Surveying and mapping geographic information data collection system based on cloud computing

Abstract

This invention discloses a cloud computing-based surveying and mapping geographic information data acquisition system, relating to the field of natural resource management technology. It utilizes a data acquisition module to obtain observation point height (G), observation point latitude (W), and neighboring point data. A data processing module runs data processing algorithms to output assessment data for surveying and mapping geographic information. Based on the assessment data, in-depth analysis is performed on the processed data. A data visualization module displays the data acquisition results and assessment report, and optimizes and adjusts the system. The data processing module includes a unit reflecting the corrected ground elevation, a unit comprehensively assessing horizontal position accuracy, and a unit comprehensively assessing geographic information data quality. This invention has beneficial effects in improving data accuracy, optimizing data processing flow, enhancing system adaptability, and improving decision support capabilities. These effects collectively promote the intelligent and modern development of surveying and mapping geographic information data acquisition systems.

Description

Technical Field

[0001] This invention relates to the field of natural resource management technology, specifically to a cloud-based surveying and mapping geographic information data acquisition system. Background Technology

[0002] With the rapid development of information technology, surveying and mapping geographic information data acquisition systems are gradually transforming towards digitalization, automation, and intelligence. As an advanced computing model, cloud computing has brought revolutionary changes to the field of surveying and mapping geographic information with its powerful data processing capabilities, flexible resource allocation, and high cost-effectiveness. This system integrates multiple data sources such as high-precision measurement equipment, satellite positioning technology, and remote sensing technology, and combines the distributed storage, parallel processing, and intelligent analysis capabilities of the cloud computing platform to achieve rapid acquisition, processing, and analysis of massive geospatial data.

[0003] However, traditional surveying and mapping geographic information data acquisition systems often face data processing bottlenecks, especially when dealing with large-scale, highly complex geospatial data. They are prone to slow processing speeds and long response times. Moreover, most existing data quality assessment methods are based on simple statistical indicators and empirical formulas, which are difficult to comprehensively and accurately reflect the true quality of the data. In particular, the assessment of key indicators such as elevation correction and horizontal position accuracy lacks scientific models and parameterization methods. In addition, the integration between different data sources and processing systems is not high, resulting in serious data silos and making it difficult to achieve seamless data connection and sharing. Furthermore, due to the delays in data processing and transmission, traditional systems cannot achieve real-time or near real-time data acquisition and processing, and cannot meet the needs of rapid response and decision support. Summary of the Invention

[0004] The purpose of this invention is to provide a cloud computing-based surveying and mapping geographic information data acquisition system, which solves the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution, and the specific implementation steps are as follows:

[0006] Step S1: Use the data acquisition module to obtain the observation point height G, observation point latitude W, and neighboring point data;

[0007] Step S2: Using the data processing module, run the data processing algorithm to output the evaluation data of the surveying and mapping geographic information;

[0008] Step S3: Based on the evaluation data, conduct in-depth analysis of the processed data, and use the data visualization module to display the data collection results and evaluation report;

[0009] Step S4: Based on the evaluation report, optimize and adjust the system;

[0010] Step S5: Use the data acquisition module to store the data;

[0011] The data processing module includes a unit reflecting the corrected ground elevation, a unit for comprehensively evaluating horizontal position accuracy, and a unit for comprehensively evaluating geographic information data quality.

[0012] Optionally, the data acquisition module may use devices including a GPS receiver and a remote sensing device.

[0013] The data processing module uses equipment including servers, network devices, and data acquisition terminals;

[0014] The data visualization module uses devices including visualization equipment;

[0015] The GPS receiver is used to accurately acquire the latitude and longitude information of the observation point;

[0016] The remote sensing equipment includes a satellite remote sensor and a high-definition camera mounted on a drone, used to acquire ground image data;

[0017] The servers and network devices are deployed on a cloud platform for data processing, storage, and transmission;

[0018] The data acquisition terminal is used for on-site data acquisition;

[0019] The visualization device is used to display data analysis results and reports.

