Remote sensing data monitoring method and system based on distributed computing

Abstract

The invention discloses a remote sensing data monitoring method and system based on distributed computing, and belongs to the technical field of remote sensing data processing. The method comprises the following steps: comprehensively analyzing the environmental interference data and the remote sensing equipment performance data to obtain the environmental interference degree evaluation value of each target area and the remote sensing equipment performance evaluation value of each target area, and adjusting and feedbacking according to the remote sensing equipment performance evaluation value of each target area; comprehensively analyzing the environmental interference degree evaluation value of each target area and the remote sensing equipment performance evaluation value of each target area to obtain the remote sensing data accuracy evaluation value of each target area, and screening and obtaining each effective comparison area according to the remote sensing data accuracy evaluation value of each target area; processing the characteristic image data of each effective comparison area to obtain the abnormal change index of the area to be detected, and providing early warning feedback according to the abnormal change index of the area to be detected.

Description

Technical Field

[0001] The present invention relates to the technical field of remote sensing data processing, and in particular to a remote sensing data monitoring method and system based on distributed computing. Background Art

[0002] Remote sensing monitoring is a technical method that uses remote sensing technology for monitoring, and is mainly used for monitoring ground cover, atmosphere, ocean and near-surface conditions. With the development of global big data and the continuous advancement of remote sensing technology, remote sensing data monitoring methods have been widely used in meteorology, land, ocean, agriculture, geology and military fields, providing important support for decision-making and planning in these fields.

[0003] The existing technologies of remote sensing data monitoring methods are mainly realized through various technical means such as satellite remote sensing monitoring, drone low-altitude remote sensing monitoring and ground observation. With the development and application of new technologies such as artificial intelligence and big data, remote sensing data monitoring methods are also constantly innovating and improving.

[0004] For example, the invention patent with announcement number: CN118052356B discloses an industrial carbon emission remote sensing monitoring system based on big data, including: dividing the monitoring year into seasonal periods, and increasing the acquisition of meteorological conditions when using infrared remote sensing equipment to monitor carbon emissions in industrial areas in each seasonal period, so that when a sudden change in carbon emissions is identified, the meteorological conditions can be combined for authenticity identification, which greatly improves the accuracy of eliminating carbon emission mutation data and provides reliable data support for the analysis results. At the same time, the carbon emission data obtained in each seasonal period is classified according to the meteorological type, thereby analyzing the sensitivity of the inversion model to various abnormal weather conditions, and then based on the sensitivity analysis results, effective carbon emission data can be extracted from the carbon emission data obtained in each seasonal period.

[0005] For example, the patent application with publication number: CN118278729A discloses a flood disaster monitoring method that integrates multi-source remote sensing data, including: S1: collecting data; S2: overall investigation at the macro level; S3: drawing high-resolution topographic maps; S4: using ground means to accurately mark flood locations and confirm disaster losses; S5: integrating multi-source data to scientifically divide different severe flood disaster areas.

[0006] However, in the process of implementing the technical solution of the invention in the embodiments of the present application, the present application found that the above technology has at least the following technical problems:

[0007] In the prior art, due to errors in data acquisition under different environments, the adaptability of remote sensing data monitoring methods is limited, and there is a problem of low accuracy of monitoring results. Summary of the invention

[0008] The present invention provides a remote sensing data monitoring method and system based on distributed computing, thereby solving the problem in the prior art that the adaptability of the remote sensing data monitoring method is limited and the accuracy of the monitoring results is low due to errors in data acquisition under different environments, thereby improving the applicability of remote sensing data detection and the accuracy of detection results.

[0009] The invention provides a remote sensing data monitoring method based on distributed computing, comprising the following steps: dividing a region to be detected into target regions, acquiring remote sensing data of each target region, wherein the remote sensing data comprises environmental interference data, remote sensing equipment performance data and characteristic image data; obtaining an environmental interference degree evaluation value of each target region according to a comprehensive analysis of the environmental interference data, obtaining a remote sensing equipment performance evaluation value of each target region according to a comprehensive analysis of the remote sensing equipment performance data, and performing adjustment feedback according to the remote sensing equipment performance evaluation value of each target region; obtaining a remote sensing data accuracy evaluation value of each target region according to a comprehensive analysis of the environmental interference degree evaluation value of each target region and the remote sensing equipment performance evaluation value of each target region, and screening each effective comparison region according to the remote sensing data accuracy evaluation value of each target region; processing the characteristic image data of each effective comparison region to obtain an abnormal change index of the region to be detected, and performing early warning feedback according to the abnormal change index of the region to be detected.

[0010] Furthermore, the step of obtaining an environmental interference degree assessment value for each target area based on a comprehensive analysis of the environmental interference data includes: the environmental interference data includes air visibility, noise intensity and light intensity; obtaining a critical air visibility, a critical noise intensity, a reference light intensity and an allowable deviation light intensity from a data monitoring database; and obtaining an environmental interference degree assessment value for each target area through a comprehensive analysis.

[0011] Furthermore, the step of obtaining a remote sensing equipment performance evaluation value for each target area based on a comprehensive analysis of the remote sensing equipment performance data includes: the remote sensing equipment performance data includes spatial resolution, spectral resolution and temporal resolution; obtaining a critical spatial resolution, a reference spectral resolution, an allowable deviation spectral resolution, a reference temporal resolution and an allowable deviation temporal resolution from a data monitoring database; and obtaining a remote sensing equipment performance evaluation value for each target area through a comprehensive analysis.

