A three-dimensional modeling method and system for urban water pipe valve management and control

Abstract

The application relates to a three-dimensional modeling method and system for urban water pipe valve control, and belongs to the technical field of water pipe valve control, wherein the method comprises the following steps: acquiring modeling data of a preset modeling object, wherein the preset modeling object at least comprises a pipeline and a valve; the modeling data at least comprises structure data and a connection relationship corresponding to the modeling object; the modeling data is filtered and layered to generate multiple layers of data sets, and a three-dimensional model is constructed for each layer of data sets, wherein the data amount contained in different layers of data sets is different; real-time acquisition and analysis of display requirements are performed, and a layer of data set meeting the display requirements is loaded in real time to display a corresponding three-dimensional model; wherein the display requirements at least comprise resolution requirements. The application has the effect of effectively reducing the data amount and memory occupation under the premise of ensuring the three-dimensional modeling accuracy.

Description

Technical Field

[0001] This application relates to the field of water pipe valve control technology, and in particular to a three-dimensional modeling method and system for urban water pipe valve control. Background Technology

[0002] In the continuous development of urban construction and management, the refined management and control of urban infrastructure has become increasingly crucial. Water pipes and valves, as important components of the urban water supply system, play a vital role in ensuring the normal lives of urban residents and the stable operation of the city through effective monitoring and management of their operational status. Currently, to achieve precise management and control of these facilities, 3D modeling is often used. By constructing 3D models, the location, layout, and operational status of water pipes and valves can be displayed intuitively and comprehensively, providing strong decision-making support for urban managers.

[0003] Existing 3D modeling technologies primarily rely on 2D data. In practice, this requires collecting vast amounts of 2D data to construct 3D models, resulting in extremely large datasets. When these 3D models, built from massive amounts of 2D data, are displayed on a system, they often consume significant memory. This not only places extremely high demands on the system's hardware configuration, increasing hardware costs, but can also slow down system operation, impacting real-time data display and analysis efficiency. Consequently, it struggles to meet the demands of rapid urban development and real-time management, and therefore requires improvement. Summary of the Invention

[0004] In order to effectively reduce data volume and memory usage while ensuring the accuracy of 3D modeling, this application provides a 3D modeling method and system for urban water pipe valve management.

[0005] Firstly, this application provides a three-dimensional modeling method for the control of urban water pipe valves, including: Obtain modeling data for a preset modeling object, wherein the preset modeling object includes at least pipelines and valves; and the modeling data includes at least the structural data and connection relationships corresponding to the modeling object. The modeling data is filtered and layered to generate multi-layer datasets, and a three-dimensional model is constructed for each layer dataset. The amount of data contained in different layers datasets is different. The display requirements are acquired and analyzed in real time, and the layer dataset that meets the display requirements is loaded in real time to display the corresponding 3D model; wherein, the display requirements include at least resolution requirements.

[0006] By adopting the above technical solution, this application proposes to process the 3D model in layers and display the 3D model at a corresponding level according to actual needs. The data volume of the 3D model at different levels is different. As can be seen from the resolution requirements disclosed above, the level of detail of the modeling data displayed by the 3D model at different levels is different. Therefore, by combining the real-time display requirements, the dynamic loading and switching of 3D models with different data volumes can be realized. That is, when the resolution requirement is not high, the 3D model with less data is displayed, thereby reducing the memory pressure of the 3D model on the system. This effect is particularly significant when the data volume of the 3D model to be constructed is large.

[0007] Optionally, the method further includes: After generating multi-layer datasets, establish mapping relationships between the multi-layer datasets; and satisfy the following condition: the datasets corresponding to the same viewpoint position of different 3D models do not overlap; The real-time loading of the layer dataset that meets the display requirements and the display of the corresponding 3D model includes: Based on the resolution and corresponding layer dataset of the current 3D model, and the resolution included in the display requirements, determine the target layer dataset and corresponding target 3D model that meet the display requirements; If the resolution corresponding to the current 3D model is lower than the resolution included in the display requirements, then additional data is added to the current 3D model. The additional data is data that is included in the target layer dataset but not in the current 3D model layer dataset. If the resolution corresponding to the current 3D model is higher than the resolution included in the actual requirement, then redundant data is removed based on the current 3D model. The redundant data is data that is included in the current 3D model dataset but not included in the target dataset.