[0020] Optionally, the average height difference GC of the neighboring points based on the height G of the observation point... avg The calculation formula is as follows:

[0021] GC avg =(G-GL1)+(G-GL2)+(G-GL3)......+(G-GL n ) / N;

[0022] GC avg The average height difference between neighboring points;

[0023] G is the height of the observation point;

[0024] GL1, GL2, GL3, GL n All are the heights of neighboring points;

[0025] N represents the total number of observations from neighboring points.

[0026] Optionally, the average latitude LW of the neighboring points based on the latitude W of the observation point... avg The calculation formula is as follows:

[0027] LWavg =(LW1+LW2+LW3+......+LW n ) / N;

[0028] LW avg It is the average latitude of neighboring points;

[0029] N represents the total number of observations at neighboring points, and GC represents the average height difference between neighboring points. avg Latitude average of neighboring points LW avg The total number of neighboring point observations N used in the calculation remains consistent;

[0030] LW1+LW2+LW3+......+LW n This reflects the result of adding the dimensions of neighboring points.

[0031] Optionally, the calculation formula for the unit reflecting the corrected true ground elevation is as follows:

[0032] GCX=(G×sin(W)×a)+(GC avg / cos(LW) avg )×b);

[0033] in:

[0034] GCX is the elevation correction value;

[0035] W represents the latitude of the observation point;

[0036] Both a and b are weighting coefficients;

[0037] a is used to adjust the degree of influence of the observation point height G on the elevation correction value GCX;

[0038] b is used to adjust the degree of influence of the latitude W of the observation point on the elevation correction value GCX.

[0039] Optionally, the calculation formula for the comprehensive evaluation of the horizontal position accuracy unit is as follows:

[0040]

[0041] in:

[0042] SP represents the horizontal position accuracy value;

[0043] GJQ is the weighted average height difference, which is the average height difference weighted by the spatial distance between the observation point and its neighboring points. It is used to reflect the influence of the surrounding terrain on the horizontal position accuracy value SP.

[0044] QY is a correction factor used to adjust the influence of non-topographic factors on the horizontal position accuracy value SP.

[0045] Optionally, the calculation formula for the comprehensive evaluation unit of geographic information data quality is as follows:

[0046] ZP=(SP×G×c)+((GCX / W)×d)+EX;

[0047] EX=∑ L i=1 EX i 2 ;

[0048] in:

[0049] ZP is the comprehensive data quality assessment value;

[0050] c and d are both weighting coefficients;

[0051] c is used to adjust the degree of influence of the horizontal position accuracy value SP on the comprehensive data quality assessment value ZP;

[0052] d is used to adjust the degree of influence of the elevation correction value GCX on the comprehensive data quality assessment value ZP;

[0053] EX represents additional data quality assessment metrics, including measurement error, system noise, and data processing uncertainty.

[0054] EX i This is the value of the i-th additional data quality assessment metric;

[0055] ∑ L i=1 EX i 2 EX reflects the increase from i=1 to i=L i 2 Summation calculation.

[0056] Optionally, using the data visualization module, and based on the current comprehensive data quality assessment value ZP and the analysis of the previous X comprehensive data quality assessment values ​​ZP, the following adjustments can be made:

[0057] If the current and previous X comprehensive data quality assessment values ​​ZP on the line graph gradually increase over time, specifically forming an upward-sloping curve, it indicates that the data quality is gradually improving and there is no need to adjust the geographic information.

[0058] If the current and previous X comprehensive data quality assessment values ​​ZP on the line graph gradually decrease over time, specifically forming a downward sloping curve, it indicates that the data quality is declining, and the observation point height G, observation point latitude W, and neighboring point data should be reassessed and measured.

[0059] If the current and previous X comprehensive data quality assessment values ​​ZP on the line graph fluctuate within a certain range without a clear upward or downward trend, it indicates that it is the result of multiple factors working together. In this case, the parameters in the corrected ground elevation unit should be checked, and the number of observations should be increased and other adjustment factors should be introduced.

[0060] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0061] I. This invention utilizes the distributed storage and parallel processing capabilities of cloud computing platforms, enabling the system to efficiently process large-scale, highly complex geospatial data, thereby significantly improving data processing speed and response time.

