CN116227744A - Method and system for predicting thallium pollution in watershed water environment - Google Patents
Method and system for predicting thallium pollution in watershed water environment Download PDFInfo
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- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 title claims abstract description 34
- 238000000034 method Methods 0.000 title claims abstract description 25
- 238000001514 detection method Methods 0.000 claims abstract description 73
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- 238000012544 monitoring process Methods 0.000 claims abstract description 34
- 238000011109 contamination Methods 0.000 claims description 47
- 238000004458 analytical method Methods 0.000 claims description 12
- 238000012216 screening Methods 0.000 claims description 12
- 238000003860 storage Methods 0.000 claims description 9
- 238000004519 manufacturing process Methods 0.000 claims description 6
- 238000005457 optimization Methods 0.000 claims description 6
- 238000012986 modification Methods 0.000 description 5
- 230000004048 modification Effects 0.000 description 5
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- 238000009826 distribution Methods 0.000 description 3
- 238000005259 measurement Methods 0.000 description 3
- 238000005065 mining Methods 0.000 description 3
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- 239000003344 environmental pollutant Substances 0.000 description 2
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- 231100000719 pollutant Toxicity 0.000 description 2
- 241001465754 Metazoa Species 0.000 description 1
- 238000003723 Smelting Methods 0.000 description 1
- 230000004075 alteration Effects 0.000 description 1
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- 229910052793 cadmium Inorganic materials 0.000 description 1
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- 238000002474 experimental method Methods 0.000 description 1
- 229910001385 heavy metal Inorganic materials 0.000 description 1
- 238000001095 inductively coupled plasma mass spectrometry Methods 0.000 description 1
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- G06Q10/00—Administration; Management
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- Y02A20/00—Water conservation; Efficient water supply; Efficient water use
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Abstract
The application discloses a method and a system for predicting thallium pollution in a watershed water environment, wherein the system for predicting thallium pollution in the watershed water environment comprises the following steps: a plurality of monitoring subsystems and prediction centers; wherein, the supervision subsystem: for sending a prediction request; receiving and executing a data acquisition instruction, and sending detection data; receiving a prediction result, and controlling and managing thallium pollution according to the prediction result; prediction center: for performing the steps of: receiving a prediction request, and obtaining a migration transformation model according to the prediction request; transmitting a data acquisition instruction and receiving detection data; and analyzing the detection data by using a migration transformation model to obtain a prediction result, and sending the prediction result. According to the method and the device, the time and the concentration of thallium pollution to be migrated to the target node can be accurately predicted by using the migration transformation model created according to the characteristics of the predicted river basin, so that thallium pollution in the river basin can be conveniently managed and controlled in time according to the prediction result.
Description
Technical Field
The application relates to the technical field of water environment risk prediction, in particular to a method and a system for predicting thallium pollution in a watershed water environment.
Background
Thallium is a typical rare dispersive element found in nature. Thallium and its compounds are extremely toxic and highly accumulative, and have much greater toxicity to animals and plants than other heavy metals such as lead, cadmium, mercury, etc. In the current thallium river pollution accident, a mode of sending a thallium river sample to a laboratory for analysis or a mode of carrying out on-site monitoring by adopting an environmental emergency monitoring method of trace thallium in surface water is generally adopted to analyze thallium pollution generated in a flow field, so the following problems exist in the existing monitoring mode:
(1) The upstream of most of the watercourses is a lake and a reservoir, the downstream is a drinking water source, and the vehicle-mounted ICP-MS (related to the use of power and argon) is inconvenient to use in most of remote places, and a rapid and effective monitoring method does not exist on site.
(2) The collected samples are sent to a laboratory for analysis, so that the time consumption is long, and the upstream tracing difficulty is high.
(3) The water layers of the watershed are distributed differently, and when thallium pollutants enter the lake and the reservoir and flow along with the water body, the time and the concentration of the thallium pollutants transferred to a downstream sensitive water source can not be accurately predicted, so that effective treatment measures are difficult to be taken by environmental emergency disposal personnel.
Disclosure of Invention
The invention aims to provide a method and a system for predicting thallium pollution in a watershed water environment, which can accurately predict the time and concentration of thallium pollution migrating to a target node by using a migration transformation model created according to the characteristics of a predicted watershed, and are convenient for timely controlling and managing thallium pollution in the watershed according to a prediction result.