[0012] Furthermore, the step of performing adjustment feedback according to the remote sensing equipment performance evaluation value of each target area includes: obtaining the remote sensing equipment performance evaluation threshold from the data monitoring database; comparing the remote sensing equipment performance evaluation value of each target area with the remote sensing equipment performance evaluation threshold; if the remote sensing equipment performance evaluation value of a target area is greater than or equal to the remote sensing equipment performance evaluation threshold, performing abnormal detection of the area to be detected; if the remote sensing equipment performance evaluation value of a target area is less than the remote sensing equipment performance evaluation threshold, performing adjustment feedback on the remote sensing equipment of the target area.

[0013] Furthermore, the step of obtaining the remote sensing data accuracy evaluation value of each target area through comprehensive analysis based on the environmental interference degree evaluation value of each target area and the remote sensing equipment performance evaluation value of each target area includes: obtaining the critical environmental interference degree evaluation value and the critical remote sensing equipment performance evaluation value from the data monitoring database; and obtaining the remote sensing data accuracy evaluation value of each target area through comprehensive analysis.

[0014] Furthermore, the remote sensing data accuracy evaluation value of each target area is obtained as follows:

[0015] ;

[0016] In the formula, represents the accuracy evaluation value of remote sensing data of the i-th target area, Indicates the impact factor of remote sensing data accuracy assessment corresponding to the environmental interference degree assessment value, Indicates the remote sensing data accuracy assessment impact factor corresponding to the remote sensing equipment performance evaluation value, represents the environmental interference level assessment value of the i-th target area, Indicates the critical environmental interference level assessment value, represents the performance evaluation value of the remote sensing equipment in the i-th target area, represents the critical remote sensing equipment performance evaluation value, e is a natural constant, i is the label of each target area, i=1,2,3,...,N, N is the total number of target areas.

[0017] Furthermore, the step of screening and obtaining each valid comparison area according to the remote sensing data accuracy evaluation value of each target area includes: obtaining the remote sensing data accuracy evaluation threshold from the data monitoring database; comparing the remote sensing data accuracy evaluation value of each target area with the remote sensing data accuracy evaluation threshold, if the remote sensing data accuracy evaluation value of a group of areas is greater than or equal to the remote sensing data accuracy evaluation threshold, then marking the group of areas as a valid comparison area, if the remote sensing data accuracy evaluation value of a group of areas is less than the remote sensing data accuracy evaluation threshold, then marking the group of areas as an invalid comparison area; and obtaining each valid comparison area by statistics.

[0018] Furthermore, the characteristic image data of each effective comparison area is processed to obtain the abnormal change index of the area to be detected, which includes: the characteristic image data of each effective comparison area includes the grayscale value of each pixel point of the initial characteristic image of each effective comparison area and the grayscale value of each pixel point of the characteristic image of each effective comparison area at each monitoring time point; the allowable deviation grayscale value is obtained from the data monitoring database; and the abnormal change index of the area to be detected is obtained by comprehensive analysis.

[0019] Furthermore, the step of providing early warning feedback based on the abnormal change index of the area to be detected includes: obtaining an abnormal change index threshold from a data monitoring database; comparing the abnormal change index of the area to be detected with the abnormal change index threshold; if the abnormal change index is greater than or equal to the abnormal change index threshold, providing early warning feedback for the area to be detected; if the abnormal change index is less than the abnormal change index threshold, no processing is performed.

[0020] The embodiment of the present application provides a remote sensing data monitoring system based on distributed computing, including a data acquisition module, a data analysis module, a region screening module, an early warning feedback module and a data monitoring database; wherein the data acquisition module is used to divide the area to be detected into target areas, and obtain remote sensing data of each target area, wherein the remote sensing data includes environmental interference data, remote sensing equipment performance data and characteristic image data; the data analysis module is used to obtain an environmental interference degree assessment value of each target area according to a comprehensive analysis of the environmental interference data, obtain a remote sensing equipment performance assessment value of each target area according to a comprehensive analysis of the remote sensing equipment performance data, and perform adjustment feedback according to the remote sensing equipment performance assessment value of each target area; the region screening module is used to obtain a remote sensing data accuracy assessment value of each target area according to a comprehensive analysis of the environmental interference degree assessment value of each target area and the remote sensing equipment performance assessment value of each target area, and obtain each effective comparison area according to the remote sensing data accuracy assessment value of each target area; the early warning feedback module is used to process the characteristic image data of each effective comparison area, obtain an abnormal change index of the area to be detected, and perform early warning feedback according to the abnormal change index of the area to be detected.

[0021] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0022] 1. The present invention provides a remote sensing data monitoring method and system based on distributed computing, so that the area to be detected can be efficiently divided into target areas, and environmental interference data and remote sensing equipment performance data can be comprehensively analyzed, thereby achieving an accurate assessment of the accuracy of remote sensing data of each target area, effectively screening out effective comparison areas, and finally obtaining abnormal change indicators of the area to be detected and providing early warning feedback.

[0023] 2. The present invention screens the remote sensing data of each target area according to its accuracy assessment value, thereby being able to identify target areas with high data quality and small errors as effective comparison areas, thereby achieving precise utilization of remote sensing data and improving the accuracy and reliability of abnormal change index calculation.

[0024] 3. The present invention provides early warning feedback based on abnormal change indicators in the area to be detected, so as to timely discover and respond to abnormal situations in the area, thereby achieving rapid intervention and effective management of potential risks or problems. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 A flow chart of a remote sensing data monitoring method based on distributed computing provided in an embodiment of the present application.

