Method and device for predicting standard reaching time of water quality of keiske lake in arid area, computer equipment and program product
By constructing a hydrodynamic and water environment model and combining monitoring data with geographic information, the problem of accurately predicting the time when water quality in tailwater lakes in arid areas will reach standards was solved, and efficient water quality management guidance was achieved.
Patent Information
- Application Number
- CN202510963899.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-07-14
- Publication Date
- 2025-10-17
AI Technical Summary
Existing technologies make it difficult to accurately predict when the water quality of tailwater lakes in arid areas will meet standards. Traditional methods have low inversion accuracy and cannot effectively guide the design of water environment management measures.
By establishing a hydrodynamic model and a water environment model, combining water quantity and water quality monitoring data, and using geographic information data to build a water environment model, the changing relationship of pollutants is inverted and calculated, and the time required for pollutants to reach the preset water quality indicators is obtained.
It improves the prediction accuracy of the time when lake water quality reaches standards, reduces monitoring costs, and can scientifically guide the design of water environment management measures.
Smart Images
Figure CN120805472A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present application relates to the technical field of environment, in particular to a tail-lag lake water quality compliance time prediction method and device in arid regions, computer equipment and program product. BACKGROUND
[0002] Tail-lag lakes in arid regions generally have environmental problems such as imbalance of water and salt relationship and water quality pollution exceeding standard. How to reasonably determine the tail-lag lake water quality compliance time in arid regions so as to scientifically design corresponding water quality restoration measures is a key problem to be solved at present.
[0003] However, there is generally a lack of monitoring data for lake groundwater at present, which makes it difficult for traditional technologies to quantify the hydraulic exchange of the lake, thereby limiting the accurate assessment and prediction of the lake water quality compliance time. Meanwhile, in the traditional lake water environment model in arid regions, the groundwater source inflow and outflow of the lake is predicted by using empirical parameters, water balance calculation results or adjacent point groundwater monitoring data, and the inversion accuracy is less than 70%. In this case, it is difficult to accurately determine the water quality target pollutant concentration compliance time and guide the design of water environment treatment measures or projects. SUMMARY
[0004] Therefore, it is necessary to provide a tail-lag lake water quality compliance time prediction method, device, computer equipment and computer program product in arid regions, which can accurately predict the lake water quality compliance time.
[0005] In a first aspect, in one embodiment, the present application provides a tail-lag lake water quality compliance time prediction method in arid regions, the method comprising:
[0006] establishing a water dynamic model of a target lake area according to water quantity monitoring data and water quality monitoring data of the target lake area; the water dynamic model is used to characterize the water quantity exchange of the groundwater in the target lake area;
[0007] constructing a water environment model of the target lake area based on geographic information data of the target lake area and in combination with the water dynamic model; the water environment model is used to invert and calculate the change relationship of the target pollutant with time in the target lake area;
[0008] obtaining a target prediction result for the target lake area through inversion and optimization debugging of the water environment model; the target prediction result is used to characterize the time required for the target pollutant to reach a preset water quality index.
[0009] In one embodiment, the water dynamic model of the target lake area is established according to the water quantity monitoring data and the water quality monitoring data of the target lake area, comprising:
[0010] determining a water balance relationship of the target lake area based on the water quantity monitoring data;
[0011] According to the water quantity monitoring data and the water quality monitoring data, a mass balance relationship of the target lake region is determined;
[0012] Based on the mass balance relationship, an ordinary differential equation is established; the ordinary differential equation represents a dynamic change process of the water quality monitoring data with water quantity exchange;
[0013] According to the ordinary differential equation, a hydrodynamic model is constructed in combination with the water quantity balance relationship and the mass balance relationship.
[0014] In one of the embodiments, based on geographic information data of the target lake region, a water environment model of the target lake region is constructed in combination with the hydrodynamic model, including:
[0015] The hydrodynamic model is connected to the hydrodynamic module, and the hydrodynamic model connected to the hydrodynamic module is coupled with the water quality module in code to construct the water environment model.
[0016] The hydrodynamic model is connected to the hydrodynamic module, and the hydrodynamic model connected to the hydrodynamic module is coupled with the water quality module in code to construct the water environment model.
[0017] In one of the embodiments, connecting the hydrodynamic model to the hydrodynamic module includes:
[0018] Based on the hydrodynamic model, the groundwater input water quantity and the groundwater output water quantity of the target lake region are obtained;
[0019] The groundwater input water quantity and the groundwater output water quantity are taken as boundary conditions of the hydrodynamic module, so that the hydrodynamic model is connected to the hydrodynamic module.
