Disclosure of Invention
The invention aims to provide a mapping geographic information data acquisition system based on cloud computing, which solves the problems in the background technology.
In order to achieve the above purpose, the present invention provides the following technical solutions, which specifically include the following implementation steps:
Step S1, acquiring the observation point height G, the observation point latitude W and the adjacent point data by utilizing a data acquisition module;
s2, using a data processing module to run a data processing algorithm and outputting evaluation data of mapping geographic information;
step S3, based on the evaluation data, carrying out deep analysis on the processed data, and displaying a data acquisition result and an evaluation report by using a data visual module;
Step S4, based on the evaluation report, optimizing and adjusting the system;
S5, storing the data by utilizing a data acquisition module;
the data processing module comprises a ground real elevation unit after correction, a comprehensive horizontal position accuracy evaluation unit and a comprehensive geographic information data quality evaluation unit.
Optionally, the device used by the data acquisition module comprises a GPS receiver and remote sensing equipment;
The equipment used by the data processing module comprises a server and network equipment, and a data acquisition terminal;
the equipment used by the data visualization module comprises visualization equipment;
the GPS receiver is used for accurately acquiring longitude and latitude information of the observation point;
The remote sensing equipment comprises a satellite remote sensor and a high-definition camera carried by an unmanned aerial vehicle, and is used for acquiring ground image data;
the server and the network equipment are deployed on the cloud platform and are used for data processing, storage and transmission;
The data acquisition terminal is used for acquiring field data;
The visualization device is used for displaying data analysis results and reports.
Optionally, the calculation formula of the average height difference GCavg of the neighboring points based on the observation point height G is as follows:
GCavg=（G-GL1）+（G-GL2）+（G-GL3）......+（G-GLn)/N;
GCavg is the average height difference of the neighboring points;
g is the height of the observation point;
GL1,GL2,GL3,GLn is the proximity point height;
n is the total number of neighbor point observations.
Optionally, the calculation formula of the adjacent point latitude average LWavg based on the adjacent observation point latitude W is as follows:
LWavg=（LW1+LW2+LW3+......+LWn)/N;
LWavg is the average of the latitude of the neighboring points;
N is the total observed quantity of the adjacent points, and the average height difference GCavg of the adjacent points is consistent with the total observed quantity N of the adjacent points participating in calculation by the average latitude LWavg of the adjacent points;
LW1+LW2+LW3+......+LWn reflects the result value of the adjacent point dimension addition.
Optionally, the calculation formula of the ground real elevation unit after correction is as follows:
GCX=(G×sin(W)×a)+(GCavg/cos（LWavg)×b);
Wherein:
GCX is an elevation correction value;
w is the latitude of the observation point;
a and b are weight coefficients;
a is used for adjusting the influence degree of the height G of the observation point on the elevation correction value GCX;
b is used for adjusting the influence degree of the latitude W of the observation point on the elevation correction value GCX.
Optionally, the calculation formula of the overall evaluation horizontal position accuracy unit is as follows:
;
Wherein:
SP is the precision value of horizontal position;
GJQ is a weighted average height difference, and GJQ is an average height difference weighted according to a spatial distance between an observation point and an adjacent point, and is used for reflecting the influence of surrounding topography on a horizontal position accuracy value SP;
QY is a correction factor for adjusting the influence of the non-topography factor on the horizontal position accuracy value SP.
Optionally, the calculation formula for comprehensively evaluating the geographic information data quality unit is as follows:
ZP=(SP×G×c)+((GCX/W)×d)+EX;
EX=∑Li=1EXi2;
Wherein:
ZP is a comprehensive data quality assessment value;
c and d are weight coefficients;
c is used for adjusting the influence degree of the horizontal position accuracy value SP on the comprehensive data quality evaluation value ZP;
d is used for adjusting the influence degree of the elevation correction value GCX on the comprehensive data quality evaluation value ZP;
EX is an additional data quality evaluation index value, and comprises measurement errors, system noise and uncertainty of data processing;
EXi is the i-th additional data quality assessment index value;
ΣLi=1EXi2 reflects the sum calculation of EXi2 added from i=1 to i=l.
