Surveying and mapping data acquisition system and method based on geospatial information data surveying and mapping

Abstract

The invention relates to the technical field of geographic surveying and mapping, and discloses a surveying and mapping data acquisition method based on geographic space information data surveying and mapping, and the method comprises the following steps: 1, obtaining original geographic space information data by using a plurality of sensors, the sensors including but not limited to remote sensing satellites, unmanned aerial vehicles and ground laser scanners; step 2, preprocessing the obtained original data, including denoising, correction and normalization; and 3, integrating normalized data from different sensors through a data fusion algorithm by adopting a multi-source data fusion technology. According to the method, the geographic area is surveyed and mapped through the multiple sensors to obtain the original data, after the original surveying and mapping data are preprocessed, the normalized data from the different sensors are integrated and adopted through the data fusion algorithm by adopting the multi-source data fusion technology, the limitation of a single data source is overcome, and the comprehensiveness and consistency of the data are improved.

Description

Technical Field

[0001] The present invention relates to the field of geographic surveying and mapping technology, and in particular to a surveying and mapping data acquisition system and method based on geographic spatial information data surveying and mapping. Background Art

[0002] With the rapid development of science and technology, geospatial information is increasingly used in various fields, including urban planning, environmental monitoring, traffic management, agricultural monitoring, etc. The accurate collection and processing of geospatial information data is the basis for realizing these applications. Traditional surveying and mapping methods often rely on manual measurement and ground surveys, which are inefficient and easily affected by human factors, and cannot meet the needs of modern society for high-precision and high-efficiency surveying and mapping data. In recent years, with the development of remote sensing technology, global positioning system (GPS) and geographic information system (GIS), the way surveying and mapping data are collected has undergone fundamental changes. Remote sensing technology can obtain a wide range of geographic information through platforms such as satellites or drones, while GPS provides accurate location information and GIS provides strong support for data storage, analysis and visualization.

[0003] However, despite the significant progress made in data collection by existing technologies, there are still some problems to be solved. For example, how to efficiently integrate data from different sensors to ensure data consistency and accuracy; how to improve the accuracy and reliability of data collection in complex geographical environments. These problems need to be solved urgently. Summary of the invention

[0004] 1. Technical issues to be resolved

[0005] In view of the deficiencies in the prior art, the present invention provides a surveying and mapping data acquisition system and method based on geographic spatial information data surveying and mapping, which has the advantages of being able to efficiently integrate data from different sensors, ensure data accuracy, and improve data acquisition precision, thereby solving the problems raised in the background technology.

[0006] (II) Technical solution

[0007] In order to achieve the above-mentioned purpose of being able to efficiently integrate data from different sensors, ensure the accuracy of data, and improve the accuracy of data collection, the present invention provides the following technical solutions: a surveying and mapping data collection method based on geographic spatial information data surveying and mapping, which is carried out in the following steps:

[0008] Step 1: Use a variety of sensors to obtain raw geospatial information data, including but not limited to remote sensing satellites, drones, and ground laser scanners;

[0009] Step 2: Preprocess the acquired raw data, including denoising, correction and normalization;

[0010] Step 3: Use multi-source data fusion technology to integrate the normalized data from different sensors through a data fusion algorithm. The formula of the data fusion algorithm is as follows:

[0011]

[0012] Among them, D f is the fused data, D i is the data of the i-th sensor, W i is the weight associated with the i-th sensor data, and n is the number of sensors;

[0013] Step 4: Use machine learning algorithms to achieve real-time synchronization to ensure data consistency and accuracy. The data consistency check algorithm is based on the following formula:

[0014]

[0015] Where C is the data consistency index, n is the number of sensors;

[0016] Step 5: Use artificial intelligence technology to automatically identify and optimize data processing parameters to support multiple data analysis methods;

[0017] Step 6: Select appropriate data analysis methods for spatial analysis or time series analysis according to needs, and output the final surveying and mapping data.

[0018] Preferably, the denoising process in step 2 adopts wavelet transform technology, the correction process includes radiation correction and geometric correction, and the normalization process adopts the following formula:

[0019]

[0020] Where F′ is the normalized data, F is the initial data measured by each sensor, and σ is the standard deviation of the data.

[0021] Preferably, ensuring data consistency in step 4 is a data synchronization mechanism based on timestamps.