[0026] Figure 2 A graph showing changes in remote sensing data accuracy assessment values ​​for remote sensing data monitoring based on distributed computing provided in an embodiment of the present application.

[0027] Figure 3 A schematic diagram of the structure of a remote sensing data monitoring system based on distributed computing provided in an embodiment of the present application. DETAILED DESCRIPTION

[0028] The embodiments of the present application provide a remote sensing data monitoring method and system based on distributed computing, which solves the problem in the prior art that the adaptability of the remote sensing data monitoring method is limited and the accuracy of the monitoring results is low due to errors in data acquisition under different environments. The environmental interference degree assessment value of each target area is obtained by comprehensive analysis of the environmental interference data, and the remote sensing equipment performance assessment value of each target area is obtained by comprehensive analysis of the remote sensing equipment performance data, and adjustment feedback is performed according to the remote sensing equipment performance assessment value of each target area; the remote sensing data accuracy assessment value of each target area is obtained by comprehensive analysis according to the environmental interference degree assessment value of each target area and the remote sensing equipment performance assessment value of each target area, and each valid comparison area is obtained by screening according to the remote sensing data accuracy assessment value of each target area; the characteristic image data of each valid comparison area is processed to obtain the abnormal change index of the area to be detected, and early warning feedback is performed according to the abnormal change index of the area to be detected, thereby improving the applicability of remote sensing data detection and the accuracy of detection results.

[0029] The technical solution in the embodiment of the present application is to solve the above-mentioned problem that the adaptability of the remote sensing data monitoring method is limited and the accuracy of the monitoring results is low due to the errors in data acquisition under different environments. The overall idea is as follows:

[0030] The method divides the area to be detected into target areas, obtains remote sensing data of each target area, and the remote sensing data includes environmental interference data, remote sensing equipment performance data and characteristic image data; obtains environmental interference degree evaluation values ​​of each target area according to comprehensive analysis of environmental interference data, obtains remote sensing equipment performance evaluation values ​​of each target area according to comprehensive analysis of remote sensing equipment performance data, and performs adjustment feedback according to the remote sensing equipment performance evaluation values ​​of each target area; obtains remote sensing data accuracy evaluation values ​​of each target area according to comprehensive analysis of environmental interference degree evaluation values ​​of each target area and remote sensing equipment performance evaluation values ​​of each target area, and obtains effective comparison areas according to remote sensing data accuracy evaluation values ​​of each target area; processes characteristic image data of each effective comparison area to obtain abnormal change index of the area to be detected, and performs early warning feedback according to the abnormal change index of the area to be detected, thereby achieving the purpose of fine and efficient monitoring of the area to be detected.

[0031] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0032] like Figure 1 As shown, it is a flow chart of a remote sensing data monitoring method based on distributed computing provided in an embodiment of the present application, the method comprising the following steps: dividing the area to be detected into target areas, acquiring remote sensing data of each target area, wherein the remote sensing data comprises environmental interference data, remote sensing equipment performance data and characteristic image data; obtaining an environmental interference degree assessment value of each target area according to a comprehensive analysis of the environmental interference data, obtaining a remote sensing equipment performance assessment value of each target area according to a comprehensive analysis of the remote sensing equipment performance data, and performing adjustment feedback according to the remote sensing equipment performance assessment value of each target area; obtaining a remote sensing data accuracy assessment value of each target area according to a comprehensive analysis of the environmental interference degree assessment value of each target area and the remote sensing equipment performance assessment value of each target area, and screening each valid comparison area according to the remote sensing data accuracy assessment value of each target area; processing the characteristic image data of each valid comparison area to obtain an abnormal change index of the area to be detected, and performing early warning feedback according to the abnormal change index of the area to be detected.

[0033] In this embodiment, when the area to be detected is divided into target areas, the resolution and total number of pixels of the image of the area to be detected are first determined, and the number of pixels that each target area should contain is calculated according to the expected number of pixels of each target area, and then the area to be detected is divided into multiple uniform target areas according to the number of pixels. By dividing the area to be detected into target areas and acquiring remote sensing data respectively, the present invention can analyze the data characteristics of each area more carefully and reduce data confusion and errors; through accurate data analysis and early warning feedback, limited resources can be concentrated on the areas that need the most attention, improving resource utilization efficiency; processing the characteristic image data of each effective comparison area can extract abnormal change information and promptly notify relevant personnel to take measures.

[0034] In addition, the data monitoring database is used to store relevant data of the remote sensing data monitoring method based on distributed computing, including: critical air visibility, critical noise intensity, reference light intensity, allowable deviation light intensity, environmental interference degree assessment factor corresponding to air visibility, and remote sensing equipment performance assessment threshold, etc. The data in the data monitoring database can be directly collected through sensors built on platforms such as remote sensing satellites or lidar and other technologies, and can also be directly queried from public data sources such as the Digital Earth Open Platform and the Geospatial Data Cloud.

[0035] Furthermore, the step of obtaining an environmental interference degree assessment value for each target area based on a comprehensive analysis of the environmental interference data includes: the environmental interference data includes air visibility, noise intensity and light intensity; obtaining a critical air visibility, a critical noise intensity, a reference light intensity and an allowable deviation light intensity from a data monitoring database; and obtaining an environmental interference degree assessment value for each target area through a comprehensive analysis.

