A method and system for processing two-dimensional and three-dimensional spatiotemporal geographic information.

Abstract

This invention relates to the field of geographic information processing technology, providing a method and system for processing two-dimensional and three-dimensional spatiotemporal geographic information. The method includes: collecting two-dimensional and three-dimensional data from different sources, integrating location and time information to construct a unified spatiotemporal network, thereby improving data integration. A dynamic model is set up to track and analyze the changes in geographic features in three-dimensional space and their behavior on a two-dimensional plane in real time to predict future trends. The mutual influence of different geographic features in the spatiotemporal network is analyzed, a spatiotemporal influence matrix is ​​constructed, and the dynamic relationship between two-dimensional and three-dimensional features is quantified. An anomaly detection model is established, combining historical data to identify potential abnormal events and generate alarm information to provide decision support. By integrating two-dimensional and three-dimensional data, the analytical capabilities for traffic flow, population dynamics, and environmental changes are significantly improved, providing a scientific basis for urban management and resource allocation, and promoting smart cities and sustainable development.

Description

Technical Field

[0001] This invention belongs to the field of geographic information processing technology, and in particular relates to a method and system for processing two-dimensional and three-dimensional spatiotemporal geographic information. Background Technology

[0002] With the acceleration of urbanization and the rapid development of information technology, the acquisition and processing of spatiotemporal geographic information has become increasingly important. Traditional Geographic Information Systems (GIS) often focus on static data and lack the ability to monitor and analyze dynamic changes in real time. This results in insufficient timeliness and accuracy of data when dealing with fields such as urban management, traffic flow monitoring, and environmental protection, making it difficult to meet the needs of modern society for efficient decision-making.

[0003] Currently, many existing technologies still face numerous challenges when processing two-dimensional and three-dimensional data. Traditional methods typically process two-dimensional data (such as maps and road networks) separately from three-dimensional data (such as building heights and terrain information), making it difficult to effectively reflect the correlation between the data. This approach not only reduces the efficiency of analysis but may also lead to information loss and misunderstanding, thereby affecting the scientific nature of decision-making.

[0004] Furthermore, with the development of IoT and big data technologies, real-time data collection has become possible. However, how to effectively integrate dynamic data from different sources, especially in complex urban environments, remains a pressing problem. Existing technologies often lack a unified framework for processing multidimensional data, leading to severe information silos and limiting the application potential of the data. Summary of the Invention

[0005] The purpose of this invention is to provide a method for processing two-dimensional and three-dimensional spatiotemporal geographic information, aiming to solve the technical problems existing in the prior art as identified in the background section.

[0006] This invention is implemented as follows: a method for processing two-dimensional and three-dimensional spatiotemporal geographic information, the method comprising:

[0007] Collect spatiotemporal geographic information from different sources, including two-dimensional and three-dimensional data, and integrate the collected two-dimensional and three-dimensional data in terms of location and time information, placing them in a unified spatiotemporal network;

[0008] Dynamic modeling is set up on the spatiotemporal network to track and analyze the changes of geographic features in three-dimensional space and their behavior on a two-dimensional plane in real time, and predict the future behavioral trends of geographic features based on the analysis results;

[0009] Analyze the interactions between different geographic features in the spatiotemporal network, capture the relationship between two-dimensional and three-dimensional geographic features, and analyze their dynamic influence relationships;

[0010] Establish an anomaly detection model that combines dynamic influence relationships and future behavioral trends to determine whether there are potential abnormal events and generate warning messages.

[0011] As a further aspect of the present invention, the integration of the collected two-dimensional and three-dimensional data in terms of location and time information, and placing them in a unified spatiotemporal grid, specifically includes:

[0012] Data is collected from the identified data sources to obtain the required two-dimensional and three-dimensional data, including real-time acquisition of static and dynamic data;

[0013] Geographic features are extracted from the collected two-dimensional and three-dimensional data, including location information and event information, and spatiotemporal attributes, including spatial attributes and temporal attributes, are constructed for each geographic feature.

[0014] A multi-grid structure is established as a spatiotemporal network to integrate the acquired two-dimensional data, three-dimensional data, and corresponding spatiotemporal attributes, placing them within a unified spatiotemporal network.

