Multi-floor three-dimensional model construction method and device, computer equipment and storage medium

Abstract

The invention relates to a multi-floor three-dimensional model construction method and device, computer equipment and a storage medium. The method comprises the following steps: performing floor construction according to floor parameter data of each floor to obtain a plurality of floor construction models and corresponding sequence data; under the condition that model splicing is conducted on the adjacent floors, coordinate point marking is conducted on the floor parameter data of the adjacent floors, and upper-layer coordinate point position data and lower-layer coordinate point position data are obtained; performing calculation processing according to the floor parameter data of the adjacent floor, the upper-layer coordinate point position data and the lower-layer coordinate point position data to obtain vertical corresponding data of the upper-layer coordinate point position data corresponding to the lower layer; and performing model splicing according to the floor parameter data of the adjacent floors, the upper layer mark data and the vertical corresponding data to obtain a target multi-floor model. By the adoption of the method, the multi-layer three-dimensional model can be generated and spliced by collecting the point cloud, the air pressure and the distance data, the modeling efficiency and accuracy are improved, and a convenient and reliable technical guarantee is provided.

Description

Technical Field

[0001] The present application relates to the technical field of three-dimensional model construction, and in particular to a method, device, computer equipment and storage medium for constructing a multi-floor three-dimensional model. Background Art

[0002] As the demand for digital transformation in the construction industry continues to grow, digital twin technology, as an emerging application direction, is becoming an important tool in the field of building information modeling (BIM) and smart buildings. Digital twin technology can provide support for various stages of building design, construction, and operation by collecting various types of building data in real time and generating a three-dimensional virtual model that is highly consistent with the real environment. Especially in the modeling process of multi-story buildings, quickly and accurately constructing a three-dimensional model is of great significance for subsequent monitoring, analysis, and management.

[0003] Among the related technologies, multi-story building model construction methods based on emerging technologies such as 3D Gaussian Splatting, photogrammetry, and image recognition have been applied in some practical scenarios. These technologies collect two-dimensional images and point cloud data of buildings, and use computer vision, deep learning and other algorithms to process the data to generate three-dimensional digital models of buildings. 3DGS technology can accurately capture the details of the building surface and reconstruct a highly realistic three-dimensional model through point cloud data.

[0004] However, in the related technologies, there are still many challenges when dealing with multi-story buildings, especially when there are large differences between floors. For multi-story buildings, especially when there are large differences in the structure and room types of the upper and lower adjacent floors, it is difficult to automatically identify the overlapping areas of adjacent floors, resulting in the inability to correctly splice the building models of each floor together. Also, when dealing with multi-story buildings, the lack of effective algorithms may lead to incorrect docking or inconsistency in the generated 3D model, affecting the accuracy and practicality of the overall model. Summary of the invention

[0005] Based on this, it is necessary to provide a multi-story three-dimensional model construction method, device, computer equipment, computer-readable storage medium and computer program product that can simplify the three-dimensional modeling of multi-story houses, automatically correct the order and orientation of floors, and improve efficiency and accuracy in response to the above technical problems.

[0006] In a first aspect, the present application provides a method for constructing a multi-floor three-dimensional model. The method comprises:

[0007] Perform floor construction according to the floor parameter data of each floor, and obtain multiple floor construction models and their corresponding sequence data;

[0008] When the adjacent layers are modeled, coordinate point marking is performed on the floor parameter data of the adjacent layers to obtain the upper layer coordinate point position data and the lower layer coordinate point position data;

[0009] Calculate and process the floor parameter data of the adjacent layers, the upper layer coordinate point position data and the lower layer coordinate point position data to obtain the vertical corresponding data of the upper layer coordinate point position data on the lower layer;

[0010] The model is spliced ​​according to the floor parameter data of adjacent layers, the upper layer marking data and the vertical corresponding data to obtain the target multi-floor model.

[0011] In one embodiment, the floor is constructed according to the floor parameter data of each floor, and a plurality of floor construction models and their corresponding sequence data are obtained, including:

[0012] When constructing each floor, construct the floor according to the floor point cloud data of the current floor to obtain the current floor construction model;

[0013] When all the floor construction models are constructed, all the floor construction models are sorted by number according to the floor number data to obtain a model number mapping relationship;

[0014] The model number mapping relationship is judged and adjusted based on the floor air pressure data, and finally the correct number mapping relationship is obtained;

[0015] Based on all the floor construction models and the correctly adjusted label mapping relationship, multiple floor construction models and their corresponding sequence data are determined.

[0016] In one embodiment, when adjacent layers are modeled, the floor parameter data of the adjacent layers are screened and marked, and before obtaining the upper layer marking data and the lower layer marking data, the method further includes:

[0017] Grouping is performed according to the plurality of floor construction models and their corresponding sequence data to obtain a plurality of adjacent layer groups;

[0018] Adjacent layer floor parameter data is determined based on a plurality of adjacent layer groups.

[0019] In one embodiment, when adjacent layers are modeled, coordinate point marking is performed on the floor parameter data of the adjacent layers to obtain upper layer coordinate point position data and lower layer coordinate point position data, including:

[0020] Acquire upper layer parameter data and model scale parameters of an adjacent upper layer; wherein the upper layer parameter data includes upper layer point cloud data;

[0021] Perform data screening according to the upper point cloud data to obtain the upper horizontal distance data, the upper longitudinal distance data and the upper vertical distance data;

[0022] The upper layer coordinate point position data is determined based on the model scale parameter, the upper layer transverse distance data, the upper layer longitudinal distance data and the upper layer vertical distance data.

