Geographic information data acquisition and processing system based on big data

Abstract

The invention discloses a geographic information data acquisition and processing system based on big data. The system comprises a data acquisition module, a data processing module, a system state monitoring module and a comprehensive evaluation module. The data acquisition module acquires environmental parameters such as terrain, atmospheric pressure, geomagnetic intensity, solar radiation intensity and the like through an unmanned aerial vehicle carrying a sensor, and adjusts an acquisition strategy according to different geographic areas. The data processing module comprises a primary calibration sub-module and a secondary precision calibration sub-module which are respectively used for primary calibration and precision correction of data. The system state monitoring module monitors the collection process and the environmental parameter change in real time and generates an early warning signal. And the comprehensive evaluation module evaluates the data by applying a multi-parameter nonlinear combination algorithm and generates a geographic information analysis report. According to the system, high-precision data acquisition and large-range coverage are realized, and effective geographic information decisions are supported.

Description

Technical Field

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

[0002] With the rapid development of big data technology, geographic information systems (GIS) have been widely used in many fields such as urban planning, environmental monitoring, natural resource management and disaster warning. However, the traditional geographic information data collection method mainly relies on fixed sensors and manual collection, with a long data update cycle and limited collection range, which cannot cope with complex and changing geographical environments. This fixed method is difficult to achieve efficient coverage in areas with high altitudes and complex terrains, especially in dynamically changing environments, and cannot adapt in real time.

[0003] In addition, existing data processing systems usually rely on simple linear models for calibration and evaluation, ignoring the impact of various environmental parameters such as atmospheric pressure, geomagnetic intensity, and solar radiation intensity on data collection accuracy, resulting in large errors in collected data. Traditional systems also lack a dynamic feedback mechanism and cannot adjust data processing models according to real-time environmental conditions. Therefore, in complex geographical environments, the data processing accuracy of existing systems is insufficient and cannot provide effective support for accurate decision-making.

[0004] In view of this, there is an urgent need for a geographic information data collection and processing system that combines big data technology and can dynamically adjust data collection and processing strategies to improve the coverage and accuracy of data collection, and can perform comprehensive evaluations based on real-time monitoring data to meet multi-scenario and multi-dimensional geographic information needs. Summary of the invention

[0005] The purpose of the present invention is to provide a geographic information data acquisition and processing system based on big data, which has the advantages of dynamically adapting to various geographical environments, improving data acquisition and processing accuracy, and realizing comprehensive evaluation, thereby solving the problems of low acquisition efficiency and insufficient data processing accuracy of existing systems.

[0006] To achieve the above-mentioned purpose, the present invention provides the following technical solutions: a geographic information data acquisition and processing system based on big data, comprising a data acquisition module, a data processing module, a system status monitoring module and a comprehensive evaluation module, wherein the data acquisition module is used to collect multi-dimensional geographic information data, and can adjust the acquisition method according to different geographical regions and environmental conditions to ensure the comprehensiveness and accuracy of the data;

[0007] The data processing module performs preliminary processing and calibration on the collected data, generates intermediate processing results, and performs precision correction on the data to ensure the accuracy of the data;

[0008] The system status monitoring module is used to monitor the operating status of the data acquisition module and the changes in environmental parameters. When an abnormal situation is detected, the system automatically generates an early warning signal and notifies the management personnel;

[0009] The comprehensive assessment module generates comprehensive assessment results based on processed and calibrated data to support geographic information processing and decision-making.

[0010] Preferably, the data processing module includes two submodules:

[0011] The preliminary calibration submodule is used to perform preliminary data processing based on the collected terrain data, atmospheric pressure, geomagnetic intensity, and solar radiation intensity, correct the errors generated during the collection process, and generate preliminary calibration results;

[0012] The secondary precision calibration submodule is used to further correct the preliminary calibration results in combination with external parameters to generate accurate calibration data.

[0013] Preferably, the execution process of the system includes the following steps:

[0014] S1. Data collection steps:

[0015] S11, flight path planning and execution: the sensor-carrying drone cruises according to the preset flight path and altitude, covering the designated geographical area to ensure the comprehensiveness of data collection and the accuracy of regional coverage; S12, environmental parameter collection: the multi-band sensor on the drone is used to collect multiple environmental parameters, including terrain data, atmospheric pressure, geomagnetic intensity, solar radiation intensity, groundwater level changes, and surface albedo, to ensure the synchronous collection of multi-dimensional data; S13, real-time data transmission: the collected data is transmitted to the ground control center in real time through the wireless communication module of the drone for preliminary storage to provide data support for subsequent data processing steps;

[0016] S2. Preliminary data processing and calibration steps:

[0017] S21. Preliminary data processing: The system processes the collected raw data through a preliminary data calibration algorithm, mainly based on terrain data, atmospheric pressure, geomagnetic intensity and solar radiation intensity for preliminary calibration, correcting the noise and errors generated during data collection to ensure the basic accuracy of the data; S22. Generate preliminary calibration results: After processing, the system generates preliminary calibration results, which will be used in subsequent precision correction steps to ensure the accuracy of subsequent processing;

[0018] S3. Second precision calibration steps:

[0019] S31. External parameter detection: The system uses a secondary precision calibration algorithm, combined with real-time monitoring of external parameters, and uses the time series processing function of external parameters to analyze the change trend of external parameters, dynamically adjust the calibration model, and ensure that the calibration model can adapt to changes in the external environment; S32. Calibration result correction: Based on the secondary precision calibration algorithm and combined with the time series processing function of external parameters, the system further corrects the preliminary calibration results to ensure the accuracy and consistency of the calibration data under different environmental conditions and time changes, and outputs the corrected accurate data;

[0020] S4. System monitoring and early warning steps:

[0021] S41, status monitoring: the system status monitoring module monitors the flight status of the drone, the working status of the sensor, and the changes in atmospheric pressure, geomagnetic intensity, solar radiation intensity, ground humidity, wind speed fluctuation rate, groundwater level changes, and surface albedo environmental parameters in real time to ensure the stability of the collection and operation process; S42, warning and notification: when the system detects an abnormal situation, the system automatically triggers a warning signal and notifies the ground operator to intervene;

[0022] S5. Comprehensive assessment and decision support steps:

[0023] S51. Multi-parameter comprehensive evaluation: The system uses a comprehensive evaluation algorithm and a multi-parameter nonlinear combination algorithm based on calibrated data to incorporate key parameters such as groundwater level changes and surface albedo into the evaluation process to generate a comprehensive evaluation result. S52. Generate an evaluation report: The system generates a geographic information analysis report based on the comprehensive evaluation results, and displays the evaluation results in a visual way for the operator's reference and decision-making.