[0062] Second, by introducing units that reflect the corrected ground elevation, units that comprehensively evaluate horizontal position accuracy, and units that comprehensively evaluate geographic information data quality, this invention systematically establishes a scientific data quality evaluation model. This model can comprehensively and accurately evaluate the elevation correction value, horizontal position accuracy, and overall data quality of the data. In particular, the introduction of weighting coefficients a and b and neighboring point data in the corrected ground elevation unit, as well as the consideration of the weighted average height difference GJQ and correction factor QY in the comprehensive evaluation of horizontal position accuracy unit, makes the evaluation results more consistent with the actual situation.

[0063] Third, this invention utilizes a cloud computing platform to support seamless connection and integration of multiple data sources and processing systems, breaking down data silos and realizing data sharing and interoperability.

[0064] Fourth, the present invention, based on the real-time data processing and analysis capabilities of cloud computing, enables the system to achieve real-time and near-real-time data acquisition and processing, providing a strong guarantee for rapid response and decision support. Attached Figure Description

[0065] Figure 1 This is a flowchart of the method for a cloud-based surveying and mapping geographic information data acquisition system.

[0066] Figure 2 This is a schematic diagram of the line drawing of the present invention. Figure 1 ;

[0067] Figure 3 This is a schematic diagram of the line drawing of the present invention. Figure 2 ;

[0068] Figure 4 This is a schematic diagram of the line drawing of the present invention. Figure 3 . Detailed Implementation

[0069] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0070] This cloud-based surveying and mapping geographic information data acquisition system differs from traditional systems. Traditional systems suffer from slow processing speeds and long response times, particularly in evaluating key indicators such as elevation correction and horizontal position accuracy, lacking scientific models and parameterization methods. Furthermore, low integration and data processing and transmission delays lead to severe data silos, failing to meet the demands for rapid response and decision support. In contrast, this algorithm unit improves data accuracy, optimizes data processing workflows, enhances system adaptability, and improves decision support capabilities. These combined effects promote the intelligent and modern development of surveying and mapping geographic information data acquisition systems.

[0071] Example 1, please refer to Figures 1 to 4 This implementation provides a cloud-based surveying and mapping geographic information data acquisition system, and the specific implementation steps are as follows:

[0072] Step S1: Use the data acquisition module to obtain the observation point height G, observation point latitude W, and neighboring point data;

[0073] Step S2: Using the data processing module, run the data processing algorithm to output the evaluation data of the surveying and mapping geographic information;

[0074] Step S3: Based on the evaluation data, conduct in-depth analysis of the processed data, and use the data visualization module to display the data collection results and evaluation report;

[0075] Step S4: Based on the evaluation report, optimize and adjust the system;

[0076] Step S5: Use the data acquisition module to store the data;

[0077] The data processing module includes a unit reflecting the corrected ground elevation, a unit for comprehensively evaluating the accuracy of horizontal position, and a unit for comprehensively evaluating the quality of geographic information data.

[0078] The data acquisition module uses equipment including a GPS receiver and remote sensing devices;

[0079] The data processing module uses equipment including servers, network devices, and data acquisition terminals;

[0080] The data visualization module uses visualization equipment;

[0081] GPS receivers are used to accurately acquire latitude and longitude information of observation points;

[0082] Remote sensing equipment includes satellite remote sensors and high-definition cameras carried by drones, used to acquire ground image data;

[0083] Servers and network devices are deployed on cloud platforms for data processing, storage, and transmission;

[0084] Data acquisition terminals are used for on-site data acquisition;

[0085] Visualization devices are used to display data analysis results and reports.

[0086] Through the above methods, steps, modules, units, and equipment configurations, this invention can construct a cloud computing-based surveying and mapping geographic information data acquisition system, achieving efficient, accurate, and comprehensive data acquisition, processing, and evaluation.