[0036] The environmental interference level assessment value of each target area is obtained in the following manner:

[0037] ;

[0038] In the formula, represents the environmental interference level assessment value of the i-th target area, Indicates the impact factor of environmental interference assessment corresponding to air visibility rate, Indicates the impact factor of environmental interference assessment corresponding to noise intensity, Indicates the impact factor of environmental interference assessment corresponding to light intensity, represents the air visibility rate of the i-th target area, represents the critical air visibility rate, represents the noise intensity of the i-th target area, represents the critical noise intensity, represents the light intensity of the i-th target area, represents the reference light intensity, Represents the allowable deviation light intensity, where i is the label of each target area, i=1,2,3,...,N, and N is the total number of target areas.

[0039] , and The environmental interference degree assessment impact factors corresponding to the air visibility rate, noise intensity and light intensity preset in the data monitoring database respectively represent the numerical values ​​of the influence of the air visibility rate, noise intensity and light intensity on the environmental interference degree assessment value, and can be directly obtained from the data monitoring database when used. These relationships are organized into "mapping sets", that is, lookup tables or corresponding rules. When the value of a certain environmental interference data is actually monitored, this value can be searched according to the corresponding mapping set, and then the corresponding environmental interference degree assessment impact factor can be obtained. This impact factor is a number between 0 and 1, which represents the evaluation result of the current environmental interference data on the environmental interference degree. Specifically, for air visibility, there is a mapping set of air visibility and environmental interference assessment factors corresponding to air visibility. By inputting the value of air visibility, an environmental interference assessment factor can be obtained; for noise intensity, there is a mapping set of noise intensity and environmental interference assessment factors corresponding to noise intensity. By inputting the value of noise intensity, an environmental interference assessment factor can also be obtained; and for light intensity, there is a mapping set of light intensity and environmental interference assessment factors corresponding to light intensity. By inputting the value of light intensity, an environmental interference assessment factor can also be obtained. These mapping relationships can be many-to-one (multiple environmental parameter values ​​correspond to the same impact factor) or one-to-one (one environmental parameter value corresponds to a unique impact factor).

[0040] In this embodiment, air visibility is a physical quantity that describes the transparency of the atmosphere. It refers to the maximum distance at which an observer can see distant objects under specific atmospheric conditions. It can be directly measured by meteorological observation equipment (such as a visibility meter); noise intensity can be directly measured by a noise measuring instrument (such as a sound level meter); and light intensity can be directly measured by an illuminance meter at the monitoring point on the remote sensing sensor. The three factors affect each other. For example, air pollution may reduce visibility and may also affect the propagation and attenuation of noise (when air pollution is serious, sound waves will be continuously absorbed by pollutants in the air during propagation, resulting in faster attenuation of noise). At the same time, atmospheric pollutants such as haze will scatter and absorb sunlight, reducing the amount of light reaching the ground and reducing light intensity. The environmental interference degree assessment value obtained through comprehensive analysis can reflect the overall status of regional environmental quality and provide a scientific basis for environmental protection and governance.

[0041] Furthermore, the step of obtaining a remote sensing equipment performance evaluation value for each target area based on a comprehensive analysis of the remote sensing equipment performance data includes: the remote sensing equipment performance data includes spatial resolution, spectral resolution and temporal resolution; obtaining a critical spatial resolution, a reference spectral resolution, an allowable deviation spectral resolution, a reference temporal resolution and an allowable deviation temporal resolution from a data monitoring database; and obtaining a remote sensing equipment performance evaluation value for each target area through a comprehensive analysis.

[0042] The remote sensing equipment performance evaluation value of each target area is obtained as follows:

[0043] ;

[0044] In the formula, represents the performance evaluation value of the remote sensing equipment in the i-th target area, Indicates the impact factor of remote sensing equipment performance evaluation corresponding to spatial resolution, Indicates the impact factor of remote sensing equipment performance evaluation corresponding to spectral resolution, Indicates the impact factor of remote sensing equipment performance evaluation corresponding to time resolution, represents the spatial resolution of the i-th target area, represents the critical spatial resolution, represents the spectral resolution of the i-th target area, represents the reference spectral resolution, represents the allowable deviation spectral resolution, represents the temporal resolution of the i-th target area, represents the reference time resolution, Indicates the allowed deviation time resolution.

[0045] , and The remote sensing equipment performance evaluation impact factors corresponding to the spatial resolution, spectral resolution and temporal resolution preset in the data monitoring database respectively represent the numerical values ​​of the degree of influence of spatial resolution, spectral resolution and temporal resolution on the remote sensing equipment performance evaluation value, which can be directly obtained from the data monitoring database when used. These relationships are organized into a "mapping set", that is, a lookup table or corresponding rules. When the value of a remote sensing equipment performance data is actually monitored, this value can be found according to the corresponding mapping set, and then the corresponding remote sensing equipment performance evaluation impact factor can be obtained. This impact factor is a number between 0 and 1, which represents the evaluation result of the current remote sensing equipment performance data on the accuracy of remote sensing data. Specifically, for spatial resolution, there are spatial resolution and the remote sensing equipment performance evaluation impact factor corresponding to the spatial resolution as a mapping set. By inputting the spatial resolution, the corresponding remote sensing equipment performance evaluation impact factor can be obtained; for spectral resolution, there are spectral resolution and the remote sensing equipment performance evaluation impact factor corresponding to the spectral resolution as a mapping set. By inputting the spectral resolution, the corresponding remote sensing equipment performance evaluation impact factor can be obtained; for temporal resolution, there are temporal resolution and the remote sensing equipment performance evaluation impact factor corresponding to the temporal resolution as a mapping set. By inputting the temporal resolution, the corresponding remote sensing equipment performance evaluation impact factor can be obtained. These mapping relationships can be many-to-one or one-to-one.

