Intelligent acquisition and processing method and system for multi-source heterogeneous geographic information data

Abstract

The application discloses a kind of multi-source heterogeneous geographic information data intelligent collection processing method and system, it is related to data processing technical field, the method includes: the multiple sensors of initialization mobile device are listened to and collect multiple sensor multi-source heterogeneous geographic information data stream, carry out time synchronization, space alignment and data fusion, obtain geographic information fusion data stream, in combination with map data and user input, generate structured geographic information data package;Through dynamic legend generation module, structured geographic information data package is read and calculated, generates target geographic legend, and it is superimposed to map view and visualized display.The application solves the technical problem that the existing geographic information collection method relies on single data source, and the data collection accuracy is insufficient, and the reliability is poor in complex environment, achieves the technical effect that the data collection accuracy and reliability in complex environment are improved by multi-source data fusion and dynamic legend generation.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, specifically to an intelligent acquisition and processing method and system for multi-source heterogeneous geographic information data. Background Technology

[0002] In the field of geographic information acquisition and processing, traditional methods primarily rely on a single data source, such as GPS, for positioning and geographic information collection. However, single data sources suffer from insufficient accuracy and poor reliability in complex environments, such as urban canyons, indoor spaces, or areas with signal interference. Furthermore, traditional methods lack the ability to fuse and process multi-source data, making it difficult to fully utilize the advantages of different sensors. Simultaneously, in terms of geographic information visualization, existing map legends are mostly static designs, unable to dynamically adjust according to map zoom levels and device screen characteristics, resulting in a poor user experience. Summary of the Invention

[0003] This application provides an intelligent acquisition and processing method and system for multi-source heterogeneous geographic information data, which solves the technical problems of existing geographic information acquisition methods relying on a single data source and having insufficient data acquisition accuracy and poor reliability in complex environments.

[0004] The first aspect of this application provides an intelligent acquisition and processing method for multi-source heterogeneous geographic information data. The method includes: initializing multiple sensors of a mobile device, including GPS, a gyroscope, and mobile network base station positioning, and monitoring and acquiring multi-source heterogeneous geographic information data streams from the multiple sensors; performing time synchronization, spatial alignment, and data fusion on the multi-source heterogeneous geographic information data streams to obtain a geographic information fused data stream; combining the geographic information fused data stream, map data, and user input to generate a structured geographic information data package; and reading and calculating the structured geographic information data package through a dynamic legend generation module to generate a target geographic legend, and drawing and overlaying the target geographic legend onto a map view for visualization.

[0005] A second aspect of this application provides an intelligent acquisition and processing system for multi-source heterogeneous geographic information data. The system includes: a heterogeneous data acquisition module for initializing multiple sensors on a mobile device, including GPS, a gyroscope, and mobile network base station positioning, and monitoring and acquiring multi-source heterogeneous geographic information data streams from the multiple sensors; a geographic information fusion module for performing time synchronization, spatial alignment, and data fusion on the multi-source heterogeneous geographic information data streams to obtain a fused geographic information data stream; a structured processing module for combining the fused geographic information data stream, map data, and user input to generate a structured geographic information data package; and a dynamic legend generation module for reading and calculating the structured geographic information data package through the dynamic legend generation module to generate a target geographic legend, and then drawing and overlaying the target geographic legend onto a map view for visualization.

[0006] One or more technical solutions provided in this application have at least the following technical effects or advantages: This application provides an intelligent acquisition and processing method and system for multi-source heterogeneous geographic information data, relating to the field of data processing technology. By integrating multi-source sensor data such as GPS, gyroscope, and base station positioning, it performs time synchronization, spatial alignment, and weighted fusion of multi-source heterogeneous geographic information to generate a high-precision geographic information fusion data stream. Combined with map data and user input, it generates a structured geographic information data package. Through a dynamic legend generation module, it achieves adaptive adjustment and visualization of legends. This solves the technical problems of existing geographic information acquisition methods that rely on a single data source and suffer from insufficient data acquisition accuracy and poor reliability in complex environments. It achieves the technical effect of improving the accuracy and reliability of data acquisition in complex environments through multi-source data fusion and dynamic legend generation. Attached Figure Description

[0007] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0008] Figure 1 A schematic flowchart of an intelligent acquisition and processing method for multi-source heterogeneous geographic information data provided in this application embodiment; Figure 2 This is a schematic diagram of the structure of an intelligent acquisition and processing system for multi-source heterogeneous geographic information data provided in an embodiment of this application.

[0009] Figure labeling: Heterogeneous data acquisition module 11, geographic information fusion module 12, structured processing module 13, dynamic legend generation module 14. Detailed Implementation

[0010] This application provides an intelligent acquisition and processing method and system for multi-source heterogeneous geographic information data, which solves the technical problems of existing geographic information acquisition methods relying on a single data source and having insufficient data acquisition accuracy and poor reliability in complex environments.