[0015] As a further aspect of the present invention, the step of setting up dynamic modeling on a spatiotemporal network, tracking and analyzing the changes of geographical features in three-dimensional space and their behavior on a two-dimensional plane in real time, and predicting the future behavioral trends of geographical features based on the analysis results, specifically includes:

[0016] Based on the collected two-dimensional data, three-dimensional data, and spatiotemporal attributes, a dynamic model is constructed, and all spatiotemporal attributes collected at the current moment are used as input;

[0017] Dynamic models are used to monitor changes in geographic features in three-dimensional space, including changes in location, morphology, and behavior in a two-dimensional plane. Based on the monitoring results, the behavioral trends of geographic features are analyzed, and future trends and potential behavioral patterns are predicted.

[0018] As a further aspect of the present invention, the prediction of future trends and potential behavioral patterns specifically includes:

[0019] Define the neighborhood of each geographic feature and capture all neighborhood features related to that geographic feature within that neighborhood;

[0020] Based on a dynamic model, and combined with neighborhood characteristics, the future changing trends of each geographic feature are analyzed:

[0021] ;

[0022] in, Indicates time Temporal geographical features state, A set of neighborhood features representing geographical features. The influence coefficient represents the neighborhood characteristics. Geographical features The intensity of the impact, Representing neighborhood features and geographical features The differences between states, that is, the influence of neighborhood features on geographical features.

[0023] As a further aspect of the present invention, the analysis of the mutual influence between different geographical features in the spatial network, capturing the relationship between two-dimensional and three-dimensional geographical features, and analyzing their dynamic influence relationships specifically includes:

[0024] Based on all the spatiotemporal attributes constructed, a relationship model between each geographic feature is built, the dynamic correlation of different geographic features in the spatiotemporal network is analyzed, and it is determined and recorded whether there are two-dimensional features that affect the three-dimensional features.

[0025] Create a spatiotemporal influence matrix to record the strength and direction of the interaction between different geographic features in time and space, where each element of the matrix represents the influence weight between different geographic features;

[0026] Identify the periodicity of changes in different geographical features over time, and define it as a dynamic influence relationship.

[0027] As a further aspect of the present invention, the analysis of the dynamic correlation of different geographical features in the spatiotemporal network, and the determination of whether there are two-dimensional features that affect the three-dimensional features, specifically involves:

[0028] ;

[0029] in, Representing two-dimensional features and three-dimensional features The amount of mutual information between them is directly proportional to the dependency relationship. Representing two-dimensional features and three-dimensional features The probability of them happening simultaneously Two-dimensional features The marginal probability of occurrence Three-dimensional features The marginal probability of occurrence;

[0030] The identification of the periodicity of the changing trends of different geographical features over time specifically includes:

[0031] Based on the obtained spatiotemporal attributes, information about each geographic feature under different time and spatial attributes is extracted and arranged to generate a time series.

[0032] Periodicity can be identified by calculating the correlation between the results of each geographic feature in a time series at different time points.

[0033] ;

[0034] in, Indicates the time series with time delay The autocorrelation coefficient under the following conditions Geographical feature observations representing time, Represents the mean of a time series. This represents the total number of samples in the time series.

[0035] As a further aspect of the present invention, the establishment of an anomaly detection model, which combines dynamic influence relationships and future behavioral trends to determine whether potential abnormal events exist and generate warning information, specifically includes:

[0036] An anomaly detection model is constructed based on historical data and spatiotemporal attributes, and the spatiotemporal data collected in real time is input into the anomaly detection model.

[0037] The established anomaly detection model is used to monitor real-time data and determine whether any abnormal events exist.

[0038] ;

[0039] in, Indicates the error value. This represents the actual observed value at time t. This is the influence coefficient;

[0040] Set an error threshold; if the error value exceeds the error threshold, an abnormal event is considered to have occurred.

[0041] Another object of the present invention is to provide a system for processing two-dimensional and three-dimensional spatiotemporal geographic information, the system comprising:

[0042] The data integration module is used to collect spatiotemporal geographic information from different sources, including two-dimensional and three-dimensional data, and to integrate the collected two-dimensional and three-dimensional data in terms of location and time information, placing them in a unified spatiotemporal grid.

[0043] The geographic feature change tracking and analysis module is used to set up dynamic modeling on the spatiotemporal network, track and analyze the changes of geographic features in three-dimensional space and their behavior on the two-dimensional plane in real time, and predict the future behavior trend of geographic features based on the analysis results.

[0044] The feature interaction analysis module is used to analyze the interaction between different geographic features in the spatiotemporal network, capture the relationship between two-dimensional and three-dimensional geographic features, and analyze their dynamic influence relationships.