[0008] By adopting the above technical solution, when updating the displayed 3D model according to actual needs, the update method used in this application is not to load and switch the 3D model itself, but to find the data to be supplemented or deleted (hereinafter referred to as the difference data) based on the resolution difference, and only adjust some data. Compared with the switching of the 3D model itself, this operation can improve the system's response speed to display needs and improve the update efficiency of the 3D model. As the resolution requirement increases, the amount of data of the 3D model to be switched will increase. If the 3D model itself that meets the corresponding resolution requirement is loaded directly, all the data contained in the 3D model (i.e., the layer dataset) needs to be loaded. The difference data mentioned above is contained in the layer dataset. In other words, the system response time required to load all the data of the entire layer dataset is bound to be greater than the system response time required to load the difference data alone. Therefore, the loading and switching scheme proposed in this application is more efficient.

[0009] Optionally, the method further includes: Acquire multi-source data and extract data features; wherein the multi-source data includes at least geological data and environmental monitoring data; The step of filtering and stratifying the modeling data to generate multi-layer datasets includes: The modeling data is filtered, and initial layers are obtained after preliminary layering based on a preset layering strategy. The slice thickness and fineness of each initial layer are then determined. The preset layering strategy includes at least resolution. Based on the extracted data features and the degree of difference of the data features in different regions of each initial level, several local regions are divided for each initial level. Based on the data features of the local regions, the slice thickness and fineness of each local region are determined. Based on slice thickness and data granularity, layer datasets for each initial level and region datasets for each local region are generated.

[0010] By adopting the above technical solution, in the process of hierarchical modeling, multi-source data is further integrated on the basis of the preset hierarchical strategy. For example, geological data, environmental monitoring data and other data that may affect the distribution or changes of water pipes and valves are considered. Based on such data, adaptive hierarchical modeling is realized, that is, the slice thickness and fineness of different areas of each level are adaptively adjusted to achieve fine multi-level hierarchical modeling.

[0011] Optionally, the step of dividing each initial level into several local regions based on the extracted data features and the degree of difference of the data features in different regions of each initial level, and determining the slice thickness and fineness of each local region based on the data features of the local regions, includes: For the extracted data features, predict the changing trend of the data features within a specified time period. Based on the degree of difference in the changing trend of the data features in different regions of each initial level, divide each initial level into several local regions. Based on the degree of difference in the changing trend of each data feature within different time periods of the specified time period, divide the specified time period into several sub-time periods. Based on the changing trend of each data feature in each sub-period and in each local region, determine the slice thickness and fineness of each local region in each sub-period. Based on the chronological order of all the sub-time periods, and in real time according to the progress of the current moment, the slice thickness and data fineness of each local region at the current moment are updated in real time.

[0012] By adopting the above technical solution, multi-source data contains data characteristics that change over time (such as low water level, pollutant concentration, temperature data, etc. covered by environmental monitoring data). This solution further proposes to predict the changing trend of the aforementioned data characteristics in the future (such as within a specified time period) through time series prediction methods, and integrate this changing trend into the adaptive stratification scheme of the volume mentioned above, thereby optimizing the dynamic adjustment of slice thickness and fineness, and further realizing refined multi-level stratified modeling.

[0013] Optionally, the method further includes: The correlation between slice thickness and fineness of each local region at different initial levels was analyzed and determined, and a mapping model was established. The step of determining the slice thickness and fineness of each local region in each sub-time period based on the changing trend of each data feature in each sub-time period and in each local region includes: Based on the changing trend of each data feature in each sub-time period and in each local region, whenever the current time falls into the target sub-time period, the slice thickness and fineness of all target local regions contained in the target initial level are determined in the target sub-time period. Then, the slice thickness and fineness of each target local region in the target sub-time period, excluding the target initial level, are inferred and output through a mapping model. Based on the inference results of the mapping model, the slice thickness and fineness of all target local regions in other initial levels besides the target initial level are adjusted. Here, the target sub-time period refers to any sub-time period, the target initial level refers to any initial level, and the target local region refers to any local region contained in the target initial level.

[0014] By adopting the above technical solution, since the initial levels are spatially continuous, adjusting the slice thickness and fineness of a certain initial level may affect the data distribution of adjacent levels. For example, if the slice thickness of a certain initial level is reduced (to capture more details), it may be necessary to adjust the adjacent initial levels accordingly to avoid discontinuities or data discontinuities. In addition, prediction results (such as environmental change rate) may propagate spatially, thus affecting multiple initial levels. For example, if the predicted environmental change rate of a certain local area is high, it may be necessary to adjust the slicing strategy of that area in multiple initial levels simultaneously. Therefore, this application proposes that when the slice thickness and fineness of a local area in any initial level are adjusted, the slice thickness and fineness of that local area in other initial levels will be adjusted in a coordinated manner. The adjustment rule is based on a pre-built mapping model. By learning and summarizing the correlation between the slice thickness and fineness of the local area in different initial levels through the mapping model, the slice thickness and fineness of the local area in each level can be inferred, thereby achieving efficient dynamic adjustment of the slice thickness and fineness of all initial levels.