[0087] In this embodiment, the system utilizes the cooperation of three algorithm units within a cloud-based surveying and mapping geographic information data acquisition system. Each unit undertakes a different computational purpose and together constitutes the core framework for comprehensive evaluation and optimization of geospatial data quality. The system combines the results of three calculations: GCX (elevation correction value), SP (horizontal position accuracy value), and ZP (horizontal position accuracy value). GCX represents the elevation correction value; during the surveying and mapping geographic information data acquisition process, due to the influence of terrain undulations, Earth's curvature, and atmospheric refraction, directly observed elevation values ​​often require correction to accurately reflect the true elevation of the ground. This evaluation not only considers the influence of terrain factors but also incorporates corrections for external factors such as meteorological conditions, making the evaluation results closer to reality. This helps improve the overall performance of the data acquisition system. ZP is the comprehensive data quality assessment value. This assessment method not only considers the spatial location accuracy of the data, but also incorporates multiple other dimensions related to data quality, including signal strength, measurement error, and equipment stability. This provides a more comprehensive and accurate data quality assessment result, which helps system administrators to identify and resolve data quality issues in a timely manner, thereby improving the overall reliability and stability of the data acquisition system. The current and previous X comprehensive data quality assessment values ​​ZP can also influence the calculation of the ground true elevation unit and the comprehensive assessment horizontal position accuracy unit after correction. This closed-loop feedback mechanism helps the system to continuously optimize itself and improve the overall data acquisition and processing capabilities.

[0088] Please see Figures 1 to 4 The calculation formula reflecting the corrected true ground elevation unit is as follows:

[0089] GCX=(G×sin(W)×a)+(GCavg / cos(LW) avg )×b);

[0090] in:

[0091] GCX is the elevation correction value;

[0092] W represents the latitude of the observation point;

[0093] Both a and b are weighting coefficients;

[0094] a is used to adjust the degree of influence of the observation point height G on the elevation correction value GCX;

[0095] b is used to adjust the degree of influence of the latitude W of the observation point on the elevation correction value GCX.

[0096] In this embodiment: First, in this algorithm unit, the observation point height G is obtained through on-site measurement using a GPS receiver and remote sensing equipment. These devices can accurately measure the vertical distance of the observation point relative to a known reference surface, such as the geoid or sea level, i.e., the observation point height G. However, in a surveying and mapping geographic information data acquisition system, determining the highest point is usually not achieved directly through a single formula, but rather based on the elevation data of multiple observation points, namely GL1, GL2, GL3, GL4, GL5, GL6, GL7, GL8, GL9, GL9, GL1, GL2, GL3, GL9 ... n Based on the comparison, the system will sort the elevation data of all observation points, and the point with the largest elevation value will be the highest point.

[0097] The latitude W of the observation point is obtained through the GPS system and satellite positioning technology. Modern GPS receivers can provide very accurate latitude and longitude information. Latitude is an important parameter describing the location of a point on Earth. It affects the influence of the Earth's curvature on elevation measurement, so it needs to be taken into account in elevation correction calculations.

[0098] This algorithm unit comprehensively considers the observation point height G, observation point latitude W, weight coefficients a and b, and the average height difference GC between neighboring points. avg The average latitude of its neighboring points, LW avg This method enables accurate calculation of elevation correction values ​​(GCX). It not only considers the characteristics of the observation point itself, but also incorporates the influence of the surrounding terrain, thereby greatly improving the accuracy of elevation correction. This is especially important for areas with complex terrain and large changes in elevation, and can provide more reliable basic data for subsequent geographic information analysis and applications.

[0099] Furthermore, this algorithm unit, through weight coefficients a and b and neighboring point data, enables the unit reflecting the true ground elevation after correction to dynamically adjust the elevation correction value according to different terrain conditions and environmental factors. This enhanced environmental adaptability allows the system to maintain high measurement accuracy under different geographical environments and climatic conditions, providing strong support for the widespread application of surveying and mapping geographic information data.

[0100] Because the corrected ground elevation units can dynamically adjust elevation correction values ​​according to different terrain conditions and environmental factors, the data acquisition strategy can be more flexible and efficient. In complex terrain areas, the system can automatically adjust acquisition parameters based on real-time data to reduce errors and improve efficiency. This intelligent data acquisition strategy is particularly important for large-scale, high-precision surveying projects.

[0101] Please see Figures 1 to 4 The calculation formula for comprehensively evaluating the horizontal position accuracy unit is as follows:

[0102]

[0103] in:

[0104] SP represents the horizontal position accuracy value;

[0105] GJQ is the weighted average height difference, which is the average height difference weighted by the spatial distance between the observation point and its neighboring points. It is used to reflect the influence of the surrounding terrain on the horizontal position accuracy value SP.

