CN116913047A - River basin agriculture non-point source pollutant tracing method and early warning system - Google Patents
River basin agriculture non-point source pollutant tracing method and early warning system Download PDFInfo
- Publication number
- CN116913047A CN116913047A CN202310872217.0A CN202310872217A CN116913047A CN 116913047 A CN116913047 A CN 116913047A CN 202310872217 A CN202310872217 A CN 202310872217A CN 116913047 A CN116913047 A CN 116913047A
- Authority
- CN
- China
- Prior art keywords
- value
- remote monitoring
- rainfall
- coefficient
- module
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 239000003344 environmental pollutant Substances 0.000 title claims description 24
- 231100000719 pollutant Toxicity 0.000 title claims description 24
- 238000000034 method Methods 0.000 title claims description 23
- 238000001514 detection method Methods 0.000 claims abstract description 122
- 238000012544 monitoring process Methods 0.000 claims abstract description 100
- 238000011156 evaluation Methods 0.000 claims abstract description 73
- 238000007405 data analysis Methods 0.000 claims abstract description 26
- 230000036284 oxygen consumption Effects 0.000 claims abstract description 17
- 238000012423 maintenance Methods 0.000 claims abstract description 16
- YUWBVKYVJWNVLE-UHFFFAOYSA-N [N].[P] Chemical compound [N].[P] YUWBVKYVJWNVLE-UHFFFAOYSA-N 0.000 claims abstract description 12
- 238000003911 water pollution Methods 0.000 claims description 30
- 239000002689 soil Substances 0.000 claims description 24
- 230000007246 mechanism Effects 0.000 claims description 22
- 239000010865 sewage Substances 0.000 claims description 21
- 230000008569 process Effects 0.000 claims description 6
- 238000011157 data evaluation Methods 0.000 abstract description 2
- 230000006872 improvement Effects 0.000 abstract description 2
- 238000011158 quantitative evaluation Methods 0.000 abstract description 2
- IJGRMHOSHXDMSA-UHFFFAOYSA-N Atomic nitrogen Chemical compound N#N IJGRMHOSHXDMSA-UHFFFAOYSA-N 0.000 description 10
- OAICVXFJPJFONN-UHFFFAOYSA-N Phosphorus Chemical compound [P] OAICVXFJPJFONN-UHFFFAOYSA-N 0.000 description 5
- 238000005516 engineering process Methods 0.000 description 5
- 229910052757 nitrogen Inorganic materials 0.000 description 5
- 229910052698 phosphorus Inorganic materials 0.000 description 5
- 239000011574 phosphorus Substances 0.000 description 5
- 239000013049 sediment Substances 0.000 description 3
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 3
- 239000000356 contaminant Substances 0.000 description 2
- 239000000463 material Substances 0.000 description 2
- 239000004576 sand Substances 0.000 description 2
- 238000004162 soil erosion Methods 0.000 description 2
- 238000007792 addition Methods 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000007716 flux method Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000003672 processing method Methods 0.000 description 1
- 238000012163 sequencing technique Methods 0.000 description 1
- 238000003900 soil pollution Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B21/00—Alarms responsive to a single specified undesired or abnormal condition and not otherwise provided for
- G08B21/02—Alarms for ensuring the safety of persons
- G08B21/12—Alarms for ensuring the safety of persons responsive to undesired emission of substances, e.g. pollution alarms
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01D—MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
- G01D21/00—Measuring or testing not otherwise provided for
- G01D21/02—Measuring two or more variables by means not covered by a single other subclass
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
- G01N33/24—Earth materials
-
- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B31/00—Predictive alarm systems characterised by extrapolation or other computation using updated historic data
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N2021/1765—Method using an image detector and processing of image signal
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N2021/1793—Remote sensing
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N2021/1793—Remote sensing
- G01N2021/1797—Remote sensing in landscape, e.g. crops
Abstract
The invention relates to the field of drainage basin maintenance supervision, in particular to a remote monitoring module which is used for acquiring remote monitoring parameters of a region needing drainage basin maintenance evaluation and sending the remote monitoring parameters to a data analysis module; the data analysis module is used for obtaining a remote monitoring coefficient according to the remote monitoring parameter and sending the remote monitoring coefficient to the river basin supervision platform; the traceability detection module is used for acquiring field detection parameters of the evaluation area, acquiring field detection coefficients according to the field detection parameters and sending the field detection coefficients to the river basin supervision platform; the on-site detection parameters comprise a nitrogen-phosphorus value, an oxygen consumption value and a chromaticity value; the invention realizes 'quasi-real-time' remote sensing monitoring 'fine' project management, improves the accuracy of quantitative evaluation through multiple data analysis and evaluation, and has the advantages of real-time performance, fine and accurate performance and efficiency improvement, so as to improve the efficiency and accuracy of basin maintenance work.