To achieve the above object, the present application provides a system for predicting thallium pollution in a watershed water environment, comprising: a plurality of monitoring subsystems and prediction centers; wherein, the supervision subsystem: for sending a prediction request; receiving and executing a data acquisition instruction, and sending detection data; receiving a prediction result, and controlling and managing thallium pollution according to the prediction result; prediction center: for performing the steps of: receiving a prediction request, and obtaining a migration transformation model according to the prediction request; wherein the prediction request comprises at least: a pollution source location and a guard zone location; transmitting a data acquisition instruction and receiving detection data; and analyzing the detection data by using a migration transformation model to obtain a prediction result, and sending the prediction result.
As above, each of the supervisory subsystems includes: the system comprises a management end, a plurality of detection nodes and at least one monitoring end; wherein, the management end: for sending a prediction request; receiving a data acquisition instruction, and sending detection requests to a plurality of detection nodes according to the data acquisition instruction; receiving detection data and sending all detection data to a prediction center; receiving a prediction result sent by a prediction center, sending a control instruction to a monitoring end according to the prediction result, and simultaneously issuing a treatment task according to the prediction result, wherein the treatment task at least comprises: determining treatment points, treatment time, treatment modes and treatment personnel; detecting node: receiving and executing a detection request, and sending the acquired detection data to a management end; monitoring end: receiving and executing a control instruction, wherein the control instruction comprises: production and discharge are stopped.
As above, the prediction center at least includes: the system comprises a login unit, a model configuration unit, a prediction unit and a storage unit; wherein, login unit: the method is used for registering the supervision subsystem and finishing the login of the supervision subsystem; model configuration unit: after logging is completed, receiving a prediction request of a monitoring subsystem, and obtaining a migration transformation model according to the prediction request; prediction unit: the device is used for sending a data acquisition instruction and receiving detection data; inputting the detection data into a migration transformation model, analyzing the detection data by the migration transformation model to obtain a prediction result, and sending the prediction result; and a storage unit: for storing a base information base, a parameter information base and a sample database.
As above, the model configuration unit at least includes: the system comprises a pre-analysis module, a configuration module and a model optimization module; wherein, pre-analysis module: after logging is completed, receiving a prediction request of the monitoring subsystem, pre-analyzing the prediction request to obtain attribution drainage basin information, obtaining prediction drainage basin data according to the prediction request and the attribution drainage basin information, and sending the prediction drainage basin data to the configuration module; and (3) a configuration module: configuring an initialization model according to the predicted river basin data to obtain a migration transformation model; model optimization module: updating the output precision according to the new sample data to obtain new output precision, and optimizing the current migration transformation model by utilizing the new output precision to obtain a new migration transformation model.
The application also provides a method for predicting thallium pollution in a watershed water environment, which comprises the following steps: the prediction center receives a prediction request of the monitoring subsystem, and a migration transformation model is obtained according to the prediction request; wherein the prediction request comprises at least: a pollution source location and a guard zone location; the prediction center sends a data acquisition instruction to the supervision subsystem and receives detection data uploaded after the supervision subsystem executes the data acquisition instruction; the prediction center analyzes the detection data by using the migration transformation model to obtain a prediction result, and sends the prediction result to the supervision subsystem, and the supervision subsystem manages and controls thallium pollution according to the prediction result.
As above, the predicting center receives the predicting request of the monitoring subsystem, and the sub-steps of obtaining the migration transformation model according to the predicting request are as follows: pre-analyzing the prediction request to obtain attribution basin information; obtaining predicted drainage basin data according to the predicted request and the attribution drainage basin information; and configuring the initialization model according to the predicted river basin data to obtain a migration transformation model.
As above, the sub-steps of configuring the initialization model according to the predicted watershed data to obtain the migration transformation model are as follows: calling an initialization model, and configuring the initialization model according to the predicted river basin data to obtain an operation model; and obtaining sample data, calculating output precision according to the sample data, configuring the precision of the operation model by utilizing the output precision, and obtaining the migration transformation model after the configuration is completed.
As described above, the data of the treatment node collected in real time after the current prediction is used as new sample data, the new sample data is used to update the output precision, so as to obtain new output precision, and the new output precision is used to optimize the current migration transformation model, so as to obtain the new migration transformation model.
As above, the expression of the new output accuracy is as follows:
； wherein ,/>
The new output precision is obtained; />
Is->
Thallium contamination predicted concentrations for the new sample data; />
Is->
The thallium of the new sample data pollutes the measured concentration,
，/>
the total number of the new sample data; />
Is->
Average value of thallium contamination predicted concentration of each new sample data; />
Is->
The thallium of the new sample data pollutes the average of the measured concentrations.
As above, the sub-steps of pre-analyzing the prediction request to obtain the home domain information are as follows: acquiring a plurality of management watershed data corresponding to management end information in a prediction request, and taking each management watershed data as primary selection data; and screening the plurality of primary selection data according to pollution source information and protection area information in the prediction request to obtain attribution basin information.