[0106] QY is a correction factor used to adjust the influence of non-topographic factors on the horizontal position accuracy value SP.

[0107] In this embodiment, firstly, the comprehensive evaluation of the horizontal position accuracy unit achieves a comprehensive evaluation of the horizontal position accuracy value SP by combining the latitude W of the observation point, the elevation correction value GCX, the weighted average height difference GJQ based on spatial distance, and the meteorological condition correction factor QY. This evaluation method not only considers the influence of terrain factors on horizontal position accuracy, but also incorporates the correction of external factors of meteorological conditions, making the evaluation results closer to the real situation.

[0108] The introduction of the weighted average height difference GJQ and meteorological condition correction factor QY in the comprehensive evaluation of horizontal position accuracy unit optimizes the data processing flow. By considering neighboring point data through weighted averaging, the influence of surrounding terrain on horizontal position accuracy can be more reasonably reflected. The addition of meteorological condition correction can adjust the influence of non-terrain factors on the evaluation results in real time, improving the real-time performance and accuracy of data processing.

[0109] In summary, this algorithm unit comprehensively evaluates the horizontal position accuracy value SP, considering not only the influence of terrain factors but also the correction for external factors such as meteorological conditions. This comprehensive evaluation method enables the system to more accurately determine the reliability and validity of the data, thereby reducing subsequent processing errors and resource waste caused by data quality issues. The evaluation results of the comprehensive horizontal position accuracy unit can provide an important reference for data acquisition quality control. During the data acquisition process, the system can adjust the acquisition plan, optimize equipment configuration, and take other measures in a timely manner based on the evaluation results of the horizontal position accuracy value SP to ensure data quality. This real-time quality control mechanism helps improve the overall efficiency and accuracy of data acquisition.

[0110] Please see Figures 1 to 4 The calculation formula for comprehensively evaluating the quality unit of geographic information data is as follows:

[0111] ZP=(SP×G×c)+((GCX / W)×d)+EX;

[0112] EX=∑ L i=1 EX i 2 ;

[0113] in:

[0114] ZP is the comprehensive data quality assessment value;

[0115] c and d are both weighting coefficients;

[0116] c is used to adjust the degree of influence of the horizontal position accuracy value SP on the comprehensive data quality assessment value ZP;

[0117] d is used to adjust the degree of influence of the elevation correction value GCX on the comprehensive data quality assessment value ZP;

[0118] EX represents additional data quality assessment metrics, including measurement error, system noise, and data processing uncertainty.

[0119] EX i This is the value of the i-th additional data quality assessment metric;

[0120] ∑ L i=1 EX i 2 EX reflects the increase from i=1 to i=L i 2 Summation calculation.

[0121] In this embodiment, the algorithm unit is first based on;

[0122] This algorithm unit integrates the horizontal position accuracy value SP, the elevation correction value GCX, and multiple additional data quality assessment index values ​​EX to achieve a comprehensive evaluation of the integrated data quality assessment value ZP. This evaluation method not only considers the spatial position accuracy of the data, but also incorporates other dimensions related to data quality, including signal strength, measurement error, and equipment stability, thereby providing a more comprehensive and accurate data quality assessment result.

[0123] Among them, the weight coefficients c and d in the comprehensive evaluation of geographic information data quality unit allow for dynamic adjustment of the influence of different evaluation indicators on the comprehensive data quality evaluation value ZP according to actual needs. This flexibility enables the system to be optimized in a targeted manner according to different application scenarios and data characteristics, promoting the continuous improvement of data quality.

[0124] The Comprehensive Data Quality Assessment Value (ZP) provides strong support for data-driven decision-making. In the field of surveying and mapping geographic information, high-quality data is an important foundation for making scientific decisions and plans. Through the assessment of the Comprehensive Data Quality Assessment Value (ZP), decision-makers can more accurately understand the overall quality of the data, thereby formulating more scientific strategies for data collection, processing, and application. This data-based decision-making approach helps improve the scientific nature and effectiveness of decision-making.