Description
Technical Field
The invention relates to the field of river basin maintenance supervision, in particular to a river basin agricultural non-point source pollutant tracing method and an early warning system.
Background
Chinese patent CN110487987B discloses a method for tracing and resolving FRN-CSSI combined tracing of river basin agriculture non-point source pollution, comprising the following steps: (1) sample collection of FRN tracing and CSS I tracing techniques; (2) sample analysis of FRN tracing and CSSI traceability techniques; (3) Sand conveying rates of different land utilization types/vegetation types in the river basin; (4) the contribution proportion of sediment sources of different vegetation types in the river basin; (5) analyzing sediment and pollutant sources;
in the prior art, the FRN and CSSI combined tracing technology is adopted, so that the land utilization source of river basin sediment pollutants and a load flux method of an input water system thereof can be quantified, and key control areas of soil erosion and pollution in the river basin can be quantitatively identified and divided according to soil erosion sand production rates and land utilization areas of different land utilization types; however, the technology cannot perform primary detection on remote sensing images, and performs further detection by tracing and tracking after detection results, so that the detection efficiency and accuracy are effectively improved.
Disclosure of Invention
In order to overcome the technical problems, the invention aims to provide a river basin agriculture non-point source pollutant tracing method and an early warning system: the method comprises the steps of obtaining remote monitoring parameters of an area needing to be subjected to drainage basin maintenance evaluation through a remote monitoring module, obtaining remote monitoring coefficients through a data analysis module according to the remote monitoring parameters, generating an early warning instruction through a drainage basin supervision platform according to the remote monitoring coefficients, sounding an early warning alarm after receiving the early warning instruction through a danger alarm module, obtaining the position of an evaluation area and a field detection mechanism, sending the position of the evaluation area to a tracing detection module of the field detection mechanism, obtaining the field detection parameters of the evaluation area through the tracing detection module, obtaining the field detection coefficients according to the field detection parameters, wherein the field detection parameters comprise nitrogen and phosphorus values, oxygen consumption values and chromaticity values, obtaining the danger coefficients through the drainage basin supervision platform according to the remote monitoring coefficients and the field detection coefficients, generating the danger alarm instruction according to the danger coefficients, and sounding the danger alarm after receiving the danger alarm instruction through the danger alarm module. .
The aim of the invention can be achieved by the following technical scheme:
a watershed agricultural non-point source contaminant pre-warning system, comprising:
the remote monitoring module is used for acquiring remote monitoring parameters of the area needing to be subjected to drainage basin maintenance evaluation and sending the remote monitoring parameters to the data analysis module; the remote monitoring parameters comprise a sewage ratio, a gradient value and a rainfall value; the specific process of the remote monitoring module for obtaining the remote monitoring parameters is as follows:
marking a region needing to be subjected to drainage basin maintenance evaluation as an evaluation region, and acquiring a remote sensing image of the evaluation region;
acquiring the total area of an evaluation area and the area of pollutants in the evaluation area according to the remote sensing image, marking the total area and the area of pollutants as a total surface value, acquiring the ratio of the total surface value and the total surface value, and marking the ratio as a pollution surface ratio;
acquiring the average gradient of the evaluation area according to the remote sensing image, and marking the average gradient as a gradient value;
acquiring rainfall conditions in historical data of an evaluation area, acquiring total rainfall, rainfall times and total rainfall duration in preset time, marking the total rainfall, the rainfall times and the total rainfall duration as a rainfall value, a rainfall value and a rainfall value in sequence, and analyzing the rainfall value, the rainfall value and the rainfall value to obtain a rainfall value;
the sewage ratio, the gradient value and the rainfall value are sent to a data analysis module;
the data analysis module is used for obtaining a remote monitoring coefficient according to the remote monitoring parameter and sending the remote monitoring coefficient to the river basin supervision platform;
the river basin supervision platform is used for generating an early warning instruction according to the remote monitoring coefficient and sending the early warning instruction to the danger alarm module; the system is also used for obtaining a danger coefficient according to the remote monitoring coefficient and the site detection coefficient, generating a danger alarm instruction according to the danger coefficient and sending the danger alarm instruction to the danger alarm module;
the dangerous alarm module is used for sounding an early warning alarm after receiving an early warning instruction, acquiring the position of the evaluation area and the on-site detection mechanism, and transmitting the position of the evaluation area to the traceability detection module of the on-site detection mechanism; the system is also used for sounding a dangerous alarm after receiving the dangerous alarm instruction;
the traceability detection module is used for acquiring field detection parameters of the evaluation area, acquiring field detection coefficients according to the field detection parameters and sending the field detection coefficients to the river basin supervision platform; the on-site detection parameters comprise a nitrogen-phosphorus value, an oxygen consumption value and a chromaticity value.