The method and the device can accurately predict the time and the concentration of thallium pollution transferred to the target node (such as a protection zone node) by utilizing the migration transformation model created according to the characteristics of the predicted river basin, and are convenient for timely controlling and managing thallium pollution in the river basin according to the prediction result.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following description will briefly introduce the drawings that are required to be used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are only some embodiments described in the present application, and other drawings may also be obtained according to these drawings for a person having ordinary skill in the art.
FIG. 1 is a schematic diagram of an embodiment of a system for predicting thallium contamination in a watershed water environment;
figure 2 is a flow chart of one embodiment of a method for predicting thallium contamination in a watershed water environment.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are some, but not all embodiments of the invention. 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.
As shown in fig. 1, the present application provides a system for predicting thallium contamination in a watershed water environment, comprising: a plurality of supervision subsystems 110 and a prediction center 120.
Wherein the supervisory subsystem 110: for sending a prediction request; receiving and executing a data acquisition instruction, and sending detection data; and receiving the prediction result, and controlling and managing thallium pollution according to the prediction result.
Prediction center 120: for performing the steps of:
receiving a prediction request, and obtaining a migration transformation model according to the prediction request; wherein the prediction request comprises at least: a pollution source location and a guard zone location;
transmitting a data acquisition instruction and receiving detection data;
and analyzing the detection data by using a migration transformation model to obtain a prediction result, and sending the prediction result.
Further, each of the supervision subsystems 110 includes: a management end 1101, a plurality of detection nodes 1102, and at least one monitoring end 1103.
Wherein, the management end 1101: for sending a prediction request; receiving a data acquisition instruction, and sending detection requests to a plurality of detection nodes according to the data acquisition instruction; receiving detection data and sending all detection data to a prediction center; receiving a prediction result sent by a prediction center, sending a control instruction to a monitoring end according to the prediction result, and simultaneously issuing a treatment task according to the prediction result, and timely controlling and treating thallium pollution, wherein the treatment task at least comprises: determining treatment points, treatment time, treatment modes and treatment personnel.
In particular, administrative end 1101 is a terminal of a government agency/government entity that manages a plurality of watercourses and a plurality of monitoring ends.
Detection node 1102: and receiving and executing the detection request, and sending the acquired detection data to the management end.
Specifically, the detection node 1102 is provided with a device for sampling different water layers in a drainage basin (such as a main flow, a tributary flow and the like), a device for performing emergency detection on the samples, and a communication device for transmitting detection data obtained by detection to a management end. One detection node corresponds to one sampling point in the predicted stream domain.
Monitoring end 1103: receiving and executing a control instruction, wherein the control instruction comprises: production and discharge are stopped.
Specifically, the monitoring terminal 1103 is a terminal of a subject (e.g., mining site, smeltery, industrial park, etc.) capable of producing thallium contamination within the management scope of the management terminal.
Further, the prediction center at least includes: the system comprises a login unit, a model configuration unit, a prediction unit and a storage unit.
Wherein, login unit: the method is used for registering the supervision subsystem and finishing the registration of the supervision subsystem.
Model configuration unit: after logging is completed, a prediction request of the monitoring subsystem is received, and a migration transformation model is obtained according to the prediction request.
Prediction unit: the method comprises the steps of sending a data acquisition instruction, receiving detection data, inputting the detection data into a migration transformation model, analyzing the detection data by the migration transformation model, obtaining a prediction result, and sending the prediction result.
And a storage unit: for storing a base information base, a parameter information base and a sample database.
Specifically, the basic information base includes: a plurality of supervision packets, each supervision packet comprising at least: the management system comprises a management end name, a management end ID, at least one monitoring end name, at least one monitoring end ID, a plurality of detection nodes and a plurality of management river basin data.
The parameter information base includes: a plurality of parameter data packets, each parameter data packet comprising at least: the method comprises the steps of river reach marking, river reach length, river reach width, river reach aquifer distribution, thickness of each layer of the river reach aquifer, thallium pollution dispersion coefficient, pollution source node heat generation rate per unit volume and curved surface segments.
The sample database comprises at least: the measured data of a plurality of sample points of each river reach at least comprises: thallium pollution actual measurement concentration, distance between sampling point and pollution source starting point, and curved surface section shared by sampling point and pollution source starting point.
Further, the model configuration unit at least includes: the system comprises a pre-analysis module, a configuration module and a model optimization module.
