset upper limit value of the minimum neighborhood sample number, a specific calculation formula corresponding to the minimum neighborhood sample number corresponding to the three-dimensional point cloud data is obtained, wherein the specific calculation formula is as follows:
wherein P is the minimum neighborhood sample number corresponding to the three-dimensional point cloud data, b is the lower limit value of the preset minimum neighborhood sample number, c is the upper limit value of the preset minimum neighborhood sample number, and R is the adjustment coefficient of the minimum neighborhood sample number.
The invention also provides a digital twin data driving system based on the GIS+BIM technology, which comprises a memory and a processor, wherein the processor executes a computer program stored in the memory to realize the method.
The technical scheme of the invention has the beneficial effects that:
in the embodiment of the invention, three-dimensional point cloud data of the urban road are acquired by using a laser scanning mode, and data points in the three-dimensional point cloud data are divided into high-density data points and low-density data points. And obtaining the road leveling coefficient of the three-dimensional point cloud data according to the discreteness and the number of the high-density data points in the three-dimensional point cloud data, the density of all the high-density data points and the difference of the nearest distances between the high-density data points and the low-density data points. Because the road defect area is an important analysis area for road maintenance and maintenance, when the road flatness coefficient is smaller, the road defect and detail information of the defect are more, the smaller minimum neighborhood sample number is selected, and the detail information in the road defect area is protected. And respectively obtaining the noise influence degree in the three-dimensional point cloud data according to the difference between the nearest distances of the high-density data points and the low-density data points in the three-dimensional point cloud data, and selecting a larger minimum neighborhood sample number when the noise influence degree is larger, so as to improve the denoising effect. According to the noise influence degree and the road leveling coefficient in the three-dimensional point cloud data, the minimum neighborhood sample number corresponding to the three-dimensional point cloud data is obtained, an outlier in the three-dimensional point cloud data is obtained by using a DBSCAN density clustering algorithm, the outlier in the three-dimensional point cloud data is deleted, new three-dimensional point cloud data is obtained, and therefore a visual road digital twin model is built by using a GIS+BIM technology. The method and the device acquire high-quality three-dimensional point cloud data through efficient denoising, and ensure the accuracy of road visualization, analysis and decision making of the digital twin model.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of steps of a digital twin data driving method based on GIS+BIM technology.
Detailed Description
In order to further describe the technical means and effects adopted by the present invention to achieve the preset purpose, the following detailed description is given below of the digital twin data driving method and system based on the gis+bim technology according to the present invention, and the detailed description of the specific implementation, structure, features and effects thereof is given below. In the following description, different "one embodiment" or "another embodiment" means that the embodiments are not necessarily the same. Furthermore, the particular features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.
Unless defined otherwise, 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 invention belongs.
The digital twin data driving method and system based on the GIS+BIM technology provided by the invention are specifically described below with reference to the accompanying drawings.
Referring to fig. 1, a flowchart of steps of a digital twin data driving method based on gis+bim technology according to an embodiment of the present invention is shown, where the method includes the following steps:
step S001: collecting three-dimensional point cloud data of an urban road in a laser scanning mode, and marking the minimum value in Euclidean distances between each data point and other data points as the nearest distance of each data point according to the three-dimensional coordinates of the data points in the three-dimensional point cloud data; obtaining a distance threshold value by using an Ojin algorithm according to the nearest distances of all data points in the three-dimensional point cloud data; recording the data points with the nearest distance less than or equal to the distance threshold value as high-density data points; data points with a closest distance greater than the distance threshold are noted as low density data points.
The collected road point cloud data may contain noise or invalid points, the existing DBSCAN density clustering algorithm is generally used for filtering noise data points in the road point cloud data, but the noise data points are affected by defects such as cracks and pits on a road, road defects with different degrees are needed to achieve a good denoising effect through the DBSCAN density clustering algorithm with different parameter sizes, the main parameters of the DBSCAN density clustering algorithm are known to be the neighborhood radius and the minimum neighborhood sample number, and the high-efficiency denoising of the road point cloud data is achieved through analyzing the influence of the road defects and the noise in the road point cloud data according to the embodiment, so that the minimum neighborhood sample number is self-adaptive.
