CN118209605B - Arsenic intelligent monitoring method and system based on electrochemical sensor - Google Patents
Arsenic intelligent monitoring method and system based on electrochemical sensor Download PDFInfo
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- 229910052785 arsenic Inorganic materials 0.000 title claims abstract description 581
- RQNWIZPPADIBDY-UHFFFAOYSA-N arsenic atom Chemical compound [As] RQNWIZPPADIBDY-UHFFFAOYSA-N 0.000 title claims abstract description 580
- 238000012544 monitoring process Methods 0.000 title claims abstract description 231
- 238000000034 method Methods 0.000 title claims abstract description 52
- 239000013049 sediment Substances 0.000 claims abstract description 91
- 238000004458 analytical method Methods 0.000 claims abstract description 81
- 238000012937 correction Methods 0.000 claims abstract description 28
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims abstract description 18
- 230000008569 process Effects 0.000 claims abstract description 15
- 238000010586 diagram Methods 0.000 claims description 71
- 238000001514 detection method Methods 0.000 claims description 53
- 238000013508 migration Methods 0.000 claims description 34
- 230000005012 migration Effects 0.000 claims description 34
- 230000000153 supplemental effect Effects 0.000 claims description 20
- 238000012545 processing Methods 0.000 claims description 17
- 238000012549 training Methods 0.000 claims description 13
- 239000002245 particle Substances 0.000 claims description 11
- 230000002596 correlated effect Effects 0.000 claims description 5
- 239000003550 marker Substances 0.000 claims description 5
- 238000012216 screening Methods 0.000 claims description 5
- 239000013589 supplement Substances 0.000 claims description 5
- 238000012806 monitoring device Methods 0.000 claims description 3
- 230000000694 effects Effects 0.000 abstract description 8
- 238000012360 testing method Methods 0.000 description 7
- 230000000875 corresponding effect Effects 0.000 description 6
- 238000012986 modification Methods 0.000 description 4
- 230000004048 modification Effects 0.000 description 4
- 238000012795 verification Methods 0.000 description 4
- 230000008485 antagonism Effects 0.000 description 3
- 238000010801 machine learning Methods 0.000 description 3
- 230000000295 complement effect Effects 0.000 description 2
- -1 arsenic ions Chemical class 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 238000012512 characterization method Methods 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 230000036541 health Effects 0.000 description 1
- 238000003062 neural network model Methods 0.000 description 1
- 230000000750 progressive effect Effects 0.000 description 1
- 230000004044 response Effects 0.000 description 1
- 230000035945 sensitivity Effects 0.000 description 1
- 239000002689 soil Substances 0.000 description 1
- 238000001179 sorption measurement Methods 0.000 description 1
- 231100000701 toxic element Toxicity 0.000 description 1
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Abstract
The application provides an arsenic intelligent monitoring method and system based on an electrochemical sensor, and relates to the technical field of water quality monitoring, wherein the method comprises the following steps: acquiring hydrologic characteristic information and sediment characteristic information of a plurality of positions in a target area; obtaining a first arsenic enrichment profile; obtaining a second arsenic enrichment profile; fitting to obtain an arsenic enrichment distribution map based on the first arsenic enrichment distribution map and the second arsenic enrichment distribution map, and setting a plurality of marking points; setting and obtaining a plurality of basic monitoring precision information, correcting the plurality of basic monitoring precision information according to fitting deviation degree of a plurality of mark points in the fitting process, obtaining and detecting the precision information according to the plurality of corrections, and monitoring arsenic content at the plurality of mark points through an arsenic monitoring module. The application can solve the technical problem of poor monitoring accuracy caused by lack of arsenic enrichment distribution analysis and monitoring accuracy analysis on monitoring points in the prior art, and achieves the technical effects of improving the arsenic content monitoring efficiency and monitoring accuracy.
Description
Technical Field
The application relates to the technical field of water quality monitoring, in particular to an arsenic intelligent monitoring method and system based on an electrochemical sensor.
Background
Arsenic is a toxic element widely existing in nature, and pollution in water body forms a serious threat to human health and ecosystem. The electrochemical sensor has the advantages of quick response, low cost, simple operation and the like, and has wide application prospect in arsenic content monitoring.
The existing monitoring method is to set monitoring points according to experience to monitor arsenic content, but due to the fact that water in water flows and sediment in water areas are affected, arsenic content at different positions is different, arsenic enrichment distribution analysis and monitoring precision analysis of the monitoring points are lacking in the prior art, and therefore the fact that the monitoring points are set inaccurately and the maximum arsenic content in the water areas is difficult to monitor accurately is caused, accuracy of monitoring results is affected, and reference to water area environment management is low.
In summary, in the prior art, because of the lack of arsenic enrichment distribution analysis and monitoring accuracy analysis on the monitoring points, the technical problem of poor monitoring accuracy exists.
Disclosure of Invention
The application aims to provide an arsenic intelligent monitoring method and system based on an electrochemical sensor, which are used for solving the technical problem of poor monitoring accuracy caused by lack of arsenic enrichment distribution analysis and monitoring accuracy analysis of monitoring points in the prior art.
In view of the above problems, the application provides an arsenic intelligent monitoring method and system based on an electrochemical sensor.
In a first aspect, the present application provides an electrochemical sensor-based arsenic intelligent monitoring method, which is implemented by an electrochemical sensor-based arsenic intelligent monitoring system, wherein the method comprises: acquiring hydrologic characteristic information and sediment characteristic information of a plurality of positions in a target area through a hydrologic monitoring module, wherein the target area is a water flow area to be subjected to arsenic monitoring, the hydrologic characteristic information comprises flow speed, flow rate and flow direction, and the sediment characteristic information comprises sediment particle size information and sediment scale information; according to the multiple hydrologic characteristic information, carrying out arsenic migration enrichment distribution analysis to obtain first arsenic enrichment distribution information, and carrying out arsenic enrichment supplement analysis treatment on the target area to obtain a first arsenic enrichment distribution map; carrying out arsenic migration enrichment distribution analysis according to the characteristic information of the multiple sediments to obtain second arsenic enrichment distribution information, and processing to obtain a second arsenic enrichment distribution map; fitting to obtain an arsenic enrichment distribution diagram based on the first arsenic enrichment distribution diagram and the second arsenic enrichment distribution diagram, and setting a plurality of mark points in the arsenic enrichment distribution diagram as position points to be subjected to arsenic monitoring analysis; and setting and obtaining a plurality of basic monitoring precision information according to the arsenic enrichment information of the plurality of mark points, correcting the plurality of basic monitoring precision information according to the fitting deviation degree of the plurality of mark points in the fitting process, obtaining and detecting the precision information according to the plurality of corrections, and monitoring the arsenic content at the plurality of mark points through an arsenic monitoring module.
