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

Abstract

The invention discloses a surveying and mapping geographic information data acquisition system based on cloud computing, and relates to the technical field of natural resource management, a data acquisition module is used for acquiring observation point height G, observation point latitude W and adjacent point data, a data processing module is used for operating a data processing algorithm and outputting evaluation data of surveying and mapping geographic information, and the surveying and mapping geographic information is acquired based on the evaluation data. The data processing module is used for processing the real elevation of the ground and deeply analyzing the processed data, the data visual module is used for displaying a data acquisition result and an evaluation report, the system is optimized and adjusted, and the data processing module comprises a unit for reflecting the corrected real elevation of the ground, a unit for comprehensively evaluating the horizontal position precision and a unit for comprehensively evaluating the geographic information data quality. The surveying and mapping geographic information data acquisition system has the beneficial effects of improving the data precision, optimizing the data processing flow, enhancing the system adaptability and improving the decision support capability, and the effects jointly promote the intelligent and modern development of the surveying and mapping geographic information data acquisition system.

Description

Technical Field

[0001] The present invention relates to the technical field of natural resource management, and in particular to a surveying and mapping geographic information data acquisition system based on cloud computing. Background Art

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

[0003] However, traditional surveying and mapping geographic information data acquisition systems often face data processing bottlenecks, especially when processing large-scale, highly complex geographic spatial data. They are prone to slow processing speeds and long response times. Existing data quality assessment methods are mostly based on simple statistical indicators and empirical formulas, which are difficult to fully and accurately reflect the true quality of the data. In particular, there is a lack of scientific models and parameterization methods for the evaluation of key indicators such as elevation correction and horizontal position accuracy. In addition, the integration between different data sources and processing systems is not high, resulting in serious data silos, making it difficult to achieve seamless data docking and sharing. Due to delays in data processing and transmission, traditional systems find it difficult to achieve real-time and quasi-real-time data collection and processing, and cannot meet the needs of rapid response and decision support. Summary of the Invention

[0004] The purpose of the present invention is to provide a surveying and mapping geographic information data acquisition system based on cloud computing, which solves the problems raised in the above background technology.

[0005] To achieve the above objectives, the present invention provides the following technical solutions, and the specific implementation steps are as follows: Step S1, using the data acquisition module to obtain the observation point height G, the observation point latitude W and the adjacent point data; Step S2, using the data processing module to run the data processing algorithm and output the evaluation data of the surveying and mapping geographic information; Step S3: Based on the evaluation data, the processed data is deeply analyzed, and the data collection results and evaluation report are displayed using the data visualization module; Step S4: Optimize and adjust the system based on the evaluation report; Step S5, using the data acquisition module to store the data; The data processing module includes a unit for reflecting the corrected ground true elevation, a unit for comprehensively evaluating the horizontal position accuracy, and a unit for comprehensively evaluating the quality of geographic information data.

[0006] Optionally, the equipment used by the data acquisition module includes a GPS receiver and a remote sensing device; The equipment used in the data processing module includes servers, network equipment, and data acquisition terminals; The equipment used in the data visualization module includes visualization equipment; The GPS receiver is used to accurately obtain the latitude and longitude information of the observation point; The remote sensing equipment includes satellite remote sensors and high-definition cameras carried by drones, which are used to obtain 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 collection; The visualization device is used to display data analysis results and reports.

[0007] Optionally, the average height difference GC of the adjacent points based on the height G of the observation point avg The calculation formula is as follows: GC avg =(G-GL1)+(G-GL2)+(G-GL3)......+(G-GL n ) / N; GC avg is the average height difference of adjacent points; G is the height of the observation point; GL1, GL2, GL3, GL n are the heights of adjacent points; N is the total number of neighboring point observations.

[0008] Optionally, the latitude average value LW of the adjacent points based on the latitude W of the observation point avg The calculation formula is as follows: LW avg =(LW1+LW2+LW3+......+LW n ) / N; LW avg is the average latitude of adjacent points; N is the total number of observations of adjacent points, and the average height difference GC of adjacent points avg and the average latitude of the neighboring points LW avg The total number of neighboring point observations N involved in the calculation remains consistent; LW1+LW2+LW3+......+LW nReflects the result value of adding the dimensions of adjacent points.