[0024] Preferably, the steps of the preliminary data calibration algorithm include:

[0025] S1. Input data: the original geographic information data D received by the measurement data acquisition module input and environmental parameters for correcting the measured data, including atmospheric pressure P atm 、Geomagnetic intensity M geo and solar radiation intensity S solar ; S2, calibration calculation formula: The calibration algorithm calibrates the input data according to the following formula to calculate the preliminary calibration result R1:

[0026] where α P , α M and α SThey are the calibration coefficients of atmospheric pressure, geomagnetic intensity and solar radiation intensity respectively, and P0, M0 and S0 are the corresponding standard reference values; S3, output calibration results: the preliminary calibration result R1 is the intermediate processing result of the geographic information data, which is used for further correction and optimization in subsequent steps.

[0027] Preferably, the secondary precision calibration algorithm specifically includes the following steps:

[0028] S1. Input data: Receive the preliminary calibration result R1 and external environmental parameters, including ground humidity H ground and wind speed fluctuation rate V wind ; S2, calibration calculation formula: The secondary calibration algorithm further corrects the preliminary calibration result through the following formula to generate an accurate calibration result R2: Among them, β H and β V are the correction coefficients of ground humidity and wind speed fluctuation rate, respectively, H0 and V0 are standard humidity and standard wind speed fluctuation rate; S3, output calibration result: generate secondary precision calibration result R2, which is used for further comprehensive evaluation or directly applied to geographic information data analysis.

[0029] Preferably, the comprehensive evaluation algorithm specifically includes the following steps:

[0030] S1. Input data: receiving the preliminary calibration result R1 and the secondary precision calibration result R2, as well as external comprehensive parameters, including groundwater level change W ground and the surface albedo A surface ; S2, comprehensive evaluation calculation formula: The comprehensive evaluation algorithm generates the final comprehensive evaluation result R through the following formula final : Where ω1 and ω2 are dynamic weights, which are adjusted based on the influence of different parameters; γ1, γ2 and γ3 are adjustment coefficients; W0 is the standard groundwater level; ε is a constant to avoid the denominator being zero. S3. Output comprehensive evaluation results: The comprehensive evaluation results R final Used as the final output of geographic information data processing system to support decision making and analysis.

[0031] Preferably, the dynamic weight calculation function in the comprehensive evaluation algorithm is adjusted according to the characteristics of the input data, and its calculation method is:

[0032] Calculation of weight ω1: ω1=g1(R1,W ground )=a1+b1×log(R1+1)+c1×W ground ; where a1, b1, c1 are constants; R1 is the preliminary calibration result, W groundThe weight ω1 is dynamically adjusted according to the change of R1 and the groundwater level; the calculation of the weight ω2 is: Among them, a2, b2, c2 are constants, A surface is the surface albedo, R2 is the result of quadratic precision calibration, and ε is a constant to avoid the denominator being zero.

[0033] Preferably, the time series processing function of the external parameters includes:

[0034] Time series function of groundwater level change: W ground (t) = W0 + A w ×sin(ω w t+φ w ), where W0 is the base groundwater level, A w is the amplitude of groundwater level change, ω w is the frequency of change, φ w is the phase angle, t is the time variable;

[0035] Time series function of surface albedo: A surface (t) = A0 + B a ×e -λt , where A0 is the initial surface albedo, B a is the amplitude of albedo change, λ is the attenuation coefficient, and t is the time variable.

[0036] Compared with the prior art, the present invention has the following beneficial effects:

[0037] 1. Adjust the acquisition strategy in real time: The present invention realizes the real-time acquisition of environmental parameters such as atmospheric pressure, geomagnetic intensity, and solar radiation intensity by setting up a drone carrying sensors and combining it with a multi-band sensor. It can also automatically adjust the acquisition strategy according to different geographical areas and environmental conditions to ensure large-scale coverage and high-precision data acquisition.

[0038] 2. Multi-level calibration and precision optimization: The present invention uses a preliminary calibration submodule and a secondary precision calibration submodule, combined with external parameters to perform multi-level calibration, which significantly reduces the impact of environmental factors on data accuracy and further improves the accuracy of data processing.

[0039] 3. Feedback loop mechanism: The secondary precision calibration algorithm of the present invention is not only based on the preliminary calibration results, but also dynamically adjusts the calibration model in combination with external parameters, and feeds the corrected data back to the preliminary calibration process to form a feedback loop. This mechanism significantly improves the dynamic adaptability of the calibration results and ensures that the accuracy and consistency of the data are maintained under different environmental conditions.

[0040] 4. Cross-input enhancement algorithm connection: The preliminary calibration result is not only used for secondary precision calibration, but also as one of the inputs of the comprehensive evaluation algorithm; the output result of the secondary calibration is also used as an important input of the comprehensive evaluation algorithm to ensure that the results of each algorithm can further improve the calculation accuracy of subsequent steps.