[0022] Preferably, the step 5 supports multiple data analysis methods including spatial analysis and time series analysis, wherein the spatial analysis is based on the following formula:

[0023] S=∫ΩH(x)dx

[0024] Where S is the spatial analysis result, H(x) is the data value at the spatial position x, dx represents the volume element at the spatial position x, and Ω is the analysis area.

[0025] Preferably, the time series analysis algorithm in step six is ​​based on a time series prediction model and is used to predict geographic space change trends.

[0026] Preferably, step six also includes a feedback loop for adjusting the sensor's acquisition strategy according to the data analysis results.

[0027] Preferably, the data acquisition system includes: a data acquisition module, the data acquisition module is used to acquire original geospatial information data through a variety of sensors, and the data acquisition module is connected to a preprocessing module, the preprocessing module is used to denoise, correct and normalize the acquired original data, and the preprocessing module sends the preprocessed data to a data fusion module, the data fusion module adopts multi-source data fusion technology, and integrates the normalized data from different sensors through a data fusion algorithm, the data fusion module sends the integrated data to a synchronization processing module, the synchronization processing module uses a machine learning algorithm to achieve real-time synchronization to ensure data consistency and accuracy, the data fusion module is also connected to an optimization module, the optimization module uses artificial intelligence technology to automatically identify and optimize data processing parameters to support a variety of data analysis methods, the synchronization processing module is connected to an analysis output module, the analysis output module selects an appropriate data analysis method for spatial analysis or time series analysis according to demand, and outputs the final surveying and mapping data.

[0028] Preferably, the analysis output module has a user-defined function.

[0029] Compared with the prior art, the present invention provides a surveying and mapping data acquisition system and method based on surveying and mapping of geographic spatial information data, which has the following beneficial effects:

[0030] 1. The present invention provides a surveying and mapping data acquisition system and method based on geographic spatial information data mapping. The system maps a geographic area through multiple sensors to obtain raw data. After preprocessing the raw surveying and mapping data, the system uses multi-source data fusion technology to integrate the normalized data from different sensors through a data fusion algorithm, thereby overcoming the limitations of a single data source and improving the comprehensiveness and consistency of the data.

[0031] 2. The present invention provides a surveying and mapping data acquisition system and method based on geographic spatial information data surveying and mapping, which realizes real-time data synchronization through machine learning algorithms, ensures the temporal consistency of data from different sensors, and can accurately capture the changes of geographic spatial data over time. The optimization module uses artificial intelligence technology to automatically identify and optimize data processing parameters, thereby improving the efficiency of data processing and ensuring the accuracy of data processing. The data acquisition system combined with the method can make the obtained geographic surveying and mapping data more accurate and reliable, and is suitable for geographic areas with complex terrain. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1It is a schematic diagram of the process of the present invention;

[0033] Figure 2 Schematic diagram of the system architecture of the present invention. DETAILED DESCRIPTION

[0034] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0035] See also Figure 1-Figure 2 , a surveying and mapping data collection method based on geographic spatial information data surveying and mapping, is carried out in the following steps:

[0036] Step 1: Use a variety of sensors to obtain raw geospatial information data, including but not limited to remote sensing satellites, drones, and ground laser scanners;

[0037] Step 2: Preprocess the acquired raw data, including denoising, correction and normalization;

[0038] Step 3: Use multi-source data fusion technology to integrate the normalized data from different sensors through the data fusion algorithm. The data fusion algorithm formula is as follows:

[0039]

[0040] Among them, D f is the fused data, D i is the data of the i-th sensor, W i is the weight associated with the i-th sensor data, and n is the number of sensors;

[0041] Step 4: Use machine learning algorithms to achieve real-time synchronization to ensure data consistency and accuracy. The data consistency check algorithm is based on the following formula:

[0042]

[0043] Where C is the data consistency index, n is the number of sensors;

[0044] Step 5: Use artificial intelligence technology to automatically identify and optimize data processing parameters to support multiple data analysis methods;

[0045] Step 6: Select appropriate data analysis methods for spatial analysis or time series analysis according to needs, and output the final surveying and mapping data.