[0046] In this embodiment, spatial resolution is the size of the smallest unit that can be distinguished in detail on the remote sensing image, which is used to characterize the ability of the image to distinguish the details of the ground target. It can be obtained by actually photographing the ground target and measuring the smallest unit size that can be clearly distinguished in the image using measurement tools such as image processing software; spectral resolution is the wavelength width when the instrument reaches 50% of the maximum spectral response, which can be obtained by using special equipment such as spectrometers to actually photograph the ground target and measure its spectral characteristics; time resolution refers to the time interval between two adjacent detections when the same target is repeatedly detected, which can be obtained by analyzing the historical observation frequency of the satellite and calculating the difference between the time of two adjacent observations. There is a correlation between the three. For example, improving spatial resolution may reduce temporal resolution and spectral resolution, because remote sensing equipment requires more time and energy to capture finer ground details; similarly, improving spectral resolution may also lead to a reduction in spatial resolution and temporal resolution. The performance evaluation value of remote sensing equipment obtained by comprehensive analysis can accurately evaluate the performance and application effect of remote sensing equipment, and provide strong support for the application of remote sensing technology.

[0047] Furthermore, the step of performing adjustment feedback according to the remote sensing equipment performance evaluation value of each target area includes: obtaining the remote sensing equipment performance evaluation threshold from the data monitoring database; comparing the remote sensing equipment performance evaluation value of each target area with the remote sensing equipment performance evaluation threshold; if the remote sensing equipment performance evaluation value of a target area is greater than or equal to the remote sensing equipment performance evaluation threshold, performing abnormal detection of the area to be detected; if the remote sensing equipment performance evaluation value of a target area is less than the remote sensing equipment performance evaluation threshold, performing adjustment feedback on the remote sensing equipment of the target area.

[0048] In this embodiment, the remote sensing device performance evaluation threshold refers to a limit value used to evaluate whether the performance of the remote sensing device meets the predetermined standard. When the performance evaluation value of the remote sensing device is lower than the threshold, it means that there may be problems with the performance of the device or it needs to be optimized. It is necessary to conduct an on-site inspection of the remote sensing device in a timely manner, including the appearance of the device, the connection line, the sensor status, etc., and determine the specific cause of the device performance problem based on the result of the problem diagnosis, such as sensor aging, poor line contact, software failure, etc., and formulate specific solutions for the cause of the problem. For example, for the problem of sensor aging, you can consider replacing a new sensor; for the problem of poor line contact, you can check and reconnect the line; for the problem of software failure, you can try to update the software or reinstall the software. After the adjustment is completed, the performance of the remote sensing device is verified to ensure that the performance of the device reaches the remote sensing device performance evaluation threshold, and the equipment is regularly maintained and maintained to ensure the long-term stable operation of the equipment; and when the performance evaluation value of the remote sensing device is greater than or equal to the threshold, it means that the remote sensing device has good performance and subsequent operations can be performed. By adjusting the feedback, it can be ensured that the performance of the remote sensing equipment meets the predetermined standards, thereby improving the accuracy and reliability of the remote sensing data. At the same time, timely adjustment of devices with remote sensing equipment performance evaluation values ​​less than the threshold can reduce the extension of detection time and waste of resources caused by equipment failure or errors.

[0049] Furthermore, the step of obtaining the remote sensing data accuracy evaluation value of each target area through comprehensive analysis based on the environmental interference degree evaluation value of each target area and the remote sensing equipment performance evaluation value of each target area includes: obtaining the critical environmental interference degree evaluation value and the critical remote sensing equipment performance evaluation value from the data monitoring database; and obtaining the remote sensing data accuracy evaluation value of each target area through comprehensive analysis.

[0050] In this embodiment, the greater the environmental interference level evaluation value, the greater the difficulty and error in obtaining remote sensing data, thereby affecting the accuracy of the data; the higher the remote sensing equipment performance evaluation value, the higher the accuracy and stability of data acquisition, and the stronger the data processing and analysis capabilities, thereby helping to improve the accuracy of remote sensing data. Both of them affect the accuracy of remote sensing data. For example, the increase in environmental interference will increase the requirements for equipment performance, and the quality of equipment performance will also affect the ability to adapt to environmental interference. The remote sensing data accuracy evaluation value obtained by comprehensive analysis reflects the accuracy level of remote sensing data under given environmental and equipment conditions, which helps decision makers optimize the acquisition and processing strategies of remote sensing data.

[0051] Furthermore, the remote sensing data accuracy evaluation value of each target area is obtained as follows:

[0052] ;

[0053] In the formula, represents the accuracy evaluation value of remote sensing data of the i-th target area, Indicates the impact factor of remote sensing data accuracy assessment corresponding to the environmental interference degree assessment value, Indicates the remote sensing data accuracy assessment impact factor corresponding to the remote sensing equipment performance evaluation value, represents the environmental interference level assessment value of the i-th target area, Indicates the critical environmental interference level assessment value, represents the performance evaluation value of the remote sensing equipment in the i-th target area, represents the critical remote sensing equipment performance evaluation value, and e is a natural constant.