[0020] In one of the embodiments, the geographic information data includes water quality data, hydrogeological data, meteorological data, pollution source data and spatial attribute data; the hydrodynamic module and the water quality module are determined based on the geographic information data by the water environment simulation software, including:
[0021] The hydrodynamic module is determined according to the hydrogeological data, the meteorological data and the spatial attribute data by the water environment simulation software;
[0022] The water quality module is determined according to the water quality data and the pollution source data by the water environment simulation software.
[0023] In one of the embodiments, the water environment simulation software includes MIKE3 software; the MIKE3 software is used to construct a MIKE3-ECOLAB model.
[0024] In a second aspect, in one of the embodiments, the present application provides a device for predicting water quality compliance time of a tail-lag lake in an arid region, the device including:
[0025] The water power model construction module is configured to construct a water power model of the target lake region according to water quantity monitoring data and water quality monitoring data of the target lake region, and the water power model is configured to represent water quantity exchange of groundwater in the target lake region.
[0026] The water environment model construction module is configured to construct a water environment model of the target lake region based on geographic information data of the target lake region and in combination with the water power model, and the water environment model is configured to inverse and calculate a change relationship of the target pollutant with time in the target lake region.
[0027] The prediction result acquisition module is configured to acquire a target prediction result for the target lake region through inversion and optimization debugging of the water environment model, and the target prediction result is configured to represent a required time for the target pollutant to reach a preset water quality index.
[0028] In a third aspect, in an embodiment, the present application provides a computer device, comprising a memory and a processor, the memory stores a computer program, and the processor implements the steps in the method embodiments of the first aspect when executing the computer program.
[0029] In a fourth aspect, in an embodiment, the present application provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the steps in the method embodiments of the first aspect.
[0030] In a fifth aspect, in an embodiment, the present application provides a computer program product, comprising a computer program, and the computer program is executed by a processor to implement the steps in the method embodiments of the first aspect.
[0031] The drought region tail-lake water quality compliance time prediction method, device, computer device and computer program product can construct a water power model for a target lake region according to water quantity monitoring data and water quality monitoring data of the target lake region, the water power model can be used to represent water quantity exchange of groundwater in the target lake region, then a water environment model of the target lake region can be constructed based on geographic information data of the target lake region and by using the obtained water power model, the water environment model can be used to inverse and calculate a change relationship of a target pollutant with time in the target lake region, and finally a target prediction result for the target lake region can be acquired through the obtained water environment model, and the required time for the target pollutant to reach a preset water quality index can be known through the target prediction result. The present application can improve the accuracy of lake water quality compliance time prediction. BRIEF DESCRIPTION OF DRAWINGS
[0032] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related art, the following will briefly introduce the drawings needed to be used in the description of the embodiments of the present application or the related art. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other related drawings can also be obtained on the basis of these drawings without creative effort.
[0033] Figure 1 An application environment diagram of the water quality standard reaching time prediction method of the tail-lag lake in the arid region in one embodiment;
[0034] Figure 2 A flowchart of the water quality standard reaching time prediction method of the tail-lag lake in the arid region in one embodiment;
[0035] Figure 3 A flowchart of establishing an ordinary differential equation in one embodiment;
[0036] Figure 4 A flowchart of constructing a water environment model in one embodiment;
[0037] Figure 5 A flowchart of connecting a water dynamic model to a water dynamic module in one embodiment;
[0038] Figure 6 A flowchart of determining a water dynamic module and a water quality module in one embodiment;
[0039] Figure 7 A structural block diagram of the water quality standard reaching time prediction device of the tail-lag lake in the arid region in one embodiment;
[0040] Figure 8 An internal structure diagram of the computer device in one embodiment. DETAILED DESCRIPTION
[0041] In order to make the purposes, technical solutions and advantages of the present application more clear, the present application will be further described in detail below with reference to the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application, and are not used to limit the present application.
[0042] The water quality standard reaching time prediction method of the tail-lag lake in the arid region provided by the embodiments of the present application can be applied to, for example Figure 1The application environment is shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data required by the server 104 to process. The data storage system can be integrated on the server 104, or placed on the cloud or other network servers. Among them, the terminal 102 can be, but is not limited to, various personal computers, notebook computers, smart phones, tablet computers, Internet of Things devices, and portable wearable devices. The Internet of Things device can be a smart television, a smart vehicle device, a projection device, etc. The portable wearable device can be a smart watch, a smart bracelet, a head-mounted device, etc. The head-mounted device can be a virtual reality (VR) device, an augmented reality (AR) device, smart glasses, etc. The server 104 can be a stand-alone physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services.