And acquiring three-dimensional point cloud data of the urban road by using a laser scanning mode, and marking the minimum value in Euclidean distances between each data point and other data points as the nearest distance of each data point according to the three-dimensional coordinates of the data points in the three-dimensional point cloud data, wherein the smaller the nearest distance is, the denser the data points in the local area are indicated. And obtaining a distance threshold value by using an Ojin algorithm according to the nearest distances of all data points in the three-dimensional point cloud data, marking the data points with the nearest distances smaller than or equal to the distance threshold value as high-density data points, and marking the data points with the nearest distances larger than the distance threshold value as low-density data points. The method of the Sedrin algorithm is a well-known technique, and the specific method is not described here.
What needs to be described is: the data points in the three-dimensional point cloud data are discrete points in space, each data point is represented by three-dimensional coordinates, and Euclidean distance between the data points can be known according to the three-dimensional coordinates of the data points. It is known that laser beams scanned by a laser at defects such as cracks, pits, etc. on a flat road surface will be reflected or scattered, resulting in more return signals, thus generating denser data points, so that high density data points should be the defective areas of the road, and low density data points should be the normal areas of the road. Because of the randomness of the noise, the nearest distance of the noise data points is not constant, the known Ojin algorithm can divide the data into two types, so that when only one high-density data point or one low-density data point exists, road defects and noise are not likely to exist in the three-dimensional point cloud data, the three-dimensional point cloud data do not need to be subjected to denoising processing, the three-dimensional point cloud data can be directly used for constructing a digital twin model, and when a plurality of high-density data points exist, the three-dimensional point cloud data are likely to exist, and the three-dimensional point cloud data need to be subjected to denoising processing described below.
Step S002: obtaining a reference high-density data point corresponding to each high-density data point and the discreteness of the high-density data points in the three-dimensional point cloud data according to the Euclidean distance between all the high-density data points in the three-dimensional point cloud data; recording the number of the reference high-density data points corresponding to each high-density data point as the density of each high-density data point; and obtaining the road leveling coefficient of the three-dimensional point cloud data according to the discreteness and the number of the high-density data points in the three-dimensional point cloud data, the density of all the high-density data points and the difference of the nearest distances between the high-density data points and the low-density data points.
And recording other high-density data points corresponding to the distance threshold value and with the Euclidean distance between each high-density data point and other high-density data points in the three-dimensional point cloud data as reference high-density data points corresponding to each high-density data point. The number of reference high-density data points corresponding to each high-density data point is recorded as the concentration of each high-density data point. And calculating Euclidean distance between any two high-density data points in the three-dimensional point cloud data, and recording the average value of Euclidean distances between all the high-density data points in the three-dimensional point cloud data as the discreteness of the high-density data points in the three-dimensional point cloud data. It should be noted that, the maximum value of the nearest distances of all the high-density data points is a distance threshold value, so that each high-density data point must have a corresponding reference high-density data point, and the nearest distance of the low-density data point is greater than the distance threshold value, so that the data points corresponding to the Euclidean distance between the data points being less than or equal to the distance threshold value can only be high-density data points.
The data law of a flat road surface is known, and the data is similar, the difference between noise data points on the flat road surface and surrounding data points is obvious, and the noise data points are easy to identify. The data characteristics of the defect areas such as pavement cracks and pits are changeable and complex, noise identification of the defect areas is difficult due to randomness of noise, and the road defect areas are key analysis areas for road maintenance and maintenance. Therefore, according to the data analysis of the high-density data points, the calculation formula of the road leveling coefficient a of the three-dimensional point cloud data is as follows:
where a is the road flatness coefficient of the three-dimensional point cloud data,is the mean value of the nearest distances of all high-density data points in the three-dimensional point cloud data, +.>Is the mean of the nearest distances of all low-density data points in the three-dimensional point cloud data,the number of high-density data points in the three-dimensional point cloud data is S, the number of data points in the three-dimensional point cloud data is S, and C is the three-dimensional pointDiscretization of high-density data points in cloud data, +.>For the concentration of the ith high-density data point in the three-dimensional point cloud data,is the average value of the densities of all high-density data points in the three-dimensional point cloud data, and is +.>For the maximum concentration of the ith high-density data point and all corresponding reference high-density data points in the three-dimensional point cloud data,/the method comprises the following steps of>For the minimum density of the ith high-density data point and all the corresponding reference high-density data points in the three-dimensional point cloud data, the description is that when the densities of the ith high-density data point and all the corresponding reference high-density data points are equal>Andshould be equal. />The present embodiment uses +.>To present inverse proportion relation and normalization processing, and the implementer can set inverse proportion function and normalization function according to actual situation.
