A three-dimensional modeling method and system for distributed heating pipe network

Abstract

The present application relates to the technical field of three-dimensional modeling, and specifically to a three-dimensional modeling method and system for distributed heating pipe networks, the method comprising: obtaining point cloud models of each preset area on the heating pipe network; obtaining the spatial distribution sequence of each preset area and the point cloud spacing difference coefficient of each element therein through the distance distribution characteristics of the point cloud data in the point cloud model to its fitting surface; obtaining a pipe network point cloud set; dividing the pipe network point cloud set into cubes, obtaining the quantity weight of each cube, and obtaining the weighted distribution quantity of the pipe network point cloud set; obtaining a threshold coefficient of the point cloud data during the search process; obtaining the distance threshold of the pipe network point cloud set and the number of identified holes in the heating pipe network, identifying the point cloud data in the pipe network point cloud set as hole boundary points, modeling the heating pipe network, and filling the holes. The present application aims to improve the accuracy and integrity of the three-dimensional model by improving the accuracy of hole identification.

Description

Technical Field

[0001] The present application relates to the field of three-dimensional modeling technology, and in particular to a three-dimensional modeling method and system for distributed heating pipe networks. Background Art

[0002] A distributed heating network is a heating method that disperses heat source equipment near users. Multiple small heat sources, such as gas boilers, heat pumps, and solar collectors, provide heat to local areas, reducing heat losses associated with long-distance transmission. 3D modeling of distributed heating networks can visually demonstrate their structure and layout, providing managers with a clearer understanding of the spatial distribution of the entire distributed heating system, enabling quicker location of faulty equipment and components, and shortening repair times.

[0003] The 3D modeling of heating pipe networks requires scanning to obtain point cloud data. However, due to the complex shape of the heating pipe network, incomplete scanning, insensitivity to scanning in certain areas, and scanner stability issues, the point cloud model of the heating pipe network contains holes. Traditional techniques generally use thresholds to identify the boundaries of holes and determine their shape and size. However, due to the different pipe specifications and pipe routes used in different regions of the heating pipe network, fixed thresholds are difficult to adapt to complex network structures, resulting in low accuracy in hole identification in the point cloud model, which in turn affects the effectiveness of the 3D modeling of the heating pipe network. Summary of the Invention

[0004] In view of the above, it is necessary to provide a three-dimensional modeling method and system for distributed heating pipe networks. Compared with traditional three-dimensional modeling methods and systems for distributed heating pipe networks, it can improve the accuracy of hole identification and thus improve the accuracy and integrity of the three-dimensional model.

[0005] In a first aspect, an embodiment of the present application provides a three-dimensional modeling method for a distributed heating network, the method comprising the following steps:

[0006] Obtain the point cloud model and fitting surface of each preset area on the heating pipe network respectively;

[0007] The spatial distribution sequence of each preset area and the point cloud spacing difference coefficient of each element therein are obtained by using the distance distribution characteristics of the point cloud data in the point cloud model to its fitting surface; normal point cloud data are screened from all the point cloud models using the point cloud spacing difference coefficient to form a pipe network point cloud set;

[0008] The pipe network point cloud set is divided into cubes. By analyzing the difference in the number of point cloud data in each cube and all other cubes, the quantity weight of each cube is obtained. Combined with the number of point cloud data in each cube, the weighted distribution quantity of the pipe network point cloud set is obtained.

[0009] Obtaining a threshold coefficient of the point cloud data during the search process by comparing the weighted distribution quantity with the number of point cloud data;

[0010] For the pipe network point cloud set, the initial neighbor points of each point cloud data are found through the weighted distribution number, and the distance threshold of the pipe network point cloud set is obtained by combining the maximum distance between each point cloud data and its initial neighbor point with the threshold coefficient; the number of identified boundaries of the heating pipe network holes is obtained through the weighted distribution number and the threshold coefficient, and the point cloud data as the hole boundary points are identified through the identified number and the distance threshold, so as to model the heating pipe network and fill the holes.

[0011] In one embodiment, the process of obtaining the spatial distribution sequence of each preset area and the point cloud spacing difference coefficient of each element therein is as follows:

[0012] For each preset area, calculate the distance between each point cloud data in the point cloud model and its fitting surface, which is recorded as the fitting distance;

[0013] Arrange the fitting distances of all point cloud data in ascending order to form a spatial distribution sequence and remove duplicates;

[0014] Calculate the difference between each element in a spatially distributed sequence and its adjacent previous element;

[0015] Count the maximum and minimum values ​​of all the differences corresponding to each element and all previous elements;

[0016] The point cloud spacing difference coefficient of each element in the spatial distribution sequence is the difference between the maximum value and the minimum value.