Optionally, the data visualization module is utilized, and based on the analysis of the current comprehensive data quality evaluation value ZP and the previous X times of the comprehensive data quality evaluation value ZP, the following is adjusted:
if the current and previous X times of comprehensive data quality evaluation values ZP are gradually increased along with time on the line graph, and a curve inclined upwards is formed in a concrete way, the data quality is gradually improved, and geographic information is not required to be adjusted;
If the current and previous X comprehensive data quality evaluation values ZP gradually decrease with time on the line graph, a downward inclined curve is formed, which indicates that the data quality is decreasing, and the observation point height G, the observation point latitude W and the adjacent point data should be reevaluated and measured;
If the current and previous X comprehensive data quality evaluation values ZP fluctuate within a certain range and there is no clear rising and falling trend on the line graph, it indicates that the result is due to the combined action of multiple factors, each parameter in the corrected ground real elevation unit should be checked and reflected, the observation times should be increased, and other factor adjustment factors should be introduced.
Compared with the prior art, the invention has the following beneficial effects:
1. The invention utilizes the distributed storage and parallel processing capability of the cloud computing platform, so that the system can efficiently process large-scale and high-complexity geospatial data, thereby remarkably improving the data processing speed and response time.
2. According to the invention, by reflecting the introduction of the corrected ground real elevation unit, the comprehensive evaluation horizontal position accuracy unit and the comprehensive evaluation geographic information data quality unit, a scientific data quality evaluation model is established by the system, so that the elevation correction value, the horizontal position accuracy and the comprehensive data quality of data can be comprehensively and accurately evaluated, and particularly, the introduction of the weight coefficients a and b and the adjacent point data in the corrected ground real elevation unit and the consideration of the weighted average height difference GJQ and the correction factor QY in the comprehensive evaluation horizontal position accuracy unit are reflected, so that the evaluation result is more in line with the actual situation.
3. According to the invention, the cloud computing platform is utilized to support seamless butt joint and integration of various data sources and processing systems, so that the phenomenon of data island is broken, and the sharing and intercommunication of data are realized.
4. The cloud computing-based real-time data processing and analyzing capability enables the system to realize real-time and quasi-real-time data acquisition and processing, and provides powerful guarantee for quick response and decision support.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The mapping geographic information data acquisition system based on cloud computing is different from the traditional mapping geographic information data acquisition system, the traditional mapping geographic information data acquisition system is low in processing speed and long in response time, particularly, for the evaluation of key indexes of elevation correction and horizontal position accuracy, a scientific model and a parameterization method are lacked, due to the fact that the integration level is low and the data processing and transmission delay are caused, the data island phenomenon is serious, the requirements of quick response and decision support cannot be met, and the algorithm unit has the beneficial effects of improving the data accuracy, optimizing the data processing flow, enhancing the system adaptability and improving the decision support capability, and the effects jointly promote the intelligent development and the modern development of the mapping geographic information data acquisition system.
Referring to fig. 1 to 4, the present embodiment provides a mapping geographic information data acquisition system based on cloud computing, which specifically includes the following implementation steps:
Step S1, acquiring the observation point height G, the observation point latitude W and the adjacent point data by utilizing a data acquisition module;
s2, using a data processing module to run a data processing algorithm and outputting evaluation data of mapping geographic information;
step S3, based on the evaluation data, carrying out deep analysis on the processed data, and displaying a data acquisition result and an evaluation report by using a data visual module;
Step S4, based on the evaluation report, optimizing and adjusting the system;
S5, storing the data by utilizing a data acquisition module;
The data processing module comprises a ground real elevation unit after correction, a comprehensive horizontal position accuracy evaluation unit and a comprehensive geographic information data quality evaluation unit;
the equipment used by the data acquisition module comprises a GPS receiver and remote sensing equipment;
the equipment used by the data processing module comprises a server and network equipment, and a data acquisition terminal;
the equipment used by the data visualization module comprises visualization equipment;
the GPS receiver is used for accurately acquiring longitude and latitude information of the observation point;
The remote sensing equipment comprises a satellite remote sensor and a high-definition camera carried by an unmanned aerial vehicle, and is used for acquiring ground image data;
the server and the network equipment are deployed on the cloud platform and are used for data processing, storage and transmission;
the data acquisition terminal is used for acquiring field data;
the visualization device is used for displaying data analysis results and reports.
