CN113705131B - Water body pollution diffusion simulation prediction method and device based on particle motion - Google Patents
Water body pollution diffusion simulation prediction method and device based on particle motion Download PDFInfo
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Abstract
A water body pollution diffusion simulation prediction method and a device based on particle motion comprise the steps of dividing pollutants in a simulation area into a large number of pollutant particles, and calculating the motion state change of each pollutant particle; decomposing the motion state change process of the pollutant particles into three independent sub-processes, namely directional motion, random motion and random attenuation, performing independent calculation and analysis on each sub-process, and finally superposing the sub-processes to obtain the whole diffusion process of the pollutant particles; and continuously iterating the diffusion process of the pollutant particles, counting the pollutant particles in the sub-areas of the simulation area at selected time points, and representing the pollutant concentration at the corresponding position according to the number of the particles in each sub-area. The invention can realize the high-efficiency diffusion simulation prediction of various pollutants in the water body, particularly in the large-range water body (such as the ocean), thereby solving the problems of the analysis and the prediction of the diffusion process, the concentration distribution and the diffusion path of the pollutants.
Description
Technical Field
The invention relates to an analysis and prediction method for a diffusion process of pollutants in a water body, in particular to a simulation prediction method and a simulation prediction device for the diffusion of the pollutants in the water body based on particle motion.
Background
With the rapid development of global economy, the influence of human activities on the surrounding environment is increasing, and the problems of environmental pollution and ecological destruction caused by the human activities are receiving more and more attention. In the marine field, a series of events caused by human factors, such as industrial wastewater discharge, offshore crude oil leakage and the like, make marine ecological environment face a serious challenge. In order to describe and predict the diffusion behavior of pollutants in water more accurately, various diffusion models have been developed so far at home and abroad, such as a LAKECO model for a lake ecosystem, a vortex resolution model for ocean diffusion analysis, and the like, and diffusion analysis software combining finite element technology, such as TIDAL, HydroGeoSphere, MIKE3, and the like, appears. However, these diffusion simulation prediction methods and analysis software have respective drawbacks, such as not considering the attenuation of radioactive elements, setting complex simulation parameters, requiring multiple environmental data as input, and the like, and particularly in the large-scale diffusion scene such as the ocean, it is difficult to sufficiently acquire the required input data, such as the wind field, temperature, and the like, which change with time, and there are problems that the simulation time is too long and is unacceptable.
It is to be noted that the information disclosed in the above background section is only for understanding the background of the present application and thus may include information that does not constitute prior art known to a person of ordinary skill in the art.
Disclosure of Invention
The invention mainly aims to overcome the defects of the background technology and provide a water body pollution diffusion simulation and prediction method and device based on particle motion.
In order to achieve the purpose, the invention adopts the following technical scheme:
a water body pollution diffusion simulation prediction method based on particle motion comprises the steps of dividing pollutants in a simulation area into a large number of pollutant particles, and calculating the motion state change of each pollutant particle in the simulation area; decomposing the motion state change process of the pollutant particles into three independent sub-processes, namely directional motion, random motion and random attenuation, performing independent calculation and analysis on each sub-process, and finally superposing the sub-processes to obtain the whole diffusion process of the pollutant particles; and continuously iterating the diffusion process of the pollutant particles, counting the pollutant particles in the sub-areas of the simulation area at selected time points, and representing the pollutant concentration at the corresponding sub-area according to the number of the particles in each sub-area.
Further:
the random motion is computationally analyzed as follows: dirtStep of time for dye particles
The displacement magnitude obeying parameter is
Rayleigh distribution of wherein
The dispersion coefficient of the pollutants in the water body to be analyzed; when calculating the displacement caused by random motion, the displacement direction angle of each pollutant particle can be respectively obtained by a program for generating random numbers
Generated in uniform distribution, magnitude of displacement
Generated according to a rayleigh distribution.