[0011] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0012] It should be noted that the terms "first," "second," etc., in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or modules not explicitly listed or inherent to such processes, methods, products, or devices.

[0013] Example 1, as Figure 1 As shown, this application provides an intelligent acquisition and processing method for multi-source heterogeneous geographic information data, the method comprising: P10: Initialize multiple sensors of the mobile device, including GPS, gyroscope and mobile network base station positioning, and listen to and collect multi-source heterogeneous geographic information data streams from the multiple sensors.

[0014] Specifically, the first step requires initializing multiple sensors on the mobile device, including the Global Positioning System (GPS), gyroscopes, and mobile network base station positioning systems. This process is fundamental to acquiring multi-source heterogeneous geographic information data. By integrating data from multiple sensors, the accuracy and robustness of geographic information acquisition can be significantly improved.

[0015] Specifically, the GPS module is first started and initialized. GPS technology calculates the device's position by receiving signals from at least four satellites, providing information such as longitude, latitude, and altitude. In outdoor environments, GPS can provide high-precision positioning data, typically with an error range within a few meters. For the data acquisition process in this application, GPS positioning provides the core location data of the device. However, GPS signals may be interfered with in some complex environments, such as urban canyons or indoors, leading to a decrease in positioning accuracy. To address this issue, this application introduces a gyroscope and a mobile network base station positioning system as auxiliary sensors to enhance the reliability and accuracy of positioning.

[0016] A gyroscope is a sensor that measures the rotation angle and angular velocity of a mobile device in three-dimensional space. Using a gyroscope, the system can monitor the device's attitude changes in real time, and thus calculate the device's relative position changes using inertial navigation principles when GPS signals are lost or weakened. This inertial navigation-based assisted positioning method can provide relatively accurate positioning information in a short time, effectively compensating for the inadequacy of GPS signals. Meanwhile, mobile network base station positioning systems can calculate the approximate location of the device by utilizing the signal strength and time difference between the mobile device and surrounding base stations when GPS signals are unavailable. Although this positioning method is generally less accurate, it can effectively provide device location in complex environments such as urban areas with tall buildings or underground locations, and is a reliable alternative, especially when GPS is unavailable.

[0017] After sensor initialization, the system begins listening to and acquiring multi-source heterogeneous geographic information data streams from these sensors. Multi-source heterogeneity refers to data originating from different sensors, and these data differ in format, accuracy, and timestamps. For example, GPS data is typically provided in latitude and longitude coordinates, while gyroscope data is output in the form of angular velocity and rotation angle, and base station positioning data is presented in the form of base station signal strength and distance estimates. These data require specialized processing for time synchronization and spatial alignment to ensure the accuracy of subsequent data fusion.

[0018] Furthermore, to achieve effective data acquisition, it is necessary to ensure the frequency and accuracy of data collection to guarantee that the data from each sensor can meet the requirements of subsequent processing. For applications with high real-time requirements, the system needs to configure the sensor's acquisition frequency, for example, updating data once per second, and adjust the sampling accuracy and data storage strategy according to the actual application scenario to ensure that data can be collected stably and accurately under different environments.

[0019] This multi-source data fusion strategy not only improves the accuracy and reliability of positioning but also enhances the system's adaptability to complex environments. For example, in geological surveys, data collectors may need to collect data in various environments such as mountains, urban canyons, or indoors. By combining GPS, gyroscope, and base station positioning data, the system can continuously provide accurate geographic information in these different scenarios, improving the efficiency and quality of field data collection.

[0020] P20: Perform time synchronization, spatial alignment, and data fusion on the multi-source heterogeneous geographic information data stream to obtain a geographic information fused data stream.

[0021] Furthermore, step P20 in this embodiment of the application also includes: P21: Synchronize the multi-source heterogeneous geographic information data stream according to the collection timestamp to obtain a multi-source heterogeneous geographic information sequence data stream; P22: Perform spatial coordinate transformation and alignment on the multi-source heterogeneous geographic information sequence data stream to obtain a usable multi-source heterogeneous geographic information data stream; P23: Perform weighted data fusion on the usable multi-source heterogeneous geographic information data stream to obtain a geographic information fused data stream.

[0022] It should be understood that processing multi-source heterogeneous geographic information data streams is necessary to ensure that data collected by different sensors can be accurately aligned in time and space, and to obtain high-quality geographic information fusion data streams through data fusion.

[0023] First, the collected multi-source heterogeneous geographic information data streams are time-synchronized according to their acquisition timestamps. Since the sampling frequencies and time bases of different sensors may differ, directly using the raw data can lead to temporal inconsistencies, affecting the accuracy of data fusion. Therefore, the system needs to extract the timestamp information from each sensor's data to unify all data streams onto a common time base. This involves matching the timestamps of different sensor data to ensure that each data point corresponds to a unified time window, generating a multi-source heterogeneous geographic information sequence data stream. This ensures that data from different data sources can be compared and fused at the same point in time. During the time synchronization process, a high-precision timestamp parsing algorithm can be used to ensure the consistency of the time base of data from different sensors.