[0009] Optionally, the calculation formula reflecting the corrected ground truth elevation unit is as follows: GCX=(G×sin(W)×a)+(GC avg / cos(LW avg ) × b); in: GCX is the elevation correction value; W is the latitude of the observation point; a and b are weight coefficients; a is used to adjust the influence of the observation point height G on the elevation correction value GCX; b is used to adjust the influence of the observation point latitude W on the elevation correction value GCX.

[0010] Optionally, the calculation formula for the comprehensive evaluation horizontal position accuracy unit is as follows: ; in: SP is the horizontal position accuracy value; GJQ is the weighted mean height difference, which is the weighted mean height difference between the observation point and the adjacent points according to the spatial distance. It is used to reflect the influence of the surrounding terrain on the horizontal position accuracy value SP. QY is the correction factor, which is used to adjust the impact of non-topographic factors on the horizontal position accuracy value SP.

[0011] Optionally, the calculation formula for the comprehensive evaluation of geographic information data quality unit 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 weight coefficients; c is used to adjust the influence of the horizontal position precision value SP on the comprehensive data quality assessment value ZP; d is used to adjust the impact of the elevation correction value GCX on the comprehensive data quality assessment value ZP; EX is the additional data quality evaluation indicator value, including measurement error, system noise, and data processing uncertainty; EX i is the value of the i-th additional data quality assessment indicator; ∑ L i=1 EXi 2 Reflects EX added from i=1 to i=L i 2 The sum calculation of .

[0012] Optionally, the data visualization module is used and based on the analysis of the current comprehensive data quality evaluation value ZP and the previous X comprehensive data quality evaluation values ​​ZP, the adjustment is as follows: If the current and previous X comprehensive data quality assessment values ​​ZP gradually increase over time on the line graph, 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 gradually decrease over time on the line graph, forming a downward sloping curve, it indicates that the data quality is deteriorating 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 times' comprehensive data quality assessment values ​​ZP fluctuate within a certain range on the line graph without a clear upward or downward trend, it indicates that multiple factors are acting together. In this case, the various parameters in the unit reflecting the corrected ground truth elevation should be checked, and the number of observations should be increased or other adjustment factors should be introduced.

[0013] Compared with the prior art, the present invention has the following beneficial effects: 1. The present invention utilizes the distributed storage and parallel processing capabilities of the cloud computing platform to enable the system to efficiently process large-scale, highly complex geospatial data, thereby significantly improving data processing speed and response time.

[0014] 2. The present invention systematically establishes a scientific data quality assessment model by introducing a unit that reflects the corrected ground true elevation, a unit for comprehensively evaluating the horizontal position accuracy, and a unit for comprehensively evaluating the quality of geographic information data. This model can comprehensively and accurately evaluate the elevation correction value, horizontal position accuracy, and comprehensive data quality of the data. In particular, the introduction of the weight coefficients a, b and neighboring point data in the unit that reflects the corrected ground true elevation, as well as the consideration of the weighted average height difference GJQ and the correction factor QY in the unit for comprehensively evaluating the horizontal position accuracy, make the assessment results more in line with the actual situation.

[0015] 3. The present invention utilizes the cloud computing platform to support seamless connection and integration of multiple data sources and processing systems, breaking the data silo phenomenon and realizing data sharing and intercommunication.

[0016] 4. The real-time data processing and analysis capabilities of the present invention based on cloud computing enable the system to achieve real-time and quasi-real-time data collection and processing, providing a strong guarantee for rapid response and decision support. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 This is a method flow chart of the surveying and mapping geographic information data acquisition system based on cloud computing; Figure 2 Schematic diagram of the present invention Figure 1 ; Figure 3 Schematic diagram of the present invention Figure 2 ; Figure 4 Schematic diagram of the present invention Figure 3 . DETAILED DESCRIPTION

[0018] The following will provide a clear and complete description of the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0019] This cloud computing-based surveying and mapping geographic information data acquisition system is different from the traditional surveying and mapping geographic information data acquisition system. The traditional surveying and mapping geographic information data acquisition system has slow processing speed and long response time, especially for the evaluation of key indicators of elevation correction and horizontal position accuracy. It lacks scientific models and parameterization methods. Due to low integration and delays in data processing and transmission, it leads to serious data island phenomenon and cannot meet the needs of rapid response and decision support. This algorithm unit has the beneficial effects of improving data accuracy, optimizing data processing flow, enhancing system adaptability and improving decision support capabilities. These effects jointly promote the intelligent and modern development of surveying and mapping geographic information data acquisition systems.