Wherein, pre-analysis module: after logging is completed, a prediction request of the monitoring subsystem is received, the prediction request is subjected to pre-analysis to obtain attribution drainage basin information, the prediction drainage basin data is obtained according to the prediction request and the attribution drainage basin information, and the prediction drainage basin data is sent to the configuration module.
And (3) a configuration module: and configuring the initialization model according to the predicted river basin data to obtain a migration transformation model.
Model optimization module: updating the output precision according to the new sample data to obtain new output precision, and optimizing the current migration transformation model by utilizing the new output precision to obtain a new migration transformation model.
As shown in fig. 2, the application provides a method for predicting thallium pollution in a watershed water environment, which comprises the following steps:
s210: the prediction center receives a prediction request of the monitoring subsystem, and a migration transformation model is obtained according to the prediction request; wherein the prediction request comprises at least: management side information, pollution source information and protection area information.
Further, the prediction center receives a prediction request of the monitoring subsystem, and the sub-steps of obtaining the migration transformation model according to the prediction request are as follows:
s2101: and pre-analyzing the prediction request to obtain the attribution basin information.
Further, the pre-analyzing the prediction request to obtain the home domain information includes the following sub-steps:
s21011: and acquiring a plurality of management stream domain data corresponding to the management end information in the prediction request, and taking each management stream domain data as primary selection data.
Specifically, the management side information in the prediction request at least includes: a management end name and a management end ID. One management side corresponds to one management side name and one management side ID.
Further, the sub-steps of obtaining a plurality of management domain data corresponding to the management side information in the prediction request, and taking each management domain data as a primary selected data are as follows:
u1: generating a first query request according to the management side information, and sending the first query request, wherein the first query request at least comprises: the first access content, the first access location and the management side information.
Specifically, the first access location is a base information repository. The first access content is acquisition management basin data. The management river basin data are related data of the river basin detected, managed and managed by the management end. And after the pre-analysis module generates a first query request according to the management end information, the first query request is sent to the storage unit.
U2: and receiving a plurality of management watershed data obtained according to the first query request, and taking each management watershed data as one primary selection data.
Specifically, the storage unit accesses the basic information base according to the first query request, obtains a supervision data packet consistent with the management end information in the basic information base, sends a plurality of management drainage basin data contained in the supervision data packet to the pre-analysis module, and the pre-analysis module receives the plurality of management drainage basin data and takes each management drainage basin data as a primary selection data.
Wherein, the management of the river basin data at least comprises: drainage basin area and drainage basin model.
The watershed area represents: the flow-through area of a basin.
The watershed model is a 3D model constructed through an existing 3D model and used for showing basic information of the watershed. The basin model comprises: and each river reach sub-model is used for displaying the length, the width and the water-bearing layer distribution of the river reach and the thickness of each water-bearing layer of the river reach. The length, width, water-bearing layer distribution and thickness of each layer of the water-bearing layer of the river reach, which are actually corresponding to the river reach, are obtained according to actual measurement and are stored in a parameter information base for the 3D model. One river reach sub-model corresponds to one river reach mark.
S21012: and screening the plurality of primary selection data according to pollution source information and protection area information in the prediction request to obtain attribution basin information.
Further, the sub-steps of screening the plurality of primary selection data according to pollution source information and protection area information in the prediction request to obtain the home domain information are as follows:
u1': and screening the primary selection data for the first time according to the pollution source information, wherein the primary selection data corresponding to the river basin of each pollution source corresponding to the pollution source information is used as screening data.
Specifically, one pollution source information corresponds to one pollution source. The source of contamination is a host capable of producing thallium contamination, for example: mining sites, smelters, industrial parks, etc. Each pollution source information includes at least: the name of the source, the type of source, the scale of the contamination and the area of the source.
Wherein the type of contamination source indicates the type of body capable of producing thallium contamination, for example: mining, smelting, etc. The contamination scale is determined based on the maximum concentration of thallium contamination that the source of contamination can produce per unit time. One pollution scale corresponds to one concentration range, and the pollution scale corresponding to the concentration range to which the maximum concentration of thallium pollution which can be generated by one pollution source in unit time belongs is the scale of the pollution source. The pollution source area is used to represent the actual location area where the pollution source is located.
When the river basin flows through the pollution source area, thallium pollution generated by the pollution source enters the river basin and migrates and spreads along with the water body flow, so that the primary selection data corresponding to the river basin of the pollution source corresponding to the pollution source information flowing through each river basin is used as screening data, and the primary selection data corresponding to the river basin of the pollution source not flowing through the pollution source information is removed.
U2': and screening the plurality of screening data for the second time according to the protection area information, and taking the screening data corresponding to the drainage basin of the protection area corresponding to the protection area information as attribution drainage basin information.