[0024] If the sampling timestamps of different sensors are not completely consistent, the system can use interpolation algorithms to correct these discrepancies. For example, linear interpolation or spline interpolation can be used to map the data collected by the sensors onto a standard timestamp sequence, ensuring time synchronization of all data streams. The main purpose of this step is to eliminate inconsistencies caused by time misalignment, providing a foundation for subsequent spatial alignment and data fusion.

[0025] After time synchronization is completed, spatial coordinate transformation and alignment are then performed on the multi-source heterogeneous geographic information sequence data stream. Since geographic information data acquired by different sensors may be based on different coordinate systems, such as GPS data typically based on the WGS-84 coordinate system, while mobile network base station positioning data may be based on the local coordinate system, it is necessary to unify these data into a standard geographic coordinate system.

[0026] During coordinate transformation, a standard coordinate system, such as WGS-84 or the local UTM coordinate system, can be selected as a reference, and data from different sensors can be uniformly converted to data in this coordinate system. Specifically, coordinate transformation algorithms, such as affine transformation or projection transformation, can be used to convert data in the original coordinate system to data in the target coordinate system, thereby ensuring spatial consistency of the data and eliminating spatial deviations caused by differences in sensor positions or coordinate systems.

[0027] Finally, weighted data fusion is performed on the available multi-source heterogeneous geographic information data streams. Since data from different sensors vary in accuracy and reliability, simply aggregating these data may lead to biases in the fusion results. Therefore, the system employs a weighted fusion algorithm, assigning different weights based on the accuracy and reliability of each sensor's data. For example, GPS data has higher accuracy in open environments, so it receives a higher weight in such environments; while in environments with weak GPS signals, the weights of gyroscope and base station positioning data are correspondingly increased.

[0028] Data fusion processes can employ techniques such as weighted averaging, Kalman filtering, or particle filtering. These algorithms, combined with assigned weights, weighted calculations are performed on data from multiple sensors to obtain a comprehensive and more accurate geographic information data stream. For example, if GPS signals exhibit significant errors at certain times due to obstruction or reflection, these errors can be corrected using data from other sensors, such as gyroscopes or mobile base station positioning, thereby providing more accurate position and attitude data.

[0029] During the weighted fusion process, the system also considers the timeliness and availability of the data, dynamically adjusting the weights of each data source. If the data quality of certain sensors is poor, the system will automatically reduce their weights to prevent erroneous data from affecting the final results. The fused geographic information data stream has higher accuracy, stability, and robustness, effectively meeting the data accuracy requirements under different environments.

[0030] Furthermore, step P23 in this embodiment of the application also includes: P23-1: Perform structured transformation on the available multi-source heterogeneous geographic information data stream to obtain a multi-source structured geographic information data stream; P23-2: Assign weights to the available multi-source heterogeneous geographic information data stream according to the acquisition accuracy of the multiple sensors to determine the multi-source data weight coefficient set; P23-3: Perform weighted data fusion on the multi-source structured geographic information data stream based on the multi-source data weight coefficient set to obtain a geographic information fused data stream.

[0031] Optionally, the weighted data fusion process was further refined to generate a high-quality geographic information fusion data stream.

[0032] First, the available multi-source heterogeneous geographic information data streams undergo structured transformation. Data collected by different sensors typically have different formats and types. For example, a GPS sensor may provide location data such as longitude, latitude, and altitude, while a gyroscope provides direction and angular velocity data, and mobile network base station positioning may provide information such as base station ID and signal strength. These data have different representations, and to facilitate subsequent processing and analysis, the system needs to convert these different types of data into a unified, structured data format.

[0033] For example, structured transformation involves data standardization, such as converting GPS data, gyroscope data, and base station positioning data into a standardized data format that includes fields such as timestamps, geographic coordinates, sensor type, and data precision. Through this transformation, the system can ensure structural consistency of data from different sources, thereby facilitating subsequent weight allocation and data fusion.

[0034] Next, the available multi-source heterogeneous geographic information data streams are weighted according to the acquisition accuracy of each sensor. Since different sensors have varying accuracy and reliability under different environments, the system needs to assign different weights to the output data of each sensor based on factors such as sensor accuracy, signal strength, and environmental conditions, thus determining a set of multi-source data weight coefficients. Specifically, the weight coefficients are determined based on the inherent accuracy characteristics of the sensors and their actual performance under current environmental conditions. For example, GPS typically has high accuracy in open environments, and therefore receives a higher weight under normal circumstances; while in environments where GPS signals are interfered with, the weights of gyroscope and base station positioning data may be increased accordingly. The system can dynamically calculate the monitoring accuracy of each sensor using a pre-set accuracy evaluation model, combined with real-time environmental data such as signal strength and noise levels, and then generate corresponding weight coefficients based on the monitoring accuracy. This accuracy evaluation model can be constructed using machine learning based on historical geographic information monitoring data. The model inputs are environmental data and sensor type, and the output is the sensor weight coefficients. These weight coefficients reflect the reliability and importance of different sensor data under current conditions, providing a quantitative basis for subsequent weighted fusion.