In a second aspect, the present application also provides an electrochemical sensor-based arsenic intelligent monitoring system for performing the electrochemical sensor-based arsenic intelligent monitoring method according to the first aspect, wherein the system comprises: the hydrologic monitoring module is used for collecting hydrologic characteristic information and sediment characteristic information of a plurality of positions in a target area through the hydrologic monitoring module, wherein the target area is a water flow area to be monitored by arsenic, the hydrologic characteristic information comprises flow speed, flow rate and flow direction, and the sediment characteristic information comprises sediment particle size information and sediment scale information; the first arsenic migration enrichment distribution analysis module is used for carrying out arsenic migration enrichment distribution analysis according to the plurality of hydrologic characteristic information to obtain first arsenic enrichment distribution information, and carrying out arsenic enrichment supplementary analysis treatment on the target area to obtain a first arsenic enrichment distribution map; the second arsenic migration enrichment distribution analysis module is used for carrying out arsenic migration enrichment distribution analysis according to the characteristic information of the multiple sediments to obtain second arsenic enrichment distribution information, and processing to obtain a second arsenic enrichment distribution map; the arsenic enrichment distribution map fitting module is used for fitting to obtain an arsenic enrichment distribution map based on the first arsenic enrichment distribution map and the second arsenic enrichment distribution map, and a plurality of mark points are arranged in the arsenic enrichment distribution map and used as position points for arsenic monitoring analysis; and the arsenic content monitoring module is used for setting and obtaining a plurality of basic monitoring precision information according to the arsenic enrichment information of the plurality of mark points, correcting the plurality of basic monitoring precision information according to fitting deviation degree of the plurality of mark points in the fitting process, obtaining and detecting the precision information according to the plurality of corrections, and carrying out arsenic content monitoring on the plurality of mark points through the arsenic monitoring module.
One or more technical schemes provided by the application have at least the following technical effects or advantages:
Acquiring hydrologic characteristic information and sediment characteristic information of a plurality of positions in a target area through a hydrologic monitoring module, wherein the target area is a water flow area to be subjected to arsenic monitoring, the hydrologic characteristic information comprises flow speed, flow rate and flow direction, and the sediment characteristic information comprises sediment particle size information and sediment scale information; according to the multiple hydrologic characteristic information, carrying out arsenic migration enrichment distribution analysis to obtain first arsenic enrichment distribution information, and carrying out arsenic enrichment supplement analysis treatment on the target area to obtain a first arsenic enrichment distribution map; carrying out arsenic migration enrichment distribution analysis according to the characteristic information of the multiple sediments to obtain second arsenic enrichment distribution information, and processing to obtain a second arsenic enrichment distribution map; fitting to obtain an arsenic enrichment distribution diagram based on the first arsenic enrichment distribution diagram and the second arsenic enrichment distribution diagram, and setting a plurality of mark points in the arsenic enrichment distribution diagram as position points to be subjected to arsenic monitoring analysis; and setting and obtaining a plurality of basic monitoring precision information according to the arsenic enrichment information of the plurality of mark points, correcting the plurality of basic monitoring precision information according to the fitting deviation degree of the plurality of mark points in the fitting process, obtaining and detecting the precision information according to the plurality of corrections, and monitoring the arsenic content at the plurality of mark points through an arsenic monitoring module. And respectively carrying out arsenic migration enrichment distribution analysis by analyzing hydrologic characteristic information and sediment characteristic information to obtain a first arsenic enrichment distribution diagram and a second arsenic enrichment distribution diagram, fitting the first arsenic enrichment distribution diagram and the second arsenic enrichment distribution diagram to obtain a plurality of mark points with the maximum arsenic enrichment information, carrying out monitoring precision analysis on the plurality of mark points, and carrying out arsenic content monitoring on the plurality of mark points so as to achieve the technical effect of improving the arsenic content monitoring efficiency and monitoring accuracy.
The foregoing description is only an overview of the present application, and is intended to be implemented in accordance with the teachings of the present application in order that the same may be more clearly understood and to make the same and other objects, features and advantages of the present application more readily apparent. It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the application or to delineate the scope of the application. Other features of the present application will become apparent from the description that follows.
Drawings
In order to more clearly illustrate the application or the technical solutions of the prior art, the following brief description will be given of the drawings used in the description of the embodiments or the prior art, it being obvious that the drawings in the description below are only exemplary and that other drawings can be obtained from the drawings provided without the inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of an intelligent arsenic monitoring method based on an electrochemical sensor according to the application;
FIG. 2 is a schematic diagram of the structure of the electrochemical sensor-based arsenic intelligent monitoring system of the present application.
Reference numerals illustrate: the system comprises a hydrologic monitoring module 11, a first arsenic migration enrichment distribution analysis module 12, a second arsenic migration enrichment distribution analysis module 13, an arsenic enrichment distribution map fitting module 14 and an arsenic content monitoring module 15.
Detailed Description
The application provides the arsenic intelligent monitoring method and the arsenic intelligent monitoring system based on the electrochemical sensor, which solve the technical problem of poor monitoring accuracy caused by lack of arsenic enrichment distribution analysis and monitoring accuracy analysis of monitoring points in the prior art. And respectively carrying out arsenic migration enrichment distribution analysis by analyzing hydrologic characteristic information and sediment characteristic information to obtain a first arsenic enrichment distribution diagram and a second arsenic enrichment distribution diagram, fitting the first arsenic enrichment distribution diagram and the second arsenic enrichment distribution diagram to obtain a plurality of mark points with the maximum arsenic enrichment information, carrying out monitoring precision analysis on the plurality of mark points, and carrying out arsenic content monitoring on the plurality of mark points so as to achieve the technical effect of improving the arsenic content monitoring efficiency and monitoring accuracy.
In the following, the technical solutions of the present application will be clearly and completely described with reference to the accompanying drawings, and it should be understood that the described embodiments are only some embodiments of the present application, but not all embodiments of the present application, and that the present application is not limited by the exemplary embodiments described herein. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application. It should be further noted that, for convenience of description, only some, but not all of the drawings related to the present application are shown.