[0023] In one embodiment, when adjacent layers are modeled, coordinate point marking is performed on the floor parameter data of the adjacent layers to obtain upper layer coordinate point position data and lower layer coordinate point position data, further comprising:

[0024] Acquire lower layer parameter data of an adjacent lower layer; wherein the lower layer parameter data includes lower layer point cloud data;

[0025] Perform data screening according to the lower layer point cloud data to obtain the lower layer horizontal distance data, the lower layer longitudinal distance data and the lower layer vertical distance data;

[0026] The lower layer coordinate point position data is determined based on the model scale parameter, the lower layer lateral distance data, the lower layer longitudinal distance data and the lower layer vertical distance data.

[0027] In one embodiment, the vertical corresponding data of the upper layer coordinate point position data on the lower layer is obtained by performing calculation and processing based on the floor parameter data of the adjacent layers, the upper layer coordinate point position data and the lower layer coordinate point position data, including:

[0028] Calculate the horizontal spacing according to the upper horizontal distance data and the lower horizontal distance data to obtain the coordinate horizontal spacing;

[0029] Calculate the vertical spacing according to the horizontal spacing of coordinates, the distance between adjacent coordinate points and the adjacent layer height to obtain the vertical spacing of coordinates;

[0030] Data conversion is performed according to adjacent layer heights, coordinate transverse spacing, coordinate longitudinal spacing and model scale parameters to obtain adjacent model layer heights, model coordinate transverse spacing and model coordinate longitudinal spacing;

[0031] The upper layer coordinate point position data is vertically transformed according to the model coordinate horizontal spacing, the model coordinate vertical spacing and the lower layer coordinate point position data to obtain the vertical corresponding data of the upper layer coordinate point position data corresponding to the lower layer.

[0032] In one embodiment, model splicing is performed according to the floor parameter data of adjacent layers, the upper layer coordinate point position data and the vertical corresponding data to obtain the target multi-floor model, including:

[0033] Perform data adjustment according to the upper layer coordinate point position data and the vertical corresponding data to obtain adjusted vertical data;

[0034] determining adjustment data based on the adjusted vertical data and the vertical corresponding data;

[0035] Adjusting the lower layer construction model based on the adjustment data and the lower layer point cloud data to obtain an adjusted lower layer model;

[0036] Adjust the orientation of the model based on the direction data in the floor parameter data;

[0037] After all adjacent layers are adjusted, a target multi-floor model is obtained.

[0038] In a second aspect, the present application also provides a device for constructing a multi-floor three-dimensional model. The device comprises:

[0039] A model construction module is used to construct floors according to the floor parameter data of each floor, and obtain multiple floor construction models and their corresponding sequence data;

[0040] A data screening module is used to mark the coordinate points of the floor parameter data of the adjacent layers when the adjacent layers are modeled together, and obtain the upper layer coordinate point position data and the lower layer coordinate point position data;

[0041] A data processing module, used for performing calculation and processing according to the floor parameter data of the adjacent layers, the upper layer coordinate point position data and the lower layer coordinate point position data, to obtain the vertical corresponding data of the upper layer coordinate point position data corresponding to the lower layer;

[0042] The model splicing module is used to perform model splicing according to the floor parameter data of adjacent layers, the upper layer marking data and the vertical corresponding data to obtain the target multi-floor model.

[0043] In a third aspect, the present application further provides a computer device. The computer device includes a memory and a processor, the memory stores a computer program, and the processor implements the following steps when executing the computer program:

[0044] Perform floor construction according to the floor parameter data of each floor, and obtain multiple floor construction models and their corresponding sequence data;

[0045] When the adjacent layers are modeled, coordinate point marking is performed on the floor parameter data of the adjacent layers to obtain the upper layer coordinate point position data and the lower layer coordinate point position data;

[0046] Calculate and process the floor parameter data of the adjacent layers, the upper layer coordinate point position data and the lower layer coordinate point position data to obtain the vertical corresponding data corresponding to the upper layer mark data;

[0047] The model is spliced ​​according to the floor parameter data of adjacent layers, the upper layer marking data and the vertical corresponding data to obtain the target multi-floor model.

[0048] In a fourth aspect, the present application further provides a computer-readable storage medium. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the following steps are implemented:

[0049] Perform floor construction according to the floor parameter data of each floor, and obtain multiple floor construction models and their corresponding sequence data;

[0050] When the adjacent layers are modeled, coordinate point marking is performed on the floor parameter data of the adjacent layers to obtain the upper layer coordinate point position data and the lower layer coordinate point position data;

[0051] Calculate and process the floor parameter data of the adjacent layers, the upper layer coordinate point position data and the lower layer coordinate point position data to obtain the vertical corresponding data corresponding to the upper layer mark data;

[0052] The model is spliced ​​according to the floor parameter data of adjacent layers, the upper layer marking data and the vertical corresponding data to obtain the target multi-floor model.

[0053] In a fifth aspect, the present application further provides a computer program product. The computer program product includes a computer program, and when the computer program is executed by a processor, the following steps are implemented:

[0054] Perform floor construction according to the floor parameter data of each floor, and obtain multiple floor construction models and their corresponding sequence data;

[0055] When the adjacent layers are modeled, coordinate point marking is performed on the floor parameter data of the adjacent layers to obtain the upper layer coordinate point position data and the lower layer coordinate point position data;

[0056] Calculate and process the floor parameter data of the adjacent layers, the upper layer coordinate point position data and the lower layer coordinate point position data to obtain the vertical corresponding data corresponding to the upper layer mark data;

[0057] The model is spliced ​​according to the floor parameter data of adjacent layers, the upper layer marking data and the vertical corresponding data to obtain the target multi-floor model.