[0035] Finally, the structured geographic information data stream is weighted and fused based on a set of weighting coefficients from multiple data sources. During the fusion process, the system combines data from each sensor, performing a weighted average according to their respective weights, or employing advanced fusion algorithms such as Kalman filtering and particle filtering to reduce the impact of unreliable data and enhance the robustness of the final result. For example, in a specific fusion calculation, the system might multiply GPS data by a higher weighting coefficient, and multiply gyroscope data and base station positioning data by their respective weighting coefficients, then perform a comprehensive calculation using these weighted data to obtain the final geographic location information. Through this weighted fusion method, the system can fully utilize the advantages of each sensor under different environmental conditions to generate a high-precision, high-reliability geographic information fusion data stream.

[0036] Furthermore, after determining the set of weight coefficients for multi-source data, step P23-2 in this embodiment of the application further includes: P23-21: Analyze the impact of the sensor acquisition environment on the accuracy of the multiple sensors to obtain the accuracy impact factors of the multiple sensors; P23-22: Dynamically correct the weight coefficient set of the multi-source data based on the accuracy impact factors of the multiple sensors.

[0037] In one possible embodiment of this application, after determining the set of weight coefficients for multi-source data, an analysis of the impact of the sensor acquisition environment on accuracy and a dynamic correction mechanism for the weight coefficients can be further introduced, thereby further improving the quality and reliability of the fused data.

[0038] First, an accuracy impact analysis is performed on multiple sensors. The core of this process is to evaluate the performance of different sensors in the current data acquisition environment, particularly how their accuracy is affected by environmental factors. For example, GPS accuracy may be affected by building obstructions, multipath effects, or satellite signal strength; gyroscope accuracy may be affected by device jitter or temperature changes; and the accuracy of mobile network base station positioning may be affected by the base station's signal coverage and signal strength.

[0039] To accurately assess the impact of these environmental factors on sensor accuracy, the system performs a detailed analysis of the operating environment of each sensor. For example, by pre-establishing a sensor performance model and combining it with real-time acquired environmental data, such as signal strength, interference levels, and temperature, the system analyzes the acquisition accuracy of each sensor, thereby obtaining multiple sensor accuracy influencing factors. The sensor performance model is built based on a neural network model and can predict the sensor's performance in the current environment based on real-time acquired environmental data, such as signal strength, interference levels, and temperature. By inputting the real-time acquired environmental data into this model, the system obtains the predicted performance values ​​of the sensors in the current environment and outputs the acquisition accuracy influencing factors for each sensor. These influencing factors are quantified values ​​representing the degree of change in sensor accuracy under the current environment. For example, an influencing factor of 1.2 indicates that the sensor error under the current environment has increased by 20% compared to normal conditions.

[0040] Next, the weight coefficient set of multi-source data is dynamically adjusted based on the aforementioned factors affecting acquisition accuracy. The purpose of this process is to adjust the weight coefficient of each sensor data point according to the sensor performance under current environmental conditions. Specifically, if the acquisition accuracy of a sensor is significantly reduced due to environmental factors, its weight coefficient will decrease accordingly; conversely, if a sensor exhibits high accuracy under the current environment, its weight coefficient will increase. For example, in an urban canyon environment, GPS signals may be severely interfered with. In this case, the system will reduce the weight coefficient of GPS data while increasing the weight coefficients of gyroscope and base station positioning data to ensure the overall accuracy of the fused data. Through this dynamic adjustment mechanism, the system can adapt to environmental changes in real time and optimize weight allocation, thereby generating high-quality geographic information fusion data streams under different conditions.

[0041] Furthermore, after obtaining the geographic information fusion data stream, step P23-3 of this application embodiment also includes: P23-31: Based on the data acquisition accuracy influencing factors of the multiple sensors, perform data error analysis to determine the data error coefficients of the multiple sensors; P23-32: Perform adaptive calibration and compensation on the geographic information fusion data stream according to the data error coefficients of the multiple sensors.

[0042] Specifically, error analysis and adaptive calibration compensation can be performed on the obtained geographic information fusion data stream to further improve the accuracy and reliability of the data.

[0043] After obtaining the geographic information fusion data stream, the system needs to evaluate the error sources of each sensor by analyzing the factors affecting the acquisition accuracy of each sensor. Accuracy-influencing factors typically include environmental factors such as signal interference, obstruction, and weather conditions; sensor physical characteristics such as accuracy, resolution, and measurement range; and equipment operating conditions such as temperature, vibration, and equipment aging. The system can analyze the changes in these influencing factors using historical sensor data, real-time feedback data, and environmental monitoring data such as signal strength and weather information, and based on this, evaluate the error level of each sensor under different conditions.