Example 1
Referring to fig. 1, the application provides an arsenic intelligent monitoring method based on an electrochemical sensor, the method is applied to an arsenic intelligent monitoring device based on the electrochemical sensor, the device comprises a hydrologic monitoring module and an arsenic monitoring module, the arsenic monitoring module is arranged based on the electrochemical sensor, and the method specifically comprises the following steps:
Step one: acquiring hydrologic characteristic information and sediment characteristic information of a plurality of positions in a target area through a hydrologic monitoring module, wherein the target area is a water flow area to be subjected to arsenic monitoring, the hydrologic characteristic information comprises flow speed, flow rate and flow direction, and the sediment characteristic information comprises sediment particle size information and sediment scale information;
Specifically, the application provides an arsenic intelligent monitoring method based on an electrochemical sensor, which is applied to an arsenic intelligent monitoring device based on the electrochemical sensor, wherein the device comprises a hydrological monitoring module and an arsenic monitoring module. The hydrologic monitoring module is specially used for collecting hydrologic characteristic information and sediment characteristic information in the target area. The hydrologic characteristic information mainly comprises flow velocity, flow rate and flow direction, so that the hydrologic monitoring module is provided with existing equipment such as a flow velocity meter, a flow meter and a flow direction meter. The hydrologic monitoring module needs to acquire sediment characteristic information, so the hydrologic monitoring module is also provided with existing equipment such as sediment samplers, particle size analyzers and the like. The arsenic monitoring module is arranged based on an electrochemical sensor, and the electrochemical sensor converts arsenic ions into measurable electric signals through specific chemical reactions, so that real-time monitoring of arsenic content is realized.
The hydrologic monitoring module is arranged at a plurality of positions in the target area, and for example, if the target area is a reservoir, the hydrologic monitoring module can be arranged at an outlet and an inlet of the reservoir and an area possibly at risk of arsenic pollution, and is particularly arranged by a person skilled in the art in combination with practical experience. And then starting a hydrologic monitoring module to acquire hydrologic characteristic information and sediment characteristic information of a plurality of positions in the target area, wherein the hydrologic characteristic information comprises flow velocity, flow rate and flow direction, and the sediment characteristic information comprises sediment particle size information and sediment scale information. The flow speed, flow rate and flow direction of water flow can influence the arsenic distribution, for example, arsenic can diffuse to other areas along with the water flow direction, so that the arsenic distribution analysis can be carried out by collecting hydrologic characteristic information; where the deposit layer is thicker or the particles are finer, arsenic adsorption may be stronger, so these areas may be potential areas of higher arsenic content, and thus arsenic profile analysis may also be performed by collecting deposit characterization information.
Step two: according to the multiple hydrologic characteristic information, carrying out arsenic migration enrichment distribution analysis to obtain first arsenic enrichment distribution information, and carrying out arsenic enrichment supplement analysis treatment on the target area to obtain a first arsenic enrichment distribution map;
Specifically, according to a plurality of pieces of hydrologic characteristic information of a plurality of positions, the distribution of arsenic in the plurality of positions is predicted by a pre-trained hydrologic arsenic enrichment distribution predictor, and first arsenic enrichment distribution information is obtained. And then carrying out arsenic enrichment supplemental analysis processing on the target area based on the generation of the antagonism network, namely, the first arsenic enrichment distribution information obtained at the moment is not accurate enough, so that the arsenic enrichment supplemental analysis processing is carried out on the first arsenic enrichment distribution information based on the generation of the antagonism network training arsenic enrichment supplemental processing channel based on the arsenic monitoring data record in the history, and the first arsenic enrichment distribution map is obtained.
Step three: carrying out arsenic migration enrichment distribution analysis according to the characteristic information of the multiple sediments to obtain second arsenic enrichment distribution information, and processing to obtain a second arsenic enrichment distribution map;
specifically, according to the characteristic information of a plurality of sediments, the distribution of arsenic at a plurality of positions is predicted by a pre-trained sediment arsenic enrichment distribution predictor, second arsenic enrichment distribution information is obtained, and arsenic enrichment complementary analysis processing of the target area is also carried out on the second arsenic enrichment distribution information, so that a second arsenic enrichment distribution map is obtained.
Step four: fitting to obtain an arsenic enrichment distribution diagram based on the first arsenic enrichment distribution diagram and the second arsenic enrichment distribution diagram, and setting a plurality of mark points in the arsenic enrichment distribution diagram as position points to be subjected to arsenic monitoring analysis;
Specifically, based on the first arsenic enrichment distribution diagram and the second arsenic enrichment distribution diagram, first arsenic enrichment information and second arsenic enrichment information corresponding to a plurality of positions respectively can be obtained, and the first arsenic enrichment information and the second arsenic enrichment information of the plurality of positions are weighted and calculated to obtain arsenic enrichment information corresponding to the plurality of positions respectively, so that the arsenic enrichment distribution diagram is generated. Screening a plurality of position points with the maximum arsenic enrichment information in the arsenic enrichment distribution map for marking, for example, marking by using a unique color or symbol, generating a plurality of marking points, and taking the plurality of marking points as the position points to be subjected to arsenic monitoring analysis. Arsenic content monitoring is then specifically performed for a plurality of marker points.
Step five: and setting and obtaining a plurality of basic monitoring precision information according to the arsenic enrichment information of the plurality of mark points, correcting the plurality of basic monitoring precision information according to the fitting deviation degree of the plurality of mark points in the fitting process, obtaining and detecting the precision information according to the plurality of corrections, and monitoring the arsenic content at the plurality of mark points through an arsenic monitoring module.
Specifically, the detection limit information and the detection frequency information are the lowest value at which the presence of arsenic can be detected and quantitatively analyzed at a plurality of mark points, and the monitoring sensitivity to arsenic is measured. The detection frequency information refers to the number of times of detection in a certain time period, such as several times per second, for arsenic monitoring. The arsenic enrichment information of a plurality of mark points can be analyzed and decided through a pre-trained basic detection precision decision maker, so that a plurality of basic monitoring precision information can be obtained. And further calculating and weighting the deviation degree between the first arsenic enrichment information and the second arsenic enrichment information of the plurality of mark points and the arsenic enrichment information respectively in the fitting process to be used as the fitting deviation degree of the plurality of mark points. Ideally, the first arsenic enrichment information and the second arsenic enrichment information obtained by analyzing the hydrologic characteristics and the sediment characteristics should be consistent, and the smaller the fitting deviation degree is, the closer the actually obtained first arsenic enrichment information and second arsenic enrichment information is, the higher the detection accuracy is, and the lower the detection accuracy is otherwise. The fitting deviation degree can be configured with a correction coefficient, and then the correction is carried out on the plurality of basic monitoring precision information by utilizing the reciprocal of the correction coefficient, namely the detection frequency is amplified by the reciprocal of the correction coefficient, the detection limit is reduced, and a plurality of corrected detection precision information after correction is obtained.
And further carrying out arsenic content monitoring on a plurality of mark points through an electrochemical sensor in the arsenic monitoring module according to the plurality of correction detection precision information to obtain arsenic content monitoring results of the plurality of mark points, thereby achieving the technical effect of improving the monitoring efficiency and accuracy.
