CN117035498B - High-altitude water quality and quantity detection method based on remote sensing hydrologic data model - Google Patents
High-altitude water quality and quantity detection method based on remote sensing hydrologic data model Download PDFInfo
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Abstract
The invention relates to the technical field of high-altitude hydrologic detection, and is used for solving the problems that in the existing high-altitude water quality and water quantity detection mode, accurate assessment of the water quality state of a water body is difficult to achieve, so that the health state of the water body cannot be accurately analyzed, and analysis of the change trend of the water body is difficult to achieve, so that early warning of disaster conditions cannot be performed, and particularly relates to a high-altitude water quality and water quantity detection method based on a remote sensing hydrologic data model. According to the invention, the monitoring of the water quality and water quantity state of the water body in the high-altitude area is realized through a remote sensing technology, the evaluation of the water quality and the health state of the water body is clarified, and the clear output of the change trend of the water body is realized by adopting a data comparison analysis and data comprehensive analysis mode, so that data support is provided for disaster early warning and emergency response; through the detection and analysis of the water quality and the water quantity of the high altitude, the method not only provides a basis for protecting the health of water resources, environment and ecological systems, but also lays a foundation for preventing and alleviating the occurrence of water-related disasters.
Description
Technical Field
The invention relates to the technical field of high-altitude hydrologic detection, in particular to a high-altitude water quality and quantity detection method based on a remote sensing hydrologic data model.
Background
The high-altitude hydrology refers to analysis of hydrologic information of a water body from a high-altitude area, and the high-altitude hydrologic water quality and water quantity detection refers to a process of monitoring and evaluating water quality and water quantity of a Gao Kongou area (such as a lake, a river, a reservoir and the like), and the accurate detection of water quality and water quantity can effectively realize water resource management and environmental protection.
However, in the existing method for detecting the water quality and the water quantity of high-altitude water, accurate assessment of the water quality state of the water body is difficult to achieve, so that the health state of the water body cannot be accurately analyzed, analysis of the water body change trend is also difficult to achieve, early warning of disaster situations cannot be achieved, and ecological environment harmfulness is increased.
In order to solve the above-mentioned defect, a technical scheme is provided.
Disclosure of Invention
The invention aims to provide a high-altitude water quality and quantity detection method based on a remote sensing hydrological data model.
The aim of the invention can be achieved by the following technical scheme: a high-altitude water quality and quantity detection method based on a remote sensing hydrologic data model comprises the following steps:
step one: acquiring water quality state information and water quantity state information related to high-altitude hydrology through various remote sensing technologies, and sending various information to a cloud database for storage;
step two: according to the data monitored by the remote sensing technology, judging and analyzing the water body type of the unknown water body, and outputting the determined water body type of the unknown water body;
step three: monitoring the water quality state information of the target water body, and analyzing the water quality state of the target water body to obtain the water quality grade of the target water body, wherein the water quality grade comprises a high-grade water quality grade, a medium-grade water quality grade and a poor-grade water quality grade;
step four: according to the obtained water quality grade of the target water body, carrying out health transformation evaluation analysis on the water quality grade of the target water body, outputting text word early warning, and displaying and explaining the water quality state of the target water body through a display terminal;
step five: the water quantity state information of the target water body is monitored, so that the water quantity state of the target water body is analyzed, a flood early warning signal, a drought early warning signal and a normal water quantity early warning signal are generated, and display and explanation are carried out through a display terminal.
Preferably, the determining and analyzing are performed on the water body type of the unknown water body, and the specific analysis process is as follows:
201: receiving electromagnetic waves reflected or radiated by the earth surface by adopting a remote sensing technology, generating an electromagnetic spectrum, and dividing the electromagnetic spectrum in the electromagnetic spectrum into different wave bands according to the wavelength range;
202: the method comprises the steps of monitoring reflectivities in reflection characteristic information of different wavebands of an unknown water body in real time, taking time as an abscissa, taking reflectivities of wavebands at corresponding time points as an ordinate, establishing a waveband reflection coordinate system, and drawing to obtain reflectivity curves of the wavebands of the unknown water body;
203: selecting a characteristic shape wave band from a wave band reflection coordinate system, extracting the shape of a reflectivity curve of the characteristic shape wave band of the unknown water body, and comparing the shape with a reflectivity curve shape judgment table stored in a cloud database, thereby outputting the water body type of the unknown water body, wherein each shape of the reflectivity curve of the characteristic shape wave band has one water body type corresponding to the water body type;
204: selecting a characteristic peak wave band from a wave band reflection coordinate system, extracting the peak position of a reflectivity curve of the characteristic peak wave band of an unknown water body, and comparing the peak position with a reflectivity curve peak position judgment table stored in a cloud database, thereby outputting the water body type of the unknown water body, wherein each peak position of the reflectivity curve has a water body type corresponding to the water body type;
205: comparing the reflectivities of different wave bands of the unknown water body to obtain reflectivity ratio values among the wave bands of the unknown water body, and comparing the reflectivity ratio values with a reflectivity ratio judgment table stored in a cloud database to output water body types of the unknown water body, wherein each reflectivity ratio value has one water body type corresponding to the water body type;
206: the results output in steps 203, 204 and 205 are statistically analyzed, specifically: if the water types output in steps 203, 204 and 205 are the same water type, or the water types output in any two steps 203, 204 and 205 are the same water type, the water type is used as the final judgment result of the unknown water, so that the determined water type of the unknown water is obtained.