[0044] For example, a GPS sensor may have high positioning accuracy in open environments, but in urban canyons or underground spaces, interference from buildings or underground facilities can reduce its accuracy. Similarly, the accuracy of a gyroscope may be affected by factors such as equipment vibration and temperature changes. Therefore, the system needs to calculate an accuracy impact factor based on these external conditions to quantify the potential error of each sensor.

[0045] Next, by establishing an error model, the accuracy influencing factors of each sensor are analyzed to determine the data error coefficient for each sensor. This error model can be a computational model built based on various statistical and machine learning methods, such as least squares and Kalman filtering. By inputting the accuracy influencing factors of each sensor into the error model for analysis, the data error coefficient of each sensor can be output. This coefficient is used to quantify the error magnitude of each sensor under specific conditions. For example, if the error coefficient of a sensor is 1.2, it means that the data from that sensor may deviate from the actual value by 20% under specific environmental conditions.

[0046] Next, adaptive calibration compensation is performed on the geographic information fusion data stream based on the error coefficients of data from multiple sensors. The purpose of this process is to adjust the fused data stream according to the error level of each sensor's data, thereby reducing errors and improving data accuracy. Calibration compensation adjusts each data point in the fused data stream by applying error coefficients. For example, if the error coefficient of a certain sensor's data is high, the system will adjust the contribution of that sensor's data to the fused data stream accordingly to reduce its impact on the final result. This adaptive calibration compensation mechanism can dynamically adjust according to the sensor's error level, thereby ensuring that the fused data stream maintains high accuracy and reliability under different environmental conditions. In this way, the system can effectively reduce errors caused by variations in sensor accuracy or environmental factors, further optimizing the quality of the geographic information fusion data stream.

[0047] P30: Combine the aforementioned geographic information fusion data stream, map data, and user input to generate a structured geographic information data package.

[0048] Optionally, a structured geographic information data package can be generated by combining geographic information fusion data streams, map data, and user input. This process involves transforming raw data collected from multiple sources into a structured data format that is easy to process and display later.

[0049] First, the fused geographic information data is integrated with map data. Map data, as a crucial component of geographic information, typically includes basic geographic information such as topographic maps, satellite imagery, road networks, and administrative divisions. This map data provides the spatial context and reference framework for the fused geographic information data stream. Therefore, Geographic Information System (GIS) technology can be used to spatially match and integrate the fused geographic information data with the map data. For example, if the location coordinates in the geographic information data stream point to a certain urban area, the system will match that location with the city boundary data on the map, thereby confirming the geographical location corresponding to the data stream. Simultaneously, geographic features extracted from the map data, such as buildings, roads, and natural resources, are mapped to the location information in the geographic information data stream, ensuring that each data point not only contains device location information but also can be combined with specific geographic features.

[0050] Simultaneously, user input is incorporated into the data integration process. Users can input specific queries, annotation information, points of interest (POIs), etc., through mobile devices or relevant application interfaces, enabling the system to provide customized geographic information services based on user needs. For example, a user can mark a specific location on a map and input related descriptions. The system combines these user inputs with the fused geographic information data and map data to generate a structured geographic information data package containing user annotations. The system supports multiple user input methods, such as voice input and gesture annotation, which can improve the convenience and flexibility of user interaction.

[0051] After these operations are completed, all the data is structured and organized to form the final structured geographic information data package. This data package uses a standardized format, such as JSON, XML, or database format, and contains necessary metadata, such as timestamps, coordinate system information, sensor data, map data, and user input, to facilitate subsequent processing, storage, and visualization. The structured geographic information data package includes geographic location information, map background information, user input information, timestamps, data sources, and data quality indicators. For example, the fused high-precision geographic location data includes longitude, latitude, and altitude information; map background information includes road names, building outlines, and administrative divisions associated with the geographic location; and user input information includes personalized content such as user annotations, query requests, and descriptions of points of interest. Simultaneously, the data package also records the time of data generation and the sensor information used, as well as quality information such as the error range and accuracy assessment of the fused data, providing reference for data users.

[0052] During the generation of structured geographic information data packets, the system employs standardized data formats and encoding rules to ensure the compatibility and scalability of the packets. For example, it uses geographic information exchange standards (such as GeoJSON and GML) to encode the data, enabling seamless exchange and sharing between different geographic information systems and application platforms. Furthermore, data verification and quality assessment mechanisms ensure the generated data packets possess high accuracy and reliability. For instance, data quality indicators are included in the data packets, allowing data users to understand the data's error range and accuracy level, thus enabling them to apply the data appropriately according to their specific needs.

[0053] P40: The structured geographic information data package is read and calculated by the dynamic legend generation module to generate a target geographic legend, and the target geographic legend is drawn and overlaid on the map view for visualization.