[0058] The above-mentioned multi-floor three-dimensional model construction method, device, computer equipment, storage medium and computer program product collect data for each floor of the multiple floors to obtain floor parameter data of each floor, including but not limited to point cloud, direction, distance, air pressure and other data and sequence numbers of multiple floors, and then use the floor parameter data of each layer to construct a three-dimensional model of each layer, that is, a multiple floor construction model, and the air pressure data of each layer verifies and adjusts the sequence numbers of the multiple floors to obtain the correct sequence numbers, and the correct sequence numbers are matched one by one with the multiple floor construction models, and then the models of adjacent layers are spliced ​​based on the correct sequence numbers, by determining the upper layer coordinate point position data and the lower layer coordinate point position data in the model of the adjacent layer, and calculating the vertical corresponding point of the upper layer coordinate point in the lower layer, and then combining the direction data to adjust the position of the lower layer model so that the adjacent layers are spliced ​​at the correct position, by collecting point cloud, air pressure and distance data, the three-dimensional model of the multi-story house is automatically generated and spliced, which simplifies the modeling process, reduces manual intervention, improves modeling efficiency and accuracy, and provides convenient and reliable technical guarantee for the business. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 A diagram of an application environment of a method for constructing a multi-floor three-dimensional model in an embodiment;

[0060] Figure 2 A schematic diagram of a process of constructing a multi-floor three-dimensional model in one embodiment;

[0061] Figure 3 A schematic diagram of a structure for calculating vertical corresponding data in one embodiment;

[0062] Figure 4 A schematic diagram of a flow chart of a model splicing step in an embodiment;

[0063] Figure 5 A schematic diagram of the structure of model splicing in one embodiment;

[0064] Figure 6 A structural block diagram of a device for constructing a multi-floor three-dimensional model in one embodiment;

[0065] Figure 7 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0066] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0067] The multi-floor three-dimensional model construction method provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown. Among them, the acquisition terminal 102 communicates with the cloud side platform 104 through the network. The data storage system can store the data that the cloud side platform 104 needs to process. The data storage system can be integrated on the cloud side platform 104, or it can be placed on the cloud or other network servers. By collecting the point cloud, direction, distance, air pressure and other data of each layer, a three-dimensional model of multiple floors is constructed, and the air pressure data is used to verify and adjust the order of the floors. Based on the correct order, the mark points are selected to calculate the vertical corresponding points, and the position of the lower model is adjusted in combination with the direction data to achieve accurate splicing of adjacent floors. Among them, the acquisition terminal 102 can be, but is not limited to, smart terminals and other measurement tools. Smart terminals refer to mobile devices with processing capabilities, capable of running various applications and interacting with other devices or networks. Common smart terminals include smartphones, tablets, smart watches, etc. Smart terminals are usually equipped with multiple sensors (such as accelerometers, gyroscopes, GPS, laser rangefinders, etc.), and can perform data collection, measurement, analysis and other functions through applications (Apps). Other measuring tools refer to any tools or equipment used to measure distance and floor height other than smart terminals, including but not limited to laser rangefinders, tape measures, and professional distance measuring instruments. The cloud-side platform 104 can be implemented with an independent server or a server cluster consisting of multiple servers.

[0068] In one embodiment, Figure 2 As shown, a method for constructing a multi-story three-dimensional model is provided. Figure 1 The cloud-side platform in the example is used to illustrate the process, which includes the following steps:

[0069] Step 202, construct the floors according to the floor parameter data of each floor, and obtain multiple floor construction models and their corresponding sequence data.

[0070] Among them, floor parameter data includes floor point cloud data, floor label data and floor air pressure data. Point cloud is a data set composed of a large number of points in three-dimensional space. Each point contains three-dimensional coordinates (x, y, z) and may contain other attributes such as color, intensity, normal, etc. Point cloud data is widely used in many fields, including but not limited to autonomous driving, robot navigation, architectural modeling, virtual reality, medical imaging, geographic information systems, etc., because it can accurately capture the geometric shape and surface characteristics of objects.

[0071] Floor parameter data also includes directional data, which refers to data information used to describe the direction of an object or phenomenon in space, geographic information, or other related fields. Directional data is usually related to attributes such as position, angle, and orientation, and is used to characterize the spatial or temporal direction of an object, event, or activity. Directional data usually uses the geographic North Pole as a reference point.

[0072] Specifically, the operator collects data from each of the multiple floors using a collection terminal or other measurement tools to obtain data for each floor and number each floor according to the floor, thereby obtaining floor parameter data for each floor. After collection, the collection terminal transmits the floor parameter data of all floors to the cloud-side platform.

[0073] After the cloud-side platform receives the floor parameter data of all floors, it uses the floor air pressure data to verify the floor label data and corrects the wrong label data so that the floor point cloud data of each floor corresponds to the correct floor label. At the same time, the floor model corresponding to the floor point cloud data is constructed based on the floor point cloud data, that is, multiple floor construction models and their corresponding sequence data are obtained.

[0074] Step 204, when the adjacent layers are modeled, coordinate point marking is performed on the floor parameter data of the adjacent layers to obtain upper layer coordinate point position data and lower layer coordinate point position data.