[0020] For example 1, please refer to Figures 1 to 4 This implementation provides a surveying and mapping geographic information data acquisition system based on cloud computing. The specific implementation steps are as follows: Step S1, using the data acquisition module to obtain the observation point height G, the observation point latitude W and the adjacent point data; Step S2, using the data processing module to run the data processing algorithm and output the evaluation data of the surveying and mapping geographic information; Step S3: Based on the evaluation data, the processed data is deeply analyzed, and the data collection results and evaluation report are displayed using the data visualization module; Step S4: Optimize and adjust the system based on the evaluation report; Step S5, using the data acquisition module to store the data; The data processing module includes a unit reflecting the corrected ground true elevation, a unit comprehensively evaluating the horizontal position accuracy, and a unit comprehensively evaluating the quality of geographic information data. The equipment used in the data acquisition module includes GPS receivers and remote sensing equipment; The equipment used in the data processing module includes servers, network equipment, and data acquisition terminals; The equipment used in the data visualization module includes visualization equipment; The GPS receiver is used to accurately obtain the latitude and longitude information of the observation point; Remote sensing equipment includes satellite remote sensors and high-definition cameras carried by drones, which are used to obtain ground image data; Servers and network equipment are deployed on the cloud platform for data processing, storage, and transmission; The data acquisition terminal is used for on-site data collection; Visualization equipment is used to display data analysis results and reports.

[0021] Through the above method steps, modules, units and equipment configurations, the present invention can build a surveying and mapping geographic information data acquisition system based on cloud computing to achieve efficient, accurate and comprehensive data acquisition, processing and evaluation.

[0022] In the embodiment, the system cooperates with each other through three algorithm units. In the surveying and mapping geographic information data collection system based on cloud computing, each of the three algorithm units has different calculation purposes and together constitutes the core framework for comprehensive evaluation and optimization of geospatial data quality. The three operation results of GCX, SP and ZP are combined. GCX is the elevation correction value. In the process of surveying and mapping geographic information data collection, due to the influence of terrain undulations, earth curvature, and atmospheric refraction factors, the directly observed height value often needs to be corrected to accurately reflect the true elevation of the ground. SP is the horizontal position accuracy value. This evaluation not only takes into account the influence of terrain factors, but also incorporates the correction of external factors such as meteorological conditions, making the evaluation result closer to the actual situation. It helps improve the overall performance of the data acquisition system. ZP is a comprehensive data quality assessment value. This assessment method not only considers the spatial position accuracy of the data, but also incorporates multiple 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. This helps system administrators promptly identify and resolve data quality issues, improving the overall reliability and stability of the data acquisition system. The results of the current and previous X comprehensive data quality assessment values ​​ZP can also affect the calculation of the unit reflecting the corrected ground truth elevation and the unit for comprehensive horizontal position accuracy assessment. This closed-loop feedback mechanism helps the system continuously optimize itself and improve the overall data acquisition and processing capabilities.

[0023] See also Figures 1 to 4 , the calculation formula reflecting the corrected ground truth elevation unit is as follows: GCX=(G×sin(W)×a)+(GC avg / cos(LW avg ) × b); in: GCX is the elevation correction value; W is the latitude of the observation point; a and b are weight coefficients; a is used to adjust the influence of the observation point height G on the elevation correction value GCX; b is used to adjust the influence of the observation point latitude W on the elevation correction value GCX.