The directional motion is computationally analyzed as follows: the water body flow velocity at which the pollutant particles are located is
The components in two orthogonal directions are respectively
And
at a time step size
The displacement of the contaminant particles due to said directed movement is of the order of magnitude
The displacement components in the two directions are respectively
And
(ii) a The displacement of random motion and the displacement of directional motion are added according to vectors to obtain the pollutant particles
The actual displacement of the inner.
The random attenuation is computationally analyzed as follows: step of time
Any contaminant particles in the container can be removed by
Or
The probability of (a) disappears and,
is the decay constant of the contaminant in the body of water to be analyzed.
Determining the amount of pollutant represented by each pollutant particle according to the total amount of the pollutant discharged each time;
at the initial moment, calculating the initial pollutant amount of each pollution source region according to the pollutant concentration of each pollution source and the size of the pollution source region, then calculating the equivalent pollutant particle amount, and then generating a corresponding amount of pollutant particles and uniformly and randomly putting the pollutant particles into the pollution source region; for each pollutant particle, a single number is used for identifying, and the state of each pollutant particle comprises the position coordinate of the pollutant particle and whether attenuation occurs; when a contaminant particle is generated, it is given a globally unique number, initialized with the coordinates of its location and marked as not attenuated.
Iteratively calculating a state of the contaminant particles, comprising:
from the present moment
Initially, the following steps are performed cyclically:
for each pollution source, judging whether pollutants are discharged at the current moment or not according to a predetermined pollutant discharge schedule and discharge amount, if so, generating a corresponding amount of pollutant particles and adding the pollutant particles to an area where the pollution source is located;
traversing all the contaminant particles which are not attenuated, calculating the state change of the contaminant particles and updating the state change; wherein the change in state of each contaminant particle is calculated as follows: first, calculate
Probability of attenuation of internal contaminant particles
Then a random program is used to simulate the probability of one occurrence as
An independent event of (2); if the event occurs, determining that the contaminant particles are attenuated, and marking the contaminant particles as attenuated; otherwise, the following contents are continuously executed;
randomly generating the displacement direction of the pollutants according to the uniform distribution and the parameters of
The Rayleigh distribution randomly generates the displacement of the pollutant to obtain the displacement of random motion, and adds the displacement of the random motion to the position of the pollutant particles to obtain a new position; if the new position is in the simulation area, updating the position of the pollutant particles to the new position; calculating the displacement of the directional movement of the pollutant particles according to the water body flow velocity v of the position of the pollutant particles
Adding the position of the contaminant particles to the displacement of the directional motionObtaining a new position; if the new position is in the simulation area, the position of the pollutant particles is updated to the new position again;
the current time
Increase of
Repeating the above steps until
The simulation end time is reached.
Counting the contaminant particles contained in each sub-region at a selected point in time, the contaminant concentration at the respective sub-region being characterized by the number of particles in the respective sub-region, comprising: the contaminant concentration at a selected point in time for each sub-region is obtained by multiplying the number of contaminant particles by the amount represented by each contaminant particle and dividing by the area or volume of the sub-region.
Further comprising: the contaminant diffusion path is analyzed based on the state of at least some or all of the contaminant particles at each selected point in time.
Further comprising: and carrying out visual display in a graphic and animation mode according to the simulation result data.
A water body pollution diffusion simulation prediction device based on particle motion comprises: a processor and a computer readable storage medium storing a computer program which, when executed by the processor, performs the water body pollution diffusion simulation prediction method.
The invention has the following beneficial effects:
the invention can realize the high-efficiency diffusion simulation prediction of various pollutants (including radioactive elements) in the water body, particularly in the large-range water body (such as the ocean), thereby solving the analysis and prediction problems of the diffusion process, the concentration distribution and the diffusion path of the pollutants.
Drawings
FIG. 1 is a schematic illustration of the determination of relative contaminant concentrations based on particles in each sub-zone according to one embodiment of the present invention.