As a further scheme of the invention: the specific process of obtaining the remote monitoring coefficient by the data analysis module is as follows:
analyzing the sewage ratio, the gradient value and the rainfall value to obtain a remote monitoring coefficient;
and sending the remote monitoring coefficient to a river basin supervision platform.
As a further scheme of the invention: the specific process of acquiring the field detection parameters by the traceability detection module is as follows:
and randomly selecting a plurality of detection points in the evaluation area, acquiring the soil hardness, the soil humidity and the soil density of each acquisition point, marking the soil hardness, the soil humidity and the soil density as a nitrogen-phosphorus value, an oxygen consumption value and a chromaticity value in sequence, and analyzing the nitrogen-phosphorus value, the oxygen consumption value and the chromaticity value to obtain a water pollution value.
As a further scheme of the invention: and sequencing the water pollution values of all the detection points in order from large to small, and marking the water pollution value of the middle position as a field detection coefficient if the water pollution value of the middle position is only one.
As a further scheme of the invention: if the middle position is more than one water pollution value, marking the average value of all the water pollution values at the middle position as a field detection coefficient;
and sending the field detection coefficient to a river basin supervision platform.
A river basin agriculture non-point source pollutant tracing method comprises the following steps:
step T1: the remote monitoring module marks the area needing to be subjected to drainage basin maintenance evaluation as an evaluation area, and acquires a remote sensing image of the evaluation area;
step T2: the remote monitoring module obtains the total area of the evaluation area and the greening area in the evaluation area according to the remote sensing image, marks the total area as a total face value and a sewage face value, and obtains the ratio of the sewage face value to the total face value and marks the ratio as a sewage face ratio;
step T3: the remote monitoring module obtains the average gradient of the evaluation area according to the remote sensing image and marks the average gradient as a gradient value;
step T4: the remote monitoring module acquires rainfall conditions in historical data of an evaluation area, acquires total rainfall, rainfall times and total rainfall duration in preset time, marks the total rainfall, the rainfall times and the total rainfall duration as a rainfall value, a rainfall value and a rainfall value in sequence, and analyzes the rainfall value, the rainfall value and the rainfall value to obtain a rainfall value;
step T5: the remote monitoring module sends the sewage ratio, the gradient value and the rainfall value to the data analysis module;
step T6: the data analysis module analyzes the sewage ratio, the gradient value and the rainfall value to obtain a remote monitoring coefficient;
step T7: the data analysis module sends the remote monitoring coefficient to the river basin supervision platform;
step T8: the river basin supervision platform compares the remote monitoring coefficient with a preset remote monitoring threshold value: if the remote monitoring coefficient is larger than the remote monitoring threshold value, generating an early warning instruction and sending the early warning instruction to the danger alarm module;
step T9: the dangerous alarm module sounds an early warning alarm after receiving an early warning instruction, acquires the position of an evaluation area, acquires a drainage basin supervision detection mechanism with the minimum distance from the position of the evaluation area, marks the drainage basin supervision detection mechanism as a field detection mechanism, and sends the position of the evaluation area to a tracing detection module of the field detection mechanism;
step T10: the tracing detection module randomly selects a plurality of detection points in the evaluation area, acquires the soil hardness, the soil humidity and the soil density of each acquisition point, marks the soil hardness, the soil humidity and the soil density as a nitrogen-phosphorus value, an oxygen consumption value and a chromaticity value in sequence, and analyzes the nitrogen-phosphorus value, the oxygen consumption value and the chromaticity value to obtain a water pollution value;
step T11: the tracing detection module sorts the water pollution values of all detection points in sequence from large to small, if the middle position has only one water pollution value, the water pollution value of the middle position is marked as a field detection coefficient, and if the middle position has more than one water pollution value, the average value of all the water pollution values of the middle position is marked as the field detection coefficient;
step T12: the traceability detection module sends the field detection coefficient to the river basin supervision platform;
step T13: the river basin supervision platform obtains the product of the remote monitoring coefficient and the on-site detection coefficient and marks the product as a dangerous coefficient;
step T14: the river basin supervision platform compares the risk coefficient with a preset risk threshold value: if the risk coefficient is greater than the risk threshold value, a risk alarm instruction is generated and sent to the risk alarm module;
step T15: and the danger alarm module sounds a danger alarm after receiving the danger alarm instruction.