[0054] Furthermore, in generating the target geographic legend, step P40 of this embodiment also includes: P41: The structured geographic information data package is read through the dynamic legend generation module to obtain geographic information legend parameters, and the current scale value, screen pixel density and device parameters of the map view are obtained at the same time; P42: The geographic information legend parameters are dynamically calculated based on the current scale value, screen pixel density and device parameters to generate a target geographic legend, which includes legend length, width and text size.

[0055] It should be understood that the dynamic legend generation module processes the structured geographic information data package to generate the target geographic legend, and then draws and overlays it onto the map view for visualization. This ensures that the legend display is compatible with the map zoom level, screen display density, and device characteristics, so that users can clearly and intuitively understand the geographic information displayed on the map.

[0056] In the specific implementation process, the dynamic legend generation module first reads the structured geographic information data package to obtain geographic information legend parameters. These parameters are the foundation for generating the legend and typically include key information such as the type, scope, and units of the geographic information. Simultaneously, it obtains the current map view's scale value, screen pixel density, and device parameters. The scale value reflects the proportional relationship between the actual distance on the map and the pixels displayed on the screen; the screen pixel density determines the image's display accuracy on the screen; and device parameters include information such as the device's resolution and display size. These parameters directly affect the legend's display effect on the screen and therefore need to be considered during legend generation. For example, the current scale value determines the proportional relationship between the legend and the map, the screen pixel density affects the legend's clarity, and the device parameters determine the legend's layout and size.

[0057] Next, based on the acquired current scale value, screen pixel density, and device parameters, the geographic information legend parameters are dynamically calculated to generate the target geographic legend. The core of this process is ensuring that the legend accurately reflects the actual scale of the geographic information under different conditions and remains visually clear and readable. Specifically, the target geographic legend includes visual elements such as the legend's length, width, and text size. These elements are dynamically adjusted according to the scale value and screen parameters. For example, when the map zoom is large, the legend's length and width may need to be increased accordingly so that users can clearly see the legend's content; while when the map zoom is small, the legend's size may need to be reduced to fit a smaller display area. Furthermore, screen pixel density and device parameters also affect the legend's display effect. When the device has a high pixel density, the legend's details and text need to be adjusted to a finer size, while on devices with low pixel density, the legend may need to be enlarged to ensure clear and readable display.

[0058] Through this dynamic calculation method, the target geographic legend can adaptively adjust its visual presentation to suit different map views and device conditions. Ultimately, the generated target geographic legend is drawn and overlaid on the map view for visualization. This process not only enhances the intuitiveness and usability of map information but also ensures a consistent user experience across different devices and display conditions.

[0059] Furthermore, step P42 in this embodiment of the application also includes: P42-1: Dynamically adapt and calculate the geographic information legend parameters based on the current scale value and the screen pixel density to obtain an adapted geographic information legend; P42-2: Use the device parameters to perform constraint optimization on the adapted geographic information legend to generate a target geographic legend.

[0060] Specifically, the generation process of the target geographic legend can be further refined to ensure that the generated legend can achieve the best visualization effect under different devices and display conditions.

[0061] First, the geographic information legend parameters are dynamically adapted based on the current map scale and screen pixel density. This process aims to ensure that the legend's visual elements, such as length, width, and text size, are adjusted in real-time according to the map scale and screen pixel density, maintaining a consistent scale between the legend and the map and ensuring clear readability at different zoom levels. Specifically, the system calculates the basic dimensions of the legend based on the map scale. For example, if the map scale is 1:10000, the system calculates the actual distance represented by 1 centimeter on the map, such as 1 centimeter on the map equaling 100 meters in reality. Subsequently, these dimensions are adjusted based on the screen pixel density to ensure the legend remains clear and legible on high-resolution screens. For example, for high-pixel-density screens, the text size and line thickness of the legend are appropriately increased to improve readability.

[0062] After completing the dynamic adaptation calculation, the adapted geographic information legend is further constrained and optimized using device parameters. Device parameters include the device's display resolution, screen size, and display scale, which further affect the legend's display effect. Although the previous step has already performed adaptation calculations based on the scale and screen pixel density, differences in device parameters, such as the device's screen size and resolution, can still affect the legend's display.

[0063] Therefore, the legend needs further adjustment based on the specific display characteristics of the device, such as screen size, resolution, and device type, to ensure optimal display across different devices. For example, for small mobile device screens, the system optimizes the legend layout and limits its maximum size to prevent it from occupying too much space; while for large desktop monitors, the system can provide more detailed legend information, including longer text descriptions and more complex graphic elements. Furthermore, the system considers the device's display mode, such as landscape or portrait mode, and optimizes the legend accordingly. For instance, in portrait mode, the legend might be designed with a vertical layout to fit the screen's vertical space; while in landscape mode, it might adopt a horizontal layout to fully utilize the screen's width.