[0054] In this embodiment, and The remote sensing data accuracy assessment impact factors corresponding to the environmental interference degree assessment value and the remote sensing equipment performance assessment value preset in the data monitoring database respectively represent the numerical values ​​of the degree of influence of the environmental interference degree assessment value and the remote sensing equipment performance assessment value on the remote sensing data accuracy assessment value, and can be directly obtained from the data monitoring database when used. These relationships are organized into a "mapping set", that is, a lookup table or corresponding rules. When the value of a certain remote sensing data is actually monitored, this value can be searched according to the corresponding mapping set, and then a corresponding remote sensing data accuracy assessment impact factor can be obtained. This impact factor is a number between 0 and 1, which represents the evaluation result of the current parameter on the accuracy of the remote sensing data. Specifically, for the environmental interference degree evaluation value, there is an environmental interference degree evaluation value and a remote sensing data accuracy evaluation impact factor corresponding to the environmental interference degree evaluation value as a mapping set, and by inputting the environmental interference degree evaluation value, the corresponding remote sensing data accuracy evaluation impact factor can be obtained; for the remote sensing equipment performance evaluation value, there is a remote sensing equipment performance evaluation value and a remote sensing data accuracy evaluation impact factor corresponding to the remote sensing equipment performance evaluation value as a mapping set, and by inputting the remote sensing equipment performance evaluation value, the corresponding remote sensing data accuracy evaluation impact factor can be obtained. These mapping relationships can be many-to-one or one-to-one.

[0055] The remote sensing data accuracy assessment impact factor corresponding to the environmental interference degree assessment value is set to 0.6, the remote sensing data accuracy assessment impact factor corresponding to the remote sensing equipment performance assessment value is set to 0.4, the environmental interference degree assessment value is set to 1.2, the critical environmental interference degree assessment value is set to 1.3, and the critical remote sensing equipment performance assessment value is set to 1.2. When the remote sensing equipment performance assessment value continues to increase, the remote sensing data accuracy assessment value is calculated. As shown in Table 1, the remote sensing data accuracy assessment value data table of remote sensing data monitoring based on distributed computing.

[0056] Table 1 Remote sensing data accuracy evaluation value table for remote sensing data monitoring based on distributed computing

[0057]

[0058] like Figure 2 As shown in Table 1 and Figure 2 It can be seen that when the remote sensing data accuracy assessment influencing factor corresponding to the environmental interference degree assessment value, the remote sensing data accuracy assessment influencing factor corresponding to the remote sensing equipment performance assessment value, the environmental interference degree assessment value, the critical environmental interference degree assessment value and the critical remote sensing equipment performance assessment value remain unchanged, and the remote sensing equipment performance evaluation value continues to increase, the remote sensing data accuracy assessment value also continues to increase.

[0059] Furthermore, the step of screening and obtaining each valid comparison area according to the remote sensing data accuracy evaluation value of each target area includes: obtaining the remote sensing data accuracy evaluation threshold from the data monitoring database; comparing the remote sensing data accuracy evaluation value of each target area with the remote sensing data accuracy evaluation threshold, if the remote sensing data accuracy evaluation value of a group of areas is greater than or equal to the remote sensing data accuracy evaluation threshold, then marking the group of areas as a valid comparison area, if the remote sensing data accuracy evaluation value of a group of areas is less than the remote sensing data accuracy evaluation threshold, then marking the group of areas as an invalid comparison area; and obtaining each valid comparison area by statistics.

[0060] In this embodiment, the remote sensing data accuracy assessment threshold is a standard limit value for measuring the accuracy of remote sensing data. As a standard for screening data, the remote sensing data accuracy assessment threshold can ensure that only data that reaches or exceeds the threshold is used for subsequent analysis and comparison, which helps to eliminate low-quality data; in the data screening process, invalid comparison areas are excluded, reducing the need for further analysis and processing of these areas, helping to optimize resource allocation, focusing limited resources on effective comparison areas, and improving work efficiency and research quality.

[0061] Furthermore, the characteristic image data of each effective comparison area is processed to obtain the abnormal change index of the area to be detected, which includes: the characteristic image data of each effective comparison area includes the grayscale value of each pixel point of the initial characteristic image of each effective comparison area and the grayscale value of each pixel point of the characteristic image of each effective comparison area at each monitoring time point; the allowable deviation grayscale value is obtained from the data monitoring database; and the abnormal change index of the area to be detected is obtained by comprehensive analysis.

[0062] The method for obtaining the abnormal change index of the area to be detected is as follows:

[0063] ;

[0064] ;

[0065] In the formula, Indicates the abnormal change index of the area to be detected. It represents the difference degree index of the characteristic images between the kth monitoring time point and the k-1th monitoring time point of the jth effective comparison area, where the characteristic image difference degree index is used to quantify the difference degree between the characteristic images at different time points. Represents the gray value of the xth pixel of the feature image at the kth monitoring time point in the jth effective comparison area, Represents the gray value of the xth pixel of the feature image at the k-1th monitoring time point in the jth effective comparison area, represents the gray value of the xth pixel of the feature image at the Kth monitoring time point of the jth effective comparison area, that is, the gray value of the xth pixel of the final feature image of the jth effective comparison area, It represents the gray value of the xth pixel of the feature image at the first monitoring time point of the jth effective comparison area, that is, the gray value of the xth pixel of the initial feature image of the jth effective comparison area, where j is the number of each effective comparison area. Indicates the allowable deviation grayscale value, j=1,2,3,...,M, M is the total number of valid comparison areas, k is the number of each monitoring time point, k=1,2,3,...,K, K is the total number of monitoring time points, x is the number of each pixel point, x=1,2,3,...,Y, Y is the total number of pixels.

