Three-dimensional geographic information system data fusion method and system

Abstract

The invention relates to a three-dimensional geographic information system data fusion method and system, and relates to the field of data processing, and the method comprises the steps: obtaining a static three-dimensional data source, and carrying out the spatial consistency modeling to construct a static three-dimensional geographic information model; obtaining dynamic three-dimensional data sources to be fused, extracting key attributes of the dynamic three-dimensional data sources, performing dynamic grade division, and outputting multiple groups of dynamic three-dimensional data sources; a hierarchical fusion scheduler is set to call a plurality of groups of dynamic three-dimensional data sources step by step to perform data fusion on the static three-dimensional geographic information model from low to high, and a fused three-dimensional geographic information model is output, so that the technical problem that the three-dimensional geographic information system cannot perform flexible and efficient fusion according to the dynamic characteristics of the data sources is solved.

Description

Technical Field

[0001] The present invention relates to the field of data processing, and in particular to a three-dimensional geographic information system data fusion method and system. Background Art

[0002] With the widespread application and development of three-dimensional geographic information systems (3D GIS), the system needs to integrate more and more multi-source heterogeneous data, which show significant differences. On the one hand, some data sources such as traffic density, camera images, and meteorological sensors have high real-time and dynamic requirements, high update frequency, and drastic changes, and need to reflect the latest status information in a timely manner. On the other hand, data sources such as vegetation growth, construction progress, and terrain evolution have relatively long update cycles and slow changes, and pay more attention to long-term trends and stability. When dealing with these data sources with significant differences, existing 3D geographic information system data fusion methods often use a unified time step or a single time base for fusion. However, this method ignores the dynamic characteristics of the data source, resulting in poor fusion effect and unable to meet the requirements of data accuracy and real-time performance in practical applications. For example, for data sources with high frequency updates, the latest status may not be reflected in time due to the long fusion cycle; while for data sources with low frequency updates, unnecessary computational overhead and errors may be introduced due to frequent fusion. Summary of the invention

[0003] The present invention aims to solve the technical problem that a three-dimensional geographic information system in the prior art cannot be flexibly and efficiently integrated according to the dynamic characteristics of data sources, and provides a three-dimensional geographic information system data fusion method and system.

[0004] The technical solution of the present invention to solve the above technical problems is as follows: In a first aspect, the present invention provides a three-dimensional geographic information system data fusion method, the method comprising: accessing a three-dimensional geographic information system to obtain a static three-dimensional data source, performing spatial consistency modeling on the static three-dimensional data source to construct a static three-dimensional geographic information model; obtaining a dynamic three-dimensional data source to be fused, extracting key attributes of the dynamic three-dimensional data source, including spatial position, data type, data update frequency and degree of change, and data timestamp range; dynamically classifying the dynamic three-dimensional data source according to the key attributes, and outputting multiple groups of dynamic three-dimensional data sources, the dynamic levels at least including a low dynamic level, a medium dynamic level, and a high dynamic level; setting a hierarchical fusion scheduler, the hierarchical fusion scheduler calling the multiple groups of dynamic three-dimensional data sources step by step from low to high to perform data fusion on the static three-dimensional geographic information model, and outputting a fused three-dimensional geographic information model.

[0005] In a second aspect, the present invention provides a three-dimensional geographic information system data fusion system, the system comprising: a data acquisition module, used to access the three-dimensional geographic information system to obtain a static three-dimensional data source, perform spatial consistency modeling on the static three-dimensional data source to construct a static three-dimensional geographic information model; an attribute extraction module, used to obtain a dynamic three-dimensional data source to be fused, extract key attributes of the dynamic three-dimensional data source, including spatial position, data type, data update frequency and degree of change, and data timestamp range; a level classification module, used to dynamically classify the dynamic three-dimensional data source according to the key attributes, and output multiple groups of dynamic three-dimensional data sources, the dynamic levels at least including a low dynamic level, a medium dynamic level and a high dynamic level; a data fusion module, used to set a hierarchical fusion scheduler, the hierarchical fusion scheduler calls the multiple groups of dynamic three-dimensional data sources step by step from low to high to perform data fusion on the static three-dimensional geographic information model, and outputs a fused three-dimensional geographic information model.

[0006] The beneficial effects of the present invention are: by accessing a static three-dimensional data source to build a model, and obtaining a dynamic three-dimensional data source to extract key attributes and dynamically grade the data, and then using a hierarchical fusion scheduler to fuse them step by step, the effective integration and flexible and efficient fusion of multi-source three-dimensional geographic information is achieved, thereby improving the data processing capability and application effect of the three-dimensional geographic information system. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] Figure 1 A schematic flow chart of a three-dimensional geographic information system data fusion method provided by the present invention.

[0008] Figure 2 This is a structural schematic diagram of the three-dimensional geographic information system data fusion system provided by the present invention.

[0009] Explanation of reference numerals: data acquisition module 11 , attribute extraction module 12 , level classification module 13 , data fusion module 14 . DETAILED DESCRIPTION

[0010] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0011] In the description of the present invention, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0012] In the description of the present invention, the term "for example" is used to mean "used as an example, illustration or explanation". Any embodiment described as "for example" in the present invention is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is given to enable any technician in the field to implement and use the present invention. In the following description, details are listed for the purpose of explanation. It should be understood that a person of ordinary skill in the art can recognize that the present invention can be implemented without using these specific details. In other examples, well-known structures and processes will not be elaborated in detail to avoid obscuring the description of the present invention with unnecessary details. Therefore, the present invention is not intended to be limited to the embodiments shown, but is consistent with the widest scope consistent with the principles and features disclosed in the present invention.