[0045] The anomaly detection module is used to build an anomaly detection model, combine dynamic influence relationships and future behavioral trends to determine whether there are potential abnormal events, and generate warning information.

[0046] The beneficial effects of this invention are:

[0047] This method integrates the dynamic characteristics of time and space to construct a spatiotemporal network that enables comprehensive data analysis. The unified data processing framework enhances data integration, allowing decision-makers to access rich geographic information on a single platform. This strengthens the understanding of complex geographic features and their interactions, contributing to optimized urban planning and resource management.

[0048] The real-time tracking capability of dynamic models enables more accurate analysis of traffic flow and crowd dynamics, reflecting the impact of these dynamics on the surrounding environment in a timely manner. This helps managers quickly identify changes in traffic flow, reduce congestion, and improve urban traffic efficiency. By capturing the relationship between two-dimensional features (such as roads and rivers) and three-dimensional features (such as building height), users can identify the synergistic effects of these features, providing important evidence for scientific resource allocation and urban development strategies.

[0049] In anomaly detection, algorithms combining spatiotemporal state transition models effectively identify potential abnormal events, such as traffic accidents or environmental pollution. This comprehensive analysis improves the accuracy of anomaly identification, accelerates response speed, and ensures that managers can take timely measures to mitigate potential impacts. Attached Figure Description

[0050] Figure 1 A flowchart illustrating a method for processing two-dimensional and three-dimensional spatiotemporal geographic information provided in an embodiment of the present invention;

[0051] Figure 2 A flowchart provided for embodiments of the present invention, which integrates the collected two-dimensional and three-dimensional data in terms of location and time information and places them in a unified spatiotemporal grid;

[0052] Figure 3 A flowchart for real-time tracking and analysis of changes in geographic features in three-dimensional space and behavior on a two-dimensional plane, and prediction of future behavioral trends of geographic features based on the analysis results, provided for embodiments of the present invention;

[0053] Figure 4 A flowchart for capturing the relationship between two-dimensional and three-dimensional geographic features and analyzing their dynamic influence relationships, provided by an embodiment of the present invention;

[0054] Figure 5 A flowchart for determining whether a potential abnormal event exists, provided as an embodiment of the present invention;

[0055] Figure 6 This is a structural block diagram of a spatiotemporal geographic information two-dimensional and three-dimensional data processing system provided in an embodiment of the present invention. Detailed Implementation

[0056] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0057] Figure 1 A flowchart illustrating a method for processing two-dimensional and three-dimensional spatiotemporal geographic information provided in an embodiment of the present invention is shown below. Figure 1 As shown, the method includes:

[0058] S100 collects spatiotemporal geographic information from different sources, including two-dimensional and three-dimensional data, and integrates the collected two-dimensional and three-dimensional data in terms of location and time information, placing them in a unified spatiotemporal network.

[0059] This step involves the comprehensive collection of two-dimensional and three-dimensional data from various sources. This data is not limited to static information (such as maps, building heights and shapes) but also includes dynamic information (such as traffic flow and climate change). During this process, data collection needs to fully consider both location and temporal information, ensuring effective integration of all data within a single spatiotemporal framework. Specifically, by extracting geographic features from the collected two-dimensional and three-dimensional data, key spatial, temporal, and comprehensive spatiotemporal attributes can be identified. These attributes provide the foundation for subsequent analysis and modeling.

[0060] Next, in the process of constructing the spatiotemporal grid, a multi-dimensional grid structure can be established to integrate different types of data, forming a unified spatiotemporal grid. This grid is not only a carrier for data storage but also a fundamental framework for spatiotemporal data analysis. Within this framework, all spatiotemporal attributes are integrated, laying a solid foundation for subsequent dynamic modeling and impact analysis. Through this integration method, users can more intuitively obtain a comprehensive view of geographical features, thereby enabling more efficient decision-making in fields such as urban planning and environmental monitoring.

[0061] By systematically integrating data from different sources and types, this approach not only provides rich foundational information for subsequent analysis but also enhances data usability and reliability. Through this comprehensive data processing method, users can gain a more complete understanding of dynamic changes in geospatial environments and obtain crucial spatiotemporal information in a timely manner. Furthermore, a unified spatiotemporal framework clarifies the relationships between different data sets, thereby promoting cross-analysis and comprehensive utilization of the data. In practical applications, this method can significantly improve the accuracy and efficiency of urban management, resource allocation, and environmental monitoring, contributing to more scientific and forward-looking decision-making.