[0024] In this embodiment: First, in this algorithm unit, the height G of the observation point is obtained by field measurement using a GPS receiver and remote sensing equipment. These equipment 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 height G of the observation point. In a surveying and mapping geographic information data acquisition system, the highest point is usually not determined directly through a single formula, but is based on the elevation data of multiple observation points, i.e., GL1, GL2, GL3, GL4, GL5, GL6, GL7, GL8, GL9, GL10, GL11, GL22, GL33, GL12, GL13, GL14, GL15, GL16, GL17, GL18, GL19, GL21, GL22, GL34, GL19, GL11, GL23, GL11, GL24, GL35 n After comparison, the system will sort the elevation data of all observation points, and the point with the largest elevation value is the highest point; The latitude W of the observation point is obtained through the GPS system and satellite positioning technology. Modern GPS receivers can provide very accurate longitude and latitude information. Latitude is an important parameter that describes the location of a point on the earth. It affects the effect of the earth's curvature on elevation measurement, so it needs to be taken into account in the elevation correction calculation. 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 of the adjacent points. avg and the average latitude of its neighboring points LW avg , achieving accurate calculation of the elevation correction value GCX. This calculation method not only takes into account 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 altitude changes, and can provide more reliable basic data for subsequent geographic information analysis and applications; In addition, this algorithm unit uses weight coefficients a and b and neighboring point data to enable the unit reflecting the corrected ground truth elevation to dynamically adjust the elevation correction value according to different terrain conditions and environmental factors. This enhanced environmental adaptability enables the system to maintain high measurement accuracy in different geographical environments and climatic conditions, providing strong support for the widespread application of surveying and mapping geographic information data. Since the unit reflecting the corrected ground true elevation can dynamically adjust the elevation correction value according to different terrain conditions and environmental factors, this makes the data collection strategy more flexible and efficient. In complex terrain areas, the system can automatically adjust the collection parameters based on real-time data to reduce errors and improve efficiency. This intelligent data collection strategy is particularly important for large-scale, high-precision surveying and mapping projects.

[0025] See also Figures 1 to 4 , the calculation formula for comprehensively evaluating the horizontal position accuracy unit is as follows: ; in: SP is the horizontal position accuracy value; GJQ is the weighted mean height difference, which is the weighted mean height difference between the observation point and the adjacent points according to the spatial distance. It is used to reflect the influence of the surrounding terrain on the horizontal position accuracy value SP. QY is the correction factor, which is used to adjust the impact of non-topographic factors on the horizontal position accuracy value SP.

[0026] In this embodiment, first, the comprehensive horizontal position accuracy evaluation unit realizes a comprehensive evaluation of the horizontal position accuracy value SP by combining the observation point latitude W, 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 impact of terrain factors on horizontal position accuracy, but also incorporates the correction of external factors such as meteorological conditions, making the evaluation result closer to the actual situation. The introduction of the weighted average height difference GJQ and the meteorological condition correction factor QY in the comprehensive horizontal position accuracy assessment unit optimizes the data processing process. By taking into account the data of neighboring points in a weighted average manner, it can more reasonably reflect the impact of the surrounding terrain on the horizontal position accuracy. The addition of the meteorological condition correction term can adjust the impact of non-topographic factors on the assessment results in real time, improving the real-time and accuracy of data processing. In summary, this algorithm unit not only considers the influence of terrain factors but also incorporates the correction of external factors of meteorological conditions by comprehensively evaluating the horizontal position precision value SP. This comprehensive evaluation method enables the system to more accurately judge 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 evaluation of the horizontal position precision unit can provide an important reference for data acquisition quality control. During the data acquisition process, the system can timely adjust the acquisition plan, optimize the equipment configuration and take other measures to ensure data quality according to the evaluation results of the horizontal position precision value SP. This real-time quality control mechanism helps to improve the overall efficiency and accuracy of data acquisition.

[0027] See also Figures 1 to 4, the calculation formula for comprehensively evaluating the quality unit of geographic information data 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 weight coefficients; c is used to adjust the influence of the horizontal position precision value SP on the comprehensive data quality assessment value ZP; d is used to adjust the impact of the elevation correction value GCX on the comprehensive data quality assessment value ZP; EX is the additional data quality evaluation indicator value, including measurement error, system noise, and data processing uncertainty; EX i is the value of the i-th additional data quality assessment indicator; ∑ L i=1 EX i 2 Reflects EX added from i=1 to i=L i 2 The sum calculation of .