[0015] Optionally, the method further includes: Based on a preset partitioning strategy, reusable modules are generated, and each reusable module corresponds to a module dataset composed of corresponding data selected from the modeling data. A 3D model is constructed for each reuse module based on the module dataset, the module dataset and 3D model of the reuse module are stored, and an integration interface is defined for each reuse module. The construction of a 3D model for each layer of the dataset includes: A 3D model is constructed based on each layer of the dataset, and the 3D model of the integrable reusable module is called first. The corresponding 3D model is integrated into the 3D model corresponding to the layer dataset through the corresponding integration interface to assist in the construction of the 3D model corresponding to each layer of the dataset; wherein, the integrable reusable module satisfies the following condition: the corresponding module dataset is included in the layer dataset.

[0016] By adopting the above technical solution, the modeling data is classified and summarized to generate module datasets and corresponding reusable modules. A three-dimensional model is built separately for each reusable module. When three-dimensional modeling is required for each level, it can be determined first whether there is an integrable reusable module. If so, the three-dimensional model corresponding to the reusable module is called to improve modeling efficiency and reduce the workload of repetitive modeling.

[0017] Optionally, the module dataset storing the multiplexing module includes: The module dataset of the multiplexing module is compressed and stored based on a preset data compression algorithm.

[0018] By adopting the above technical solution and using data compression technology to compress the data corresponding to the multiplexing module, the data space occupied can be significantly reduced, while improving storage and transmission efficiency.

[0019] Secondly, this application provides a 3D modeling system for urban water pipe valve control, including, The data acquisition module is used to acquire modeling data of a preset modeling object, wherein the preset modeling object includes at least pipelines and valves; and the modeling data includes at least the structural data and connection relationships corresponding to the modeling object. The hierarchical modeling module is used to filter and stratify the modeling data to generate multi-layer datasets, and to build a three-dimensional model for each layer dataset, wherein the amount of data contained in different layers datasets is different. A switching display module is used to acquire and analyze display requirements in real time, and load layer datasets that meet the display requirements in real time to display the corresponding 3D model; wherein, the display requirements include at least resolution requirements.

[0020] Thirdly, this application provides a three-dimensional modeling device for urban water pipe valve control, including a memory and a processor, wherein the memory stores a computer program that can be loaded by the processor and executed as described in any of the first aspects.

[0021] Fourthly, this application provides a computer-readable storage medium storing a computer program that can be loaded by a processor and executed as described in any of the first aspects.

[0022] In summary, this application includes the following beneficial technical effects: This application proposes to process 3D models in layers and display individual 3D models according to actual needs. The data volume of 3D models at different layers is different. As can be seen from the resolution requirements disclosed above, the level of detail of the modeling data displayed by 3D models at different layers is different. Therefore, by combining the real-time display requirements, the dynamic loading and switching of 3D models with different data volumes can be realized. That is, when the resolution requirement is not high, the 3D model with less data is displayed, thereby reducing the memory pressure of 3D models on the system. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1This is a flowchart illustrating a three-dimensional modeling method for urban water pipe valve control disclosed in an embodiment of this application.

[0025] Figure 2 This is a structural block diagram of a three-dimensional modeling system for urban water pipe valve control disclosed in an embodiment of this application.

[0026] Explanation of reference numerals in the attached diagram: 201, Data acquisition module; 202, Layered modeling module; 203, Display switching module. Detailed Implementation

[0027] The following is in conjunction with the appendix Figure 1-2 This application will be described in further detail.

[0028] This application discloses a three-dimensional modeling method for urban water pipe valve control (hereinafter referred to as the three-dimensional modeling method), the execution subject of which is a three-dimensional modeling system for urban water pipe valve control (hereinafter referred to as the three-dimensional modeling system). The following will be discussed in conjunction with the attached... Figure 1 This section details the specific steps of the 3D modeling system in implementing 3D modeling methods.

[0029] S101, Obtain modeling data of preset modeling objects, wherein the preset modeling objects include at least pipelines and valves; the modeling data includes at least the structural data and connection relationships corresponding to the modeling objects.