The invention has the beneficial effects that:
according to the river basin agricultural non-point source pollutant early warning system, remote monitoring parameters of a region needing to be subjected to river basin maintenance evaluation are obtained through a remote monitoring module, the remote monitoring parameters comprise a sewage surface ratio, a gradient value and a rainfall value, a remote monitoring coefficient is obtained through a data analysis module according to the remote monitoring parameters, an early warning instruction is generated through a river basin supervision platform according to the remote monitoring coefficient, an early warning alarm is sounded after the early warning instruction is received through a danger alarm module, the position of an evaluation region and a field detection mechanism are obtained, the position of the evaluation region is sent to a tracing detection module of the field detection mechanism, the field detection parameter of the evaluation region is obtained through the tracing detection module, the field detection coefficient is obtained according to the field detection parameter, the field detection parameter comprises a nitrogen and phosphorus value, an oxygen consumption value and a chromaticity value, the danger coefficient is obtained through the river basin supervision platform according to the remote monitoring coefficient and the field detection coefficient, and a danger alarm instruction is generated according to the danger coefficient, and the danger alarm is sounded after the danger alarm instruction is received through the danger alarm module; the river basin agricultural non-point source pollutant early warning system firstly acquires a remote sensing image by utilizing a satellite remote sensing technology, then performs data acquisition on an area needing to be subjected to river basin maintenance evaluation through the remote sensing image to acquire remote monitoring parameters, and the remote monitoring coefficient acquired according to the remote monitoring parameters is used for pre-evaluating the risk degree of the river basin loss of the area, wherein the larger the remote monitoring coefficient is, the higher the risk degree of the river basin loss is, then performs field detection on the area with early warning to acquire a field detection coefficient, and the risk coefficient acquired according to the remote monitoring coefficient and the field detection coefficient is used for final evaluation on the risk degree of the river basin loss of the area, and the larger the risk coefficient is, the higher the risk degree of the river basin loss is; the river basin agricultural non-point source pollutant early warning system organically combines a remote sensing technology method, a field detection method and a data comprehensive processing method, achieves 'quasi-real-time' remote sensing monitoring 'fine' project management, improves the accuracy of quantitative evaluation through multiple data analysis and evaluation, has the advantages of instantaneity, fine and accurate performance and efficiency improvement, improves the efficiency and the accuracy of river basin maintenance work, and enables a tracing detection module to track, check and verify the detection of the remote sensing technology and improve the accuracy and the efficiency of the detection of the river basin agricultural non-point source pollutant.
Drawings
The invention is further described below with reference to the accompanying drawings.
FIG. 1 is a schematic block diagram of a river basin agricultural non-point source contaminant pre-warning system of the present invention.
Detailed Description
The technical solutions of the embodiments of the present invention will be clearly and completely described below in conjunction with the embodiments of the present invention, and it is apparent that the described embodiments are only some embodiments of the present invention, 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.
