A method for simulating the dynamic fate and transport of pollutants in a lake supplied with reclaimed water

Abstract

The application discloses a kind of methods for simulating the dynamic fate distribution of pollutants in lake supplied by reclaimed water, and relates to the technical field of lake pollutant simulation distribution, comprising the following steps: establishing a lake supply simulation device, simulating the supply of reclaimed water to the lake, and collecting simulation data to obtain pollutant simulation change data;And unit division processing is carried out to obtain concentration diffusion unit data, and peak concentration analysis is carried out to obtain peak concentration distribution data;And distribution change analysis is carried out to obtain pollutant distribution change data;Establish a concentration prediction model to predict the time and area of the occurrence of the peak concentration of pollutants;The application is used to solve the problem that when the dynamic distribution of pollutants in the lake supplied by reclaimed water is obtained, it mainly depends on manual sampling, it is difficult to capture the dynamic change of the diffusion of pollutants in the lake, and the discharge scheme of reclaimed water lacks reliable scientific basis, and the lake may cause water quality to exceed the standard due to excessive discharge and improper discharge time.

Description

Technical Field

[0001] This invention relates to the field of lake pollutant distribution simulation technology, specifically a method for simulating the dynamic distribution of pollutants in lakes replenished by reclaimed water. Background Technology

[0002] Lake pollutant simulation distribution technology refers to a comprehensive technical system that uses lake water as a research carrier, integrates hydrodynamics, environmental chemistry, hydrometeorology, data science and computer simulation technology, and constructs mathematical models or data-driven models to quantitatively describe or predict the migration, diffusion, transformation and degradation processes of pollutants in the spatial range of lakes over time, and finally presents the spatial distribution characteristics and dynamic change laws of pollutant concentration.

[0003] Current methods for obtaining dynamic data on pollutant distribution in lakes replenished with reclaimed water rely primarily on traditional manual sampling. This method has inherent temporal and spatial limitations. When reclaimed water is replenished, pollutants undergo instantaneous diffusion and concentration fluctuations within the lake, and manual sampling cannot achieve real-time, continuous monitoring. It struggles to cover key instantaneous points of pollutant diffusion and cannot fully capture the spatiotemporal changes in pollutant distribution after entering the lake. This results in a lag in understanding the actual distribution of pollutants within the lake, failing to provide accurate dynamic data support for subsequent management. Furthermore, existing methods only passively acquire pollutant concentration data, lacking proactive simulation and prediction capabilities. This lack of reliable scientific basis for reclaimed water discharge schemes means that lakes may experience water quality exceeding standards due to excessive or inappropriate reclaimed water replenishment, potentially causing sudden shocks to the lake ecosystem. Therefore, current methods for obtaining dynamic data on pollutant distribution in lakes replenished with reclaimed water rely primarily on manual sampling, which struggles to capture the dynamic changes in pollutant diffusion within the lake. Moreover, the lack of reliable scientific basis for reclaimed water discharge schemes means that lakes may experience water quality exceeding standards due to excessive or inappropriate discharge. Summary of the Invention

[0004] This invention aims to at least partially solve one of the technical problems in the prior art. It establishes a lake replenishment simulation device to simulate the replenishment of lakes with reclaimed water and collects simulation data to obtain simulated pollutant change data. This data is then processed into unit divisions to obtain concentration diffusion unit data, followed by peak concentration analysis to obtain peak concentration distribution data. Distribution change analysis is then performed to obtain pollutant distribution change data. A concentration prediction model is established, and based on the simulated pollutant change data, the timing and region of pollutant concentration peaks are predicted. This addresses the existing problem that obtaining information on the dynamic distribution of pollutants in lakes replenished with reclaimed water mainly relies on manual sampling, making it difficult to capture the dynamic changes in pollutant diffusion within the lake. Furthermore, the reclaimed water discharge scheme lacks reliable scientific basis, and lakes may experience water quality exceeding standards due to excessive discharge or inappropriate discharge timing.

[0005] To achieve the above objectives, this application provides a method for simulating the dynamic fate and distribution of pollutants in lakes replenished by reclaimed water, comprising the following steps:

[0006] Establish a lake replenishment simulation device, use the device to simulate the replenishment of lakes with reclaimed water, and collect simulation data to obtain simulated pollutant change data;

[0007] The data on simulated changes in pollutants are divided into units to obtain concentration diffusion unit data, and peak concentration analysis is performed to obtain peak concentration distribution data.

[0008] Based on the concentration diffusion unit data and the pollutant simulation change data, the distribution change data of pollutants were obtained by performing distribution change analysis.

[0009] Establish a concentration prediction model and predict the time and region of pollutant concentration peaks based on simulated pollutant change data.

[0010] Furthermore, a lake recharge simulation device is established to simulate the recharge of lakes with reclaimed water, and simulation data is collected to obtain simulated pollutant change data, including the following sub-steps:

[0011] The lake to be simulated is denoted as the first lake. A physical model of the first lake is established and denoted as the lake model. A lake replenishment simulation device is established, which includes the lake model, a water quality flow sensing and control device, a water supply pipe, a reclaimed water discharge outlet, and a detachable baffle.

[0012] Based on the material composition at the bottom of the first lake, the same material was sampled to fill the bottom of the lake model, and the lake model was filled with water from the first lake.

[0013] Multiple water quality sensor probes are evenly placed in the lake model and recorded as water quality sensor points. A plane rectangular coordinate system is established under the top-down view of the lake model and recorded as the lake model coordinate system. The coordinate position of each water quality sensor point in the lake model coordinate system is obtained.

