CN118050329A

CN118050329A - Drainage basin water quality inversion method and equipment based on national control section and sentry images

Info

Publication number: CN118050329A
Application number: CN202410444176.XA
Authority: CN
Inventors: 俞雷; 胡良金; 李文苹; 侯从强
Original assignee: Beijing Sixiang Aishu Technology Co ltd
Current assignee: Beijing Sixiang Aishu Technology Co ltd
Priority date: 2024-04-15
Filing date: 2024-04-15
Publication date: 2024-05-17

Abstract

The application discloses a drainage basin water quality inversion method and equipment based on national control section and sentry images, belongs to the technical field of electric digital data processing, and aims to solve the technical problem that the existing national control section monitoring drainage basin water quality can only provide limited point position data and cannot comprehensively reflect space change. The method comprises the following steps: acquiring a sentry image in the scope of a river basin to be processed, and preprocessing remote sensing image data corresponding to the sentry image to obtain a target sentry image; acquiring national control section monitoring station data in the range of the river basin to be treated, and cleaning and sorting the national control section monitoring station data to obtain target national control section monitoring station data; according to the data of the target sentinel images and the target national control section monitoring station, a basin water quality inversion model is built, inversion is carried out on the water quality condition of the basin range to be processed through the basin water quality inversion model, the complementarity of the remote sensing image data and the actual measurement station data is comprehensively considered, and the accuracy and the reliability of basin water quality inversion are improved.

Description

Drainage basin water quality inversion method and equipment based on national control section and sentry images

Technical Field

The application relates to the technical field of electric digital data processing, in particular to a drainage basin water quality inversion method and equipment based on national control section and sentry images.

Background

Water is a valuable resource of life and is of irreplaceable importance for human survival and development. However, with the acceleration of industrialization progress, the continuous improvement of urban level and the continuous increase of population, the problems of water resource shortage, water pollution, water poverty and the like are increasingly highlighted, and the sustainable development of human society is brought into a serious challenge. In particular, in recent years, the effects of climate change have become increasingly pronounced, and many inland water bodies are threatened by unprecedented deterioration of water quality.

At present, the traditional water quality monitoring method can reflect the water quality condition to a certain extent, but has a plurality of limitations. For example, the method of field sampling and laboratory analysis, while capable of providing relatively accurate water quality data, is difficult to realize real-time continuous monitoring due to complex operation, time and effort consumption, and often results in a monitoring result with a large hysteresis. In addition, because the selection number of sampling points is limited, the covered water area is smaller, and the obtained water quality monitoring result has only partial representative significance, so that the water quality condition of the whole water body is difficult to comprehensively reflect.

In order to solve these problems, a real-time water quality monitoring method based on sensor technology has been attracting attention in recent years. The method can monitor the change of the water quality index in real time and continuously by arranging the sensor network in the water body, thereby realizing timely understanding and early warning of the water quality condition. However, even if huge monitoring sites are established nationwide, water quality indexes are monitored regularly, but the water quality indexes still cannot be obtained in most water areas because the water quality indexes can only cover partial areas of partial water.

Particularly for basin water quality monitoring, the problems of the national control section monitoring method are more remarkable. State control section monitoring is usually performed by selecting a few monitoring points to represent the water quality condition of the whole river basin, but due to factors such as complexity of the river basin, distribution of water flow, topography and the like, the selected limited monitoring points may not fully reflect the spatial variation condition of the water quality of the whole river basin. In addition, due to insufficient density of monitoring points, data obtained by national control section monitoring often cannot cover the whole river basin, particularly a large river basin, which further limits the representativeness and reliability of the monitoring data.

Disclosure of Invention

The embodiment of the application provides a drainage basin water quality inversion method and equipment based on national control section and sentinel images, which are used for solving the technical problem that the existing national control section monitoring drainage basin water quality can only provide limited point location data and cannot comprehensively reflect space change.

In one aspect, the embodiment of the application provides a drainage basin water quality inversion method based on national control section and sentinel images, which comprises the following steps:

acquiring a sentry image in a to-be-processed river basin range, and preprocessing remote sensing image data corresponding to the sentry image to obtain a target sentry image;

Acquiring national control section monitoring station data in the range of the river basin to be processed, and cleaning and sorting the national control section monitoring station data to obtain target national control section monitoring station data;

according to the target sentinel images and the target national control section monitoring station data, a drainage basin water quality inversion model is constructed, and inversion is carried out on the water quality condition of the drainage basin range to be processed through the drainage basin water quality inversion model.

In one implementation manner of the present application, the preprocessing is performed on remote sensing image data corresponding to the sentinel image to obtain a target sentinel image, and specifically includes:

Determining whether the data level of the remote sensing image data corresponding to the sentry image is a designated level, if so, determining that the remote sensing image data of the designated level has completed calibration and correction operation, and if not, performing radiation calibration on the remote sensing image data of a non-designated level;

performing geometric correction on the calibrated remote sensing image data based on the reference image, and performing atmospheric correction on the calibrated and geometrically corrected remote sensing image data through a Bopu response function to obtain a plurality of calibrated and corrected remote sensing image data to be processed;

And calculating an image band value corresponding to the remote sensing image data to be processed through normalizing the difference water body index to obtain a water body range of a target area in the to-be-processed river basin range, and splicing and cutting the remote sensing image data to be processed according to the water body range to obtain a corresponding target sentinel image.