[0041] 5. Comprehensive evaluation and intelligent decision support: Through the comprehensive evaluation module, the system generates reliable geographic information evaluation results based on a multi-parameter nonlinear combination algorithm, combined with key parameters such as groundwater level changes and surface albedo. These results further improve the accuracy of data evaluation through cross-feedback and weighted combination of algorithms, and effectively support intelligent decision-making. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 The overall architecture of the geographic information data acquisition and processing system based on big data of the present invention;

[0043] Figure 2 It is a structural decomposition diagram of the data processing module of the present invention;

[0044] Figure 3 This is a flow chart of the execution steps of geographic information data collection and processing of the present invention. DETAILED DESCRIPTION

[0045] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0046] See also Figures 1 to 3 The present invention relates to a geographic information data acquisition and processing system based on big data, aiming to provide a system that can efficiently acquire and process data in different geographical environments. The system of the present invention includes four main modules: data acquisition module, data processing module, system status monitoring module and comprehensive evaluation module. Each module plays a vital role in the entire data acquisition and processing process, ensuring the comprehensiveness and accuracy of the system.

[0047] Data acquisition module: The data acquisition module is the starting point of the system and is responsible for collecting multi-dimensional geographic information data. It collects data through drones carrying sensors. The flight path and altitude of the drone can be dynamically adjusted according to different geographical areas and environmental conditions to ensure that areas with complex terrain (such as mountains, wetlands, etc.) can also be accurately covered. Multi-band sensors can collect multiple environmental parameters in real time, including but not limited to terrain data, atmospheric pressure, geomagnetic intensity, solar radiation intensity, etc. These data are crucial for subsequent data processing and calibration.

[0048] The data collection module has an adaptive function, and the drone will adjust the collection strategy based on real-time feedback when flying in different geographical areas. For example, in high-altitude areas, the system will automatically optimize the collection parameters according to the local air pressure and climate conditions to ensure the accuracy and consistency of the collected data.

[0049] Data processing module: The data processing module is the core part of this system, responsible for processing and calibrating the collected data to ensure the accuracy of the data. The data processing module contains two submodules: the preliminary calibration submodule and the secondary precision calibration submodule.

[0050] Preliminary calibration submodule: After data collection is completed, the initial calibration submodule first performs preliminary processing on the raw data. This submodule performs preliminary calibration on the data based on environmental parameters (such as atmospheric pressure, geomagnetic intensity, solar radiation intensity, etc.) to correct the noise and errors that may be generated during the acquisition process. The calibration at this stage is mainly aimed at correcting large errors, with the aim of generating a reasonable intermediate data set to provide a basis for subsequent fine calibration.

[0051] For example, in areas where the geomagnetic intensity varies greatly, the module can use algorithms to correct the impact of fluctuations in geomagnetic intensity on measurement data and generate more accurate preliminary calibration results.

[0052] Secondary precision calibration submodule: After the initial calibration, the secondary precision calibration submodule will process the data more finely. This stage of calibration is not only based on the initial calibration results, but also combines external environmental parameters (such as ground humidity and wind speed fluctuation rate, etc.) to further correct the accuracy of the data. This multi-level calibration can effectively reduce the impact of environmental factors on data accuracy, especially in a dynamically changing environment.

[0053] Through secondary precision calibration, the system can introduce real-time changes in the external environment (for example, a sudden increase in wind speed or a drastic change in humidity) into the calibration model, dynamically correct the initial calibration results, and thus output more accurate calibration data.

[0054] System status monitoring module: The main task of the system status monitoring module is to ensure the safety and stability of the entire data collection and processing process. This module monitors the operating status of the data acquisition module in real time, including the flight status of the drone, the working status of the sensor, and the changes in the collected environmental parameters (such as atmospheric pressure, geomagnetic intensity, solar radiation intensity, etc.). Through this monitoring function, the system can effectively identify and respond to abnormal situations during operation.

[0055] If the system detects an abnormality (such as flight failure, sensor failure or drastic fluctuations in environmental parameters), the monitoring module will automatically generate an early warning signal and notify the management personnel to take measures in time. This function ensures the high reliability of the system and avoids data loss or errors during the collection process.

[0056] Comprehensive evaluation module: The comprehensive evaluation module is a key functional module in the present invention, which is responsible for comprehensive analysis and evaluation of the calibrated data. This module uses a multi-parameter nonlinear combination algorithm to perform weighted analysis on multiple environmental parameters (such as groundwater level changes, surface albedo, etc.) to generate a comprehensive evaluation result. This result can be used in a variety of application scenarios, including urban planning, environmental monitoring, natural resource management, etc.

[0057] By combining the nonlinear relationship of different parameters, the comprehensive assessment module can effectively improve the accuracy of the assessment and generate reliable geographic information analysis reports. In addition, the module also supports visual display, and operators can intuitively view the assessment results through a graphical interface to provide support for decision-making.

[0058] Data collection stage: The UAV cruises according to the preset path, collects geographic information data and environmental parameters in real time, and transmits the data to the ground control center. Preliminary calibration stage: The system uses the preliminary calibration submodule to perform preliminary processing on the collected raw data, eliminate noise and large errors, and generate preliminary calibration results. Secondary precision calibration stage: Combined with external environmental parameters, the system further corrects the preliminary calibration results through the secondary precision calibration submodule and outputs accurate calibration data. System monitoring stage: The system status monitoring module monitors the changes of UAVs, sensors and environmental parameters in real time to ensure the stability of the collection process, and issues early warning signals when abnormal conditions are detected. Comprehensive evaluation stage: The system comprehensive evaluation module conducts a comprehensive analysis of the calibrated data, generates an evaluation report, and displays the evaluation results through visualization tools for decision support by operators.

[0059] The execution process of the geographic information data acquisition and processing system based on big data of the present invention is divided into five main steps: data acquisition, preliminary data processing and calibration, secondary precision calibration, system monitoring and early warning, comprehensive evaluation and decision support. These steps realize the efficient acquisition and accurate processing of geographic information data through the collaborative work of algorithms, and provide effective decision support.