What needs to be described is:representing the relative density of road defects, it is known that when the density of point cloud data is greater, i.e., the distance between data points is smaller, the DBSCAN density clustering algorithm can more easily cluster that by reducing the minimum number of neighborhood samplesIsolated points that do not meet the core point condition are marked as noise points, which helps to reduce the impact on the real data points in dense areas and improve the denoising effect. Thus->Smaller, smaller minimum neighborhood sample numbers are required. />Representing the distribution characteristics of road defects>The larger the value, the larger the road defect area in the three-dimensional point cloud data is, and the more widely distributed is, namely the defects distributed at different positions have different probabilities of being different defects, so that the more the detail information in the road defect area should be,/>Representation->Is included in the above formula (c). />For the concentration variance of all high-density data points,as the weight of the density of the ith high-density data point, when the depth change of the crack or pit of the road is gentle, the density change of the data point is small, i.e. +.>The smaller the defect degree is, the smaller the weight is, and when the crack or pit depth of the road is changed steeply, the density change of the data points is smaller, namelyThe larger the defect level is, the larger the weight is, thereby using the weighted variance of the densities of all high density data points>Representing the degree of change in the concentration of high-density data points in three-dimensional point cloud data, the greater the degree of change in the concentration, i.e., the greater the difference between road defects, the more detailed information should be in the region of a road defect, and therefore ∈ ->The amount of detail information representing the road defect. It is known that when analyzing and processing point cloud data, a smaller minimum number of neighborhood samples can capture finer detail information, so that the larger the amount of detail information, the smaller the minimum number of neighborhood samples is required. To this end use->Normalized to inverse proportion>The smaller the value of A, the smaller the minimum neighborhood sample number is required.
Step S003: obtaining the noise influence degree in the three-dimensional point cloud data according to the difference between the nearest distances of the high-density data point and the low-density data point in the three-dimensional point cloud data; and obtaining the minimum neighborhood sample number corresponding to the three-dimensional point cloud data according to the noise influence degree and the road leveling coefficient in the three-dimensional point cloud data.
When the noise influence in the known point cloud data is larger, the condition of the core point can be defined more strictly by increasing the minimum neighborhood sample number, and more neighborhood samples are required when the clustering clusters are selected, so that the points in the dense area can be classified into one clustering cluster only, the interference of noise points is reduced, and the quality and accuracy of the data are improved. The road defect characteristics are only analyzed, so that the influence degree of noise in the three-dimensional point cloud data needs to be further analyzed, and the accurate minimum neighborhood sample number is obtained.
From the shape characteristics of the pits of the road fracture, it is known that for a fracture defect, the depth of the fracture corresponding to the data points traversed along the fracture path from any one data point should be similar, i.e., the closest distances of the data points are similar. For pit defects, the pit defects are round, so the corresponding pit depths of the data points on the circular arc taking the pit defect boundary as a template should be similar, namely the nearest distances of the data points on the circular arc are similar. Thus, there is a feature that the nearest distance between the data point of the defect and a certain data point around the defect is similar, and the nearest distance between the noise data point and the surrounding data point is different due to the randomness of noise.
And calculating absolute values of differences between the nearest distances of each high-density data point and all corresponding reference high-density data points in the three-dimensional point cloud data, and recording the minimum value of the absolute values of differences between the nearest distances of each high-density data point and all corresponding reference high-density data points as the noise influence degree of each high-density data point, wherein the larger the value is, the larger the noise influence is.
The main parameters of the known DBSCAN density clustering algorithm are a neighborhood radius and a minimum neighborhood sample number, the neighborhood radius a set in this embodiment is 3, the lower limit value b of the minimum neighborhood sample number is 15, and the upper limit value c of the minimum neighborhood sample number is 25, which is described as an example, and other values may be set in other embodiments, which are not limited in this embodiment.