[0043] In an exemplary embodiment, as shown in Figure 2 , a method for predicting the compliance time of water quality of a tail-lake in an arid region is provided. The method is applied to the server 104 in Figure 1 for example, and includes the following steps S202 to S206. Among them:
[0044] Step S202, according to the water quantity monitoring data and the water quality monitoring data of the target lake area, a hydrodynamic model of the target lake area is established.
[0045] Among them, the hydrodynamic model can be used to characterize the water exchange of the groundwater in the target lake area.
[0046] Exemplarily, the water quantity monitoring data can refer to the monitoring data of the input and output of the water quantity in the target lake area. For example, the water quantity monitoring data can include the river recharge amount, the groundwater recharge amount, the rainfall amount, the evaporation amount, and the groundwater outflow amount of the target lake.
[0047] The water quality monitoring data can refer to the concentration monitoring data of the mineralization degree of the lake water or the input and output of the preset compound in the target lake area. For example, the water quality monitoring data can include the concentration monitoring data of some inorganic compounds in the target lake area, such as the concentration data of the river water, the concentration data of the groundwater input, the concentration data of the groundwater output, and the concentration data of the lake water.
[0048] Specifically, based on the mass balance, the target lake area can be generalized as a black box, the water input of which can include river input, rainfall input, groundwater input, etc., and the water output of which can include groundwater output and evaporation output, etc. The input and output process of the preset compound (e.g., a certain pollutant) monitored by the water quality monitoring data is similar to the water input and output of the target lake area, but in fact most of the pollutants are difficult to be output by evaporation, based on which, when constructing a water dynamic model (also referred to as a water quantity and water quality steady-state model) taking the pollutant as the calculation object, the evaporation water output of the target lake can no longer be considered. Specifically, the server can analyze the water exchange of the groundwater of the target lake area according to the water quantity monitoring data and the water quality monitoring data of the target lake area, and thus establish a water dynamic model for the target lake area. Optionally, the above target lake can be a tail-lag lake (i.e., a tail-lag type lake).
[0049] It can be understood that the specific monitoring index types of the above water quantity monitoring data and water quality monitoring data are not limited to the implementation manners mentioned in the above embodiments, as long as they can represent the water input and output of the target lake and the input and output of the preset compound (or the target pollutant), and the specific monitoring index types of the water quantity monitoring data and the water quality monitoring data are not specifically limited in the embodiments of the present application.
[0050] In step S204, a water environment model of the target lake area is constructed based on the geographic information data of the target lake area and in combination with the water dynamic model.
[0051] The water environment model can be used to inverse and calculate the change relationship of the target pollutant in the target lake area over time.
[0052] Exemplarily, the geographic information data can refer to natural environmental data affecting the water quality and quantity of the target lake area, for example, the geographic information data of the target lake area can include water quality data, hydrogeological data, meteorological data, spatial data, attribute data, and pollution source data of the target lake area.
[0053] It can be understood that the specific monitoring index types of the above geographic information data are not limited to the implementation manners mentioned in the above embodiments, as long as they can represent the water input and output of the target lake and the input and output of the preset compound (or the target pollutant), and the specific monitoring index types of the geographic information data are not specifically limited in the embodiments of the present application.
[0054] Specifically, the geographic information data can be used as the basic data for constructing the water environment model, and the lake hydrodynamic process is simulated in combination with the water dynamic model, so that the water environment model representing the change of the water quality of the target lake area over time can be constructed according to the calculated pollutant diffusion.
[0055] Step S206, through inversion and optimization debugging of the water environment model, a target prediction result for the target lake region is obtained.
[0056] The target prediction result can be used to represent the required time for the target pollutant to reach the preset water quality index. Optionally, the target prediction result can be a water quality change time series graph of the target pollutant in the target lake region.
[0057] Exemplarily, the water quality change condition simulated by the water quality change time series graph can be compared with the actual condition, and the model parameters of the water environment model are adjusted and optimized to further improve the accuracy of the simulation of the water environment model.
[0058] It can be understood that by changing the boundary conditions of the water environment model, the required time for the target pollutant in the target lake region to reach the preset water quality target can be predicted. Optionally, the target pollutant can include carbon pollutants, nitrogen pollutants, phosphorus pollutants, and other water pollutants.
[0059] Specifically, the server can automatically simulate and calculate the degradation coefficients of carbon, nitrogen, phosphorus, and other water pollutants and the corresponding metabolic time through the constructed water environment model, so that the water quality change time series graph of the target pollutant in the target lake region can be obtained. According to the time series graph, the time required for the target pollutant to reach the preset water quality index or the preset water quality target limit value can be accurately predicted.