In one implementation manner of the present application, the method for obtaining the national control section monitoring site data in the scope of the river basin to be treated, and cleaning and sorting the national control section monitoring site data to obtain target national control section monitoring site data specifically includes:

Determining national control section monitoring station data corresponding to a to-be-processed river basin range from a water quality monitoring data source to acquire the national control section monitoring station data in the to-be-processed river basin range; the state control section monitoring site data at least comprises: longitude and latitude coordinates, section names, the basin to which the longitude and latitude coordinates belong and monitoring time;

Judging whether repeated data exists in the national control section monitoring site data, if so, removing redundancy from the national control section monitoring site data to obtain first national control section monitoring site data, and determining whether the first national control section monitoring site data is missing;

Under the condition that data are missing in the first national control section monitoring site data, null filling is carried out on the first national control section monitoring site data to obtain second national control section monitoring site data, and whether abnormal data exist in the second national control section monitoring site data is determined;

And deleting the abnormal data to obtain third national control section monitoring site data, and unifying the data format of the third national control section monitoring site data to obtain target national control section monitoring site data.

In one implementation manner of the application, the constructing a drainage basin water quality inversion model according to the target sentinel image and the target national control section monitoring site data specifically comprises:

Constructing a characteristic wave band based on the target sentinel image and the target national control section monitoring station data, and carrying out correlation analysis on the characteristic wave band;

And determining the magnitude relation between the correlation and a preset correlation threshold value, and determining a drainage basin water quality inversion model from a plurality of linear regression models corresponding to various wave band combinations according to the magnitude relation.

In one implementation manner of the present application, the method for constructing a characteristic band based on the target sentinel image and the target national control section monitoring station data, and performing correlation analysis on the characteristic band specifically includes:

Determining a corresponding pixel position on the target sentinel image according to longitude and latitude coordinates in the target national control section monitoring station data so as to extract an image band value of the pixel position;

And matching the data of each target national control section monitoring site with the image band value of the corresponding pixel position of the target sentinel image, and calculating the correlation between the data of the target national control section monitoring site and different band combinations through the pearson correlation coefficient.

In one implementation manner of the present application, the determining a magnitude relation between the correlation and a preset correlation threshold, and determining a drainage basin water quality inversion model in a plurality of linear regression models corresponding to a plurality of band combinations according to the magnitude relation specifically includes:

the method comprises the steps of comparing correlation between different wave band combinations and target national control section monitoring station data with a preset correlation threshold value respectively to determine a plurality of wave band combinations with correlation larger than the preset correlation threshold value, and sequencing the plurality of wave band combinations according to the sequence of the correlation from high to low;

Establishing a linear regression model between an image band value of the sentry image and national control section monitoring site data aiming at each band combination in the plurality of band combinations, and respectively checking the linear regression model corresponding to each band combination to obtain corresponding precision evaluation;

And determining a drainage basin water quality inversion model corresponding to the drainage basin range to be processed in a plurality of linear regression models corresponding to the plurality of wave band combinations according to the prediction precision and the model decision coefficient in the precision evaluation.

In one implementation manner of the present application, the acquiring the sentinel image in the scope of the to-be-processed river basin specifically includes:

Acquiring a water quality inversion requirement, and determining a corresponding to-be-processed drainage basin range and a drainage basin position according to the water quality inversion requirement;

inquiring remote sensing image data according to the to-be-processed drainage basin range and the drainage basin position, and determining a sentry image corresponding to the to-be-processed drainage basin range so as to download the sentry image.

On the other hand, the embodiment of the application also provides a device for acquiring the saliency area, which comprises:

The sentry image acquisition unit is used for acquiring sentry images in the scope of the to-be-processed river basin, and preprocessing remote sensing image data corresponding to the sentry images to obtain target sentry images;

The national control section monitoring station data acquisition unit is used for acquiring national control section monitoring station data in the range of the river basin to be processed, and cleaning and sorting the national control section monitoring station data to obtain target national control section monitoring station data;

the model construction inversion unit is used for constructing a drainage basin water quality inversion model according to the target sentry images and the target national control section monitoring station data, and inverting the water quality condition of the drainage basin range to be processed through the drainage basin water quality inversion model.

On the other hand, the embodiment of the application also provides drainage basin water quality inversion equipment based on the national control section and the sentry image, which comprises:

At least one processor;

And a memory communicatively coupled to the at least one processor;

The storage stores instructions executable by the at least one processor, so that the at least one processor can execute a river basin water quality inversion method based on national control section and sentry images.

On the other hand, the embodiment of the application also provides a nonvolatile computer storage medium which stores computer executable instructions, wherein the computer executable instructions realize the drainage basin water quality inversion method based on the national control section and the sentry images when being executed.

The embodiment of the application provides a drainage basin water quality inversion method and equipment based on national control section and sentry images, which at least comprise the following beneficial effects:

The noise and the abnormal value can be effectively removed by preprocessing the sentry images and cleaning and finishing the national control section monitoring station data, the accuracy and the reliability of the data are improved, and a high-quality data base is provided for the subsequent water quality inversion work; the drainage basin water quality inversion model is constructed based on the preprocessed target sentinel images and the target national control section monitoring site data, so that the accuracy of model input can be ensured, the prediction accuracy and reliability of the model are improved, and the water quality inversion result is more approximate to the actual situation; the constructed basin water quality inversion model is utilized, so that the water quality condition of the basin range to be treated can be quickly inverted, the time-consuming sampling and laboratory analysis processes in the traditional water quality monitoring method are avoided, and the efficiency and the instantaneity of water quality monitoring are greatly improved; by accurately inverting the water quality condition of the flow field, timely and comprehensive water quality information can be provided for related departments, which is helpful for formulating targeted water environment treatment and protection measures and promoting sustainable utilization of water resources and improvement of ecological environment.