[0062] like Figure 2 As shown, the integration of the collected two-dimensional and three-dimensional data in terms of location and time information, and placing them in a unified spatiotemporal grid, specifically includes:

[0063] S110, collects data from the identified data source to obtain the required two-dimensional and three-dimensional data, including real-time collection of static and dynamic data;

[0064] S120 extracts geographic features from the collected two-dimensional and three-dimensional data, including location information and event information, and constructs spatiotemporal attributes for each geographic feature, including spatial attributes and temporal attributes.

[0065] S130, establish a multi-grid structure as a spatiotemporal network, integrate the acquired two-dimensional data, three-dimensional data and corresponding spatiotemporal attributes, and place them in a unified spatiotemporal network.

[0066] S200 sets up dynamic modeling on a spatiotemporal network to track and analyze the changes of geographic features in three-dimensional space and their behavior on a two-dimensional plane in real time, and predicts the future behavioral trends of geographic features based on the analysis results.

[0067] This step involves real-time monitoring of changes in geographic features in three-dimensional space, such as changes in building height or terrain. Dynamic models help us better understand the impact of urban expansion. For example, as buildings in a region gradually increase in height during urban development, this affects surrounding environmental factors such as sunlight and wind direction. The model can capture this change and analyze its impact on residents. For instance, reduced sunshine hours may lead to decreased resident satisfaction with living in the area, thus affecting the region's real estate market.

[0068] Secondly, in terms of behavioral analysis on a two-dimensional plane, the model can track changes in traffic flow. For example, when construction occurs on a major road, traffic flow may be forced to shift to other secondary roads. Through dynamic models, changes in traffic flow can be monitored in real time, the causes of traffic congestion can be analyzed, and traffic conditions can be predicted for the next few hours. This will provide traffic management departments with decision-making support, helping them adjust traffic lights or implement temporary traffic controls during peak hours to alleviate congestion.

[0069] If a pollution incident occurs in a region, dynamic models can analyze in real time the impact of pollution sources (such as factory emissions) on surrounding water bodies or air quality. By monitoring the relationship between different geographical features (such as rivers, vegetation cover, etc.) and pollutant concentrations, models can predict pollution spread trends and provide timely early warning information to decision-makers so that emergency measures can be taken.

[0070] This step, by establishing a dynamic model, allows users not only to capture changes in geographical features in a timely manner but also to deeply understand the impact of these changes on the surrounding environment and people, thereby making more accurate decisions. This real-time monitoring and analysis capability makes work in fields such as urban management, environmental protection, and traffic management more efficient and scientific, promoting the realization of smart cities and sustainable development.

[0071] like Figure 3 As shown, the step of setting up dynamic modeling on a spatiotemporal network to track and analyze the changes of geographic features in three-dimensional space and their behavior on a two-dimensional plane in real time, and predicting the future behavioral trends of geographic features based on the analysis results, specifically includes:

[0072] S210, Based on the collected two-dimensional data, three-dimensional data and spatiotemporal attributes, construct a dynamic model and use all spatiotemporal attributes collected at the current moment as input;

[0073] S220 uses dynamic models to monitor changes in geographic features in three-dimensional space, including changes in location, morphology, and behavior in a two-dimensional plane. Based on the monitoring results, it analyzes the behavioral trends of geographic features and predicts future trends and potential behavioral patterns.

[0074] In this step, predicting future trends and potential behavioral patterns specifically includes:

[0075] Define the neighborhood of each geographic feature and capture all neighborhood features related to that geographic feature within that neighborhood;

[0076] Based on a dynamic model, and combined with neighborhood characteristics, the future changing trends of each geographic feature are analyzed:

[0077] ;

[0078] in, Indicates time Temporal geographical features state, A set of neighborhood features representing geographical features. The influence coefficient represents the neighborhood characteristics. Geographical features The intensity of the impact, Representing neighborhood features and geographical features The differences between states, that is, the influence of neighborhood features on geographical features.

[0079] S300 analyzes the mutual influence between different geographic features in the space-time network, captures the relationship between two-dimensional and three-dimensional geographic features, and analyzes their dynamic influence relationships.