Fig. 2 is a schematic view of a modular structure according to an embodiment of the present invention.
FIG. 3 is a dynamic display of the contaminant diffusion process and concentration profile according to one embodiment of the present invention.
FIG. 4 is a display of a contaminant diffusion path according to one embodiment of the present invention.
Detailed Description
The embodiments of the present invention will be described in detail below. It should be emphasized that the following description is merely exemplary in nature and is not intended to limit the scope of the invention or its application.
It will be understood that when an element is referred to as being "secured to" or "disposed on" another element, it can be directly on the other element or be indirectly on the other element. When an element is referred to as being "connected to" another element, it can be directly connected to the other element or be indirectly connected to the other element. In addition, the connection may be for either a fixed or coupled or communicating function.
It is to be understood that the terms "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," and the like are used in an orientation or positional relationship indicated in the drawings for convenience in describing the embodiments of the present invention and to simplify the description, and are not intended to indicate or imply that the referenced device or element must have a particular orientation, be constructed in a particular orientation, and be in any way limiting of the present invention.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of the embodiments of the present invention, "a plurality" means two or more unless specifically limited otherwise.
The embodiment of the invention provides a water body pollution diffusion simulation and prediction method based on particle motion, which comprises the steps of dividing pollutants in a simulation area into a large number of pollutant particles, and calculating the motion state change of each pollutant particle in the simulation area; decomposing the motion state change process of the pollutant particles into three independent sub-processes, namely directional motion, random motion and random attenuation, performing independent calculation and analysis on each sub-process, and finally superposing the sub-processes to obtain the whole diffusion process of the pollutant particles; and continuously iterating the diffusion process of the pollutant particles, counting the pollutant particles in the sub-areas of the simulation area at selected time points, and representing the pollutant concentration at the corresponding sub-area according to the number of the particles in each sub-area.
The embodiment of the invention also provides a simulation prediction device based on the water body pollution diffusion simulation prediction method.
The method and the device can realize the high-efficiency diffusion simulation prediction of various pollutants (including radioactive elements) in the water body, particularly in a large-range water body (such as ocean), so that the problems of the diffusion process, the concentration distribution and the analysis and prediction of a diffusion path of the pollutants are solved.
Specific embodiments of the present invention are further described below.
In the water body, the diffusion process of the pollutants can be represented by the concentration of the pollutants in different areas at various time points, and the change of the concentration of the pollutants is actually the change of the number of pollutant particles in a unit volume, so that the diffusion process can be analyzed from the perspective of the pollutant particles. Therefore, the invention calculates the motion of each particle in a short time by dividing the pollutant into a large number of pollutant particles, and the process is iterated continuously, and finally the pollutant concentration at the position is represented by the particle number of each area. As shown in fig. 1, by dividing the plane into zones and counting the particles in each zone, the relative contaminant concentration of each zone can be estimated. Specifically, the present invention breaks down the change in contaminant particles into three separate sub-processes, which are directed motion, random motion, and random decay, respectively. The entire diffusion process is obtained by analyzing each sub-process individually and finally stacking the sub-processes. The calculation principles of these three sub-processes are explained below.
Random motion
The random motion is a non-stop random motion, and the brownian motion is the motion. Although the random movement of individual particles is random, the random movement of a large number of particles has a statistical regularity, such as the macroscopic movement of contaminants from a high concentration region to a low concentration region. The random motion is similar to mathematical random walk, Einstein's ever analyzes the random walk problem and obtains the mean square displacement and macroscopic dispersion coefficient of the particles
The time-proportional conclusion, for a one-dimensional problem, this relationship can be expressed as
Wherein
Mean square displacement for one-dimensional random walk, i.e.