[0017] In one embodiment, screening normal point cloud data from all the point cloud models includes: taking point cloud data corresponding to a point cloud spacing difference coefficient that is smaller than a preset screening threshold as normal point cloud data.

[0018] In one embodiment, the process of obtaining the quantity weight is as follows:

[0019] Calculate the sum of the number of point cloud data in all cubes in the pipe network point cloud set, and count the maximum value of the number of point cloud data in all cubes;

[0020] Calculate the difference between the maximum value and the number of point cloud data in each cube, and record it as the number difference;

[0021] The quantity weight is the ratio of the quantity difference to the sum.

[0022] In one embodiment, the method for obtaining the weighted distribution quantity is:

[0023] Calculate the weighted sum of the number of point cloud data in all cubes, wherein the weight of the number of point cloud data in each cube is the quantity weight of each cube; the weighted distribution quantity is the result of rounding up the weighted sum.

[0024] In one embodiment, the process of obtaining the threshold coefficient is:

[0025] Calculating a difference between the weighted distribution quantity and the number of point cloud data;

[0026] Calculating a ratio of the difference value to the number of point cloud data;

[0027] The threshold coefficient is the cumulative value of the ratios of all cubes in the pipe network point cloud set.

[0028] In one embodiment, the process of obtaining the distance threshold is as follows:

[0029] Obtain the weighted distribution number of neighboring points of each point cloud data in the pipe network point cloud set, and record them as initial neighboring points;

[0030] Count the maximum distance between each point cloud data and all its initial neighboring points, and record it as the maximum distance;

[0031] Calculate the average of the maximum distances corresponding to all point cloud data;

[0032] The distance threshold is the product of the mean value and the threshold coefficient.

[0033] In one embodiment, the method for obtaining the identification number is:

[0034] Calculating a cumulative value of the normalized value of the threshold coefficient and 1;

[0035] The identification quantity is a calculation result of rounding up the product of the cumulative value and the weighted distribution quantity.

[0036] In one embodiment, identifying point cloud data as hole boundary points includes:

[0037] Searching for the identified number of neighboring points of each point cloud data in the pipe network point cloud set, which are recorded as each identified neighboring point;

[0038] When the distances between any point cloud data and all its identified neighboring points are less than or equal to the distance threshold, the point cloud data is regarded as a non-hole boundary point; otherwise, it is regarded as a hole boundary point.

[0039] In a second aspect, embodiments of the present application further provide a three-dimensional modeling system for a distributed heating network, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor, when executing the computer program, implements any of the steps of the three-dimensional modeling method for a distributed heating network described above. This application has at least the following beneficial effects:

[0040] This application takes into account the shape characteristics of the heating pipes. By analyzing the distance distribution characteristics from the point cloud data to the fitted surface in the point cloud model and calculating the point cloud spacing difference coefficient, it can effectively identify abnormal point cloud data and use only normal point cloud data for subsequent processing, thereby eliminating interfering data, which is conducive to improving the accuracy and reliability of subsequent 3D modeling and avoiding the adverse effects of abnormal data on the modeling results.

[0041] Furthermore, the pipeline point cloud set is divided into cubes. By analyzing the difference between the number of point cloud data in each cube and the overall distribution, and performing weighted summation on the number of point cloud data in all cubes, the density change of the point cloud data in the spatial distribution can be accurately reflected. Then, according to the spatial distribution density characteristics of the point cloud data in different areas, the threshold coefficient is obtained. Subsequently, the weighted summation result and the threshold coefficient are combined to determine the number of neighbor points to search and the distance threshold for judging the boundary points of the hole. Taking into account the spatial distribution density and distance characteristics of the point cloud data, sparsely distributed point cloud data can be identified as non-boundary points, and boundary points and internal points can be effectively distinguished. This avoids the problem that the traditional fixed threshold method is difficult to adapt to complex network structures, improves the accuracy and reliability of hole identification, and provides more accurate boundary information for subsequent modeling of the heating pipe network and filling of holes, which helps to achieve higher quality three-dimensional modeling.