[0030] In implementation, the modeling objects specifically include pipelines, valves, and the installation environment in which they are located. The modeling data specifically includes the structural data and connection relationships of the modeling objects. The structural data specifically includes the shape, material, type, and size of the pipelines and valves. The connection relationships specifically include the connection methods between valves and pipelines, and the corresponding relationships between them. Modeling data can be obtained by using UAV oblique photography and ground close-range photography techniques to acquire multi-view image data of the installation environment of pipelines and valves, so as to subsequently construct a structural model of the installation environment. Alternatively, design drawings of pipeline and valve connection methods uploaded by staff can also be obtained. Furthermore, the aforementioned multi-view image data can be multi-view image data recorded during the installation of pipelines and valves, so as to integrate the installation environment with the pipelines and valves in the design drawings, ultimately obtaining an installation environment with pipeline and valve distribution relationships and structure.

[0031] S102, the modeling data is filtered and layered to generate multi-layer datasets, and a 3D model is built for each layer dataset. The amount of data contained in different layers datasets is different. After generating multi-layer datasets, establish mapping relationships between the multi-layer datasets; and satisfy the following condition: the datasets corresponding to the same viewpoint position of different 3D models do not overlap; S102, "filtering and stratifying the modeling data to generate a multi-layered dataset," includes: S1021, Filter the modeling data, perform preliminary stratification based on the preset stratification strategy to obtain the initial level, and determine the slice thickness and fineness of each initial level; wherein, the preset stratification strategy includes at least resolution; S1022, Based on the extracted data features and the degree of difference of the data features in different regions of each initial level, divide each initial level into several local regions, and determine the slice thickness and fineness of each local region based on the data features of the local regions. S1023, based on slice thickness and data fineness, generates layer datasets for each initial level and region datasets for each local region.

[0032] Correspondingly, the 3D modeling method also includes the following steps: The correlation between slice thickness and fineness of each local region at different initial levels was analyzed and determined, and a mapping model was established.

[0033] In implementation, a complete and detailed initial 3D model is first constructed based on the modeling data. Then, a preset layering strategy is used to layer the initial 3D model. Specifically, the modeling data is filtered by dividing the resolution range, thereby selecting the data required for each layer to form a layer dataset. For example, the filtering method can be: reducing the number of vertices and faces through mesh simplification algorithms; or reducing data density using point cloud sampling methods (such as voxelization or random sampling). Next, the 3D modeling system constructs 3D models based on each layer dataset to form different levels of 3D models. It should be noted that the 3D models corresponding to all the aforementioned initial layers are 3D models corresponding to the same installation environment with pipelines and valves. The only difference between these 3D models is the amount of data, i.e., the model's level of detail. The bottom layer of the 3D model is the simplest (low-poly model), and the top layer of the 3D model has the highest resolution (such as a high-poly model or point cloud). The level of detail of the intermediate layers increases sequentially from bottom to top.

[0034] The above describes how the initial 3D model is sliced ​​and layered using a 3D modeling system to obtain several initial levels and the corresponding 3D model for each initial level. At this point, the slice thickness and model detail for each initial level are preset fixed values. Based on this, the 3D modeling system is used to acquire multi-source data and extract data features from it. Specifically, the multi-source data can be geological data and environmental monitoring data. The data features specifically include: 1. Geological data characteristics: such as stratigraphic complexity (calculating the degree of undulation of strata through gradient or curvature), rock type (classifying rock types through cluster analysis (such as K-Means), and fault distribution (extracting fault locations through edge detection or fault identification algorithms). 2. Environmental monitoring data characteristics: groundwater level changes (calculated by time series analysis), pollutant concentration distribution (pollutant concentration distribution map generated by spatial interpolation methods (such as Kriging interpolation)); The 3D modeling system further divides the initial layer into multiple local regions based on the specific values ​​of the aforementioned data features at different locations within the initial layer. The division of local regions can be based on the following criteria: the difference in the values ​​of more than one data feature across different local regions must exceed a preset difference threshold. Then, the system calculates the data features of each local region (such as stratigraphic complexity, time-lapse diversity, and environmental change rate). Finally, based on a pre-stored correspondence table, it readjusts the slice thickness and refinement of the initial layer at each local region. This correspondence table contains multiple data feature ranges, along with the corresponding slice thickness and refinement for each range. These data feature ranges include stratigraphic complexity ranges, environmental change rate ranges, and rock type diversity ranges. Specifically, slice thickness can be determined using stratigraphic complexity ranges and environmental change rates, while refinement can be determined using rock type diversity. For example, the undulation of the lower layers in a local region can be calculated using gradient or curvature, and the slice thickness can be determined using the following formula: Slice thickness = 1 / (1 + gradient), meaning that a larger gradient results in a smaller slice thickness, and a smaller slice thickness indicates more data details need to be captured. For example, the level of detail can be determined by the following formula: detail = 100 * (1 + rock type diversity). This formula means that the more rock types there are, the higher the level of detail, that is, the higher the level of detail and the larger the amount of data in the model at the corresponding initial level.