[0064] After dynamic adaptation calculations and constraint optimization, the final target geographic legend is generated. This target geographic legend not only dynamically adapts to changes in map scale and screen pixel density but also optimizes based on device parameters, achieving the best display effect on different devices. The target geographic legend includes visual elements such as the legend's length, width, and text size. These elements are precisely calculated and optimized to accurately reflect the actual proportional relationships of geographic information and are visually clear and easy to read. For example, a distance legend might display "1cm = 100m" on a small mobile device, while on a large desktop monitor it might display "1 centimeter represents 100 meters," with more detailed explanatory text. This adaptive and optimized mechanism ensures that users receive intuitive, accurate, and easy-to-read geographic information legends when viewing maps on different devices.

[0065] In practical applications, this dynamic legend generation mechanism provides robust support for geographic information services. For example, in mobile GIS applications, users can zoom in and out of the map to access geographic information at different scales, and the legend will automatically adjust its size and layout to ensure the accuracy and readability of the information. Simultaneously, the system optimizes the legend based on device parameters, ensuring a consistent user experience across different devices, thereby significantly enhancing the visualization and application value of geographic information.

[0066] In summary, the embodiments of this application have at least the following technical effects: This application significantly improves the accuracy and reliability of geographic information collection in complex environments by integrating multi-source sensor data such as GPS, gyroscope, and base station positioning, and performing time synchronization, spatial alignment, and weighted fusion. By combining map data and user input, it generates structured data packages, enabling personalized processing and application of geographic information. This meets diverse needs in different scenarios, provides customized geographic information services, and improves the efficiency and flexibility of data processing. Furthermore, the dynamic legend generation module dynamically adjusts the legend display, enhancing the readability of the map and the user experience.

[0067] This technology achieves the goal of improving the accuracy and reliability of data acquisition in complex environments through multi-source data fusion and dynamic legend generation.

[0068] Example 2, based on the same inventive concept as the intelligent acquisition and processing method for multi-source heterogeneous geographic information data in the foregoing examples, such as... Figure 2 As shown, this application provides an intelligent acquisition and processing system for multi-source heterogeneous geographic information data. The system and method embodiments in this application are based on the same inventive concept. The system includes: The heterogeneous data acquisition module 11 is used to initialize multiple sensors of the mobile device, including GPS, gyroscope and mobile network base station positioning, and to monitor and acquire multi-source heterogeneous geographic information data streams from the multiple sensors.

[0069] The geographic information fusion module 12 is used to perform time synchronization, spatial alignment and data fusion on the multi-source heterogeneous geographic information data stream to obtain a geographic information fusion data stream.

[0070] The structured processing module 13 is used to combine the geographic information fusion data stream, map data and user input to generate a structured geographic information data package.

[0071] The dynamic legend generation module 14 is used to read and calculate the structured geographic information data package, generate a target geographic legend, and draw and overlay the target geographic legend onto the map view for visualization.

[0072] Furthermore, the geographic information fusion module 12 is also used to perform the following steps: The multi-source heterogeneous geographic information data stream is synchronized according to the collection timestamp to obtain a multi-source heterogeneous geographic information sequence data stream; the multi-source heterogeneous geographic information sequence data stream is spatially transformed and aligned to obtain a usable multi-source heterogeneous geographic information data stream; the usable multi-source heterogeneous geographic information data stream is weighted and fused to obtain a geographic information fused data stream.

[0073] Furthermore, the geographic information fusion module 12 is also used to perform the following steps: The available multi-source heterogeneous geographic information data stream is structurally transformed to obtain a multi-source structured geographic information data stream; the available multi-source heterogeneous geographic information data stream is weighted according to the acquisition accuracy of the multiple sensors to determine a multi-source data weight coefficient set; and the multi-source structured geographic information data stream is weighted and fused based on the multi-source data weight coefficient set to obtain a geographic information fused data stream.

[0074] Furthermore, the geographic information fusion module 12 is also used to perform the following steps: An accuracy impact analysis is performed on the multiple sensors based on their acquisition environment to obtain multiple sensor acquisition accuracy impact factors; the multi-source data weight coefficient set is then dynamically corrected based on these multiple sensor acquisition accuracy impact factors.

[0075] Furthermore, the geographic information fusion module 12 is also used to perform the following steps: Data error analysis is performed based on the data acquisition accuracy influencing factors of the multiple sensors to determine the data error coefficients of the multiple sensors; adaptive calibration and compensation are then performed on the geographic information fusion data stream according to the data error coefficients of the multiple sensors.

[0076] Furthermore, the dynamic legend generation module 14 is also used to perform the following steps: The structured geographic information data package is read by the dynamic legend generation module to obtain geographic information legend parameters. At the same time, the current scale value, screen pixel density and device parameters of the map view are obtained. Based on the current scale value, screen pixel density and device parameters, the geographic information legend parameters are dynamically calculated to generate a target geographic legend, which includes legend length, width and text size.