Through the steps, modules, units and equipment configuration of the method, a mapping geographic information data acquisition system based on cloud computing can be constructed, and efficient, accurate and comprehensive data acquisition, processing and evaluation are realized.
In the embodiment, the system is mutually matched with three algorithm units, different calculation purposes are born respectively in a mapping geographic information data acquisition system based on cloud calculation, a core framework for comprehensively evaluating and optimizing the geographic space data quality is formed together, and in combination with three calculation results of GCX, SP and ZP, the GCX is an elevation correction value, in the mapping geographic information data acquisition process, due to the influences of topography fluctuation, earth curvature and atmospheric refraction factors, the directly observed elevation value always needs to be corrected to accurately reflect the real elevation of the ground, the SP is a horizontal position precision value, the evaluation not only considers the influence of topography factors, but also includes the correction of external factors of weather conditions, so that the evaluation result is more approximate to the real situation, the system is beneficial to improving the overall performance of the data acquisition system, ZP is an integrated data quality evaluation value, the evaluation mode not only considers the spatial position accuracy of data, but also comprises a plurality of other dimensions related to the data quality, including signal strength, measurement errors and equipment stability, so that a more comprehensive and accurate data quality evaluation result is provided, a system manager is helped to find and solve the data quality problem in time, the overall reliability and stability of the data acquisition system are improved, the results of the current and previous X times of integrated data quality evaluation value ZP can also influence the calculation of a ground real elevation unit and a comprehensive evaluation horizontal position accuracy unit after correction, and the closed loop feedback mechanism is beneficial to continuous self-optimization of the system and improves the overall data acquisition and processing capacity.
Referring to fig. 1 to 4, the calculation formula reflecting the corrected ground real elevation unit is as follows:
GCX=(G×sin(W)×a)+(GCavg/cos（LWavg)×b);
Wherein:
GCX is an elevation correction value;
w is the latitude of the observation point;
a and b are weight coefficients;
a is used for adjusting the influence degree of the height G of the observation point on the elevation correction value GCX;
b is used for adjusting the influence degree of the latitude W of the observation point on the elevation correction value GCX.
In the embodiment, firstly, in the algorithm unit, the height G of an observation point is measured in the field through a GPS receiver and remote sensing equipment, the equipment can accurately measure the vertical distance between the observation point and a certain known reference plane, such as a ground level surface and a sea level, namely the height G of the observation point, and in a mapping geographic information data acquisition system, the highest point is judged to be realized not directly through a single formula, but based on the altitude data of a plurality of observation points, namely GL1,GL2,GL3,GLn, the altitude data of all the observation points are obtained by comparison, and the system can sort the altitude data of all the observation points, wherein the point with the highest altitude value is the highest point;
The latitude W of the observation point is obtained through a GPS system and a satellite positioning technology, a modern GPS receiver can provide very accurate latitude and longitude information, and the latitude is an important parameter for describing a certain point on the earth and influences the influence of the curvature of the earth on the elevation measurement, so that the influence of the curvature of the earth on the elevation measurement needs to be taken into consideration in the calculation of elevation correction;
The algorithm unit realizes accurate calculation of the elevation correction value GCX by comprehensively considering the height G of the observation point, the latitude W of the observation point, the weight coefficients a and b, the average height difference GCavg of the adjacent points and the average LWavg of the latitude of the adjacent points, and the calculation mode not only considers the characteristics of the observation point, but also brings in the influence of surrounding terrains, thereby greatly improving the accuracy of elevation correction, being particularly important for areas with complex terrains and large height variation, and being capable of providing more reliable basic data for subsequent geographic information analysis and application;
in addition, the algorithm unit enables the ground real elevation unit after correction to dynamically adjust elevation correction values according to different terrain conditions and environmental factors through the weight coefficients a and b and the adjacent point data, and the environmental adaptability is enhanced, so that the system can keep higher measurement precision under different geographic environments and climatic conditions, and powerful support is provided for wide application of mapping geographic information data;
The ground real elevation unit after correction can dynamically adjust elevation correction values according to different terrain conditions and environmental factors, so that a data acquisition strategy can be more flexible and efficient, in a complex terrain area, a system can automatically adjust acquisition parameters according to real-time data, errors are reduced, efficiency is improved, and the intelligent data acquisition strategy is particularly important for large-scale and high-precision mapping projects.