Further, the second step of the present application further comprises:
acquiring regional characteristic information of the target region, and indexing to acquire a plurality of homologous regions in historical arsenic monitoring data; acquiring a plurality of sample hydrologic characteristic information sets based on arsenic monitoring data records of the plurality of homologous regions, and acquiring a first arsenic enrichment distribution information set of the samples; constructing a hydrologic arsenic enrichment distribution predictor by adopting the plurality of sample hydrologic characteristic information sets and the sample first arsenic enrichment distribution information set; and predicting the plurality of hydrologic characteristic information based on the hydrologic arsenic enrichment distribution predictor to obtain the obtained first arsenic enrichment distribution information.
Further, the application also comprises the following steps:
Acquiring a sample arsenic monitoring result distribution diagram set according to the arsenic monitoring data records of the plurality of homologous regions, and processing to acquire a sample first arsenic enrichment distribution diagram set according to arsenic content distribution in each sample arsenic monitoring result distribution diagram; constructing a generator and a discriminator based on the generation of the countermeasure network; inputting the first arsenic enrichment distribution information set of the sample into the generator by combining with random noise to generate an arsenic enrichment distribution map, inputting the arsenic enrichment distribution map into the discriminator, and performing discrimination training by combining with the first arsenic enrichment distribution map of the sample until convergence to obtain an arsenic enrichment supplemental treatment channel; and carrying out arsenic enrichment supplemental analysis treatment on the first arsenic enrichment distribution information based on the arsenic enrichment supplemental treatment channel to obtain the first arsenic enrichment distribution map.
Specifically, according to a plurality of hydrologic characteristic information, arsenic migration enrichment distribution analysis is performed, and the process of obtaining first arsenic enrichment distribution information is as follows:
The regional characteristic information comprises various natural characteristics which can influence the arsenic distribution, such as geological structure, soil type, climate condition and the like of the target region, and the natural characteristics need to be combined with actual determination. The historical arsenic monitoring data comprises an arsenic monitoring data record of a plurality of areas with different area characteristics, wherein the arsenic monitoring data record comprises historical hydrologic characteristics and historical sediment characteristics of the areas, and historical first arsenic enrichment distribution information and historical second arsenic enrichment distribution information which are respectively obtained based on the historical hydrologic characteristics and the historical sediment characteristics.
Based on the regional characteristic information, the same region as the regional characteristic information is obtained in the historical arsenic monitoring data index as a plurality of homogeneous regions. And extracting arsenic monitoring data records of the same-family areas, constructing a plurality of sample hydrologic characteristic information sets of the same-family areas by using the historical hydrologic characteristics and the historical first arsenic enrichment distribution information, and acquiring a first arsenic enrichment distribution information set of the samples. And taking the plurality of sample hydrologic characteristic information sets as input, taking the sample first arsenic enrichment distribution information set as output, and constructing a hydrologic arsenic enrichment distribution predictor based on the existing machine learning model. The sample hydrologic characteristic information set comprises hydrologic characteristic information of a plurality of positions in a same family region, namely, the sample hydrologic characteristic information set corresponds to the sample first arsenic enrichment distribution information in the sample first arsenic enrichment distribution information set. The multiple sample hydrologic characteristic information sets and the sample first arsenic enrichment distribution information sets can be divided into training sets, verification sets and test sets, the sample hydrologic characteristic information sets are input into the hydrologic arsenic enrichment distribution predictor, output supervision adjustment is carried out on the sample first arsenic enrichment distribution information sets, iterative training verification is carried out on the sample first arsenic enrichment distribution information sets, network parameters of the hydrologic arsenic enrichment distribution predictor are optimized, output tests are carried out, and the hydrologic arsenic enrichment distribution predictor with accuracy meeting the requirements is obtained.
Inputting the plurality of hydrologic characteristic information into the hydrologic arsenic enrichment distribution predictor for prediction, and outputting the obtained first arsenic enrichment distribution information, wherein the first arsenic enrichment distribution information comprises arsenic contents of a plurality of positions.
Further, carrying out arsenic enrichment supplemental analysis treatment on the target area to obtain a first arsenic enrichment distribution map, wherein the method comprises the following specific steps:
Acquiring a sample arsenic monitoring result distribution diagram set according to the arsenic monitoring data records of the plurality of homologous regions, and processing to acquire a sample first arsenic enrichment distribution diagram set according to arsenic content distribution in each sample arsenic monitoring result distribution diagram;
Arsenic content monitoring data of a plurality of positions in a same family area are contained in the arsenic monitoring data record, and are converted into distribution diagrams based on the prior art, and a sample arsenic monitoring result distribution diagram set is obtained. And integrating the sample arsenic monitoring result distribution graphs at a plurality of positions into a distribution graph according to the arsenic content distribution in each sample arsenic monitoring result distribution graph to obtain a first arsenic enrichment distribution graph of a plurality of samples.
Based on the generation countermeasure network, a generator and a discriminator are constructed, and the generator can learn the characteristics of the arsenic enrichment distribution map and generate a high-quality arsenic enrichment distribution map; and the discriminator is responsible for judging the proximity degree of the generated arsenic enrichment distribution map and the real sample, so as to guide the generator to continuously optimize. Specifically, in the training process, a first arsenic enrichment distribution information set of a sample is input into a generator together with random noise to generate an arsenic enrichment distribution map. And then inputting the generated arsenic enrichment distribution map into a discriminator, comparing the arsenic enrichment distribution map with the first arsenic enrichment distribution map of the sample, judging whether the generated arsenic enrichment distribution map is consistent with the first arsenic enrichment distribution map of the sample, and continuously iterating and optimizing network parameters until the generated arsenic enrichment distribution map is consistent with the first arsenic enrichment distribution map of the sample, so that antagonism network training is generated until convergence, and an arsenic enrichment supplemental treatment channel is obtained. And finally, carrying out arsenic enrichment supplemental analysis treatment on the first arsenic enrichment distribution information by utilizing an arsenic enrichment supplemental treatment channel, and outputting the first arsenic enrichment distribution map. Therefore, arsenic enrichment distribution analysis based on hydrologic characteristics is realized, support is provided for subsequent arsenic content monitoring, and position points with higher arsenic content are conveniently screened out for monitoring, so that monitoring efficiency is improved.
Further, the third step of the present application further comprises:
Acquiring a plurality of sample sediment characteristic information sets based on arsenic monitoring data records of the plurality of homologous regions, and acquiring a sample second arsenic enrichment distribution information set; adopting the plurality of sample sediment characteristic information sets and the sample second arsenic enrichment distribution information set to construct a sediment arsenic enrichment distribution predictor; and predicting the plurality of sediment characteristic information based on the sediment arsenic enrichment distribution predictor to obtain the obtained second arsenic enrichment distribution information.