Drawings

The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description serve to explain the application and do not constitute a limitation on the application. In the drawings:

FIG. 1 is a flow chart of a drainage basin water quality inversion method based on national control section and sentinel images, which is provided by the embodiment of the application;

FIG. 2 is a schematic structural diagram of a drainage basin water quality inversion device based on national control section and sentinel images, which is provided by the embodiment of the application;

Fig. 3 is a schematic diagram of an internal structure of a drainage basin water quality inversion device based on a national control section and a sentinel image according to an embodiment of the present application.

Detailed Description

In order to make the objects, technical solutions and advantages of the present application more apparent, the technical solutions of the present application will be clearly and completely described below with reference to specific embodiments of the present application and corresponding drawings. It will be apparent that the described embodiments are only some, but not all, embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.

The embodiment of the application provides a drainage basin water quality inversion method and equipment based on national control section and sentinel images, which solve the technical problem that the current national control section monitoring drainage basin water quality can only provide limited point location data and cannot comprehensively reflect space change.

The following describes in detail the technical solutions provided by the embodiments of the present application with reference to the accompanying drawings.

Fig. 1 is a flow chart of a drainage basin water quality inversion method based on a national control section and a sentinel image, which is provided by the embodiment of the application.

The implementation of the analysis method according to the embodiment of the present application may be a terminal device or a server, which is not particularly limited in the present application. For ease of understanding and description, the following embodiments are described in detail with reference to a server.

It should be noted that the server may be a single device, or may be a system formed by a plurality of devices, that is, a distributed server, which is not particularly limited in the present application.

As shown in fig. 1, the drainage basin water quality inversion method based on the national control section and the sentinel image provided by the embodiment of the application comprises the following steps:

101. the method comprises the steps of obtaining a sentinel image in a to-be-processed river basin range, and preprocessing remote sensing image data corresponding to the sentinel image to obtain a target sentinel image.

The sentinel image needs to select remote sensing image data with good quality, clear images and no cloud and fog shielding of the target river in the river basin range.

Specifically, the server obtains clear water quality inversion requirements, and accordingly determines the range and the position of the to-be-processed drainage basin, so that inversion work is more specific and effective, and the water quality monitoring requirements in a specific area or under specific conditions can be better met. Through the determined scope and the determined position of the to-be-processed river basin, the corresponding sentry images are accurately inquired and downloaded, unnecessary data screening and arrangement work can be avoided, the data processing flow is optimized, and the working efficiency is improved. The remote sensing image data used can be ensured to be up to date by inquiring and downloading the sentry images in real time, so that the water quality condition of the current river basin is accurately reflected, and the timeliness of the inversion result is improved. According to the specific river basin position, the sentinel images are inquired and downloaded, so that the geographic matching property and accuracy of the data can be ensured, errors caused by data mismatch are reduced, and the accuracy of water quality inversion is improved.

In one embodiment, a certain provincial environmental protection department plans to comprehensively monitor and evaluate the water quality condition of a certain key river basin in the provincial so as to formulate a targeted water environment treatment measure. To achieve this goal, the environmental sector first determines the specific requirements of water inversion, including the required monitoring accuracy, time frame, and key indicators.

Based on these needs, technical teams of environmental protection departments utilize Geographic Information System (GIS) tools to determine the extent and precise location of the basin to be treated. This river basin range covers a plurality of counties and cities, and is an important water source area and ecologically sensitive area in the province.

Then, the technical team inquires a related remote sensing image database according to the determined scope and position of the to-be-processed river basin, and screens out the sentry images matched with the scope of the river basin by utilizing the characteristics of spatial resolution and time resolution of the remote sensing image data. The sentry images are shot by high-resolution satellites, so that the information such as the topography and the topography in the river basin range, the water distribution and the like can be clearly displayed.

After the sentry images corresponding to the scope of the to-be-processed river basin are determined, technical team downloads the sentry images by using professional remote sensing image data processing software, and the downloaded image data is subjected to format conversion and preprocessing, so that a high-quality remote sensing data source is provided for subsequent water quality inversion work.

After the sentry image data is obtained, the technical team further combines other monitoring data and data, such as ground water quality monitoring site data, meteorological data and the like, and a drainage basin water quality inversion model is constructed. By the model, the water quality conditions of different areas in the outflow region can be accurately inverted, and scientific basis is provided for environmental protection departments to formulate water environment treatment strategies.

In one embodiment of the application, the server determines the data level of the remote sensing image data corresponding to the sentry image, so that only the data conforming to the specified level is ensured to be used for subsequent analysis, which is helpful for screening out high-quality data subjected to strict calibration correction, and inaccuracy of analysis results caused by using low-quality or uncorrected data is avoided. And for the remote sensing image data of a non-designated level, radiation calibration operation is carried out, so that radiation differences caused by different sensors or different shooting conditions can be eliminated or reduced, and the data is more accurate and reliable in physical sense. Geometric correction is performed based on the reference image, so that geometric distortion in the remote sensing image can be eliminated, and the image is more matched with the real geographic coordinates. The influence of atmospheric scattering and absorption on the image can be eliminated by carrying out atmospheric correction through the Bopu response function, and the definition and accuracy of image data are improved, so that the data processing flow can be optimized, and a better data base is provided for the subsequent water body range extraction.