[0080] This step, through the calculation of mutual information, quantifies the correlation between two-dimensional and three-dimensional features. This provides important quantitative evidence for determining whether two-dimensional features influence three-dimensional features. For example, in an urban environment, two-dimensional features (such as road layout) may affect three-dimensional features (such as building height and distribution). If an increase in roads is accompanied by a change in building height, a potential relationship between the two can be inferred.

[0081] Furthermore, by creating a spatiotemporal impact matrix to record the intensity and direction of interactions between different geographical features in time and space, we can identify which two-dimensional features have a significant impact on three-dimensional features within a specific time period. For example, when a newly built expressway opens, housing prices in the surrounding area may rise significantly. The spatiotemporal impact matrix can quantify the intensity of this impact, thus providing a reference for future urban planning.

[0082] Analyzing trends over time is also crucial. By identifying the periodic changes in different geographical features over time, we can more accurately predict their future behavior. For example, vegetation cover in some areas may fluctuate periodically with seasonal changes, and this information can help city managers make scientific plans when formulating greening policies.

[0083] By systematically analyzing the relationships between different geographical features, a more comprehensive understanding of dynamic changes within a geographic space can be achieved. This analysis not only enhances the connectivity of data but also deepens the understanding of complex environmental phenomena. For example, in the field of public safety, analyzing the dynamic relationships between geographical features can help identify potential risk areas in advance, such as the relationship between high crime rates and specific road layouts, thus providing a foundation for security management.

[0084] like Figure 4As shown, the analysis of the interactions between different geographic features in the spatial network captures the relationship between two-dimensional and three-dimensional geographic features and analyzes their dynamic influence relationships, specifically including:

[0085] S310, Based on all the spatiotemporal attributes constructed, construct a relationship model between each geographic feature, analyze the dynamic correlation of different geographic features in the spatiotemporal network, determine whether there are two-dimensional features that affect the three-dimensional features and record them;

[0086] S320, create a spatiotemporal influence matrix to record the strength and direction of the interaction between different geographic features in time and space, where each element of the matrix represents the influence weight between different geographic features;

[0087] S330 identifies the periodicity of changes in different geographical features over time, defining it as a dynamic influence relationship.

[0088] In this step, the analysis of the dynamic correlation of different geographical features in the spatiotemporal network, and the determination of whether there are two-dimensional features that affect the three-dimensional features, specifically involves:

[0089] ;

[0090] in, Representing two-dimensional features and three-dimensional features The amount of mutual information between them is directly proportional to the dependency relationship. Representing two-dimensional features and three-dimensional features The probability of them happening simultaneously Two-dimensional features The marginal probability of occurrence Three-dimensional features The marginal probability of occurrence;

[0091] The identification of the periodicity of the changing trends of different geographical features over time specifically includes:

[0092] Based on the obtained spatiotemporal attributes, information about each geographic feature under different time and spatial attributes is extracted and arranged to generate a time series.

[0093] Periodicity can be identified by calculating the correlation between the results of each geographic feature in a time series at different time points.

[0094] ;

[0095] in, Indicates the time series with time delay The autocorrelation coefficient under the following conditions Geographical feature observations representing time, Represents the mean of a time series. This represents the total number of samples in the time series.

[0096] S400 establishes an anomaly detection model, combining dynamic influence relationships and future behavioral trends to determine whether there are potential abnormal events and generate warning information.

[0097] By analyzing historical data and spatiotemporal attributes, a highly adaptive anomaly detection model is constructed. This model utilizes past observations and collected spatiotemporal data to identify normal behavioral patterns and sets relevant thresholds for comparison in real-time data monitoring. For example, for traffic flow data, the model can learn the normal range of traffic fluctuations; if real-time data deviates significantly from this range, an alarm will be triggered.

[0098] The anomaly detection model collects data in real time and inputs it into the established model for monitoring. By calculating the error between the current observed value and the predicted value, it can effectively determine whether an anomaly exists. For example, as mentioned in the formula... Specifically, this demonstrates how to combine the current state with historical states for analysis. If the error exceeds a set threshold, an abnormal event is identified, and an alarm message is generated promptly.

[0099] Once the model detects an anomaly, the system can quickly generate alarm information and transmit it to relevant decision-makers. This information not only includes the basic characteristics of the anomaly (such as event type, time of occurrence, and scope of impact), but may also include analysis results based on historical data, helping decision-makers quickly understand the potential impact of the event and appropriate countermeasures. For example, in traffic management, if a traffic accident occurs on a certain road segment, the system will not only issue a real-time alarm, but also provide historical traffic flow data for that segment, helping decision-makers assess the impact of the accident on overall traffic.

