CN109858699B - Water quality quantitative simulation method and device, electronic equipment and storage medium - Google Patents
Water quality quantitative simulation method and device, electronic equipment and storage medium Download PDFInfo
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Abstract
The application provides a water quality quantitative simulation method and device, electronic equipment and a storage medium. The target water area is divided into a plurality of grids, and the method comprises the following steps: performing a data prediction step for a first preset time period and a first future time period, comprising: determining first water quality prediction data of a monitoring grid in a first future time period based on a fitting function, wherein the fitting function is the fitting function of the water quality monitoring data which is determined based on the water quality monitoring data of the target water area monitoring grid in the first preset time period and changes along with time; determining second water quality prediction data of all grids in the first future time period based on a preset water quality simulation model; and determining third water quality prediction data of all grids in the first future time period based on the first water quality prediction data and the second water quality prediction data.
Description
Technical Field
The application relates to the technical field of water environment, in particular to a water quality quantitative simulation method and device, electronic equipment and a storage medium.
Background
The water quality simulation is to research the characteristics, rules and influence factors of the migration and transformation of pollutants in rivers and predict the development trend. The water quality simulation is divided into qualitative simulation and quantitative simulation. Because the continuous change of water quality in space and time is involved in the actual work, the problem of quantitative simulation and prediction of water quality is solved as a main problem, and a basis can be provided for evaluating, predicting and selecting a pollution control scheme, and formulating a water quality standard and a pollution discharge regulation.
The quantitative simulation can be divided into physical simulation, test model simulation and mathematical simulation. The mathematical simulation is a common and effective simulation mode because of the incomparable economy, flexibility and adaptability of the physical simulation and the test model simulation. The rapid development of computer technology also promotes the continuous improvement of mathematical simulation accuracy and reliability.
However, the mathematical simulation has limitations, and the mechanism of water pollution is not clear, many processes are difficult to express and simulate by mathematical methods, and the modeling must be generalized to a certain extent, so that the distortion is inevitable. The basis of the simulation modeling of the test model is that water quality data, the authenticity, systematicness and integrity of data directly influence the model precision, and particularly solve the problems of extension forecasting precision, equation dispersion and boundary condition processing in mathematical model solving and the like. Most of the existing researches are to establish a model aiming at the characteristics of a certain research object, the application expansibility of the model is poor, the analysis, calculation, query and display capabilities of mass data are deficient, the visualization degree of a simulation result is poor, the interactivity is poor and the like.
Therefore, in the process of correcting the model water quality simulation, simulation errors can be caused due to unclear water quality pollution mechanism, generalized approximation process and low input data precision. And because no more detailed and reliable hydrological water quality database exists in China at present, the reliability of the simulation result of the test model needs to be improved by introducing an observation value.
Disclosure of Invention
The embodiment of the application provides a water quality quantitative simulation method, which is characterized in that a target water area is divided into a plurality of grids, and the method comprises the following steps: performing a data prediction step for a first preset time period and a first future time period, comprising: determining first water quality prediction data of the monitoring grid in a first future time period based on a fitting function of the water quality monitoring data, which is determined by the water quality monitoring data of the target water area monitoring grid in the first preset time period, along with the change of time; determining second water quality prediction data of all grids in the first future time period based on a water quality simulation model; and determining third water quality prediction data of all grids in the first future time period based on the first water quality prediction data and the second water quality prediction data.
As an optional technical solution of the present application, after determining the third water quality prediction data of all grids in the first future time period, the method further includes: determining that the target time period does not reach a predetermined value; performing iterative steps for a second preset time period and a second future time period, comprising: determining fourth water quality prediction data of the monitoring grid in a second future time period based on the water quality monitoring data of the target water area monitoring grid in the second preset time period and the fitting function; and determining fifth water quality prediction data of all grids in the second future time period based on the fourth water quality prediction data and the third water quality prediction data.
The embodiment of the application also provides a water quality quantitative simulation device which is characterized by comprising a statistical prediction module, a model prediction module and a data assimilation module, wherein the statistical prediction module determines first water quality prediction data of a monitoring grid in a first future time period based on a fitting function of the water quality monitoring data, which is determined by the water quality monitoring data of a target water area monitoring grid in the first preset time period, and changes along with time; the model prediction module determines second water quality prediction data of all grids in the first future time period based on a water quality simulation model; and the data assimilation module determines third water quality prediction data of all grids in the first future time period based on the first water quality prediction data and the second water quality prediction data.