[0042] Furthermore, the holes are filled so that the three-dimensional model can fully display the structure and layout of the heating network, providing managers with more intuitive and accurate visual information, facilitating the rapid location of faulty equipment and components, and improving the reliability of the distributed heating system. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present application or the prior art, the following is a brief introduction to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0044] Figure 1 A flowchart of a three-dimensional modeling method for a distributed heating network provided in one embodiment of the present application;

[0045] Figure 2 Schematic diagram of the process of obtaining the point cloud spacing difference coefficient;

[0046] Figure 3 Schematic diagram of the process of obtaining hole boundary points. DETAILED DESCRIPTION

[0047] In the description of the embodiments of this application, words such as "exemplary," "or," and "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of this application should not be construed as being preferred or advantageous over other embodiments or designs. Rather, the use of words such as "exemplary," "or," and "for example" is intended to present the relevant concepts in a concrete manner.

[0048] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application relates. The terms used in this application are for the purpose of describing specific embodiments only and are not intended to limit this application. It should be understood that, unless otherwise indicated, " / " represents or.

[0049] It should also be noted that the terms "first" and "second" in this application are used to distinguish similar objects, rather than to describe a specific order or sequence.

[0050] The following describes in detail a three-dimensional modeling method and system for a distributed heating network provided by the present application with reference to the accompanying drawings.

[0051] See also Figure 1 , which shows a flowchart of a three-dimensional modeling method for a distributed heating network provided by an embodiment of the present application, the method comprising the following steps:

[0052] Step 1: Obtain the point cloud model of each preset area on the heating pipe.

[0053] Since the installation area of ​​the heating pipe network is large and the installation locations in different areas are different, this application uses a small three-dimensional laser scanner to scan the surface of the heating pipe network to facilitate the use of various complex installation scenarios. The surface of the heating pipe network is scanned by a three-dimensional laser scanner to collect a point cloud model of the heating pipe network. Since the shape and structure of the pipeline are relatively simple, the spatial point pitch of the three-dimensional point cloud data in this application is set to 1mm, and the point cloud data in the point cloud model collected for every 1 meter of pipeline is used as a data set. During the collection process, if the length of the last section of the pipeline is less than 1 meter, the point cloud data in the point cloud model collected for the last section of the pipeline is also used as a data set, recorded as a point cloud data set.

[0054] Step 2: Obtain the fitting surface of the point cloud data in the point cloud model of each preset area, and obtain the spatial distribution sequence of each preset area and the point cloud spacing difference coefficient of each element therein through the distance distribution characteristics of the point cloud data in the point cloud model to its fitting surface; use the point cloud spacing difference coefficient to screen normal points from all the point cloud models and form a pipe network point cloud set.

[0055] For heating network pipes, while their installation structures are complex and varied, the shapes of their individual components are relatively fixed, typically representing curved surfaces with a certain degree of curvature. Therefore, the spatial distribution of the point cloud data in the collected point cloud dataset also exhibits surface characteristics. When point cloud data in a point cloud dataset does not conform to these surface distribution characteristics, it indicates that the point cloud data is abnormal.

[0056] Based on the above analysis, to distinguish abnormal data, each point cloud dataset is used as input to a surface fitting algorithm, which outputs the fitted surface for each point cloud dataset. Using the point-to-surface distance formula, the distance from each point cloud data point in each point cloud dataset to its fitted surface is calculated, recorded as the fitted distance. The point-to-surface distance formula is well known and will not be further described in this application.

[0057] In this embodiment, the surface fitting algorithm is the least squares method. The least squares method is a well-known technology and will not be described in detail in this application. As other implementation methods, on the basis of being able to obtain the fitting surface of each point cloud data set, the implementer can adopt other existing technologies, such as singular value decomposition method, linear interpolation method, Lagrange interpolation method, etc., and this application does not impose any special restrictions.

[0058] In the absence of interference, the distance distribution from the point cloud data in the point cloud dataset to the fitting surface should be uniform, and the gap between the fitting distances should be relatively small. Therefore, abnormal point cloud data can be identified by the difference between the fitting distances.

[0059] For each point cloud dataset, the fitted distances of all point cloud data are sorted in ascending order to obtain a spatial distribution sequence and then duplicates are removed. For example, if the fitted distances are 3, 3, 3, 2, 2, 5, 5, 6, and 6, the spatial distribution sequence after duplicate removal is {2, 3, 5, 6}.