[0028] In this embodiment, the algorithm unit is first based on; This algorithm unit integrates the horizontal position accuracy value SP, the elevation correction value GCX, and multiple additional data quality assessment indicators EX to achieve a comprehensive assessment of the comprehensive data quality assessment value ZP. This assessment method not only considers the spatial position accuracy of the data, but also incorporates multiple 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. Among them, the weight coefficients c and d in the comprehensive evaluation unit of geographic information data quality allow the influence of different evaluation indicators on the comprehensive data quality evaluation value ZP to be dynamically adjusted according to actual needs. This flexibility enables the system to perform targeted optimization according to different application scenarios and data characteristics, promoting the continuous improvement of data quality. 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 basis for making scientific decisions and plans. Through the evaluation of the comprehensive data quality assessment value ZP, decision makers can more accurately understand the overall quality of the data, thereby formulating strategies for data collection, processing and application more scientifically. This data-based decision-making method helps to improve the scientific nature and effectiveness of decision-making.

[0029] See also Figures 1 to 4 , using the data visualization module, and based on the analysis of the current comprehensive data quality assessment value ZP and the previous X comprehensive data quality assessment values ​​ZP, the adjustment is as follows: If the current and previous X comprehensive data quality assessment values ​​ZP gradually increase over time on the line graph, 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 gradually decrease over time on the line graph, forming a downward sloping curve, it indicates that the data quality is deteriorating 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 times' comprehensive data quality assessment values ​​ZP fluctuate within a certain range on the line graph without a clear upward or downward trend, it indicates that multiple factors are acting together. In this case, the various parameters in the unit reflecting the corrected ground truth elevation should be checked, and the number of observations should be increased or other adjustment factors should be introduced.

[0030] In this embodiment, the algorithm unit, based on the evaluation results of the comprehensive data quality by the comprehensive evaluation unit for geographic information data quality, can in turn influence the parameter adjustment and optimization of the unit reflecting the corrected ground truth elevation and the unit for comprehensive evaluation of horizontal position accuracy. When it is found that the data quality of certain areas and time periods is low, the weight coefficients a and b in the unit reflecting the corrected ground truth elevation, the parameters of the neighboring point data, and the weighted average height difference GJQ and the correction factor QY in the unit for comprehensive evaluation of horizontal position accuracy can be adjusted to improve the data quality of these areas and time periods. This closed-loop feedback mechanism helps the system to continuously optimize itself and improve the overall data collection and processing capabilities. By comprehensively evaluating and optimizing the quality of comprehensive data through the geographic information data quality unit, 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, providing more reliable data support for scientific research, engineering construction, and urban planning in the field of surveying and mapping geographic information. It is worth noting that by observing the line graph, we can promptly discover the trend of declining data quality and take targeted measures to improve it. This helps ensure the accuracy and reliability of the data and provides a solid foundation for subsequent analysis and decision-making. Specifically, when data quality fluctuates, by deeply analyzing the causes of the fluctuations and adjusting the parameters in the units reflecting the corrected ground truth 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 collection and processing methods are relatively effective, but it may also indicate that there is potential for further improvement in data quality. By continuously optimizing the selection algorithm for neighboring points and improving the measurement accuracy of the observation point height, the efficiency and accuracy of the data collection and processing process can be further improved. When a downward trend occurs, a comprehensive review of the data collection and processing process should be conducted to identify and correct possible problems, such as equipment aging and errors in the data collection process, so as to restore and improve data quality. 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 developing accurate maps, planning reasonable transportation routes, and assessing environmental risks. By optimizing data collection 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 on-site surveys and re-measurements. By optimizing the data acquisition and processing system, work efficiency can be improved, labor and material costs can be reduced, and overall operational cost-effectiveness can be improved. By analyzing line graphs and adjusting the parameters in the units that reflect the corrected ground truth elevation, data fluctuations in different situations can be addressed and the stability of the data acquisition and processing system can be enhanced. This helps ensure that the system can operate stably in various environments and provide accurate and reliable data support. In summary, observing the changing trends of the current and previous X comprehensive data quality assessment values ​​ZP through line graphs and optimizing the data acquisition and processing system accordingly can bring beneficial effects in improving data quality, optimizing data acquisition and processing processes, improving decision-making quality, reducing operating costs, and enhancing system stability.