[0060] The above drought area tail-lag lake water quality compliance time prediction method can establish a water dynamic model of the target lake region according to water quantity monitoring data and water quality monitoring data of the target lake region; based on geographic information data of the target lake region, a water environment model for the target lake region can be constructed in combination with the water dynamic model; through the water environment model, a target prediction result for the target lake region can be obtained. The present application can avoid direct large-scale on-site monitoring of groundwater input and output by constructing a water dynamic model, effectively reducing the monitoring cost, and improving the accuracy of predicting the water quality compliance time of the target lake region (such as a drought area tail-lag lake).
[0061] In one of the embodiments, as shown in Figure 3 According to the water quantity monitoring data and the water quality monitoring data of the target lake region, a water dynamic model of the target lake region is established, which includes the following steps S302 to S308. Wherein:
[0062] Step S302, based on the water quantity monitoring data, the water balance relationship of the target lake region is determined.
[0063] The water quantity monitoring data can include river recharge, groundwater recharge, rainfall, evaporation, and groundwater outflow of the target lake region.
[0064] Specifically, the server can construct the water balance relationship of the target lake area according to the obtained water monitoring data.
[0065] Exemplarily, according to the following formula 1, the water balance relationship of the target lake area can be determined based on the water monitoring data:
[0066] (Formula 1)
[0067] Wherein, represents the river recharge of the target lake area, represents the groundwater recharge of the target lake area, represents the rainfall of the target lake area, represents the evaporation of the target lake area, represents the groundwater outflow of the target lake area.
[0068] Step S304, determining the mass balance relationship of the target lake area according to the water monitoring data and the water quality monitoring data.
[0069] Wherein, the water quality monitoring data can include concentration monitoring data such as river water concentration data, groundwater input concentration data, groundwater output concentration data and lake water concentration data of the target lake area.
[0070] It can be understood that the salinity and the concentration of inorganic compounds of the lake water are mainly determined by physical input and output and chemical interaction inside the lake water (for example, between the lake and the lake sediment). The mass balance relationship for salinity or inorganic compounds can be established according to the balance of the water quantity of the target lake area.
[0071] It should be noted that the selection of the above inorganic compounds (i.e. the calculation object of the pollutants) needs to consider the stability of the compound in the biogeochemical cycle process. The fewer factors affecting the stability of the compound, the more conducive to the construction and calculation of the hydrodynamic model. Alternatively, the water quality monitoring data of the embodiments of the present application can be salinity or fluoride concentration monitoring data.
[0072] Specifically, the server can determine the mass balance relationship of the target lake area according to the collected water monitoring data and water quality monitoring data.
[0073] Exemplarily, according to the following formula 2, the mass balance relationship of the target lake area can be constructed based on the water monitoring data and the water quality monitoring data:
[0074] (Formula 2)
[0075] Wherein, represents the river water concentration data, for example The mineralization degree of the river water flowing into the lake or the concentration of inorganic compounds (fluoride, etc.) in the lake water; represents the concentration data of the groundwater input, for example The mineralization degree of the groundwater flowing into the lake or the concentration of inorganic compounds (fluoride, etc.) can be obtained. represents the concentration data of the lake water, for example The mineralization degree of the lake water or the concentration of inorganic compounds (fluoride, etc.) can be obtained. It can be understood that the concentration data of the lake water is actually equal to the mineralization degree or the concentration of inorganic compounds of the groundwater output from the target lake. Alternatively, in formula 2, in order to distinguish the mineralization degree and the concentration of inorganic compounds data, C S represents the mineralization degree of the water body, and C F represents the concentration of inorganic compounds (fluoride, etc.) in the water body.
[0076] In step S306, a common differential equation is established based on the mass balance relationship.
[0077] The common differential equation can represent the dynamic change process of the water quality monitoring data with water exchange.
[0078] Specifically, the server can further construct a corresponding common differential equation for the dynamic change process of the water quality monitoring data with water exchange according to the calculated mass balance relationship.
[0079] For example, taking the concentration data of the mineralization degree or fluoride as the water quality monitoring data, when the target lake area is in a stable state, if the groundwater output changes, the mineralization degree and the concentration of inorganic compounds such as fluoride in the target lake area will also gradually change until the target lake area reaches a new steady state.
[0080] Alternatively, the common differential equation representing the dynamic change process of the water quality monitoring data with water exchange can be represented as follows according to formula 3 as follows:
[0081] (Formula 3)
[0082] wherein, represents the volume of the lake water in the target lake area, represents time.
[0083] Further, the common differential equation can be solved according to formula 4 to formula 6 as follows.