[0060] S1. Data collection step: Data collection is the first step of the system, which uses the multi-band sensors carried by the drone to collect real-time environmental data.

[0061] S11. Flight path planning and execution: Before data collection, the system first plans the flight path of the drone. The path planning is based on the preset geographical area, and the drone cruises according to the set path and altitude to ensure that the specified area can be covered. By dynamically adjusting the flight parameters of the drone, the system can ensure that even in complex terrain (such as mountains, hills or urban buildings), it can achieve comprehensive coverage and accurate data collection. This not only improves the reliability of the data, but also ensures the integrity of the collection area. S12. Environmental parameter collection: The multi-band sensors on the drone are responsible for collecting multiple environmental parameters, including terrain data, atmospheric pressure, geomagnetic intensity, solar radiation intensity, groundwater level changes and surface albedo. These multi-dimensional data are the basis for subsequent analysis and processing and can provide rich information for the system. By synchronously collecting different environmental parameters, the system can obtain more accurate and comprehensive geographic data. S13. Real-time data transmission: After the data collection is completed, the drone transmits the collected raw data to the ground control center in real time through the wireless communication module. The real-time nature of this step ensures the continuity of the data during the collection process, and the initially stored data provides a data basis for subsequent data processing and analysis.

[0062] S2. Preliminary data processing and calibration step: The preliminary data processing and calibration step is responsible for preliminary processing of the collected raw data, eliminating noise and correcting errors in the collection process. S21. Preliminary data processing: The system first performs preliminary processing on the collected raw data through a preliminary data calibration algorithm. This step is mainly based on environmental parameters such as terrain data, atmospheric pressure, geomagnetic intensity, and solar radiation intensity for calibration. The purpose of preliminary calibration is to reduce the noise and large errors that may occur during the data collection process and ensure the generation of a stable basic data set. S22. Generate preliminary calibration results: After the preliminary data processing is completed, the system generates preliminary calibration results. These calibration results are used for subsequent precision calibration steps and provide a highly accurate data basis for subsequent data analysis and processing.

[0063] S3. Secondary precision calibration step: This step further corrects the preliminary calibration results through a more accurate algorithm to ensure the accuracy of the data under various environmental conditions. S31. External parameter detection: The system detects external parameters through a secondary precision calibration algorithm combined with real-time monitored external parameters (such as ground humidity and wind speed fluctuation rate, etc.), and uses the time series processing function of the external parameters to analyze the changing trends of these parameters. In this way, the system can dynamically adjust the calibration model to ensure that the calibration process can adapt to changes in the external environment and improve the accuracy of the calibration. S32. Calibration result correction: Based on the secondary precision calibration algorithm, the system further corrects the preliminary calibration results in combination with the time series processing function of the external parameters. The dynamic changes of external parameters will affect the data accuracy, so the system predicts and processes these changes through the time series processing function to ensure that the calibration data remains accurate and consistent under different environmental conditions. Finally, the system outputs the corrected accurate data as the final calibration result.

[0064] S4. System monitoring and early warning steps: The system status monitoring module is responsible for monitoring the operating status of the entire data collection and processing process, ensuring the stability of the system, and issuing early warnings in abnormal situations. S41. Status monitoring: The system monitors the flight status of the drone, the working status of the sensor, and the collected environmental parameters (including atmospheric pressure, geomagnetic intensity, solar radiation intensity, ground humidity, wind speed fluctuation rate, groundwater level changes, and surface albedo, etc.) in real time. This monitoring process ensures the stability of the collection process and avoids data collection failures due to sensor failures or flight anomalies. S42. Early warning and notification: When the system detects an abnormal situation (such as drone failure, sensor failure, or drastic changes in environmental parameters), the system will automatically trigger an early warning signal to notify ground operators to intervene. This automated early warning mechanism can effectively reduce the risk of data loss and improve the reliability of the collection process.

[0065] S5. Comprehensive evaluation and decision support steps: The system uses the comprehensive evaluation module to conduct multi-dimensional analysis of the calibrated data, generate comprehensive evaluation results, and provide support for decision-making. S51. Multi-parameter comprehensive evaluation: Based on the comprehensive evaluation algorithm, the system conducts multi-parameter nonlinear combination evaluation on the calibrated data. By incorporating key parameters such as groundwater level changes and surface albedo into the evaluation process, the system can generate comprehensive evaluation results. This multi-dimensional evaluation not only takes into account the impact of a single parameter, but also weights it through a nonlinear algorithm to ensure the accuracy of the evaluation results. S52. Generate an evaluation report: After completing the multi-parameter comprehensive evaluation, the system generates a geographic information analysis report. Through visualization tools, operators can intuitively view the evaluation results and make decisions based on these results. This evaluation report can not only be used for geographic information analysis, but also provide decision support for multiple fields such as urban planning and environmental protection.

[0066] The geographic information data acquisition and processing system based on big data disclosed in the present invention includes a preliminary calibration algorithm, which is designed to ensure the accuracy of subsequent data processing by eliminating preliminary errors in the collected data. The preliminary calibration algorithm involves multiple environmental parameters and adopts a set of clear calibration formulas to improve the accuracy of the data.

[0067] S1. Input data: The system first receives the original geographic information data D from the measurement data acquisition module. input , these data are collected by multi-band sensors carried by drones. The data contains terrain information and related environmental parameters (such as air pressure, atmospheric pressure, geomagnetic intensity, solar radiation intensity, etc.). These environmental parameters may introduce errors during the data collection process, so they need to be calibrated. Source of environmental parameters: The atmospheric pressure P used in this invention atm 、Geomagnetic intensity M geo and solar radiation intensity S solar Parameters such as data are collected from sensors in real time and collected through the dynamic adjustment of drones in different environments. Ensure that the source of these data is traceable and reasonably obtained in actual operations to ensure the accuracy of the data.