The server calculates the image band value through the normalized difference water Index (Modified Normalized DIFFERENCE WATER Index, MNDWI) and accurately identifies the water body area, so that the target water body range in the to-be-treated river basin range is accurately extracted, the water body identification precision is high, and the follow-up deeper analysis and research on the water body are facilitated. Then, according to the extracted water body range, the remote sensing image data are spliced and cut to obtain target sentinel images, and the target sentinel images are processed only aiming at the target water body area, so that huge data of the whole river basin range are prevented from being processed, the data processing efficiency is improved, and unnecessary calculation and analysis work is reduced.

In one embodiment, during a water quality monitoring task, a researcher needs to obtain water quality information within a certain basin. Firstly, downloading a sentry image of the river basin from a remote sensing image database, and acquiring corresponding remote sensing image data.

Next, researchers begin processing these remote sensing image data. First, the data levels of these data are checked to confirm whether the specified level is reached. If the data is just the specified level meeting the requirement, the remote sensing image data can be determined to complete the calibration and correction operation without additional radiation calibration.

If the remote sensing image data acquired by researchers does not reach the specified level, radiation calibration is required for the data with the non-specified level. By using professional remote sensing data processing software, researchers adjust the radiation characteristics of the data so as to meet the requirements of subsequent analysis.

After the radiation calibration is completed, the researchers continue to geometrically correct the data. And correcting the geometric distortion of the remote sensing image data by utilizing the reference image with known accurate geographic coordinates and matching the feature points, so as to ensure that each pixel in the image can accurately correspond to the actual geographic position.

And then, researchers use the Bopu response function to carry out atmospheric correction on the remote sensing image data subjected to calibration and geometric correction, so that the influence of atmospheric scattering and absorption on the image is eliminated, and the definition and accuracy of the data are improved.

After the series of correction operations, a researcher obtains a plurality of calibration corrected remote sensing image data to be processed. Then, using a Normalized Difference Water Index (NDWI) method, calculating the band values of the images, and successfully extracting the water range of the target area in the river basin range by setting a proper threshold value.

Finally, according to the water body range, researchers splice and cut the original remote sensing image data, and only the part related to the target water body area is reserved, so that the corresponding target sentinel image is obtained. The distribution and characteristics of the water body in the flow field can be clearly displayed through the target sentinel images, and a high-quality data source is provided for subsequent water quality inversion work.

In one embodiment, the normalized difference water index is calculated by the following formula:

In the embodiment of the application Representing normalized differential water index,/>Representing the green band,/>Representing the short wave infrared band. The spectral characteristics of shadows such as buildings in green light wave bands and near infrared wave bands are similar to those of water bodies, when short wave infrared wave bands are adopted to replace near infrared wave bands, the calculated contrast between the water body and the building index can be obviously enhanced, and the confusion degree of the calculated contrast between the water body and the building index is greatly reduced, so that the accurate extraction of water body information in towns is facilitated.

102. And acquiring national control section monitoring station data in the range of the river basin to be processed, and cleaning and finishing the national control section monitoring station data to obtain target national control section monitoring station data.

Specifically, in one embodiment of the application, the server ensures the pertinence and the accuracy of the data by screening the national control section monitoring site data corresponding to the scope of the to-be-processed river basin from the water quality monitoring data source. The integrity and accuracy of the data are further improved through the removal of the repeated data and the filling of the missing data, and a reliable data basis is provided for the subsequent water quality inversion and analysis. By removing redundancy and filling null values for the state control section monitoring site data, invalid and missing parts in the data are reduced, the data structure is optimized, and the efficiency of subsequent processing and analysis is improved. And the data is convenient to integrate and compare by unifying the data format, so that the data processing flow is further simplified. In the data processing process, abnormal data are deleted and cleaned, the authenticity and the reliability of the data can be ensured, the interference of abnormal values on the subsequent water quality inversion result is avoided, the accuracy and the accuracy of water quality inversion are improved, and more scientific basis is provided for water quality monitoring and treatment.

It should be noted that, in the embodiment of the present application, the national control section monitors site data, for example: longitude and latitude coordinates, section names, the basin to which the water treatment device belongs, monitoring time, dissolved oxygen, conductivity, turbidity, permanganate index, ammonia nitrogen, total phosphorus, total nitrogen and other information.

In one embodiment, environmental authorities have initiated water quality monitoring projects in order to fully understand the water quality condition of a river basin. Firstly, screening corresponding national control section monitoring site data from huge water quality monitoring data sources by a technical team according to a preset to-be-processed river basin range. The data record the longitude and latitude coordinates, the section names, the affiliated drainage basin and the specific monitoring time of each monitoring site in the drainage basin in detail, and provide a basis for subsequent data processing and analysis.

Immediately after obtaining these data, the technical team underwent a preliminary check of the quality of the data, with the result that the data of the partially monitored site was found to be duplicated. In order to ensure the accuracy and consistency of the data, the team decides to clear the repeated data, and uses professional data processing software to perform duplication removal operation on the national control section monitoring site data to remove redundant data, so as to obtain first national control section monitoring site data.