[0060] Calculate the difference between each element in the spatial distribution sequence and its previous adjacent element; count the maximum and minimum values ​​of all these differences corresponding to each element and all previous elements; and use the difference between the maximum and minimum values ​​as the point cloud spacing difference coefficient for each element in the spatial distribution sequence. It should be noted that the point cloud spacing difference coefficient is not calculated for the first two elements in the spatial distribution sequence.

[0061] It should be noted that the point cloud spacing difference coefficient is based on the difference between adjacent fitting distances, indicating the variation range of the point cloud data in spatial distribution. At the same time, the degree of abnormality of the spatial distribution is quantified by the maximum and minimum values. The larger the point cloud spacing difference coefficient, the greater the degree of abnormality of the point cloud data in spatial distribution. The schematic diagram of the process of obtaining the point cloud spacing difference coefficient is as follows: Figure 2 shown.

[0062] Furthermore, point cloud data corresponding to point cloud spacing difference coefficients less than a preset screening threshold are considered normal point cloud data, while point cloud data corresponding to point cloud spacing difference coefficients greater than or equal to the preset screening threshold are considered abnormal point cloud data. These abnormal point cloud data are then removed, and all normal point cloud data in the entire point cloud dataset is combined into a pipe network point cloud set. By removing abnormal point cloud data, interfering data can be effectively removed, retaining authentic and reliable point cloud data, thereby improving the accuracy and reliability of subsequent modeling and providing a more accurate data foundation for building a 3D model of the heating pipe network using point cloud data.

[0063] In this embodiment, the value of the preset screening threshold is 0.1. The value of the preset screening threshold is preset manually. On the basis of satisfying the value range of the preset screening threshold of [0, 0.3], the implementer can set the value of the preset screening threshold by himself.

[0064] Step 3: Divide the pipe network point cloud set into cubes to obtain the weighted distribution number of the pipe network point cloud set. By comparing the weighted distribution number with the number of point cloud data in each cube, the threshold coefficient of the point cloud data in the search process is obtained; the distance threshold of the pipe network point cloud set and the number of identified boundaries of the heating pipe network holes are obtained, and the point cloud data as the hole boundary points are identified.

[0065] When identifying holes in the point cloud model of the heating pipe network, two neighbor numbers need to be set: one for calculating the distance threshold and the other for determining the hole boundary points.

[0066] Step 3.1: By analyzing the difference in the number of point cloud data in each cube and all other cubes, the quantity weight of each cube is obtained. Combined with the number of point cloud data in each cube, the weighted distribution quantity of the pipe network point cloud set is obtained.

[0067] The pipe network point cloud set is divided into cubes, and the cubes without point cloud data are removed.

[0068] In this embodiment, the length, width, and height of the cube are all 1 cm. The part that does not meet the 1 cm requirement does not participate in subsequent calculations. 1 cm is only an embodiment of this application, and the implementer can set its specific value at will. This application does not impose any special restrictions.

[0069] Because heating pipes are not smooth and are made of metal, which reacts with moisture and nutrients in the air, causing corrosion, the refractive index and reflectivity of the laser light may vary at different locations on the pipe surface. This results in different amounts of point cloud data within different cubes during data collection, leading to varying degrees of point cloud data sparsity within different cubes. To prevent point cloud data within sparse cubes from being identified as hole boundaries, a weighted sum of the point cloud data within all cubes is performed.

[0070] Based on the above analysis, the quantity weight of each cube is obtained by analyzing the difference in the number of point cloud data between each cube and all other cubes. The expression is:

[0071] Where w a Indicates the quantity weight of the a-th cube; Num max Indicates the maximum value of the point cloud data in all cubes in the pipe network point cloud set; Num a 、Num b Respectively represent the number of point cloud data in the ath and bth cubes; N represents the number of all cubes in the pipe network point cloud set. max -Num a Recorded as the quantity difference.

[0072] Furthermore, the weighted distribution quantity of the pipe network point cloud set is obtained by combining the quantity weight of each cube with the number of point cloud data in each cube. The expression is:

[0073] Where wNum represents the weighted distribution number of the pipe network point cloud; Indicates the rounding up operation; N indicates the number of all cubes; w a Indicates the quantity weight of the a-th cube; Num a Represents the number of point cloud data within the a-th cube. The weighted distribution number is the number of neighbors used to calculate the distance threshold.