[0046] Specifically, the denoising process in step 2 uses wavelet transform technology, the correction process includes radiation correction and geometric correction, and the normalization process uses the following formula:

[0047]

[0048] Where F′ is the normalized data, F is the initial data measured by each sensor, and σ is the standard deviation of the data. The advantage is that wavelet transform technology can effectively distinguish signals from noise through multi-scale analysis, realize efficient denoising of original surveying and mapping data, thereby retaining more effective information and enhancing the availability and accuracy of data; due to the radiation deviation caused by environmental factors (such as lighting conditions, sensor performance, etc.), the radiation correction technology can ensure that the obtained data can truly reflect the radiation characteristics of the surface, which helps to improve the accuracy of subsequent data analysis and processing; due to the geometric deformation caused by sensor viewing angle, terrain undulation and other imaging errors, these errors can be eliminated through geometric correction, making the data position in space more accurate. Finally, through normalization processing, the surveying and mapping data measured by each sensor are substituted into the above formula, and finally the normalized surveying and mapping data of each sensor are obtained, which provides the prerequisite for the fusion of surveying and mapping data.

[0049] Specifically, ensuring data consistency in step 4 is a data synchronization mechanism based on timestamps. The advantage is that, through the timestamp synchronization mechanism, the system can update and synchronize the latest data in real time, so that the analysis of surveying and mapping data is based on the latest information, which improves the accuracy and timeliness of the output of the final surveying and mapping data. In a multi-source data environment, data from different sensors may overlap or conflict. The timestamp mechanism can identify and synchronize the latest or most relevant data, thereby reducing conflicts and inconsistencies between data.

[0050] Specifically, step 5 supports multiple data analysis methods including spatial analysis and time series analysis, where spatial analysis is based on the following formula:

[0051] S=∫ΩH(x)dx

[0052] Where S is the result of spatial analysis, H(x) is the data value at spatial position x, dx represents the volume element at spatial position x, and Ω is the analysis area. The advantage is that spatial analysis helps to understand the distribution and relationship of geographic spatial data, while time series analysis can reveal the trend of data changes over time. Users can choose the appropriate analysis method according to specific analysis needs. At the same time, spatial analysis helps to process data in geographic areas that are difficult to analyze using traditional methods, such as those with complex terrain and dense vegetation cover.

[0053] Specifically, the time series analysis algorithm in step 6 is based on the time series prediction model and is used to predict the trend of geographic space changes. The advantage is that the prediction model can provide early insight into the patterns and trends of geographic space data changes over time, such as land use changes, urban expansion speed, vegetation cover dynamics, etc., and provide decision makers with forecast information of future changes. Predicting the future trend of geographic space changes helps to effectively allocate resources, such as implementing protection measures in advance in areas of predicted land degradation, or planning infrastructure in advance in areas of predicted urban expansion.

[0054] Specifically, step six also includes a feedback loop for adjusting the sensor's acquisition strategy based on the data analysis results. The advantage is that by analyzing the results of the collected data, areas where the data quality is insufficient or there are errors can be identified. The feedback loop can adjust the sensor settings or acquisition strategies based on these analysis results, such as adjusting the sampling frequency, changing the position or angle of the sensor, etc., thereby significantly improving the accuracy of data acquisition; the feedback loop helps to more effectively allocate limited resources, such as sensor usage time, energy consumption, and data processing capabilities, by accurately controlling the data acquisition process, thereby reducing waste and reducing the overall cost of the task; at the same time, it can also quickly respond to the data analysis results and make necessary adjustments, enhancing the system's response speed and flexibility to environmental changes.

[0055] Specifically, the data acquisition system includes: a data acquisition module, which is used to acquire original geographic spatial information data through a variety of sensors, and the data acquisition module is connected to a preprocessing module, which is used to denoise, correct and normalize the acquired original data, and the preprocessing module sends the preprocessed data to a data fusion module, which adopts multi-source data fusion technology, and integrates the normalized data from different sensors through a data fusion algorithm, and sends the integrated data to a synchronization processing module, which uses a machine learning algorithm to achieve real-time synchronization to ensure data consistency and accuracy. The data fusion module is also connected to an optimization module, which uses artificial intelligence technology to automatically identify and optimize data processing parameters to support a variety of data analysis methods. The synchronization processing module is connected to an analysis output module, and the analysis output module selects an appropriate data analysis method for spatial analysis or time series analysis according to demand, and outputs the final surveying and mapping data. The advantages are that the system collects data from multiple sensors through the data acquisition module, combined with the denoising, correction and normalization processing of the preprocessing module, to ensure the high quality of the data, and provide a reliable basis for the subsequent processing of surveying and mapping data; the data fusion module adopts multi-source data fusion technology, which can effectively integrate data from different sensors, overcome the limitations of a single data source, and improve the comprehensiveness and consistency of the data; the synchronization processing module realizes real-time data synchronization through machine learning algorithms, ensures the temporal consistency of data from different sensors, and can accurately capture the changes of geographic spatial data over time; the optimization module uses artificial intelligence technology to automatically identify and optimize data processing parameters, which not only improves the efficiency of data processing, but also ensures the accuracy of data processing, and adapts to data requirements in different scenarios; the data acquisition system combined with the method can make the obtained geographic surveying and mapping data more accurate and reliable, suitable for geographical areas with complex terrain.