Preferably, the specific solution process of the reflectivity is as follows:
monitoring a remote sensing data record log, and calling the radiation energy and the incoming radiation energy of each wave band from the remote sensing data record log, and performing division calculation on the two data, wherein the method specifically comprises the following steps: reflectivity = radiant energy +.r radiant energy, whereby the reflectivity of the corresponding band is obtained.
Preferably, the water quality state information of the target water body is monitored, and the specific monitoring process is as follows:
extracting a visible light wave band of blue light and a visible light wave band of red light from different wave bands of a target water body, monitoring the reflectivity of the blue light wave band and the red light wave band of the target water body, respectively marking the reflectivity as fs1 and fs2, carrying out combined calculation processing on the two data, and according to a set data model: a reference value xfh of the suspended matter content of the target water body is obtained, wherein cax is used for representing a reference value of the water body turbidity evaluation conversion,and cax is a constant;
performing contrast matching analysis on the suspended matter content of the target water body and a turbidity degree data table stored in a cloud database, so as to obtain a turbidity index of the target water body, and marking the turbidity index as dty, wherein the obtained suspended matter content of each target water body corresponds to one turbidity index;
the reflectivity of the red wave band and the near infrared wave band of the target water body is monitored, and is respectively recorded as ry1 and ry2, and the reflectivity of the two wave bands is compared and analyzed, and according to a set formula: lg=ry1++ry2, thereby obtaining the leaf-green estimation index lg of the target water body;
comparing and matching the leaf green estimation index of the target water body with a chlorophyll data table stored in a cloud database, thereby obtaining the chlorophyll content of the target water body, and marking the chlorophyll content as cp l, wherein the obtained leaf green estimation index of each target water body corresponds to one chlorophyll content;
and monitoring the reflectivity of the blue light wave band and the red light wave band of the target water body, substituting the two items of data into a color data table stored in a cloud database for comparison and matching, thereby obtaining the chromaticity value of the target water body, and recording the chromaticity value as co l.
Preferably, the analyzing the water quality state of the target water body specifically includes the following steps:
monitoring turbidity indexes, chlorophyll content and chromaticity values in water quality state information of a target water body in real time, comprehensively analyzing three items of data, and according to a set data model: wqx =ρ1×dty+ρ2×cp l+ρ3xcol, thereby obtaining a water quality coefficient wqx of the target water body, wherein ρ1, ρ2 and ρ3 are weight factor coefficients of turbidity index, chlorophyll content and chromaticity value, respectively, and ρ1, ρ2 and ρ3 are natural numbers greater than 0;
and comparing and matching the water quality coefficient of the target water body with a water quality state judging table stored in the cloud database, thereby obtaining the water quality grade of the target water body, wherein the obtained water quality coefficient of each target water body corresponds to one water quality grade, and the water quality grade comprises a high-grade water quality grade, a medium-grade water quality grade and a poor-grade water quality grade.
Preferably, the health transformation evaluation analysis is performed on the water quality grade of the target water body, and the specific analysis process is as follows:
according to the received quality grade of the quality of the water, judging the health state of the target water body as a quality water state, triggering the text word early warning without treatment and displaying the text word early warning through a display terminal;
judging the health state of the target water body to be a medium water state according to the received medium water quality grade, triggering the medium pollution of the water quality state of the target water body, carrying out treatment-required text word early warning, and carrying out display description through a display terminal;
according to the received poor water quality grade, the health state of the target water body is judged to be a poor water state, and serious pollution exists in the water quality state of the target water body, so that a text word early warning is needed to be treated, and display and explanation are carried out through a display terminal.