[0014] Furthermore, a lake recharge simulation device is established to simulate the recharge of lakes with reclaimed water, and simulation data is collected to obtain simulated pollutant change data, including the following sub-steps:

[0015] The pollutants to be monitored are sequentially labeled as pollutant 1 - pollutant n, where n is the total number of pollutants to be monitored, and any one pollutant is designated as the first pollutant.

[0016] Collect reclaimed water for replenishing the lake, denoted as replenished reclaimed water. Based on the actual discharge flow rate and total discharge of the replenished reclaimed water in the discharge plan, set the simulated discharge flow rate and total discharge of the replenished reclaimed water.

[0017] The reclaimed water is fed into the lake model based on the simulated discharge flow and total discharge. The concentration of the first pollutant and the direction of water flow at the corresponding water quality sensing point are collected at the first time interval using a water quality index sensing probe. The collection time is recorded as the simulated change data of the first pollutant, where the first time interval is t1.

[0018] Simultaneously, simulated change data for pollutants 1 to n are repeatedly collected to obtain simulated change data for pollutants.

[0019] Furthermore, based on the simulated pollutant change data, the data is divided into units to obtain concentration diffusion unit data, and peak concentration analysis is performed to obtain peak concentration distribution data, including the following sub-steps:

[0020] Any water quality sensing point is designated as the first sensing point. Two water quality sensing points that are closest to the first sensing point and are not entirely on the same straight line as the first sensing point are designated as the second sensing points. The first sensing point and the two second sensing points are connected in sequence to form a triangular region, which is designated as a local diffusion unit.

[0021] Repeatedly acquire all local diffusion units, and denote any one of the local diffusion units as the first unit;

[0022] The simulated change data of the first pollutant are grouped according to the collection time, and any group of data at the same collection time is recorded as the data at the first time.

[0023] Based on the data at the first moment, the water quality sensing point with the highest concentration of the first pollutant and the water quality sensing point with the second highest concentration of the first pollutant in the first unit are obtained and distributed in order as the unit maximum point and the unit second maximum point. The concentration of the first pollutant at the unit maximum point is recorded as the maximum concentration of the first unit.

[0024] Obtain the geometric center of the first element, and denote the direction of the line connecting the second largest point of the element to the largest point of the element as the element gradient direction of the first element;

[0025] Calculate the ratio of the absolute difference in the concentration of the first pollutant between any two water quality sensing points in the first unit to the corresponding straight-line distance, and record it as the concentration decay rate. Calculate the average value of all concentration decay rates in the first unit, and record it as the unit decay rate of the first unit.

[0026] Furthermore, the process of dividing the pollutant simulation change data into units to obtain concentration diffusion unit data, and performing peak concentration analysis to obtain peak concentration distribution data, also includes the following sub-steps:

[0027] Repeatedly obtain the maximum concentration, gradient direction, and decay rate of all local diffusion units; and calculate the average value of all maximum concentrations and the average value of all unit decay rates, which are recorded as the first average concentration AC and the first average decay rate AR in sequence.

[0028] If the maximum concentration of the first unit is greater than AC and the unit decay rate of the first unit is less than AR, then the first unit is marked as a high-concentration unit; otherwise, it is marked as a low-concentration unit, and all high-concentration units are obtained repeatedly.

[0029] For all high-concentration cells, draw a direction arrow starting from the geometric center of each high-concentration cell along the corresponding cell gradient direction, and denot it as the gradient direction arrow of the corresponding high-concentration cell.

[0030] Observe the distribution of all gradient direction arrows and denote the region pointed to by the most gradient direction arrows as the gradient convergence region.

[0031] The high-concentration unit in the gradient convergence region pointed by the gradient direction arrow is denoted as the pointing unit, and any one of the pointing units is denoted as the first pointing unit.

[0032] Furthermore, the process of dividing the pollutant simulation change data into units to obtain concentration diffusion unit data, and performing peak concentration analysis to obtain peak concentration distribution data, also includes the following sub-steps:

[0033] Obtain the sum of the maximum concentrations of all pointing units, denoted as the total pointing concentration ZH; denote the maximum concentration of the first pointing unit as BC, and mark the coordinates of the geometric center of the first pointing unit as (BX, BY), where BX and BY represent the x-coordinate and y-coordinate of the geometric center of the first pointing unit in sequence.

[0034] Calculate the weighted abscissa QX and weighted ordinate QY of the first pointing unit, where QX = BX * BC / ZH and QY = BY * BC / ZH; repeatedly obtain the weighted abscissa and weighted ordinate of all pointing units, and sum the distributions of all weighted abscissas and weighted ordinates, and denote them as AX and AY respectively in order; denote the position with coordinates (AX, AY) as the peak concentration position;

[0035] The peak concentration locations at all acquisition times are repeatedly acquired to obtain the peak concentration distribution data of the first pollutant, and the peak concentration distribution data of the pollutant is repeatedly acquired.

[0036] Furthermore, based on the concentration diffusion unit data and the simulated pollutant change data, a distribution change analysis is performed to obtain the pollutant distribution change data, including the following sub-steps:

[0037] k1 points are uniformly selected from the lake model and denoted as distribution simulation points. The position coordinates of each distribution simulation point are obtained. Any distribution simulation point is denoted as the first distribution point, where k1 is the set number.

[0038] Based on the data at the first moment, obtain the local diffusion unit where the first distribution point is located, denoted as the first distribution unit, obtain the unit gradient direction of the first distribution unit, and obtain the water flow direction at the maximum point of the unit, denoted as the first water flow direction, calculate the angle between the unit gradient direction and the first water flow direction, denoted as BA, 0≤BA≤180;

[0039] Calculate the adaptation weight AQ between the unit gradient direction and the first water flow direction of the first distribution unit, where AQ = k2 * BA + 1; where k2 is the set slope, k3 ≤ AQ ≤ 1, k3 is the set lower limit, 0 ≤ k3.