[0035] It should be emphasized that the size, number, and position of the local regions divided by each initial level are the same and correspond one-to-one. However, due to the resolution difference of the initial levels themselves, the adjusted slice thickness and fineness of the same local region are different at different initial levels. Accordingly, the 3D modeling system of this application pre-fits the linear relationship between the slice thickness and the fineness of the local region at different initial levels and constructs a mapping model. When the slice thickness (or fineness) of the local region at one of the initial levels and the resolution range of that initial level are input into the mapping model, the mapping model will output the slice thickness (or fineness) of the local region at all other initial levels based on the aforementioned linear relationship.

[0036] Therefore, the 3D model can select one of the initial levels as the target initial level, and then readjust the slice thickness and fineness of all local regions in the target initial level. Then, the mapping model is used to obtain the slice thickness and fineness of each local region in other levels besides the target initial level, so as to achieve efficient adjustment of the slice thickness and fineness of all local regions in all initial levels.

[0037] Optionally, S1022 further includes the following sub-steps: Based on the extracted data features, predict the changing trend of the data features within a specified time period. Based on the degree of difference in the changing trend of data features in different regions of each initial level, divide each initial level into several local regions. Based on the degree of difference in the changing trend of each data feature in different time periods within the specified time period, divide the specified time period into several sub-time periods. Based on the changing trend of each data feature in each sub-time period and in each local region, whenever the current time falls into the target sub-time period, the slice thickness and fineness of all target local regions contained in the target initial level are determined in the target sub-time period. Then, the slice thickness and fineness of each target local region in the target sub-time period, excluding the target initial level, are inferred and output through the mapping model. Based on the inference results of the mapping model, the slice thickness and fineness of all target local regions in other initial levels besides the target initial level are adjusted. Here, the target sub-time period refers to any sub-time period, the target initial level refers to any initial level, and the target local region refers to any local region contained in the target initial level. Based on the chronological order of all sub-time periods, and in real time according to the progress of the current moment, the slice thickness and data refinement of each local region at the current moment are updated.

[0038] In implementation, some data in multi-source data (hereinafter referred to as time-transition data) will change over time, such as environmental monitoring data. Therefore, the above further proposes to predict the change trend of such time-transition data within a specified future time period, and to adaptively adjust the slice thickness and fineness of the local area of ​​the change in the time-transition data within the specified future time period based on the prediction results. Specifically, historical time-transition data is collected in advance and organized into a time series format (timestamp + value). Then, prediction algorithms (such as ARIMA, LSTM, Prophet, etc.) are used to predict the change trend of the time-transition data within the specified future time period to obtain the prediction results. The prediction results are specifically time-transition data in time series format. The 3D modeling system will further divide the specified time period into several sub-time periods based on the change trend, and satisfy the following: at the transition time between adjacent sub-time periods, the time-transition data undergoes a jump, that is, the difference between the time-transition data corresponding to the transition time and the time before the transition time is higher than the preset difference.

[0039] As the current time point progresses, whenever the current time point falls within any sub-time period, or when the sub-time period in which the current time point is located changes, the 3D modeling system will readjust the slice thickness and refinement of each local region based on the predicted time-transition data corresponding to that sub-time period and other non-time-transition data, in conjunction with the above scheme, to further achieve refined layering of the multi-level 3D model.

[0040] S103 acquires and analyzes display requirements in real time, loads layer datasets that meet display requirements in real time, and displays the corresponding 3D model; among which, display requirements include at least resolution requirements.

[0041] S103, "Real-time loading of layer datasets that meet display requirements and display of corresponding 3D models," includes: Based on the resolution and corresponding layer dataset of the current 3D model, determine the resolution included in the display requirements, and identify the target layer dataset and corresponding target 3D model that meet the display requirements. If the resolution of the current 3D model is lower than the resolution required for display, additional data is added to the current 3D model. The additional data is data that is included in the target layer dataset but not in the current 3D model layer dataset. If the resolution of the current 3D model is higher than the resolution required in reality, then redundant data will be removed from the current 3D model. Redundant data refers to data that is included in the current 3D model dataset but not in the target dataset.