Preferably, the monitoring of the water volume state information of the target water body includes the following specific monitoring process:
the water body temperature of the target water body is monitored in real time through the thermal infrared remote sensing image and is recorded as wd i And carrying out standard deviation analysis on the continuously monitored water temperature according to a set formula:obtaining a temperature fluctuation value sigma 1 of the target water body, wherein i is represented as continuous monitoring time, and mu 1 is represented as average data of the water body temperature under the continuous time;
the water level of the target water body is monitored in real time through the thermal infrared remote sensing image and is recorded as sw i And carrying out standard deviation analysis on the continuously monitored water temperature according to a set formula: obtaining a water level fluctuation value sigma 2 of the target water body, wherein mu 2 is represented as mean value data of the water level of the water body in continuous time;
the water quantity of the target water body in a unit time period is monitored in real time through the thermal infrared remote sensing image, the initially monitored water quantity and the finally monitored water quantity of the target water body are respectively recorded and respectively recorded as sl Initially, the method comprises And sl Terminal (A) According to the set formula: mu 3= (sl) Terminal (A) -sl Initially, the method comprises ) And m, thereby obtaining the average water quantity mu 3 of the target water body, wherein m is expressed as the duration of the unit time period.
Preferably, the water quantity state is analyzed, and the specific analysis process is as follows:
performing comparison and matching analysis on the temperature fluctuation value of the target water body and a water body temperature state judgment table stored in a cloud database, so as to obtain a water temperature feedback level of the target water body, wherein the obtained temperature fluctuation value of each target water body corresponds to one water temperature feedback level, and the water temperature feedback level comprises a first-order water temperature feedback level, a second-order water temperature feedback level and a third-order water temperature feedback level;
performing comparison matching analysis on the water level fluctuation value of the target water body and a water level state judgment table stored in a cloud database, so as to obtain a water level feedback level of the target water body, wherein the obtained water level fluctuation value of each target water body corresponds to one water level feedback level, and the water level feedback level comprises a first-order water level feedback level, a second-order water level feedback level and a third-order water level feedback level;
comparing and matching the average water quantity of the target water body with a water quantity state judgment table stored in a cloud database, thereby obtaining a water quantity feedback grade of the target water body, wherein the obtained average water quantity of each target water body corresponds to one water quantity feedback grade, and the water quantity feedback grade comprises a first-order water quantity feedback grade, a second-order water quantity feedback grade and a third-order water quantity feedback grade;
simultaneously, the feedback level state of each item of data of the target water body is called, and if the first-order water temperature feedback level, the third-order water level feedback level and the third-order water quantity feedback level are captured at the same time, a flood warning signal is generated;
if the third-order water temperature feedback level, the first-order water level feedback level and the first-order water quantity feedback level are captured at the same time, generating a drought early warning signal;
and under other conditions, generating normal water body water quantity early warning signals.
The invention has the beneficial effects that:
according to the invention, the monitoring of the water quality and the water quantity state of the water body in the high-altitude area is realized by the remote sensing technology, and the early warning evaluation of the water quality state and the water body health state of the water body is defined, so that a scientific basis is provided for realizing environmental protection;
monitoring the change trend of the water body by a remote sensing technology, including the change of the water level, the change of the water body temperature and the change of the water quantity, and adopting a data comparison analysis and data comprehensive analysis mode to clearly output the water quantity state of the water body, and discovering disaster conditions such as flooding, drought and the like in advance to provide data support for disaster early warning and emergency response;
through the detection and analysis of the water quality and the water quantity of the high altitude, the method not only provides a basis for protecting the health of water resources, environment and ecological systems, but also lays a foundation for preventing and alleviating the occurrence of water-related disasters.
Drawings
The invention is further described below with reference to the accompanying drawings.