[0040] Furthermore, the analysis of distribution changes based on concentration diffusion unit data and simulated pollutant change data, to obtain pollutant distribution change data, also includes the following sub-steps:

[0041] Calculate the straight-line distance from the first distribution point to the maximum point of the first distribution unit, denoted as DL; and obtain the angle between the direction of the line connecting the first distribution point to the maximum point of the first distribution unit and the gradient direction of the first distribution unit, denoted as the first angle;

[0042] Calculate the corrected distance XL of the first distribution point, where if the first included angle is less than k4, then XL = DL * AQ, otherwise XL = DL / AQ;

[0043] The unit decay rate and maximum concentration of the first distribution unit are distributed in sequence and denoted as DR and MC. The concentration of the first pollutant at the first distribution point is calculated as FC, where FC = MC - DR * XL.

[0044] The peak concentration locations and the concentration of the first pollutant at each distribution simulation point were repeatedly acquired at all acquisition times to obtain the pollutant distribution change data of the first pollutant; and the pollutant distribution change data of all pollutants were repeatedly acquired.

[0045] Furthermore, establishing a concentration prediction model and predicting the timing and region of pollutant concentration peaks based on simulated pollutant change data includes the following sub-steps:

[0046] The simulated change data of the first pollutant was normalized by scaling all concentrations to [0, 1], and the normalized simulation data was obtained.

[0047] An initial prediction model is constructed based on the spatiotemporal long short-term memory network. The initial prediction model includes an input layer, a core layer, and an output layer. The initial prediction model is trained using normalized simulation data. After completion, the concentration prediction model of the first pollutant is obtained.

[0048] Based on the concentration prediction model of the first pollutant and the normalized simulation data, the concentration of the first pollutant at each water quality sensing point at future time is predicted, and the predicted concentration data of the first pollutant is obtained.

[0049] Furthermore, establishing a concentration prediction model and predicting the time and region of pollutant concentration peaks based on simulated pollutant change data includes the following sub-steps:

[0050] Based on the predicted concentration data of the first pollutant, the peak concentration location and corresponding pollutant distribution change data at each water quality sensing point at the same future time are obtained using the first pollutant concentration at each future time. The location of the peak concentration of the first pollutant at each time and the pollutant concentration distribution of the first pollutant at each time are obtained and recorded as the dynamic distribution data of the first pollutant.

[0051] The dynamic distribution data of all pollutants is repeatedly acquired and retrieved sequentially. If the peak concentration of a certain pollutant at a certain moment is greater than the corresponding threshold, the corresponding emission scheme is deemed unqualified; otherwise, the corresponding emission scheme is deemed qualified.

[0052] The beneficial effects of this invention are as follows: This invention establishes a lake replenishment simulation device to simulate the replenishment of lakes with reclaimed water and collects simulation data to obtain simulated pollutant change data; based on the simulated pollutant change data, it performs unit division processing to obtain concentration diffusion unit data, and performs peak concentration analysis to obtain peak concentration distribution data; based on the concentration diffusion unit data and the simulated pollutant change data, it performs distribution change analysis to obtain pollutant distribution change data; it establishes a concentration prediction model and predicts the time and region of pollutant concentration peak occurrence based on the simulated pollutant change data; it can simulate the dynamic changes of pollutant diffusion in lakes, predict the time and region of pollutant concentration peak occurrence, and simulate the environmental risks of different emission schemes;

[0053] This invention refines the spatial region, maintains computational control, and reduces noise interference in peak concentration determination by dividing the area into local diffusion units and selecting high-concentration units. It then uses the gradient direction arrows of each high-concentration unit to find gradient convergence zones and finally calculates the peak position using weighted coordinates. This results in a more accurate and reliable peak concentration location. Furthermore, by defining the adaptation weight AQ based on the angle between the unit gradient direction and the water flow direction, and combining it with correction distance, unit attenuation rate, and maximum concentration, the invention calculates the concentration at any distribution point, reducing errors when the flow direction and concentration gradient are inconsistent. This more realistically reflects the different diffusion characteristics along and against the flow direction, improving the accuracy of concentration calculation at distribution points. Finally, by establishing a concentration prediction model using a spatiotemporal long short-term memory network, the invention predicts the time and region of pollutant concentration peaks. This can guide the optimization of reclaimed water treatment processes and the adjustment of discharge periods, ensuring the resource utilization of reclaimed water while avoiding sudden impacts on lake ecosystems and reducing the cost of subsequent water quality treatment. Attached Figure Description

[0054] Figure 1 This is a flowchart illustrating the steps of the method of the present invention;

[0055] Figure 2 This is a schematic diagram of the lake replenishment simulation device of the present invention;

[0056] Figure 3 This is a schematic diagram of a local diffusion unit of the present invention;

[0057] Figure 4 This is a schematic diagram of the structure of the electronic device of the present invention;

[0058] In the diagram, 1 is the water quality flow sensing and control device at the outlet; 2 is the detachable baffle; 3 is the water quality index sensing probe; 4 is the lake bottom filling material; 5 is the reclaimed water discharge outlet; 6 is the water supply pipe; and 7 is the water quality flow sensing and control device at the inlet. Detailed Implementation

[0059] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0060] Example 1, please refer to Figure 1 As shown, this application provides a method for simulating the dynamic fate and distribution of pollutants in a lake replenished by reclaimed water, comprising the following steps:

[0061] Step S1 involves establishing a lake recharge simulation device, simulating the recharge of the lake with reclaimed water, and collecting simulation data to obtain simulated pollutant change data. Step S1 includes the following sub-steps:

[0062] For step S101, please refer to... Figure 2 As shown, the lake to be simulated is denoted as the first lake, and a solid model of the first lake is established, denoted as the lake model; a lake replenishment simulation device is established, which includes the lake model, a water quality flow sensing and control device, a water supply pipe, a reclaimed water discharge outlet, and a detachable baffle. The shape and size of the lake replenishment simulation device can be flexibly adjusted according to the shape and size of the lake to be simulated.