Next, the technical team carefully examines the first national control section monitoring site data and finds that some sites have missing data during certain time periods. The team decides to null fill these missing data, considering that they may affect subsequent water quality analysis and assessment. According to the data trend of the adjacent time points and the data condition of the adjacent sites, the method such as interpolation method is adopted, the missing data is reasonably filled, and the second national control section monitoring site data is obtained.

However, even after the above-described processing, the technical team found some abnormal data when checking the second national control section monitoring site data. Such abnormal data may be due to instrument failure, operational errors, or other reasons, which if left untreated, may lead to misleading water quality analysis. Thus, the team decides to delete these outliers. And identifying and deleting abnormal data exceeding the range by setting a reasonable threshold range, so as to obtain the third national control section monitoring site data.

Finally, in order to facilitate subsequent data analysis and application, the technical team unifies the data format of the third national control section monitoring site data. According to the characteristics and the requirements of the data, a unified data format standard is formulated, and format conversion and arrangement are carried out on the original data by utilizing data processing software, so that the target national control section monitoring station data is finally obtained.

103. According to the target sentinel images and the target national control section monitoring station data, a basin water quality inversion model is constructed, and inversion is carried out on the water quality condition of the basin range to be processed through the basin water quality inversion model.

Specifically, the server constructs characteristic wave bands based on the target sentinel images and the target national control section monitoring station points, and carries out correlation analysis on the characteristic wave bands, so that wave band combinations highly related to water quality parameters can be screened out, information related to water quality conditions can be extracted more accurately in the subsequent water quality inversion process, and the accuracy of water quality inversion is improved. By comparing the magnitude relation between the characteristic wave bands and the preset correlation threshold, a model which is most suitable for inversion of the current basin water quality can be determined in a plurality of linear regression models corresponding to various wave band combinations, blind selection of the model can be avoided, the selected model is ensured to be more consistent with the actual situation, and accordingly prediction capacity and stability of the model are improved. The characteristic wave bands are subjected to correlation analysis, so that the wave bands with larger contribution to water inversion can be rapidly identified, indiscriminate processing of all wave bands is avoided, the workload of data processing can be reduced, the speed and the efficiency of data processing can be improved, and powerful support is provided for real-time performance and rapid response of water inversion. By constructing the drainage basin water quality inversion model, the real-time monitoring and evaluation of the water quality condition in the drainage basin can be realized, the relevant departments can grasp the water quality information in time, and targeted water environment treatment and protection measures are formulated. Meanwhile, based on an accurate water quality inversion result, scientific decision basis can be provided for water resource management and ecological protection, and sustainable utilization of water resources and healthy development of ecological environment are promoted.

In one embodiment, to explore the water quality condition of a river basin deeply, a research team performs a series of data processing and analysis work in combination with target sentinel images and target national control section monitoring site data. Firstly, a research team constructs a series of characteristic wave bands based on spectral information in target sentinel images and in combination with water quality parameter data in target national control section monitoring site data. The characteristic wave bands not only contain a plurality of spectrum channel information in the sentry images, but also integrate actual water quality data provided by the national control section monitoring station, thereby forming multidimensional and comprehensive data characteristics.

Next, a research team conducted correlation analysis on these characteristic bands. And calculating the correlation coefficient between each characteristic wave band and the water quality parameter by using a statistical method. Through the step, the team successfully identifies the characteristic wave bands with obvious correlation with the water quality parameters, and an important basis is provided for the subsequent basin water quality inversion model construction.

After obtaining the correlation results of the characteristic wave bands and the water quality parameters, the research team compares the results with a preset correlation threshold value and screens out characteristic wave band combinations with higher correlation according to a preset threshold value standard. These combinations are believed to be key factors that better reflect water quality conditions.

With these key characteristic band combinations, research teams began to build basin water quality inversion models. According to the wave band combination screened before, a plurality of linear regression models are designed. Each model is built based on different band combinations, aiming at finding the model which can predict the water quality parameters most accurately.

Through multiple tests and verification, the research team finally determines an optimal basin water quality inversion model. The model stands out in various wave band combinations, and shows higher prediction precision and stability. The system can accurately reflect the water quality condition in the flow area and can provide powerful support for water quality monitoring and management.

In one embodiment of the application, the server accurately determines the corresponding pixel position on the target sentinel image by utilizing the longitude and latitude coordinates in the target national control section monitoring station data, so that the accurate matching of the monitoring station data and the image data can be ensured, errors in data processing can be reduced, and the accuracy of subsequent analysis can be improved. By extracting the image band value of the pixel position and combining with the target state control section monitoring station data, the information of the remote sensing image can be fully utilized, richer data support is provided for water quality inversion, the accuracy and reliability of water quality inversion are enhanced, and the prediction capability of an inversion model is improved. The pearson correlation coefficient between the target national control section monitoring site data and the different wave band combinations is calculated, so that the correlation between the different wave band combinations and the water quality parameters can be quantized, which wave band combinations are more important for water quality inversion can be determined, the selection of the wave band combinations is optimized, and the inversion model effect is improved. By automatically matching the monitoring site data with the image band value and calculating the correlation, the workload of manual operation can be obviously reduced, the speed and the efficiency of data processing are improved, the quick response and the real-time monitoring of water quality inversion are realized, and timely and effective support is provided for water quality management.