[0077] Furthermore, the dynamic legend generation module 14 is also used to perform the following steps: The geographic information legend parameters are dynamically adapted based on the current scale value and the screen pixel density to obtain an adapted geographic information legend; the adapted geographic information legend is constrained and optimized using the device parameters to generate a target geographic legend.

[0078] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this specification. Additionally, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0079] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

[0080] This specification and accompanying drawings are merely illustrative examples of this application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from its scope. Therefore, if such modifications and variations fall within the scope of this application and its equivalents, this application intends to include such modifications and variations.

Claims

1. A method for intelligent acquisition and processing of multi-source heterogeneous geographic information data, characterized in that, The method includes: Initialize multiple sensors of the mobile device, including GPS, gyroscope and mobile network base station positioning, and monitor and collect multi-source heterogeneous geographic information data streams from the multiple sensors; The multi-source heterogeneous geographic information data stream is time-synchronized, spatially aligned, and fused to obtain a geographic information fused data stream. By combining the aforementioned geographic information fusion data stream, map data, and user input, a structured geographic information data package is generated; The structured geographic information data package is read and calculated by the dynamic legend generation module to generate a target geographic legend, which is then drawn and overlaid onto the map view for visualization.

2. The intelligent acquisition and processing method for multi-source heterogeneous geographic information data as described in claim 1, characterized in that, The obtained geographic information fusion data stream includes: The multi-source heterogeneous geographic information data stream is synchronized according to the collection timestamp to obtain a multi-source heterogeneous geographic information sequence data stream. Spatial coordinate transformation and alignment are performed on the multi-source heterogeneous geographic information sequence data stream to obtain a usable multi-source heterogeneous geographic information data stream. The available multi-source heterogeneous geographic information data streams are weighted and fused to obtain a geographic information fused data stream.

3. The intelligent acquisition and processing method for multi-source heterogeneous geographic information data as described in claim 2, characterized in that, The available multi-source heterogeneous geographic information data streams are weighted and fused to obtain a geographic information fused data stream, including: The available multi-source heterogeneous geographic information data stream is subjected to structured transformation to obtain a multi-source structured geographic information data stream; The available multi-source heterogeneous geographic information data streams are weighted according to the acquisition accuracy of the multiple sensors to determine the multi-source data weight coefficient set; The multi-source structured geographic information data stream is weighted and fused based on the set of weight coefficients of the multi-source data to obtain a geographic information fused data stream.

4. The intelligent acquisition and processing method for multi-source heterogeneous geographic information data as described in claim 3, characterized in that, After determining the set of weight coefficients for multi-source data, the following is included: An accuracy impact analysis was performed on the multiple sensors based on their acquisition environment to obtain multiple sensor acquisition accuracy impact factors. The weight coefficient set of the multi-source data is dynamically corrected based on the influence factors of the acquisition accuracy of the multiple sensors.

5. The intelligent acquisition and processing method for multi-source heterogeneous geographic information data as described in claim 4, characterized in that, After obtaining the geographic information fusion data stream, it includes: Data error analysis was performed based on the factors affecting the accuracy of the multiple sensors to determine the data error coefficients of the multiple sensors. The geographic information fusion data stream is adaptively calibrated and compensated according to the error coefficients of the multiple sensor data.

6. The intelligent acquisition and processing method for multi-source heterogeneous geographic information data as described in claim 1, characterized in that, Generate a target geographic legend, including: The structured geographic information data package is read by the dynamic legend generation module to obtain geographic information legend parameters, and at the same time, the current scale value, screen pixel density and device parameters of the map view are obtained. The geographic information legend parameters are dynamically calculated based on the current scale value, screen pixel density, and device parameters to generate a target geographic legend, which includes legend length, width, and text size.

7. The intelligent acquisition and processing method for multi-source heterogeneous geographic information data as described in claim 6, characterized in that, Based on the current scale value, screen pixel density, and device parameters, the geographic information legend parameters are dynamically calculated to generate the target geographic legend, including: Based on the current scale value and the screen pixel density, the geographic information legend parameters are dynamically adapted and calculated to obtain an adapted geographic information legend. The device parameters are used to constrain and optimize the adapted geographic information legend to generate the target geographic legend.

8. An intelligent acquisition and processing system for multi-source heterogeneous geographic information data, characterized in that, The system includes: A heterogeneous data acquisition module is used to initialize multiple sensors of a mobile device, including GPS, gyroscope and mobile network base station positioning, and to monitor and acquire multi-source heterogeneous geographic information data streams from the multiple sensors. The geographic information fusion module is used to perform time synchronization, spatial alignment and data fusion on the multi-source heterogeneous geographic information data stream to obtain a geographic information fusion data stream. The structured processing module is used to combine the geographic information fusion data stream, map data, and user input to generate a structured geographic information data package; The dynamic legend generation module is used to read and calculate the structured geographic information data package, generate a target geographic legend, and draw and overlay the target geographic legend onto the map view for visualization.

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