[0068] S2. Calibration calculation formula: The system uses a preliminary calibration algorithm to perform calculations based on input data and corrected environmental parameters. The calibration formula is as follows:

[0069] Formula logic: The formula is calculated by the calibration coefficient α P , α M and α S The collected environmental parameters (atmospheric pressure, geomagnetic intensity, solar radiation intensity) are compared with the corresponding standard reference values ​​P0, M0 and S0 to correct the deviation in the original data. Each calibration coefficient represents the weight of the influence of different environmental parameters on the data. Parameter source and reference value: P0: standard atmospheric pressure; M0: standard geomagnetic intensity; S0: standard solar radiation intensity. These standard reference values ​​can be obtained from relevant meteorological or geographic data literature to ensure the accuracy and traceability of parameters during the calibration process. Compared with the existing linear calibration method, this formula considers the multi-dimensional influence of environmental parameters on the data, and calibrates through nonlinear relationships, which significantly improves the accuracy of data correction. This method can more effectively correct errors in topographic measurements, especially in complex or dynamically changing environments;

[0070] S3. Output calibration results: After the above calibration process, the system generates a preliminary calibration result R1, which represents the corrected intermediate processing data. This calibration result will serve as the basis for subsequent precision calibration and optimization processing. The preliminary calibration result not only provides a basis for subsequent data processing, but also ensures the reliability of the output data in subsequent processing by further analyzing its errors.

[0071] The secondary precision calibration algorithm disclosed in the present invention aims to further correct the preliminary calibration results and improve the accuracy of the calibration results by introducing external environmental parameters (such as ground humidity and wind speed fluctuation rate). The core of the algorithm is to optimize the preliminary calibration results to ensure that the final geographic data maintains a high degree of accuracy and consistency under various complex environmental conditions.

[0072] S1. Input data: The input data of the secondary precision calibration algorithm consists of two parts:

[0073] Preliminary calibration result R1: This is the output result based on the preliminary calibration algorithm in the previous stage, which has been preliminarily corrected by multi-dimensional environmental parameters. External environmental parameters: including ground humidity: H ground and wind speed fluctuation rate V wind , these parameters have an important impact on the accuracy of data in geographic data collection. The source of external parameters: ground humidity and wind speed fluctuation rate come from real-time monitoring of external sensor data, which can dynamically reflect the actual environmental changes in the collection area. Ensure that the source of these parameters is reliable, reasonable and traceable so that accurate corrections can be made under different geographical conditions.

[0074] S2. Calibration calculation formula: The secondary precision calibration algorithm further corrects the preliminary calibration result through the following formula to generate an accurate calibration result: Based on the preliminary calibration result R1, the accuracy of ground humidity and wind speed fluctuation rate is adjusted by introducing external parameter correction factors. Each correction factor β H and β V Used to reflect the weight of the impact of ground humidity and wind speed fluctuation on the data.

[0075] H0: standard humidity; V0: standard wind speed fluctuation rate; these standard values ​​are used to measure the relative changes of actual environmental parameters, so as to dynamically adjust the calibration results. The standard reference values ​​of the parameters are derived from the standard data in common meteorological or geographical literature; β H Indicates the influence of ground humidity on calibration data; β VRepresents the correction weight of wind speed fluctuation on calibration data. By introducing these correction coefficients, the system can better reflect the multi-dimensional impact of the external environment on geographic information data; compared with the traditional single linear calibration method, the present invention adopts a multi-dimensional calibration algorithm based on external parameters, which can better adapt to changes in environmental conditions. By introducing ground humidity and wind speed fluctuation, the algorithm ensures high consistency and accuracy of data in complex and dynamic environments.

[0076] S3. Output accurate calibration results: After the second precision calibration, the system generates the final accurate calibration result R2. This result represents the data after multi-dimensional calibration and can be used for higher-precision geographic information analysis or further data processing. The second precision calibration result can be directly used in subsequent comprehensive evaluation and decision support modules. Through further comprehensive analysis of the calibration results, the system can provide users with more accurate geographic information data and provide a solid foundation for scientific decision-making.

[0077] The comprehensive evaluation algorithm of the present invention is based on the preliminary calibration results and the secondary precision calibration results, and combines external comprehensive parameters (such as groundwater level changes and surface albedo) to generate the final comprehensive evaluation results through a nonlinear combination algorithm. The purpose of this algorithm design is to achieve a comprehensive analysis of geographic information data and support decision-making in complex environments.

[0078] S1. Input data: The input data of the comprehensive evaluation algorithm include: Preliminary calibration result R1: The result from the preliminary calibration algorithm, which has been calibrated by multi-dimensional environmental parameters. Secondary precision calibration result R2: The precise result after further calibration based on external environmental parameters. External comprehensive parameters: including groundwater level change W ground and the surface albedo A surface ,These parameters have an important impact on the changes in the geographical environment and can provide more extensive data support for the assessment process. External parameter sources: Groundwater level and surface albedo are derived from real-time data from sensors and monitoring equipment, ensuring that these parameters can dynamically reflect the current geographical environment and provide a strong basis for comprehensive assessment.

[0079] S2. Comprehensive evaluation calculation formula: The comprehensive evaluation algorithm generates the final comprehensive evaluation result through the following formula: ; This formula combines the results of the preliminary calibration and the secondary precision calibration, and generates the final evaluation result through weighted processing of external comprehensive parameters. Each parameter has its specific weight (ω) in the formula, ensuring that the comprehensive evaluation can accurately reflect the geographic information under different environmental conditions.

[0080] Dynamic weights (ω1 and ω2): ω1 and ω2 are dynamic weights adjusted based on the degree of influence of different input parameters, ensuring that the system can flexibly adjust the weights in the evaluation process according to different environmental conditions. This dynamic adjustment ensures that the system can adapt to various complex environmental changes and generate more accurate evaluation results. Adjustment coefficients (γ1, γ2 and γ3): γ1, γ2 and γ3 are corresponding adjustment coefficients, which control the weights of preliminary calibration results, groundwater level changes and surface albedo in the evaluation respectively. Through these adjustment coefficients, the system can flexibly adjust the importance of different environmental parameters in the evaluation.