As an optional technical solution of the present application, the apparatus further includes an integration module, the integration module determines that the target time interval does not reach a predetermined value, performs an iteration step, determines fourth water quality prediction data of the monitoring grid in a second future time interval based on the water quality monitoring data of the target water area monitoring grid in the second preset time interval and the fitting function, and determines fifth water quality prediction data of all grids in the second future time interval based on the fourth water quality prediction data and the third water quality prediction data.
An embodiment of the present invention further provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor executes the program to implement the method described above.
An embodiment of the present application further provides a computer-readable storage medium, on which a processor program is stored, where the processor program is used in the method described above.
The technical scheme provided by the embodiment of the application solves the problem of simulation distortion caused by low density of water quality monitoring data and the fact that a model simulation result is mainly limited by integrity of hydrological water quality data, and obtains high-precision hydrological water quality data close to a true value.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a schematic flow chart of a water quality quantitative simulation method according to an embodiment of the present disclosure;
fig. 2 is a schematic flow chart of a water quality quantitative simulation method according to another embodiment of the present disclosure;
FIG. 3 is a schematic flow chart of a water quality quantitative simulation method according to another embodiment of the present disclosure;
fig. 4 is a schematic composition diagram of a water quality quantitative simulation apparatus according to an embodiment of the present application;
fig. 5 is a schematic composition diagram of a water quality quantitative simulation apparatus according to another embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, specific embodiments of the technical solutions of the present application will be described in more detail and clearly in the following with reference to the accompanying drawings and the embodiments. However, the specific embodiments and examples described below are for illustrative purposes only and are not limiting of the present application. It is intended that the present disclosure includes only some embodiments and not all embodiments, and that other embodiments may be devised by those skilled in the art with various modifications as fall within the scope of the appended claims.
It should be understood that although the terms first, second, third, etc. may be used herein to describe various elements or components, these elements or components should not be limited by these terms. These terms are only used to distinguish one element or component from another element or component. Thus, a first element or component discussed below could be termed a second element or component without departing from the teachings of the present application.
Fig. 1 is a schematic flow chart of a water quality quantitative simulation method according to an embodiment of the present application, which includes the following steps.
In step S110, first water quality prediction data of the monitoring grid in the first future time period is determined based on a fitting function, wherein the fitting function is a fitting function of the water quality monitoring data determined based on the water quality monitoring data of the target water area monitoring grid in the first preset time period, the water quality monitoring data changing with time.
In this embodiment, in order to simulate and predict data of the whole water area, the target water area is divided into a plurality of grids, and the water quality monitoring data is from one of the monitoring grids, and the hydrological water quality monitoring data of the monitoring grid in the first preset time period is obtained, where the water quality monitoring data includes water temperature, water flow, measured ammonia nitrogen, dissolved oxygen, chlorophyll concentration of a target substance in water, and the like, but is not limited thereto. The preset time period is set as required, and in this embodiment, for example, four hours may be selected as one preset time period, which is not limited to this.
And determining a fitting function of the water quality monitoring data along with the change of time based on the water quality monitoring data. Observed values needing regression fitting comprise observed quantities such as water temperature, ammonia nitrogen, dissolved oxygen and chlorophyll, and the time series change trend of the quantities is not dependent variable of the time independent variable in common knowledge. Although the water temperature is a quantity with daily variation fluctuation, the main factor for determining the water temperature is the air temperature, and the short-term forecast of the air temperature is relatively mature, so that the regression analysis is carried out by taking the air temperature as an independent variable and the water temperature as a dependent variable, and an accurate forecast value can be obtained.
However, the observed quantities of ammonia nitrogen, dissolved oxygen, chlorophyll and the like and the water temperature are different, no independent variable which has a main influence on the observed value like the air temperature exists, and certainly, a series of factors which possibly influence the observed values of ammonia nitrogen, dissolved oxygen and chlorophyll can be communicated with professionals and listed, and then the main influence factors can be found out through a principal component analysis method, but whether the influence factors can have reliable predicted values or not is also a problem.
Therefore, in this case, the influence of all independent variables on the dependent variable is summarized as the influence of time on the dependent variable, and a fitting function of the change of the water quality monitoring data with time is determined to perform fitting regression. Based on the fit function, first water quality prediction data for the monitoring grids for a first future time period is determined.