Fig. 1 is a flow chart of the method of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, the invention discloses a method for detecting water quality and water quantity in high altitude based on a remote sensing hydrological data model, which comprises the following steps:
step one: collecting water quality state information and water quantity state information related to high-altitude hydrology through various remote sensing technologies, and sending various information to a cloud database for storage, wherein the cloud database is also used for storing a reflectivity curve shape judging table, a reflectivity curve peak position judging table, a reflectivity ratio judging table, a turbidity degree data table, a chlorophyll data table, a color data table, a water quality state judging table, a water body temperature state judging table, a water body water level state judging table and a water quantity state judging table;
step two: according to the data monitored by the remote sensing technology, the water body type of the unknown water body is judged and analyzed, and the specific analysis process is as follows:
201: receiving electromagnetic waves reflected or radiated by the earth surface by adopting a remote sensing technology, generating an electromagnetic spectrum, and dividing the electromagnetic spectrum in the electromagnetic spectrum into different wave bands according to the wavelength range;
202: the method comprises the steps of monitoring reflectivities in reflection characteristic information of different wavebands of an unknown water body in real time, taking time as an abscissa, taking reflectivities of wavebands at corresponding time points as an ordinate, establishing a waveband reflection coordinate system, and drawing to obtain reflectivity curves of the wavebands of the unknown water body;
the specific solving process of the reflectivity is as follows: monitoring a remote sensing data record log, and calling the radiation energy and the incoming radiation energy of each wave band from the remote sensing data record log, and performing division calculation on the two data, wherein the method specifically comprises the following steps: reflectivity = radiant energy +.r.radiant energy, whereby reflectivity of the corresponding band is obtained;
203: selecting a characteristic shape wave band from a wave band reflection coordinate system, extracting the shape of a reflectivity curve of the characteristic shape wave band of an unknown water body, and comparing the shape with a reflectivity curve shape judgment table stored in a cloud database, thereby outputting the water body type of the unknown water body, wherein each shape of the reflectivity curve of the characteristic shape wave band has one water body type corresponding to the water body type, and the water body type comprises a lake, a river and a sea;
it should be noted that the reflectivity curve shape determination table is composed of a plurality of reflectivity curve shapes of known water body types, and the water body types can be primarily determined by comparing the reflectivity curve shape of the unknown water body with the reflectivity curve shape of the known water body types;
204: selecting a characteristic peak wave band from a wave band reflection coordinate system, extracting the peak position of a reflectivity curve of the characteristic peak wave band of an unknown water body, and comparing the peak position with a reflectivity curve peak position judgment table stored in a cloud database, thereby outputting the water body type of the unknown water body, wherein each peak position of the reflectivity curve has a water body type corresponding to the water body type;
205: comparing the reflectivities of different wave bands of the unknown water body to obtain reflectivity ratio values among the wave bands of the unknown water body, and comparing the reflectivity ratio values with a reflectivity ratio judgment table stored in a cloud database to output water body types of the unknown water body, wherein each reflectivity ratio value has one water body type corresponding to the water body type;
206: the results output in steps 203, 204 and 205 are statistically analyzed, specifically: if the water types output in steps 203, 204 and 205 are the same water type, or the water types output in any two steps 203, 204 and 205 are the same water type, the water type is used as the final judgment result of the unknown water, so that the determined water type of the unknown water is obtained.
Step three: the water quality state information of the target water body is monitored, and the specific monitoring process is as follows:
extracting a visible light wave band of blue light and a visible light wave band of red light from different wave bands of a target water body, monitoring the reflectivity of the blue light wave band and the red light wave band of the target water body, respectively marking the reflectivity as fs1 and fs2, carrying out combined calculation processing on the two data, and according to a set data model: obtaining a reference value xfh of the suspended matter content of the target water body, wherein cax is used for representing a reference value of water body turbidity evaluation conversion, and cax is a constant;
performing contrast matching analysis on the suspended matter content of the target water body and a turbidity degree data table stored in a cloud database, so as to obtain a turbidity index of the target water body, and marking the turbidity index as dty, wherein the obtained suspended matter content of each target water body corresponds to one turbidity index;
the reflectivity of the red wave band and the near infrared wave band of the target water body is monitored, and is respectively recorded as ry1 and ry2, and the reflectivity of the two wave bands is compared and analyzed, and according to a set formula: lg=ry1++ry2, thereby obtaining the leaf-green estimation index lg of the target water body;
comparing and matching the leaf green estimation index of the target water body with a chlorophyll data table stored in a cloud database, thereby obtaining the chlorophyll content of the target water body, and marking the chlorophyll content as cpl, wherein the obtained leaf green estimation index of each target water body corresponds to one chlorophyll content;
monitoring the reflectivity of a blue light wave band and a red light wave band of a target water body, substituting the two items of data into a color data table stored in a cloud database for comparison and matching, thereby obtaining a chromaticity value of the target water body, and recording the chromaticity value as col;
therefore, the water quality state of the target water body is analyzed, and the specific analysis process is as follows:
monitoring turbidity indexes, chlorophyll content and chromaticity values in water quality state information of a target water body in real time, comprehensively analyzing three items of data, and according to a set data model: wqx =ρ1×dty+ρ2×cpl+ρ3×col, thereby obtaining a water quality coefficient wqx of the target water body, wherein ρ1, ρ2 and ρ3 are weight factor coefficients of turbidity index, chlorophyll content and chromaticity value, respectively, ρ1, ρ2 and ρ3 are natural numbers larger than 0, and the weight factor coefficients are used for balancing the duty ratio weights of each item of data in formula calculation, thereby promoting the accuracy of calculation results;
and comparing and matching the water quality coefficient of the target water body with a water quality state judging table stored in the cloud database, thereby obtaining the water quality grade of the target water body, wherein the obtained water quality coefficient of each target water body corresponds to one water quality grade, and the water quality grade comprises a high-grade water quality grade, a medium-grade water quality grade and a poor-grade water quality grade.
