Urban water pipe valve control-oriented three-dimensional modeling method and system

Abstract

The invention relates to a three-dimensional modeling method and system for urban water pipe valve management and control, and belongs to the technical field of water pipe valve management and control. The method comprises the steps that modeling data of preset modeling objects are obtained, and the preset modeling objects at least comprise pipelines and valves; the modeling data at least comprises structure data and connection relations corresponding to modeling objects; the modeling data is screened and layered, multiple layers of data sets are generated, a three-dimensional model is constructed for each layer of data set, and data volumes contained in different layers of data sets are different; obtaining and analyzing a display demand in real time, loading the layer data set meeting the display demand in real time, and displaying a corresponding three-dimensional model; wherein the display demand at least comprises a resolution demand. The method has the effect of effectively reducing the data volume and the memory occupation on the premise of ensuring the three-dimensional modeling precision.

Description

Technical Field

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

[0002] In the continuous development of urban construction and management, the refined control of urban infrastructure has become increasingly critical. As an important part of the urban water supply system, water pipes and valves in the city, effective monitoring and management of their operating status play a vital role in ensuring the normal life of urban residents and the stable operation of the city. At present, in order to achieve precise control of these facilities, three-dimensional modeling is often used. By constructing a three-dimensional model, the location, layout, and operating status of water pipes and valves can be intuitively and comprehensively displayed, providing strong decision-making support for urban managers.

[0003] Existing 3D modeling technology is mainly based on 2D data. In actual operation, a large amount of 2D data needs to be collected to build a 3D model, which results in a huge amount of data. When these 3D models built with a large amount of 2D data are displayed on the system, they often take up a lot of memory space. This not only places extremely high demands on the hardware configuration of the system and increases hardware costs, but may also cause the system to run slowly, affecting the real-time display and analysis efficiency of data, making it difficult to meet the needs of rapid urban development and real-time control, so it needs to be improved. Summary of the invention

[0004] In order to effectively reduce the amount of data and memory usage while ensuring the accuracy of three-dimensional modeling, the present application provides a three-dimensional modeling method and system for urban water pipe valve control.

[0005] In a first aspect, the present application provides a three-dimensional modeling method for urban water pipe valve control, including: Acquire modeling data of a preset modeling object, wherein the preset modeling object includes at least a pipeline and a valve; the modeling data includes at least structural data and connection relationships corresponding to the modeling object; Screening and stratifying the modeling data to generate multi-layer data sets, and constructing a three-dimensional model for each layer of data sets, wherein different layers of data sets contain different amounts of data; Acquire and analyze display requirements in real time, load layer data sets that meet the display requirements in real time, and display the corresponding three-dimensional model; wherein the display requirements at least include resolution requirements.

[0006] By adopting the above technical solution, the present application proposes to perform hierarchical processing on the three-dimensional model, and display the three-dimensional model of a single layer correspondingly according to the actual requirements. Moreover, the data volumes of the three-dimensional models of different layers are different. Combining with the resolution requirements disclosed above, it can be known that the detailed degrees of the modeling data displayed by the three-dimensional models of different layers are different. Therefore, by combining the real-time display requirements, the dynamic loading and switching of the three-dimensional models with different data volumes are realized, that is, when the resolution requirement is not high, the three-dimensional model covering less data volume is displayed, so as to reduce the memory pressure of the three-dimensional model on the system. This effect is particularly significant when the data volume of the three-dimensional model to be constructed is relatively large.

[0007] Optionally, the method further includes: After generating the multi-layer data set, establishing a mapping relationship between the multi-layer data sets; and satisfying that the data sets corresponding to the same perspective position of the three-dimensional models of different layers have no intersection; The real-time loading of the layer data set that meets the display requirements and displaying the corresponding three-dimensional model includes: Based on the resolution corresponding to the current three-dimensional model and the corresponding layer data set, and the resolution included in the display display requirements, determining the target layer data set that meets the display requirements and the corresponding target three-dimensional model; If the resolution corresponding to the current three-dimensional model is lower than the resolution included in the display requirements, additional data is supplemented on the basis of the current three-dimensional model, and the additional data is the data included in the target layer data set and not included in the current three-dimensional model layer data set; If the resolution corresponding to the current three-dimensional model is higher than the resolution included in the actual requirements, redundant data is deleted on the basis of the current three-dimensional model, and the redundant data is the data included in the current three-dimensional model data set and not included in the target data set.