In one embodiment, in a certain water quality monitoring task, national control section monitoring site data of a target river basin and corresponding target sentinel images are obtained. The state control section monitoring site data record the longitude and latitude coordinates, water quality parameters and other information of each site in detail, and the sentry images provide remote sensing image data of the river basin range.

Firstly, according to longitude and latitude coordinate information in target national control section monitoring site data, accurately determining the pixel position corresponding to each site on a target sentinel image. The process utilizes the space analysis function of a Geographic Information System (GIS) to successfully spatially correspond the monitored site data with the image data through coordinate transformation and matching.

And extracting the image band values of each corresponding pixel position, wherein the band values reflect the brightness information of the sentinel images on different spectrum channels and contain rich water quality related characteristics. And (3) by extracting the band values, correlating the quantitative information of the remote sensing image with the actual water quality data of the target national control section monitoring station.

And then, matching the data of each target national control section monitoring station with the image band value of the pixel position corresponding to the target sentinel image. This step ensures that each set of data corresponds accurately, providing a reliable basis for subsequent correlation calculations.

And finally, calculating the correlation between the target national control section monitoring station data and different wave band combinations by using the Pearson correlation coefficient. The pearson correlation coefficient can quantify the degree of linear correlation between the two sets of data, helping to identify which band combinations have significant correlation with the target water quality parameter. Through the calculation process, a series of correlation coefficients are obtained, and an important basis is provided for the subsequent inversion model construction.

In one embodiment, the remote sensing image data selected is a sentinel second optical image, so that the image band value of the sentinel image subjected to pretreatment is divided by ten thousand to obtain the reflectivity value of the remote sensing image, and the reflectivity values of the remote sensing images in all wave bands of all national control section monitoring sites and corresponding water quality indexes are summarized and arranged.

The 12 wave bands of the sentinel images are combined, the combination form mainly comprises a single wave band, two wave bands, three wave bands and four wave bands, and the specific situations are as follows:

Single band: bi, where i is any one of twelve bands;

two bands: bi/bj, bi-bj, bi+bj, wherein i and j are any one of twelve wave bands;

Three wave bands: bi/(bj/bk), (bj/bk)/bi, bi/(bj-bk), (bj-bk)/bi/(bj+bk), (bj+bk)/bi, (1/bi-1/bk), where i, j, k are any one of twelve bands;

Four wave bands: (bi/bj)/(bk-bh), (bk-bh)/(bi/bj), (bi/bj)/(bk+bh), (bk+bh)/(bi/bj), (bi+bj)/(bk-bh), (bk-bh)/(bi+bj), wherein i, j, k, h is any one of twelve bands;

and then, the reflectivity of the remote sensing image of each extracted band combination is brought into the band combination, and a corresponding value is calculated.

In one embodiment, the pearson correlation coefficient is adopted to calculate the correlation between the target national control section monitoring station data and the different wave band combinations, the correlation condition between the reflectivity of the remote sensing image of the different characteristic wave band combinations and the measured data of the corresponding national control section monitoring station can be obtained, and the correlation analysis of the best combined characteristic wave band is completed by selecting 10 wave band combinations with highest correlation. The calculation formula of the adopted pearson correlation coefficient is as follows:

In the embodiment of the application Representing the pearson correlation coefficient,/>Representing the number of target national control section monitoring stations,/>Actual measurement data representing the ith national control section monitoring station,/>Band combination calculated value representing corresponding position of ith national control section monitoring station,/>Mean value of measured data of national control section monitoring stationAnd the average value of the band combination calculated values of the corresponding positions of the national control section monitoring stations is represented.

In one embodiment of the application, the server can screen out the band combination highly correlated with the water quality parameter by comparing the correlation between the different band combinations and the target national control section monitoring site data with a preset correlation threshold. The method not only improves the efficiency of data processing, but also provides a more reliable data base for subsequent model construction, is beneficial to optimizing the selection of the wave band combination, and ensures that the selected wave band combination can accurately reflect the water quality condition. By establishing a linear regression model for various wave band combinations and evaluating the precision, the selected model can be ensured to have higher prediction precision and model decision coefficients, the accuracy and reliability of the water quality inversion model can be improved, and the model can be used for predicting and evaluating the water quality condition in a flow field more accurately. By checking and comparing the linear regression models corresponding to the various wave band combinations, the basin water quality inversion model which is most suitable for the basin range to be treated can be determined, the applicability of the model is enhanced, and the model can be better adapted to the water quality characteristics and monitoring requirements of different basins.

In one embodiment, in a certain basin water quality monitoring task, a plurality of different wave band combinations and corresponding target national control section monitoring site data are obtained. These data provide rich information to analyze the correlation between band combinations and water quality parameters.

Firstly, calculating the correlation between different wave band combinations and target national control section monitoring site data, and comparing the correlation values with a preset correlation threshold value. The preset correlation threshold is set according to historical data and experience and is used for screening out the wave band combination with obvious correlation with the water quality parameter. By this step, it is possible to determine a plurality of band combinations whose correlation is greater than a preset threshold.

The band combinations are then ordered in order of high-to-low correlation. This helps to prioritize those band combinations that are more relevant to the water quality parameters to improve the accuracy of the subsequent model.

Then, a linear regression model between the image band value of the sentry image and the state control section monitoring site data is established for each band combination. These models describe the linear relationship between the band combinations and the water quality parameters, which are the basis for performing water quality inversion.