[0063] Figure 2 In the diagram, 1 represents the water quality flow sensing and control device at the outlet, and 7 represents the water quality flow sensing and control device at the inlet, used to control the flow rate of water entering and exiting the lake model; 2 represents a detachable baffle, simulating whether there is a rubber dam in the river connecting the lake. If a rubber dam exists, the baffle is installed; otherwise, it is removed; 3 represents a water quality indicator sensing probe, which can monitor the concentration of pollutants and the direction of water flow at the corresponding location in real time; 4 represents lake substrate filling; 5 represents a reclaimed water discharge outlet, used to replenish reclaimed water to the inside of the lake model; and 6 represents a water supply pipe.

[0064] Step S102: Based on the material composition of the bottom of the first lake, sample the same material to fill the bottom of the lake model, and use the lake water of the first lake to fill the lake model; different bottom materials and water bodies will change the pollutant deposition rate, dissolution behavior and diffusion rate, thereby affecting the distribution of pollutants. Therefore, in order to ensure the accuracy of the simulation, the same water body and materials should be sampled to fill the lake model.

[0065] Step S103: Evenly place multiple water quality indicator sensors in the lake model, denoted as water quality sensing points. Establish a Cartesian coordinate system from the top-down view of the lake model, denoted as the lake model coordinate system. Obtain the coordinate position of each water quality sensing point in the lake model coordinate system. The number of water quality indicator sensors can be set according to the actual application scenario. Figure 2 This is only a schematic diagram of a lake recharge simulation device; in reality, the number of water quality sensor probes would be much greater. Figure 2 The number in.

[0066] Step S104: The pollutants to be monitored are sequentially recorded as pollutant 1 - pollutant n, where n is the total number of pollutants to be monitored, and any one pollutant is recorded as the first pollutant; for example, pollutant indicators such as GOD, ammonia nitrogen, total phosphorus and total nitrogen;

[0067] Step S105: Collect reclaimed water used to replenish the lake, denoted as reclaimed water replenishment. Based on the actual discharge flow rate and total discharge amount of reclaimed water replenishment in the discharge plan, set the simulated discharge flow rate and total discharge amount of reclaimed water replenishment. Determine the simulated discharge flow rate and total discharge amount based on the ratio between the actual lake storage and the lake model storage, as well as the actual discharge flow rate and total discharge amount of reclaimed water replenishment in the discharge plan. Ensure that the discharge duration and discharge proportion are basically consistent to guarantee the accuracy of the simulation.

[0068] Step S106: Based on the simulated discharge flow rate and total discharge, the replenished reclaimed water is input into the lake model. Using a water quality index sensing probe, the concentration of the first pollutant and the direction of water flow at the corresponding water quality sensing point are collected at a first time interval, and the collection time is recorded as the simulated change data of the first pollutant. The first time interval is t1. In this embodiment, the first time interval is 0.1 seconds, which can be set according to the actual application scenario.

[0069] Step S107: Simultaneously collect simulated change data of pollutant 1 to pollutant n to obtain simulated change data of pollutants.

[0070] In the specific implementation process, different simulated emission schemes can be determined based on different actual emission schemes, thereby simulating the diffusion paths and concentration decay patterns of pollutants such as GOD, ammonia nitrogen, total phosphorus, and total nitrogen in lakes under different emission volumes and emission methods. Simulating the environmental risks of different emission schemes in advance can guide the optimization of reclaimed water treatment processes and the adjustment of emission periods, ensuring the resource utilization of reclaimed water while avoiding sudden impacts on the lake ecosystem and reducing the cost of subsequent water quality treatment.

[0071] Step S2 involves dividing the pollutant simulation change data into units to obtain concentration diffusion unit data, and then performing peak concentration analysis to obtain peak concentration distribution data. Step S2 includes the following sub-steps:

[0072] For step S201, please refer to... Figure 3 As shown, any water quality sensing point is designated as the first sensing point. Two water quality sensing points that are closest to the first sensing point but not entirely on the same straight line as the first sensing point are selected, i.e., the three points are not on the same straight line, and are designated as the second sensing points. The first sensing point and the two second sensing points are connected in sequence to form a triangular region, which is designated as a local diffusion unit. Because the water quality sensing points are uniformly distributed, the water quality sensing points located in the middle part will have multiple sets of corresponding second sensing points, i.e., they will belong to multiple local diffusion units at the same time. The closer the monitoring points are, the greater the influence of the same diffusion process, and the more uniform the characteristics such as concentration gradient and decay rate are.

[0073] Step S202: Repeat the acquisition of all local diffusion units, and record any one of the local diffusion units as the first unit;

[0074] Step S203: Group the simulated change data of the first pollutant according to the collection time, that is, the data at the same collection time are grouped together, and any group of data at the same collection time is recorded as the data at the first time.

[0075] Step S204: Based on the data at the first moment, obtain the water quality sensing point with the highest concentration of the first pollutant in the first unit and the water quality sensing point with the second highest concentration of the first pollutant, and record them as the unit maximum point and the unit second maximum point in order. Record the concentration of the first pollutant at the unit maximum point as the maximum concentration of the first unit.