[0084] Based on formula 3, the following definitions can be made:
[0085] (Formula 4)
[0086] According to the above formula 4, as shown in formula 5 and formula 6, the solution of the ordinary differential equation can be further obtained:
[0087] (Formula 5)
[0088] (Formula 6)
[0089] wherein, will decrease exponentially, and after a time / , will decrease to 1 / , / represents the characteristic time of the change of the inorganic compound concentration in the target lake area.
[0090] Step S308, according to the ordinary differential equation, the water balance relationship and the mass balance relationship are combined to construct a hydrodynamic model.
[0091] Wherein, the hydrodynamic model can be calculated according to the salinity or inorganic compound concentration data of the lake water in the steady state, combined with the mass balance relationship, to obtain the accurate groundwater input and output water quantity, and the calculation process can be implemented in the server in real time and dynamically, and different calculation parameters can be adaptively selected according to the water quality prediction demand, to improve the accuracy of simulation.
[0092] Specifically, the server can obtain the groundwater input and output water quantity when the target lake area is in different salinity or inorganic compound concentration in the steady state, by the above-mentioned ordinary differential equation and its analytical solution, combined with the water balance relationship and the mass balance relationship, based on the historical observation data of the target lake area. Thus, the groundwater water quantity exchange can be analyzed.
[0093] In some examples, the value of the groundwater input concentration data can also be checked in combination with the water balance relationship and the water level observation data of the target lake area, and the groundwater input and output water quantity can be calculated in combination with the mass balance relationship, to improve the accuracy of the hydrodynamic model simulation, and further improve the accuracy of the lake water quality prediction.
[0094] It can be understood that the water power model of the embodiment of the present application can take inorganic anions with strong exchange activity such as fluoride as the calculation object, construct a water power model, and use the actual observation data of the water body to perform model fitting and optimization, so as to realize the back calculation of the water quantity of the lake groundwater input and output under the steady state condition by using the current water quality fluoride concentration value. The advantage of the embodiment of the present application is that it can not be necessary to perform large-scale groundwater input and output field monitoring, greatly reducing the cost of model construction, and effectively solving the problem of missing monitoring data of the lake groundwater exchange.
[0095] In one embodiment, as shown in Figure 4 Based on the geographic information data of the target lake area, a water environment model of the target lake area is constructed by combining the water power model, including the following steps S402 to S404.
[0096] In step S402, the water power module and the water quality module are determined based on the geographic information data by using the water environment simulation software.
[0097] The water power module can be used to simulate the water power process of the target lake area, and the water quality module can be used to simulate the water quality state of the target lake area, such as the state of algae, carbon pollutants, nitrogen pollutants, phosphorus pollutants, etc.
[0098] For example, the water environment simulation software can be used to simulate and analyze various physical, chemical and biological processes in the target water environment, and can directly reflect the dynamic changes of the target water body to effectively evaluate the diffusion and influence of pollutants. In some examples, the water environment simulation software can use MIKE water environment software, and the MIKE water environment software includes MIKE3 software with built-in water power module and water quality module.
[0099] It can be understood that the above-mentioned water environment simulation software is not limited to the implementation manner mentioned in the above-mentioned embodiment, as long as the corresponding water power module and water quality module can be constructed, and the type of the water environment simulation software is not limited in the embodiment of the present application.
[0100] Specifically, based on the collected geographic information data, the water power module and the water quality module required for constructing the water environment module can be determined by using the MIKE3 software and other water environment simulation software.
[0101] In step S402, the water power model is connected to the water power module, and the water power module connected with the water power model is code-coupled with the water quality module to construct the water environment model.
[0102] It can be understood that the water power model can dynamically calculate the input and output water quantity of the groundwater of the target lake area according to the water quality monitoring data of the lake water in the steady state, so as to obtain the water quantity exchange of the groundwater of the target lake area. By connecting the water power model to the water power module, the water power module can more accurately simulate the water power process of the target lake area, thereby improving the accuracy of the lake water quality prediction.
[0103] Specifically, the server can connect the water power model constructed for the target lake area to the water power module of the water environment simulation software, and make the water quality module directly code-coupled with the water power module, so that the simulation of the water power transport process and the water quality change process of the target lake area can be carried out at the same time, thereby constructing the water environment model for the target lake area.
[0104] In one of the embodiments, as shown in Figure 5 The water power model is connected to the water power module, including the following steps S502 to S504. Among them:
[0105] Step S502, based on the water power model, obtaining the groundwater input water quantity and the groundwater output water quantity of the target lake area.