[0081] W0 is the standard groundwater level, which is used to measure the relative change of groundwater level. surface is the surface albedo, which reflects the reflective capacity of the surface, and ε is a constant used to prevent the denominator from being zero. These parameters are derived from actual monitoring data to ensure the accuracy and scientificity of the parameters in the evaluation process.

[0082] By introducing dynamic weights and adjustment coefficients, the evaluation formula can dynamically adjust the evaluation process according to different environmental conditions to ensure that the output results can more accurately reflect the current geographic information. Compared with the simple linear weighted evaluation method, the present invention adopts a nonlinear combination algorithm, which can process more complex associated data and improve the evaluation accuracy.

[0083] S3. Output comprehensive evaluation results: After comprehensive evaluation calculation, the system generates the final comprehensive evaluation result R final , the result will be used as the final output of the system to support decision-making and data processing.

[0084] Application of output results: The comprehensive evaluation results can be directly used for further processing or analysis of geographic information data. Based on the evaluation results, the system can provide users with a comprehensive geographic information evaluation report to support intelligent decision-making and data analysis in multiple scenarios.

[0085] The dynamic weight calculation algorithm in the present invention is used to dynamically adjust the weight coefficients in the comprehensive evaluation algorithm according to the characteristics of the input data. By using input data (such as preliminary calibration results, groundwater level changes, surface albedo, etc.), the system can flexibly adjust the weight values ​​under different environmental conditions to generate more accurate evaluation results.

[0086] S1. Calculation of dynamic weight ω1: Dynamic weight ω1 is used in the comprehensive evaluation algorithm to adjust the initial calibration result R1 and the groundwater level change W ground The weight of influence. The calculation formula is as follows:

[0087] ω1=g1(R1,W ground )=a1+b1×log(R1+1)+c1×Wground , the formula is through R1

[0088] (Preliminary calibration results) and W ground The weights are dynamically adjusted by a nonlinear combination of the initial calibration results (groundwater level changes). Specifically, the b1 coefficient in the formula measures the impact of R1 on the weight by taking the logarithm of the initial calibration result, ensuring that larger R1 values ​​are not over-amplified. At the same time, the c1 coefficient controls the impact of groundwater level changes. Finally, a1 is a constant term, representing the basic weight. R1: preliminary calibration result, derived from the preliminary calibration algorithm in the previous stage, representing the preliminary processing accuracy of the data. W ground :The change of groundwater level is an external environmental parameter monitored and fed back by sensors in real time, reflecting the impact of groundwater level fluctuations on the environment.

[0089] By introducing the nonlinear combination of groundwater level changes and preliminary calibration results, ω1 can adapt to changes under different environmental conditions, ensuring the flexibility and accuracy of the assessment process. Especially in areas with large changes in the hydrological environment, this dynamic weight adjustment mechanism can significantly improve the assessment accuracy of the system.

[0090] S2. Calculation of dynamic weight ω2: Dynamic weight ω2 is used in the comprehensive evaluation algorithm to adjust the secondary precision calibration result R2 and the surface albedo A. surface The weight of influence. The calculation formula is as follows:

[0091] The calculation of ω2 also takes into account the surface albedo A surface And the quadratic precision calibration result R2. Among them, the b2 coefficient controls the influence of the surface albedo. By introducing It can effectively prevent calculation errors when the albedo is zero. c2 is used to control the impact of the secondary calibration results on the weights, ensuring that the weight adjustment can reflect the effect of the secondary precision calibration.

[0092] R2: Second precision calibration result, which means the accurate calibration result after external parameter adjustment, which comes from the second calibration algorithm in the previous stage; A surface : Surface albedo is the external environment data collected by sensors, which reflects the surface's ability to reflect solar radiation. This parameter has an important impact on the assessment of the geographic environment. ε: Constant term, used to prevent calculation errors caused when the surface albedo is close to zero.

[0093] S3. Application of dynamic weights: By using two dynamic weights, ω1 and ω2, the system can adjust the weights according to the current environmental data and calibration results. This not only improves the adaptability of the system under complex environmental conditions, but also ensures that the evaluation results can better reflect the changes in the actual environment. The calculation formula of dynamic weights not only takes into account the nonlinear characteristics of the input data, but also introduces a constant term for basic adjustment to ensure that the weight changes in each evaluation are reasonable and controllable.

[0094] The time series processing functions in the present invention are used to dynamically analyze the changing trends of external environmental parameters (such as groundwater level changes and surface albedo). These functions describe the fluctuation characteristics of external environmental parameters through time series models, ensuring that the system can flexibly adjust the evaluation process in different time periods and generate accurate evaluation results.

[0095] S1. Time series function of groundwater level change: Groundwater level change is an important parameter in the geographical environment and is affected by natural periodic changes. Its time series function is described as follows: W ground (t) = W0 + A w ×sin(ω w t+φ w ), W0 is the reference groundwater level, which represents the standard groundwater level without external interference. w is the amplitude of groundwater level change, indicating the maximum range of groundwater level fluctuation; ω w is the frequency of change, describing the periodic law of groundwater level change and indicating the fluctuation speed of groundwater level. w is the phase angle, which indicates the starting position of the groundwater level change in the cycle. t is the time variable, which is used to describe the change of groundwater level over time. Through this time series model, the system can accurately predict the dynamic change trend of groundwater level and introduce these changes in the assessment process. Especially in long-term environmental changes, this function can capture the fluctuation characteristics of groundwater level, thus ensuring more accurate assessment results.