[0076] Step S205: Obtain the geometric center of the first unit, and denote the direction of the line connecting the second largest point of the unit to the largest point of the unit as the unit gradient direction of the first unit; the unit gradient direction reflects the core direction of the concentration increase within the first unit.

[0077] Step S206: Calculate the ratio of the absolute difference of the concentration of the first pollutant between any two water quality sensing points in the first unit to the corresponding straight-line distance, and record it as the concentration decay rate. Calculate the average value of all concentration decay rates in the first unit, and record it as the unit decay rate of the first unit. The unit decay rate reflects the rate at which the concentration in the first unit decays with distance.

[0078] Step S207: Repeatedly obtain the maximum concentration, unit gradient direction and unit decay rate of all local diffusion units; and calculate the average value of all maximum concentrations and the average value of all unit decay rates, which are recorded as the first average concentration AC and the first average decay rate AR in order; using the overall average at the current moment as the reference threshold means that the threshold will be adaptively adjusted with the change of the overall pollution situation, which is convenient to identify relatively significant high value areas under different intensity scenarios.

[0079] Step S208: If the maximum concentration of the first unit is greater than AC and the unit decay rate of the first unit is less than AR, then the first unit is marked as a high-concentration unit; otherwise, it is marked as a low-concentration unit. All high-concentration units are obtained repeatedly. A maximum concentration greater than AC indicates that the pollutant concentration in the first unit is high, and a unit decay rate less than AR indicates that the pollutant decays slowly in the first unit and the pollutant is easy to retain.

[0080] Step S209: For all high-concentration units, draw a directional arrow starting from the geometric center of each high-concentration unit along the corresponding unit gradient direction, and record it as the gradient direction arrow of the corresponding high-concentration unit; screening high-concentration units can further focus on the pollutant accumulation area, which is the area where the highest concentration is most likely to exist in the diffusion of reclaimed water.

[0081] Step S210: Observe the distribution of all gradient direction arrows and record the area pointed to by the most gradient direction arrows as the gradient convergence area. The gradient convergence area is the convergence point of the gradient in the surrounding concentration area. Since the concentration decreases along the water flow direction, the gradient direction will point to the high concentration area with high pollutant concentration. Finding the gradient convergence area by the distribution of arrows is essentially to let the local diffusion law point to the high concentration center by itself. This is more in line with physical logic than the traditional weighted average. The weighted average may deviate from the true core due to interference from distant source points.

[0082] Step S211: The high-concentration unit in the gradient convergence region pointed to by the gradient direction arrow is denoted as the pointing unit, and any one of the pointing units is denoted as the first pointing unit.

[0083] Step S212: Obtain the sum of the maximum concentrations of all pointing units, denoted as the total pointing concentration ZH; denote the maximum concentration of the first pointing unit as BC, and mark the coordinates of the geometric center of the first pointing unit as (BX, BY), where BX and BY represent the abscissa and ordinate of the geometric center of the first pointing unit in sequence.

[0084] Step S213: Calculate the weighted abscissa QX and weighted ordinate QY of the first pointing cell, where QX = BX * BC / ZH and QY = BY * BC / ZH; repeatedly obtain the weighted abscissa and weighted ordinate of all pointing cells, and sum the distributions of all weighted abscissas and weighted ordinates, and record them as AX and AY respectively in order; record the position with coordinates (AX, AY) as the peak concentration position; the larger the maximum concentration in the cell, the closer the cell is to the peak concentration position, the greater the influence on the location of the peak concentration position, and the more representative it is of the true peak concentration position; the weighted calculation can make the peak concentration position more biased towards the high concentration area, avoiding center shift due to interference;

[0085] Step S214: Repeatedly acquire the peak concentration location at all acquisition times to obtain the peak concentration distribution data of the first pollutant, and repeat the acquisition of the peak concentration distribution data of the pollutant.

[0086] In the specific implementation process, the local diffusion unit adopts a triangle because the triangle is the closed figure with the fewest sides in the two-dimensional plane. It can cover the two-dimensional space and calculate the local diffusion characteristics, such as gradient direction and attenuation rate, simply and accurately through the coordinates and concentration of 3 points. If a quadrilateral or circle is used, the more points there are, the more likely feature conflicts will occur, and it will be impossible to determine a unified local diffusion law.

[0087] Step S3 involves performing distribution change analysis based on concentration diffusion unit data and pollutant simulation change data to obtain pollutant distribution change data. Step S3 includes the following sub-steps:

[0088] Step S301: Select k1 points uniformly from the lake model, denoted as distribution simulation points, and obtain the position coordinates of each distribution simulation point; denot any one distribution simulation point as the first distribution point, where k1 is the set number; expand the unitized information generated by the discrete sensing points (observation points) into a continuous or semi-continuous spatial concentration field for easy visualization; k1 can be flexibly set according to the actual application scenario. The larger k1 is, the more refined the pollutant distribution is, but the computational load will also be greater. In this embodiment, k1=400;

[0089] Step S302: Based on the data at the first moment, obtain the local diffusion unit where the first distribution point is located, denoted as the first distribution unit, obtain the unit gradient direction of the first distribution unit, and obtain the water flow direction at the maximum point of the unit, denoted as the first water flow direction, calculate the angle between the unit gradient direction and the first water flow direction, denoted as BA, 0≤BA≤180; the diffusion laws of different units are significantly different, and the concentration of the target point must be derived from the law of its unit to avoid using incorrect laws and causing deviations in the calculation results;

[0090] Step S303: Calculate the adaptation weight AQ between the unit gradient direction and the first water flow direction of the first distribution unit, where AQ = k2 * BA + 1; where k2 is the set slope, k3 ≤ AQ ≤ 1, k3 is the set lower limit, 0 ≤ k3; the adaptation weight AQ is used to correct the influence of water flow on the concentration decay rate. When the unit gradient direction is in the same direction as the first water flow direction, the concentration decay rate is corrected to be smaller, which conforms to the physical law of slow downstream diffusion; when they are in opposite directions, the concentration decay rate is amplified, which conforms to the characteristic of fast countercurrent diffusion; where AQ and the corresponding k2 can be set according to the actual application scenario. In this embodiment, 0.4 ≤ AQ ≤ 1, that is, when BA = 0, AQ = 1; when BA = 180, AQ = 0.4, so k2 = -1 / 300;

[0091] Step S304: Calculate the straight-line distance from the first distribution point to the maximum point of the first distribution unit, denoted as DL; and obtain the angle between the direction of the line connecting the first distribution point to the maximum point of the first distribution unit and the gradient direction of the first distribution unit, denoted as the first angle.