[0106] Specifically, the server can obtain the groundwater input water quantity and the groundwater output water quantity of the target lake area by using the water power model based on the obtained water quality monitoring data of the target lake area, such as the salinity or inorganic compound concentration data of the target lake area.
[0107] Step S504, connecting the groundwater input water quantity and the groundwater output water quantity as the boundary conditions of the water power module, and connecting the water power model to the water power module.
[0108] Exemplarily, the water power model of the MIKE3-ECOLAB model can include a plurality of boundary conditions, such as the water surface boundary condition involving the water surface wind stress and the lake bottom boundary condition involving the lake bottom stress. It can be understood that the water power model can more accurately simulate the water power process of the target lake area by reasonably setting the boundary conditions.
[0109] Specifically, the server can connect the groundwater input water quantity and the groundwater output water quantity of the water power model to the water power module of the water environment simulation software as the boundary conditions of the water power module, so that the water power module can more accurately simulate the water power process of the target lake area, thereby effectively improving the accuracy of predicting the lake water quality compliance time.
[0110] In one of the embodiments, as shown in Figure 6As shown, the geographic information data includes water quality data, hydrogeological data, meteorological data, pollution source data, and spatial attribute data. Through the water environment simulation software, the water power module and the water quality module are determined based on the geographic information data, including the following steps S602 to step S604. Among them:
[0111] Step S602, through the water environment simulation software, the water power module is determined according to the hydrogeological data, meteorological data and spatial attribute data.
[0112] Specifically, the server can determine the water power module of the water environment simulation software based on the collected hydrogeological data, meteorological data and spatial attribute data.
[0113] Exemplarily, the hydrogeological data can use the hydrological data such as water level, flow rate and flow velocity of the target lake area, and the geological data such as DEM (Digital Elevation Model) lake bottom topographic data and lake inlet river elevation data.
[0114] Optionally, the meteorological data can use the daily precipitation, daily air temperature (maximum temperature and minimum temperature), daily relative humidity, daily solar radiation and daily average wind speed of the target lake area. Optionally, the meteorological data can use the atmospheric assimilation data set CMADSV1.0 from the SWAT model, which is a spatial resolution of 1 / 3°x1 / 3°, a time span of 10 years of daily data.
[0115] In some examples, the spatial attribute data can include land use type, soil type data and soil attribute data of the target lake area. Among them, the land use type data can use the 1:250000 land use raster data published by the Department of Earth System Science of Tsinghua University. The soil type data can use the soil type data in the World Soil Database published by the Food and Agriculture Organization of the United Nations, with a resolution of 1:1000000. The soil attribute data represents the movement of water and air in the soil profile, and most of the soil attribute data can be directly queried from the HWSD (Harmonized World Soil Database) soil data set, and a few data can be calculated using the soil property calculation tool SPAW (Soil Plant Atmosphere Water). Soil attribute data can include: soil layer number, soil hydrology grouping, maximum root depth of soil profile, soil wet density, saturated hydraulic conductivity, saturated hydraulic conductivity, etc.
[0116] Step S604, through the water environment simulation software, the water quality module is determined according to the water quality data and the pollution source data.
[0117] Specifically, the server can further determine the water quality module of the water environment simulation software based on the collected water quality data and pollution source data.
[0118] Exemplarily, the water quality data can be water quality data of a lake and surrounding rivers, sewage outlets, groundwater, and the like that flow into a target lake area, and the data monitoring period can be set to ten years. Further, the water quality data can be selected from all data in Tables 1 and 2 in the Surface Water Environmental Quality Standard (GB 3838-2002).
[0119] Optionally, the pollution source data can be land source pollution source input monitoring data of the target lake area in a ten-year period. In some examples, the pollution source data can be calculated by using environmental system data or in combination with the Technical Guidelines for Pollution Source Strength Calculation (HJ 884-2018).
[0120] In one embodiment, the water environment simulation software includes MIKE3 software, and the MIKE3 software can be used to build a MIKE3-ECOLAB model.
[0121] It can be understood that MIKE3 can well simulate the processes of three-dimensional water dynamics, temperature, salinity changes, transport of viscous and non-viscous sediments, water nutrient salt circulation, eutrophication, and toxic substance transport.
[0122] Specifically, the present application builds a water environment model for the target lake area based on the MIKE3-ECOLAB model of the MIKE3 software, which can more accurately and accurately predict the lake water quality compliance time.
[0123] It should be understood that although each step in the flowchart involved in each of the above-described embodiments is displayed in sequence according to the arrow, these steps are not necessarily executed in sequence according to the arrow. Unless otherwise specified herein, the execution of these steps is not strictly limited in sequence, and these steps can be executed in other orders. Moreover, at least part of the steps in the flowchart involved in each of the above-described embodiments can include multiple steps or stages, which are not necessarily executed at the same time, but can be executed at different times, and the execution sequence of these steps or stages is not necessarily sequential, but can be executed in rotation or alternation with at least part of other steps or steps or stages in other steps.