[0066] In this embodiment, the grayscale value of each pixel of the characteristic image can be directly measured by a spectrometer, image sensor, etc. By comparing the changes in the grayscale value of the characteristic image at different time points, the abnormal changes in the image can be captured more accurately, not only the changes in the short term can be found, but also the long-term trend changes can be identified; the setting of the allowable deviation grayscale value (∆G) enables the method to adjust the sensitivity according to different detection requirements and adapt to different monitoring scenarios and fault tolerance ranges.

[0067] Furthermore, the step of providing early warning feedback based on the abnormal change index of the area to be detected includes: obtaining an abnormal change index threshold from a data monitoring database; comparing the abnormal change index of the area to be detected with the abnormal change index threshold; if the abnormal change index is greater than or equal to the abnormal change index threshold, providing early warning feedback for the area to be detected; if the abnormal change index is less than the abnormal change index threshold, no processing is performed.

[0068] In this embodiment, the abnormal change index threshold is a critical value used to determine whether the abnormal change in the area to be detected has reached a level that requires an early warning. When the abnormal change index of the area to be detected reaches or exceeds the threshold, the early warning system is immediately triggered, and according to the characteristics and actual situation of the area to be detected, the most appropriate communication method is selected to send the early warning information, including SMS, email, telephone, social media, radio, television, etc., to ensure that the early warning information can cover all potentially affected people and areas. This helps to promptly discover related issues such as abnormal ground use and take measures to deal with them, preventing the problem from further deteriorating or causing greater losses.

[0069] like Figure 3As shown, it is a schematic diagram of the structure of the remote sensing data monitoring system based on distributed computing provided in the embodiment of the present application. The remote sensing data monitoring system based on distributed computing provided in the embodiment of the present application includes: a data acquisition module, a data analysis module, a region screening module, an early warning feedback module and a data monitoring database; wherein, the data acquisition module is used to divide the area to be detected into target areas, and obtain remote sensing data of each target area, wherein the remote sensing data includes environmental interference data, remote sensing equipment performance data and feature image data; the data analysis module is used to obtain the environmental interference degree evaluation value of each target area according to the comprehensive analysis of the environmental interference data, and obtain the environmental interference degree evaluation value of each target area according to the remote sensing equipment performance data and feature image data. The equipment performance data is comprehensively analyzed to obtain the remote sensing equipment performance evaluation value of each target area, and adjustment feedback is performed according to the remote sensing equipment performance evaluation value of each target area; the area screening module is used to obtain the remote sensing data accuracy evaluation value of each target area according to the environmental interference degree evaluation value of each target area and the remote sensing equipment performance evaluation value of each target area, and each effective comparison area is screened according to the remote sensing data accuracy evaluation value of each target area; the early warning feedback module is used to process the characteristic image data of each effective comparison area, obtain the abnormal change index of the area to be detected, and perform early warning feedback according to the abnormal change index of the area to be detected.

[0070] In summary, the embodiments of the present application divide the area to be detected into target areas to obtain remote sensing data of each target area, wherein the remote sensing data includes environmental interference data, remote sensing equipment performance data and characteristic image data; an environmental interference degree assessment value of each target area is obtained based on a comprehensive analysis of the environmental interference data, a remote sensing equipment performance assessment value of each target area is obtained based on a comprehensive analysis of the remote sensing equipment performance data, and adjustment feedback is performed based on the remote sensing equipment performance assessment value of each target area; a remote sensing data accuracy assessment value of each target area is obtained based on a comprehensive analysis of the environmental interference degree assessment value of each target area and the remote sensing equipment performance assessment value of each target area, and each valid comparison area is obtained by screening based on the remote sensing data accuracy assessment value of each target area; the characteristic image data of each valid comparison area is processed to obtain an abnormal change index of the area to be detected, and early warning feedback is performed based on the abnormal change index of the area to be detected, thereby improving the applicability of remote sensing data detection and the accuracy of detection results.

[0071] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0072] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0073] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0074] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0075] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0076] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A remote sensing data monitoring method based on distributed computing, characterized in that: The following steps are involved: Divide the area to be detected into target areas, and obtain remote sensing data of each target area, wherein the remote sensing data includes environmental interference data, remote sensing equipment performance data, and feature image data; Obtain environmental interference degree assessment values ​​for each target area based on comprehensive analysis of environmental interference data, obtain remote sensing equipment performance assessment values ​​for each target area based on comprehensive analysis of remote sensing equipment performance data, and perform adjustment feedback based on remote sensing equipment performance assessment values ​​for each target area; Comprehensively analyze the environmental interference degree assessment value of each target area and the remote sensing equipment performance assessment value of each target area to obtain the remote sensing data accuracy assessment value of each target area, and select each effective comparison area according to the remote sensing data accuracy assessment value of each target area; Process the characteristic image data of each effective comparison area to obtain the abnormal change index of the area to be detected, and provide early warning feedback based on the abnormal change index of the area to be detected; The step of obtaining the remote sensing data accuracy evaluation value of each target area by comprehensive analysis based on the environmental interference degree evaluation value of each target area and the remote sensing equipment performance evaluation value of each target area comprises: obtaining the critical environmental interference degree evaluation value and the critical remote sensing equipment performance evaluation value from the data monitoring database; Comprehensive analysis is performed to obtain the accuracy assessment value of remote sensing data for each target area; The remote sensing data accuracy evaluation value of each target area is obtained as follows: ； In the formula, represents the accuracy assessment value of remote sensing data of the i-th target area, Indicates the impact factor of remote sensing data accuracy assessment corresponding to the environmental interference degree assessment value, Indicates the remote sensing data accuracy assessment impact factor corresponding to the remote sensing equipment performance evaluation value, represents the environmental interference level assessment value of the i-th target area, Indicates the critical environmental interference level assessment value, represents the performance evaluation value of the remote sensing equipment in the i-th target area, represents the critical remote sensing equipment performance evaluation value, e is a natural constant, i is the label of each target area, i=1,2,3,...,N, N is the total number of target areas.