[0100] By combining real-time monitoring with dynamic models, this method enables rapid response to emergencies and provides data support for decision-making. For example, in environmental monitoring, if a sudden deterioration in air quality is detected in a certain area, the model can quickly identify this anomaly and issue an alert to management personnel, prompting them to take timely measures, such as activating emergency plans or conducting on-site investigations. This rapid response capability significantly improves the timeliness and effectiveness of event response and reduces potential hazards.

[0101] Furthermore, the established anomaly detection model is not limited to a single data source but can also comprehensively consider the complex relationships between multiple spatiotemporal features, which is particularly important for highly dynamic environments (such as urban traffic and environmental monitoring). By enhancing the interconnectivity between data, users can more comprehensively understand and respond to changes, thus promoting the realization of intelligent decision-making.

[0102] like Figure 5 As shown, the establishment of the anomaly detection model, which combines dynamic influence relationships and future behavioral trends, determines whether there are potential abnormal events and generates warning information, specifically includes:

[0103] S410 constructs an anomaly detection model based on historical data and spatiotemporal attributes, and inputs the spatiotemporal data collected in real time into the anomaly detection model;

[0104] S420 uses a pre-built anomaly detection model to monitor real-time data and determine whether any abnormal events exist.

[0105] ;

[0106] in, Indicates the error value. This represents the actual observed value at time t. This is the influence coefficient;

[0107] S430: Set an error threshold. If the error value is greater than the error threshold, an abnormal event is identified.

[0108] Figure 6 A structural block diagram of a spatiotemporal geographic information two-dimensional and three-dimensional data processing system provided in an embodiment of the present invention is shown below. Figure 6 As shown, the system includes:

[0109] The data integration module 100 is used to collect spatiotemporal geographic information from different sources, including two-dimensional data and three-dimensional data, and to integrate the collected two-dimensional data and three-dimensional data in terms of location and time information, and place them in a unified spatiotemporal network.

[0110] The geographic feature change tracking and analysis module 200 is used to set up dynamic modeling on the spatiotemporal network, track and analyze the changes of geographic features in three-dimensional space and their behavior on the two-dimensional plane in real time, and predict the future behavior trend of geographic features based on the analysis results.

[0111] The feature interaction analysis module 300 is used to analyze the interaction between different geographic features in the spatiotemporal network, capture the relationship between two-dimensional and three-dimensional geographic features, and analyze their dynamic influence relationships.

[0112] The anomaly detection module 400 is used to establish an anomaly detection model, combine dynamic influence relationships and future behavioral trends to determine whether there are potential abnormal events, and generate warning information.

[0113] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0114] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0115] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

[0116] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for processing two-dimensional and three-dimensional spatiotemporal geographic information, characterized in that, The method includes: Collect spatiotemporal geographic information from different sources, including two-dimensional and three-dimensional data, and integrate the collected two-dimensional and three-dimensional data in terms of location and time information, placing them in a unified spatiotemporal network; Dynamic modeling is set up on the spatiotemporal network to track and analyze the changes of geographic features in three-dimensional space and their behavior on a two-dimensional plane in real time, and predict the future behavioral trends of geographic features based on the analysis results; Analyze the interactions between different geographic features in the spatiotemporal network, capture the relationship between two-dimensional and three-dimensional geographic features, and analyze their dynamic influence relationships; Establish an anomaly detection model, combining dynamic influence relationships and future behavioral trends, to determine whether there are potential abnormal events and generate warning information; Specifically, the analysis of the interactions between different geographic features in the spatial network, capturing the relationship between two-dimensional and three-dimensional geographic features, and analyzing their dynamic influence relationships includes: Based on all the spatiotemporal attributes constructed, a relationship model between each geographic feature is built, the dynamic correlation of different geographic features in the spatiotemporal network is analyzed, and it is determined and recorded whether there are two-dimensional features that affect the three-dimensional features. Create a spatiotemporal influence matrix to record the strength and direction of the interaction between different geographic features in time and space, where each element of the matrix represents the influence weight between different geographic features; Identify the periodicity of the changing trends of different geographical features over time, and define it as a dynamic influence relationship; The analysis of the dynamic correlation of different geographical features in the spatiotemporal network, and the determination of whether there are two-dimensional features that affect the three-dimensional features, specifically involves: ； in, Representing two-dimensional features and three-dimensional features The amount of mutual information between them is directly proportional to the dependency relationship. Representing two-dimensional features and three-dimensional features The probability of them happening simultaneously Two-dimensional features The marginal probability of occurrence Three-dimensional features The marginal probability of occurrence; The identification of the periodicity of the changing trends of different geographical features over time specifically includes: Based on the obtained spatiotemporal attributes, information about each geographic feature under different time and spatial attributes is extracted and arranged to generate a time series; Periodicity can be identified by calculating the correlation between the results of each geographic feature in a time series at different time points. ； in, Indicates the time series with time delay The autocorrelation coefficient under the following conditions Geographical feature observations representing time, Represents the mean of a time series. This represents the total number of samples in the time series.