[0124] Based on the same inventive concept, the embodiments of the present application also provide a device for predicting the time when the water quality of the tail-end lake in an arid area will reach the standard, which is used to implement the aforementioned method for predicting the time when the water quality of the tail-end lake in an arid area will reach the standard. The implementation scheme for solving the problem provided by this device is similar to the implementation scheme described in the aforementioned method. Therefore, the specific limitations of one or more embodiments of the device for predicting the time when the water quality of the tail-end lake in an arid area will be referred to the limitations of the method for predicting the time when the water quality of the tail-end lake in an arid area will be referred to above, and will not be repeated here.
[0125] In an exemplary embodiment, Figure 7 As shown, a device 700 for predicting the time when the water quality of the tail lake in an arid area reaches the standard is provided. The device 700 includes:
[0126] The hydrodynamic model building module 702 is used to build a hydrodynamic model of the target lake area based on the water quantity monitoring data and water quality monitoring data of the target lake area; the hydrodynamic model is used to characterize the water quantity exchange of groundwater in the target lake area;
[0127] The water environment model construction module 704 is used to construct a water environment model of the target lake area based on the geographic information data of the target lake area and in combination with the hydrodynamic model; the water environment model is used to invert and calculate the change relationship of the target pollutants in the target lake area over time;
[0128] The prediction result acquisition module 706 is used to obtain the target prediction results for the target lake area through inversion and optimization debugging of the water environment model; the target prediction results are used to characterize the time required for the target pollutant to reach the preset water quality index.
[0129] In one embodiment, the hydrodynamic model building module 702 is further configured to:
[0130] Determine the water balance relationship of the target lake area based on water monitoring data;
[0131] Determine the mass balance relationship of the target lake area based on water quantity monitoring data and water quality monitoring data;
[0132] Based on the mass balance relationship, an ordinary differential equation is established; the ordinary differential equation represents the dynamic change process of water quality monitoring data with water volume exchange;
[0133] Based on ordinary differential equations, a hydrodynamic model is constructed by combining the water balance relationship and the mass balance relationship.
[0134] In one embodiment, the water environment model building module 704 is further configured to:
[0135] Through water environment simulation software, the water dynamics module and water quality module are determined based on geographic information data;
[0136] The water power model is connected to the water power module, and the water power module connected with the water power model is code-coupled with the water quality module to build the water environment model.
[0137] In one of the embodiments, the water environment model building module 704 is further configured to:
[0138] Based on the water power model, the underground water input and output of the target lake area are obtained;
[0139] The underground water input and output are taken as the boundary conditions of the water power module, so that the water power model is connected to the water power module.
[0140] In one of the embodiments, the geographic information data includes water quality data, hydrogeological data, meteorological data, pollution source data and spatial attribute data; the water environment model building module 704 is further configured to:
[0141] The water power module is determined according to the hydrogeological data, meteorological data and spatial attribute data through the water environment simulation software.
[0142] The water quality module is determined according to the water quality data and pollution source data through the water environment simulation software.
[0143] In one of the embodiments, the water environment simulation software includes MIKE3 software; the MIKE3 software is used to build a MIKE3-ECOLAB model.
[0144] The above-mentioned various modules in the drought area tail-lake water quality compliance time prediction device can be all or partially realized by software, hardware and combinations thereof. The above-mentioned various modules can be embedded in or independent of the processor in the computer device in hardware form, or can be stored in the memory in the computer device in software form, so as to be called and executed by the processor to perform the operations corresponding to the above-mentioned various modules.
[0145] In one exemplary embodiment, a computer device, which can be a server, is provided, and an internal structure diagram of the computer device can be as shown in Figure 8As shown in the figure. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through the system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capability. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store water quantity monitoring data, water quality monitoring data and geographic information data for the target lake area. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with external terminals through network connection. The computer program is executed by the processor to realize a drought area tail-lag lake water quality compliance time prediction method.
[0146] Those skilled in the art can understand that, Figure 8 The skilled in the art can understand that,
[0147] In one embodiment, a computer device is also provided, including a memory and a processor, the memory stores a computer program, and the processor executes the computer program to realize the steps in each method embodiment described above.
[0148] In one embodiment, a computer readable storage medium is provided, which stores a computer program, and the computer program is executed by a processor to realize the steps in each method embodiment described above.