[0096] S2. Time series function of surface albedo: The surface albedo has a great influence on the geographical environment, especially in the scenario of affecting surface temperature and solar radiation reflection. Its time series function is described as follows: A surface (t) = A0 + B a ×e -λt , A0 is the initial surface albedo, which indicates the surface albedo at the initial moment. a is the amplitude of albedo change, indicating the amplitude of albedo change over time. λ is the attenuation coefficient, indicating the rate at which surface albedo gradually decays over time, which is usually related to environmental factors (such as vegetation, climate change, etc.). t is the time variable, describing the change of albedo over time.

[0097] S3. Application of external parameter time series processing: Time series processing of groundwater level and surface albedo: Through time series functions, the system can dynamically monitor the changing trends of external parameters and adjust the weights and input data in the evaluation process based on these changes. Especially in long-term geographical evaluations, these time series functions can help the system capture the periodic changes and attenuation trends of the natural environment. Dynamic adjustment during the evaluation process: The time series function of external parameters ensures that the system can adjust parameters in real time during execution, so that the evaluation process can dynamically respond to environmental changes. This dynamic adjustment mechanism not only improves the accuracy of the evaluation, but also enhances the adaptability of the system in complex environments.

[0098] Example 1: Geographic Information Collection and Processing System Based on Atmospheric Pressure and Geomagnetic Intensity Calibration

[0099] Traditional geographic information collection systems often cannot guarantee data accuracy in high-altitude areas and complex terrains, especially the fluctuations in atmospheric pressure and geomagnetic intensity, which can easily cause serious errors in data collection. This embodiment innovatively improves data accuracy in special geographical areas (such as mountains, hills and high-altitude areas) by calibrating atmospheric pressure and geomagnetic intensity in real time.

[0100] The innovation of this embodiment is that the algorithm combining atmospheric pressure correction and geomagnetic intensity correction ensures high-precision geographic data collection in complex terrain. This embodiment is widely used in the fields of natural resource exploration, disaster warning and mountain infrastructure planning.

[0101] Through the calibration algorithm of the present invention, the system can dynamically correct the data errors caused by the fluctuation of environmental parameters. Compared with the existing fixed calibration method, the application of the present invention in complex terrain has better adaptability and accuracy.

[0102] In order to verify the advantages of the present invention under complex terrain conditions, multiple groups of experiments were conducted and 7 comparison projects were selected to demonstrate the performance differences between the present invention and the prior art under different conditions.

[0103]

[0104] It can be seen from the above comparison items that the present invention is significantly superior to the prior art in terms of acquisition accuracy, data error correction efficiency, and the ability to handle atmospheric pressure and geomagnetic intensity fluctuations. In addition, the present invention has higher acquisition coverage and data transmission stability in mountainous environments. Therefore, the present invention has a wider application prospect in complex terrain conditions.

[0105] Example 2: Geographic Information Collection and Processing System Based on Groundwater Level Change and Surface Albedo

[0106] In the process of long-term environmental monitoring, the existing geographic information collection system is difficult to effectively process long-term changes, especially in areas where groundwater level changes and surface albedo decay are large. This embodiment dynamically adjusts long-term environmental data by introducing time series functions of groundwater level changes and surface albedo.

[0107] The innovation of this embodiment is that, by combining the periodic model of groundwater level changes and the attenuation model of surface albedo, the system can respond to environmental changes in real time, and is particularly suitable for long-term hydrological monitoring, ecological environmental assessment, and climate change research.

[0108] By introducing the time series processing function, the present invention exhibits extremely high environmental adaptability over a long time scale and can accurately capture and correct groundwater level fluctuations and changes in surface albedo.

[0109] In order to verify the superiority of the present invention under long-term monitoring conditions, a number of comparative projects were designed to demonstrate the performance differences between the present invention and the prior art in groundwater level monitoring and surface albedo analysis.

[0110]

[0111] The comparison results show that the present invention exhibits extremely high accuracy and stability in long-term monitoring of groundwater level fluctuations and surface albedo attenuation. At the same time, the system can handle environmental changes with a faster response speed, ensuring the efficiency and reliability of data processing. In addition, the present invention also has significant advantages over the existing technology in terms of resource consumption, and is particularly suitable for deployment in monitoring systems that require long-term, low-consumption operation.

[0112] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A geographic information data acquisition and processing system based on big data, comprising a data acquisition module, a data processing module, a system status monitoring module and a comprehensive evaluation module, characterized in that: The data collection module is used to collect multi-dimensional geographic information data, and can adjust the collection method according to different geographical regions and environmental conditions to ensure the comprehensiveness and accuracy of the data; The data processing module performs preliminary processing and calibration on the collected data, generates intermediate processing results, and performs precision correction on the data to ensure the accuracy of the data; The system status monitoring module is used to monitor the operating status of the data acquisition module and the changes in environmental parameters. When an abnormal situation is detected, the system automatically generates an early warning signal and notifies the management personnel; The comprehensive assessment module generates comprehensive assessment results based on processed and calibrated data to support geographic information processing and decision-making.

2. The geographic information data acquisition and processing system based on big data according to claim 1 is characterized in that: The data processing module includes two submodules: The preliminary calibration submodule is used to perform preliminary data processing based on the collected terrain data, atmospheric pressure, geomagnetic intensity, and solar radiation intensity, correct the errors generated during the collection process, and generate preliminary calibration results; The secondary precision calibration submodule is used to further correct the preliminary calibration results in combination with external parameters to generate accurate calibration data.