[0031] For example 2, please refer to Figures 1 to 4 , based on the average height difference GC of the neighboring points near the observation point height G avg The calculation formula is as follows: GC avg =(G-GL1)+(G-GL2)+(G-GL3)......+(G-GL n ) / N; GC avg is the average height difference of adjacent points; G is the height of the observation point; GL1, GL2, GL3, GLn are the heights of adjacent points; N is the total number of observations at neighboring points; Based on the average latitude LW of the neighboring points near the observation point latitude W avg The calculation formula is as follows: LW avg =(LW1+LW2+LW3+......+LW n ) / N; LW avg is the average latitude of adjacent points; N is the total number of observations of adjacent points, and the average height difference GC of adjacent points avg and the average latitude of the neighboring points LW avg The total number of neighboring point observations N involved in the calculation remains consistent; LW1+LW2+LW3+......+LW n Reflects the result value of adding the dimensions of adjacent points.

[0032] In this embodiment, the average height difference GC of adjacent points is calculated. avg , which enables the system to more accurately assess the terrain undulations in 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; Average height difference of adjacent points GC avg The calculation results can provide a basis for the formulation of data collection strategies. For example, in areas with large height changes, the system can calculate the average height difference GC of adjacent points. avg Dynamically adjust the density of collection points to ensure data continuity and representativeness; In the cloud computing environment, the average height difference GC of adjacent points avg The calculation can be processed in parallel efficiently, which greatly shortens the data processing time. At the same time, the average height difference GC of the adjacent points avg It can also be used as one of the indicators for data quality assessment, helping to identify and eliminate outliers and improve the accuracy and reliability of data processing; Average latitude of neighboring points LW avg The calculation of helps to obtain the average latitude of the area around the observation point, which is of great significance for the spatial analysis and positioning services of geographic information systems. By accurately calculating the average latitude of neighboring points LW avg , can improve the accuracy of geographic coordinates and reduce positioning errors; Combined with the average height difference of adjacent points GC avg and the average latitude of the neighboring points LW avgThe calculation results of the system can more comprehensively understand the terrain characteristics of the area where the observation point is located, which is of great significance for terrain analysis, hydrological simulation, and disaster warning applications, and helps to improve the comprehensive service capabilities of the system. On the cloud computing platform, the average latitude of the neighboring points LW avg The calculation can make full use of the elastic expansion capability of cloud resources to achieve efficient data processing and storage, and update the average latitude LW of neighboring points in real time. avg ,The system can dynamically adjust data processing procedures and resource allocation, optimize system performance, and improve user experience; In summary, the average height difference GC of adjacent points avg and the average latitude of the neighboring points LW avg 's calculations play an important role in surveying and mapping geographic information data acquisition systems based on cloud computing. They not only improve the accuracy and efficiency of data acquisition and processing, but also enhance the intelligence level and comprehensive service capabilities of the system.

[0033] While 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 can be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. The surveying and mapping geographic information data acquisition system based on cloud computing is characterized by: The specific implementation steps are as follows: Step S1, using the data acquisition module to obtain the observation point height G, the observation point latitude W and the adjacent point data; Step S2, using the data processing module to run the data processing algorithm and output the evaluation data of the surveying and mapping geographic information; Step S3, based on the evaluation data, perform in-depth analysis on the processed data, and use the data visualization module to display the data collection results and the evaluation report; Step S4, optimizing and adjusting the system based on the evaluation report; Step S5, using the data acquisition module to store the data; The data processing module includes a unit for reflecting the corrected ground true elevation, a unit for comprehensively evaluating the horizontal position accuracy and a unit for comprehensively evaluating the quality of geographic information data.

2. The surveying and mapping geographic information data acquisition system based on cloud computing according to claim 1 is characterized in that: The equipment used in the data acquisition module includes a GPS receiver and a remote sensing device; The equipment used in the data processing module includes servers, network equipment, and data acquisition terminals; The equipment used by the data visualization module includes visualization equipment.