Specifically, according to the characteristic information of a plurality of sediments, arsenic migration enrichment distribution analysis is performed, and the step of obtaining second arsenic enrichment distribution information is as follows:
And extracting arsenic monitoring data records of the same-family regions, constructing a plurality of sample sediment characteristic information sets of the same-family regions by using the historical sediment characteristics and the historical second arsenic enrichment distribution information, and acquiring a sample second arsenic enrichment distribution information set. And taking the characteristic information sets of the plurality of sample sediments as input, taking the second arsenic enrichment distribution information set of the sample as output, and constructing a sediment arsenic enrichment distribution predictor based on the existing machine learning model. One sample sediment characteristic information set comprises sediment characteristic information of a plurality of positions in a same family area, namely, one sample sediment characteristic information set corresponds to one sample second arsenic enrichment distribution information in a sample second arsenic enrichment distribution information set. The multiple sample sediment characteristic information sets and the sample second arsenic enrichment distribution information sets can be divided into training sets, verification sets and test sets, the sample sediment characteristic information sets are input into sediment arsenic enrichment distribution predictors, output supervision adjustment is carried out on the sample second arsenic enrichment distribution information sets, iterative training verification is carried out on the sample sediment characteristic information sets, network parameters of the sediment arsenic enrichment distribution predictors are optimized, output tests are carried out, and sediment arsenic enrichment distribution predictors with accuracy meeting requirements are obtained.
Inputting the sediment characteristic information into the sediment arsenic enrichment distribution predictor for prediction, and outputting the obtained second arsenic enrichment distribution information, wherein the second arsenic enrichment distribution information comprises arsenic contents of a plurality of positions obtained based on sediment characteristic analysis.
Further, by adopting the same method as the method for acquiring the first arsenic enrichment distribution map, a sample arsenic monitoring result distribution map set obtained based on sediment characteristics is acquired according to arsenic monitoring data records of the plurality of homologous regions, and a sample second arsenic enrichment distribution map set is acquired through processing according to arsenic content distribution in each sample arsenic monitoring result distribution map. Based on generating the countermeasure network, a generator and a arbiter are constructed. And generating an arsenic enrichment distribution map by adopting the sample second arsenic enrichment distribution information set and combining a random noise input generator, inputting the arsenic enrichment distribution map into a discriminator, and performing discrimination training by combining the sample second arsenic enrichment distribution map until convergence to obtain a second arsenic enrichment supplemental treatment channel. And carrying out arsenic enrichment supplemental analysis treatment on the second arsenic enrichment distribution information based on a second arsenic enrichment supplemental treatment channel to obtain the second arsenic enrichment distribution map. Support is provided for subsequent arsenic content monitoring, and position points with higher arsenic content are conveniently screened out for monitoring, so that monitoring efficiency is improved.
Further, the fourth step of the present application further comprises:
Acquiring first arsenic enrichment information and second arsenic enrichment information of all position points in the target area according to the first arsenic enrichment distribution diagram and the second arsenic enrichment distribution diagram; according to the first arsenic enrichment information and the second arsenic enrichment information of all the position points, calculating and fitting to obtain the arsenic enrichment information of all the position points, and generating an arsenic enrichment distribution map; and screening a plurality of position points with the maximum arsenic enrichment information in the arsenic enrichment distribution map as the plurality of marking points.
Specifically, the step of setting a plurality of marker points in the arsenic enrichment profile as location points to be subjected to arsenic monitoring analysis includes:
The first arsenic enrichment distribution map comprises arsenic enrichment information of a plurality of positions obtained based on hydrologic characteristic information, and the second arsenic enrichment distribution map comprises arsenic enrichment information of a plurality of positions obtained based on sediment information, namely, the same position has corresponding arsenic enrichment information in the first arsenic enrichment distribution map and the second arsenic enrichment distribution map. Based on the first arsenic enrichment information and the second arsenic enrichment information of all the position points in the target area are extracted from the first arsenic enrichment distribution map and the second arsenic enrichment distribution map. And carrying out weighted average on the first arsenic enrichment information and the second arsenic enrichment information of all the position points to calculate the result as the arsenic enrichment information of all the position points, and integrating to generate an arsenic enrichment distribution map. And screening a plurality of position points with the maximum arsenic enrichment information in the arsenic enrichment distribution diagram as the plurality of mark points, namely, the arsenic enrichment information of the plurality of mark points is the maximum, and the corresponding arsenic content is the maximum probability in actual detection, so that the arsenic content monitoring result can be obtained only by carrying out arsenic content monitoring on the plurality of mark points in the follow-up process, the monitoring efficiency is improved, the phenomenon that the arsenic content monitoring result is lower due to improper monitoring positions is avoided, and the monitoring accuracy is improved.
Further, the fifth step of the present application further comprises:
Acquiring a sample arsenic enrichment information set, and acquiring a sample detection limit information set and a sample detection frequency set; combining the sample detection limit information set and the sample detection frequency set to obtain a sample basic monitoring precision information set; and constructing a basic detection precision decision-making device by adopting the sample detection limit information set and the sample basic monitoring precision information set, and inputting decisions to the arsenic enrichment information of the plurality of mark points to obtain the plurality of basic monitoring precision information.
Further, the fifth step of the present application further comprises:
Respectively calculating to obtain a first arsenic enrichment deviation set and a second arsenic enrichment deviation set according to the arsenic enrichment information of all the position points in the arsenic enrichment distribution diagram and the arsenic enrichment information of all the position points in the first arsenic enrichment distribution diagram and the second arsenic enrichment distribution diagram; calculating to obtain a plurality of fitting deviation degrees according to the first arsenic enrichment deviation set and the second arsenic enrichment deviation set; and distributing a plurality of correction coefficients according to the fitting deviation degrees, and correcting the plurality of basic monitoring precision information to obtain a plurality of pieces of corrected detection precision information, wherein the magnitude of the fitting deviation degrees and the magnitude of the correction coefficients are positively correlated.
Specifically, according to the arsenic enrichment information of the plurality of mark points, the steps of obtaining a plurality of basic monitoring precision information are set as follows:
A sample arsenic enrichment information set may be obtained based on the arsenic monitoring data record and configured with a suitable sample detection limit information set and sample detection frequency set by one skilled in the art in combination with actual experience. And combining the sample detection limit information and the sample detection frequency corresponding to the same sample arsenic enrichment information in the sample detection limit information set and the sample detection frequency set to obtain a sample basic monitoring precision information set. Further, the sample detection limit information set and the sample basic monitoring precision information set are adopted to construct a basic detection precision decision device, that is, the basic detection precision decision device is constructed based on the existing machine learning model, such as a neural network model, the sample detection limit information set and the sample basic monitoring precision information set can be divided into a training set and a testing set, the sample detection limit information set is adopted as input, the sample basic monitoring precision information set is adopted as output, all data in the training set are trained, and model output accuracy testing is carried out through data in the testing set, so that the basic detection precision decision device with accuracy meeting requirements is obtained. Inputting the arsenic enrichment information of the plurality of mark points into a basic detection precision decision-making device for decision analysis, and outputting the plurality of basic monitoring precision information.