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

[0009] Optionally, the method further includes: Obtain multi-source data and extract data features; wherein, the multi-source data includes at least geological data and environmental monitoring data; The step of screening and stratifying the modeling data to generate a multi-layer data set includes: Screen the modeling data, perform preliminary stratification based on a preset stratification strategy to obtain initial levels, and determine the slice thickness and fineness of each initial level; wherein, the preset stratification strategy includes at least resolution; According to the extracted data features and the degree of difference of the data features in different regions of each initial level, divide several local regions for each initial level respectively, and determine the slice thickness and fineness of each local region according to the data features of the local region; Based on the slice thickness and data fineness, generate the layer data set of each initial level and the regional data set corresponding to each local region respectively.

[0010] By adopting the above technical solution, in the process of hierarchical modeling, on the basis of the preset stratification strategy, multi-source data is further incorporated, such as considering data that affects the distribution or change of water pipes and valves, such as geological data and environmental monitoring data, and adaptive hierarchical modeling is realized based on such data, that is, the slice thickness and fineness of different regions of each level are adaptively adjusted to achieve refined multi-level hierarchical modeling.

[0011] Optionally, the step of dividing several local regions for each initial level respectively according to 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 according to the data features of the local region includes: For the extracted data features, predict the change trend of the data features within a specified time period. Based on the degree of difference in the change trends of the data features in different regions of each initial level, divide several local regions for each initial level. Based on the degree of difference in the change trends of each data feature in different time periods within the specified time period, divide the specified time period into several sub-periods; According to the change trend of each data feature at each sub-period in each local region, determine the slice thickness and fineness of each local region at each sub-period; According to the sequence of all the sub-periods and in real time as the current moment progresses, update in real time the slice thickness and data fineness of each local region at the current moment.

[0012] By adopting the above technical solution, there are data features in multi-source data that change over time (such as the groundwater level, pollutant concentration, temperature data, etc. covered by environmental monitoring data). This solution further proposes to predict the change trend of the aforementioned data features in the future (such as within a specified time period) through a time series prediction method, and integrate this change trend into the adaptive hierarchical scheme described above, so as to optimize the dynamic adjustment of the slice thickness and fineness, and further achieve refined multi-level hierarchical modeling.

[0013] Optionally, the method further includes: Analyze and determine the correlation relationship between the slice thickness and fineness corresponding to each local region between different initial levels, and establish a mapping model; The step of determining the slice thickness and fineness of each local region at each sub-period according to the change trend of each data feature at each sub-period in each local region includes: According to the change trend of each data feature at each sub-period in each local region, whenever the current moment falls into a target sub-period, determine the slice thickness and fineness of all target local regions included in the target initial level at the target sub-period, and then infer and output the slice thickness and fineness corresponding to each target local region within the target sub-period except for the target initial level through the mapping model, and adjust the slice thickness and fineness of all the target local regions in other initial levels except the target initial level according to the inference result of the mapping model; where the target sub-period refers to any sub-period, the target initial level refers to any initial level, and the target local region refers to any local region included in the target initial level.

[0014] By adopting the above technical solution, since the initial levels are spatially continuous, the slice thickness and fineness of a certain initial level may affect the data distribution of the adjacent levels after adjustment. For example, when the slice thickness of a certain initial level is reduced (to capture more details), the initial level adjacent to it may need to be adjusted accordingly to avoid faults or data discontinuity. In addition, the prediction results (such as the rate of environmental change) may propagate in space, thereby affecting multiple initial levels. For example, if the predicted value of the rate of environmental change in a local area is high, it may be necessary to adjust the slicing strategies of the area in multiple initial levels at the same time. Therefore, the present application proposes that when the slice thickness and fineness of a local area in any initial level are adjusted, the present application will adjust the slice thickness and fineness of the local area in other initial levels in a linked manner, and the adjustment rule is based on a pre-constructed mapping model. The mapping model learns and summarizes the correlation between the slice thickness and fineness corresponding to the local area at different initial levels, and finally infers the slice thickness and fineness of the local area in each level, thereby realizing efficient dynamic adjustment of the slice thickness and fineness of all initial levels.