3. The surveying and mapping geographic information data acquisition system based on cloud computing according to claim 2 is characterized in that: The GPS receiver is used to accurately obtain the latitude and longitude information of the observation point; The remote sensing equipment includes satellite remote sensors and high-definition cameras carried by drones, which are used to obtain 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.

4. The surveying and mapping geographic information data acquisition system based on cloud computing according to claim 3 is characterized in that: Based on the average height difference GC of the neighboring points near the observation point height G avg The calculation formula is as follows: GC avg =(G-GL1)+(G-GL2)+(G-GL3)......+(G-GL n ) / N; GC avg is the average height difference of adjacent points; G is the height of the observation point; GL1, GL2, GL3, GL n are the heights of adjacent points; N is the total number of neighboring point observations.

5. The surveying and mapping geographic information data acquisition system based on cloud computing according to claim 4 is characterized in that: Based on the latitude W of the observation point, the latitude average value LW of the neighboring points avg The calculation formula is as follows: <h2 style=";text-align:left;direction:ltr">LW<h2 style=";text-align:left;direction:ltr"> avg <h2 style=";text-align:left;direction:ltr"> =(LW1+LW2+LW3+......+LW<h2 style=";text-align:left;direction:ltr"> n <h2 style=";text-align:left;direction:ltr"> ( / N) LW avg is the average latitude of neighboring points; N is the total number of observations of neighboring points, and the average height difference of neighboring points GC avg The average latitude of the neighboring points LW avg The total number of neighboring point observations N involved in the calculation remains consistent; LW1+LW2+LW3+......+LW n Reflects the result value of adding the dimensions of adjacent points.

6. The surveying and mapping geographic information data acquisition system based on cloud computing according to claim 5 is characterized in that: The calculation formula reflecting the corrected ground true elevation unit is as follows: GCX=(G×sin(W)×a)+(GC avg / cos(LW avg )×b); in: GCX is the elevation correction value; W is the latitude of the observation point; a and b are weight coefficients; a is used to adjust the influence of the observation point height G on the elevation correction value GCX; b is used to adjust the influence of the observation point latitude W on the elevation correction value GCX.

7. The surveying and mapping geographic information data acquisition system based on cloud computing according to claim 6 is characterized in that: The calculation formula for the overall evaluation horizontal position accuracy unit is as follows: ； in: SP is the horizontal position accuracy value; GJQ is the weighted mean height difference, which is the weighted average height difference between the observation point and the adjacent points according to the spatial distance, and is used to reflect the influence of the surrounding terrain on the horizontal position accuracy value SP; QY is a correction factor, which is used to adjust the impact of non-terrain factors on the horizontal position accuracy value SP.

8. The surveying and mapping geographic information data acquisition system based on cloud computing according to claim 7 is 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 weight coefficients; c is used to adjust the influence of the horizontal position accuracy value SP on the comprehensive data quality assessment value ZP; d is used to adjust the influence of the elevation correction value GCX on the comprehensive data quality assessment value ZP; EX is the additional data quality evaluation indicator value, including measurement error, system noise, and data processing uncertainty; EX i is the value of the i-th additional data quality assessment indicator; ∑ L i=1 EX i 2 Reflects the EX from i=1 to i=L i 2 The sum calculation of .

9. The surveying and mapping geographic information data acquisition system based on cloud computing according to claim 8, characterized in that: Using the data visualization module, and based on the analysis of the current comprehensive data quality evaluation value ZP and the previous X comprehensive data quality evaluation values ​​ZP, the following adjustments are made: If the current and previous X comprehensive data quality assessment values ​​ZP gradually increase over time on the line graph, forming an upward sloping curve, it means 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 gradually decrease over time on the line graph, specifically forming a downward sloping curve, it means that the data quality is declining, and the observation point height G, observation point latitude W and neighboring point data should be re-evaluated and measured; If the current and previous X comprehensive data quality assessment values ​​ZP fluctuate within a certain range on the line graph without a clear upward or downward trend, it indicates that it is the result of the combined effect of multiple factors. The parameters in the corrected ground true elevation unit should be checked, and the number of observations should be increased as well as other adjustment factors should be introduced.

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