[0074] It should be noted that: in the point cloud data processing, by calculating the weighted distribution number of the pipe network point cloud set, the density change of the point cloud data in the point cloud model in the spatial distribution can be accurately reflected; the weighted distribution number is based on the difference between the number of point cloud data in each cube and the overall distribution to reflect the density change amplitude of the point cloud in the spatial distribution; at the same time, by calculating the weighted total distribution number, the overall characteristics of the spatial distribution can be quantified; it can effectively improve the accuracy and reliability of the heating pipe network point cloud data processing, and lay the foundation for subsequent analysis and application.

[0075] Step 3.2: Obtain a threshold coefficient of the point cloud data during the search process by comparing the weighted distribution quantity with the number of point cloud data.

[0076] When determining the boundary points of a hole, the distance difference between neighboring points is a key factor in identifying boundary points. When searching for neighboring points in point cloud data, the search direction is omnidirectional, that is, the search is conducted in all directions. If the same number of neighboring points is searched for non-boundary points and boundary points, the search distance for boundary points will be longer. The weighted distribution number of the heating water pipe point cloud data represents the spatial distribution density characteristics of the point cloud data. Therefore, by comparing the weighted distribution number with the number of point cloud data, the threshold coefficient of the point cloud data during the search process can be obtained. The expression is:

[0077] Where μ represents the threshold coefficient of the point cloud data in the search process; wNum represents the weighted distribution number of the pipe network point cloud set; Num a Represents the number of point cloud data in the ath cube; N represents the number of all cubes; |*| represents the absolute value operation.

[0078] It should be noted that: when the threshold coefficient of the point cloud is larger during the search process, the greater the change in the distribution density of the point cloud in space is. Therefore, a larger search distance is required to identify sparsely distributed point cloud data as non-boundary points, effectively distinguish boundary points from internal points, and avoid misidentifying point cloud data in sparse areas as hole boundary points.

[0079] Step 3.3, for the pipe network point cloud set, find the initial neighbor points of each point cloud data through the weighted distribution number, and obtain the distance threshold of the pipe network point cloud set through the maximum distance between each point cloud data and its initial neighbor point in combination with the threshold coefficient; obtain the number of identified boundaries of the heating pipe holes through the weighted distribution number and the threshold coefficient, and identify the point cloud data as the hole boundary points through the identified number and the distance threshold.

[0080] Obtain the weighted distribution of neighboring points for each point cloud data point in the pipe network point cloud set, record them as initial neighboring points, calculate the maximum distance between each point cloud data point and all of its initial neighboring points, record them as the maximum distance, calculate the mean of the maximum distances corresponding to all point cloud data points, and use the product of the mean and the threshold coefficient as the distance threshold for the pipe network point cloud set. The distance between a point cloud data point and its initial neighboring points is the Euclidean distance.

[0081] In this embodiment, the neighboring points of each point cloud data are searched through a KD tree (kd-tree). The KD tree is a well-known technology and will not be described in detail in this application.

[0082] The number of identified heating pipe hole boundaries is obtained by combining the weighted distribution number and the threshold coefficient. The expression is:

[0083] Where K represents the number of identified boundaries of heating pipe holes; μ ′ Indicates the normalized value of the threshold coefficient of the point cloud data during the search process; wNum represents the weighted distribution number of the pipe network point cloud set. In this embodiment, the sigmoid function is used to obtain the normalized value of the threshold coefficient; Indicates rounding up. The number of identified points is the number of neighbors used to determine the hole boundary point.

[0084] Furthermore, the identified number of neighboring points of each point cloud data in the pipe network point cloud set is searched and recorded as each identified neighboring point; when the distance between any point cloud data and all its identified neighboring points is less than or equal to the distance threshold, the any point cloud data is regarded as a non-hole boundary point, otherwise, the any point cloud data is regarded as a hole boundary point. The distance between the point cloud data and its identified neighboring points is the Euclidean distance. The flowchart of the hole boundary point acquisition is shown in FIG. Figure 3 shown.

[0085] Step 6: Obtain holes based on hole boundary points, model the heating pipe network and fill the holes.