[0015] Optionally, the method further includes: Based on the preset division strategy, a reuse module is generated, and each reuse module corresponds to a module data set composed of corresponding data screened from the modeling data; Constructing a three-dimensional model for each reuse module according to the module data set, storing the module data set and the three-dimensional model of the reuse module, and defining an integration interface for each reuse module; The three-dimensional model is constructed for each layer of data set, including: A three-dimensional model is constructed according to each layer of data set, and the three-dimensional model of the integrable reuse module is preferentially called, and the corresponding three-dimensional model is integrated into the three-dimensional model corresponding to the layer data set through the corresponding integration interface to assist in constructing the three-dimensional model corresponding to each layer of data set; wherein the integrable reuse module satisfies: the corresponding module data set is included in the layer data set.

[0016] By adopting the above technical solution, the modeling data is classified and summarized, and a module data set and corresponding reuse modules are generated. A three-dimensional model is constructed separately for each reuse module. When three-dimensional modeling is required for each level, it is possible to prioritize whether there is an integrable reuse module. If so, the three-dimensional model corresponding to the reuse module is called to improve modeling efficiency and reduce the workload of repeated modeling.

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

[0018] By adopting the above technical solution, compressing the data corresponding to the multiplexing module by using data compression technology can significantly reduce the data occupied space and improve the storage and transmission efficiency at the same time.

[0019] In a second aspect, the present application provides a three-dimensional modeling system for urban water pipe valve control, including a data acquisition module, configured to obtain modeling data of a preset modeling object, where the preset modeling object at least includes pipelines and valves; and the modeling data at least includes structure data and connection relationships corresponding to the modeling object; a hierarchical modeling module, configured to screen and layer the modeling data to generate multiple layers of data sets, and construct three-dimensional models for each layer of data set respectively, where the data volumes included in different layers of data sets are different; a switching display module, configured to obtain and analyze display requirements in real time, and load in real time the layer data set that meets the display requirements to display the corresponding three-dimensional model; where the display requirements at least include resolution requirements.

[0020] In a third aspect, the present application provides a three-dimensional modeling device for urban water pipe valve control, including a memory and a processor, and a computer program capable of being loaded and executed by the processor as described in any method of the first aspect is stored on the memory.

[0021] In a fourth aspect, the present application provides a computer-readable storage medium storing a computer program capable of being loaded and executed by the processor as described in any method of the first aspect.

[0022] In summary, the present application includes the following beneficial technical effects: The present application proposes to perform hierarchical processing on the three-dimensional model, and display the three-dimensional model of a single layer correspondingly according to actual requirements, and the data volumes of the three-dimensional models of different layers are different. Combining with the resolution requirements disclosed above, it can be known that the detailed degrees of the modeling data displayed by the three-dimensional models of different layers are different. Therefore, by combining the real-time display requirements, the dynamic loading and switching of three-dimensional models with different data volumes are realized, that is, when the resolution requirement is not high, a three-dimensional model covering a smaller data volume is displayed, so as to reduce the memory pressure of the three-dimensional model on the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0024] Figure 1It is a schematic flowchart of a three-dimensional modeling method for urban water pipe valve control disclosed in an embodiment of the present application.

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

[0026] Explanation of reference numerals: 201, data acquisition module; 202, hierarchical modeling module; 203, switching display module. Detailed implementation manner

[0027] The following will further elaborate on the present application in conjunction with the attached Figure 1-2 for a more detailed description of the present application.

[0028] An embodiment of the present application discloses a three-dimensional modeling method for urban water pipe valve control (hereinafter simply 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 simply referred to as the three-dimensional modeling system). The following will specifically elaborate on the specific execution steps of the three-dimensional modeling system for the three-dimensional modeling method in conjunction with the attached Figure 1 to specifically describe the specific execution steps of the three-dimensional modeling system for the three-dimensional modeling method.

[0029] S101, obtain the modeling data of the preset modeling object, where the preset modeling object at least includes pipelines and valves; the modeling data at least includes the structural data and connection relationships corresponding to the modeling object.

[0030] In implementation, the modeling object specifically includes pipelines, valves, and the installation environment where the pipeline valves are located; the modeling data is specifically the structural data, connection relationships, etc. of the modeling object, where the structural data specifically includes the shape, material, type, size, etc. of the pipelines and valves; the connection relationship is specifically the connection method between the valve and the pipeline and the corresponding relationship of the mutual connection. The method of obtaining the modeling data can specifically adopt drone oblique photography and ground close-range photography techniques to obtain multi-view image data of the installation environment where the pipelines and valves are located, so as to construct a structural model of the installation environment subsequently, and it is also possible to obtain design drawings of the connection method of pipeline valves uploaded by the staff; and the foregoing multi-view influence data can be multi-view image data recorded during the installation process of the pipelines and valves, so as to fuse the installation environment with the pipeline valves in the design drawings, and finally obtain an installation environment with the distribution relationship and structure of the pipelines and valves.