Further, according to the fitting deviation degree of a plurality of mark points in the fitting process, the plurality of basic monitoring precision information is corrected, and the method comprises the following steps: according to the arsenic enrichment information of all the position points in the arsenic enrichment distribution diagram and the arsenic enrichment information of all the position points in the first arsenic enrichment distribution diagram and the second arsenic enrichment distribution diagram, a first arsenic enrichment deviation set and a second arsenic enrichment deviation set are obtained, in short, a plurality of first arsenic enrichment information and a plurality of second arsenic enrichment information belonging to a plurality of mark points are extracted from the first arsenic enrichment distribution diagram and the second arsenic enrichment distribution diagram, and deviation between the plurality of first arsenic enrichment information and the arsenic enrichment information of a plurality of mark points is calculated respectively, so that the first arsenic enrichment deviation set is obtained. And respectively calculating deviations between the plurality of second arsenic enrichment information and the arsenic enrichment information of the plurality of mark points to obtain the arsenic enrichment deviation set.
And calculating the deviation degree of the first arsenic enrichment deviation and the second arsenic enrichment deviation of the same mark point in the first arsenic enrichment deviation set and the second arsenic enrichment deviation set, dividing the difference value of the first arsenic enrichment deviation and the second arsenic enrichment deviation by the first arsenic enrichment deviation, and obtaining a plurality of fitting deviation degrees based on calculation results. The smaller the fitting deviation degree is, the higher the monitoring precision of the corresponding marked point is, and the lower the monitoring precision is on the contrary. And acquiring a plurality of correction coefficients based on the fitting deviation degrees, wherein the magnitude of the fitting deviation degrees and the magnitude of the correction coefficients are positively correlated. Specifically, a plurality of fitting deviation intervals can be divided by a person skilled in the art, and a plurality of correction coefficients are configured for the fitting deviation intervals according to the principle that the magnitude of the fitting deviation is positively correlated with the magnitude of the correction coefficient.
And further, the plurality of basic monitoring precision information is corrected, the correction coefficient is used for amplifying the detection frequency in the plurality of pieces of corrected detection precision information and reducing the detection limit, and by way of example, the detection limit can be multiplied by the plurality of correction coefficients, and the detection frequency can be multiplied by the reciprocal of the plurality of correction coefficients, so that the plurality of pieces of basic monitoring precision information are corrected, and the plurality of pieces of corrected detection precision information are obtained. And carrying out arsenic content monitoring at a plurality of mark points through an arsenic monitoring module according to the corrected detection precision information, so as to achieve the technical effect of improving the monitoring accuracy.
In summary, the electrochemical sensor-based arsenic intelligent monitoring method provided by the application has the following technical effects:
Acquiring hydrologic characteristic information and sediment characteristic information of a plurality of positions in a target area through a hydrologic monitoring module, wherein the target area is a water flow area to be subjected to arsenic monitoring, the hydrologic characteristic information comprises flow speed, flow rate and flow direction, and the sediment characteristic information comprises sediment particle size information and sediment scale information; according to the multiple hydrologic characteristic information, carrying out arsenic migration enrichment distribution analysis to obtain first arsenic enrichment distribution information, and carrying out arsenic enrichment supplement analysis treatment on the target area to obtain a first arsenic enrichment distribution map; carrying out arsenic migration enrichment distribution analysis according to the characteristic information of the multiple sediments to obtain second arsenic enrichment distribution information, and processing to obtain a second arsenic enrichment distribution map; fitting to obtain an arsenic enrichment distribution diagram based on the first arsenic enrichment distribution diagram and the second arsenic enrichment distribution diagram, and setting a plurality of mark points in the arsenic enrichment distribution diagram as position points to be subjected to arsenic monitoring analysis; and setting and obtaining a plurality of basic monitoring precision information according to the arsenic enrichment information of the plurality of mark points, correcting the plurality of basic monitoring precision information according to the fitting deviation degree of the plurality of mark points in the fitting process, obtaining and detecting the precision information according to the plurality of corrections, and monitoring the arsenic content at the plurality of mark points through an arsenic monitoring module. And respectively carrying out arsenic migration enrichment distribution analysis by analyzing hydrologic characteristic information and sediment characteristic information to obtain a first arsenic enrichment distribution diagram and a second arsenic enrichment distribution diagram, fitting the first arsenic enrichment distribution diagram and the second arsenic enrichment distribution diagram to obtain a plurality of mark points with the maximum arsenic enrichment information, carrying out monitoring precision analysis on the plurality of mark points, and carrying out arsenic content monitoring on the plurality of mark points so as to achieve the technical effect of improving the arsenic content monitoring efficiency and monitoring accuracy.
Example 2
Based on the same inventive concept as the electrochemical sensor-based arsenic intelligent monitoring method in the foregoing embodiment, the present application further provides an electrochemical sensor-based arsenic intelligent monitoring system, referring to fig. 2, the system includes:
The hydrologic monitoring module 11 is configured to collect hydrologic characteristic information and sediment characteristic information of a plurality of positions in a target area through the hydrologic monitoring module, where the target area is a water flow area to be monitored by arsenic, the hydrologic characteristic information includes a flow velocity, a flow rate and a flow direction, and the sediment characteristic information includes sediment particle size information and sediment scale information;
The first arsenic migration enrichment distribution analysis module 12 is configured to perform arsenic migration enrichment distribution analysis according to the plurality of hydrologic feature information to obtain first arsenic enrichment distribution information, and perform arsenic enrichment complementary analysis processing on the target area to obtain a first arsenic enrichment distribution map;
the second arsenic migration enrichment distribution analysis module 13 is configured to perform arsenic migration enrichment distribution analysis according to the feature information of the multiple sediments, obtain second arsenic enrichment distribution information, and process to obtain a second arsenic enrichment distribution map;
The arsenic enrichment distribution map fitting module 14 is configured to obtain an arsenic enrichment distribution map by fitting based on the first arsenic enrichment distribution map and the second arsenic enrichment distribution map, and set a plurality of mark points in the arsenic enrichment distribution map as location points to be subjected to arsenic monitoring analysis;
the arsenic content monitoring module 15 is configured to obtain a plurality of basic monitoring precision information according to the arsenic enrichment information of the plurality of mark points, correct the plurality of basic monitoring precision information according to fitting deviation degrees of the plurality of mark points in the fitting process, obtain and detect the precision information according to the plurality of corrections, and monitor the arsenic content at the plurality of mark points through the arsenic monitoring module.