Mean of the particle displacement over time squared. For the two-dimensional problem, since the square of the actual displacement distance is equal to the sum of the squares of the displacements in two directions, and the displacements in two directions generally have the property of independent and same distribution, there are
On the other hand, from a probabilistic perspective, two-dimensional random walks are independent of each other in both directions and follow the same 0-mean normal distribution.Therefore, the displacement of the two-dimensional random walk follows Rayleigh distribution, the displacement direction follows uniform distribution, and the displacement direction and the uniform distribution are independent. Are used separately
And
indicating the magnitude and direction of displacement of the particles,
and
representing the corresponding probability density function, then
The expected value of the square of the displacement magnitude is then
By the desired definition
That is to say that
Thus, for random motion in microscopic diffusion analysis, the contaminant particles are in
The displacement magnitude obeying parameter is
The rayleigh distribution of (a) is,wherein
The dispersion coefficient is measured macroscopically. When calculating the displacement caused by random motion, the displacement of each pollutant particle can be obtained by a program generating random numbers, and the direction angle of the displacement is noticed
Generating, shifting according to uniform distribution
Generated according to a rayleigh distribution.
Directional movement
For the two-dimensional problem, assume that the magnitude of the ocean current velocity at the location of the contaminant particles is
The components in two orthogonal directions are respectively
And
then after a short time
The displacement of the pollutant particles due to the directional movement is of the magnitude
The displacement components in the two directions are respectively
And
. It should be noted that the premise of calculating the directional motion displacement is that the magnitude and direction of the motion speed of the pollutant particles in a time step are not changed greatly,therefore, it is not only easy to use
The value of (A) should not be too large. The displacement of random motion and the displacement of directional motion are added according to the vector to obtain the pollutant particles
The actual displacement of the inner.
Random attenuation
For contaminant decay processes, such as decay, degradation, conversion, etc., decay constants are generally used
To characterize. From a microscopic point of view, the decay constant represents the probability of a single particle to decay within a unit of time, and therefore, random decay can be achieved in this way: at a time step
Inside, any contaminant particle has
The probability of (2) disappears. When in use
When small, the probability of disappearance can be approximated
And (4) calculating.
The specific process of the embodiment of the method is as follows:
(1) determining basic information of pollution diffusion process
The basic information of the process of pollution diffusion includes: the number of pollution sources, the positions, the discharge modes and the time schedules (one-time discharge or multiple discharge) of the pollution sources and the amount of discharged pollutants; ② boundaries for spreading of pollution, e.g. coasts, seabed, dykes, connected but negligible to the body of water to be analyzedOther water bodies; dispersion coefficient of pollutant in water body to be analyzed
For subsequent random motion calculation; fourthly, the flow field distribution of the water body to be analyzed in the simulation time period, namely the water flow speed of each point in the given time; attenuation constant of pollutant in water body to be analyzed
。
(2) Determining a time step and a quantity represented by each contaminant particle
Comprehensively considering the precision requirement of simulation and the time consumption of calculation, selecting a proper simulation time step length
. The amount represented by each pollutant particle is determined based on the total amount of each emission of pollutant. For example, in the single-source pollution problem, 1 ton of pollutant is charged into the pollution source area at a time, and if 1 ton of pollutant is divided into 1000 pollutant particles, each pollutant particle represents 1kg of pollutant.
(3) Initiating contaminant particles that contaminate a diffusion simulation zone
For the area to be simulated, the concentration of the pollutants at each position can be represented by the density of the pollutant particles, so that the concentration of the pollutants at each position can be calculated by recording the position of each pollutant at a specific moment. At the initial moment, the initial pollutant amount of each area is calculated according to the pollutant concentration of each pollution source and the size of the pollution source area, then the equivalent pollutant particle amount is calculated, and then the corresponding amount of pollutant particles are generated and uniformly and randomly put into the pollution source area. For each contaminant particle, identified by a separate number, the status of each contaminant particle includes its location coordinates and whether attenuation has occurred. When a contaminant particle is generated, it is given a globally unique number, initialized with the coordinates of its location and marked as not attenuated.