[0125] Please see Figures 1 to 4 Using the data visualization module, and based on the current comprehensive data quality assessment value ZP and the analysis of the previous X comprehensive data quality assessment values ​​ZP, the following adjustments are made:

[0126] If the current and previous X comprehensive data quality assessment values ​​ZP on the line graph gradually increase over time, specifically forming an upward-sloping curve, it indicates that the data quality is gradually improving and there is no need to adjust the geographic information.

[0127] If the current and previous X comprehensive data quality assessment values ​​ZP on the line graph gradually decrease over time, specifically forming a downward sloping curve, it indicates that the data quality is declining, and the observation point height G, observation point latitude W, and neighboring point data should be reassessed and measured.

[0128] If the current and previous X comprehensive data quality assessment values ​​ZP on the line graph fluctuate within a certain range without a clear upward or downward trend, it indicates that it is the result of multiple factors working together. In this case, the parameters in the corrected ground elevation unit should be checked, and the number of observations should be increased and other adjustment factors should be introduced.

[0129] In this embodiment, the algorithm unit, based on the evaluation results of the comprehensive evaluation geographic information data quality unit on the overall data quality, can in turn influence the parameter adjustment and optimization of the unit reflecting the corrected ground true elevation and the unit reflecting the comprehensive evaluation horizontal position accuracy. When it is found that the data quality of certain areas and time periods is low, the data quality of these areas and time periods can be improved by adjusting the weight coefficients a and b in the unit reflecting the corrected ground true elevation, the parameters of the neighboring point data, and the weighted average height difference GJQ and correction factor QY in the unit reflecting the comprehensive evaluation horizontal position accuracy. This closed-loop feedback mechanism helps the system to continuously optimize itself and improve the overall data acquisition and processing capabilities.

[0130] By comprehensively evaluating and optimizing the quality of geographic information data units, the performance of the entire data acquisition and processing system can be improved. When the system can continuously provide high-quality data, it will be more conducive to subsequent geographic information analysis and application, and provide more reliable data support for scientific research, engineering construction and urban planning in the field of surveying and mapping geographic information.

[0131] It is worth noting that by observing the line graph, we can promptly identify trends of declining data quality and take targeted measures to improve it. This helps ensure the accuracy and reliability of the data, providing a solid foundation for subsequent analysis and decision-making.

[0132] Specifically, when data quality fluctuates, by deeply analyzing the causes of the fluctuations and adjusting the parameters in the ground elevation units that reflect the corrected true elevation, data quality can be stabilized, and the impact of random errors and environmental factors on the data can be reduced. An upward trend line graph indicates that the current data acquisition and processing methods are relatively effective, but it may also suggest the potential for further improvement in data quality. By continuously optimizing the algorithm for selecting neighboring points and improving the measurement accuracy of the observation point height, the efficiency and accuracy of the data acquisition and processing process can be further improved. When there is a downward trend, a comprehensive review of the data acquisition and processing process should be conducted to identify and correct potential problems, such as equipment aging and errors in the data acquisition process, thereby restoring and improving data quality.

[0133] Accurate and reliable data is the foundation for making high-quality decisions. By ensuring data quality, we can reduce erroneous decisions caused by data problems and improve the accuracy and reliability of decisions. In the field of surveying and mapping, high-quality data is of great significance for creating accurate maps, planning reasonable transportation routes, and assessing environmental risks. By optimizing data acquisition and processing systems, we can ensure that these fields have more accurate and reliable data support. Improving data quality can reduce duplication of work and waste of resources caused by data errors. For example, in geographic information systems, accurate data can reduce unnecessary field surveys and remeasurement work.

[0134] Optimizing the data acquisition and processing system can improve work efficiency, reduce manpower and material costs, and thus improve overall operational cost-effectiveness. By analyzing the line graph and adjusting the parameters in the ground elevation unit that reflects the correction, it is possible to cope with data fluctuations under different conditions and enhance the stability of the data acquisition and processing system. This helps to ensure that the system can operate stably in various environments and provide accurate and reliable data support.

[0135] In summary, observing the changing trends of the current and previous X comprehensive data quality assessment values ​​ZP using line graphs and optimizing the data acquisition and processing system accordingly can bring beneficial effects such as improving data quality, optimizing data acquisition and processing processes, enhancing decision-making quality, reducing operating costs, and strengthening system stability.