Example 1:
referring to fig. 1, the embodiment is a river basin agriculture non-point source pollutant early warning system, which includes the following modules: the system comprises a remote monitoring module, a data analysis module, a river basin supervision platform, a danger alarm module and a tracing detection module;
the remote monitoring module is used for acquiring remote monitoring parameters of an area needing to be subjected to drainage basin agricultural non-point source maintenance evaluation and sending the remote monitoring parameters to the data analysis module; the remote monitoring parameters comprise a sewage ratio LM, a gradient value PD and a rainfall value JY;
the data analysis module is used for obtaining a remote monitoring coefficient YC according to the remote monitoring parameters and sending the remote monitoring coefficient YC to the river basin supervision platform;
the river basin supervision platform is used for generating an early warning instruction according to the remote monitoring coefficient YC and sending the early warning instruction to the danger alarm module; the system is also used for obtaining a danger coefficient WX according to the remote monitoring coefficient YC and the site detection coefficient XC, generating a danger alarm instruction according to the danger coefficient WX, and sending the danger alarm instruction to the danger alarm module;
the dangerous alarm module is used for sounding an early warning alarm after receiving an early warning instruction, acquiring the position of the evaluation area and the on-site detection mechanism, and sending the position of the evaluation area to the traceability detection module of the on-site detection mechanism; the system is also used for sounding a dangerous alarm after receiving the dangerous alarm instruction;
the traceability detection module is used for acquiring field detection parameters of the evaluation area, acquiring a field detection coefficient XC according to the field detection parameters, and sending the field detection coefficient XC to the river basin supervision platform; wherein, the on-site detection parameters comprise a nitrogen-phosphorus value RY, an oxygen consumption value RS and a chromaticity value RM.
Example 2:
referring to fig. 1, the embodiment is a method for tracing non-point source pollutants in river basin agriculture, comprising the following steps:
step T1: the remote monitoring module marks the area needing to be subjected to drainage basin maintenance evaluation as an evaluation area, and acquires a remote sensing image of the evaluation area;
step T2: the remote monitoring module obtains the total area of the evaluation area and the area of the pollutant in the evaluation area according to the remote sensing image, marks the total area as a total face value and a dirty face value, obtains the ratio between the dirty face value and the total face value, and marks the ratio as a dirty face ratio LM;
step T3: the remote monitoring module obtains the average gradient of the evaluation area according to the remote sensing image and marks the average gradient as a gradient value PD;
step T4: the method comprises the steps that a remote monitoring module obtains rainfall conditions in historical data of an evaluation area, obtains rainfall total quantity, rainfall times and rainfall total duration in preset time, marks the rainfall total quantity, the rainfall times and the rainfall total duration as a rainfall value YL, a rainfall value YC and a rainfall value YS in sequence, substitutes the rainfall value YL, the rainfall value YC and the rainfall value YS into a formula JY=j1XYL+j2XYC+j3 XYS to obtain a rainfall value JY, wherein j1, j2 and j3 are respectively preset proportionality coefficients of the rainfall value YL, the rainfall value YC and the rainfall value YS, and j 1+j2+j3=1, j1 is more than 0 and less than j2 and less than 1, j 1=0.30, j 2=0.32, and j 3=0.38;
step T5: the remote monitoring module sends the sewage ratio LM, the gradient value PD and the rainfall value JY to the data analysis module;
step T6: the data analysis module substitutes the sewage ratio LM, the gradient value PD and the rainfall value JY into a formula Obtaining a remote monitoring coefficient YC, wherein y1, y2 and y3 are preset weight coefficients of a sewage surface ratio LM, a gradient value PD and a rainfall value JY respectively, and y3 is more than y1 and more than y2 and more than 4.65, and y1=4.94, y2=4.78 and y3=5.20 are taken;
step T7: the data analysis module sends the remote monitoring coefficient YC to a river basin supervision platform;
step T8: the basin supervision platform compares the remote monitoring coefficient YC with a preset remote monitoring threshold YCy: if the remote monitoring coefficient YC is larger than the remote monitoring threshold YCy, generating an early warning instruction and sending the early warning instruction to a danger alarm module;
step T9: the dangerous alarm module sounds an early warning alarm after receiving an early warning instruction, acquires the position of an evaluation area, acquires a drainage basin supervision detection mechanism with the minimum distance from the position of the evaluation area, marks the drainage basin supervision detection mechanism as a field detection mechanism, and sends the position of the evaluation area to a tracing detection module of the field detection mechanism;