[0092] Step S305: Calculate the corrected distance XL of the first distribution point. If the first included angle is less than k4, then XL = DL * AQ; otherwise, XL = DL / AQ. In this embodiment, k4 = 45°, which can be flexibly set. If the first included angle is less than k4, the corrected XL becomes smaller, indicating that pollutants are more likely to diffuse along this direction. Therefore, the concentration is higher at the same physical distance. If the first included angle is not less than k4, the corrected XL becomes smaller or larger, indicating that the diffusion of pollutants in this direction is inhibited, and the concentration is lower at the same physical distance.

[0093] Step S306: The unit decay rate and maximum concentration of the first distribution unit are distributed in sequence and denoted as DR and MC. The concentration of the first pollutant at the first distribution point is calculated as FC, where FC = MC - DR * XL.

[0094] Step S307: Repeatedly acquire the peak concentration location and the first pollutant concentration at each distribution simulation point at all acquisition times to obtain the pollutant distribution change data of the first pollutant; and repeat the acquisition of the pollutant distribution change data of all pollutants.

[0095] In the specific implementation process, if the distribution simulation point is selected from the edge inside the lake model and is not contained by the local diffusion unit, the local diffusion unit closest to the distribution simulation point can be obtained as the first distribution unit of the distribution simulation point and then processed.

[0096] Step S4: Establish a concentration prediction model and predict the time and region of pollutant concentration peaks based on simulated pollutant change data; Step S4 includes the following sub-steps:

[0097] Step S401: Normalize the simulated change data of the first pollutant by scaling all concentrations to [0, 1] to obtain normalized simulation data; avoid the model being biased towards large numerical features due to dimensional differences.

[0098] Step S402: Construct an initial prediction model based on the spatiotemporal long short-term memory network. The initial prediction model includes an input layer, a core layer, and an output layer. Train the initial prediction model using normalized simulation data. After completion, obtain the concentration prediction model for the first pollutant. The diffusion patterns of different pollutants may be different, so separate concentration prediction models need to be established for different pollutants.

[0099] Step S403: Based on the concentration prediction model of the first pollutant and the normalized simulation data, predict the concentration of the first pollutant at each water quality sensing point at future time, and obtain the predicted concentration data of the first pollutant.

[0100] Step S404: Based on the predicted concentration data of the first pollutant, the peak concentration location and corresponding pollutant distribution change data at each water quality sensing point at the same future time are obtained using the first pollutant concentration at each future time. The location of the peak concentration of the first pollutant at each time and the pollutant concentration distribution of the first pollutant at each time are obtained. That is, unit division processing is performed, then peak concentration analysis is performed, and then distribution change analysis is performed. This is recorded as the dynamic distribution data of the first pollutant. The dynamic distribution data of the first pollutant includes simulated data and data predicted using simulated data.

[0101] Step S405: Repeatedly acquire dynamic distribution data of all pollutants and search them sequentially. If the peak concentration of a pollutant at a certain moment is greater than the corresponding threshold, the corresponding emission scheme is deemed unqualified; otherwise, the corresponding emission scheme is deemed qualified. The corresponding threshold can be set according to the actual application scenario and relevant standards. Based on the simulated and predicted data, the environmental risks of different emission schemes are obtained, and the time and area of ​​the peak concentration of pollutants during the emission process are obtained to avoid the lake from exceeding water quality standards due to excessive discharge or improper timing of discharge.

[0102] In the specific implementation process, the Spatiotemporal Long Short-Term Memory Network (ST-LSTM) is used. The core advantage of using the ST-LSTM network to build a prediction model is that it can simultaneously capture the time dependence of pollutant concentration changes, such as the degradation and diffusion effects of pollutants over time, as well as spatial correlations, such as the diffusion of pollutants from the emission outlet to surrounding monitoring points and the mutual influence of concentrations in different regions.

[0103] Example 2, please refer to Figure 4 As shown, Figure 4A schematic diagram of an electronic device is provided, which may include a processor, a communication interface, a memory, and a communication bus. The processor, communication interface, and memory communicate with each other via the communication bus. The memory stores computer-readable instructions, which the processor can call. When the processor executes a computer-readable instruction, it performs steps such as those in a method for simulating the dynamic distribution of pollutants in a lake replenished by reclaimed water, to achieve the following functions: establishing a lake replenishment simulation device; simulating the replenishment of a lake by reclaimed water using the simulation device and collecting simulation data to obtain simulated pollutant change data; dividing the simulated pollutant change data into units to obtain concentration diffusion unit data, and performing peak concentration analysis to obtain peak concentration distribution data; performing distribution change analysis based on the concentration diffusion unit data and the simulated pollutant change data to obtain pollutant distribution change data; establishing a concentration prediction model and predicting the time and region of pollutant concentration peaks based on the simulated pollutant change data.