(4) Iterative computation of states of contaminant particles
Current time of day
The following steps are executed in a loop until
The simulation end time is reached.
And for each pollution source, judging whether pollutants need to be discharged at the current moment or not according to the pollutant discharge schedule and the discharge amount, and if so, generating a corresponding amount of pollutant particles and adding the pollutant particles into the area where the pollution source is located.
All contaminant particles that have not been attenuated are traversed, their state changes are calculated and updated. Wherein the change in state of each contaminant particle is calculated as follows. First, calculate
Probability of attenuation of internal contaminant particles
Then a random program is used to simulate the probability of one occurrence as
Independent events of (2). If the event occurs, determining that the contaminant particles are attenuated, and marking the contaminant particles as attenuated; otherwise, the following is continued. Randomly generating the displacement direction of the pollutants according to the uniform distribution and the parameters of
The Rayleigh distribution randomly generates the displacement of the pollutant to obtain the displacement of random motion, and adds the displacement of the random motion to the position of the pollutant particle to obtain a new position. If the new position is within the simulation zone (not beyond the boundary), the position of the contaminant particle is updated to the new position. According to the position of the contaminating particlesVelocity of ocean current
Calculating the displacement of the directional movement of the pollutant particles
And adding the position of the pollutant particles to the displacement of the directional motion to obtain a new position. If the new position is within the simulation zone (not beyond the boundary), the position of the contaminant particle is updated to the new position again.
Increase the current time
That is to say
。
(5) Calculating a contaminant concentration profile
And (3) carrying out proper grid division on the polluted region, counting pollutant particles contained in each sub-region at each time point, multiplying the quantity represented by each pollutant particle by the number of the pollutant particles, and dividing by the area or the volume of the sub-region to obtain the pollutant concentration of each sub-region at each time point.
(6) Obtaining simulation results
For the condition that only the pollutant concentration distribution is needed, the pollutant concentration of each sub-area obtained in the process (5) is reserved; when the pollutant diffusion path needs to be analyzed, the state of all pollutant particles at each time point needs to be preserved.
Based on the diffusion simulation prediction method, another embodiment provides a two-dimensional diffusion simulation device applicable to global oceans.
The implementation process of the simulation device can be divided into the following 5 modules: the device comprises a boundary management module, a flow field management module, a diffusion simulation module, a concentration calculation module and a result display module. The relationship between the modules is shown in fig. 2, and the functions are as follows:
(1) boundary management module
For providing boundary information in the sea area, mainly the boundary between sea and land. The specific functions of the module are as follows: for each input longitude and latitude, a Boolean value can be output to indicate whether the position represented by the longitude and latitude is outside the simulated sea area, so as to determine the boundary of pollution diffusion.
(2) Flow field management module
For providing flow field information at the sea surface. The specific functions are as follows: and outputting the ocean current speed at the corresponding sea surface at the corresponding moment for each input time and longitude and latitude.
(3) Diffusion simulation module
The core content of the iteration of the diffusion process is the management of the pollution particle list. The specific functions are as follows: reading boundary information from a boundary management module, setting pollution source information and initializing a pollutant particle list; in each time step, adding pollutant particles discharged by a pollution source into a pollutant particle list, then sequentially calculating the state change of the pollutant particles caused by random attenuation, random motion and directional motion, and iterating the process; the resulting particulate state is stored in a tabular data file.
(4) Concentration conversion module
For conversion of contaminant particulate state to contaminant concentration. The specific functions are as follows: calculating the pollutant concentration according to the division of the polluted area and the state of the pollutant particles at each moment; and storing the obtained pollutant concentration distribution in a grid data file.
(5) Result display module
And displaying the simulation result. The specific functions are as follows: and reading simulation result data from the table data file or the grid data file, and performing visual display in a graphic and animation mode.