Specifically, one protection area information corresponds to one protection area. The protected zone is an area that cannot be affected by thallium contamination, for example: sensitive water sources, cities, towns, villages, etc. Each protection zone information includes at least: the name of the protection zone, the type of the protection zone, the protection zone size and the protection zone area.
Wherein the type of protection zone indicates the type of body that needs to be protected from thallium contamination, for example: sensitive water sources, living areas (such as towns and villages), and production areas (such as cultivated lands and pastures), etc. The scale of the protection area is judged according to the occupied area. The scale of a protection area corresponds to a coverage area, and the scale of the protection area corresponding to the coverage area to which the occupation area of the protection area belongs is the scale of the protection area. The protection area is used for indicating the actual position area where the protection area is located.
When the river basin flows through the protection area, thallium pollution in the river basin can affect the protection area, so that screening data corresponding to the river basin flowing through the protection area corresponding to the protection area information is used as attribution river basin information, and screening data corresponding to the river basin not flowing through the protection area corresponding to the protection area information is removed.
S2102: and obtaining predicted drainage basin data according to the predicted request and the attribution drainage basin information.
Further, according to the prediction request and the attribution basin information, the substeps of obtaining the prediction basin data are as follows:
s21021: and carrying out upstream section marking on the river basin model in the attribution river basin information according to pollution source information in the prediction request, carrying out downstream section marking on the river basin model in the attribution river basin information according to protection area information in the prediction request, taking a river reach sub-model positioned between the upstream section marking and the downstream section marking in the river basin model as a predicted river basin model, and taking the river reach marking of the river reach sub-model as a river reach marking of the predicted river basin model.
S21022: generating a second query request according to the river reach mark of the predicted river basin model, and sending the second query request, wherein the second query request at least comprises: a river reach mark, a second access content and a second access location.
Specifically, the second access content is to obtain prediction parameter data. The second access location is a parameter information base.
S21023: and receiving the predicted parameter data obtained according to the second query request, and taking the predicted parameter data and the predicted drainage basin model as predicted drainage basin data. Wherein the prediction parameter data at least comprises: the dispersion coefficient of thallium pollution, the rate of heat generation per unit volume of pollution source nodes and the curved surface section.
Specifically, the storage unit accesses the parameter information base according to the second query request to obtain a parameter data packet consistent with the river reach mark in the parameter information base, and takes the dispersion coefficient of thallium pollution, the rate of heat generation per unit volume of the pollution source node and the curved surface section in the parameter data packet as prediction parameter data.
The material flux, the medium permeability, the material dynamic viscosity, the fluid pressure, the gravitational acceleration and the delay factor in the curved surface section related to the process of obtaining the curved surface section in an existing mode are all obtained from a parameter data packet, and data (such as the material flux, the medium permeability, the material dynamic viscosity, the fluid pressure, the gravitational acceleration and the delay factor) in the parameter data packet are all obtained through actual measurement and calculation in advance.
S2103: and configuring the initialization model according to the predicted river basin data to obtain a migration transformation model.
Further, the initialization model is configured according to the predicted river basin data, and the sub-steps of obtaining the migration transformation model are as follows:
s21031: and calling an initialization model, and configuring the initialization model according to the predicted river basin data to obtain an operation model.
Further, the expression of the initialization model is as follows:
wherein ,
is a pollution source node->
Thallium contamination migration diffusion to the protection zone node->
And protection zone node->
Time of thallium contamination concentration reaching thallium contamination predicted concentration +.>
Is a derivative of (2); />
For protecting zone node->
Is a space volume of (2); />
For protecting zone node->
Is a derivative of the mass of the substance; />
For protecting zone node->
Is a space volume and pollution source node->
The curved surface integral of the space volume of (2) is approximately discretized into a curved surface section shared by two points; />
For thallium contamination from a contamination source node->
To the protection zone node->
Is of the diffusion coefficient of (2)；/>
To be from pollution source node->
To the protection zone node->
Thallium contamination prediction concentration of (a); />
Is a pollution source node->
Rate of heat generation per unit volume.
wherein ,
；
wherein ,
to be from pollution source node->
To the protection zone node->
Thallium contamination predicted concentration,/-, for>
To predict +.>
Thallium contamination concentration of the individual detection nodes, +.>
，/>
The total number of the nodes is detected; />
Is the first/>
The distance between each detection node and the starting point of the pollution source; />
Is->
An average value of thallium contamination concentrations of the individual detection nodes; />
Is->
Average value of distances between each detection node and the starting point of the pollution source; />
Is a pollution source node->
To the protection zone node->
Is a distance of (2); />
Is a regression constant.