[0013] Embodiment 1:

[0014] like Figure 1 As shown, an embodiment of the present invention provides a three-dimensional geographic information system data fusion method, the method comprising: S10: Accessing a three-dimensional geographic information system to obtain a static three-dimensional data source, and performing spatial consistency modeling on the static three-dimensional data source to construct a static three-dimensional geographic information model.

[0015] For example, in the process of constructing a static 3D geographic information model in this solution, it is first necessary to access various types of static 3D data sources from the 3D geographic information system. These data sources include various types of data, such as terrain data, building data, vegetation data, etc., which usually exist in different formats and coordinate systems. In order to integrate these data into a unified model, they need to be spatially consistent modeled.

[0016] The core of spatial consistency modeling is to perform format conversion and geometric splicing on multi-source static data. Specifically, it is to convert data in different formats and coordinate systems into a unified format and coordinate system through various technical means, such as coordinate conversion and projection transformation, to ensure that they can be accurately aligned in space. At the same time, the geometric shape of the data needs to be adjusted and optimized to eliminate gaps and overlaps between the data and form a complete and continuous three-dimensional geographic information model.

[0017] In the process of building a model, in addition to considering the accuracy and consistency of three-dimensional geometric data (such as points, surfaces, and volumes), it is also necessary to pay attention to the accuracy and integrity of semantic data. Semantic data describes the attributes and characteristics of geographical entities, such as building types, road names, etc. They are of great significance for understanding and applying three-dimensional geographical information models. Therefore, in the modeling process, it is necessary to define and describe semantic data in detail and establish an accurate attribute database.

[0018] In addition, spatial topological structure and hierarchical organizational structure are also important components of building a static three-dimensional geographical information model. Spatial topological structure describes the spatial relationships between geographical entities, such as adjacency, inclusion relationships, etc. They have an important impact on the geometric consistency and spatial analysis capabilities of the model. The hierarchical organizational structure is used to organize and manage the data in the model, such as optimizing the display and rendering efficiency of the model through layer structure or LOD (Level of Detail) structure.

[0019] For example, when building a three-dimensional geographical information model of a city, it is necessary to perform format conversion and geometric splicing on multi-source static data such as terrain data, building data, and vegetation data to ensure their accurate spatial alignment. At the same time, it is also necessary to accurately define and describe semantic data such as the types of buildings and the names of roads. In addition, consider the spatial topological structure and hierarchical organizational structure of the model, such as distinguishing different types of geographical entities by establishing different layers, or optimizing the display efficiency of the model by establishing an LOD structure. Through these steps, an accurate, complete, and efficient static three-dimensional geographical information model can be built.

[0020] S20: Obtain the dynamic three-dimensional data sources to be fused, and extract the key attributes of the dynamic three-dimensional data sources, including spatial location, data type, data update frequency and degree of change, and data timestamp range.

[0021] Optionally, further obtain the dynamic three-dimensional data sources to be fused. The dynamic data sources come from a variety of different sensors and devices, such as GPS locators, meteorological sensors, cameras, etc. They collect and transmit various location-related information in real time. In order to effectively utilize these data sources, it is necessary to extract the key attributes of the information from them.

[0022] Extract the key attributes of dynamic 3D data sources, mainly including spatial location, data type, data update frequency and degree of change, and data timestamp range. Among them, spatial location is the basic attribute of data, which describes the specific location of data in 3D space and is the basis of data fusion. Data type indicates the nature and purpose of data, such as temperature, humidity, image, etc. Different types of data may require different processing methods during the fusion process. Data update frequency and degree of change reflect the dynamic characteristics of data. Data sources with high update frequency, such as traffic density data, may be updated every second, while data sources with long update cycles, such as vegetation growth data, may be updated only once a year. The degree of change describes how fast the data changes over time. For example, meteorological data may change rapidly over time, while terrain data is relatively stable. These data characteristics are crucial to determining the priority and fusion strategy of data fusion. The data timestamp range records the time information of the data, which is of great significance for determining the timeliness of data and tracing back historical data. By comparing the timestamps of different data sources, it can ensure that the latest data is used in the fusion process and avoid time conflicts between data.

[0023] For example, in urban traffic management, it may be necessary to fuse traffic density data from GPS locators, traffic monitoring image data from cameras, and weather data from meteorological sensors. When extracting the key attributes of these data sources, it is noted that traffic density data is updated frequently and changes rapidly, while meteorological data may be updated less frequently but changes relatively stably. At the same time, these data sources all have clear spatial location and timestamp information. Furthermore, based on these key attributes, appropriate fusion strategies can be formulated, such as giving priority to fusing traffic density data with high update frequency and rapid changes to reflect traffic conditions in real time; and for meteorological data, appropriate fusion cycles can be selected according to the degree of its change.

[0024] S30: Dynamically classifying the dynamic three-dimensional data source according to the key attributes, and outputting multiple groups of dynamic three-dimensional data sources, wherein the dynamic classes at least include a low dynamic class, a medium dynamic class, and a high dynamic class.

[0025] In detail, dynamic 3D data sources are dynamically graded based on multiple key attributes, including spatial location, data type, timestamp range, update frequency and change intensity. Specifically, the spatial locations of all dynamic 3D data sources need to be uniformly converted into points, lines and surfaces in a 3D geographic coordinate system to ensure the consistency and comparability of the data in space. At the same time, data types are classified, such as point clouds, vectors, rasters or time series attribute values, so that appropriate algorithms and methods can be used for different types of data in subsequent processing. The accuracy of the timestamp range is also an important basis for dynamic grade division, which usually needs to be accurate to seconds, minutes or hours to ensure that the timeliness and dynamic changes of the data can be accurately reflected. When extracting fields, the location (location), data type (data_type), update frequency (frequency), change intensity (amplitude) and timestamp range (timestamp_range) information of each data source are recorded.