[0042] In implementation, for segmented multi-layered 3D models, the 3D modeling system also sets a loading distance threshold for each initial layer of the 3D model. This aims to dynamically load different layers of 3D models based on changes in viewpoint or distance. Based on the current viewpoint, the distance between each position and the current viewpoint's position is determined. The resolution of the corresponding position is determined based on the distance. Then, based on the resolution range, the initial layer of the 3D model corresponding to that resolution range is selected as the display model for that position. In other words, for positions that are far from the current viewpoint's position, a low-resolution 3D model is loaded to display the structure at that position, while for positions that are close to the current viewpoint's position, a high-resolution 3D model is loaded to display the structure at that position. Users can manually define distances by zooming in and out, and switch perspectives using preset adjustment buttons to trigger display requests. The corresponding 3D modeling system will receive these manually triggered display requests and, in conjunction with the above strategy, determine the 3D model matched for each location. When it is necessary to load and display the matched 3D model, this application proposes to compare the resolution range of the matched 3D model (hereinafter referred to as the loaded model) with the resolution range of the current 3D model to determine the loading method. The loading methods include: 1. Further reducing the resolution based on the current 3D model, i.e., deleting redundant data; 2. Further increasing the resolution based on the current 3D model, i.e., supplementing additional data. The redundant data and the additional data are respectively the data in the layer dataset corresponding to the loaded model that are different from the layer dataset corresponding to the current 3D model.

[0043] Optionally, the 3D modeling method also includes the following steps: Based on a preset partitioning strategy, reusable modules are generated, and each reusable module corresponds to a module dataset composed of corresponding data selected from the modeling data. A 3D model is built for each reuse module based on the module dataset. The module dataset of the reuse module is compressed and stored based on a preset data compression algorithm. An integration interface is defined for each reuse module. The "Constructing a 3D model for each layer of the dataset" in S102 includes: A 3D model is constructed based on each layer of the dataset, and the 3D model of the integrable reusable module is called first. The corresponding 3D model is integrated into the 3D model corresponding to the layer dataset through the corresponding integration interface to assist in the construction of the 3D model corresponding to each layer of the dataset. The integrable reusable module satisfies the following condition: the corresponding module dataset is included in the layer dataset.

[0044] In practice, for example, the partitioning strategy may include: 1. partitioning modules based on data characteristics such as geological complexity, distribution of time delay types, and environmental change rate; 2. partitioning modules based on the type and function of urban infrastructure such as water pipes, valves, and roads; 3. partitioning modules based on the structural units or datasets that frequently appear in the three-dimensional models constructed in historical periods.

[0045] The 3D modeling system also defines a unique identifier for each reuse module, and builds and stores a 3D model for each reuse module based on the module dataset corresponding to each reuse module. It uses a preset data compression algorithm (such as Draco, Quantized Mesh, etc.) to compress the data contained in the module dataset of the reuse module before storage. At the same time, it designs a unified integration interface to facilitate the rapid integration of reuse modules into the model and improve the efficiency of building subsequent initial-level 3D models.

[0046] This application also discloses a 3D modeling system for the control of urban water pipe valves. (Refer to...) Figure 2 ,include: The data acquisition module 201 is used to acquire modeling data of preset modeling objects, wherein the preset modeling objects include at least pipelines and valves; the modeling data includes at least the structural data and connection relationships corresponding to the modeling objects; The hierarchical modeling module 202 is used to filter and stratify the modeling data to generate multi-layer datasets, and to build a three-dimensional model for each layer dataset. The amount of data contained in different layers datasets is different. The switching display module 203 is used to acquire and analyze display requirements in real time, and load the layer dataset that meets the display requirements in real time to display the corresponding 3D model; among which, the display requirements include at least resolution requirements.

[0047] Optionally, the layered modeling module 202 is also used to establish a mapping relationship between the multi-layered datasets after generating the multi-layered datasets; and satisfy the following: the datasets corresponding to the same viewpoint position of the three-dimensional models of different layers do not overlap; The switching display module 203 is also used to determine the target layer dataset and the corresponding target 3D model that meet the display requirements based on the resolution and the corresponding layer dataset corresponding to the current 3D model; it is also used to supplement additional data on the current 3D model if the resolution corresponding to the current 3D model is lower than the resolution included in the display requirements, the additional data being data included in the target layer dataset but not included in the current 3D model layer dataset; it is also used to delete redundant data on the current 3D model if the resolution corresponding to the current 3D model is higher than the resolution included in the actual requirements, the redundant data being data included in the current 3D model dataset but not included in the target dataset.