Specifically, the origin of the pollution source is the detection node closest to the pollution source.
The specific value of (2) is set according to the case of a plurality of experiments performed in advance.
S21032: and obtaining sample data, calculating output precision according to the sample data, configuring the precision of the operation model by utilizing the output precision, and obtaining the migration transformation model after the configuration is completed.
Further, the expression of the output accuracy is as follows:
wherein ,
the output precision is; />
Is->
Thallium contamination prediction concentration for each sample data; />
Is->
Thallium contamination measured concentration of individual sample data, < >>
，/>
The total number of the sample data; />
Is->
Average value of thallium contamination predicted concentration of individual sample data; />
Is->
The thallium of each sample data pollutes the average of the measured concentrations.
Specifically, the sample data is measured data obtained from a sample database at a sampling point on a prediction drainage basin before the current prediction.
S220: the prediction center sends a data acquisition instruction to the supervision subsystem and receives detection data uploaded after the supervision subsystem executes the data acquisition instruction, wherein the detection data at least comprises: detection time, thallium contamination concentration of the detection node, and location of the detection node.
Specifically, the prediction center sends a data acquisition instruction to the management end, the management end receives the data acquisition instruction, sends detection requests to a plurality of detection nodes according to the data acquisition instruction, the detection nodes execute the detection requests and send the acquired detection data to the management end, and after the management end gathers all the detection data, the management end sends all the detection data to the prediction center.
S230: the prediction center analyzes the detection data by using a migration transformation model to obtain a prediction result, and sends the prediction result to the supervision subsystem, and the supervision subsystem manages and controls thallium pollution according to the prediction result, wherein the prediction result at least comprises: thallium contamination prediction concentration, prediction time, and hazard level.
Specifically, the prediction center inputs the detection data into a migration transformation model, the migration transformation model analyzes the detection data to obtain a prediction result, and the prediction result is sent to the management end. The management end sends a management and control instruction to the monitoring end according to the prediction result, wherein the management and control instruction comprises: production and discharge are stopped. And the monitoring end closes production and discharge according to the control instruction. And the management end simultaneously issues a treatment task according to the prediction result, and timely controls and treats thallium pollution. Wherein, the treatment task at least comprises: determining treatment points, treatment time, treatment modes and treatment personnel.
Further, when setting the input migration transformation model
Time of output of the migration transformation model +.>
Namely, pollution source node->
Thallium contamination migration diffusion to the protection zone node->
And protection zone node->
The predicted time for thallium pollution concentration to reach emergency pre-warning concentration is set to +.>
Namely, from the pollution source node->
To the protection zone node->
The thallium pollution prediction concentration of the system is the hazard degree of the emergency early warning grade. Wherein (1)>
For protecting zone node->
Pollution concentration threshold, < ">>
For protecting zone node->
Current thallium contaminates the measured concentration.
Further, when setting pollution source node
And protection zone node->
When one node in the middle is used as a treatment node, the treatment node is used as a new protection area node m', and the output +.>
To be from pollution source node->
Thallium contamination predicted concentration to the protection zone node m', time of output +.>
Namely, pollution source node->
Thallium pollution of the protection zone node m ' is migrated and diffused to the protection zone node m ', and the thallium pollution concentration of the protection zone node m ' reaches the prediction time of the emergency early warning concentration. By setting the treatment node and taking the treatment node as a new protection area node m', the time for a treatment person to reach the treatment node to treat thallium pollution and the thallium pollution concentration to treat can be accurately predicted, so that thallium pollution can be timely early-warned and timely treated, and serious damage caused by thallium pollution is avoided.
Further, the data of the treatment nodes collected in real time after the prediction is used as new sample data, the output precision is updated by using the new sample data to obtain new output precision, and the current migration transformation model is optimized by using the new output precision to obtain a new migration transformation model, so that the output precision of the prediction in the next time is improved.
Further, the expression of the new output accuracy is as follows:
wherein ,
the new output precision is obtained; />
Is->
Thallium contamination predicted concentrations for the new sample data; />
Is->
Thallium contamination measured concentration of new sample data,/->
，/>
The total number of the new sample data; />
Is->
Average value of thallium contamination predicted concentration of each new sample data; />
Is->
The thallium of the new sample data pollutes the average of the measured concentrations.
Further, the prediction result is displayed through the prediction drainage basin model.
The method and the device can accurately predict the time and the concentration of thallium pollution transferred to the target node (such as a protection zone node) by utilizing the migration transformation model created according to the characteristics of the predicted river basin, and are convenient for timely controlling and managing thallium pollution in the river basin according to the prediction result.