Referring to fig. 1 to 4, the calculation formula of the overall evaluation horizontal position accuracy unit is as follows:
;
Wherein:
SP is the precision value of horizontal position;
GJQ is a weighted average height difference, and GJQ is an average height difference weighted according to a spatial distance between an observation point and an adjacent point, and is used for reflecting the influence of surrounding topography on a horizontal position accuracy value SP;
QY is a correction factor for adjusting the influence of the non-topography factor on the horizontal position accuracy value SP.
In the embodiment, firstly, the comprehensive evaluation of the horizontal position accuracy unit realizes the 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 the spatial distance and the meteorological condition correction factor QY, and the evaluation mode not only considers the influence of the topography factors on the horizontal position accuracy, but also includes the correction of the external factors of the meteorological condition, so that the evaluation result is more similar to the real situation;
The introduction of the weighted average height difference GJQ and the meteorological condition correction factor QY in the horizontal position precision unit is comprehensively evaluated, the data processing flow is optimized, the influence of surrounding topography on the horizontal position precision can be more reasonably reflected by considering the adjacent point data in a weighted average mode, and the influence of non-topography factors on the evaluation result can be adjusted in real time by adding the meteorological condition correction term, so that the real-time performance and accuracy of data processing are improved;
In summary, the algorithm unit comprehensively evaluates the horizontal position accuracy value SP, not only considers the influence of the topography factor, but also includes the correction of the external factor of the meteorological condition, the comprehensive evaluation mode enables the system to judge the reliability and the effectiveness of the data more accurately, thereby reducing the subsequent processing errors and the resource waste caused by the data quality problem, the evaluation result of the comprehensive evaluation horizontal position accuracy unit can provide important reference for the data acquisition quality control, in the data acquisition process, the system can adjust the acquisition scheme, optimize the equipment configuration and take other measures in time according to the evaluation result of the horizontal position accuracy value SP to ensure the data quality, and the real-time quality control mechanism is helpful for improving the overall efficiency and the accuracy of the data acquisition.
Referring to fig. 1 to 4, the calculation formula for comprehensively evaluating the geographic information data quality unit is as follows:
ZP=(SP×G×c)+((GCX/W)×d)+EX;
EX=∑Li=1EXi2;
Wherein:
ZP is a comprehensive data quality assessment value;
c and d are weight coefficients;
c is used for adjusting the influence degree of the horizontal position accuracy value SP on the comprehensive data quality evaluation value ZP;
d is used for adjusting the influence degree of the elevation correction value GCX on the comprehensive data quality evaluation value ZP;
EX is an additional data quality evaluation index value, and comprises measurement errors, system noise and uncertainty of data processing;
EXi is the i-th additional data quality assessment index value;
ΣLi=1EXi2 reflects the sum calculation of EXi2 added from i=1 to i=l.