[0075] The adjacent layers include adjacent upper layers and adjacent lower layers.

[0076] Specifically, the floor point cloud data can be divided into data groups of adjacent layers by constructing models of multiple floors and their corresponding sequential data. Since the upper layer coordinate points and the lower layer coordinate points have been selected in the adjacent upper floor and the adjacent lower floor respectively, and the distance data have been collected when collecting data. By analyzing the distance data collected at the upper layer coordinate points and the distance data collected at the lower layer coordinate points, the upper layer coordinate point positions and the lower layer coordinate point positions are marked in the upper layer model and the lower layer model in equal proportion, and the upper layer coordinate point position data and the lower layer coordinate point position data are obtained at the same time.

[0077] Step 206, performing calculation processing based on the floor parameter data of the adjacent layers, the upper layer coordinate point position data and the lower layer coordinate point position data, to obtain the vertical corresponding data of the upper layer coordinate point position data corresponding to the lower layer.

[0078] Specifically, when modeling adjacent layers, the distance data collected at the upper coordinate points and the distance data collected at the lower coordinate points are determined from the floor data of the upper floors and the lower floors, respectively. The distance data includes lateral distance data, longitudinal distance data, and vertical distance data, and then the distance data and the model scaling ratio of the model are used to mark the coordinate points in the model, and the lateral spacing between the upper coordinate points and the lower coordinate points can be determined by the distance data collected at the upper coordinate points and the distance data collected at the lower coordinate points. For example, if a coordinate point is selected in the staircase area between floors, the total lateral distance of the staircase area is fixed, or the distance between the walls on both sides of the staircase area is fixed, so the lateral spacing between the upper coordinate points and the lower coordinate points is determined to facilitate the subsequent accurate calculation of the position data.

[0079] At the same time, in the process of collecting data in the lower layer, the straight-line distance between the lower layer coordinate point and the upper layer coordinate point and the floor height between the upper and lower layers are measured. The straight-line distance, floor height and horizontal spacing are input into the Pythagorean theorem formula to calculate the longitudinal spacing between the upper layer coordinate point and the lower layer coordinate point. Combined with the position data of the lower layer coordinate point, the position data corresponding to the upper layer coordinate point position data at the vertical position of the lower layer can be determined. Because the coordinate points are set in equal proportion, the vertical corresponding data of the upper layer coordinate point position data corresponding to the lower layer can be obtained.

[0080] Step 208, model splicing is performed according to the floor parameter data of adjacent layers, the upper layer marking data and the vertical corresponding data to obtain the target multi-floor model.

[0081] Specifically, when the adjacent layers are modeled, the vertical corresponding points of the upper layer coordinate points on the lower layer and their corresponding vertical corresponding data are used to construct the normal line, and the splicing axis system is constructed using the normal line and the direction data of the adjacent layers, and the floor construction model of the lower layer is placed under the floor construction model of the upper layer to obtain the splicing model. After processing all adjacent layers, the target multi-floor model is obtained.

[0082] In the above-mentioned multi-floor three-dimensional model construction method, data is collected for each of the multiple floors to obtain floor parameter data of each floor, including but not limited to point cloud, direction, distance, air pressure and other data and sequence numbers of multiple floors, and then the floor parameter data of each layer is used to construct a three-dimensional model of each layer, that is, a multiple-floor construction model, and the air pressure data of each layer is used to verify and adjust the sequence numbers of the multiple floors to obtain the correct sequence numbers, and the correct sequence numbers are matched one by one with the multiple floor construction models, and then the models of adjacent layers are spliced ​​based on the correct sequence numbers, by determining the upper layer coordinate point position data and the lower layer coordinate point position data in the model of the adjacent layer, and calculating the vertical corresponding point of the upper layer coordinate point in the lower layer, and then combining the direction data to adjust the position of the lower layer model so that the adjacent layers are spliced ​​at the correct position, by collecting point cloud, air pressure and distance data, the three-dimensional model of the multi-story house is automatically generated and spliced, which simplifies the modeling process, reduces manual intervention, improves modeling efficiency and accuracy, and provides convenient and reliable technical guarantee for the business.

[0083] In an exemplary embodiment, the floor construction is performed according to the floor parameter data of each floor to obtain a plurality of floor construction models and their corresponding sequence data, including:

[0084] When constructing each floor, the floor is constructed according to the floor point cloud data of the current floor to obtain the current floor construction model; when all the floor construction models are constructed, all the floor construction models are sorted by number according to the floor label data to obtain the floor label mapping relationship; the floor label mapping relationship is judged and adjusted based on the floor air pressure data to obtain the correct label mapping relationship; based on all the floor construction models and the correct label mapping relationship, multiple floor construction models and their corresponding sequence data are determined.

[0085] Among them, the floor parameter data includes floor point cloud data, floor number data and floor air pressure data.

[0086] Exemplarily, after receiving the floor parameter data of all floors, the cloud-side platform uses the floor parameter data of each floor to construct a floor construction model for each floor, and establishes a mapping relationship between the floor construction model of each floor and the floor label data, thereby determining the upper and lower floor position relationship of the floor construction model.

[0087] At the same time, a mapping relationship between the floor construction model and the floor air pressure data of each floor is established. Then, the floor number data corresponding to the floor construction model is adjusted and modified according to the size relationship of the floor air pressure data until the size sequence of the floor air pressure data is the same as or opposite to the size sequence of the floor number data.