[0031] S102, screen and layer the modeling data to generate multiple layers of data sets, and construct three-dimensional models for each layer of data set respectively, where the amount of data contained in different layers of data sets is different; After generating multiple layers of data sets, establish a mapping relationship between the multiple layers of data sets; and satisfy: there is no intersection in the data sets corresponding to the same viewing position of the three-dimensional models of different layers; Among them, "screening and stratifying the modeling data to generate multiple layers of data sets" in S102 includes: S1021, screening the modeling data, obtaining an initial level after preliminary stratification based on a preset stratification strategy, and determining the slice thickness and fineness of each initial level; wherein, the preset stratification strategy includes at least resolution; S1022, according to the extracted data features and the degree of difference of the data features in different regions of each initial level, dividing several local regions for each initial level respectively, and determining the slice thickness and fineness of each local region according to the data features of the local region; S1023, generating a layer data set for each initial level and a regional data set corresponding to each local region respectively based on the slice thickness and data fineness.

[0032] Correspondingly, the three-dimensional modeling method further includes the following steps: Analyze and determine the correlation relationship between the slice thickness and fineness corresponding to each of the local regions between different initial levels, and establish a mapping model.

[0033] In implementation, first construct a complete and detailed initial three-dimensional model according to the modeling data, and then stratify the initial three-dimensional model using a preset stratification strategy. Specifically, the stratification basis can be determined by dividing the resolution range to screen the modeling data, so as to screen out the data required for each layer to form a layer data set. Exemplarily, the screening method can specifically be: reducing the number of vertices and faces through a mesh simplification algorithm; using a point cloud sampling method (such as voxelization or random sampling) to reduce the data density. Then, the three-dimensional modeling system constructs three-dimensional models respectively according to each layer data set obtained by screening to form three-dimensional models at different levels. It should be noted that the three-dimensional models corresponding to all the foregoing initial levels are three-dimensional models corresponding to the same installation environment with pipelines and valves, and the difference between these three-dimensional models is only the corresponding data volume, that is, the model fineness is different. The three-dimensional model at the bottom layer is the simplest (low polygon model), and the three-dimensional model at the top layer has the highest resolution (such as a high polygon model or a point cloud); the model fineness of the middle multiple layers increases sequentially from bottom to top.

[0034] As above, after slicing and stratifying the initial three-dimensional model through a three-dimensional modeling system, several initial levels and the three-dimensional models corresponding to each initial level are obtained. At this time, the slice thickness and model fineness corresponding to each initial level are preset fixed values. On this basis, the three-dimensional modeling system is used to obtain multi-source data and extract data features from the multi-source data. The multi-source data here can specifically be geological data and environmental monitoring data, and the data features specifically include: 1. Geological data characteristics: such as formation complexity (calculating the undulation degree of the formation through gradient or curvature), rock type (classifying rock types through clustering analysis (such as K-Means)), fault distribution (extracting fault positions through edge detection or fault recognition algorithms); 2. Environmental monitoring data characteristics: groundwater level change (calculating the water level change rate through time series analysis), pollutant concentration distribution (generating a pollutant concentration distribution map through spatial interpolation methods (such as Kriging interpolation)); The 3D modeling system is used to further divide the initial level into multiple local regions according to the specific values at different positions in the initial level based on the above data characteristics. The basis for dividing the local regions can be that the difference degree of the values of more than one data characteristic in different local regions is greater than the preset difference degree; then calculate the data characteristics of each local region (such as formation complexity, delay type diversity, and environmental change rate); then, based on the pre-stored correspondence table, re-adjust the slice thickness and fineness of the initial level at each local region. Among them, the correspondence table contains multiple data characteristic ranges, as well as the slice thickness and fineness corresponding to each data characteristic range; the data characteristic ranges here include formation complexity range, environmental change rate range, and rock type quantity range; specifically, the slice thickness can be determined by the formation complexity range and environmental change rate, and the fineness can be determined by the rock type diversity. Exemplarily, the gradient or curvature can be used to calculate the undulation degree of the lower layer of the local region, and the slice thickness can be determined according to the following formula: slice thickness = 1 / (1 + gradient), that is, the greater the gradient, the smaller the slice thickness, and the smaller the slice thickness indicates that more data details need to be captured. Exemplarily, the fineness can be determined according to the following formula: fineness = 100 * (1 + rock type diversity). This formula means that the more rock types, the higher the fineness, that is, the higher the model fineness and the larger the data volume of the corresponding initial level.