While preferred embodiments of the present application have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the scope of the present application be interpreted as including the preferred embodiments and all alterations and modifications that fall within the scope of the present application. It will be apparent to those skilled in the art that various modifications and variations can be made in 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 protection of the present application and the equivalents thereof, the present application is intended to cover such modifications and variations.
Claims (10)
1. A system for predicting thallium contamination in a watershed water environment, comprising: a plurality of monitoring subsystems and prediction centers;
wherein, the supervision subsystem: for sending a prediction request; receiving and executing a data acquisition instruction, and sending detection data; receiving a prediction result, and controlling and managing thallium pollution according to the prediction result;
prediction center: for performing the steps of:
receiving a prediction request, and obtaining a migration transformation model according to the prediction request; wherein the prediction request comprises at least: a pollution source location and a guard zone location;
transmitting a data acquisition instruction and receiving detection data;
and analyzing the detection data by using a migration transformation model to obtain a prediction result, and sending the prediction result.
2. The system for predicting thallium contamination in a watershed water environment of claim 1, wherein each supervision subsystem comprises: the system comprises a management end, a plurality of detection nodes and at least one monitoring end;
wherein, the management end: for sending a prediction request; receiving a data acquisition instruction, and sending detection requests to a plurality of detection nodes according to the data acquisition instruction; receiving detection data and sending all detection data to a prediction center; receiving a prediction result sent by a prediction center, sending a control instruction to a monitoring end according to the prediction result, and simultaneously issuing a treatment task according to the prediction result, wherein the treatment task at least comprises: determining treatment points, treatment time, treatment modes and treatment personnel;
detecting node: receiving and executing a detection request, and sending the acquired detection data to a management end;
monitoring end: receiving and executing a control instruction, wherein the control instruction comprises: production and discharge are stopped.
3. The system for predicting thallium contamination in a watershed water environment of claim 2, wherein the prediction center comprises at least: the system comprises a login unit, a model configuration unit, a prediction unit and a storage unit;
wherein, login unit: the method is used for registering the supervision subsystem and finishing the login of the supervision subsystem;
model configuration unit: after logging is completed, receiving a prediction request of a monitoring subsystem, and obtaining a migration transformation model according to the prediction request;
prediction unit: the device is used for sending a data acquisition instruction and receiving detection data; inputting the detection data into a migration transformation model, analyzing the detection data by the migration transformation model to obtain a prediction result, and sending the prediction result;
and a storage unit: for storing a base information base, a parameter information base and a sample database.
4. A system for predicting thallium contamination in a watershed water environment as in claim 3, wherein the model configuration unit comprises at least: the system comprises a pre-analysis module, a configuration module and a model optimization module;
wherein, pre-analysis module: after logging is completed, receiving a prediction request of the monitoring subsystem, pre-analyzing the prediction request to obtain attribution drainage basin information, obtaining prediction drainage basin data according to the prediction request and the attribution drainage basin information, and sending the prediction drainage basin data to the configuration module;
and (3) a configuration module: configuring an initialization model according to the predicted river basin data to obtain a migration transformation model;
model optimization module: updating the output precision according to the new sample data to obtain new output precision, and optimizing the current migration transformation model by utilizing the new output precision to obtain a new migration transformation model.
5. The method for predicting thallium pollution in the watershed water environment is characterized by comprising the following steps:
the prediction center receives a prediction request of the monitoring subsystem, and a migration transformation model is obtained according to the prediction request; wherein the prediction request comprises at least: a pollution source location and a guard zone location;
the prediction center sends a data acquisition instruction to the supervision subsystem and receives detection data uploaded after the supervision subsystem executes the data acquisition instruction;
the prediction center analyzes the detection data by using the migration transformation model to obtain a prediction result, and sends the prediction result to the supervision subsystem, and the supervision subsystem manages and controls thallium pollution according to the prediction result.
6. The method for predicting thallium contamination in a watershed water environment of claim 5, wherein the predicting center receives a prediction request from the monitoring subsystem, and the sub-steps of obtaining the migration transformation model based on the prediction request are as follows:
pre-analyzing the prediction request to obtain attribution basin information;
obtaining predicted drainage basin data according to the predicted request and the attribution drainage basin information;
and configuring the initialization model according to the predicted river basin data to obtain a migration transformation model.
7. The method for predicting thallium contamination in a watershed water environment of claim 6, wherein the sub-steps of configuring the initialization model according to the predicted watershed data to obtain the migration transformation model are as follows:
calling an initialization model, and configuring the initialization model according to the predicted river basin data to obtain an operation model;
and obtaining sample data, calculating output precision according to the sample data, configuring the precision of the operation model by utilizing the output precision, and obtaining the migration transformation model after the configuration is completed.