2. The method according to claim 1, characterized in that, The process of integrating the collected two-dimensional and three-dimensional data in terms of location and time information and placing them in a unified spatiotemporal network specifically includes: Data is collected from the identified data sources to obtain the required two-dimensional and three-dimensional data, including real-time acquisition of static and dynamic data; Geographic features are extracted from the collected two-dimensional and three-dimensional data, including location information and event information, and spatiotemporal attributes, including spatial attributes and temporal attributes, are constructed for each geographic feature. A multidimensional grid structure is established as a spatiotemporal grid, which integrates the acquired two-dimensional data, three-dimensional data and corresponding spatiotemporal attributes, and places them in a unified spatiotemporal grid.

3. The method according to claim 2, characterized in that, The process of setting up dynamic modeling on a spatiotemporal network to track and analyze the changes of geographic features in three-dimensional space and their behavior on a two-dimensional plane in real time, and predicting the future behavioral trends of geographic features based on the analysis results, specifically includes: Based on the collected two-dimensional data, three-dimensional data, and spatiotemporal attributes, a dynamic model is constructed, and all spatiotemporal attributes collected at the current moment are used as input; Dynamic models are used to monitor changes in geographic features in three-dimensional space, including changes in location, morphology, and behavior in a two-dimensional plane. Based on the monitoring results, the behavioral trends of geographic features are analyzed, and future trends and potential behavioral patterns are predicted.

4. The method according to claim 3, characterized in that, The prediction of future trends and potential behavioral patterns specifically includes: Define the neighborhood of each geographic feature and capture all neighborhood features related to that geographic feature within that neighborhood; Based on a dynamic model, and combined with neighborhood characteristics, the future changing trends of each geographic feature are analyzed: ； in, Indicates time Temporal geographical features state, A set of neighborhood features representing geographical features. The influence coefficient represents the neighborhood characteristics. Geographical features The intensity of the impact, Representing neighborhood features and geographical features The differences between states, that is, the influence of neighborhood features on geographical features.

5. The method according to claim 1, characterized in that, The establishment of an anomaly detection model, which combines dynamic influence relationships and future behavioral trends, determines whether there are potential abnormal events and generates warning information, specifically includes: An anomaly detection model is constructed based on historical data and spatiotemporal attributes, and the spatiotemporal data collected in real time is input into the anomaly detection model. The established anomaly detection model is used to monitor real-time data and determine whether any abnormal events exist. ； in, Indicates the error value. This represents the actual observed value at time t. This is the influence coefficient; Set an error threshold; if the error value exceeds the error threshold, an abnormal event is considered to have occurred.

6. The method according to claim 1, characterized in that, The system for implementing the spatiotemporal geographic information two-dimensional and three-dimensional data processing method includes: The data integration module is used to collect spatiotemporal geographic information from different sources, including two-dimensional and three-dimensional data, and to integrate the collected two-dimensional and three-dimensional data in terms of location and time information, placing them in a unified spatiotemporal grid. The geographic feature change tracking and analysis module is used to set up dynamic modeling on the spatiotemporal network, track and analyze the changes of geographic features in three-dimensional space and their behavior on the two-dimensional plane in real time, and predict the future behavior trend of geographic features based on the analysis results. The feature interaction analysis module is used to analyze the interaction between different geographic features in the spatiotemporal network, capture the relationship between two-dimensional and three-dimensional geographic features, and analyze their dynamic influence relationships. The anomaly detection module is used to build an anomaly detection model, combine dynamic influence relationships and future behavioral trends to determine whether there are potential abnormal events, and generate warning information.

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