[0149] In one embodiment, a computer program product is provided, which includes a computer program, and the computer program is executed by a processor to realize the steps in each method embodiment described above.
[0150] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer readable storage medium, and when executed, can include the processes of the above-mentioned embodiment methods. Any reference to memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. The non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical storage, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. The volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration but not limitation, the RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The database involved in the embodiments provided in the present application can include at least one of a relational database and a non-relational database. The non-relational database can include a distributed database based on a block chain, etc., without being limited thereto. The processor involved in the embodiments provided in the present application can be a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., without being limited thereto.
[0151] The technical features of the above embodiments can be combined arbitrarily. In order to make the description simple, all possible combinations of the technical features in the above embodiments are not described, however, as long as the combinations of the technical features do not exist contradictory, they should be considered as the scope of the present application.
[0152] The above-described embodiments are merely illustrative of several embodiments of the present application, and the description is relatively specific and detailed, but should not be understood as a limitation on the scope of the patent. It should be noted that for those skilled in the art, without departing from the concept of the present application, a number of modifications and improvements can be made, which are all within the scope of the present application. Therefore, the scope of protection of the present application should be subject to the appended claims.
Claims
1. A method for predicting the time for water quality of tail lakes in arid areas to reach standards, characterized in that: The method comprises: Establishing a hydrodynamic model of the target lake region based on water quantity monitoring data and water quality monitoring data of the target lake region; the hydrodynamic model is used to characterize the water quantity exchange of groundwater in the target lake region; Based on the geographic information data of the target lake area and in combination with the hydrodynamic model, a water environment model of the target lake area is constructed; the water environment model is used to invert and calculate the relationship between the target pollutants in the target lake area and time; Through the inversion and optimization debugging of the water environment model, the target prediction results for the target lake area are obtained; the target prediction results are used to characterize the time required for the target pollutants to reach the preset water quality indicators.
2. The method according to claim 1, characterized in that Based on the water quantity monitoring data and water quality monitoring data of the target lake area, a hydrodynamic model of the target lake area is established, including: Determining the water balance relationship of the target lake area based on the water monitoring data; Determining a mass balance relationship of the target lake area based on the water quantity monitoring data and the water quality monitoring data; Based on the mass balance relationship, an ordinary differential equation is established; the ordinary differential equation represents the dynamic change process of the water quality monitoring data with water volume exchange; The hydrodynamic model is constructed based on the ordinary differential equation and in combination with the water balance relationship and the mass balance relationship.
3. The method according to claim 1, characterized in that The step of constructing a water environment model of the target lake area based on the geographic information data of the target lake area and in combination with the hydrodynamic model includes: Determine a water power module and a water quality module based on the geographic information data using water environment simulation software; The hydrodynamic model is connected to the hydrodynamic module, and the hydrodynamic module connected to the hydrodynamic model is code-coupled with the water quality module to construct the water environment model.
4. The method according to claim 3, characterized in that Connecting the hydrodynamic model to the hydrodynamic module includes: Based on the hydrodynamic model, obtaining the groundwater input and output of the target lake area; The groundwater input water volume and the groundwater output water volume are used as boundary conditions of the hydrodynamic module, so that the hydrodynamic model is connected to the hydrodynamic module.
5. The method according to claim 3, characterized in that The geographic information data includes water quality data, hydrogeological data, meteorological data, pollution source data, and spatial attribute data; the water environment simulation software determines the hydrodynamic module and water quality module based on the geographic information data, including: Determining the hydrodynamic module according to the hydrogeological data, the meteorological data and the spatial attribute data using the water environment simulation software; The water quality module is determined according to the water quality data and the pollution source data through the water environment simulation software.
6. The method according to any one of claims 3 to 5, characterized in that The water environment simulation software includes MIKE3 software; the MIKE3 software is used to construct the MIKE3-ECOLAB model.
7. A device for predicting the time when the water quality of the tail lake in an arid area will reach the standard, characterized in that: The device comprises: A hydrodynamic model building module is used to establish a hydrodynamic model of the target lake area based on the water quantity monitoring data and water quality monitoring data of the target lake area; the hydrodynamic model is used to characterize the water quantity exchange of groundwater in the target lake area; A water environment model construction module is used to construct a water environment model of the target lake area based on the geographic information data of the target lake area in combination with the hydrodynamic model; the water environment model is used to invert and calculate the relationship between the change of target pollutants in the target lake area over time; The prediction result acquisition module is used to obtain the target prediction result for the target lake area through inversion and optimization debugging of the water environment model; the target prediction result is used to characterize the time required for the target pollutant to reach the preset water quality index.
8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.
9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.
10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.