Further, the first arsenic migration enrichment distribution analysis module 12 in the system is further configured to:
acquiring regional characteristic information of the target region, and indexing to acquire a plurality of homologous regions in historical arsenic monitoring data;
Acquiring a plurality of sample hydrologic characteristic information sets based on arsenic monitoring data records of the plurality of homologous regions, and acquiring a first arsenic enrichment distribution information set of the samples;
Constructing a hydrologic arsenic enrichment distribution predictor by adopting the plurality of sample hydrologic characteristic information sets and the sample first arsenic enrichment distribution information set;
And predicting the plurality of hydrologic characteristic information based on the hydrologic arsenic enrichment distribution predictor to obtain the obtained first arsenic enrichment distribution information.
Further, the first arsenic migration enrichment distribution analysis module 12 in the system is further configured to:
Acquiring a sample arsenic monitoring result distribution diagram set according to the arsenic monitoring data records of the plurality of homologous regions, and processing to acquire a sample first arsenic enrichment distribution diagram set according to arsenic content distribution in each sample arsenic monitoring result distribution diagram;
Constructing a generator and a discriminator based on the generation of the countermeasure network;
Inputting the first arsenic enrichment distribution information set of the sample into the generator by combining with random noise to generate an arsenic enrichment distribution map, inputting the arsenic enrichment distribution map into the discriminator, and performing discrimination training by combining with the first arsenic enrichment distribution map of the sample until convergence to obtain an arsenic enrichment supplemental treatment channel;
and carrying out arsenic enrichment supplemental analysis treatment on the first arsenic enrichment distribution information based on the arsenic enrichment supplemental treatment channel to obtain the first arsenic enrichment distribution map.
Further, the second arsenic migration enrichment distribution analysis module 13 in the system is further configured to:
Acquiring a plurality of sample sediment characteristic information sets based on arsenic monitoring data records of the plurality of homologous regions, and acquiring a sample second arsenic enrichment distribution information set;
Adopting the plurality of sample sediment characteristic information sets and the sample second arsenic enrichment distribution information set to construct a sediment arsenic enrichment distribution predictor;
and predicting the plurality of sediment characteristic information based on the sediment arsenic enrichment distribution predictor to obtain the obtained second arsenic enrichment distribution information.
Further, the arsenic enrichment profile fitting module 14 in the system is also configured to:
Acquiring first arsenic enrichment information and second arsenic enrichment information of all position points in the target area according to the first arsenic enrichment distribution diagram and the second arsenic enrichment distribution diagram;
According to the first arsenic enrichment information and the second arsenic enrichment information of all the position points, calculating and fitting to obtain the arsenic enrichment information of all the position points, and generating an arsenic enrichment distribution map;
And screening a plurality of position points with the maximum arsenic enrichment information in the arsenic enrichment distribution map as the plurality of marking points.
Further, the arsenic content monitoring module 15 in the system is also configured to:
acquiring a sample arsenic enrichment information set, and acquiring a sample detection limit information set and a sample detection frequency set;
combining the sample detection limit information set and the sample detection frequency set to obtain a sample basic monitoring precision information set;
And constructing a basic detection precision decision-making device by adopting the sample detection limit information set and the sample basic monitoring precision information set, and inputting decisions to the arsenic enrichment information of the plurality of mark points to obtain the plurality of basic monitoring precision information.
Further, the arsenic content monitoring module 15 in the system is also configured to:
Respectively calculating to obtain a first arsenic enrichment deviation set and a second arsenic enrichment deviation set according to the arsenic enrichment information of all the position points in the arsenic enrichment distribution diagram and the arsenic enrichment information of all the position points in the first arsenic enrichment distribution diagram and the second arsenic enrichment distribution diagram;
calculating to obtain a plurality of fitting deviation degrees according to the first arsenic enrichment deviation set and the second arsenic enrichment deviation set;
and distributing a plurality of correction coefficients according to the fitting deviation degrees, and correcting the plurality of basic monitoring precision information to obtain a plurality of pieces of corrected detection precision information, wherein the magnitude of the fitting deviation degrees and the magnitude of the correction coefficients are positively correlated.
The embodiments in this specification are described in a progressive manner, and each embodiment focuses on the difference from the other embodiments, and the foregoing electrochemical sensor-based arsenic intelligent monitoring method and specific example in the first embodiment of fig. 1 are equally applicable to the electrochemical sensor-based arsenic intelligent monitoring system in this embodiment, and by the foregoing detailed description of the electrochemical sensor-based arsenic intelligent monitoring method, those skilled in the art can clearly know the electrochemical sensor-based arsenic intelligent monitoring system in this embodiment, so that the detailed description is omitted herein for brevity of the specification. For the system disclosed in the embodiment, since the system corresponds to the method disclosed in the embodiment, the description is simpler, and the relevant points refer to the description of the method section.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present application without departing from the spirit or scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the present application and the equivalent techniques thereof, the present application is also intended to include such modifications and variations.
Claims (8)
1. The method is applied to an electrochemical sensor-based arsenic intelligent monitoring device, the device comprises a hydrologic monitoring module and an arsenic monitoring module, the arsenic monitoring module is arranged based on the electrochemical sensor, and the method comprises the following steps:
Acquiring hydrologic characteristic information and sediment characteristic information of a plurality of positions in a target area through a hydrologic monitoring module, wherein the target area is a water flow area to be subjected to arsenic monitoring, the hydrologic characteristic information comprises flow speed, flow rate and flow direction, and the sediment characteristic information comprises sediment particle size information and sediment scale information;
According to the multiple hydrologic characteristic information, carrying out arsenic migration enrichment distribution analysis to obtain first arsenic enrichment distribution information, and carrying out arsenic enrichment supplement analysis treatment on the target area to obtain a first arsenic enrichment distribution map;
Carrying out arsenic migration enrichment distribution analysis according to the characteristic information of the multiple sediments to obtain second arsenic enrichment distribution information, and processing to obtain a second arsenic enrichment distribution map;
fitting to obtain an arsenic enrichment distribution diagram based on the first arsenic enrichment distribution diagram and the second arsenic enrichment distribution diagram, and setting a plurality of mark points in the arsenic enrichment distribution diagram as position points to be subjected to arsenic monitoring analysis;
And setting and obtaining a plurality of basic monitoring precision information according to the arsenic enrichment information of the plurality of mark points, correcting the plurality of basic monitoring precision information according to the fitting deviation degree of the plurality of mark points in the fitting process, obtaining and detecting the precision information according to the plurality of corrections, and monitoring the arsenic content at the plurality of mark points through an arsenic monitoring module.