Therefore, the calculation formula of the minimum neighborhood sample number P corresponding to the three-dimensional point cloud data is as follows:
wherein P is the minimum neighborhood sample number corresponding to the three-dimensional point cloud data, b is the lower limit value of the minimum neighborhood sample number, c is the upper limit value of the minimum neighborhood sample number, R is the adjustment coefficient of the minimum neighborhood sample number, A is the road leveling coefficient of the three-dimensional point cloud data，For the first weight, ++>For the second weight, V is the standard deviation of the nearest distances of all low-density data points in the three-dimensional point cloud data, +.>For the noise influence degree of the ith high-density data point in the three-dimensional point cloud data, +.>For the number of high density data points in the three-dimensional point cloud data, +.>The present embodiment uses an exponential function based on natural constantsTo show inverse proportion relation and normalization processing, then +.>For normalization processing, the implementer may set an inverse proportion function and a normalization function according to the actual situation. First weight set in this embodiment +.>Second weightIn the description of this example, other values may be set in other embodiments, and the present example is not limited thereto.
What needs to be described is: the low density data points should be normal areas of the road, so the nearest distances of all the low density data points should be similar, and the standard deviation V of the nearest distances of all the low density data points corresponds to the average value of the differences between the nearest distances of all the low density data points and the average value of the nearest distances, and the larger V indicates the larger noise influence degree in the normal areas of the road. WhileRepresenting the noise impact level of a road defect area, which is the mean value of the smallest nearest distance difference between all high density data points and their surroundings,/A->The larger the noise influence degree in the road defect area is, the greater the noise influence degree is. From this, V and +.>Meaning of the data values of (1) are all the closest distance differences of the data points, so the sum of the two is weighted +.>Representing the noise influence degree in the three-dimensional point cloud data, wherein the smaller the noise influence degree is, the larger the minimum neighborhood sample number is needed, thereby using A and +.>The larger the adjustment coefficient R, R representing the minimum neighborhood sample number is, the larger the minimum neighborhood sample number is required, and since the road defect area is the key analysis area for road maintenance and care, the present embodiment gives ∈ ->And a larger weight. And obtaining the minimum neighborhood sample number P corresponding to the three-dimensional point cloud data according to the lower limit value b of the minimum neighborhood sample number and the upper limit value c of the minimum neighborhood sample number.
Step S004: obtaining outliers in the three-dimensional point cloud data by using a DBSCAN density clustering algorithm according to the minimum neighborhood sample number corresponding to the three-dimensional point cloud data; deleting outliers in the three-dimensional point cloud data to obtain new three-dimensional point cloud data; and constructing a visualized road digital twin model by using a GIS+BIM technology according to the new three-dimensional point cloud data.
And obtaining outliers in the three-dimensional point cloud data by using a DBSCAN density clustering algorithm according to the minimum neighborhood sample number P and the neighborhood radius a corresponding to the three-dimensional point cloud data. And deleting outliers in the three-dimensional point cloud data to obtain new three-dimensional point cloud data. It should be noted that, the outlier is a non-core point not assigned to any cluster, and the outlier in the three-dimensional point cloud data is a noise point.
And constructing a visualized road digital twin model by using a GIS+BIM technology according to the new three-dimensional point cloud data. In the visualized road digital twin model, the manager can directly observe the defect area on the road, so that the defect area on the road is repaired.
The DBSCAN density clustering algorithm and the GIS+BIM technology are all known technologies, and specific methods are not described herein.
The present invention has been completed.
In summary, in the embodiment of the present invention, three-dimensional point cloud data of an urban road is collected by using a laser scanning manner, and data points in the three-dimensional point cloud data are divided into high-density data points and low-density data points. Obtaining road leveling coefficients of the three-dimensional point cloud data according to the discreteness and the number of high-density data points in the three-dimensional point cloud data, the density of all the high-density data points and the difference of the nearest distances between the high-density data points and the low-density data points, obtaining noise influence degrees in the three-dimensional point cloud data according to the difference between the nearest distances between the high-density data points and the low-density data points in the three-dimensional point cloud data, obtaining the minimum neighborhood sample number corresponding to the three-dimensional point cloud data, obtaining outliers in the three-dimensional point cloud data by using a DBSCAN density clustering algorithm, deleting the outliers in the three-dimensional point cloud data, obtaining new three-dimensional point cloud data, and constructing a visualized road digital twin model by using a GIS+BIM technology. According to the invention, through self-adapting the minimum neighborhood sample number, the denoising effect of the three-dimensional point cloud data under different noise influence degrees and road leveling coefficients is improved, the high-quality three-dimensional point cloud data is obtained, and the accuracy of road visualization, analysis and decision making by the digital twin model is ensured.