Step four: according to the obtained water quality grade of the target water body, carrying out health transformation evaluation analysis on the water quality grade of the target water body, wherein the specific analysis process is as follows:
according to the received quality grade of the quality of the water, judging the health state of the target water body as a quality water state, triggering the text word early warning without treatment and displaying the text word early warning through a display terminal;
judging the health state of the target water body to be a medium water state according to the received medium water quality grade, triggering the medium pollution of the water quality state of the target water body, carrying out treatment-required text word early warning, and carrying out display description through a display terminal;
according to the received poor water quality grade, the health state of the target water body is judged to be a poor water state, and serious pollution exists in the water quality state of the target water body, so that a text word early warning is needed to be treated, and display and explanation are carried out through a display terminal.
Step five: the water quantity state information of the target water body is monitored, and the specific monitoring process is as follows:
the water body temperature of the target water body is monitored in real time through the thermal infrared remote sensing image and is recorded as wd i And carrying out standard deviation analysis on the continuously monitored water temperature according to a set formula:obtaining a temperature fluctuation value sigma 1 of the target water body, wherein i is represented as continuous monitoring time, and mu 1 is represented as average data of the water body temperature under the continuous time;
the water level of the target water body is monitored in real time through the thermal infrared remote sensing image and is recorded as sw i And carrying out standard deviation analysis on the continuously monitored water temperature according to a set formula: obtaining a water level fluctuation value sigma 2 of the target water body, wherein mu 2 is represented as mean value data of the water level of the water body in continuous time;
the water quantity of the target water body in a unit time period is monitored in real time through the thermal infrared remote sensing image, the initially monitored water quantity and the finally monitored water quantity of the target water body are respectively recorded and respectively recorded as sl Initially, the method comprises And sl Terminal (A) According to the set formula: mu 3= (sl) Terminal (A) -sl Initially, the method comprises ) M, where m is expressed as the duration of a unit time period, thereby obtaining an average water volume μ3 of the target water body;
the water quantity state of the target water body is analyzed, and the specific analysis process is as follows:
performing comparison and matching analysis on the temperature fluctuation value of the target water body and a water body temperature state judgment table stored in a cloud database, so as to obtain a water temperature feedback level of the target water body, wherein the obtained temperature fluctuation value of each target water body corresponds to one water temperature feedback level, and the water temperature feedback level comprises a first-order water temperature feedback level, a second-order water temperature feedback level and a third-order water temperature feedback level;
performing comparison matching analysis on the water level fluctuation value of the target water body and a water level state judgment table stored in a cloud database, so as to obtain a water level feedback level of the target water body, wherein the obtained water level fluctuation value of each target water body corresponds to one water level feedback level, and the water level feedback level comprises a first-order water level feedback level, a second-order water level feedback level and a third-order water level feedback level;
comparing and matching the average water quantity of the target water body with a water quantity state judgment table stored in a cloud database, thereby obtaining a water quantity feedback grade of the target water body, wherein the obtained average water quantity of each target water body corresponds to one water quantity feedback grade, and the water quantity feedback grade comprises a first-order water quantity feedback grade, a second-order water quantity feedback grade and a third-order water quantity feedback grade;
simultaneously, the feedback level state of each item of data of the target water body is called, and if the first-order water temperature feedback level, the third-order water level feedback level and the third-order water quantity feedback level are captured at the same time, a flood warning signal is generated;
if the third-order water temperature feedback level, the first-order water level feedback level and the first-order water quantity feedback level are captured at the same time, generating a drought early warning signal;
under other conditions, generating normal water body water quantity early warning signals;
and display the explanation through the display terminal.