step T10: the traceability detection module randomly selects a plurality of detection points in the evaluation area, acquires the nitrogen and phosphorus value, the oxygen consumption value and the chromaticity value of the water area of each acquisition point, marks the water area as a nitrogen and phosphorus value RY, an oxygen consumption value RS and a chromaticity value RM in sequence, and substitutes the nitrogen and phosphorus value RY, the oxygen consumption value RS and the chromaticity value RM into a formula The water pollution value TR is obtained, wherein r1, r2 and r3 are respectively a rainfall value YL, a rainfall value YC and a preset proportionality coefficient of a rainfall value YS, and r1+r2+r3=1, 0 < r2 < r3 < r1 < 1, r1=0.45, r2=0.21 and r3=0.34;
step T11: the tracing detection module sorts the water pollution values TR of all detection points according to the sequence from large to small, if the middle position has only one water pollution value TR, the water pollution value TR of the middle position is marked as a field detection coefficient XC, and if the middle position has more than one water pollution value TR, the average value of all the water pollution values TR of the middle position is marked as the field detection coefficient XC;
step T12: the traceability detection module sends the field detection coefficient XC to the river basin supervision platform;
step T13: the river basin supervision platform obtains the product of the remote monitoring coefficient YC and the site detection coefficient XC, and marks the product as a dangerous coefficient WX;
step T14: the watershed supervision platform compares the risk coefficient WX with a preset risk threshold WXy: if the risk coefficient WX is larger than the risk threshold WXy, a risk alarm instruction is generated and sent to the risk alarm module;
step T15: and the danger alarm module sounds a danger alarm after receiving the danger alarm instruction.
In the description of the present specification, the descriptions of the terms "one embodiment," "example," "specific example," and the like, mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The foregoing is merely illustrative and explanatory of the invention, as various modifications and additions may be made to the particular embodiments described, or in a similar manner, by those skilled in the art, without departing from the scope of the invention or exceeding the scope of the invention as defined in the claims.
Claims (6)
1. The river basin agriculture non-point source pollutant early warning system is characterized by comprising:
the remote monitoring module is used for acquiring remote monitoring parameters of the area needing to be subjected to drainage basin maintenance evaluation and sending the remote monitoring parameters to the data analysis module; the remote monitoring parameters comprise a sewage ratio, a gradient value and a rainfall value; the specific process of the remote monitoring module for obtaining the remote monitoring parameters is as follows:
marking a region needing to be subjected to drainage basin maintenance evaluation as an evaluation region, and acquiring a remote sensing image of the evaluation region;
acquiring the total area of an evaluation area and the area of pollutants in the evaluation area according to the remote sensing image, marking the total area and the area of pollutants as a total surface value, acquiring the ratio of the total surface value and the total surface value, and marking the ratio as a pollution surface ratio;
acquiring the average gradient of the evaluation area according to the remote sensing image, and marking the average gradient as a gradient value;
acquiring rainfall conditions in historical data of an evaluation area, acquiring total rainfall, rainfall times and total rainfall duration in preset time, marking the total rainfall, the rainfall times and the total rainfall duration as a rainfall value, a rainfall value and a rainfall value in sequence, and analyzing the rainfall value, the rainfall value and the rainfall value to obtain a rainfall value;
the sewage ratio, the gradient value and the rainfall value are sent to a data analysis module;
the data analysis module is used for obtaining a remote monitoring coefficient according to the remote monitoring parameter and sending the remote monitoring coefficient to the river basin supervision platform;
the river basin supervision platform is used for generating an early warning instruction according to the remote monitoring coefficient and sending the early warning instruction to the danger alarm module; the system is also used for obtaining a danger coefficient according to the remote monitoring coefficient and the site detection coefficient, generating a danger alarm instruction according to the danger coefficient and sending the danger alarm instruction to the danger alarm module;
the dangerous alarm module is used for sounding an early warning alarm after receiving an early warning instruction, acquiring the position of the evaluation area and the on-site detection mechanism, and transmitting the position of the evaluation area to the traceability detection module of the on-site detection mechanism; the system is also used for sounding a dangerous alarm after receiving the dangerous alarm instruction;
the traceability detection module is used for acquiring field detection parameters of the evaluation area, acquiring field detection coefficients according to the field detection parameters and sending the field detection coefficients to the river basin supervision platform; the on-site detection parameters comprise a nitrogen-phosphorus value, an oxygen consumption value and a chromaticity value.