[0035] It should be emphasized that the size, quantity, and position of the local regions divided from each initial level are the same and correspond one by one. However, due to the resolution difference of the initial level itself, the adjusted slice thickness and fineness of the same local region are different in different initial levels. Correspondingly, the 3D modeling system of the present application has pre-determined the linear relationship formed by fitting the slice thickness corresponding to the local region in different initial levels, as well as the linear relationship of the fineness, and constructed a mapping model. When the slice thickness (or fineness) corresponding to the local region in one of the initial levels and the resolution range corresponding to this initial level are input into the mapping model, the mapping model will output the slice thickness (or fineness) corresponding to the local region in all other initial levels based on the aforementioned linear relationship.

[0036] Therefore, the 3D modeling model can select one of the initial levels as the target initial level, then readjust the slice thickness and fineness of all local regions in the target initial level respectively, and then use the mapping model to obtain the slice thickness and fineness corresponding to each local region of the other levels except the target initial level, so as to efficiently adjust the slice thickness and fineness of all local regions in all initial levels.

[0037] Optionally, S1022 further includes the following sub-steps: For the extracted data features, predict the change trend of the data features within a specified time period, divide several local regions for each initial level based on the difference degree of the change trends of the data features in different regions of each initial level, and divide the specified time period into several sub-periods based on the difference degree of the change trends of each data feature in different time periods within the specified time period; According to the change trend of each data feature in each sub-period and in each local region, whenever the current moment falls into the target sub-period, determine the slice thickness and fineness of all target local regions included in the target initial level at the target sub-period, and then infer and output the slice thickness and fineness corresponding to each target local region within the target sub-period except in the target initial level through the mapping model, and adjust the slice thickness and fineness of all target local regions in the other initial levels except the target initial level according to the inference result of the mapping model; wherein, the target sub-period refers to any sub-period, the target initial level refers to any initial level, and the target local region refers to any local region included in the target initial level; According to the sequence of all sub-periods, and in real time according to the progress of the current moment, update the slice thickness and data fineness of each local region at the current moment in real time.

[0038] In implementation, some data in the multi-source data (hereinafter referred to as time-varying data) will change over time, such as environmental detection data. Therefore, the above further proposes to predict the change trend of such time-varying data within a future specified time period, and adaptively adjust the slice thickness and fineness of the local regions where the time-varying data changes within the future specified time period according to the prediction result. Among them, collect the historical time-varying data in advance, organize the time-varying data into a time series format (timestamp + value), and then use a prediction algorithm (such as ARIMA, LSTM, Prophet, etc.) to predict the change trend of the time-varying data within a future specified time period to obtain the prediction result. The prediction result is specifically the time-varying data in the time series format. The 3D modeling system will further divide the specified time period into several sub-periods based on this change trend, and satisfy: at the transition moment between adjacent sub-periods, the time-varying data undergoes a jump, that is, the difference between the time-varying data corresponding to the transition moment and its previous moment is higher than the preset difference.

[0039] As the current time point progresses, whenever the current time point falls within any sub-period, or when the sub-period in which the current time is located changes, the 3D modeling system will re-adjust the slice thickness and fineness of each local area according to the predicted time-varying data and other non-time-varying data corresponding to the sub-period, and further realize the refined layering of the multi-level 3D model.

[0040] S103. Obtain and analyze the display requirements in real time, and load the layer dataset that meets the display requirements in real time to display the corresponding 3D model; where the display requirements at least include resolution requirements.

[0041] Among them, "loading the layer dataset that meets the display requirements in real time to display the corresponding 3D model" in S103 includes: Based on the resolution corresponding to the current 3D model and the corresponding layer dataset, and displaying the resolution included in the display requirements, determine the target layer dataset that meets the display requirements and the corresponding target 3D model; If the resolution corresponding to the current 3D model is lower than the resolution included in the display requirements, additional data will be supplemented on the basis of the current 3D model. The additional data is the data that is included in the target layer dataset and not included 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 requirements, redundant data will be deleted on the basis of the current 3D model. The redundant data is the data that is included in the current 3D model dataset and not included in the target dataset.