[0104] Furthermore, when the logical instructions in the aforementioned memory can be implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0105] Example 3: This application also provides a computer-readable storage medium storing a computer program. When executed by a processor, the computer program performs the steps of the above-described method for simulating the dynamic distribution of pollutants in a lake replenished by reclaimed water, to achieve the following functions: establishing a lake replenishment simulation device, simulating the replenishment of a lake by reclaimed water using the lake replenishment simulation device, and collecting simulation data to obtain simulated pollutant change data; performing unit division processing based on the simulated pollutant change data to obtain concentration diffusion unit data, and performing peak concentration analysis to obtain peak concentration distribution data; performing distribution change analysis based on the concentration diffusion unit data and the simulated pollutant change data to obtain pollutant distribution change data; establishing a concentration prediction model, and predicting the time and region of the pollutant concentration peak based on the simulated pollutant change data.

[0106] Based on the above description of the embodiments, the embodiments of the present invention can be provided as methods, systems, or computer program products. Based on this understanding, the above technical solutions, in essence or in terms of their contribution to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or certain parts of the embodiments.

[0107] In the embodiments provided in this application, it should be understood that the disclosed system or method can be implemented in other ways. The embodiments described above are merely illustrative. For example, the division of modules or units is only a logical functional division, and there may be other division methods in actual implementation. Furthermore, multiple modules or units may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the coupling or direct coupling or communication connection shown or discussed may be through some communication interfaces. The indirect coupling or communication connection between systems, modules, and units may be electrical, mechanical, or other forms.

[0108] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A method for simulating the dynamic fate and distribution of pollutants in a lake replenished by reclaimed water, characterized in that, Includes the following steps: Establish a lake replenishment simulation device, use the device to simulate the replenishment of lakes with reclaimed water, and collect simulation data to obtain simulated pollutant change data; The data on simulated changes in pollutants are divided into units to obtain concentration diffusion unit data, and peak concentration analysis is performed to obtain peak concentration distribution data. Based on the concentration diffusion unit data and the pollutant simulation change data, the distribution change data of pollutants were obtained by performing distribution change analysis. Establish a concentration prediction model and predict the time and region of pollutant concentration peaks based on simulated pollutant change data; Based on the simulated pollutant change data, the data is divided into units to obtain concentration diffusion unit data. Peak concentration analysis is then performed to obtain peak concentration distribution data, including the following sub-steps: Any water quality sensing point is designated as the first sensing point. Two water quality sensing points that are closest to the first sensing point and are not entirely on the same straight line as the first sensing point are designated as the second sensing points. The first sensing point and the two second sensing points are connected in sequence to form a triangular region, which is designated as a local diffusion unit. Repeatedly acquire all local diffusion units, and denote any one of the local diffusion units as the first unit; The simulated change data of the first pollutant are grouped according to the collection time, and any group of data at the same collection time is recorded as the data at the first time. Based on the data at the first moment, the water quality sensing point with the highest concentration of the first pollutant and the water quality sensing point with the second highest concentration of the first pollutant in the first unit are obtained and distributed in order as the unit maximum point and the unit second maximum point. The concentration of the first pollutant at the unit maximum point is recorded as the maximum concentration of the first unit. Obtain the geometric center of the first element, and denote the direction of the line connecting the second largest point of the element to the largest point of the element as the element gradient direction of the first element; Calculate the ratio of the absolute difference in the concentration of the first pollutant between any two water quality sensing points in the first unit to the corresponding straight-line distance, and record it as the concentration decay rate. Also calculate the average value of all concentration decay rates in the first unit, and record it as the unit decay rate of the first unit. Repeatedly obtain the maximum concentration, gradient direction, and decay rate of all local diffusion units; and calculate the average value of all maximum concentrations and the average value of all unit decay rates, which are recorded as the first average concentration AC and the first average decay rate AR in sequence. If the maximum concentration of the first unit is greater than AC and the unit decay rate of the first unit is less than AR, then the first unit is marked as a high-concentration unit; otherwise, it is marked as a low-concentration unit, and all high-concentration units are obtained repeatedly. For all high-concentration cells, draw a direction arrow starting from the geometric center of each high-concentration cell along the corresponding cell gradient direction, and denot it as the gradient direction arrow of the corresponding high-concentration cell. Observe the distribution of all gradient direction arrows and denote the region pointed to by the most gradient direction arrows as the gradient convergence region. The high-concentration unit in the gradient convergence region pointed by the gradient direction arrow is denoted as the pointing unit, and any one of the pointing units is denoted as the first pointing unit; Obtain the sum of the maximum concentrations of all pointing units, denoted as the total pointing concentration ZH; denote the maximum concentration of the first pointing unit as BC, and mark the coordinates of the geometric center of the first pointing unit as (BX, BY), where BX and BY represent the x-coordinate and y-coordinate of the geometric center of the first pointing unit in sequence. Calculate the weighted abscissa QX and weighted ordinate QY of the first pointing unit, where QX = BX * BC / ZH and QY = BY * BC / ZH; repeatedly obtain the weighted abscissa and weighted ordinate of all pointing units, and sum the distributions of all weighted abscissas and weighted ordinates, and denote them as AX and AY respectively in order; denote the position with coordinates (AX, AY) as the peak concentration position; The peak concentration locations at all acquisition times are repeatedly acquired to obtain the peak concentration distribution data of the first pollutant, and the peak concentration distribution data of the pollutant is repeatedly acquired.