8. The method for predicting thallium pollution in a watershed water environment according to claim 7, wherein the data of the treatment nodes collected in real time after the current prediction is used as new sample data, the new sample data is used for updating the output precision to obtain new output precision, and the new output precision is used for optimizing the current migration transformation model to obtain a new migration transformation model.
9. The method for predicting thallium contamination in a watershed water environment of claim 8, wherein the expression for the new output accuracy is as follows:
wherein ,
the new output precision is obtained; />
Is->
Thallium contamination predicted concentrations for the new sample data; />
Is->
Thallium contamination measured concentration of new sample data,/->
，/>
The total number of the new sample data; />
Is->
Average value of thallium contamination predicted concentration of each new sample data; />
Is->
Novel sampleThe thallium contamination of this data was the average of the measured concentrations.
10. The method for predicting thallium contamination in a watershed water environment of claim 9, wherein the pre-analyzing the predicted request, the sub-step of obtaining the home watershed information is as follows:
acquiring a plurality of management watershed data corresponding to management end information in a prediction request, and taking each management watershed data as primary selection data;
and screening the plurality of primary selection data according to pollution source information and protection area information in the prediction request to obtain attribution basin information.
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Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104200322A (en) * | 2014-09-01 | 2014-12-10 | 北京科技大学 | Integrated watershed management system |
| CN104392100A (en) * | 2014-10-29 | 2015-03-04 | 南京南瑞集团公司 | Pollution source diffusion early-warning method based on water quality on-line monitoring system |
| CN107563139A (en) * | 2017-09-06 | 2018-01-09 | 郑州大学 | A kind of point source sudden water pollution source accident source percentage contribution computational methods |
| CN110909484A (en) * | 2019-12-03 | 2020-03-24 | 中国环境科学研究院 | Watershed grey water footprint evaluation method and water environment treatment strategy making method |
| CN113267607A (en) * | 2021-05-11 | 2021-08-17 | 浙江大学 | Characteristic parameter identification system for field organic pollutant migration process |
| JP6980242B1 (en) * | 2021-03-22 | 2021-12-15 | 国立研究開発法人国立環境研究所 | Environmental impact prediction method for pollutant spills and environmental impact prediction program for pollutant spills |
| CN114169266A (en) * | 2021-12-13 | 2022-03-11 | 桂林理工大学 | River pollution source analysis and pollutant migration calculation method and system |
| CN115587667A (en) * | 2022-11-04 | 2023-01-10 | 深圳市生态环境智能管控中心 | Pollution analysis management module and method for water environment management system of intelligent environment-friendly platform |
-
2023
- 2023-05-06 CN CN202310500403.1A patent/CN116227744B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104200322A (en) * | 2014-09-01 | 2014-12-10 | 北京科技大学 | Integrated watershed management system |
| CN104392100A (en) * | 2014-10-29 | 2015-03-04 | 南京南瑞集团公司 | Pollution source diffusion early-warning method based on water quality on-line monitoring system |
| CN107563139A (en) * | 2017-09-06 | 2018-01-09 | 郑州大学 | A kind of point source sudden water pollution source accident source percentage contribution computational methods |
| CN110909484A (en) * | 2019-12-03 | 2020-03-24 | 中国环境科学研究院 | Watershed grey water footprint evaluation method and water environment treatment strategy making method |
| JP6980242B1 (en) * | 2021-03-22 | 2021-12-15 | 国立研究開発法人国立環境研究所 | Environmental impact prediction method for pollutant spills and environmental impact prediction program for pollutant spills |
| CN113267607A (en) * | 2021-05-11 | 2021-08-17 | 浙江大学 | Characteristic parameter identification system for field organic pollutant migration process |
| CN114169266A (en) * | 2021-12-13 | 2022-03-11 | 桂林理工大学 | River pollution source analysis and pollutant migration calculation method and system |
| CN115587667A (en) * | 2022-11-04 | 2023-01-10 | 深圳市生态环境智能管控中心 | Pollution analysis management module and method for water environment management system of intelligent environment-friendly platform |
Non-Patent Citations (2)
| Title |
|---|
| 付俊娥 等: "流域水污染模拟预测及应急处理系统研究——以淮河为例", 《GIS技术》, no. 3, pages 116 - 110 * |
| 常文凯: "大气污染物随机扩散模型", 《北方环境》, vol. 24, no. 3, pages 33 - 35 * |
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