[0048] Optionally, a multi-source data acquisition module is used to acquire multi-source data and extract data features; wherein, the multi-source data includes at least geological data and environmental monitoring data; The hierarchical modeling module 202 is also used to filter modeling data, perform preliminary hierarchical stratification based on a preset hierarchical strategy to obtain initial hierarchies, and determine the slice thickness and fineness of each initial hierarchies; wherein, the preset hierarchical strategy includes at least resolution; it is also used to divide each initial hierarchies into several local regions according to the extracted data features and the degree of difference of the data features in different regions of each initial hierarchies, and determine the slice thickness and fineness of each local region according to the data features of the local regions; based on the slice thickness and data fineness, it generates the layer dataset of each initial hierarchies and the region dataset corresponding to each local region.

[0049] Optionally, the hierarchical modeling module 202 is also used to predict the changing trend of the extracted data features within a specified time period, divide each initial level into several local regions based on the degree of difference in the changing trends of data features in different regions of each initial level, divide the specified time period into several sub-time periods based on the degree of difference in the changing trends of each data feature within different time periods of the specified time period; it is also used to determine the slice thickness and fineness of each local region in each sub-time period based on the changing trend of each data feature in each sub-time period and in each local region; and update the slice thickness and data fineness of each local region in real time according to the order of all sub-time periods and in real time according to the progress of the current moment.

[0050] Optionally, it also includes a hierarchical linkage module, which is used to analyze and determine the correlation between the slice thickness and fineness of each local area at different levels, and to establish a mapping model; The hierarchical modeling module 202 is also used to determine the slice thickness and fineness of all target local regions contained in the target initial level during the target sub-time period, based on the changing trend of each data feature in each sub-time period and in each local region. Whenever the current time falls into the target sub-time period, it determines the slice thickness and fineness of each target local region in the target sub-time period, excluding the target initial level, through a mapping model. Based on the prediction results of the mapping model, it adjusts the slice thickness and fineness of all target local regions in other initial levels besides the target initial level. Here, the target sub-time period refers to any sub-time period, the target initial level refers to any initial level, and the target local region refers to any local region contained in the target initial level.

[0051] Optionally, it also includes an integration and reuse module, which is used to generate reuse modules based on a preset partitioning strategy, and each reuse module corresponds to a module dataset composed of corresponding data selected from the modeling data; a three-dimensional model is constructed for each reuse module based on the module dataset, the module dataset and three-dimensional model of the reuse module are stored, and an integration interface is defined for each reuse module. The hierarchical modeling module 202 is also used to construct a 3D model based on each layer dataset, and prioritizes calling the 3D model of the integrable reusable module. The corresponding 3D model is integrated into the 3D model corresponding to the layer dataset through the corresponding integration interface to assist in constructing the 3D model corresponding to each layer dataset. The integrable reusable module satisfies the following condition: the corresponding module dataset is included in the layer dataset.

[0052] Optionally, the integrated multiplexing module is also used to compress and store the module dataset of the multiplexed data based on a preset data compression algorithm.

[0053] This application also discloses a three-dimensional modeling device for urban water pipe valve control. The three-dimensional modeling device for urban water pipe valve control includes a memory and a processor. The memory stores a computer program that can be loaded by the processor and executed as described above for the three-dimensional modeling method for urban water pipe valve control.

[0054] This application also discloses a computer-readable storage medium that stores a computer program that can be loaded by a processor and executed as described above for a three-dimensional modeling method for urban water pipe valve control. The computer-readable storage medium includes, for example, various media capable of storing program code, such as a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0055] It should be noted that in this paper, relational terms such as first and second are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations.

[0056] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit the scope of protection of the application. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on these embodiments, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