Examples of the invention
The device is realized through two programming languages of C # and python, wherein a program written by C # is mainly responsible for generation and iteration of a pollution particle list, and a simulation result is stored in a table data file or a grid data file; and the program written by python is mainly responsible for the visual display of the simulation result.
(1) Boundary data
The boundary data is open source ETOPO1 terrain data (https:// www.ngdc.noaa.gov/mgg/global /), contains global elevation grid data divided by longitude and latitude, and has the data precision of 1/60 degrees. The boundary management module reads terrain data from the Etopo1 terrain data, and takes the part (land) with the elevation greater than or equal to 0 as a default boundary and the part (sea) with the elevation less than 0 as a simulated water body area.
(2) Ocean current data
Ocean current data is open source OSCAR data, comprises sea surface ocean current data between 80 degrees S and 80 degrees N since 1980, and has the data accuracy of 1/3 degrees and 5 days. And the flow field management module reads flow field data, namely ocean surface ocean current velocity of a specified position at a specified time from the OSCAR data, and provides the flow field data to the diffusion simulation module for calculation in the migration process.
(3) XLSX-based contaminant particle status data storage
Depositing contaminant particles in two dimensions, time and number, as tabular data in XLSX format, wherein each cell may store the following text: empty text indicating that the particle of the corresponding number has decayed at the corresponding time; two or three floating point numbers in text format separated by line breaks represent the spatial coordinates of the particle that has not yet decayed.
(4) Netcdf-based pollutant concentration data storage
The Network Common Data Form (NetCDF) is a mesh Data format suitable for environment Data, and generally takes ". nc" as a file suffix. The device writes the concentration distribution matrix into the NetCDF file in sequence according to the simulated time nodes in the concentration conversion module.
(5) Matplotlib-based result visualization
Matplotlib is an open source graphics library in python, and can draw and store graphics and animations in various formats. The device realizes the dynamic display of the pollutant diffusion process and the concentration distribution through the functions of a contour line color map and the like, as shown in figure 3; the display of the contaminant diffusion path is achieved by the line-graph function, as shown in fig. 4.
The background of the present invention may contain background information related to the problem or environment of the present invention and does not necessarily describe the prior art. Accordingly, the inclusion in the background section is not an admission of prior art by the applicant.
The foregoing is a more detailed description of the invention in connection with specific/preferred embodiments and is not intended to limit the practice of the invention to those descriptions. It will be apparent to those skilled in the art that various substitutions and modifications can be made to the described embodiments without departing from the spirit of the invention, and these substitutions and modifications should be considered to fall within the scope of the invention. In the description herein, references to the description of the term "one embodiment," "some embodiments," "preferred embodiments," "an example," "a specific example," or "some examples" or the like are intended to mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Various embodiments or examples and features of various embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction. Although embodiments of the present invention and their advantages have been described in detail, it should be understood that various changes, substitutions and alterations can be made herein without departing from the scope of the claims.