[0042] In implementation, for the segmented multi-layer 3D model, the 3D modeling system is also used to set a loading distance threshold for each initial level of the 3D model, aiming to dynamically load 3D models of different levels according to changes in the viewing angle or distance. Based on the current viewing angle, the distance between each position and the position where the current viewing angle is located is determined, the resolution of the corresponding position is determined according to the distance, and then the 3D model of the initial level corresponding to the resolution range where the resolution is located is selected as the display model at that position. In other words, for positions far from the position where the current viewing angle is located, a 3D model with low resolution is loaded to display the structure of that position, and for positions close to the position where the current viewing angle is located, a 3D model with high resolution is loaded to display the structure of that position. The user can artificially trigger and define the distance size in the form of zooming in and out, and switch the viewing angle through preset adjustment buttons, so as to complete the triggering of the display requirement. The corresponding 3D modeling system will receive the artificially triggered display requirement, and then determine the 3D model matched for each position in combination with the above strategy. When it is necessary to load and display the matched 3D model, this application proposes: comparing 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 on the basis of the current 3D model, that is, deleting redundant data; 2. Further increasing the resolution on the basis of the current 3D model, that is, supplementing additional data, and the redundant data and additional data are respectively the data in the layer dataset corresponding to the loaded model that is different from the layer dataset corresponding to the current 3D model.

[0043] Optionally, the 3D modeling method further includes the following steps: Generate a reuse module based on a preset partitioning strategy, and each reuse module corresponds to a module dataset composed of the corresponding data selected from the modeling data; Construct a 3D model for each reuse module according to the module dataset, compress and store the module dataset of the reuse module based on a preset data compression algorithm, and define an integration interface for each reuse module; The "constructing a 3D model for each layer dataset respectively" in S102 includes: Construct a 3D model according to each layer dataset, and preferentially call the 3D model of the reusable module that can be integrated. Integrate the corresponding 3D model 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; among them, the reusable module that can be integrated satisfies: the corresponding module dataset is included in the layer dataset.

[0044] In implementation, exemplarily, the partitioning strategy may include: 1. partitioning modules according to data characteristics such as geological complexity, delay type distribution, environmental change rate, etc.; 2. partitioning modules according to the types and functions of urban infrastructure such as water pipes, valves, roads, etc.; 3. partitioning modules according to the smallest structural units or datasets that frequently appear summarized from the three-dimensional models constructed in historical periods.

[0045] The three-dimensional modeling system is also used to define a unique identifier for each reuse module, and construct and store a three-dimensional model for each reuse module respectively according to the module dataset corresponding to each reuse module. The data included in the module dataset of the reuse module is compressed and stored by using a preset data compression algorithm (such as Draco, Quantized Mesh, etc.). At the same time, a unified integration interface is designed to facilitate the quick integration of the reuse module into the model, improving the construction efficiency of the subsequent initial-level three-dimensional model.

[0046] An embodiment of the present application also discloses a three-dimensional modeling system for urban water pipe and valve control. Refer to Figure 2 , including: A data acquisition module 201, configured to acquire modeling data of a preset modeling object, where the preset modeling object at least includes pipelines and valves; the modeling data at least includes structural data and connection relationships corresponding to the modeling object; A hierarchical modeling module 202, configured to screen and layer the modeling data to generate multi-layer datasets, and construct three-dimensional models for each layer of dataset respectively, where the data volumes included in different layers of datasets are different; A switching display module 203, configured 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; where the display requirements at least include resolution requirements.

[0047] Optionally, the hierarchical modeling module 202 is further configured to establish a mapping relationship between the multi-layer datasets after generating the multi-layer datasets; and satisfy: there is no intersection in the datasets corresponding to the same perspective position of the three-dimensional models of different layers; The switching display module 203 is further configured to determine a target layer dataset that meets the display requirements and a corresponding target 3D model based on the resolution and the corresponding layer dataset satisfied by the current 3D model, and the resolution included in the display requirements; and is further configured to supplement additional data to the current 3D model if the resolution corresponding to the current 3D model is lower than the resolution included in the display requirements, where the additional data is data that is included in the target layer dataset but not included in the current 3D model layer dataset; and is further configured to delete redundant data from the current 3D model if the resolution corresponding to the current 3D model is higher than the resolution included in the display requirements, where the redundant data is data that is included in the current 3D model dataset but not included in the target dataset.