The foregoing is merely illustrative of the structures of this invention and various modifications, additions and substitutions for those skilled in the art can be made to the described embodiments without departing from the scope of the invention or from the scope of the invention as defined in the accompanying claims.
Claims (4)
1. A high-altitude water quality and quantity detection method based on a remote sensing hydrological data model is characterized by comprising the following steps of:
step one: acquiring water quality state information and water quantity state information related to high-altitude hydrology through various remote sensing technologies, and sending various information to a cloud database for storage;
step two: according to the data monitored by the remote sensing technology, judging and analyzing the water body type of the unknown water body, and outputting the determined water body type of the unknown water body;
step three: monitoring the water quality state information of the target water body, and analyzing the water quality state of the target water body to obtain the water quality grade of the target water body, wherein the water quality grade comprises a high-grade water quality grade, a medium-grade water quality grade and a poor-grade water quality grade;
step four: according to the obtained water quality grade of the target water body, carrying out health transformation evaluation analysis on the water quality grade of the target water body, outputting text word early warning, and displaying and explaining the water quality state of the target water body through a display terminal;
step five: monitoring the water quantity state information of the target water body, analyzing the water quantity state of the target water body, generating a flood early warning signal, a drought early warning signal and a normal water quantity early warning signal, and displaying and explaining through a display terminal;
the water quality state information of the target water body is monitored, and the specific monitoring process is as follows:
extracting a visible light wave band of blue light and a visible light wave band of red light from different wave bands of a target water body, monitoring the reflectivity of the blue light wave band and the red light wave band of the target water body, respectively marking the reflectivity as fs1 and fs2, carrying out combined calculation processing on the two data, and according to a set data model: obtaining a reference value xfh of the suspended matter content of the target water body, wherein cax is used for representing a reference value of water body turbidity evaluation conversion, and cax is a constant;
performing contrast matching analysis on the suspended matter content of the target water body and a turbidity degree data table stored in a cloud database, so as to obtain a turbidity index of the target water body, and marking the turbidity index as dty, wherein the obtained suspended matter content of each target water body corresponds to one turbidity index;
the reflectivity of the red wave band and the near infrared wave band of the target water body is monitored, and is respectively recorded as ry1 and ry2, and the reflectivity of the two wave bands is compared and analyzed, and according to a set formula: lg=ry1++ry2, thereby obtaining the leaf-green estimation index lg of the target water body;
comparing and matching the leaf green estimation index of the target water body with a chlorophyll data table stored in a cloud database, thereby obtaining the chlorophyll content of the target water body, and marking the chlorophyll content as cpl, wherein the obtained leaf green estimation index of each target water body corresponds to one chlorophyll content;
monitoring the reflectivity of a blue light wave band and a red light wave band of a target water body, substituting the two items of data into a color data table stored in a cloud database for comparison and matching, thereby obtaining a chromaticity value of the target water body, and recording the chromaticity value as col;
the water quality state of the target water body is analyzed, and the specific analysis process is as follows:
monitoring turbidity indexes, chlorophyll content and chromaticity values in water quality state information of a target water body in real time, comprehensively analyzing three items of data, and according to a set data model: wqx =ρ1×dty+ρ2×cpl+ρ3×col, thereby obtaining a water quality coefficient wqx of the target water body, wherein ρ1, ρ2 and ρ3 are weight factor coefficients of turbidity index, chlorophyll content and chromaticity value, respectively, and ρ1, ρ2 and ρ3 are natural numbers greater than 0;
performing contrast matching analysis on the water quality coefficient of the target water body and a water quality state judging table stored in a cloud database, thereby obtaining the water quality grade of the target water body, wherein the water quality coefficient of each obtained target water body corresponds to one water quality grade, and the water quality grade comprises a high-grade water quality grade, a medium-grade water quality grade and a poor-grade water quality grade;
the water quantity state information of the target water body is monitored, and the specific monitoring process is as follows:
the water body temperature of the target water body is monitored in real time through the thermal infrared remote sensing image and is recorded as wd i And carrying out standard deviation analysis on the continuously monitored water temperature according to a set formula:obtaining a temperature fluctuation value sigma 1 of the target water body, wherein i is represented as continuous monitoring time, and mu 1 is represented as average data of the water body temperature under the continuous time;