[0088] In an exemplary embodiment, when adjacent layers are modeled, the floor parameter data of the adjacent layers are screened and marked, and before obtaining the upper layer marked data and the lower layer marked data, the method further includes:

[0089] The plurality of floor construction models and their corresponding sequence data are grouped to obtain a plurality of adjacent floor groups; and the adjacent floor parameter data are determined based on the plurality of adjacent floor groups.

[0090] Exemplarily, after the modification of the floor number data is completed, multiple groups of data of adjacent floor numbers are divided in order, for example, if the set of floor number data is {1,2,3...n}, then the groups {1,2}, {2,3},..., {n-1,n} are obtained. And the floor parameter data of multiple adjacent layers are obtained according to the grouping.

[0091] In an exemplary embodiment, when adjacent layers are modeled, coordinate point marking is performed on the floor parameter data of the adjacent layers to obtain upper layer coordinate point position data and lower layer coordinate point position data, including:

[0092] Obtain upper layer parameter data and model scale parameters of the adjacent upper layer; wherein the upper layer parameter data includes upper layer point cloud data; perform data screening according to the upper layer point cloud data to obtain upper layer lateral distance data, upper layer longitudinal distance data and upper layer vertical distance data; determine upper layer coordinate point position data based on the model scale parameters, upper layer lateral distance data, upper layer longitudinal distance data and upper layer vertical distance data.

[0093] The adjacent layers include adjacent upper layers and adjacent lower layers.

[0094] For example, after selecting the collection mark point, i.e., the coordinate point, on the upper floor of the adjacent layer, the operator uses the collection terminal or other measurement tools to collect data of the upper floor to obtain the upper floor parameter data and the distance data collected at the coordinate point, and the upper floor parameter data includes the upper floor point cloud data. And subsequently, when the model is constructed, the scaling ratio of the model is determined.

[0095] The distance data of the coordinate points in the model are determined by utilizing the scaling ratio of the model, and then the corresponding positions in the model are marked and the position data of the upper-layer coordinate points are determined.

[0096] In an exemplary embodiment, when adjacent layers are modeled, coordinate point marking is performed on the floor parameter data of the adjacent layers to obtain upper layer coordinate point position data and lower layer coordinate point position data, and further includes:

[0097] Acquire lower layer parameter data of an adjacent lower layer; wherein the lower layer parameter data includes lower layer point cloud data; perform data screening according to the lower layer point cloud data to obtain lower layer lateral distance data, lower layer longitudinal distance data and lower layer vertical distance data; determine lower layer coordinate point position data based on the model scale parameter, the lower layer lateral distance data, the lower layer longitudinal distance data and the lower layer vertical distance data.

[0098] For example, after selecting the collection mark point, i.e., the coordinate point, on the upper floor of the adjacent layer, the operator uses the collection terminal or other measurement tools to collect data of the upper floor to obtain the upper floor parameter data and the distance data collected at the coordinate point, and the upper floor parameter data includes the upper floor point cloud data. And subsequently, when the model is constructed, the scaling ratio of the model is determined.

[0099] The distance data of the coordinate points in the model are determined by utilizing the scaling ratio of the model, and then the corresponding positions in the model are marked and the position data of the upper-layer coordinate points are determined.

[0100] In an exemplary embodiment,

[0101] Calculating and processing the floor parameter data of the adjacent layer, the upper layer coordinate point position data and the lower layer coordinate point position data to obtain vertical corresponding data of the upper layer coordinate point position data corresponding to the lower layer, including:

[0102] The horizontal spacing is calculated based on the upper layer horizontal distance data and the lower layer horizontal distance data to obtain the coordinate horizontal spacing; the vertical spacing is calculated based on the coordinate horizontal spacing, the distance between adjacent coordinate points and the adjacent layer height to obtain the coordinate vertical spacing; the data is converted based on the adjacent layer height, the coordinate horizontal spacing, the coordinate vertical spacing and the model scale parameter to obtain the adjacent model layer height, the model coordinate horizontal spacing and the model coordinate vertical spacing; the upper layer coordinate point position data is vertically converted based on the model coordinate horizontal spacing, the model coordinate vertical spacing and the lower layer coordinate point position data to obtain the vertical corresponding data of the upper layer coordinate point position data corresponding to the lower layer.

[0103] The floor parameter data of adjacent layers include upper layer lateral distance data, lower layer lateral distance data, adjacent coordinate point distances and adjacent floor heights.

[0104] For example, by collecting the coordinate data of the upper and lower floors, the horizontal, longitudinal and vertical distance data of the two can be obtained respectively. According to these distance data and the known model scaling ratio, the position of each coordinate point can be accurately marked in the model. At the same time, by measuring the space between the floors, the horizontal spacing between the upper coordinate point and the lower coordinate point can be determined. For example, the total horizontal distance of the staircase area or the distance between the walls on both sides of the staircase is fixed. Through this information, the horizontal spacing between the upper and lower coordinate points can be determined, providing a basis for subsequent position calculations.

[0105] When collecting data on the lower layer, measure the straight-line distance and layer height between the upper and lower layer coordinate points. Use the Pythagorean theorem formula to bring the straight-line distance, layer height and horizontal spacing into the calculation to get the vertical spacing between the upper and lower layer coordinate points. Combined with the position data of the lower layer coordinate points, the corresponding position of the upper layer coordinate points in the vertical direction of the lower layer can be determined. Since the coordinate points are set in equal proportions, the position data of the upper layer coordinate points in the vertical direction of the lower layer can be accurately calculated. If the horizontal spacing between the upper and lower layer coordinate points is zero, such as Figure 3 The vertical corresponding points are calculated as shown.