[0048] Optionally, the multi-source data acquisition module is configured to acquire multi-source data and extract data features; where the multi-source data includes at least geological data and environmental monitoring data. The hierarchical modeling module 202 is further configured to screen modeling data, obtain initial levels after preliminary hierarchical division based on a preset hierarchical strategy, and determine the slice thickness and fineness of each initial level; where the preset hierarchical strategy includes at least resolution; and is further configured to divide a plurality of local regions for each initial level respectively according to the extracted data features and the difference degree of the data features in different regions of each initial level, and determine the slice thickness and fineness of each local region according to the data features of the local region; and generate a layer dataset for each initial level and a regional dataset corresponding to each local region respectively based on the slice thickness and data fineness.

[0049] Optionally, the hierarchical modeling module 202 is further configured to predict the change trend of the data features within a specified time period for the extracted data features, divide a plurality of local regions for each initial level respectively according to the difference degree of the change trends of the data features in different regions of each initial level, and divide the specified time period into a plurality of sub-periods according to the difference degree of the change trends of each data feature within different time periods of the specified time period; and is further configured to determine the slice thickness and fineness of each local region at each sub-period according to the change trend of each data feature in each local region at each sub-period; and update the slice thickness and data fineness of each local region at the current moment in real time according to the sequence of all sub-periods and the advancement of the current moment in real time.

[0050] Optionally, it further includes a hierarchical linkage module configured to analyze and determine the correlation relationship between the slice thickness and fineness corresponding to each local region between different levels, and establish a mapping model. The hierarchical modeling module 202 is further configured to determine the slice thickness and fineness of all target local regions included in the target initial level at the target sub-period whenever the current moment falls into the target sub-period according to the change trend of each data feature in each sub-period within each local region, and then infer and output the slice thickness and fineness corresponding to each target local region within the target sub-period except for those in the target initial level through the mapping model, and adjust the slice thickness and fineness of all target local regions in other initial levels except for the target initial level according to the inference result of the mapping model; wherein, the target sub-period refers to any sub-period, the target initial level refers to any initial level, and the target local region refers to any local region included in the target initial level.

[0051] Optionally, it further includes an integrated reuse module, which is configured to generate a reuse module based on a preset partitioning strategy, and each reuse module corresponds to a module dataset composed of the corresponding data screened from the modeling data; construct a three-dimensional model for each reuse module according to the module dataset, store the module dataset and three-dimensional model of the reuse module, and define an integration interface for each reuse module. The hierarchical modeling module 202 is further configured to construct a three-dimensional model according to each layer of dataset, and preferentially call the three-dimensional model of the reusable module that can be integrated, and integrate the corresponding three-dimensional model into the three-dimensional model corresponding to the layer dataset through the corresponding integration interface to assist in constructing the three-dimensional model corresponding to each layer of dataset; wherein, the reusable module that can be integrated satisfies that the corresponding module dataset is included in the layer dataset.

[0052] Optionally, the integrated reuse module is further configured to compress and store the module dataset of the reuse data based on a preset data compression algorithm.

[0053] An embodiment of the present 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. A computer program capable of being loaded and executed by the processor, such as the three-dimensional modeling method for urban water pipe valve control as described above, is stored on the memory.

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

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

[0056] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit the protection scope of the application. Obviously, the described embodiments are only some embodiments of the present application, rather than all embodiments. Based on these embodiments, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope to be protected by the present application.

Claims

1. A three-dimensional modeling method for urban water pipe valve control, characterized in that, Including: Obtain the modeling data of a preset modeling object, where the preset modeling object at least includes pipelines and valves; the modeling data at least includes the structural data and connection relationships corresponding to the modeling object; Screen and layer the modeling data to generate multiple layers of data sets, and construct 3D models for each layer of data set respectively, where the data volumes included in different layers of data sets are different; Obtain and analyze the display requirements in real time, and load the layer data set that meets the display requirements in real time to display the corresponding 3D model; where the display requirements at least include resolution requirements.

2. The three-dimensional modeling method for urban water pipe valve control according to claim 1, wherein The method further includes: After generating multiple layers of data sets, establish a mapping relationship between the multiple layers of data sets; and satisfy: there is no intersection between the data sets corresponding to the same viewing position of 3D models in different layers; The real-time loading of the layer data set that meets the display requirements and displaying the corresponding 3D model includes: Based on the resolution corresponding to the current 3D model and the corresponding layer data set, and the resolution included in the display display requirements, determine the target layer data set that meets the display requirements and the corresponding target 3D model; If the resolution corresponding to the current 3D model is lower than the resolution included in the display requirements, supplement additional data on the basis of the current 3D model, where the additional data is data that is included in the target layer data set and not included in the current 3D model layer data set; If the resolution corresponding to the current 3D model is higher than the resolution included in the real display requirements, delete redundant data on the basis of the current 3D model, where the redundant data is data that is included in the current 3D model data set and not included in the target data set.