[0026] Furthermore, based on the above key attributes, dynamic three-dimensional data sources are divided into different dynamic levels, including at least low dynamic level, medium dynamic level and high dynamic level. For example, for real-time traffic and passenger flow data, due to its high frequency and drastic change, it is usually classified as high dynamic level (L1), and its D value range can be set to be greater than 0.8. This type of data needs to be updated frequently and processed quickly to reflect the dynamic changes of traffic and passenger flow in real time. Among them, the D value is an indicator used to quantify the dynamic characteristics of dynamic three-dimensional data sources, which comprehensively reflects the update frequency and change intensity of the data. Specifically, the D value is calculated by a certain algorithm or model to measure the degree of dynamic change of the data source in time and space. That is, the specific data source dynamic classification method is realized by the D value. For engineering progress and equipment status data, the change frequency and intensity are moderate, and it is usually classified as medium dynamic level (L2), and its D value range is between 0.5 and 0.8. This type of data needs to be updated and processed periodically to track changes in engineering progress and equipment status. As for vegetation and planning update data, due to their slow changes and long cycles, they are usually classified as low dynamic level (L3), with a D value of less than or equal to 0.5. This type of data does not need to be updated frequently and can be processed and updated at longer time intervals.

[0027] This dynamic classification method can more effectively manage and process dynamic 3D data sources, improving the efficiency and accuracy of data fusion. For example, in urban planning, different update strategies and processing methods can be used for data sources of different dynamic levels to ensure the real-time and accuracy of urban planning models.

[0028] S40: Set up a hierarchical fusion scheduler, which hierarchically calls the multiple groups of dynamic 3D data sources to perform data fusion on the static 3D geographic information model from low to high, and outputs a fused 3D geographic information model.

[0029] Specifically, in the process of setting up the hierarchical fusion scheduler for data fusion, first sort the multiple groups of dynamic 3D data sources according to their dynamic levels from low to high. These dynamic data sources include vegetation growth data with a low dynamic level, building construction progress data with a medium dynamic level, and traffic flow density data with a high dynamic level, etc. The core role of the hierarchical fusion scheduler is to hierarchically call these dynamic data sources and perform data fusion on the static 3D geographic information model according to their dynamic levels.

[0030] Specifically, the hierarchical fusion scheduler will first call the data source with a low dynamic level, such as vegetation growth data, align it with the static 3D geographic information model, and check for status conflicts. If there are conflicts, perform preliminary fusion on the model according to the low-dynamic data source to generate a first-order fused 3D geographic information model. Subsequently, the scheduler will call the data source with a medium dynamic level, such as building construction progress data, align it with the first-order fused model and check for status conflicts. If there are conflicts, perform fusion according to the medium-dynamic data source to generate a second-order fused 3D geographic information model. Finally, the scheduler will call the data source with a high dynamic level, such as traffic flow density data, for the final fusion process to generate the final fused 3D geographic information model.

[0031] In this process, the hierarchical fusion scheduler can flexibly adjust the fusion strategy according to the dynamic levels and data characteristics of different data sources to ensure the accuracy and efficiency of data fusion. For example, when processing urban traffic data, the hierarchical fusion scheduler can give priority to fusing the traffic flow density data with high-frequency updates to reflect the traffic conditions in real time, while for the vegetation growth data with low-frequency updates, the fusion update can be performed less frequently. Through this hierarchical fusion method, multi-source heterogeneous dynamic 3D data can be flexibly and effectively integrated and utilized, providing more accurate and efficient data support for the application of 3D geographic information systems.

[0032] In a preferred embodiment, the hierarchical fusion scheduler calls the multiple groups of dynamic three-dimensional data sources step by step to perform data fusion on the static three-dimensional geographic information model from low to high, including: the hierarchical fusion scheduler calls the low-group dynamic three-dimensional data source; aligns the low-group dynamic three-dimensional data source to the static three-dimensional geographic information model, and determines whether the low-group dynamic three-dimensional data source has a status conflict with the data of the static three-dimensional geographic information model; if the low-group dynamic three-dimensional data source has a status conflict with the data of the static three-dimensional geographic information model, executes the low-group dynamic three-dimensional data source priority, fuses the static three-dimensional geographic information model according to the low-group dynamic three-dimensional data source, and stores a first-order fused three-dimensional geographic information model.

[0033] Specifically, in the data fusion process of the hierarchical fusion scheduler, the low-dynamic level three-dimensional data source is called first. These low-dynamic data sources, such as vegetation growth data or terrain evolution data, usually change slowly and have a long period. The scheduler aligns these data sources with the static three-dimensional geographic information model. This step is to ensure the consistency of the two in spatial position and data format. Next, the scheduler will determine whether there is a state conflict between the data of the low-dynamic three-dimensional data source and the static three-dimensional geographic information model. A state conflict refers to the situation where the two have different attribute values ​​or geometric shapes at the same spatial location. For example, an area in the static model is marked as a forest, while the area in the low-dynamic data source is updated to a vacant land due to logging activities, which constitutes a state conflict.

[0034] If a state conflict is detected, the hierarchical fusion scheduler will decide to fuse the low-dynamic 3D data source based on the preset priority rules. This is because the low-dynamic data source, although changing slowly, may contain the latest and more accurate geographic information. The scheduler will update the static 3D geographic information model according to the information of the low-dynamic data source to generate a first-order fused 3D geographic information model.