the water level of the target water body is monitored in real time through the thermal infrared remote sensing image and is recorded as sw i And carrying out standard deviation analysis on the continuously monitored water temperature according to a set formula: obtaining a water level fluctuation value sigma 2 of the target water body, wherein mu 2 is represented as mean value data of the water level of the water body in continuous time;
the water quantity of the target water body in a unit time period is monitored in real time through the thermal infrared remote sensing image, the initially monitored water quantity and the finally monitored water quantity of the target water body are respectively recorded and respectively recorded as sl Initially, the method comprises And sl Terminal (A) According to the set formula: mu 3= (s l) Terminal (A) -s l Initially, the method comprises ) M, where m is expressed as the duration of a unit time period, thereby obtaining an average water volume μ3 of the target water body;
the water quantity state is analyzed, and the specific analysis process is as follows:
performing comparison and matching analysis on the temperature fluctuation value of the target water body and a water body temperature state judgment table stored in a cloud database, so as to obtain a water temperature feedback level of the target water body, wherein the obtained temperature fluctuation value of each target water body corresponds to one water temperature feedback level, and the water temperature feedback level comprises a first-order water temperature feedback level, a second-order water temperature feedback level and a third-order water temperature feedback level;
performing comparison matching analysis on the water level fluctuation value of the target water body and a water level state judgment table stored in a cloud database, so as to obtain a water level feedback level of the target water body, wherein the obtained water level fluctuation value of each target water body corresponds to one water level feedback level, and the water level feedback level comprises a first-order water level feedback level, a second-order water level feedback level and a third-order water level feedback level;
comparing and matching the average water quantity of the target water body with a water quantity state judgment table stored in a cloud database, thereby obtaining a water quantity feedback grade of the target water body, wherein the obtained average water quantity of each target water body corresponds to one water quantity feedback grade, and the water quantity feedback grade comprises a first-order water quantity feedback grade, a second-order water quantity feedback grade and a third-order water quantity feedback grade;
simultaneously, the feedback level state of each item of data of the target water body is called, and if the first-order water temperature feedback level, the third-order water level feedback level and the third-order water quantity feedback level are captured at the same time, a flood warning signal is generated;
if the third-order water temperature feedback level, the first-order water level feedback level and the first-order water quantity feedback level are captured at the same time, generating a drought early warning signal;
and under other conditions, generating normal water body water quantity early warning signals.
2. The method for detecting the water quality and the water quantity of the high altitude based on the remote sensing hydrological data model as claimed in claim 1, wherein the determination analysis is carried out on the water body type of the unknown water body, and the specific analysis process is as follows:
201: receiving electromagnetic waves reflected or radiated by the earth surface by adopting a remote sensing technology, generating an electromagnetic spectrum, and dividing the electromagnetic spectrum in the electromagnetic spectrum into different wave bands according to the wavelength range;
202: the method comprises the steps of monitoring reflectivities in reflection characteristic information of different wavebands of an unknown water body in real time, taking time as an abscissa, taking reflectivities of wavebands at corresponding time points as an ordinate, establishing a waveband reflection coordinate system, and drawing to obtain reflectivity curves of the wavebands of the unknown water body;
203: selecting a characteristic shape wave band from a wave band reflection coordinate system, extracting the shape of a reflectivity curve of the characteristic shape wave band of the unknown water body, and comparing the shape with a reflectivity curve shape judgment table stored in a cloud database, thereby outputting the water body type of the unknown water body, wherein each shape of the reflectivity curve of the characteristic shape wave band has one water body type corresponding to the water body type;
204: selecting a characteristic peak wave band from a wave band reflection coordinate system, extracting the peak position of a reflectivity curve of the characteristic peak wave band of an unknown water body, and comparing the peak position with a reflectivity curve peak position judgment table stored in a cloud database, thereby outputting the water body type of the unknown water body, wherein each peak position of the reflectivity curve has a water body type corresponding to the water body type;
205: comparing the reflectivities of different wave bands of the unknown water body to obtain reflectivity ratio values among the wave bands of the unknown water body, and comparing the reflectivity ratio values with a reflectivity ratio judgment table stored in a cloud database to output water body types of the unknown water body, wherein each reflectivity ratio value has one water body type corresponding to the water body type;
206: the results output in steps 203, 204 and 205 are statistically analyzed, specifically: if the water types output in steps 203, 204 and 205 are the same water type, or the water types output in any two steps 203, 204 and 205 are the same water type, the water type is used as the final judgment result of the unknown water, so that the determined water type of the unknown water is obtained.