3. The geographic information data acquisition and processing system based on big data according to claim 2 is characterized in that: The execution process of the system includes the following steps: S1. Data collection steps: S11. Flight path planning and execution: The sensor-carrying UAV cruises according to a preset flight path and altitude, covering a designated geographical area to ensure comprehensive data collection and accuracy of regional coverage; S12. Environmental parameter collection: The multi-band sensors on the drone are used to collect multiple environmental parameters, including terrain data, atmospheric pressure, geomagnetic intensity, solar radiation intensity, groundwater level changes, and surface albedo, to ensure the synchronous collection of multi-dimensional data; S13, real-time data transmission: the collected data is transmitted to the ground control center in real time through the wireless communication module of the UAV for preliminary storage to provide data support for subsequent data processing steps; S2. Preliminary data processing and calibration steps: S21. Preliminary data processing: The system processes the collected raw data through a preliminary data calibration algorithm, mainly based on terrain data, atmospheric pressure, geomagnetic intensity and solar radiation intensity, to correct the noise and errors generated during the data collection process and ensure the basic accuracy of the data; S22, generating preliminary calibration results: after processing, the system generates preliminary calibration results, which will be used in subsequent precision correction steps to ensure the accuracy of subsequent processing; S3. Second precision calibration steps: S31, External parameter detection: The system uses a secondary precision calibration algorithm, combined with real-time monitoring of external parameters, and uses the time series processing function of external parameters to analyze the change trend of external parameters, dynamically adjust the calibration model, and ensure that the calibration model can adapt to changes in the external environment; S32, calibration result correction: the system further corrects the preliminary calibration results based on the secondary precision calibration algorithm and the time series processing function of the external parameters to ensure the accuracy and consistency of the calibration data under different environmental conditions and time changes, and outputs the corrected accurate data; S4. System monitoring and early warning steps: S41, status monitoring: The system status monitoring module monitors the flight status of the drone, the working status of the sensor, and the changes in atmospheric pressure, geomagnetic intensity, solar radiation intensity, ground humidity, wind speed fluctuation rate, groundwater level changes, and surface albedo environmental parameters in real time to ensure the stability of the acquisition and operation process; S42, Warning and Notification: When the system detects an abnormal situation, it automatically triggers a warning signal and notifies ground operators to intervene; S5. Comprehensive assessment and decision support steps: S51, Multi-parameter comprehensive evaluation: The system uses a comprehensive evaluation algorithm, based on calibrated data, and applies a multi-parameter nonlinear combination algorithm to incorporate key parameters such as groundwater level changes and surface albedo into the evaluation process to generate a comprehensive evaluation result; S52. Generate evaluation report: The system generates a geographic information analysis report based on the comprehensive evaluation results, and displays the evaluation results in a visual way for operators to refer to and make decisions.

4. The geographic information data acquisition and processing system based on big data according to claim 3 is characterized in that: The steps of the preliminary data calibration algorithm include: S1. Input data: the original geographic information data D received by the measurement data acquisition module input and environmental parameters for correcting the measured data, including atmospheric pressure P atm 、Geomagnetic intensity M geo and solar radiation intensity S solar ; S2, calibration calculation formula: The calibration algorithm calibrates the input data according to the following formula to calculate the preliminary calibration result R1: where α P , α M and α S are the calibration coefficients of atmospheric pressure, geomagnetic intensity and solar radiation intensity, respectively, and P0, M0 and S0 are the corresponding standard reference values; S3. Output calibration results: The preliminary calibration result R1 is the intermediate processing result of the geographic information data, which is used for further correction and optimization in subsequent steps.

5. The geographic information data acquisition and processing system based on big data according to claim 3 is characterized in that: The secondary precision calibration algorithm specifically includes the following steps: S1. Input data: Receive the preliminary calibration result R1 and external environmental parameters, including ground humidity H ground and wind speed fluctuation rate V wind ; S2, calibration calculation formula: The secondary calibration algorithm further corrects the preliminary calibration result through the following formula to generate an accurate calibration result R2: Among them, β H and β V are the correction coefficients of ground humidity and wind speed fluctuation rate, respectively, H0 and V0 are the standard humidity and standard wind speed fluctuation rate; S3. Output calibration results: Generate a secondary precision calibration result R2, which is used for further comprehensive evaluation or directly applied to geographic information data analysis.

6. The geographic information data acquisition and processing system based on big data according to claim 3 is characterized in that: The comprehensive evaluation algorithm specifically includes the following steps: S1. Input data: receiving the preliminary calibration result R1 and the secondary precision calibration result R2, as well as external comprehensive parameters, including groundwater level change W ground and the surface albedo A surface ; S2. Comprehensive evaluation calculation formula: The comprehensive evaluation algorithm generates the final comprehensive evaluation result R through the following formula: final : Where ω1 and ω2 are dynamic weights, which are adjusted based on the influence of different parameters; γ1, γ2 and γ3 are adjustment coefficients; W0 is the standard groundwater level; ε is a constant to avoid the denominator being zero; S3. Output comprehensive evaluation result: the comprehensive evaluation result R final Used as the final output of geographic information data processing system to support decision making and analysis.

7. The geographic information data acquisition and processing system based on big data according to claim 3 is characterized in that: The dynamic weight calculation function in the comprehensive evaluation algorithm is adjusted according to the characteristics of the input data, and its calculation method is: Calculation of weight ω1: ω1=g1(R1,W ground )=a1+b1×log(R1+1)+c1×W ground ; where a1, b1, c1 are constants; R1 is the preliminary calibration result, W ground For the change of groundwater level, the weight ω1 is dynamically adjusted according to the change of R1 and groundwater level; Calculation of weight ω2: Among them, a2, b2, c2 are constants, A surface is the surface albedo, R2 is the result of quadratic precision calibration, and ε is a constant to avoid the denominator being zero.

8. The geographic information data acquisition and processing system based on big data according to claim 3 is characterized by: The time series processing function of the external parameters includes: Time series function of groundwater level change: W ground (t) = W0 + A w ×sin(ω w t+φ w ), where W0 is the base groundwater level, A w is the amplitude of groundwater level change, ω w is the frequency of change, φ w is the phase angle, t is the time variable; Time series function of surface albedo: A surface (t) = A0 + B a ×e -λt , where A0 is the initial surface albedo, B a is the amplitude of albedo change, λ is the attenuation coefficient, and t is the time variable.

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