In this embodiment, the present algorithm unit is first based on;
the algorithm unit realizes comprehensive evaluation of the comprehensive data quality evaluation value ZP by integrating the horizontal position precision value SP, the elevation correction value GCX and a plurality of additional data quality evaluation index values EX, and the evaluation mode not only considers the spatial position precision of data, but also includes a plurality of other dimensions related to the data quality, including signal strength, measurement errors and equipment stability, so that a more comprehensive and accurate data quality evaluation result is provided;
Wherein, the weight coefficients c and d in the overall evaluation geographic information data quality unit allow the influence degree of different evaluation indexes on the comprehensive data quality evaluation value ZP to be dynamically adjusted according to the actual demand, the flexibility enables the system to perform targeted optimization according to different application scenes and data characteristics, and promotes continuous improvement of data quality;
The comprehensive data quality evaluation value ZP provides powerful support for data-driven decision making, high-quality data is an important basis for making scientific decisions and planning in the field of mapping geographic information, and a decision maker can more accurately know the overall quality condition of the data through evaluation of the comprehensive data quality evaluation value ZP, so that strategies for data acquisition, processing and application are more scientifically made, and the decision making mode based on the data is beneficial to improving the scientificity and the effectiveness of the decision.
Referring to fig. 1 to 4, the data visualization module is utilized, and based on the analysis of the current comprehensive data quality evaluation value ZP and the previous X times of the comprehensive data quality evaluation value ZP, the following is adjusted:
if the current and previous X times of comprehensive data quality evaluation values ZP are gradually increased along with time on the line graph, and a curve inclined upwards is formed in a concrete way, the data quality is gradually improved, and geographic information is not required to be adjusted;
If the current and previous X comprehensive data quality evaluation values ZP gradually decrease with time on the line graph, a downward inclined curve is formed, which indicates that the data quality is decreasing, and the observation point height G, the observation point latitude W and the adjacent point data should be reevaluated and measured;
If the current and previous X comprehensive data quality evaluation values ZP fluctuate within a certain range and there is no clear rising and falling trend on the line graph, it indicates that the result is due to the combined action of multiple factors, each parameter in the corrected ground real elevation unit should be checked and reflected, the observation times should be increased, and other factor adjustment factors should be introduced.
In this embodiment, the algorithm unit can adversely affect the parameter adjustment and optimization of the ground real elevation unit after correction and the comprehensive evaluation horizontal position accuracy unit based on the evaluation result of the comprehensive evaluation of the geographic information data quality unit, and when the data quality of some areas and time periods is found to be lower, the data quality of these areas and time periods can be improved by adjusting the parameters of the weight coefficients a and b and the adjacent point data in the ground real elevation unit after correction, and the weighted average elevation difference GJQ and the correction factor QY in the comprehensive evaluation horizontal position accuracy unit, and the closed loop feedback mechanism is helpful for continuous self-optimization of the system, and the overall data acquisition and processing capacity is improved;
The comprehensive data quality can be evaluated and optimized by comprehensively evaluating the geographic information data quality unit, so that the performance of the whole data acquisition and processing system can be promoted, and when the system can continuously provide high-quality data, the system is more beneficial to the subsequent analysis and application of geographic information, and more reliable data support is provided for scientific research, engineering construction and urban planning in the field of mapping geographic information;
It is worth noting that, by observing the line graph, the trend of the data quality degradation can be found in time, so that the improvement is carried out by adopting targeted measures, which is helpful for ensuring the accuracy and reliability of the data and providing a solid foundation for subsequent analysis and decision-making;
Specifically, when the data quality fluctuates, the fluctuation reasons are deeply analyzed, parameters in the corrected ground real elevation units are adjusted and reflected, the data quality can be stabilized, the influence of random errors and environmental factors on the data is reduced, a line graph of the rising trend shows that the current data acquisition and processing method is effective, but the potential for further improving the data quality can be possibly prompted, the efficiency and accuracy of the data acquisition and processing flow can be further improved by continuously optimizing a selection algorithm of adjacent points and improving the measurement accuracy of the observation point height, and when the trend is reduced, the data acquisition and processing flow is comprehensively examined, and possible problems such as equipment aging and errors in the data acquisition process are identified and corrected, so that the data quality is recovered and improved;
accurate and reliable data is the basis for making high-quality decisions, error decisions caused by data problems can be reduced by ensuring the data quality, the accuracy and reliability of the decisions are improved, the high-quality data has important significance for making accurate maps, planning reasonable traffic routes and evaluating environmental risks in the field of mapping geographic information, more accurate and reliable data support can be ensured in the fields by optimizing a data acquisition and processing system, repeated work and resource waste caused by data errors can be reduced by improving the data quality, and for example, in a geographic information system, unnecessary field investigation and re-measurement work can be reduced by accurate data;
The working efficiency can be improved, the cost of manpower and material resources can be reduced, the overall operation cost efficiency can be improved, the data fluctuation under different conditions can be dealt with by analyzing the line graph and adjusting and reflecting the parameters in the corrected ground real elevation unit, the stability of the data acquisition and processing system can be enhanced, the system can be ensured to stably operate under various environments, and accurate and reliable data support can be provided;
In summary, the line graph is used to observe the change trend of the current and previous X times of comprehensive data quality evaluation value ZP and optimize the data acquisition and processing system, so that the beneficial effects of improving the data quality, optimizing the data acquisition and processing flow, improving the decision quality, reducing the operation cost and enhancing the system stability can be brought.