[0035] For example, in urban planning, a static 3D geographic information model may contain the initial state of a building. However, over time, some buildings may have been renovated or expanded. When a low-dynamic 3D data source (such as building update data) is aligned with a static model, if a state conflict is found (i.e., the building state is inconsistent), the scheduler will give priority to the information from the low-dynamic data source and update the static model to reflect the latest state of the building. In this way, the first-order fused 3D geographic information model contains more accurate and real-time geographic information, providing a basis for subsequent data fusion and application.

[0036] In a preferred embodiment, after storing the first-order fused three-dimensional geographic information model, it includes: the hierarchical fusion scheduler calls the middle group dynamic three-dimensional data source; aligns the middle group dynamic three-dimensional data source to the static three-dimensional geographic information model, and determines whether the middle group dynamic three-dimensional data source has a state conflict with the data of the first-order fused three-dimensional geographic information model; if the middle group dynamic three-dimensional data source has a state conflict with the data of the first-order fused three-dimensional geographic information model, executes the middle group dynamic three-dimensional data source, fuses the first-order fused three-dimensional geographic information model according to the middle group dynamic three-dimensional data source, and stores the second-order fused three-dimensional geographic information model.

[0037] Similarly, after storing the first-order fused 3D geographic information model, the hierarchical fusion scheduler will continue to call the medium-dynamic level of 3D data sources. These medium-dynamic data sources, such as project progress data or equipment status data, have moderate frequency and intensity of change. The scheduler first aligns these medium-dynamic data sources with the static 3D geographic information model (more accurately, with the current first-order fused 3D geographic information model, because it is based on the fusion result of the static model and the low-dynamic data source). The purpose of alignment is to ensure the consistency of the medium-dynamic data source with the first-order fusion model in spatial position and data format, so as to prepare for subsequent data fusion.

[0038] Next, the scheduler determines whether there is a state conflict between the data in the dynamic 3D data source and the first-order fusion 3D geographic information model. State conflicts may occur at the same spatial location, and the dynamic data source provides different attribute values ​​or geometric shapes from the first-order fusion model. For example, a road is marked as unblocked in the first-order fusion model, but the road in the dynamic data source is updated to a congested state due to construction activities, which constitutes a state conflict.

[0039] If a state conflict is detected, the hierarchical fusion scheduler will decide to fuse the medium dynamic 3D data source based on the preset priority rules. This is because the medium dynamic data source contains more timely and accurate geographic information and reflects recent changes. The scheduler will update the first-order fused 3D geographic information model according to the information of the medium dynamic data source to generate a second-order fused 3D geographic information model.

[0040] For example, in urban traffic management, the first-order fused 3D geographic information model may contain the initial unblocked state of the road. However, as construction activities proceed, some roads may become congested. When the dynamic 3D data source (such as traffic monitoring data) is aligned with the first-order fusion model, if a state conflict is found (i.e., the road state is inconsistent), the dispatcher will give priority to using the information from the dynamic data source to update the first-order fusion model to reflect the latest congestion state of the road. In this way, the second-order fused 3D geographic information model contains more accurate and real-time traffic information, providing strong support for traffic management and planning.

[0041] In a preferred embodiment, after storing the second-order fused three-dimensional geographic information model, it includes: the hierarchical fusion scheduler calls a high-group dynamic three-dimensional data source; aligns the high-group dynamic three-dimensional data source to the second-order fused three-dimensional geographic information model, and determines whether the high-group dynamic three-dimensional data source has a state conflict with the data of the second-order fused three-dimensional geographic information model; if the high-group dynamic three-dimensional data source has a state conflict with the data of the second-order fused three-dimensional geographic information model, executes the high-group dynamic three-dimensional data source priority, fuses the second-order fused three-dimensional geographic information model according to the high-group dynamic three-dimensional data source, and stores a third-order fused three-dimensional geographic information model; and outputs the third-order fused three-dimensional geographic information model as the final fused three-dimensional geographic information model.

[0042] Furthermore, after storing the second-order fused 3D geographic information model, the hierarchical fusion scheduler continues its data fusion process, and at this time, it calls high-dynamic level 3D data sources. These high-dynamic data sources, such as real-time traffic flow data or crowd density data, have high frequency and drastic changes. The scheduler first aligns these high-dynamic data sources with the current second-order fused 3D geographic information model to ensure the consistency of the two in spatial position and data format, providing a basis for subsequent data fusion.

[0043] The scheduler then determines whether there is a state conflict between the high-dynamic 3D data source and the data in the second-order fusion 3D geographic information model. Since the high-dynamic data sources change frequently, they contain the latest information that is inconsistent with the second-order fusion model. For example, the traffic flow of a road in the second-order fusion model is marked as medium, but the real-time traffic flow data of the road in the high-dynamic data source indicates that it is currently in a peak congestion state, which constitutes a state conflict.

[0044] If a state conflict is detected, the hierarchical fusion scheduler will decide to fuse the high-dynamic 3D data source based on the preset priority rules. This is because the high-dynamic data source provides the most timely and accurate geographic information, reflecting the current real-time situation. The scheduler will update the second-order fused 3D geographic information model according to the information of the high-dynamic data source to generate a third-order fused 3D geographic information model.

[0045] For example, in urban traffic management, a second-order fused 3D geographic information model may contain road status updated based on a medium dynamic data source. However, as real-time traffic flow changes, some roads may quickly change from smooth to congested. When a high dynamic 3D data source (such as real-time traffic monitoring data) is aligned with the second-order fusion model, if a state conflict is found (i.e., the real-time state of the road is inconsistent), the dispatcher will prioritize the information from the high dynamic data source to update the second-order fusion model to reflect the latest real-time state of the road.

[0046] Finally, the generated three-order fusion 3D geographic information model is output as the final fusion 3D geographic information model. This model integrates the information of static 3D geographic information model, low dynamic level data source, medium dynamic level data source and high dynamic level data source, providing the most comprehensive, accurate and real-time 3D geographic information, and providing strong support for applications in urban planning, traffic management, emergency response and other fields.