[0086] The network point cloud set is triangulated to obtain a triangular mesh, and all the nearest abnormal point cloud data are connected to obtain the holes in the point cloud model of the heating network. First, the minimum angle method is used to triangulate the hole polygons to form an initial mesh, and then the least squares mesh and the radial function implicit surface are fused to obtain the implicit surface. On the basis of the implicit surface, the least second-order derivative method is used to optimize the curvature of the surface so that the curvature change trend around the hole is consistent, thereby achieving hole filling. Among them, the specific process of filling the holes and the minimum angle method, least squares mesh, radial function implicit surface and least second-order derivative method involved are all well-known technologies, and the specific content will not be repeated in this application. It should be noted that: if a triangular mesh already exists in the hole, the triangular mesh will be deleted and then the hole will be filled.

[0087] In this embodiment, a greedy triangulation method is used to triangulate the pipe network point cloud to obtain a triangular mesh. The greedy triangulation method is a well-known technology and will not be described in detail in this application.

[0088] Based on the same inventive concept as the above method, an embodiment of the present application also provides a three-dimensional modeling system for a distributed heating pipe network, comprising a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, the steps of any one of the above-mentioned three-dimensional modeling methods for a distributed heating pipe network are implemented.

[0089] In summary, this application takes into account the shape characteristics of the heating pipes, analyzes the distance distribution characteristics from the point cloud data to the fitted surface in the point cloud model, and calculates the point cloud spacing difference coefficient. This can effectively identify abnormal point cloud data and only use normal point cloud data for subsequent processing, thereby eliminating interfering data, which is beneficial to improving the accuracy and reliability of subsequent 3D modeling and avoiding the adverse effects of abnormal data on the modeling results.

[0090] Furthermore, the pipeline point cloud set is divided into cubes. By analyzing the difference between the number of point cloud data in each cube and the overall distribution, and performing weighted summation on the number of point cloud data in all cubes, the density change of the point cloud data in the spatial distribution can be accurately reflected. Then, according to the spatial distribution density characteristics of the point cloud data in different areas, the threshold coefficient is obtained. Subsequently, the weighted summation result and the threshold coefficient are combined to determine the number of neighbor points to search and the distance threshold for judging the boundary points of the hole. Taking into account the spatial distribution density and distance characteristics of the point cloud data, sparsely distributed point cloud data can be identified as non-boundary points, and boundary points and internal points can be effectively distinguished. This avoids the problem that the traditional fixed threshold method is difficult to adapt to complex network structures, improves the accuracy and reliability of hole identification, and provides more accurate boundary information for subsequent modeling of the heating pipe network and filling of holes, which helps to achieve higher quality three-dimensional modeling.

[0091] Furthermore, the holes are filled so that the three-dimensional model can fully display the structure and layout of the heating network, providing managers with more intuitive and accurate visual information, facilitating the rapid location of faulty equipment and components, and improving the reliability of the distributed heating system.

[0092] The flowcharts and block diagrams in the accompanying drawings show the possible implementation architectures, functions and operations of the systems, methods and computer program products according to the embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, program segment or part of the code, and the module, program segment or part of the code contains one or more executable instructions for implementing the specified logical functions. In some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, or they can sometimes be executed in the opposite order, which can depend on the functions involved. In the descriptions corresponding to the flowcharts and block diagrams in the accompanying drawings, the operations or steps corresponding to different boxes can also occur in an order different from that disclosed in the description, and sometimes there is no specific order between different operations or steps. For example, two consecutive operations or steps can actually be executed substantially in parallel, or they can sometimes be executed in the opposite order, which can depend on the functions involved. Each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented by a dedicated hardware-based system that performs the specified function or action, or may be implemented by a combination of dedicated hardware and computer instructions.

[0093] It is obvious to those skilled in the art that the present application is not limited to the details of the above exemplary embodiments, and that the present application can be implemented in other specific forms without departing from the basic characteristics of the present application. Therefore, from all perspectives, the above embodiments of the present application should be regarded as exemplary and non-restrictive.