2. The method for simulating the dynamic fate and distribution of pollutants in a lake replenished by reclaimed water according to claim 1, characterized in that, Establishing a lake recharge simulation device, simulating the recharge of lakes with reclaimed water, and collecting simulation data to obtain simulated pollutant change data includes the following sub-steps: The lake to be simulated is denoted as the first lake. A physical model of the first lake is established and denoted as the lake model. A lake replenishment simulation device is established, which includes the lake model, a water quality flow sensing and control device, a water supply pipe, a reclaimed water discharge outlet, and a detachable baffle. Based on the material composition at the bottom of the first lake, the same material was sampled to fill the bottom of the lake model, and the lake model was filled with water from the first lake. Multiple water quality sensor probes are evenly placed in the lake model and recorded as water quality sensor points. A plane rectangular coordinate system is established under the top-down view of the lake model and recorded as the lake model coordinate system. The coordinate position of each water quality sensor point in the lake model coordinate system is obtained.

3. The method for simulating the dynamic fate and distribution of pollutants in a lake replenished by reclaimed water according to claim 2, characterized in that, Establishing a lake recharge simulation device, simulating the recharge of lakes with reclaimed water, and collecting simulation data to obtain simulated pollutant change data includes the following sub-steps: The pollutants to be monitored are sequentially labeled as pollutant 1 - pollutant n, where n is the total number of pollutants to be monitored, and any one pollutant is designated as the first pollutant. Collect reclaimed water for replenishing the lake, denoted as replenished reclaimed water. Based on the actual discharge flow rate and total discharge of the replenished reclaimed water in the discharge plan, set the simulated discharge flow rate and total discharge of the replenished reclaimed water. The reclaimed water is fed into the lake model based on the simulated discharge flow and total discharge. The concentration of the first pollutant and the direction of water flow at the corresponding water quality sensing point are collected at the first time interval using a water quality index sensing probe. The collection time is recorded as the simulated change data of the first pollutant, where the first time interval is t1. Simultaneously, simulated change data for pollutants 1 to n are repeatedly collected to obtain simulated change data for pollutants.

4. The method for simulating the dynamic fate and distribution of pollutants in a lake replenished by reclaimed water according to claim 3, characterized in that, Based on the concentration diffusion unit data and the simulated pollutant change data, the distribution change analysis was performed to obtain the pollutant distribution change data, including the following sub-steps: k1 points are uniformly selected from the lake model and denoted as distribution simulation points. The position coordinates of each distribution simulation point are obtained. Any distribution simulation point is denoted as the first distribution point, where k1 is the set number. Based on the data at the first moment, obtain the local diffusion unit where the first distribution point is located, denoted as the first distribution unit, obtain the unit gradient direction of the first distribution unit, and obtain the water flow direction at the maximum point of the unit, denoted as the first water flow direction, calculate the angle between the unit gradient direction and the first water flow direction, denoted as BA, 0≤BA≤180; Calculate the adaptation weight AQ between the unit gradient direction and the first water flow direction of the first distribution unit, where AQ = k2 * BA + 1; where k2 is the set slope, k3 ≤ AQ ≤ 1, k3 is the set lower limit, 0 ≤ k3.

5. The method for simulating the dynamic fate and distribution of pollutants in a lake replenished by reclaimed water according to claim 4, characterized in that, Based on the concentration diffusion unit data and the simulated pollutant change data, the distribution change analysis to obtain the pollutant distribution change data also includes the following sub-steps: Calculate the straight-line distance from the first distribution point to the maximum point of the first distribution unit, denoted as DL; and obtain the angle between the direction of the line connecting the first distribution point to the maximum point of the first distribution unit and the gradient direction of the first distribution unit, denoted as the first angle; Calculate the corrected distance XL of the first distribution point, where if the first included angle is less than k4, then XL = DL * AQ, otherwise XL = DL / AQ; The unit decay rate and maximum concentration of the first distribution unit are distributed in sequence and denoted as DR and MC. The concentration of the first pollutant at the first distribution point is calculated as FC, where FC = MC - DR * XL. The peak concentration locations and the concentration of the first pollutant at each distribution simulation point were repeatedly acquired at all acquisition times to obtain the pollutant distribution change data of the first pollutant; and the pollutant distribution change data of all pollutants were repeatedly acquired.

6. The method for simulating the dynamic fate and distribution of pollutants in a lake replenished by reclaimed water according to claim 5, characterized in that, Establishing a concentration prediction model and predicting the time and region of pollutant concentration peaks based on simulated pollutant change data includes the following sub-steps: The simulated change data of the first pollutant was normalized by scaling all concentrations to [0, 1], and the normalized simulation data was obtained. An initial prediction model is constructed based on the spatiotemporal long short-term memory network. The initial prediction model includes an input layer, a core layer, and an output layer. The initial prediction model is trained using normalized simulation data. After completion, the concentration prediction model of the first pollutant is obtained. Based on the concentration prediction model of the first pollutant and the normalized simulation data, the concentration of the first pollutant at each water quality sensing point at future time is predicted, and the predicted concentration data of the first pollutant is obtained.

7. The method for simulating the dynamic fate and distribution of pollutants in a lake replenished by reclaimed water according to claim 6, characterized in that, Establishing a concentration prediction model and predicting the time and region of pollutant concentration peaks based on simulated pollutant change data also includes the following sub-steps: Based on the predicted concentration data of the first pollutant, the peak concentration location and corresponding pollutant distribution change data at each water quality sensing point at the same future time are obtained using the first pollutant concentration at each future time. The location of the peak concentration of the first pollutant at each time and the pollutant concentration distribution of the first pollutant at each time are obtained and recorded as the dynamic distribution data of the first pollutant. The dynamic distribution data of all pollutants is repeatedly acquired and retrieved sequentially. If the peak concentration of a certain pollutant at a certain moment is greater than the corresponding threshold, the corresponding emission scheme is deemed unqualified; otherwise, the corresponding emission scheme is deemed qualified.

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