[0136] Example 2, please refer to Figures 1 to 4 The average height difference GC of neighboring points based on the height G of the observation point. avg The calculation formula is as follows:

[0137] GC avg =(G-GL1)+(G-GL2)+(G-GL3)......+(G-GL n ) / N;

[0138] GC avg The average height difference between neighboring points;

[0139] G is the height of the observation point;

[0140] GL1, GL2, GL3, GL n All are the heights of neighboring points;

[0141] N represents the total number of observations at neighboring points;

[0142] Based on the average latitude LW of neighboring points near the observation point W. avg The calculation formula is as follows:

[0143] LW avg =(LW1+LW2+LW3+......+LW n ) / N;

[0144] LW avg It is the average latitude of neighboring points;

[0145] N represents the total number of observations at neighboring points, and GC represents the average height difference between neighboring points. avg Latitude average of neighboring points LW avg The total number of neighboring point observations N used in the calculation remains consistent;

[0146] LW1+LW2+LW3+......+LWn This reflects the result of adding the dimensions of neighboring points.

[0147] In this embodiment, the average height difference (GC) of neighboring points is calculated. avg This enables the system to more accurately assess the terrain undulations of the area where the observation point is located, which is crucial for generating high-precision elevation models, especially in complex terrain areas such as mountainous and hilly areas.

[0148] Neighboring point average height difference (GC) avg The calculation results can provide a basis for formulating data acquisition strategies. For example, in areas with large height variations, the system can use the average height difference (GC) of neighboring points to determine the optimal data acquisition strategy. avg The density of data collection points is dynamically adjusted to ensure data continuity and representativeness.

[0149] In a cloud computing environment, the average height difference (GC) of nearest neighbors avg The computation can be efficiently parallelized, greatly shortening data processing time. Meanwhile, the average height difference (GC) of neighboring points... avg It can also serve as one of the indicators for data quality assessment, helping to identify and remove outliers and improve the accuracy and reliability of data processing;

[0150] Average latitude of neighboring points (LW) avg The calculation helps to obtain the average latitude of the area surrounding the observation point, which is of great significance for spatial analysis and positioning services of geographic information systems. This is achieved by accurately calculating the average latitude (LW) of neighboring points. avg This can improve the accuracy of geographic coordinates and reduce positioning errors;

[0151] Combined with the average height difference of neighboring points (GC) avg and the average latitude of neighboring points LW avg The calculation results allow the system to gain a more comprehensive understanding of the terrain features of the area where the observation point is located. This is of great significance for terrain analysis, hydrological simulation, and disaster early warning applications, and helps to improve the system's comprehensive service capabilities. On the cloud computing platform, the average latitude of nearby points (LW) avg The computing capabilities can fully utilize the elastic scalability of cloud resources to achieve efficient data processing and storage by updating the average latitude (LW) of neighboring points in real time. avg The system can dynamically adjust data processing flow and resource allocation to optimize system performance and improve user experience;

[0152] In summary, the average height difference (GC) of neighboring points avg and the average latitude of neighboring points LW avgComputation plays a crucial role in cloud-based surveying and mapping geographic information data acquisition systems. It not only improves the accuracy and efficiency of data acquisition and processing, but also enhances the system's intelligence level and comprehensive service capabilities.