3. The method for detecting the water quality and the water quantity of the high altitude based on the remote sensing hydrological data model as claimed in claim 2, wherein the specific solving process of the reflectivity is as follows;
monitoring a remote sensing data record log, and calling the radiation energy and the incoming radiation energy of each wave band from the remote sensing data record log, and performing division calculation on the two data, wherein the method specifically comprises the following steps: reflectivity = radiant energy +.r radiant energy, whereby the reflectivity of the corresponding band is obtained.
4. The method for detecting the water quality and the water quantity of the high altitude based on the remote sensing hydrological data model as claimed in claim 1, wherein the health transformation evaluation analysis is carried out on the water quality grade of the target water body, and the specific analysis process is as follows:
according to the received quality grade of the quality of the water, judging the health state of the target water body as a quality water state, triggering the text word early warning without treatment and displaying the text word early warning through a display terminal;
judging the health state of the target water body to be a medium water state according to the received medium water quality grade, triggering the medium pollution of the water quality state of the target water body, carrying out treatment-required text word early warning, and carrying out display description through a display terminal;
according to the received poor water quality grade, the health state of the target water body is judged to be a poor water state, and serious pollution exists in the water quality state of the target water body, so that a text word early warning is needed to be treated, and display and explanation are carried out through a display terminal.
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Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH0938631A (en) * | 1995-07-26 | 1997-02-10 | Hitachi Ltd | Wide water basin monitor system applying remote sensing |
| CN102721644A (en) * | 2012-06-21 | 2012-10-10 | 中国科学院对地观测与数字地球科学中心 | Method and device for processing remote sensing data of water environment |
| CN103175513A (en) * | 2013-03-01 | 2013-06-26 | 戴会超 | System and method for monitoring hydrology and water quality of river basin under influence of water projects based on Internet of Things |
| CN107564257A (en) * | 2017-08-30 | 2018-01-09 | 商洛学院 | A kind of hydrography data acquisition display systems |
| CN112051222A (en) * | 2020-08-30 | 2020-12-08 | 山东锋士信息技术有限公司 | River and lake water quality monitoring method based on high-resolution satellite image |
| CN112557307A (en) * | 2020-12-09 | 2021-03-26 | 武汉新烽光电股份有限公司 | Space-air-ground integrated lake and reservoir water quality monitoring fusion data method |
| CN112734694A (en) * | 2020-12-19 | 2021-04-30 | 闫耀伟 | Water quality monitoring method based on big data |
| CN116399402A (en) * | 2023-04-18 | 2023-07-07 | 南京晓庄学院 | Fault early warning system of wireless sensor for ecological environment monitoring |
-
2023
- 2023-07-13 CN CN202310855512.5A patent/CN117035498B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH0938631A (en) * | 1995-07-26 | 1997-02-10 | Hitachi Ltd | Wide water basin monitor system applying remote sensing |
| CN102721644A (en) * | 2012-06-21 | 2012-10-10 | 中国科学院对地观测与数字地球科学中心 | Method and device for processing remote sensing data of water environment |
| CN103175513A (en) * | 2013-03-01 | 2013-06-26 | 戴会超 | System and method for monitoring hydrology and water quality of river basin under influence of water projects based on Internet of Things |
| CN107564257A (en) * | 2017-08-30 | 2018-01-09 | 商洛学院 | A kind of hydrography data acquisition display systems |
| CN112051222A (en) * | 2020-08-30 | 2020-12-08 | 山东锋士信息技术有限公司 | River and lake water quality monitoring method based on high-resolution satellite image |
| CN112557307A (en) * | 2020-12-09 | 2021-03-26 | 武汉新烽光电股份有限公司 | Space-air-ground integrated lake and reservoir water quality monitoring fusion data method |
| CN112734694A (en) * | 2020-12-19 | 2021-04-30 | 闫耀伟 | Water quality monitoring method based on big data |
| CN116399402A (en) * | 2023-04-18 | 2023-07-07 | 南京晓庄学院 | Fault early warning system of wireless sensor for ecological environment monitoring |
Non-Patent Citations (4)
| Title |
|---|
| 2008～2013年太湖蓝藻遥感监测成果分析;梁文广;王冬梅;诸晓华;谢义林;;人民长江;20141128(第S2期);全文 * |
| 应用MODIS监测太湖水体叶绿素a浓度季节变化研究;王世新;焦云清;周艺;祝令亚;阎福礼;;遥感信息;20080215(第01期);全文 * |
| 水质遥感监测方法的探讨;蔡丽娜;刘平波;智长贵;;测绘与空间地理信息;20080825(第04期);全文 * |
| 河流水情要素遥感研究进展;史卓琳 等;《地理科学进展》;20200430;全文 * |
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