After the modeling is completed, a linear regression model corresponding to each band combination is checked. In the checking process, methods such as cross verification, residual error analysis and the like are adopted to evaluate the prediction precision and stability of the model. By this step, the accuracy evaluation corresponding to each band combination can be obtained, including the indexes such as the prediction accuracy and the model determination coefficient.

And finally, comprehensively considering the performance of the linear regression model corresponding to the different wave band combinations according to the prediction precision and the model decision coefficient in the precision evaluation. Through careful comparison and analysis, an optimal basin water quality inversion model corresponding to the basin range to be processed can be determined. The model is excellent in prediction precision and model determination coefficient, can accurately reflect the water quality condition in a flow field, and provides powerful support for water quality monitoring and management.

In one embodiment, remote sensing image data of target sentry images of corresponding water quality indexes are established for the selected 10 wave band combinations) Monitoring site data (/ >) with target national control section) Is a linear regression model of (c). The linear regression model is as follows:

It should be noted that, in the embodiment of the present application, a and b each represent a model fitting parameter, which is calculated according to an actual situation.

In one embodiment, the decision coefficients are selectedAnd verifying the stability of different linear regression models, and simultaneously selecting the actual measurement data of monitoring stations which do not participate in modeling and the inversion result of the corresponding model for accuracy verification.

Determining coefficientsThe system is obtained by calculating national control section monitoring site data participating in modeling and remote sensing image data corresponding to target sentry images, and the calculation formula is as follows:

In the embodiment of the application Representing the sum of squares of residuals of a linear regression model,/>Representing dependent variable remote sensing image data/>Is a global fluctuation of (c). Determining coefficient/>The value of (2) is at most 1 and at least 0, when the coefficient/> isdeterminedThe closer to 1 the value of (c) is, the better the stability of the representation model is; when determining the coefficient/>The closer to 0 the value of (c) is, the worse the stability of the representation model is.

The rest state control section monitoring station data which does not participate in modeling and the corresponding model inversion result are used for verifying the inversion accuracy of the linear regression modelFinally, the server determines the coefficient R2 and the accuracy/>, of the modelAnd comprehensively selecting an optimal water quality inversion model from the plurality of linear regression models. The calculation formula is as follows:

In the embodiment of the application Representing the result calculated by using a linear regression model, accuracy/>The value of (2) is in the range of 0-1, and is expressed in terms of the accuracy/>The closer the value is to 100%, the higher the accuracy of the linear regression model is indicated.

In one embodiment, two water quality indexes of permanganate index and total phosphorus are selected as examples from remote sensing image data of a parallel rest area of Pan Long He and Qiong Jiang Zhiliu in Sichuan, and the drainage basin water quality inversion method based on a national control section and a parallel rest image is described.

Step one, acquiring a sentinel image of L2A in a working area river basin range. It should be noted that, in the embodiment of the application, the river inversion working area of the settling area actually comprises two water bodies at the junction of Sichuan and Chongqing, most of the water inversion area falls into Sichuan-Jikuan settling area of Sichuan-Ning city, a small part of the water inversion area falls into Yingyang city to county, anyue county of Sichuan-Jiguo, and a small part of the water inversion area falls into Chongqing Tongnan area. The whole working area is positioned between 30.130-30.470 degrees in north latitude, 105.185-105.660 degrees in east longitude, the coverage area is about 1668.5km, and the total length of the inversion river reach is about 150km. Because the working area can be completely covered by the sentinel image, the image mosaic processing is not needed.

And extracting water body information in the scenery sentry images by using SNAP software in combination with the improved normalized difference water body index, correcting the water body information to obtain complete river boundary vector data of a certain section of flat Long He and agaragar Jiang Zhiliu, and further cutting out a sentry remote sensing image base map of a working area according to a working area range vector.

And step two, acquiring 12 national control section monitoring site data available in a working area, and sorting the monitoring site data according to the sequence of section names, monitoring time and water quality indexes to realize classification of the monitoring data according to two water quality indexes of permanganate indexes and total phosphorus.

And thirdly, constructing a water quality inversion model according to the preprocessed target sentinel image obtained in the first step and the target national control section monitoring site data obtained in the second step.

Regarding the selection and correlation analysis of the optimal band combination of the inversion, the 10 combination modes with the highest water quality correlation of the permanganate index and the total phosphorus are as follows:

respectively establishing linear regression models for the 10 wave band combinations, and comprehensively analyzing the decision coefficient R2 and the models The optimal water inversion model is selected as follows:

The optimal characteristic wave band combination selected by the permanganate index is b 4/(5/1), and the established water quality inversion model is that . It should be noted that, in the embodiment of the present application, the model determination coefficient R2 is 0.8023,/>88.87%.

The optimal characteristic wave band combination selected by total phosphorus is b 5/(3-4), and the established water quality inversion model is that. It should be noted that, in the embodiment of the present application, the model determination coefficient R2 is 0.8139,/>84.63%.

And fourthly, inverting the water quality condition of the river in the river basin range by constructing a completed river basin water quality inversion model.

And (3) gradually realizing the calculation of the reflectivity value of the remote sensing image, the calculation of the combined remote sensing image value of different wavebands and the calculation of the inversion value of the model by using a waveband calculation mode in ENVI software, thereby obtaining the inversion result of the water quality index of the target area in the range of the river basin, and further obtaining the complete water quality index distribution map of the river basin.