Claims

1. A three-dimensional modeling method for a distributed heating network, characterized in that: The method comprises the following steps: Obtain the point cloud model and fitting surface of each preset area on the heating pipe network respectively; The spatial distribution sequence of each preset area and the point cloud spacing difference coefficient of each element therein are obtained by using the distance distribution characteristics of the point cloud data in the point cloud model to its fitting surface; normal point cloud data are screened from all the point cloud models using the point cloud spacing difference coefficient to form a pipe network point cloud set; The pipe network point cloud set is divided into cubes. By analyzing the difference in the number of point cloud data in each cube and all other cubes, the quantity weight of each cube is obtained. Combined with the number of point cloud data in each cube, the weighted distribution quantity of the pipe network point cloud set is obtained. Obtaining a threshold coefficient of the point cloud data during the search process by comparing the weighted distribution quantity with the number of point cloud data; For the pipe network point cloud set, the initial neighbor points of each point cloud data are found through the weighted distribution number, and the distance threshold of the pipe network point cloud set is obtained by combining the maximum distance between each point cloud data and its initial neighbor point with the threshold coefficient; the number of identified boundaries of the heating pipe network holes is obtained through the weighted distribution number and the threshold coefficient, and the point cloud data as the hole boundary points are identified through the identified number and the distance threshold, so as to model the heating pipe network and fill the holes.

2. A three-dimensional modeling method for a distributed heating network according to claim 1, characterized in that: The process of obtaining the spatial distribution sequence of each preset area and the point cloud spacing difference coefficient of each element therein is as follows: For each preset area, calculate the distance between each point cloud data in the point cloud model and its fitting surface, which is recorded as the fitting distance; Arrange the fitting distances of all point cloud data in ascending order to form a spatial distribution sequence and remove duplicates; Calculate the difference between each element in a spatially distributed sequence and its adjacent previous element; Count the maximum and minimum values ​​of all the differences corresponding to each element and all previous elements; The point cloud spacing difference coefficient of each element in the spatial distribution sequence is the difference between the maximum value and the minimum value.

3. A three-dimensional modeling method for a distributed heating network according to claim 1, characterized in that: The screening of normal point cloud data from all the point cloud models includes: taking point cloud data corresponding to a point cloud spacing difference coefficient that is smaller than a preset screening threshold as normal point cloud data.

4. A three-dimensional modeling method for a distributed heating network according to claim 1, characterized in that: The process of obtaining the quantity weight is as follows: Calculate the sum of the number of point cloud data in all cubes in the pipe network point cloud set, and count the maximum value of the number of point cloud data in all cubes; Calculate the difference between the maximum value and the number of point cloud data in each cube, and record it as the number difference; The quantity weight is the ratio of the quantity difference to the sum.

5. A three-dimensional modeling method for a distributed heating network according to claim 1, characterized in that: The method for obtaining the weighted distribution quantity is: Calculate the weighted sum of the number of point cloud data in all cubes, wherein the weight of the number of point cloud data in each cube is the quantity weight of each cube; the weighted distribution quantity is the result of rounding up the weighted sum.

6. A three-dimensional modeling method for a distributed heating network according to claim 1, characterized in that: The process of obtaining the threshold coefficient is as follows: Calculating a difference between the weighted distribution quantity and the number of point cloud data; Calculating a ratio of the difference value to the number of point cloud data; The threshold coefficient is the cumulative value of the ratios of all cubes in the pipe network point cloud set.

7. A three-dimensional modeling method for a distributed heating network according to claim 1, characterized in that: The process of obtaining the distance threshold is as follows: Obtain the weighted distribution number of neighboring points of each point cloud data in the pipe network point cloud set, and record them as initial neighboring points; Count the maximum distance between each point cloud data and all its initial neighboring points, and record it as the maximum distance; Calculate the average of the maximum distances corresponding to all point cloud data; The distance threshold is the product of the mean value and the threshold coefficient.

8. A three-dimensional modeling method for a distributed heating network according to claim 1, characterized in that: The method for obtaining the identification number is: Calculating a cumulative value of the normalized value of the threshold coefficient and 1; The identification quantity is a calculation result of rounding up the product of the cumulative value and the weighted distribution quantity.

9. A three-dimensional modeling method for a distributed heating network according to claim 1, characterized in that: The identifying of point cloud data as hole boundary points includes: Searching for the identified number of neighboring points of each point cloud data in the pipe network point cloud set, which are recorded as each identified neighboring point; When the distances between any point cloud data and all its identified neighboring points are less than or equal to the distance threshold, the point cloud data is regarded as a non-hole boundary point; otherwise, it is regarded as a hole boundary point.

10. A three-dimensional modeling system for a distributed heating network, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the processor implements the steps of a three-dimensional modeling method for a distributed heating network as described in any one of claims 1 to 9.

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