[0153] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A cloud computing-based surveying and mapping geographic information data acquisition system, characterized in that, The specific implementation steps are as follows: Step S1: Use the data acquisition module to obtain the observation point height G, observation point latitude W, and neighboring point data; Step S2: Using the data processing module, run the data processing algorithm to output the evaluation data of the surveying and mapping geographic information; Step S3: Based on the evaluation data, conduct in-depth analysis of the processed data, and use the data visualization module to display the data collection results and evaluation report; Step S4: Based on the evaluation report, optimize and adjust the system; Step S5: Use the data acquisition module to store the data; The data processing module includes a unit reflecting the corrected ground elevation, a unit for comprehensively evaluating horizontal position accuracy, and a unit for comprehensively evaluating geographic information data quality. The data acquisition module uses equipment including a GPS receiver and remote sensing devices; The data processing module uses equipment including servers, network devices, and data acquisition terminals; The data visualization module uses devices including visualization equipment; The GPS receiver is used to accurately acquire the latitude and longitude information of the observation point; The remote sensing equipment includes a satellite remote sensor and a high-definition camera mounted on a drone, used to acquire ground image data; The servers and network devices are deployed on a cloud platform for data processing, storage, and transmission; The data acquisition terminal is used for on-site data acquisition; The visualization device is used to display data analysis results and reports; a difference in average height of neighboring points GC adjacent to the observation point height G avg The formula for calculating Gc is as follows: GC avg =(G-GL1)+(G-GL2)+(G-GL3)......+(G-GL N ) / N; GC avg The average height difference between neighboring points; G is the height of the observation point; GL1, GL2, GL3, GL n All are the heights of neighboring points; N represents the total number of observations at neighboring points; Based on the average latitude LW of the neighboring points near the observation point W. avg The calculation formula is as follows: LW avg =(LW1+LW2+LW3+......+LW N ) / N; LW avg It is the average latitude of neighboring points; Neighboring point average height difference (GC) avg Latitude average of neighboring points LW avg The total number of neighboring point observations N used in the calculation remains consistent; LW1+LW2+LW3+......+LW N The result reflects the sum of the dimensions of neighboring points; The calculation formula for the corrected ground elevation unit is as follows: GCX=(G×sin(W)×a)+(GC avg / cos(LW avg )×b); in: GCX is the elevation correction value; W represents the latitude of the observation point; Both a and b are weighting coefficients; a is used to adjust the degree of influence of the observation point height G on the elevation correction value GCX; b is used to adjust the degree of influence of the latitude W of the observation point on the elevation correction value GCX.

2. The cloud computing-based surveying and mapping geographic information data acquisition system according to claim 1, characterized in that: The calculation formula for the comprehensive evaluation of horizontal position accuracy unit is as follows: ； in: SP represents the horizontal position accuracy value; GJQ is the weighted average height difference, which is the average height difference weighted by the spatial distance between the observation point and its neighboring points. It is used to reflect the influence of the surrounding terrain on the horizontal position accuracy value SP. QY is a correction factor used to adjust the influence of non-topographic factors on the horizontal position accuracy value SP.

3. The cloud computing-based surveying and mapping geographic information data acquisition system according to claim 2, characterized in that: The calculation formula for the comprehensive evaluation unit of geographic information data quality is as follows: ZP=(SP×G×c)+((GCX / W)×d)+EX; EX=∑ L i=1 EX i 2 ; in: ZP is the comprehensive data quality assessment value; c and d are both weighting coefficients; c is used to adjust the degree of influence of the horizontal position accuracy value SP on the comprehensive data quality assessment value ZP. d is used to adjust the degree of influence of the elevation correction value GCX on the comprehensive data quality assessment value ZP; EX represents additional data quality assessment metrics, including measurement error, system noise, and data processing uncertainty. EX i This is the value of the i-th additional data quality assessment metric; ∑ L i=1 EX i 2 EX reflects the increase from i=1 to i=L i 2 Summation calculation.

4. The cloud computing-based surveying and mapping geographic information data acquisition system according to claim 3, characterized in that: Using the data visualization module, and based on the current comprehensive data quality assessment value ZP and the analysis of the previous X comprehensive data quality assessment values ​​ZP, the adjustments are as follows: If the current and previous X comprehensive data quality assessment values ​​ZP on the line graph gradually increase over time, specifically forming an upward-sloping curve, it indicates that the data quality is gradually improving and there is no need to adjust the geographic information. If the current and previous X comprehensive data quality assessment values ​​ZP on the line graph gradually decrease over time, specifically forming a downward sloping curve, it indicates that the data quality is declining, and the observation point height G, observation point latitude W, and neighboring point data should be reassessed and measured. If the current and previous X comprehensive data quality assessment values ​​ZP on the line graph fluctuate within a certain range without a clear upward or downward trend, it indicates that it is the result of multiple factors working together. In this case, the parameters in the corrected ground elevation unit should be checked, and the number of observations should be increased and other adjustment factors should be introduced.

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