[0106] In an exemplary embodiment, Figure 4 As shown, the model is spliced ​​according to the floor parameter data of the adjacent layers, the upper layer marking data and the vertical corresponding data to obtain the target multi-floor model, including:

[0107] Step 402, performing data adjustment according to the upper layer coordinate point position data and the vertical corresponding data to obtain adjusted vertical data.

[0108] Exemplarily, the coordinate system of the upper layer marking data is set as the adjacent layer coordinate system, and then the distance between the upper layer coordinate point and the vertical marking point in the three-axis direction is adjusted to obtain the adjusted vertical corresponding data, that is, the adjusted vertical data.

[0109] Step 404: determining adjustment data based on the adjusted vertical data and the vertical corresponding data.

[0110] Exemplarily, after the adjusted vertical data is obtained, the adjusted data in the three-axis directions are calculated using the adjusted vertical data and the vertical corresponding data.

[0111] Step 406: adjust the lower layer construction model based on the adjustment data and the lower layer point cloud data to obtain an adjusted lower layer model.

[0112] For example, the floor point cloud data in the floor parameter data of the lower building model is corrected using the adjustment data in the three-axis direction to obtain the lower point cloud data in the adjacent layer coordinate system, and then the lower building model is placed directly below the upper building model, that is, the vertical mark point is directly below the upper mark, that is, the two vertically corresponding points of the upper and lower layers form a normal line with the normal line as the axis. The splicing process is as follows Figure 5 shown.

[0113] Step 408: Adjust the orientation of the model based on the direction data in the floor parameter data.

[0114] Exemplarily, during data collection, upper layer directional data and lower layer directional data are collected, and the model is adjusted using the north-south data in the directional data.

[0115] Step 410: After all adjacent floors have been adjusted, a target multi-floor model is obtained.

[0116] Illustratively, by performing floor splicing from the top layer through the above steps, the lower layer construction models of all adjacent layers can be placed directly below the upper layer construction models, thereby obtaining a multi-floor three-dimensional model, namely, a target multi-floor model.

[0117] In an exemplary embodiment, a method for constructing a multi-story three-dimensional model is provided, comprising the following steps:

[0118] When constructing each floor, the floor is constructed according to the floor point cloud data of the current floor to obtain the current floor construction model.

[0119] When constructing each floor, the floor is constructed according to the floor point cloud data of the current floor to obtain the current floor construction model.

[0120] When all the floor construction models are constructed, all the floor construction models are sorted by number according to the floor number data to obtain the floor number mapping relationship.

[0121] The floor number mapping relationship is judged and adjusted based on the floor air pressure data to obtain the correct number mapping relationship.

[0122] Based on all the floor construction models and the correct label mapping relationship, multiple floor construction models and their corresponding sequence data are determined.

[0123] The multiple floor construction models and their corresponding sequence data are grouped to obtain multiple adjacent layer groups.

[0124] Adjacent layer floor parameter data is determined based on a plurality of adjacent layer groups.

[0125] The upper layer parameter data and model scale parameters of the adjacent upper layer are obtained, wherein the upper layer parameter data includes the upper layer point cloud data.

[0126] Data screening is performed based on the upper point cloud data to obtain upper horizontal distance data, upper longitudinal distance data and upper vertical distance data.

[0127] The upper layer coordinate point position data is determined based on the model scale parameter, the upper layer transverse distance data, the upper layer longitudinal distance data and the upper layer vertical distance data.

[0128] Obtain lower layer parameter data of an adjacent lower layer, wherein the lower layer parameter data includes lower layer point cloud data.

[0129] Data is screened according to the lower layer point cloud data to obtain the lower layer horizontal distance data, the lower layer longitudinal distance data and the lower layer vertical distance data.

[0130] The lower layer coordinate point position data is determined based on the model scale parameter, the lower layer lateral distance data, the lower layer longitudinal distance data and the lower layer vertical distance data.

[0131] The lateral spacing is calculated according to the upper lateral distance data and the lower lateral distance data to obtain the coordinate lateral spacing.

[0132] The vertical spacing is calculated according to the horizontal spacing of coordinates, the distance between adjacent coordinate points and the adjacent layer height to obtain the vertical spacing of coordinates.

[0133] Data conversion is performed according to adjacent layer heights, coordinate transverse spacing, coordinate longitudinal spacing and model scale parameters to obtain adjacent model layer heights, model coordinate transverse spacing and model coordinate longitudinal spacing.

[0134] The upper layer coordinate point position data is vertically transformed according to the model coordinate horizontal spacing, the model coordinate vertical spacing and the lower layer coordinate point position data to obtain the vertical corresponding data of the upper layer coordinate point position data corresponding to the lower layer.

[0135] Data adjustment is performed according to the upper layer coordinate point position data and the vertical corresponding data to obtain adjusted vertical data.

[0136] Adjustment data is determined based on the adjusted vertical data and the vertical correspondence data.

[0137] The lower layer construction model is adjusted based on the adjustment data and the lower layer point cloud data to obtain an adjusted lower layer model.

[0138] The orientation of the model is adjusted based on the direction data in the floor parameter data.

[0139] After all adjacent layers are adjusted, a target multi-floor model is obtained.