[0047] In a preferred embodiment, the hierarchical fusion scheduler is connected to a data fusion network, and the data fusion network is used to fuse the static three-dimensional geographic information model according to the low group dynamic three-dimensional data source, fuse the first-order fused three-dimensional geographic information model according to the middle group dynamic three-dimensional data source, and fuse the second-order fused three-dimensional geographic information model according to the high group dynamic three-dimensional data source; wherein the data fusion network includes a geometric fusion network for updating geometric data, an attribute fusion network for updating entity attribute data, and a time fusion network for updating fused entity timestamps.

[0048] Preferably, the hierarchical fusion scheduler is closely connected with the data fusion network, and they work together to achieve seamless fusion of multi-source 3D data. The data fusion network is a comprehensive processing system that includes multiple sub-networks dedicated to different types of data fusion, ensuring that the static 3D geographic information model can be effectively fused with low-group, medium-group and high-group dynamic 3D data sources step by step.

[0049] When the hierarchical fusion scheduler calls low-level dynamic 3D data sources, these data sources mainly focus on long-term changes in geometry and entity attributes, such as renovation of buildings or evolution of terrain. At this time, the geometric fusion network in the data fusion network comes into play, which is responsible for aligning and integrating the geometric information in the low-dynamic data source with the static 3D geographic information model. If the low-dynamic data source provides an updated building model, the geometric fusion network will replace the old version in the static model with a new, more accurate version by geometric replacement. At the same time, the attribute fusion network is also involved, which is responsible for updating the attribute fields of buildings, roads, or nodes. For example, if the low-dynamic data source contains new uses for buildings or new speed limit information for roads, the attribute fusion network will ensure that this information is accurately reflected in the first-order fused 3D geographic information model. In addition, the time fusion network is responsible for updating the timestamps of the fused entities to ensure that each element in the model has accurate time information, which is crucial for subsequent data analysis and application.

[0050] Furthermore, when the dynamic 3D data source of the middle group is called, the data fusion network comes into play again. At this time, it may involve the update of the project progress or equipment status. These data sources provide information about the progress of building construction or the status of road construction. The geometric fusion network will create a transparent layer on the model by overlaying layers to show the current construction status or thermal distribution. The attribute fusion network continues to update the attribute information of related entities, such as marking the road status as "under construction".

[0051] When high-level dynamic 3D data sources are called, these data sources usually contain high-frequency changing information such as real-time traffic flow or crowd density. The data fusion network responds quickly, the geometry fusion network displays the current traffic conditions by updating the layers in real time, and the attribute fusion network updates the real-time status attributes of roads or regions, such as marking a road as "congested". The time fusion network ensures that all these updates have accurate timestamps, so that the final fused 3D geographic information model can fully and accurately reflect the current geographical environment and dynamic changes.

[0052] Through this hierarchical fusion approach and the collaborative work of each sub-network in the data fusion network, a three-dimensional model that contains both static geographic information and multi-source dynamic data can be generated, providing powerful data support for urban planning, traffic management and other fields.

[0053] In a preferred embodiment, the output fused three-dimensional geographic information model also includes a rollback recovery unit; wherein, the rollback recovery unit is used to receive a preset rollback timestamp, perform data backtracking on the fused three-dimensional geographic information model according to the preset rollback timestamp, and restore it to the historical model state corresponding to the preset rollback timestamp.

[0054] In detail, in the process of outputting the fused 3D geographic information model, in addition to generating a comprehensive, accurate and real-time 3D model, an important functional unit, the rollback recovery unit, is also integrated. The rollback recovery unit is designed to provide data backtracking capabilities to ensure that the historical state of the model can be restored when needed.

[0055] The core function of the rollback recovery unit is to receive a preset rollback timestamp. This timestamp can be a specific moment that the user dynamically queries based on needs, such as "query the comprehensive status of a certain location at a certain time." By supporting such dynamic queries, the rollback recovery unit can flexibly locate any time point in the model history.

[0056] Once the preset rollback timestamp is received, the rollback recovery unit will perform data backtracking on the fused 3D geographic information model according to this timestamp. This means that it will retrieve and apply all relevant data source updates before this time point, while ignoring all subsequent updates. In this way, the model can be accurately restored to the historical state corresponding to the preset rollback timestamp.

[0057] For example, in urban planning, if decision makers want to know the traffic and passenger flow conditions in a certain area one month ago, they can input the specific timestamp of one month ago through the rollback recovery unit. The unit will quickly backtrack the model data and restore it to the state at that point in time, thereby showing the traffic flow, passenger density and related geographical information at that time. This capability is of great significance for analyzing historical data, evaluating policy effects or providing decision support.

[0058] In short, the addition of the rollback recovery unit enables the fused three-dimensional geographic information model to not only have the ability to update in real time, but also has a powerful historical data backtracking function, providing more comprehensive and flexible data support for various application scenarios.

[0059] In a preferred embodiment, the dynamic three-dimensional data source is dynamically graded according to the key attributes, and multiple groups of dynamic three-dimensional data sources are output, including: establishing a mapping relationship between the key attributes and the dynamic correlation degree, and calculating the dynamic index of each dynamic three-dimensional data source based on the mapping relationship to obtain the dynamic index of each dynamic three-dimensional data source; setting a clustering K value, wherein the clustering K value is greater than or equal to 3; using the dynamic index of each dynamic three-dimensional data source as an input vector, performing DBSCAN clustering according to the clustering K value, and outputting clustering results, wherein the clustering results include dynamic grade labels; and dividing all dynamic three-dimensional data sources into multiple groups of dynamic three-dimensional data sources according to the dynamic grade labels.