Claims

1. A three-dimensional modeling method for urban water pipe valve control, characterized in that, include: Obtain modeling data for a preset modeling object, wherein the preset modeling object includes at least pipelines and valves; and the modeling data includes at least the structural data and connection relationships corresponding to the modeling object. The modeling data is filtered and layered to generate multi-layer datasets, and a three-dimensional model is constructed for each layer dataset. The amount of data contained in different layers datasets is different. The system acquires and analyzes display requirements in real time, loads layer datasets that meet the display requirements in real time, and displays the corresponding 3D model; wherein, the display requirements include at least resolution requirements; The method further includes: Acquire multi-source data and extract data features; wherein the multi-source data includes at least geological data and environmental monitoring data; The step of filtering and stratifying the modeling data to generate multi-layer datasets includes: The modeling data is filtered, and initial layers are obtained after preliminary layering based on a preset layering strategy. The slice thickness and fineness of each initial layer are then determined. The preset layering strategy includes at least resolution. Based on the extracted data features and the degree of difference of the data features in different regions of each initial level, several local regions are divided for each initial level. Based on the data features of the local regions, the slice thickness and fineness of each local region are determined. Based on slice thickness and data fineness, layer datasets for each initial level and region datasets for each local region are generated. The step involves dividing each initial level into several local regions based on the extracted data features and the degree of difference in these features across different regions. The slice thickness and fineness of each local region are then determined based on its data features. For the extracted data features, predict the changing trend of the data features within a specified time period. Based on the degree of difference in the changing trend of the data features in different regions of each initial level, divide each initial level into several local regions. Based on the degree of difference in the changing trend of each data feature within different time periods of the specified time period, divide the specified time period into several sub-time periods. Based on the changing trend of each data feature in each sub-period and in each local region, determine the slice thickness and fineness of each local region in each sub-period. Based on the chronological order of all the sub-time periods, and in real time according to the progress of the current moment, the slice thickness and fineness of each local region at the current moment are updated in real time. The method further includes: The correlation between slice thickness and fineness of each local region at different initial levels was analyzed and determined, and a mapping model was established. The step of determining the slice thickness and fineness of each local region in each sub-time period based on the changing trend of each data feature in each sub-time period and in each local region includes: Based on the changing trend of each data feature in each sub-time period and in each local region, whenever the current time falls into the target sub-time period, the slice thickness and fineness of all target local regions contained in the target initial level are determined in the target sub-time period. Then, the slice thickness and fineness of each target local region in the target sub-time period, excluding the target initial level, are predicted and output through a mapping model. Based on the prediction results of the mapping model, the slice thickness and fineness of all target local regions in other initial levels besides the target initial level are adjusted. Here, the target sub-time period refers to any sub-time period, the target initial level refers to any initial level, and the target local region refers to any local region contained in the target initial level. The method further includes: After generating multi-layer datasets, establish mapping relationships between the multi-layer datasets; and satisfy the following condition: the datasets corresponding to the same viewpoint position of different 3D models do not overlap; The real-time loading of the layer dataset that meets the display requirements and the display of the corresponding 3D model includes: Based on the resolution and corresponding layer dataset of the current 3D model, and the resolution included in the display requirements, determine the target layer dataset and corresponding target 3D model that meet the display requirements; If the resolution corresponding to the current 3D model is lower than the resolution included in the display requirements, then additional data is added to the current 3D model. The additional data is data that is included in the target layer dataset but not in the current 3D model layer dataset. If the resolution corresponding to the current 3D model is higher than the resolution included in the display requirements, then redundant data is removed based on the current 3D model. The redundant data is data that is included in the current 3D model dataset but not included in the target layer dataset.

2. The three-dimensional modeling method for urban water pipe valve control according to claim 1, characterized in that, The method further includes: Based on a preset partitioning strategy, reusable modules are generated, and each reusable module corresponds to a module dataset composed of corresponding data selected from the modeling data. A 3D model is constructed for each reuse module based on the module dataset, the module dataset and 3D model of the reuse module are stored, and an integration interface is defined for each reuse module. The construction of a 3D model for each layer of the dataset includes: A 3D model is constructed based on each layer of the dataset, and the 3D model of the integrable reusable module is called first. The corresponding 3D model is integrated into the 3D model corresponding to the layer dataset through the corresponding integration interface to assist in the construction of the 3D model corresponding to each layer of the dataset; wherein, the integrable reusable module satisfies the following condition: the corresponding module dataset is included in the layer dataset.

3. The three-dimensional modeling method for urban water pipe valve control according to claim 2, characterized in that, The module dataset storing the multiplexing module includes: The module dataset of the multiplexing module is compressed and stored based on a preset data compression algorithm.

4. A three-dimensional modeling system for urban water pipe valve control, applied to the three-dimensional modeling method for urban water pipe valve control as described in claim 1, characterized in that, The 3D modeling system for urban water pipe valve control includes, The data acquisition module (201) is used to acquire modeling data of a preset modeling object, wherein the preset modeling object includes at least pipelines and valves; and the modeling data includes at least the structural data and connection relationships corresponding to the modeling object. The hierarchical modeling module (202) is used to filter and hierarchically divide the modeling data to generate multi-layer datasets, and to construct a three-dimensional model for each layer dataset, wherein the amount of data contained in different layers datasets is different; The switching display module (203) is used to acquire and analyze display requirements in real time, and load the layer dataset that meets the display requirements in real time to display the corresponding three-dimensional model; wherein, the display requirements include at least resolution requirements.

5. A three-dimensional modeling device for urban water pipe valve control, characterized in that, It includes a memory and a processor, wherein the memory stores a computer program that can be loaded by the processor and executed as described in any one of claims 1 to 3.

6. A computer-readable storage medium, characterized in that, The computer program is stored that can be loaded by a processor and executed as described in any one of claims 1 to 3.

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