[0140] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0141] Based on the same inventive concept, the embodiment of the present application also provides a multi-floor 3D model construction device for implementing the multi-floor 3D model construction method involved above. The implementation solution provided by the device to solve the problem is similar to the implementation solution recorded in the above method, so the specific limitations in one or more multi-floor 3D model construction device embodiments provided below can refer to the limitations of the multi-floor 3D model construction method above, and will not be repeated here.

[0142] In one embodiment, Figure 6 As shown, a multi-floor three-dimensional model construction device is provided, including: a model construction module 602, a data screening module 604, a data processing module 606 and a model splicing module 608, wherein:

[0143] The model construction module 602 is used to construct floors according to the floor parameter data of each floor, and obtain multiple floor construction models and their corresponding sequence data.

[0144] The data screening module 604 is used to mark the coordinate points of the floor parameter data of the adjacent layers when the adjacent layers are modeled together, and obtain the coordinate point position data and the lower layer coordinate point position data.

[0145] The data processing module 606 is used to perform calculations based on the floor parameter data of the adjacent layers, the upper layer coordinate point position data and the lower layer coordinate point position data to obtain the vertical corresponding data of the upper layer coordinate point position data on the lower layer.

[0146] The model splicing module 608 is used to perform model splicing according to the floor parameter data of adjacent layers, the upper layer marking data and the vertical corresponding data to obtain the target multi-floor model.

[0147] In an exemplary embodiment, the model construction module 602 is also used to, when constructing each floor, construct the floor according to the floor point cloud data of the current floor to obtain the current floor construction model; when all the floor construction models are constructed, all the floor construction models are sorted according to the floor label data to obtain the floor label mapping relationship; the floor label mapping relationship is adjusted based on the floor air pressure data to obtain the correct label mapping relationship; and multiple floor construction models and their corresponding sequence data are determined based on all the floor construction models and the correct label mapping relationship.

[0148] In an exemplary embodiment, the multi-floor three-dimensional model construction device is used to group multiple floor construction models and their corresponding sequence data to obtain multiple adjacent layer groups; and determine adjacent layer floor parameter data based on the multiple adjacent layer groups.

[0149] In an exemplary embodiment, the data screening module 604 is also used to obtain upper layer parameter data and model scale parameters of an adjacent upper layer; wherein the upper layer parameter data includes upper layer point cloud data; data screening is performed based on the upper layer point cloud data to obtain upper layer lateral distance data, upper layer longitudinal distance data and upper layer vertical distance data; and upper layer coordinate point position data is determined based on the model scale parameters, upper layer lateral distance data, upper layer longitudinal distance data and upper layer vertical distance data.

[0150] In an exemplary embodiment, the data screening module 604 is also used to obtain lower layer parameter data of an adjacent lower layer; wherein the lower layer parameter data includes lower layer point cloud data; data screening is performed based on the lower layer point cloud data to obtain lower layer lateral distance data, lower layer longitudinal distance data and lower layer vertical distance data; and lower layer coordinate point position data is determined based on the model scale parameters, lower layer lateral distance data, lower layer longitudinal distance data and lower layer vertical distance data.

[0151] In an exemplary embodiment, the data processing module 606 is also used to calculate the horizontal spacing based on the upper layer horizontal distance data and the lower layer horizontal distance data to obtain the coordinate horizontal spacing; calculate the vertical spacing based on the coordinate horizontal spacing, the distance between adjacent coordinate points and the adjacent layer height to obtain the coordinate vertical spacing; perform data conversion based on the adjacent layer height, the coordinate horizontal spacing, the coordinate vertical spacing and the model scale parameter to obtain the adjacent model layer height, the model coordinate horizontal spacing and the model coordinate vertical spacing; perform vertical conversion on the upper layer coordinate point position data based on the model coordinate horizontal spacing, the model coordinate vertical spacing and the lower layer coordinate point position data to obtain the vertical corresponding data of the upper layer coordinate point position data corresponding to the lower layer.

[0152] In an exemplary embodiment, the model splicing module 608 is also used to adjust the data according to the upper-layer coordinate point position data and the vertical corresponding data to obtain the adjusted vertical data; determine the adjustment data based on the adjusted vertical data and the vertical corresponding data; adjust the lower-layer construction model based on the adjustment data and the lower-layer point cloud data to obtain the adjusted lower-layer model; adjust the orientation of the model based on the direction data in the floor parameter data; and obtain the target multi-floor model after completing the adjustment of all adjacent layers.

[0153] Each module in the above-mentioned multi-floor three-dimensional model construction device can be implemented in whole or in part by software, hardware, or a combination thereof. Each of the above-mentioned modules can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute the operations corresponding to each of the above modules.

[0154] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 7 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store floor acquisition data. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for constructing a multi-floor three-dimensional model is implemented.

[0155] Those skilled in the art will understand that Figure 7 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0156] In one embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0157] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0158] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0159] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant laws, regulations and standards of relevant countries and regions.

[0160] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0161] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0162] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A method for constructing a multi-story three-dimensional model, characterized in that: The method comprises: Perform floor construction according to the floor parameter data of each floor, and obtain multiple floor construction models and their corresponding sequence data; In the case of model splicing of adjacent layers, coordinate point marking is performed on the floor parameter data of the adjacent layers to obtain upper layer coordinate point position data and lower layer coordinate point position data; Calculate and process the floor parameter data of the adjacent layer, the upper layer coordinate point position data and the lower layer coordinate point position data to obtain vertical corresponding data of the upper layer coordinate point position data on the lower layer; Model splicing is performed according to the floor parameter data of adjacent layers, the upper layer coordinate point position data and the vertical corresponding data to obtain a target multi-floor model.