[0060] Optionally, in the data fusion process of the dynamic 3D GIS, the dynamic grading of the dynamic 3D data source begins with establishing a mapping relationship between key attributes and dynamic correlation. Key attributes include update frequency, change intensity, timestamp range, etc., which together determine the dynamic characteristics of the data source. By deeply analyzing the inherent connection between these attributes and the dynamic changes of the data source, a corresponding weight or correlation coefficient can be assigned to each attribute, thereby building a comprehensive mapping relationship.

[0061] Then, based on the mapping relationship, the dynamic index calculation is performed for each dynamic three-dimensional data source. The calculation process comprehensively considers the key attributes of the data source, and obtains a comprehensive index that can reflect the dynamic characteristics of the data source through weighted summation or other mathematical operations. This dynamic index not only quantifies the dynamic degree of the data source, but also provides a basis for subsequent clustering analysis.

[0062] Next, set the clustering K value, which is usually greater than or equal to 3, to ensure the diversity and accuracy of the clustering results. The selection of the clustering K value can be adjusted according to the specific application scenario and data characteristics. Then, the dynamic indicators of each dynamic three-dimensional data source are used as the input vector, and the DBSCAN (density-based spatial clustering application and noise discovery) algorithm is used for cluster analysis. The DBSCAN algorithm can automatically identify dense areas in the data and divide them into different clusters. In this process, there is no need to pre-specify the center point of each cluster, and the algorithm will automatically determine it according to the distribution characteristics of the data. The result of the cluster analysis is to assign a dynamic level label to each dynamic three-dimensional data source. These labels include "high dynamic level", "medium dynamic level" and "low dynamic level", which intuitively reflect the dynamic characteristics of the data source. Finally, all dynamic three-dimensional data sources are divided into multiple groups according to these dynamic level labels, and each group contains data sources with similar dynamic characteristics.

[0063] For example, in urban traffic management, real-time traffic flow data can be divided into high dynamic level groups because they have high frequency and drastic changes; engineering progress data can be divided into medium dynamic level groups because they have moderate frequency and intensity of change; and vegetation growth data can be divided into low dynamic level groups because they change slowly and have a long period. Through this dynamic level division method, dynamic three-dimensional data sources can be managed and processed more effectively, and the efficiency and accuracy of data fusion can be improved.

[0064] In a preferred embodiment, the hierarchical fusion scheduler includes a plurality of fusion update cycles, wherein each dynamic level corresponds to a fusion update cycle, and the fusion update cycle is in an inversely proportional functional relationship with the size of the dynamic level.

[0065] Specifically, the hierarchical fusion scheduler is a key component in the dynamic 3D GIS, which is responsible for managing and coordinating the fusion update process of data sources with different dynamic levels. In this scheduler, each dynamic level is assigned a specific fusion update cycle. These update cycles are inversely proportional to the size of the dynamic level, which means that the higher the dynamic level, the shorter the corresponding fusion update cycle.

[0066] Specifically, the data source of dynamic level L1 has the highest dynamicity, so its fusion update cycle can be set to 5 seconds. This means that every 5 seconds, the system will perform a fusion update on the L1 data source to ensure the real-time and accuracy of the data. For example, in a real-time traffic monitoring system, the L1 data source may include real-time traffic flow data, which needs to be updated frequently to reflect the latest traffic conditions. In contrast, the data source of dynamic level L2 has medium dynamicity, and its fusion update cycle can be set to 1 hour. This means that the system will perform a fusion update on the L2 data source every hour. For example, in a project progress monitoring system, the L2 data source may include project progress data, which changes relatively slowly but still needs to be updated regularly to reflect the latest progress of the project. The data source of dynamic level L3 has the lowest dynamicity, and its fusion update cycle is set to the longest 1 week. This means that the system will perform a fusion update on the L3 data source once a week. For example, in a vegetation growth monitoring system, the L3 data source may include vegetation growth data, which changes very slowly, so it can be updated once a longer time.

[0067] Through the design of this hierarchical fusion scheduler, the system can reasonably allocate update resources according to the dynamic characteristics of different data sources to ensure the efficiency and accuracy of data fusion.

[0068] The 3D GIS data fusion method provided by the embodiment of the present invention has at least the following technical effects: 1. Through the hierarchical fusion scheduler, the fusion process of multiple groups of dynamic three-dimensional data sources is realized from low to high level. This hierarchical fusion method not only improves the efficiency of data fusion, but also ensures that data sources of different dynamic levels can be processed in a targeted manner according to their characteristics, thereby improving the accuracy and real-time performance of the fusion results.

[0069] 2. A dynamic grading method based on key attributes is proposed. By establishing a mapping relationship between key attributes and dynamic correlation, calculating dynamic indicators, and using the DBSCAN clustering algorithm for clustering analysis, the automatic classification of dynamic three-dimensional data sources is realized. This dynamic grading method can accurately reflect the dynamic characteristics of the data source and provide strong support for subsequent data fusion.

[0070] 3. The rollback recovery unit allows users to receive a preset rollback timestamp and perform data backtracking on the fused 3D geographic information model according to the timestamp to restore it to the historical model state. This function enhances the flexibility and reliability of the system, allowing users to easily backtrack to the historical state of the model when needed, providing more possibilities for decision support and data analysis.

[0071] Embodiment 2:

[0072] like Figure 2 As shown, based on the same inventive concept as the 3D GIS data fusion method provided in Embodiment 1, the embodiment of the present invention also provides a 3D GIS data fusion system, the system comprising: The data acquisition module 11 is used to access the three-dimensional geographic information system to acquire a static three-dimensional data source, and to perform spatial consistency modeling on the static three-dimensional data source to construct a static three-dimensional geographic information model.