Claims (6)
1. A water body pollution diffusion simulation prediction method based on particle motion is characterized by comprising the steps of dividing pollutants in a simulation area into a large number of pollutant particles, and calculating the motion state change of each pollutant particle in the simulation area; decomposing the motion state change process of the pollutant particles into three independent sub-processes, namely directional motion, random motion and random attenuation, performing independent calculation and analysis on each sub-process, and finally superposing the sub-processes to obtain the whole diffusion process of the pollutant particles; continuously iterating the diffusion process of the pollutant particles, counting the pollutant particles in the sub-areas of the simulation area subdivision at a selected time point, and representing the pollutant concentration at the corresponding sub-area by the number of the particles in each sub-area;
the random motion is computationally analyzed as follows: time step of contaminant particles
The displacement magnitude obeying parameter is
Rayleigh distribution of wherein
The dispersion coefficient of the pollutants in the water body to be analyzed; when calculating the displacement caused by random motion, the displacement of each pollutant particle and the displacement direction angle are respectively obtained by a program for generating random numbers
Generated in uniform distribution, magnitude of displacement
Generating according to Rayleigh distribution;
the directional motion is computationally analyzed as follows: the water body flow velocity at which the pollutant particles are located is
The components in two orthogonal directions are respectively
And
at a time step size
The displacement of the contaminant particles due to said directed movement is of the order of magnitude
The displacement components in the two directions are respectively
And
(ii) a The displacement of random motion and the displacement of directional motion are added according to vectors to obtain the pollutant particles
The actual displacement within;
the random attenuation is computationally analyzed as follows: step of time
Any contaminant particles in the container can be removed by
Or
The probability of (a) disappears and,
is the attenuation constant of the pollutant in the water body to be analyzed;
iteratively calculating a state of the contaminant particles, comprising:
from the present moment
Initially, the following steps are performed cyclically:
for each pollution source, judging whether pollutants are discharged at the current moment or not according to a predetermined pollutant discharge schedule and discharge amount, if so, generating a corresponding amount of pollutant particles and adding the pollutant particles to an area where the pollution source is located;
traversing all the contaminant particles which are not attenuated, calculating the state change of the contaminant particles and updating the state change; wherein the change in state of each contaminant particle is calculated as follows: first, calculate
Probability of attenuation of internal contaminant particles
Then a random program is used to simulate the probability of one occurrence as
An independent event of (2); if the event occurs, determining that the contaminant particles are attenuated, and marking the contaminant particles as attenuated; otherwise, the following contents are continuously executed;
randomly generating the displacement direction of the pollutants according to the uniform distribution and the parameters of
The Rayleigh distribution randomly generates the displacement of the pollutant to obtain the displacement of random motion, and adds the displacement of the random motion to the position of the pollutant particles to obtain a new position; if the new position is in the simulation area, updating the position of the pollutant particles to the new position; according to the water flow velocity of the position of the pollutant particles
Calculating the displacement of the directional movement of the pollutant particles
Adding the position of the pollutant particles to the displacement of the directional motion to obtain a new position; if the new position is in the simulation area, the position of the pollutant particles is updated to the new position again;
the current time
Increase of
Repeating the above steps until
The simulation end time is reached.
2. The simulated method for predicting water body pollution diffusion as claimed in claim 1, wherein the amount of pollutants represented by each pollutant particle is determined according to the total amount of pollutants discharged each time;
at the initial moment, calculating the initial pollutant amount of each pollution source region according to the pollutant concentration of each pollution source and the size of the pollution source region, then calculating the equivalent pollutant particle amount, and then generating a corresponding amount of pollutant particles and uniformly and randomly putting the pollutant particles into the pollution source region; for each pollutant particle, a single number is used for identifying, and the state of each pollutant particle comprises the position coordinate of the pollutant particle and whether attenuation occurs; when a contaminant particle is generated, it is given a globally unique number, initialized with the coordinates of its location and marked as not attenuated.
3. The method of any one of claims 1 to 2, wherein counting the number of contaminant particles contained in each sub-region at a selected point in time, and characterizing the contaminant concentration at the respective sub-region by the number of particles in each sub-region, comprises: the contaminant concentration at a selected point in time for each sub-region is obtained by multiplying the number of contaminant particles by the amount represented by each contaminant particle and dividing by the area or volume of the sub-region.
4. The water body pollution diffusion simulation prediction method of any one of claims 1 to 2, further comprising: the contaminant diffusion path is analyzed based on the state of at least some or all of the contaminant particles at each selected point in time.
5. The water body pollution diffusion simulation prediction method of any one of claims 1 to 2, further comprising: and carrying out visual display in a graphic and animation mode according to the simulation result data.
6. A water body pollution diffusion simulation prediction device based on particle motion is characterized by comprising: a processor and a computer readable storage medium storing a computer program which, when executed by the processor, performs the water body pollution diffusion simulation prediction method of any one of claims 1 to 5.
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