3. The three-dimensional modeling method for urban water pipe valve control according to claim 1, characterized in that, The method further includes: Obtain multi-source data and extract data features; where the multi-source data at least includes geological data and environmental monitoring data; The screening and layering of the modeling data to generate multiple layers of data sets includes: Screen the modeling data, obtain an initial level after preliminary layering based on a preset layering strategy, and determine the slice thickness and fineness of each initial level; where the preset layering strategy at least includes resolution; According to 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 respectively, and determine the slice thickness and fineness of each local region according to the data features of the local region; Based on the slice thickness and data fineness, generate the layer data set of each initial level and the regional data set corresponding to each local region respectively.

4. The three-dimensional modeling method for urban water pipe valve control according to claim 3, characterized in that The dividing each initial level into several local regions respectively according to 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 according to the data features of the local region includes: Predict the change trend of the extracted data features within a specified time period. Based on the degree of difference in the change trends of the data features in different regions of each initial level, divide several local regions for each initial level. Based on the degree of difference in the change trends of each data feature in different time periods within the specified time period, divide the specified time period into several sub-periods; Determine the slice thickness and fineness of each local region at each sub-period according to the change trend of each data feature at each sub-period and within each local region; According to the order of all the sub-periods, and in real time as the current time progresses, update the slice thickness and data fineness of each local region at the current time in real time.

5. The three-dimensional modeling method for urban water pipe valve control according to claim 4, characterized in that The method further includes: Analyze and determine the correlation relationship between the slice thickness and fineness corresponding to each local region between different initial levels, and establish a mapping model; The determining the slice thickness and fineness of each local region at each sub-period according to the change trend of each data feature at each sub-period and within each local region includes: According to the change trend of each data feature at each sub-period and within each local region, whenever the current time falls into the target sub-period, determine the slice thickness and fineness of all target local regions included in the target initial level at the target sub-period, and then infer and output the slice thickness and fineness corresponding to each target local region within the target sub-period except within the target initial level through the mapping model, and adjust the slice thickness and fineness of all the target local regions within other initial levels except the target initial level according to the inference result of the mapping model; where, the target sub-period refers to any sub-period, the target initial level refers to any initial level, and the target local region refers to any local region included in the target initial level.

6. The three-dimensional modeling method for urban water pipe valve control according to claim 3, characterized in that The method further includes: Generate a reuse module based on a preset partitioning strategy, and each reuse module corresponds to a module data set composed of the corresponding data selected from the modeling data; Construct a 3D model for each reuse module according to the module data set, store the module data set and 3D model of the reuse module, and define an integration interface for each reuse module; The constructing a 3D model for each layer data set respectively includes: Construct a 3D model according to each layer data set, and preferably call the 3D model of the reusable module that can be integrated, and integrate the corresponding 3D model into the 3D model corresponding to the layer data set through the corresponding integration interface to assist in constructing the 3D model corresponding to each layer data set; where, the reusable module that can be integrated satisfies that the corresponding module data set is included in the layer data set.

7. The three-dimensional modeling method for urban water pipe valve control according to claim 6, characterized in that The storing the module data set of the reuse module includes: Compress the module data set of the reuse module based on a preset data compression algorithm and then store it.

8. A three-dimensional modeling system for urban water pipe valve control and management, characterized in that, Include, The data acquisition module (201) is used to obtain the modeling data of a preset modeling object, where the preset modeling object at least includes pipelines and valves; the modeling data at least includes the structure data and connection relationships corresponding to the modeling object. The hierarchical modeling module (202) is used to screen and layer the modeling data to generate multi-layer data sets, and construct three-dimensional models for each layer of data set respectively, where the data volumes included in different layers of data sets are different. The switching display module (203) is used to obtain and analyze the display requirements in real time, and load the layer data set that meets the display requirements in real time to display the corresponding three-dimensional model; where the display requirements at least include resolution requirements.

9. A three-dimensional modeling device for urban water pipe valve control, characterized in that, It includes a memory and a processor, and a computer program capable of being loaded and executed by the processor as described in any one of claims 1 to 7 is stored on the memory.

10. A computer-readable storage medium, characterized in that, A computer program capable of being loaded and executed by the processor as described in any one of claims 1 to 7 is stored.

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