The proportion statistics of the grading results of the river permanganate index in the settling zone are as follows:

the proportion statistics of the grading result of the total phosphorus index of the river in the settling area are as follows:

The above is a method embodiment of the present application. Based on the same inventive concept, the embodiment of the application also provides a drainage basin water quality inversion device based on the national control section and the sentry image, and the structure of the drainage basin water quality inversion device is shown in figure 2.

Fig. 2 is a schematic structural diagram of a drainage basin water quality inversion device based on a national control section and a sentinel image according to an embodiment of the present application. As shown in fig. 2, the apparatus includes: the system comprises a sentinel image acquisition unit 201, a national control section monitoring station data acquisition unit 202 and a model construction inversion unit 203.

The sentinel image obtaining unit 201 is configured to obtain a sentinel image within a scope of a river basin to be processed, and perform preprocessing on remote sensing image data corresponding to the sentinel image to obtain a target sentinel image;

the national control section monitoring station data acquisition unit 202 is used for acquiring national control section monitoring station data in the range of the river basin to be processed, and cleaning and sorting the national control section monitoring station data to obtain target national control section monitoring station data;

The model construction inversion unit 203 is configured to construct a drainage basin water quality inversion model according to the target sentinel image and the target national control section monitoring station data, and invert the water quality condition of the drainage basin range to be processed through the drainage basin water quality inversion model.

Fig. 3 is a schematic diagram of an internal structure of a drainage basin water quality inversion device based on a national control section and a sentinel image according to an embodiment of the present application. As shown in fig. 3, the apparatus includes:

At least one processor;

and a memory communicatively coupled to the at least one processor;

Wherein the memory stores instructions executable by the at least one processor, the instructions being executable by the at least one processor to enable the at least one processor to:

Acquiring a sentinel image in the scope of a river basin to be processed, and preprocessing remote sensing image data corresponding to the sentinel image to obtain a target sentinel image;

according to the target sentinel images and the target national control section monitoring station data, a basin water quality inversion model is constructed, and inversion is carried out on the water quality condition of the basin range to be processed through the basin water quality inversion model.

The embodiment of the application also provides a nonvolatile computer storage medium, which stores computer executable instructions, and the computer executable instructions can be executed:

The embodiments of the present application are described in a progressive manner, and the same and similar parts of the embodiments are all referred to each other, and each embodiment is mainly described in the differences from the other embodiments. In particular, for the apparatus and medium embodiments, the description is relatively simple, as it is substantially similar to the method embodiments, with reference to the section of the method embodiments being relevant.

The foregoing describes certain embodiments of the present application. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims can be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

The devices and media provided in the embodiments of the present application are in one-to-one correspondence with the methods, so that the devices and media also have similar beneficial technical effects as the corresponding methods, and since the beneficial technical effects of the methods have been described in detail above, the beneficial technical effects of the devices and media are not repeated here.

It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.

The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

In one typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.

The memory may include volatile memory in a computer-readable medium, random Access Memory (RAM) and/or nonvolatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of computer-readable media.

Computer readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media for a computer include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device. Computer-readable media, as defined herein, does not include transitory computer-readable media (transmission media), such as modulated data signals and carrier waves.

It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article or apparatus that comprises the element.

The foregoing is merely exemplary of the present application and is not intended to limit the present application. Various modifications and variations of the present application will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. which come within the spirit and principles of the application are to be included in the scope of the claims of the present application.

Claims

1. The river basin water quality inversion method based on the national control section and the sentry images is characterized by comprising the following steps:

2. The drainage basin water quality inversion method based on the national control section and the sentry image according to claim 1, wherein the preprocessing is performed on the remote sensing image data corresponding to the sentry image to obtain a target sentry image, specifically comprising:

3. The drainage basin water quality inversion method based on the national control section and the sentinel image according to claim 1, wherein the method is characterized by obtaining national control section monitoring site data in the drainage basin to be processed, and cleaning and sorting the national control section monitoring site data to obtain target national control section monitoring site data, and specifically comprises the following steps:

4. The drainage basin water quality inversion method based on the national control section and the sentinel image according to claim 1, wherein the construction of the drainage basin water quality inversion model according to the target sentinel image and the target national control section monitoring station data specifically comprises the following steps:

5. The drainage basin water quality inversion method based on national control section and guard post image according to claim 4, wherein the constructing a characteristic wave band based on the target guard post image and the target national control section monitoring station data and performing correlation analysis on the characteristic wave band specifically comprises:

6. The drainage basin water quality inversion method based on the national control section and the sentinel image of claim 4, wherein the determining of the magnitude relation between the correlation and the preset correlation threshold value, and determining the drainage basin water quality inversion model in a plurality of linear regression models corresponding to a plurality of wave band combinations according to the magnitude relation, specifically comprises:

7. The drainage basin water quality inversion method based on the national control section and the sentry image according to claim 1, wherein the step of obtaining the sentry image in the range of the drainage basin to be processed specifically comprises the following steps:

8. Drainage basin water quality inversion device based on state accuse section and sentry image, its characterized in that, the device includes:

9. Drainage basin water quality inversion equipment based on state accuse section and sentry image, its characterized in that, equipment includes:

At least one processor;

And a memory communicatively coupled to the at least one processor;

Wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform a state control section and sentinel image based basin water inversion method as claimed in any one of claims 1 to 7.

10. A non-volatile computer storage medium storing computer executable instructions which, when executed, implement a method of river basin water inversion based on national control section and sentinel images as claimed in any one of claims 1 to 7.