2. The remote sensing data monitoring method based on distributed computing as claimed in claim 1, characterized in that: The step of obtaining the environmental interference degree assessment value of each target area based on comprehensive analysis of the environmental interference data comprises: The environmental interference data includes air visibility, noise intensity and light intensity; Obtain critical air visibility, critical noise intensity, reference light intensity and allowable deviation light intensity from the data monitoring database; Comprehensive analysis is performed to obtain the environmental interference level assessment value of each target area.

3. The remote sensing data monitoring method based on distributed computing as claimed in claim 1, characterized in that: The step of obtaining the remote sensing equipment performance evaluation value of each target area based on comprehensive analysis of the remote sensing equipment performance data comprises: The remote sensing equipment performance data, including spatial resolution, spectral resolution and temporal resolution; Obtain critical spatial resolution, reference spectral resolution, allowable deviation spectral resolution, reference time resolution and allowable deviation time resolution from a data monitoring database; Comprehensive analysis is performed to obtain the performance evaluation value of remote sensing equipment in each target area.

4. The remote sensing data monitoring method based on distributed computing as claimed in claim 1, characterized in that: The step of adjusting and feeding back the remote sensing equipment performance evaluation value of each target area comprises: Obtaining remote sensing equipment performance evaluation thresholds from a data monitoring database; The remote sensing equipment performance evaluation value of each target area is compared with the remote sensing equipment performance evaluation threshold. If the remote sensing equipment performance evaluation value of a target area is greater than or equal to the remote sensing equipment performance evaluation threshold, anomaly detection is performed on the area to be detected. If the remote sensing equipment performance evaluation value of a target area is less than the remote sensing equipment performance evaluation threshold, adjustment feedback is performed on the remote sensing equipment in the target area.

5. The remote sensing data monitoring method based on distributed computing as claimed in claim 1, characterized in that: The step of selecting each effective comparison area according to the remote sensing data accuracy evaluation value of each target area comprises: Obtaining remote sensing data accuracy assessment thresholds from a data monitoring database; Compare the remote sensing data accuracy assessment value of each target area with the remote sensing data accuracy assessment threshold. If the remote sensing data accuracy assessment value of a group of areas is greater than or equal to the remote sensing data accuracy assessment threshold, then mark the group of areas as valid comparison areas. If the remote sensing data accuracy assessment value of a group of areas is less than the remote sensing data accuracy assessment threshold, then mark the group of areas as invalid comparison areas. The effective comparison areas are obtained by statistics.

6. The remote sensing data monitoring method based on distributed computing as claimed in claim 1, characterized in that: The step of processing the characteristic image data of each effective comparison area to obtain the abnormal change index of the area to be detected includes: The characteristic image data of each effective comparison area includes the grayscale value of each pixel point of the initial characteristic image of each effective comparison area and the grayscale value of each pixel point of the characteristic image of each effective comparison area at each monitoring time point; Obtain the grayscale value of the allowable deviation from the data monitoring database; Comprehensive analysis is performed to obtain abnormal change indicators in the area to be detected.

7. The remote sensing data monitoring method based on distributed computing as claimed in claim 1, characterized in that: The step of providing early warning feedback according to the abnormal change index of the area to be detected includes: Obtaining abnormal change indicator thresholds from a data monitoring database; The abnormal change index of the area to be detected is compared with the abnormal change index threshold. If the abnormal change index is greater than or equal to the abnormal change index threshold, an early warning feedback is given to the area to be detected. If the abnormal change index is less than the abnormal change index threshold, no processing is performed.

8. A remote sensing data monitoring system based on distributed computing, applying the remote sensing data monitoring method based on distributed computing as claimed in any one of claims 1 to 7, characterized in that: It includes data acquisition module, data analysis module, regional screening module, early warning feedback module and data monitoring database; The data acquisition module is used to divide the area to be detected into target areas and acquire remote sensing data of each target area, wherein the remote sensing data includes environmental interference data, remote sensing equipment performance data and feature image data; The data analysis module is used to obtain an environmental interference degree evaluation value of each target area based on a comprehensive analysis of the environmental interference data, obtain a remote sensing equipment performance evaluation value of each target area based on a comprehensive analysis of the remote sensing equipment performance data, and perform adjustment feedback based on the remote sensing equipment performance evaluation value of each target area; The area screening module is used to obtain the remote sensing data accuracy evaluation value of each target area based on the environmental interference degree evaluation value of each target area and the remote sensing equipment performance evaluation value of each target area through comprehensive analysis, and to screen and obtain each valid comparison area based on the remote sensing data accuracy evaluation value of each target area; The early warning feedback module is used to process the characteristic image data of each effective comparison area to obtain the abnormal change index of the area to be detected, and to provide early warning feedback according to the abnormal change index of the area to be detected.

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