2. The watershed agricultural non-point source pollutant tracing method and the early warning system according to claim 1, wherein the specific process of obtaining the remote monitoring coefficient by the data analysis module is as follows:
analyzing the sewage ratio, the gradient value and the rainfall value to obtain a remote monitoring coefficient;
and sending the remote monitoring coefficient to a river basin supervision platform.
3. The method and the system for tracing the river basin agricultural non-point source pollutants according to claim 1, wherein the specific process of acquiring the on-site detection parameters by the tracing detection module is as follows:
and randomly selecting a plurality of detection points in the evaluation area, acquiring the soil hardness, the soil humidity and the soil density of each acquisition point, marking the soil hardness, the soil humidity and the soil density as a nitrogen-phosphorus value, an oxygen consumption value and a chromaticity value in sequence, and analyzing the nitrogen-phosphorus value, the oxygen consumption value and the chromaticity value to obtain a water pollution value.
4. The watershed agricultural non-point source pollutant tracing method and the early warning system according to claim 1, wherein the water pollution values of all detection points are ranked in order from large to small, and if only one water pollution value exists in the middle position, the water pollution value in the middle position is marked as a field detection coefficient.
5. The watershed agricultural non-point source pollutant tracing method and the early warning system according to claim 1, wherein if the middle position is more than one water pollution value, the average value of all the water pollution values at the middle position is marked as a field detection coefficient;
and sending the field detection coefficient to a river basin supervision platform.
6. The river basin agriculture non-point source pollutant tracing method is characterized by comprising the following steps of:
step T1: the remote monitoring module marks the area needing to be subjected to drainage basin maintenance evaluation as an evaluation area, and acquires a remote sensing image of the evaluation area;
step T2: the remote monitoring module obtains the total area of the evaluation area and the greening area in the evaluation area according to the remote sensing image, marks the total area as a total face value and a sewage face value, and obtains the ratio of the sewage face value to the total face value and marks the ratio as a sewage face ratio;
step T3: the remote monitoring module obtains the average gradient of the evaluation area according to the remote sensing image and marks the average gradient as a gradient value;
step T4: the remote monitoring module acquires rainfall conditions in historical data of an evaluation area, acquires total rainfall, rainfall times and total rainfall duration in preset time, marks the total rainfall, the rainfall times and the total rainfall duration as a rainfall value, a rainfall value and a rainfall value in sequence, and analyzes the rainfall value, the rainfall value and the rainfall value to obtain a rainfall value;
step T5: the remote monitoring module sends the sewage ratio, the gradient value and the rainfall value to the data analysis module;
step T6: the data analysis module analyzes the sewage ratio, the gradient value and the rainfall value to obtain a remote monitoring coefficient;
step T7: the data analysis module sends the remote monitoring coefficient to the river basin supervision platform;
step T8: the river basin supervision platform compares the remote monitoring coefficient with a preset remote monitoring threshold value: if the remote monitoring coefficient is larger than the remote monitoring threshold value, generating an early warning instruction and sending the early warning instruction to the danger alarm module;
step T9: the dangerous alarm module sounds an early warning alarm after receiving an early warning instruction, acquires the position of an evaluation area, acquires a drainage basin supervision detection mechanism with the minimum distance from the position of the evaluation area, marks the drainage basin supervision detection mechanism as a field detection mechanism, and sends the position of the evaluation area to a tracing detection module of the field detection mechanism;
step T10: the tracing detection module randomly selects a plurality of detection points in the evaluation area, acquires the soil hardness, the soil humidity and the soil density of each acquisition point, marks the soil hardness, the soil humidity and the soil density as a nitrogen-phosphorus value, an oxygen consumption value and a chromaticity value in sequence, and analyzes the nitrogen-phosphorus value, the oxygen consumption value and the chromaticity value to obtain a water pollution value;
step T11: the tracing detection module sorts the water pollution values of all detection points in sequence from large to small, if the middle position has only one water pollution value, the water pollution value of the middle position is marked as a field detection coefficient, and if the middle position has more than one water pollution value, the average value of all the water pollution values of the middle position is marked as the field detection coefficient;