In a second embodiment, referring to fig. 1 to 4, the calculation formula of the average height difference GCavg of neighboring points based on the observation point height G is as follows:
GCavg=（G-GL1）+（G-GL2）+（G-GL3）......+（G-GLn)/N;
GCavg is the average height difference of the neighboring points;
g is the height of the observation point;
GL1,GL2,GL3,GLn is the proximity point height;
N is the total quantity of the neighbor point observations;
The calculation formula of the adjacent point latitude average LWavg based on the adjacent observation point latitude W is as follows:
LWavg=（LW1+LW2+LW3+......+LWn)/N;
LWavg is the average of the latitude of the neighboring points;
N is the total observed quantity of the adjacent points, and the average height difference GCavg of the adjacent points is consistent with the total observed quantity N of the adjacent points participating in calculation by the average latitude LWavg of the adjacent points;
LW1+LW2+LW3+......+LWn reflects the result value of the adjacent point dimension addition.
In this embodiment, by calculating the average height difference GCavg of the neighboring points, the system can more accurately evaluate the topography fluctuation of the area where the observation point is located, which is critical for generating a high-precision elevation model, especially in complex topography areas such as mountainous areas and hilly areas;
the calculation result of the average height difference GCavg of the neighboring points can provide a basis for the formulation of a data acquisition strategy, for example, in a region with a large height change, the system can dynamically adjust the density of the acquisition points according to the average height difference GCavg of the neighboring points so as to ensure the continuity and representativeness of the data;
In a cloud computing environment, the calculation of the average height difference GCavg of the adjacent points can be efficiently performed in parallel, so that the data processing time is greatly shortened, meanwhile, the average height difference GCavg of the adjacent points can be used as one of indexes for evaluating the data quality, so that abnormal values can be identified and removed, and the accuracy and the reliability of data processing are improved;
The calculation of the adjacent point latitude average value LWavg is beneficial to obtaining the average value of the latitude of the area around the observation point, which has important significance for the space analysis and positioning service of the geographic information system, and the precision of the geographic coordinates can be improved and the positioning error can be reduced by accurately calculating the adjacent point latitude average value LWavg;
By combining the calculation results of the average height difference GCavg of the adjacent points and the average latitude value LWavg of the adjacent points, the system can more comprehensively know the topographic features of the area where the observation points are located, has important significance for topographic analysis, hydrologic simulation and disaster early warning application, is beneficial to improving the comprehensive service capability of the system, on a cloud computing platform, the calculation of the average latitude value LWavg of the adjacent points can fully utilize the elastic expansion capability of cloud resources, realize efficient data processing and storage, and can dynamically adjust the data processing flow and resource configuration, optimize the system performance and improve the user experience by updating the average latitude value LWavg of the adjacent points in real time;
In summary, the calculation of the average altitude difference GCavg of the neighboring points and the average latitude LWavg of the neighboring points plays an important role in the mapping geographic information data acquisition system based on cloud computing, and they not only improve the accuracy and efficiency of data acquisition and processing, but also improve the intelligent level and comprehensive service capability of the system.
Although embodiments of the present 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 therein without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.