[0073] The attribute extraction module 12 is used to obtain the dynamic three-dimensional data source to be fused, and extract key attributes of the dynamic three-dimensional data source, including spatial location, data type, data update frequency and change degree, and data timestamp range.

[0074] The level classification module 13 is used to dynamically classify the dynamic three-dimensional data source according to the key attributes and output multiple groups of dynamic three-dimensional data sources, wherein the dynamic levels at least include a low dynamic level, a medium dynamic level and a high dynamic level.

[0075] The data fusion module 14 is used to set a hierarchical fusion scheduler, which calls the multiple groups of dynamic three-dimensional data sources step by step to perform data fusion on the static three-dimensional geographic information model from low to high, and outputs a fused three-dimensional geographic information model.

[0076] Furthermore, the data fusion module 14 is further configured to perform the following steps: The hierarchical fusion scheduler calls a low-group dynamic three-dimensional data source; aligns the low-group dynamic three-dimensional data source to the static three-dimensional geographic information model, and determines whether the low-group dynamic three-dimensional data source has a state conflict with the data of the static three-dimensional geographic information model; if the low-group dynamic three-dimensional data source has a state conflict with the data of the static three-dimensional geographic information model, executes the low-group dynamic three-dimensional data source priority, fuses the static three-dimensional geographic information model according to the low-group dynamic three-dimensional data source, and stores a first-order fused three-dimensional geographic information model.

[0077] Furthermore, the data fusion module 14 is further configured to perform the following steps: The hierarchical fusion scheduler calls the middle group dynamic three-dimensional data source; aligns the middle group dynamic three-dimensional data source to the static three-dimensional geographic information model, and determines whether the middle group dynamic three-dimensional data source has a state conflict with the data of the first-order fused three-dimensional geographic information model; if the middle group dynamic three-dimensional data source has a state conflict with the data of the first-order fused three-dimensional geographic information model, executes the middle group dynamic three-dimensional data source, fuses the first-order fused three-dimensional geographic information model according to the middle group dynamic three-dimensional data source, and stores the second-order fused three-dimensional geographic information model.

[0078] Furthermore, the data fusion module 14 is further configured to perform the following steps: The hierarchical fusion scheduler calls a high-level dynamic three-dimensional data source; aligns the high-level dynamic three-dimensional data source to the second-order fused three-dimensional geographic information model, and determines whether the high-level dynamic three-dimensional data source has a state conflict with the data of the second-order fused three-dimensional geographic information model; if there is a state conflict between the high-level dynamic three-dimensional data source and the data of the second-order fused three-dimensional geographic information model, executes the high-level dynamic three-dimensional data source priority, fuses the second-order fused three-dimensional geographic information model according to the high-level dynamic three-dimensional data source, and stores the third-order fused three-dimensional geographic information model; and outputs the third-order fused three-dimensional geographic information model as the final fused three-dimensional geographic information model.

[0079] Furthermore, the data fusion module 14 is further configured to perform the following steps: The hierarchical fusion scheduler is connected to a data fusion network, which is used to fuse the static three-dimensional geographic information model according to the low-group dynamic three-dimensional data source, to fuse the first-order fused three-dimensional geographic information model according to the middle-group dynamic three-dimensional data source, and to fuse the second-order fused three-dimensional geographic information model according to the high-group dynamic three-dimensional data source; wherein the data fusion network includes a geometric fusion network for updating geometric data, an attribute fusion network for updating entity attribute data, and a time fusion network for updating fused entity timestamps.

[0080] Furthermore, the data fusion module 14 is further configured to perform the following steps: The output fused three-dimensional geographic information model also includes a rollback recovery unit; wherein the rollback recovery unit is used to receive a preset rollback timestamp, perform data backtracking on the fused three-dimensional geographic information model according to the preset rollback timestamp, and restore it to the historical model state corresponding to the preset rollback timestamp.

[0081] Furthermore, the level classification module 13 is further configured to perform the following steps: A mapping relationship between the key attribute and the dynamic correlation degree is established, and a dynamic index calculation is performed on each dynamic three-dimensional data source based on the mapping relationship to obtain the dynamic index of each dynamic three-dimensional data source; a clustering K value is set, and the clustering K value is greater than or equal to 3; the dynamic index of each dynamic three-dimensional data source is used as an input vector, DBSCAN clustering is performed according to the clustering K value, and a clustering result is output, and the clustering result includes a dynamic level label; all dynamic three-dimensional data sources are divided into multiple groups of dynamic three-dimensional data sources according to the dynamic level label.

[0082] Furthermore, the data fusion module 14 is further configured to perform the following steps: The hierarchical fusion scheduler includes a plurality of fusion update cycles, wherein each dynamic level corresponds to a fusion update cycle, and the fusion update cycle is in an inversely proportional functional relationship with the size of the dynamic level.

[0083] Through the above detailed description of the three-dimensional geographic information system data fusion method in this specification, those skilled in the art can clearly understand the three-dimensional geographic information system data fusion system in this embodiment. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part description.

[0084] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A three-dimensional geographic information system data fusion method, characterized in that: The method comprises: Accessing a three-dimensional geographic information system to obtain a static three-dimensional data source, and performing spatial consistency modeling on the static three-dimensional data source to construct a static three-dimensional geographic information model; Acquire a dynamic three-dimensional data source to be fused, and extract key attributes of the dynamic three-dimensional data source, including spatial location, data type, data update frequency and change degree, and data timestamp range; According to the key attributes, the dynamic three-dimensional data source is divided into dynamic levels, and multiple groups of dynamic three-dimensional data sources are output, wherein the dynamic levels at least include a low dynamic level, a medium dynamic level, and a high dynamic level; A hierarchical fusion scheduler is provided, and the hierarchical fusion scheduler calls the multiple groups of dynamic three-dimensional data sources step by step to perform data fusion on the static three-dimensional geographic information model from low to high, and outputs a fused three-dimensional geographic information model.