step T12: the traceability detection module sends the field detection coefficient to the river basin supervision platform;
step T13: the river basin supervision platform obtains the product of the remote monitoring coefficient and the on-site detection coefficient and marks the product as a dangerous coefficient;
step T14: the river basin supervision platform compares the risk coefficient with a preset risk threshold value: if the risk coefficient is greater than the risk threshold value, a risk alarm instruction is generated and sent to the risk alarm module;
step T15: and the danger alarm module sounds a danger alarm after receiving the danger alarm instruction.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310872217.0A CN116913047A (en) | 2023-07-17 | 2023-07-17 | River basin agriculture non-point source pollutant tracing method and early warning system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310872217.0A CN116913047A (en) | 2023-07-17 | 2023-07-17 | River basin agriculture non-point source pollutant tracing method and early warning system |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116913047A true CN116913047A (en) | 2023-10-20 |
Family
ID=88364252
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310872217.0A Pending CN116913047A (en) | 2023-07-17 | 2023-07-17 | River basin agriculture non-point source pollutant tracing method and early warning system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116913047A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117411918A (en) * | 2023-12-11 | 2024-01-16 | 深圳前海翼联科技有限公司 | Monitoring alarm method and system based on IOT (Internet of things) |
-
2023
- 2023-07-17 CN CN202310872217.0A patent/CN116913047A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117411918A (en) * | 2023-12-11 | 2024-01-16 | 深圳前海翼联科技有限公司 | Monitoring alarm method and system based on IOT (Internet of things) |
| CN117411918B (en) * | 2023-12-11 | 2024-04-02 | 深圳前海翼联科技有限公司 | Monitoring alarm method and system based on IOT (Internet of things) |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Liu et al. | Assessing how spatial variations of land use pattern affect water quality across a typical urbanized watershed in Beijing, China | |
| Liu et al. | Insights into the long-term pollution trends and sources contributions in Lake Taihu, China using multi-statistic analyses models | |
| Cheng et al. | What is the relationship between land use and surface water quality? A review and prospects from remote sensing perspective | |
| CN107609742B (en) | Water environment early warning integrated management platform | |
| Ogwueleka | Use of multivariate statistical techniques for the evaluation of temporal and spatial variations in water quality of the Kaduna River, Nigeria | |
| Parajuli et al. | Comparison of AnnAGNPS and SWAT model simulation results in USDA‐CEAP agricultural watersheds in south‐central Kansas | |
| CN111461167A (en) | Pollution source positioning method, device, equipment and storage medium based on big data | |
| CN110376343B (en) | Method and device for accurately diagnosing pollution source and electronic device | |
| Angello et al. | Spatio-temporal evaluation and quantification of pollutant source contribution in Little Akaki River, Ethiopia: conjunctive application of Factor Analysis and multivariate receptor model | |
| Chen et al. | Artificial neural network modelling of concentrations of nitrogen, phosphorus and dissolved oxygen in a non‐point source polluted river in Zhejiang Province, southeast China | |
| CN116913047A (en) | River basin agriculture non-point source pollutant tracing method and early warning system | |
| CN114943917B (en) | Algorithm for visually identifying aeration quantity of aerobic tank of sewage plant | |
| Ogbozige et al. | Water quality assessment and mapping using inverse distance weighted interpolation: A case of River Kaduna, Nigeria | |
| Virro et al. | Random forest-based modeling of stream nutrients at national level in a data-scarce region | |
| Saraswati et al. | Water quality index performance for river pollution control based on better ecological point of view (a case study in Code, Winongo, Gadjah Wong streams) | |
| CN114663764A (en) | Method, device, medium and terminal equipment for zoning soil environment quality of cultivated land | |
| Yu et al. | Using machine learning to reveal spatiotemporal complexity and driving forces of water quality changes in Hong Kong marine water | |
| Behrouz et al. | What are the relevant sources and factors affecting event mean concentrations (EMCs) of nutrients and sediment in stormwater? | |
| CN116166669A (en) | Water pollution tracing method, device, equipment and storage medium | |
| CN114611626A (en) | Water environment information processing method, device, equipment and storage medium | |
| Celen et al. | Contribution for pollution sources and their assessment in urban and industrial sites of Ergene River Basin, Turkey | |
| Wu et al. | Simulation of nitrogen and phosphorus loads in the Dongjiang River basin in South China using SWAT | |
| CN116483807B (en) | Construction method of soil pollutant environment and toxicity database | |
| Cau et al. | Assessment of alternative land management practices using hydrological simulation and a decision support tool: Arborea agricultural region, Sardinia | |
| CN116430000A (en) | River ecological environment monitoring system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |