Method for simulating dynamic fate distribution of pollutants in reclaimed water replenishment lake

Abstract

The invention discloses a method for simulating dynamic fate distribution of pollutants in a reclaimed water replenishment lake, and relates to the technical field of lake pollutant simulation distribution, and the method comprises the following steps: establishing a lake replenishment simulation device, simulating the reclaimed water replenishment lake, and collecting simulation data to obtain pollutant simulation change data; carrying out unit division processing to obtain concentration diffusion unit data, and carrying out peak concentration analysis to obtain peak concentration distribution data; carrying out distribution change analysis to obtain pollutant distribution change data; establishing a concentration prediction model, and predicting the occurrence time and region of a pollutant concentration peak value; the method is used for solving the problems that when the dynamic distribution of pollutants in a reclaimed water supply lake is obtained at present, manual sampling is mainly depended, the dynamic change of diffusion of the pollutants in the lake is difficult to capture, and a reliable scientific basis is lacked in a reclaimed water discharge scheme; the problem that the water quality exceeds the standard due to excessive discharge and improper discharge opportunity of the lake is solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of lake pollutant simulation distribution, in particular to a method for simulating dynamic distribution of pollutants in a lake replenished by reclaimed water. BACKGROUND

[0002] Lake pollutant simulation distribution technology refers to a comprehensive technical system that takes lake water as a research carrier, integrates hydrodynamics, environmental chemistry, hydrology and meteorology, data science and computer simulation technology, quantitatively describes or predicts the migration, diffusion, transformation and degradation process of pollutants in the spatial range of the lake over time, and finally presents the spatial distribution characteristics and dynamic change law of pollutant concentration.

[0003] The existing method for obtaining the dynamic distribution of pollutants in a lake replenished by reclaimed water mainly relies on the traditional method of manual sampling, which has natural spatial and temporal limitations. When the lake is replenished by reclaimed water, pollutants will spread instantaneously and fluctuate in concentration in the lake along with the flow of the replenishing water. Manual sampling cannot achieve real-time continuous monitoring, making it difficult to cover the key instantaneous nodes of pollutant diffusion and unable to fully capture the spatiotemporal variation law of the dynamic distribution of pollutants from the lake into the lake. This results in a lag in the understanding of the actual distribution of pollutants in the lake and the inability to provide accurate dynamic data support for subsequent control. Moreover, the existing method only passively obtains pollutant concentration data and lacks the ability to actively simulate and predict. There is a lack of reliable scientific basis for the discharge scheme of reclaimed water, and the lake may exceed the water quality standard due to excessive replenishment of reclaimed water and improper timing of the replenishment, thereby causing a sudden impact on the lake ecosystem. Therefore, the existing method for obtaining the dynamic distribution of pollutants in a lake replenished by reclaimed water mainly relies on manual sampling, which is difficult to capture the dynamic changes of pollutant diffusion in the lake, and the discharge scheme of reclaimed water lacks reliable scientific basis, which may cause the lake to exceed the water quality standard due to excessive discharge or improper timing of the discharge. SUMMARY

[0004] The present invention aims to solve one of the technical problems in the prior art to at least a certain extent, by establishing a lake supply simulation device, using the lake supply simulation device to simulate the reclaimed water to recharge the lake, and collecting simulation data to obtain pollutant simulation change data; and performing unit division processing to obtain concentration diffusion unit data, and then performing peak concentration analysis to obtain peak concentration distribution data; and performing distribution change analysis to obtain pollutant distribution change data; establishing a concentration prediction model, and predicting the time and area of ​​the occurrence of the pollutant concentration peak based on the pollutant simulation change data, so as to solve the existing problem that when obtaining the dynamic distribution of pollutants in the reclaimed water recharge lake, it mainly relies on manual sampling, which makes it difficult to capture the dynamic changes of pollutant diffusion in the lake, and the reclaimed water discharge plan lacks a reliable scientific basis. The lake may cause water quality to exceed the standard due to excessive discharge and inappropriate discharge timing.

[0005] To achieve the above objectives, the present application provides a method for simulating the dynamic fate and distribution of pollutants in a lake supplied by recycled water, comprising the following steps: Establish a lake recharge simulation device, use it to simulate the recharge of lakes with recycled water, collect simulation data, and obtain simulated pollutant change data; Perform unit division processing based on pollutant simulation change data to obtain concentration diffusion unit data, and perform peak concentration analysis to obtain peak concentration distribution data; Perform distribution change analysis based on concentration diffusion unit data and pollutant simulation change data to obtain pollutant distribution change data; Establish a concentration prediction model and predict the time and area of ​​peak pollutant concentration based on pollutant simulation change data.

[0006] Furthermore, a lake recharge simulation device is established, and the lake recharge simulation device is used to simulate the recharge of lakes with recycled water, and simulation data is collected to obtain simulated pollutant change data, including the following sub-steps: The lake to be simulated is recorded as the first lake, and a physical model of the first lake is established, which is recorded as the lake model; a lake recharge simulation device is established, and the lake recharge simulation device includes a lake model, a water quality flow sensing control device, a water pipe, a recycled water discharge port, and a detachable baffle; According to the material composition of the bottom of the first lake, the same material is sampled to fill the bottom of the lake model, and the lake model is filled with water from the first lake; Multiple water quality index sensing probes are evenly placed in the lake model, recorded as water quality sensing points, and a plane rectangular coordinate system is established at the top-down perspective of the lake model, recorded as the lake model coordinate system, to obtain the coordinate position of each water quality sensing point in the lake model coordinate system.

[0007] Further, a lake recharge simulation device is established, the lake is simulated to be recharged by the reclaimed water by using the lake recharge simulation device, and simulation data is collected to obtain the pollutant simulation change data, including the following sub-steps: The pollutants to be monitored are sequentially recorded as pollutant 1-pollutant n, wherein n is the total number of the pollutants to be monitored, and any one pollutant is recorded as a first pollutant; The reclaimed water for recharging the lake is collected and recorded as the recharge reclaimed water, the discharge flow rate and the discharge total amount of the recharge reclaimed water in the discharge scheme are set according to the actual discharge flow rate and the discharge total amount of the recharge reclaimed water. The recharge reclaimed water is input into the lake model according to the simulated discharge flow rate and the discharge total amount, and the concentration of the first pollutant and the water flow direction of the corresponding water quality sensing point are collected at a first time interval by using the water quality index sensing probe, and the collection time is recorded as the simulation change data of the first pollutant, wherein the first time interval is t1. Meanwhile, the simulation change data of the pollutants 1-pollutant n is repeatedly collected to obtain the pollutant simulation change data.

[0008] Further, the unit division processing is performed according to the pollutant simulation change data to obtain the concentration diffusion unit data, and the peak concentration analysis is performed to obtain the peak concentration distribution data, including the following sub-steps: Any one water quality sensing point is recorded as a first sensing point, two water quality sensing points closest to the first sensing point and not on a straight line with the first sensing point are selected and recorded as second sensing points, and the first sensing point and the two second sensing points are sequentially connected to form a triangular region, which is recorded as a local diffusion unit. All the local diffusion units are repeatedly obtained, and any one local diffusion unit is recorded as a first unit. The simulation change data of the first pollutant is grouped according to the collection time, and any one group of data at the same collection time is recorded as first time data. Based on the first time data, the water quality sensing point with the maximum concentration of the first pollutant in the first unit and the water quality sensing point with the second maximum concentration of the first pollutant are obtained, and are sequentially distributed and recorded as a unit maximum point and a unit second maximum point, and the first pollutant concentration of the unit maximum point is recorded as the maximum concentration of the first unit. The geometric center of the first unit is obtained, and the direction of the line connecting the unit second maximum point to the unit maximum point is recorded as the unit gradient direction of the first unit. The ratio of the absolute difference of the first pollutant concentration of any two water quality sensing points in the first unit to the corresponding straight line distance is calculated and recorded as a concentration decay rate, and the average value of all the concentration decay rates in the first unit is calculated and recorded as the unit decay rate of the first unit.

[0009] Further, the unit division processing is performed according to the pollutant simulation change data to obtain concentration diffusion unit data, and peak concentration analysis is performed to obtain peak concentration distribution data, which further includes the following sub-steps: The maximum concentration, unit gradient direction and unit attenuation rate of each local diffusion unit are repeatedly obtained, and the average value of all maximum concentrations and the average value of all unit attenuation rates are calculated, which are respectively recorded as a first average concentration AC and a first average attenuation rate AR; If the maximum concentration of the first unit is greater than AC and the unit attenuation rate of the first unit is less than AR, the first unit is marked as a high concentration unit, otherwise it is marked as a low concentration unit, and the process of obtaining all high concentration units is repeated. For all high concentration units, a directional arrow is drawn from the geometric center of each high concentration unit along the corresponding unit gradient direction, which is recorded as the gradient direction arrow of the corresponding high concentration unit. Observe the distribution of all gradient direction arrows, and record the area pointed by the most gradient direction arrows as the gradient convergence area. The high concentration unit with the gradient direction arrow pointing to the gradient convergence area is recorded as a pointing unit, and any one pointing unit is recorded as a first pointing unit.

[0010] Further, the unit division processing is performed according to the pollutant simulation change data to obtain concentration diffusion unit data, and peak concentration analysis is performed to obtain peak concentration distribution data, which further includes the following sub-steps: The sum of the maximum concentrations of all pointing units is obtained, which is recorded as the pointing concentration sum ZH; the maximum concentration of the first pointing unit is recorded as BC, and the coordinates of the geometric center of the first pointing unit are recorded as (BX, BY), wherein BX and BY represent the horizontal coordinate and the vertical coordinate of the geometric center of the first pointing unit in sequence. The weighted horizontal coordinate QX and the weighted vertical coordinate QY of the first pointing unit are calculated, wherein QX=BX*BC / ZH, QY=BY*BC / ZH; the weighted horizontal coordinates and the weighted vertical coordinates of all pointing units are repeatedly obtained, and the sum of all weighted horizontal coordinates and all weighted vertical coordinates is calculated, which are respectively recorded as AX and AY; the position with coordinates (AX, AY) is recorded as the peak concentration position. The peak concentration position of each collection time is repeatedly obtained to obtain the peak concentration distribution data of the first pollutant, and the peak concentration distribution data of the pollutant is repeatedly obtained.

[0011] Further, the unit division processing is performed according to the pollutant simulation change data to obtain concentration diffusion unit data, and peak concentration analysis is performed to obtain peak concentration distribution data, which further includes the following sub-steps: Uniformly select k1 points from the lake model, denoted as distribution simulation points, and obtain the position coordinates of each distribution simulation point; Denote any one of the distribution simulation points as a first distribution point, where k1 is the number set; Based on the first time data, 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 of the unit maximum point, denoted as the first water flow direction, calculate the included angle between the unit gradient direction and the first water flow direction, denoted as BA, 0≤BA≤180; Calculate the fitting weight AQ of the unit gradient direction of the first distribution unit and the first water flow direction, where AQ=k2*BA+1; Wherein, k2 is the slope set, k3≤AQ≤1, k3 is the lower limit set, 0≤k3.

[0012] Further, according to the concentration diffusion unit data and the pollutant simulation change data, the distribution change analysis is carried out to obtain the pollutant distribution change data, which further includes the following sub-steps: Calculate the straight line distance from the first distribution point to the unit maximum point of the first distribution unit, denoted as DL; And obtain the included angle between the line direction from the first distribution point to the unit maximum point of the first distribution unit and the unit gradient direction of the first distribution unit, denoted as the first included angle; Calculate the correction distance XL of the first distribution point, wherein if the first included angle is less than k4, XL=DL*AQ, otherwise XL=DL / AQ; The unit attenuation rate and the maximum concentration of the first distribution unit are sequentially distributed as DR and MC, and the first pollutant concentration FC of the first distribution point is calculated, wherein FC=MC-DR*XL; Repeat to obtain the first pollutant concentration of each distribution simulation point at the peak concentration position at each collection time to obtain the pollutant distribution change data of the first pollutant; And repeat to obtain the pollutant distribution change data of all pollutants.

[0013] Further, a concentration prediction model is established, and the time and region where the peak value of the pollutant concentration appears are predicted according to the pollutant simulation change data, including the following sub-steps: The simulation change data of the first pollutant is normalized to scale all concentration sizes to [0, 1], and the normalized simulation data is obtained after completion; An initial prediction model is constructed according to the space-time long short-term memory network, the initial prediction model includes an input layer, a core layer and an output layer, the normalized simulation data is used to train the initial prediction model, and the concentration prediction model of the first pollutant is obtained after completion; According to the concentration prediction model of the first pollutant and the normalized simulation data, the first pollutant concentration of each water quality sensing point at the future time is predicted, and the predicted concentration data of the first pollutant is obtained.

[0014] Further, the step of establishing a concentration prediction model and predicting the time and area of the peak concentration of the pollutants according to the simulated variation data of the pollutants further comprises the following sub-steps: According to the predicted concentration data of the first pollutant, the peak concentration position and the corresponding pollutant distribution variation data at the corresponding time are obtained by using the concentration of the first pollutant at each water quality sensing point at the same time in the future; the position of the peak concentration of the first pollutant at each time and the concentration distribution of the first pollutant at each time are obtained; and the dynamic distribution data of the first pollutant is recorded as the dynamic distribution data of the first pollutant. The dynamic distribution data of all pollutants are repeatedly obtained, and are sequentially searched, if the peak concentration of a certain pollutant at a certain time is greater than the corresponding threshold value, it is determined that the corresponding emission scheme is unqualified, otherwise it is determined that the corresponding emission scheme is qualified.

[0015] The present application has the following advantages: the present application establishes a lake recharge simulation device, simulates the recharge of reclaimed water into a lake by using the lake recharge simulation device, collects simulation data, and obtains simulated variation data of pollutants; the simulated variation data of the pollutants is subjected to unit division processing to obtain concentration diffusion unit data, and peak concentration analysis is performed to obtain peak concentration distribution data; distribution variation analysis is performed according to the concentration diffusion unit data and the simulated variation data of the pollutants to obtain pollutant distribution variation data; a concentration prediction model is established, and the time and area of the peak concentration of the pollutants are predicted according to the simulated variation data of the pollutants; the dynamic variation of the diffusion of the pollutants in the lake can be simulated, the time and area of the peak concentration of the pollutants can be predicted, and the environmental risk of different emission schemes can be simulated. The present application can refine the spatial area, keep the calculation controllable, reduce the interference of noise on the peak value judgment, make the obtained peak concentration position more accurate and reliable, by dividing the local diffusion unit, screening the high concentration unit, using the gradient direction arrow of each high concentration unit to find the gradient convergence area, and finally calculating the peak position by using the weighted coordinates; the concentration of any distribution point can be calculated by defining the adaptive weight AQ through the included angle between the unit gradient direction and the water flow direction, and combining the correction distance, the unit attenuation rate and the maximum concentration, the error when the flow direction and the concentration gradient are inconsistent is reduced, the different diffusion characteristics along the flow direction and the counter-flow direction are more truly reflected, and the accuracy of the concentration calculation of the distribution point is improved; the time and area of the peak concentration of the pollutants can be predicted by establishing a concentration prediction model through the spatiotemporal long short-term memory network, which can guide the optimization of the reclaimed water treatment process and the adjustment of the discharge period, ensure the utilization of reclaimed water resources, avoid sudden impact on the lake ecosystem, and reduce the cost of later water quality treatment. BRIEF DESCRIPTION OF DRAWINGS

[0016] Figure 1 The step flow chart of the method of the present application; Figure 2A schematic diagram of a lake supply simulation device of the present application; Figure 3 A schematic diagram of a local diffusion unit of the present application; Figure 4 A schematic diagram of the structure of an electronic device of the present application; In the figure, 1, water quality flow sensing control device of the outlet; 2, detachable baffle; 3, water quality index sensing probe; 4, lake bottom filling; 5, reclaimed water discharge port; 6, water delivery pipe; 7, water quality flow sensing control device of the inlet. DETAILED DESCRIPTION

[0017] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of the present application.

[0018] Embodiment 1, please refer to Figure 1 As shown in the figure, the present application provides a method for simulating the dynamic distribution of pollutants in a lake supplied with reclaimed water, comprising the following steps: Step S1, establishing a lake supply simulation device, using the lake supply simulation device to simulate the supply of reclaimed water to the lake, and collecting simulation data to obtain pollutant simulation change data; step S1 comprises the following substeps: Step S101, please refer to Figure 2 As shown in the figure, for a lake to be simulated, denoted as a first lake, an entity model of the first lake is established, denoted as a lake model; a lake supply simulation device is established, which comprises the lake model, a water quality flow sensing control device, a water delivery pipe, a reclaimed water discharge port, and a detachable baffle; the lake supply simulation device can be flexibly adjusted in shape and size according to the shape and size of the lake to be simulated; Figure 2 In the figure, 1 represents the water quality flow sensing control device of the outlet, and 7 represents the water quality flow sensing control device of the inlet, which is used to control the size of the water flow entering and exiting the lake model; 2 represents the detachable baffle, which simulates whether there is a rubber dam in the river connected to the lake; if there is a rubber dam, the baffle is installed, otherwise it is removed; 3 represents the water quality index sensing probe, which can monitor the concentration of pollutants and the direction of water flow at the corresponding position in real time; 4 represents the lake bottom filling; 5 represents the reclaimed water discharge port, which is used to supply reclaimed water to the inside of the lake model; and 6 is the water delivery pipe; Step S102, according to the material composition of the bottom of the first lake, sample the same material to fill the bottom of the lake model, and fill the lake model with the lake water of the first lake; different bottom materials and water bodies will change the deposition rate, dissolution behavior and diffusion rate of pollutants, thereby affecting the distribution of pollutants, so in order to ensure the accuracy of simulation, the same water body and material are sampled to fill the lake model; Step S103, uniformly place a plurality of water quality index sensing probes in the lake model, denoted as water quality sensing points, and establish a plane rectangular coordinate system under the front view angle of the lake model, denoted as the lake model coordinate system, and obtain the coordinate position of each water quality sensing point in the lake model coordinate system; the number of water quality index sensing probes placed can be set according to the actual application scene, Figure 2 Only as a schematic diagram of a lake replenishment simulation device, the number of water quality index sensing probes placed in actual application is greater than Figure 3 The number in the above formula.

[0019] Step S104, the pollutants to be monitored are sequentially denoted as pollutant 1-pollutant n, where n is the total number of pollutants to be monitored, and any one pollutant is denoted as a first pollutant; for example, GOD, ammonia nitrogen, total phosphorus and total nitrogen, etc. Step S105, collect reclaimed water for replenishing the lake, denoted as replenishing reclaimed water, set the discharge flow rate and the discharge total amount of the replenishing reclaimed water simulation according to the actual discharge flow rate and the discharge total amount of the replenishing reclaimed water in the discharge scheme; determine the simulated discharge flow rate and the discharge total amount according to the proportional relationship between the actual lake storage capacity and the lake model storage capacity, and the actual discharge flow rate and the discharge total amount of the replenishing reclaimed water in the discharge scheme, so as to ensure that the discharge time and the discharge proportion are basically consistent, and the simulation accuracy is ensured; Step S106, input the replenishing reclaimed water into the lake model according to the simulated discharge flow rate and the discharge total amount, and use the water quality index sensing probe to collect the concentration of the first pollutant and the flow direction of the corresponding water quality sensing point at a first time interval, and record the collection time, denoted as the simulation change data of the first pollutant, wherein 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 scene; Step S107, simultaneously repeat the collection of the simulation change data of pollutant 1-pollutant n to obtain the pollutant simulation change data; In the specific implementation process, different simulation discharge schemes can be determined according to different actual discharge schemes, and then the diffusion path and concentration decay law of pollutants such as GOD, ammonia nitrogen, total phosphorus and total nitrogen in the lake under different discharge amounts and different discharge modes are simulated; the environmental risk of different discharge schemes is simulated in advance, which can guide the optimization of the reclaimed water treatment process and the adjustment of the discharge period, so as to ensure the utilization of reclaimed water resources, avoid the sudden impact on the lake ecosystem, and reduce the cost of water quality treatment in the later period.

[0020] In step S2, unit division processing is performed according to the pollutant simulation change data to obtain concentration diffusion unit data, and peak concentration analysis is performed to obtain peak concentration distribution data; step S2 includes the following sub-steps: In step S201, referring to FIG. 2, Figure 4 The two water quality sensing points closest to the first sensing point and not on the same straight line as the first sensing point are selected, that is, the three points are not on the same straight line, and are recorded as second sensing points; the first sensing point and the two second sensing points are connected in sequence to form a triangular region, which is recorded as a local diffusion unit; because the water quality sensing points are uniformly arranged, the water quality sensing points in the middle part will have multiple corresponding second sensing points, that is, they will belong to multiple local diffusion units at the same time; the closer the monitoring points, the greater the influence of the same diffusion process, and the more uniform the concentration gradient, decay rate and other characteristics; In step S202, all local diffusion units are repeatedly obtained, and any one local diffusion unit is recorded as a first unit; In step S203, the simulation change data of the first pollutant is grouped according to the collection time, that is, the data of the same collection time is a group, and any one group of data of the same collection time is recorded as first time data; In step S204, based on the first time data, the water quality sensing point with the maximum concentration of the first pollutant in the first unit and the water quality sensing point with the second maximum concentration of the first pollutant are obtained, and are recorded as the maximum point and the second maximum point of the unit in order, and the concentration of the first pollutant of the maximum point is recorded as the maximum concentration of the first unit; In step S205, the geometric center of the first unit is obtained, and the direction of the line connecting the second maximum point to the maximum point is recorded as the unit gradient direction of the first unit; the unit gradient direction reflects the core direction of the concentration increase in the first unit; In step S206, the ratio of the absolute difference of the concentration of the first pollutant of any two water quality sensing points in the first unit to the corresponding straight line distance is calculated and recorded as the concentration decay rate, and the average value of all concentration decay rates in the first unit is calculated and recorded as the unit decay rate of the first unit, which reflects the speed of the concentration decay with distance in the first unit.

[0021] Step S207: Repeatedly obtain the maximum concentration, unit gradient direction, and unit attenuation rate of all local diffusion units; and calculate the average of all maximum concentrations and the average of all unit attenuation rates, which are recorded as the first average concentration AC and the first average attenuation rate AR, respectively. Using the current overall average as the reference threshold means that the threshold will be adaptively adjusted as the overall pollution situation changes, facilitating the identification of relatively significant high-value areas in different intensity scenarios. Step S208: If the maximum concentration of the first cell is greater than AC and the cell decay rate of the first cell is less than AR, the first cell is marked as a high-concentration cell; otherwise, it is marked as a low-concentration cell, and all high-concentration cells are obtained repeatedly. If the maximum concentration is greater than AC, it indicates that the pollutant concentration in the first cell is high, and if the cell decay rate is less than AR, it indicates that the pollutant decays slowly in the first cell and is likely to remain. Step S209: For all high-concentration cells, a directional arrow is drawn starting from the geometric center of each high-concentration cell along the corresponding cell gradient direction, which is recorded as the gradient direction arrow of the corresponding high-concentration cell. Screening high-concentration cells can further focus on the pollutant accumulation area, which is the area where the highest concentration in the reclaimed water diffusion is most likely to exist; In step S210, the distribution of all gradient direction arrows is observed, and the area with the most gradient direction arrows is recorded as the gradient convergence area. The gradient convergence area is the gradient convergence point of the surrounding concentration area. Because 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 arrow distribution essentially allows the local diffusion law to point to the high concentration center. This is more in line with physical logic than the traditional weighted average, which may deviate from the true core due to interference from distant sources. Step S211 : record the high-concentration unit whose gradient direction arrow points to the gradient convergence area as a pointing unit, and record any pointing unit as a first pointing unit.

[0022] Step S212: Obtain the sum of the maximum concentrations of all pointing units, denoted as the sum of pointing concentrations ZH; denote the maximum concentration of the first pointing unit as BC, and denote 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 order; Step S213, the weighted horizontal coordinate QX and the weighted vertical coordinate QY of the first pointing unit are calculated, wherein QX=BX*BC / ZH and QY=BY*BC / ZH; the weighted horizontal coordinates and the weighted vertical coordinates of all pointing units are repeatedly obtained, and all the weighted horizontal coordinates and all the weighted vertical coordinates are summed up and sequentially recorded as AX and AY respectively; the position with coordinates (AX, AY) is recorded as the peak concentration position; the greater the maximum concentration in the unit is, the closer the unit is to the peak concentration position, and the greater the influence on the positioning of the peak concentration position is, which can better represent the true peak concentration position; the weighted calculation can make the peak concentration position more biased towards the high concentration area, avoiding center deviation caused by interference; Step S214, the peak concentration positions of all acquisition time points are repeatedly obtained to obtain the peak concentration distribution data of the first pollutant, and the peak concentration distribution data of the pollutant is repeatedly obtained; In the specific implementation process, the local diffusion unit adopts a triangle because the triangle is the closed figure with the smallest number of edges in a two-dimensional plane, which can cover a two-dimensional space and can simply and accurately calculate the local diffusion characteristics such as gradient direction and attenuation rate through the coordinates and concentrations of the three points; if a quadrilateral or a circle is used, the more points there are, the more likely it is to cause feature conflict, and thus it is impossible to determine a unified local diffusion rule.

[0023] Step S3, distribution change analysis is performed according to the concentration diffusion unit data and the pollutant simulation change data to obtain pollutant distribution change data; step S3 includes the following sub-steps: Step S301, k1 points are uniformly selected from the lake model and recorded as distribution simulation points, and the position coordinates of each distribution simulation point are obtained; any one distribution simulation point is recorded as a first distribution point, wherein k1 is the number set; the unitized information generated by the discrete sensing points (observation points) is expanded into a continuous or semi-continuous spatial concentration field, which is convenient for visualization; k1 can be flexibly set according to the actual application scene, and the greater k1 is, the more accurate the pollutant distribution obtained is, but the greater the calculation amount is; in this embodiment, k1=400; Step S302, based on the first time data, the local diffusion unit in which the first distribution point is located is obtained and recorded as a first distribution unit, the unit gradient direction of the first distribution unit is obtained, the water flow direction of the maximum point of the unit is obtained and recorded as a first water flow direction, the included angle between the unit gradient direction and the first water flow direction is calculated and recorded as BA, and 0≤BA≤180; the diffusion rules of different units are significantly different, and the concentration of the target point must be derived from the rule of the unit in which the target point is located, so as to avoid using the wrong rule and causing deviation in the calculation result; Step S303, calculate the fitting weight AQ of the unit gradient direction of the first distribution unit and the first water flow direction, wherein AQ=k2*BA+1; wherein k2 is a set slope, k3≤AQ≤1, k3 is a set lower limit, 0≤k3; the fitting weight AQ is used to correct the influence of the water flow on the concentration decay rate, when the unit gradient direction and the first water flow direction are in the same direction, the concentration decay rate is corrected to be smaller, which conforms to the physical law that the downflow diffusion is slow; when they are in opposite directions, the concentration decay rate is amplified, which conforms to the characteristics that the upflow diffusion is fast; wherein AQ and the corresponding k2 can be set according to the actual application scene, in this embodiment, 0.4≤AQ≤1, that is, when BA=0, AQ=1; when BA=180, AQ=0.4, so k2=-1 / 300; Step S304, calculate the straight line distance from the first distribution point to the unit maximum point of the first distribution unit, denoted as DL; and obtain the included angle between the line direction from the first distribution point to the unit maximum point of the first distribution unit and the unit gradient direction of the first distribution unit, denoted as the first included angle; Step S305, calculate the corrected distance XL of the first distribution point, wherein if the first included angle is less than k4, XL=DL*AQ, otherwise XL=DL / AQ; in this embodiment, k4=45°, which can be flexibly set; when the first included angle is less than k4, the corrected XL becomes smaller, which reflects that the pollutant is more likely to diffuse in this direction, and therefore, under the same physical distance, the concentration is higher; when the first included angle is not less than k4, the corrected XL becomes smaller or larger, which reflects that the diffusion of the pollutant in this direction is inhibited, and under the same physical distance, the concentration is lower; Step S306, distribute the unit decay rate and the maximum concentration of the first distribution unit in order as DR and MC, and calculate the first pollutant concentration FC of the first distribution point, wherein FC=MC-DR*XL; Step S307, repeat the acquisition of the peak concentration position at all collection times and the first pollutant concentration of each distribution simulation point to obtain the pollutant distribution change data of the first pollutant; and repeat the acquisition of the pollutant distribution change data of all pollutants; In the specific implementation process, if the distribution simulation point selects the edge inside the lake model which is not included by the local diffusion unit, the nearest local diffusion unit to the distribution simulation point can be obtained as the first distribution unit of the distribution simulation point, and subsequent processing is performed.

[0024] Step S4, establish a concentration prediction model, and predict the time and region where the peak value of the pollutant concentration appears according to the pollutant simulation change data; step S4 includes the following substeps: Step S401, normalize the simulation change data of the first pollutant to scale all concentration sizes to [0, 1], and obtain the normalized simulation data after completion; avoid the dimension difference from causing the model to be biased towards large value characteristics; Step S402, an initial prediction model is constructed according to a space-time long short-term memory network, the initial prediction model includes an input layer, a core layer and an output layer, model training is performed on the initial prediction model by using normalized simulation data, and after the training, a concentration prediction model of the first pollutant is obtained; diffusion laws of different pollutants may be different, so concentration prediction models of different pollutants need to be established respectively; Step S403, the concentration prediction model of the first pollutant and the normalized simulation data are used to predict the concentration of the first pollutant at each water quality sensing point at a future time, and prediction concentration data of the first pollutant is obtained.

[0025] Step S404, according to the prediction concentration data of the first pollutant, the concentration of the first pollutant at each water quality sensing point at the same future time is used to obtain the peak concentration position at the corresponding time and the corresponding pollutant distribution change data; the position of the pollutant concentration peak 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, peak concentration analysis is performed, and distribution change analysis is performed, which is recorded as dynamic distribution data of the first pollutant; the dynamic distribution data of the first pollutant includes simulation data and data predicted by using the simulation data; Step S405, the dynamic distribution data of all pollutants is repeatedly obtained and sequentially searched, if the pollutant concentration peak of a pollutant at a certain time is greater than a corresponding threshold value, it is determined that the corresponding emission scheme is unqualified, otherwise it is determined that the corresponding emission scheme is qualified; the corresponding threshold value can be set according to the actual application scene and the related standard; according to the simulation data and the predicted data, the environmental risk of different emission schemes is obtained, the time and region of the pollutant concentration peak in the emission process are obtained, and the water quality exceeding standard caused by excessive emission or improper emission time of the lake is avoided. In the specific implementation process, the space-time long short-term memory network is ST-LSTM; the core advantage of using the space-time long short-term memory network to construct a prediction model for prediction is that the time dependence of the concentration change of the pollutant, such as the degradation and diffusion effect of the pollutant over time, and the spatial correlation, such as the diffusion of the pollutant from the discharge port to the surrounding monitoring points and the mutual influence of the concentrations in different regions, can be captured at the same time.

[0026] Embodiment 2, please refer to Figure 4 as shown, Figure 4An example is shown in a structural diagram of an electronic device, which can include a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory complete the communication among each other through the communication bus. The memory stores computer readable instructions, and the processor can call the instructions in the memory, and when the computer readable instructions are executed by the processor, the steps in a method of simulating the dynamic distribution of pollutants in a lake supplied by reclaimed water are run to achieve the following functions: establishing a lake supply simulation device, simulating the supply of reclaimed water to the lake by using the lake supply simulation device, collecting simulation data, and obtaining pollutant simulation change data; performing unit division processing according to the pollutant simulation change data to obtain concentration diffusion unit data, and performing peak concentration analysis to obtain peak concentration distribution data; performing distribution change analysis according to the concentration diffusion unit data and the pollutant simulation change data to obtain pollutant distribution change data; and establishing a concentration prediction model and predicting the time and area of the peak concentration of pollutants according to the pollutant simulation change data.

[0027] In addition, the logical instructions in the memory described above can be implemented in the form of a software function unit and sold or used as a separate product, which can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application essentially or the part that contributes to the prior art or part of the technical solutions can be embodied in the form of a software product, which is stored in a storage medium and includes instructions for making a computer device (which can be a personal computer, a server, or a network device, etc.) execute all or part of the steps of the method described in various embodiments of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disk, and various program code storage media.

[0028] Embodiment 3, the present application also provides a computer readable storage medium, and the present application provides a storage medium having a computer program stored thereon, and the computer program is executed by a processor to run the steps in the above method of simulating the dynamic distribution of pollutants in a lake supplied by reclaimed water to achieve the following functions: establishing a lake supply simulation device, simulating the supply of reclaimed water to the lake by using the lake supply simulation device, collecting simulation data, and obtaining pollutant simulation change data; performing unit division processing according to the pollutant simulation change data to obtain concentration diffusion unit data, and performing peak concentration analysis to obtain peak concentration distribution data; performing distribution change analysis according to the concentration diffusion unit data and the pollutant simulation change data to obtain pollutant distribution change data; and establishing a concentration prediction model and predicting the time and area of the peak concentration of pollutants according to the pollutant simulation change data.

[0029] Through the description of the above embodiments, the embodiments of the present application can be provided as a method, a system or a computer program product. Based on such understanding, the above technical solutions can be embodied in the form of a software product, which can be stored in a computer readable storage medium, such as a ROM / RAM, a magnetic disk, an optical disk, etc., and includes a plurality of instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) execute the methods described in various embodiments or some parts of the embodiments.

[0030] In the embodiments provided by the present application, it should be understood that the disclosed system or method can be implemented in other manners. The above described embodiments are merely exemplary, for example, the division of modules or units can be different from the above, the implementation can be combined or integrated in other manners, and some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the modules or units can be electrical, mechanical or other forms.

[0031] Finally, it should be noted that the above embodiments are merely used to illustrate the technical solutions of the present application, rather than limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that they can still make modifications to the technical solutions recorded in the foregoing embodiments, or make equivalent replacements to some technical features; and these modifications or replacements 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 the present application.

Claims

1. A method of simulating the distribution of the dynamic fate of pollutants in a lake supplied with reclaimed water, characterized in that, The method comprises the following steps: A lake supply simulation device is established, and the lake supply simulation device is used to simulate the supply of reclaimed water to the lake, and simulation data is collected to obtain pollutant simulation change data; According to the pollutant simulation change data, unit division processing is performed to obtain concentration diffusion unit data, and peak concentration analysis is performed to obtain peak concentration distribution data; According to the concentration diffusion unit data and the pollutant simulation change data, distribution change analysis is performed to obtain pollutant distribution change data; A concentration prediction model is established, and the time and region of the occurrence of the peak concentration of the pollutant are predicted according to the pollutant simulation change data.

2. The method for simulating the distribution of the dynamic fate and transport of pollutants in a lake recharged by reclaimed water according to claim 1, characterized in that, The lake supply simulation device is established, and the lake supply simulation device is used to simulate the supply of reclaimed water to the lake, and simulation data is collected to obtain pollutant simulation change data, which comprises the following sub-steps: For the lake to be simulated, denoted as a first lake, an entity model of the first lake is established, denoted as a lake model; a lake supply simulation device is established, which comprises the lake model, a water quality flow sensing control device, a water conveying pipe, a reclaimed water discharge port, and a detachable baffle; According to the composition of the material at the bottom of the first lake, the same material is sampled to fill the bottom of the lake model, and the lake water of the first lake is used to fill the lake model; A plurality of water quality index sensing probes are uniformly placed in the lake model, denoted as water quality sensing points, and a plane rectangular coordinate system is established under the normal top view of the lake model, denoted as a lake model coordinate system, to obtain the coordinate position of each water quality sensing point in the lake model coordinate system.

3. A method of simulating the distribution of the dynamic fate of pollutants in a lake supplied with water from a water treatment plant according to claim 2, characterized in that, The lake supply simulation device is established, and the lake supply simulation device is used to simulate the supply of reclaimed water to the lake, and simulation data is collected to obtain pollutant simulation change data, which comprises the following sub-steps: The pollutants to be monitored are sequentially denoted as pollutant 1-pollutant n, wherein n is the total number of pollutants to be monitored, and any one pollutant is denoted as a first pollutant; Reclaimed water for supplying the lake is collected, denoted as supply reclaimed water, and the discharge flow rate and the total discharge amount of the supply reclaimed water are set according to the actual discharge flow rate and the total discharge amount of the supply reclaimed water in the discharge scheme; According to the simulated discharge flow rate and the total discharge amount, the supply reclaimed water is input into the lake model, and the water quality index sensing probe is used to collect the concentration of the first pollutant and the water flow direction of the corresponding water quality sensing point at a first time interval, and the time of collection is recorded, denoted as the simulation change data of the first pollutant, wherein the first time interval is t1; At the same time, the simulation change data of pollutant 1-pollutant n is repeatedly collected to obtain the pollutant simulation change data.

4. The method of simulating the distribution of pollutants in a lake receiving reclaimed water according to claim 3, wherein, According to the pollutant simulation change data, unit division processing is performed to obtain concentration diffusion unit data, and peak concentration analysis is performed to obtain peak concentration distribution data, which comprises the following sub-steps: Any one water quality sensing point is denoted as a first sensing point, and two water quality sensing points closest to the first sensing point and not on the same straight line as the first sensing point are selected and denoted as second sensing points; a triangular region is formed by sequentially connecting the first sensing point and the two second sensing points, denoted as a local diffusion unit; All local diffusion units are repeatedly obtained, and any one local diffusion unit is denoted as a first unit; Grouping the simulation change data of the first pollutant according to the collection time, and recording the data of any one group at the same collection time as the first time data; Based on the first time data, obtaining the water quality sensing point with the maximum concentration of the first pollutant and the water quality sensing point with the second maximum concentration of the first pollutant in the first unit, and recording them in order as the maximum point and the second maximum point of the unit, and recording the concentration of the first pollutant at the maximum point as the maximum concentration of the first unit; Obtaining the geometric center of the first unit, and recording the direction of the line connecting the second maximum point to the maximum point of the unit as the unit gradient direction of the first unit; Calculating 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 recording it as the concentration decay rate, and calculating the average value of all concentration decay rates in the first unit, and recording it as the unit decay rate of the first unit.

5. A method of simulating the distribution of the dynamic fate of pollutants in a lake supplied with water from a water treatment plant according to claim 4, characterized in that, The unit division processing according to the pollutant simulation change data obtains the concentration diffusion unit data, and the peak concentration analysis obtains the peak concentration distribution data, which further includes the following sub-steps: Repeating the obtaining of the maximum concentration, the unit gradient direction and the unit decay rate of all local diffusion units, and calculating the average value of all maximum concentrations and the average value of all unit decay rates, and recording them in order as the first average concentration AC and the first average decay rate AR respectively; 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, marking the first unit as a high concentration unit, otherwise marking it as a low concentration unit, and repeating the obtaining of all high concentration units; For all high concentration units, drawing a directional arrow from the geometric center of each high concentration unit along the corresponding unit gradient direction, and recording it as the gradient direction arrow of the corresponding high concentration unit; Observing the distribution direction of all gradient direction arrows, and recording the area to which the most gradient direction arrows point as the gradient convergence area; Recording the high concentration unit whose gradient direction arrow points to the gradient convergence area as the pointing unit, and recording any one pointing unit as the first pointing unit.

6. The method of simulating the distribution of the dynamic fate of contaminants in a lake supplied with reclaimed water according to claim 5, characterized in that, The unit division processing according to the pollutant simulation change data obtains the concentration diffusion unit data, and the peak concentration analysis obtains the peak concentration distribution data, which further includes the following sub-steps: Obtaining the sum of the maximum concentrations of all pointing units, and recording it as the pointing concentration sum ZH; recording the maximum concentration of the first pointing unit as BC, and recording the coordinates of the geometric center of the first pointing unit as (BX, BY), wherein BX and BY represent the horizontal coordinate and the vertical coordinate of the geometric center of the first pointing unit in order; Calculating the weighted horizontal coordinate QX and the weighted vertical coordinate QY of the first pointing unit, wherein QX=BX*BC / ZH, QY=BY*BC / ZH; repeating the obtaining of the weighted horizontal coordinates and the weighted vertical coordinates of all pointing units, and summing all weighted horizontal coordinates and all weighted vertical coordinates in order, and recording them as AX and AY respectively; recording the position with coordinates (AX, AY) as the peak concentration position; Repeating the obtaining of the peak concentration position of all collection times to obtain the peak concentration distribution data of the first pollutant, and repeating the obtaining of the peak concentration distribution data of the pollutant.

7. A method of simulating the distribution of the dynamic fate of pollutants in a lake supplied with water from a water treatment plant according to claim 6, characterized in that, The concentration diffusion unit data and the pollutant simulation change data are used for distribution change analysis to obtain pollutant distribution change data, including the following sub-steps: k1 points are uniformly selected from the lake model, denoted as distribution simulation points, and the position coordinates of each distribution simulation point are obtained; any one distribution simulation point is denoted as a first distribution point, wherein k1 is the number set; Based on the first time data, the local diffusion unit where the first distribution point is located is obtained, denoted as the first distribution unit, the unit gradient direction of the first distribution unit is obtained, and the water flow direction of the maximum point of the unit is obtained, denoted as the first water flow direction, the included angle between the unit gradient direction and the first water flow direction is calculated, denoted as BA, 0≤BA≤180; The adaptive weight AQ of the unit gradient direction of the first distribution unit and the first water flow direction is calculated, wherein AQ=k2*BA+1; wherein k2 is the slope set, k3≤AQ≤1, and k3 is the lower limit set, 0≤k3.

8. The method of simulating the distribution of the dynamic fate of contaminants in a lake supplied with reclaimed water according to claim 7, characterized in that, The concentration diffusion unit data and the pollutant simulation change data are used for distribution change analysis to obtain pollutant distribution change data, including the following sub-steps: The straight-line distance from the first distribution point to the maximum point of the first distribution unit is calculated, denoted as DL; and the included angle between the line direction from the first distribution point to the maximum point of the first distribution unit and the unit gradient direction of the first distribution unit is obtained, denoted as the first included angle; The correction distance XL of the first distribution point is calculated, wherein if the first included angle is less than k4, XL=DL*AQ, otherwise XL=DL / AQ; The unit attenuation rate and the maximum concentration of the first distribution unit are sequentially distributed, denoted as DR and MC, and the first pollutant concentration FC of the first distribution point is calculated, wherein FC=MC-DR*XL; The peak concentration position at each collection time and the first pollutant concentration of each distribution simulation point are repeatedly obtained to obtain the pollutant distribution change data of the first pollutant; and the pollutant distribution change data of all pollutants is repeatedly obtained.

9. The method of simulating the distribution of the dynamic fate of contaminants in a lake supplied with water reclaimed according to claim 8, characterized in that, A concentration prediction model is established, and the time and region of the peak value of the pollutant concentration are predicted according to the pollutant simulation change data, including the following sub-steps: The simulation change data of the first pollutant is normalized to scale all concentration sizes to [0, 1], and the normalized simulation data is obtained after completion; An initial prediction model is constructed according to the space-time long short-term memory network, the initial prediction model includes an input layer, a core layer and an output layer, the normalized simulation data is used for model training of the initial prediction model, and the concentration prediction model of the first pollutant is obtained after completion; The concentration prediction model of the first pollutant and the normalized simulation data are used to predict the first pollutant concentration of each water quality sensing point at a future time to obtain the predicted concentration data of the first pollutant.

10. The method of simulating the distribution of the dynamic fate of contaminants in a lake supplied with reclaimed water according to claim 9, characterized in that, A concentration prediction model is established, and the time and region of the peak value of the pollutant concentration are predicted according to the pollutant simulation change data, including the following sub-steps: Based on the predicted concentration data of the first pollutant, the first pollutant concentration at each water quality sensing point at the same time in the future is used to obtain the peak concentration position at the corresponding time and the corresponding pollutant distribution change data; the position where the pollutant concentration peak of the first pollutant occurs at each time and the pollutant concentration distribution of the first pollutant at each time are obtained; and these are recorded as the dynamic distribution data of the first pollutant; Repeatedly obtain the dynamic distribution data of all pollutants and search them in sequence. If the peak concentration of a pollutant at a certain moment is greater than the corresponding threshold, the corresponding emission plan is judged to be unqualified, otherwise the corresponding emission plan is judged to be qualified.

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