2. The method according to claim 1, characterized in that The hierarchical fusion scheduler calls the multiple groups of dynamic three-dimensional data sources step by step to perform data fusion on the static three-dimensional geographic information model from low to high, including: The hierarchical fusion scheduler calls the low-group dynamic three-dimensional data source; Aligning the low group dynamic three-dimensional data source to the static three-dimensional geographic information model, and determining whether there is a state conflict between the low group dynamic three-dimensional data source and the data of the static three-dimensional geographic information model; If there is a state conflict between the data of the low-group dynamic three-dimensional data source and the static three-dimensional geographic information model, the low-group dynamic three-dimensional data source priority is executed, the static three-dimensional geographic information model is fused according to the low-group dynamic three-dimensional data source, and a first-order fused three-dimensional geographic information model is stored.

3. The method according to claim 2, characterized in that After storing the first-order fused 3D geographic information model, it includes: The hierarchical fusion scheduler calls a dynamic three-dimensional data source; Aligning the middle group of dynamic three-dimensional data sources to the static three-dimensional geographic information model, and determining whether there is a state conflict between the middle group of dynamic three-dimensional data sources and the data of the first-order fusion three-dimensional geographic information model; If there is a state conflict between the data of the middle group dynamic three-dimensional data source and the first-order fused three-dimensional geographic information model, execute the middle group dynamic three-dimensional data source, fuse the first-order fused three-dimensional geographic information model according to the middle group dynamic three-dimensional data source, and store the second-order fused three-dimensional geographic information model.

4. The method according to claim 3, characterized in that After storing the second-order fused 3D geographic information model, it includes: The hierarchical fusion scheduler calls a high-level dynamic three-dimensional data source; Aligning the high-level dynamic three-dimensional data source to the second-order fused three-dimensional geographic information model, and determining whether there is a state conflict between the high-level dynamic three-dimensional data source and the data of the second-order fused three-dimensional geographic information model; If there is a state conflict between the data of the high-level dynamic three-dimensional data source and the data of the second-order fused three-dimensional geographic information model, the priority of the high-level dynamic three-dimensional data source is executed, the second-order fused three-dimensional geographic information model is fused according to the high-level dynamic three-dimensional data source, and the third-order fused three-dimensional geographic information model is stored; The third-order fused three-dimensional geographic information model is output as a final fused three-dimensional geographic information model.

5. The method according to claim 4, characterized in that The hierarchical fusion scheduler is connected to a data fusion network, and the data fusion network is used to fuse the static three-dimensional geographic information model according to the low group dynamic three-dimensional data source, fuse the first-order fused three-dimensional geographic information model according to the middle group dynamic three-dimensional data source, and fuse the second-order fused three-dimensional geographic information model according to the high group dynamic three-dimensional data source; The data fusion network includes a geometry fusion network for updating geometry data, an attribute fusion network for updating entity attribute data, and a time fusion network for updating fusion entity timestamps.

6. The method according to claim 1, characterized in that The output fused three-dimensional geographic information model also includes a rollback recovery unit; The rollback recovery unit is used to receive a preset rollback timestamp, perform data backtracking on the fused three-dimensional geographic information model according to the preset rollback timestamp, and restore it to the historical model state corresponding to the preset rollback timestamp.

7. The method according to claim 1, characterized in that The dynamic three-dimensional data source is dynamically graded according to the key attributes, and multiple groups of dynamic three-dimensional data sources are output, including: Establishing a mapping relationship between the key attribute and the degree of dynamic correlation, and calculating a dynamic index for each dynamic three-dimensional data source based on the mapping relationship to obtain a dynamic index for each dynamic three-dimensional data source; Set a cluster K value, where the cluster K value is greater than or equal to 3; Taking the dynamic index of each dynamic three-dimensional data source as an input vector, performing DBSCAN clustering according to the clustering K value, and outputting a clustering result, wherein the clustering result includes a dynamic level label; All dynamic three-dimensional data sources are divided into multiple groups of dynamic three-dimensional data sources according to the dynamic level labels.

8. The method according to claim 1, characterized in that The hierarchical fusion scheduler includes a plurality of fusion update cycles, wherein each dynamic level corresponds to a fusion update cycle, and the fusion update cycle is in an inversely proportional functional relationship with the size of the dynamic level.

9. The three-dimensional geographic information system data fusion system is characterized by: Used to implement the three-dimensional geographic information system data fusion method according to any one of claims 1 to 8, the system comprises: A data acquisition module is used to access a three-dimensional geographic information system to acquire a static three-dimensional data source, and to perform spatial consistency modeling on the static three-dimensional data source to construct a static three-dimensional geographic information model; An attribute extraction module is used to obtain the dynamic three-dimensional data source to be fused and extract key attributes of the dynamic three-dimensional data source, including spatial location, data type, data update frequency and change degree, and data timestamp range; A level classification module, used to dynamically classify the dynamic three-dimensional data source according to the key attributes, and output multiple groups of dynamic three-dimensional data sources, wherein the dynamic levels at least include a low dynamic level, a medium dynamic level and a high dynamic level; The data fusion module is used to set a hierarchical fusion scheduler, which calls the multiple groups of dynamic three-dimensional data sources step by step to perform data fusion on the static three-dimensional geographic information model from low to high, and outputs a fused three-dimensional geographic information model.

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