[0056] Specifically, the analysis output module has a user-defined function. The advantage is that the analysis output module allows users to select appropriate data analysis methods according to their needs based on the customized function. Whether it is spatial analysis or time series analysis, accurate results can be obtained, providing users with a variety of data interpretation perspectives.

[0057] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A surveying and mapping data acquisition method based on geographic spatial information data surveying and mapping, characterized in that: Proceed as follows: Step 1: Use a variety of sensors to obtain raw geospatial information data, including but not limited to remote sensing satellites, drones, and ground laser scanners; Step 2: Preprocess the acquired raw data, including denoising, correction and normalization; Step 3: Use multi-source data fusion technology to integrate the normalized data from different sensors through a data fusion algorithm. The formula of the data fusion algorithm is as follows: Among them, D f is the fused data, D i is the data of the ith sensor, W i is the weight associated with the i-th sensor data, and n is the number of sensors; Step 4: Use machine learning algorithms to achieve real-time synchronization to ensure data consistency and accuracy. The data consistency check algorithm is based on the following formula: Where C is the data consistency index, n is the number of sensors; Step 5: Use artificial intelligence technology to automatically identify and optimize data processing parameters to support multiple data analysis methods; Step 6: Select appropriate data analysis methods for spatial analysis or time series analysis according to needs, and output the final surveying and mapping data.

2. The surveying and mapping data acquisition method based on geographic spatial information data surveying and mapping according to claim 1, characterized in that: In step 2, the denoising process uses wavelet transform technology, the correction process includes radiation correction and geometric correction, and the normalization process uses the following formula: Where F′ is the normalized data, F is the initial data measured by each sensor, and σ is the standard deviation of the data.

3. The surveying and mapping data acquisition method based on geographic spatial information data surveying and mapping according to claim 1, characterized in that: In step 4, ensuring data consistency is based on a timestamp-based data synchronization mechanism.

4. The surveying and mapping data acquisition method based on geographic spatial information data surveying and mapping according to claim 1, characterized in that: Step 5 supports multiple data analysis methods including spatial analysis and time series analysis, where spatial analysis is based on the following formula: S=∫ΩH(x)dx Where S is the spatial analysis result, H(x) is the data value at the spatial position x, dx represents the volume element at the spatial position x, and Ω is the analysis area.

5. The surveying and mapping data acquisition method based on geographic spatial information data surveying and mapping according to claim 1, characterized in that: The time series analysis algorithm in step six is ​​based on a time series prediction model and is used to predict geographic space change trends.

6. The surveying and mapping data acquisition method based on geographic spatial information data surveying and mapping according to claim 1, characterized in that: The step six also includes a feedback loop for adjusting the sensor's acquisition strategy according to the data analysis results.

7. A surveying and mapping data acquisition system based on geographic spatial information data surveying and mapping, characterized in that: The data acquisition system includes: a data acquisition module, which is used to acquire original geographic spatial information data through a variety of sensors, and the data acquisition module is connected to a preprocessing module, the preprocessing module is used to denoise, correct and normalize the acquired original data, and the preprocessing module sends the preprocessed data to a data fusion module, the data fusion module adopts multi-source data fusion technology, and integrates the normalized data from different sensors through a data fusion algorithm, and the data fusion module sends the integrated data to a synchronization processing module, and the synchronization processing module uses a machine learning algorithm to achieve real-time synchronization to ensure data consistency and accuracy. The data fusion module is also connected to an optimization module, and the optimization module uses artificial intelligence technology to automatically identify and optimize data processing parameters to support a variety of data analysis methods. The synchronization processing module is connected to an analysis output module, and the analysis output module selects an appropriate data analysis method according to demand for spatial analysis or time series analysis, and outputs the final surveying and mapping data.

8. The surveying and mapping data acquisition method based on geographic spatial information data surveying and mapping according to claim 1, characterized in that: The analysis output module has a user-defined function.

Images