2. The method for constructing a multi-story three-dimensional model according to claim 1, characterized in that: The floor parameter data includes floor point cloud data, floor number data and floor air pressure data; the floor construction is performed according to the floor parameter data of each floor to obtain multiple floor construction models and their corresponding sequence data, including: When constructing each floor, construct the floor according to the floor point cloud data of the current floor to obtain the current floor construction model; When all the floor construction models are constructed, all the floor construction models are sorted by number according to the floor number data to obtain a floor number mapping relationship; The floor number mapping relationship is judged and adjusted based on the floor air pressure data to obtain a correct number mapping relationship; A plurality of floor construction models and their corresponding sequence data are determined based on all the floor construction models and the correct label mapping relationship.

3. The method for constructing a multi-story three-dimensional model according to claim 1, characterized in that: In the case of model splicing of adjacent layers, before screening and marking the floor parameter data of adjacent layers and obtaining the upper layer marking data and the lower layer marking data, the method further includes: Grouping the plurality of floor construction models and the corresponding sequence data to obtain a plurality of adjacent floor groups; The floor parameter data of adjacent layers are determined based on a plurality of adjacent layer groups.

4. The method for constructing a multi-story three-dimensional model according to claim 1, characterized in that: The adjacent layers include an adjacent upper layer and an adjacent lower layer; when the adjacent layers are modeled, coordinate point marking is performed on the floor parameter data of the adjacent layers to obtain upper layer coordinate point position data and lower layer coordinate point position data, including: Acquire upper layer parameter data and model scale parameters of an adjacent upper layer; wherein the upper layer parameter data includes upper layer point cloud data; Perform data screening according to the upper point cloud data to obtain the upper horizontal distance data, the upper longitudinal distance data and the upper vertical distance data; The upper layer coordinate point position data is determined based on the model scale parameter, the upper layer transverse distance data, the upper layer longitudinal distance data and the upper layer vertical distance data.

5. The method for constructing a multi-story three-dimensional model according to claim 4, characterized in that: In the case of model splicing of adjacent layers, coordinate point marking is performed on the floor parameter data of the adjacent layers to obtain upper layer coordinate point position data and lower layer coordinate point position data, and further includes: Acquire lower layer parameter data of an adjacent lower layer; wherein the lower layer parameter data includes lower layer point cloud data; Perform data screening according to the lower layer point cloud data to obtain the lower layer horizontal distance data, the lower layer longitudinal distance data and the lower layer vertical distance data; The lower layer coordinate point position data is determined based on the model scale parameter, the lower layer lateral distance data, the lower layer longitudinal distance data and the lower layer vertical distance data.

6. The method for constructing a multi-story three-dimensional model according to claim 1, characterized in that: The floor parameter data of the adjacent layers include upper layer lateral distance data, lower layer lateral distance data, adjacent coordinate point distances and adjacent layer heights; the calculation and processing based on the floor parameter data of the adjacent layers, the upper layer coordinate point position data and the lower layer coordinate point position data to obtain the vertical corresponding data of the upper layer coordinate point position data corresponding to the lower layer includes: Calculate the horizontal spacing according to the upper horizontal distance data and the lower horizontal distance data to obtain the coordinate horizontal spacing; Calculate the longitudinal spacing according to the coordinate transverse spacing, the distance between adjacent coordinate points and the adjacent layer heights to obtain the coordinate longitudinal spacing; Perform data conversion according to the adjacent layer heights, the coordinate transverse spacing, the coordinate longitudinal spacing and the model scale parameter to obtain adjacent model layer heights, model coordinate transverse spacing and model coordinate longitudinal spacing; The upper layer coordinate point position data is vertically transformed according to the model coordinate transverse spacing, the model coordinate longitudinal spacing and the lower layer coordinate point position data to obtain vertical corresponding data of the upper layer coordinate point position data corresponding to the lower layer.

7. The method for constructing a multi-story three-dimensional model according to claim 1, characterized in that: The step of performing model splicing according to the floor parameter data of adjacent layers, the upper layer coordinate point position data and the vertical corresponding data to obtain a target multi-floor model includes: Performing data adjustment according to the upper layer coordinate point position data and the vertical corresponding data to obtain adjusted vertical data; determining adjustment data based on the adjusted vertical data and the vertical corresponding data; Adjusting the lower layer construction model based on the adjustment data and the lower layer point cloud data to obtain an adjusted lower layer model; Adjust the orientation of the model based on the direction data in the floor parameter data; After all adjacent layers are adjusted, a target multi-floor model is obtained.

8. A multi-floor three-dimensional model construction device, characterized in that: The device comprises: A model construction module is used to construct floors according to the floor parameter data of each floor, and obtain multiple floor construction models and their corresponding sequence data; A data screening module, used for marking the coordinate points of the floor parameter data of the adjacent layers when the adjacent layers are modeled, to obtain the upper layer coordinate point position data and the lower layer coordinate point position data; A data processing module, used for performing calculation and processing according to the floor parameter data of the adjacent layers, the upper layer coordinate point position data and the lower layer coordinate point position data, to obtain vertical corresponding data of the upper layer coordinate point position data corresponding to the lower layer; The model splicing module is used to perform model splicing according to the floor parameter data of adjacent layers, the upper layer marking data and the vertical corresponding data to obtain a target multi-floor model.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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