2. The method of claim 1, wherein performing an arsenic migration enrichment distribution analysis based on the plurality of hydrologic characteristic information to obtain first arsenic enrichment distribution information comprises:
acquiring regional characteristic information of the target region, and indexing to acquire a plurality of homologous regions in historical arsenic monitoring data;
Acquiring a plurality of sample hydrologic characteristic information sets based on arsenic monitoring data records of the plurality of homologous regions, and acquiring a first arsenic enrichment distribution information set of the samples;
Constructing a hydrologic arsenic enrichment distribution predictor by adopting the plurality of sample hydrologic characteristic information sets and the sample first arsenic enrichment distribution information set;
And predicting the plurality of hydrologic characteristic information based on the hydrologic arsenic enrichment distribution predictor to obtain the obtained first arsenic enrichment distribution information.
3. The method of claim 2, wherein performing the arsenic enrichment supplemental analysis treatment of the target area to obtain a first arsenic enrichment profile comprises:
Acquiring a sample arsenic monitoring result distribution diagram set according to the arsenic monitoring data records of the plurality of homologous regions, and processing to acquire a sample first arsenic enrichment distribution diagram set according to arsenic content distribution in each sample arsenic monitoring result distribution diagram;
Constructing a generator and a discriminator based on the generation of the countermeasure network;
Inputting the first arsenic enrichment distribution information set of the sample into the generator by combining with random noise to generate an arsenic enrichment distribution map, inputting the arsenic enrichment distribution map into the discriminator, and performing discrimination training by combining with the first arsenic enrichment distribution map of the sample until convergence to obtain an arsenic enrichment supplemental treatment channel;
and carrying out arsenic enrichment supplemental analysis treatment on the first arsenic enrichment distribution information based on the arsenic enrichment supplemental treatment channel to obtain the first arsenic enrichment distribution map.
4. The method of claim 2, wherein performing the arsenic migration enrichment distribution analysis based on the plurality of deposit characteristic information to obtain second arsenic enrichment distribution information comprises:
Acquiring a plurality of sample sediment characteristic information sets based on arsenic monitoring data records of the plurality of homologous regions, and acquiring a sample second arsenic enrichment distribution information set;
Adopting the plurality of sample sediment characteristic information sets and the sample second arsenic enrichment distribution information set to construct a sediment arsenic enrichment distribution predictor;
and predicting the plurality of sediment characteristic information based on the sediment arsenic enrichment distribution predictor to obtain the obtained second arsenic enrichment distribution information.
5. The method of claim 1, wherein obtaining an arsenic enrichment profile based on the first and second arsenic enrichment profiles by fitting, disposing a plurality of marker points within the arsenic enrichment profile as location points to be subjected to arsenic monitoring analysis, comprises:
Acquiring first arsenic enrichment information and second arsenic enrichment information of all position points in the target area according to the first arsenic enrichment distribution diagram and the second arsenic enrichment distribution diagram;
According to the first arsenic enrichment information and the second arsenic enrichment information of all the position points, calculating and fitting to obtain the arsenic enrichment information of all the position points, and generating an arsenic enrichment distribution map;
And screening a plurality of position points with the maximum arsenic enrichment information in the arsenic enrichment distribution map as the plurality of marking points.
6. The method of claim 1, wherein setting up to obtain a plurality of base monitoring accuracy information based on the arsenic enrichment information of the plurality of marker points comprises:
acquiring a sample arsenic enrichment information set, and acquiring a sample detection limit information set and a sample detection frequency set;
combining the sample detection limit information set and the sample detection frequency set to obtain a sample basic monitoring precision information set;
And constructing a basic detection precision decision-making device by adopting the sample detection limit information set and the sample basic monitoring precision information set, and inputting decisions to the arsenic enrichment information of the plurality of mark points to obtain the plurality of basic monitoring precision information.
7. The method of claim 5, wherein correcting the plurality of base monitoring accuracy information based on fitting deviation of a plurality of marker points during fitting comprises:
Respectively calculating to obtain a first arsenic enrichment deviation set and a second arsenic enrichment deviation set according to the arsenic enrichment information of all the position points in the arsenic enrichment distribution diagram and the arsenic enrichment information of all the position points in the first arsenic enrichment distribution diagram and the second arsenic enrichment distribution diagram;
calculating to obtain a plurality of fitting deviation degrees according to the first arsenic enrichment deviation set and the second arsenic enrichment deviation set;
and distributing a plurality of correction coefficients according to the fitting deviation degrees, and correcting the plurality of basic monitoring precision information to obtain a plurality of pieces of corrected detection precision information, wherein the magnitude of the fitting deviation degrees and the magnitude of the correction coefficients are positively correlated.
8. An electrochemical sensor-based arsenic intelligent monitoring system, characterized by the steps for implementing the method according to any one of claims 1 to 7, said system comprising:
The hydrologic monitoring module is used for collecting hydrologic characteristic information and sediment characteristic information of a plurality of positions in a target area through the hydrologic monitoring module, wherein the target area is a water flow area to be monitored by arsenic, the hydrologic characteristic information comprises flow speed, flow rate and flow direction, and the sediment characteristic information comprises sediment particle size information and sediment scale information;
The first arsenic migration enrichment distribution analysis module is used for carrying out arsenic migration enrichment distribution analysis according to the plurality of hydrologic characteristic information to obtain first arsenic enrichment distribution information, and carrying out arsenic enrichment supplementary analysis treatment on the target area to obtain a first arsenic enrichment distribution map;
The second arsenic migration enrichment distribution analysis module is used for carrying out arsenic migration enrichment distribution analysis according to the characteristic information of the multiple sediments to obtain second arsenic enrichment distribution information, and processing to obtain a second arsenic enrichment distribution map;
The arsenic enrichment distribution map fitting module is used for fitting to obtain an arsenic enrichment distribution map based on the first arsenic enrichment distribution map and the second arsenic enrichment distribution map, and a plurality of mark points are arranged in the arsenic enrichment distribution map and used as position points for arsenic monitoring analysis;
And the arsenic content monitoring module is used for setting and obtaining a plurality of basic monitoring precision information according to the arsenic enrichment information of the plurality of mark points, correcting the plurality of basic monitoring precision information according to fitting deviation degree of the plurality of mark points in the fitting process, obtaining and detecting the precision information according to the plurality of corrections, and carrying out arsenic content monitoring on the plurality of mark points through the arsenic monitoring module.
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