The invention also provides a digital twin data driving system based on the GIS and BIM technology, which comprises a memory and a processor, wherein the processor executes a computer program stored in the memory to realize the digital twin data driving method based on the GIS and BIM technology.
The above description is only of the preferred embodiments of the present invention and is not intended to limit the invention, but any modifications, equivalent substitutions, improvements, etc. within the principles of the present invention should be included in the scope of the present invention.
Claims (5)
1. The digital twin data driving method based on the GIS and BIM technology is characterized by comprising the following steps of:
collecting three-dimensional point cloud data of an urban road in a laser scanning mode, and marking the minimum value in Euclidean distances between each data point and other data points as the nearest distance of each data point according to the three-dimensional coordinates of the data points in the three-dimensional point cloud data; obtaining a distance threshold value by using an Ojin algorithm according to the nearest distances of all data points in the three-dimensional point cloud data; recording the data points with the nearest distance less than or equal to the distance threshold value as high-density data points; recording the data points with the nearest distances larger than the distance threshold as low-density data points;
obtaining a reference high-density data point corresponding to each high-density data point and the discreteness of the high-density data points in the three-dimensional point cloud data according to the Euclidean distance between all the high-density data points in the three-dimensional point cloud data; recording the number of the reference high-density data points corresponding to each high-density data point as the density of each high-density data point; obtaining road leveling coefficients of the three-dimensional point cloud data according to the discreteness and the number of the high-density data points in the three-dimensional point cloud data, the density of all the high-density data points and the difference of the nearest distances between the high-density data points and the low-density data points;
obtaining the noise influence degree in the three-dimensional point cloud data according to the difference between the nearest distances of the high-density data point and the low-density data point in the three-dimensional point cloud data; obtaining the minimum neighborhood sample number corresponding to the three-dimensional point cloud data according to the noise influence degree and the road leveling coefficient in the three-dimensional point cloud data;
obtaining outliers in the three-dimensional point cloud data by using a DBSCAN density clustering algorithm according to the minimum neighborhood sample number corresponding to the three-dimensional point cloud data; deleting outliers in the three-dimensional point cloud data to obtain new three-dimensional point cloud data; according to the new three-dimensional point cloud data, constructing a visualized road digital twin model by using a GIS+BIM technology;
according to the Euclidean distance between all high-density data points in the three-dimensional point cloud data, the reference high-density data point corresponding to each high-density data point and the discreteness of the high-density data points in the three-dimensional point cloud data are obtained, and the method comprises the following specific steps:
calculating Euclidean distance between each high-density data point and other high-density data points in the three-dimensional point cloud data, and recording the Euclidean distance smaller than or equal to other high-density data points corresponding to a distance threshold value as reference high-density data points corresponding to each high-density data point;
calculating Euclidean distance between any two high-density data points in the three-dimensional point cloud data, and recording the average value of Euclidean distances between all the high-density data points in the three-dimensional point cloud data as the discreteness of the high-density data points in the three-dimensional point cloud data;
the method for obtaining the noise influence degree in the three-dimensional point cloud data according to the difference between the nearest distances of the high-density data point and the low-density data point in the three-dimensional point cloud data comprises the following specific steps:
in the three-dimensional point cloud data, calculating the difference of the nearest distances between each high-density data point and all corresponding reference high-density data points, and recording the minimum value in the difference of the nearest distances as the noise influence degree of each high-density data point;
the average value of the noise influence degree of all the high-density data points in the three-dimensional point cloud data is recorded as the noise influence degree of the road defect area;
calculating the standard deviation of the nearest distances of all low-density data points in the three-dimensional point cloud data, and recording the product of the standard deviation and a preset first weight plus the product of the noise influence degree of the road defect area and a preset second weight as the noise influence degree in the three-dimensional point cloud data;
the road leveling coefficient of the three-dimensional point cloud data is obtained according to the discreteness and the number of the high-density data points in the three-dimensional point cloud data, the density of all the high-density data points and the difference of the nearest distances between the high-density data points and the low-density data points, and the method comprises the following specific steps:
in the three-dimensional point cloud data, dividing the average value of the nearest distances of all high-density data points by the average value of the nearest distances of all low-density data points, and recording the average value as the relative density of the road defect;
obtaining the detail information quantity of the road defect according to the discreteness of the high-density data points in the three-dimensional point cloud data, the number of the high-density data points and the density of all the high-density data points;
obtaining road flatness coefficients of three-dimensional point cloud data according to the detailed information quantity of the road defects and the relative density of the road defects;
the method for obtaining the detail information quantity of the road defect according to the discreteness of high-density data points, the number of the high-density data points and the density of all the high-density data points in the three-dimensional point cloud data comprises the following specific steps:
marking the product of the normalized value of the number of the high-density data points in the three-dimensional point cloud data and the discreteness of the high-density data points in the three-dimensional point cloud data as the distribution characteristic of the road defect;
subtracting the minimum concentration from the maximum concentration in each high-density data point and all corresponding reference high-density data points in the three-dimensional point cloud data, and recording the obtained value as the weight of the concentration of each high-density data point;
according to the weight of the density of all the high-density data points in the three-dimensional point cloud data, marking the weighted variance of the density of all the high-density data points as the density change degree of the high-density data points in the three-dimensional point cloud data;
the product of the distribution characteristics of the road defects and the density change degree of the high-density data points in the three-dimensional point cloud data is recorded as the detail information quantity of the road defects;
the specific calculation formula corresponding to the road leveling coefficient of the three-dimensional point cloud data is obtained according to the detail information quantity of the road defect and the relative density of the road defect, and is as follows:
where a is the road flatness coefficient of the three-dimensional point cloud data,is the mean value of the nearest distances of all high-density data points in the three-dimensional point cloud data, +.>Is the mean value of the nearest distances of all low-density data points in the three-dimensional point cloud data, +.>For the number of high-density data points in the three-dimensional point cloud data, S is the number of data points in the three-dimensional point cloud data, C is the discreteness of the high-density data points in the three-dimensional point cloud data, and +.>For the density of the ith high-density data point in the three-dimensional point cloud data, +.>Is the average value of the densities of all high-density data points in the three-dimensional point cloud data, and is +.>For the maximum concentration of the ith high-density data point and all corresponding reference high-density data points in the three-dimensional point cloud data,/the method comprises the following steps of>For the minimum concentration of the ith high-density data point and all corresponding reference high-density data points in the three-dimensional point cloud data,/the method comprises the following steps of>Is an exponential function with a base of natural constant.
2. The digital twin data driving method based on the GIS+BIM technology according to claim 1, wherein the obtaining the minimum neighborhood sample number corresponding to the three-dimensional point cloud data according to the noise influence degree and the road leveling coefficient in the three-dimensional point cloud data comprises the following specific steps:
obtaining an adjustment coefficient of the minimum neighborhood sample number according to the noise influence degree in the three-dimensional point cloud data and the road leveling coefficient of the three-dimensional point cloud data;
and obtaining the minimum neighborhood sample number corresponding to the three-dimensional point cloud data according to the adjustment coefficient of the minimum neighborhood sample number and the preset lower limit value and upper limit value of the minimum neighborhood sample number.
3. The digital twin data driving method based on the GIS+BIM technology according to claim 2, wherein the specific calculation formula corresponding to the adjustment coefficient for obtaining the minimum neighborhood sample number according to the noise influence degree in the three-dimensional point cloud data and the road leveling coefficient of the three-dimensional point cloud data is as follows:
wherein R is an adjustment coefficient of the minimum neighborhood sample number, A is a road leveling coefficient of three-dimensional point cloud data,for a preset first weight, +.>For a preset second weight, V is the standard deviation of the nearest distance of all low-density data points in the three-dimensional point cloud data, +.>For the noise influence degree of the ith high-density data point in the three-dimensional point cloud data, +.>For the number of high density data points in the three-dimensional point cloud data, +.>Is an exponential function with a base of natural constant.
4. The digital twin data driving method based on the gis+bim technology according to claim 2, wherein the specific calculation formula corresponding to the minimum neighborhood sample number corresponding to the three-dimensional point cloud data is obtained according to the adjustment coefficient of the minimum neighborhood sample number, the preset lower limit value and the preset upper limit value of the minimum neighborhood sample number, and is as follows:
wherein P is the minimum neighborhood sample number corresponding to the three-dimensional point cloud data, b is the lower limit value of the preset minimum neighborhood sample number, c is the upper limit value of the preset minimum neighborhood sample number, and R is the adjustment coefficient of the minimum neighborhood sample number.
5. Digital twin data driving system based on gis+bim technology, the system comprising a memory and a processor, characterized in that the processor executes a computer program stored in the memory to implement the method according to any of the claims 1-4.
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