In step S120, second water quality prediction data of all grids in the first future time period is determined based on a preset water quality simulation model.
Common models of the water quality simulation model include, but are not limited to, a SUNTANS model and the like, and the SUNTANS model is a conventional model, and other models can be selected or established as required, without limitation. And determining second water quality prediction data of all grids in the first future time period based on a preset water quality simulation model.
In step S130, third water quality prediction data of all grids in the first future time period is determined based on the first water quality prediction data and the second water quality prediction data.
In the dynamic operation process of the water quality simulation model, namely, the first water quality prediction data is added into the second water quality prediction data, the model and the fitting function are combined to predict the water quality data of all grids in the water area in the first future time period to obtain third water quality prediction data, and the process is called data assimilation. The common data assimilation method is 3D-Var or 4D-Var. For the water body with uniform mixing, the water body characteristics can be reflected by 2D simulation.
In this embodiment, the following formula is used to perform the concrete data assimilation:
wherein xbiFor the second water quality prediction data of the ith grid, i corresponds to the number of grids, i is 1, 2, … … 100, numeral 100 is taken as an example, the actual maximum number of i is the number of grids,xai is the third water quality prediction data of the ith grid, wi, j is the influence weight of the jth monitoring grid on all grids, ovj is the first water quality prediction data of the jth grid, and j corresponds to the number of the monitoring grids.
The calculation equation of the influence weight of the monitoring grids on all grids is as follows:
wherein i is the ith grid, k is the number of monitoring grids, di，kThe distance between the ith grid and the kth monitoring grid is defined, R is an influence radius, and the influence radius can be preset as required.
In this embodiment, the influence weight, i.e. the sensitivity of the grid to the distance of the monitoring grid, can be changed by modifying the coefficients d, R. For example, the influence radius may be set to be half of the length of the target water area, that is, when the distance from the grid to the monitoring grid is greater than the length, the grid data is not influenced by the first water quality prediction data of the monitoring grid.
The technical scheme provided by the embodiment solves the problem of simulation distortion caused by low density of water quality monitoring data and the limitation of integrity of model simulation results mainly by hydrological water quality data, and obtains high-precision hydrological water quality data close to a real value.
Fig. 2 is a schematic flow chart of a water quality quantitative simulation method according to another embodiment of the present application, which includes the following steps.
In step S210, first water quality prediction data of the monitoring grid in the first future time period is determined based on a fitting function, wherein the fitting function is a fitting function of the water quality monitoring data determined based on the water quality monitoring data of the target water area monitoring grid in the first preset time period, which varies with time.
In step S220, second water quality prediction data for all grids in the first future time period is determined based on the water quality simulation model.
In step S231, the influence weight of the monitoring grid on each grid is determined.
Determining the distance between the monitoring grid and each grid, and determining the influence weight based on the distance and the preset influence radius.
The calculation equation of the influence weight of the monitoring grids on each grid is as follows:
determining the distance d between the kth monitoring grid and the ith gridi，k(ii) a Based on respective distances di，kAnd a preset influence radius R to determine the influence weight of the monitoring grid on all grids.
In step S232, a difference between the first water quality prediction data of the monitoring grid and the second water quality prediction data of the monitoring grid is determined.
In step S233, the product of the row matrix formed by the influence weights of all the monitoring grids on each grid and the column matrix formed by the above-described differences is determined.
In this embodiment, w1,1 to w1,15 are the row matrix of the influence weights of all monitoring grids on the first grid, as shown in the formula. w100,1 to w100,15 are the influence weights of all the monitoring grids on the 100 th grid to form a row matrix.
In step S234, the sum of the second water quality prediction data and the product of each grid is determined to obtain third water quality prediction data of all grids.
As shown in the above formula, the second water quality prediction data is xb1 to xb 100. And correspondingly adding the products of the row matrix formed by the influence weight and the column matrix formed by the difference to obtain third water quality prediction data of all grids.
In this embodiment, steps S210 and S220 are the same as steps S110 and S120, and are not described again.
Fig. 3 is a schematic flow chart of a water quality quantitative simulation method according to another embodiment of the present application, which includes the following steps.
In step S310, first water quality prediction data of the monitoring grid in the first future time period is determined based on a fitting function, wherein the fitting function is a fitting function of the water quality monitoring data determined based on the water quality monitoring data of the target water area monitoring grid in the first preset time period, which varies with time.
In step S320, second water quality prediction data for all grids in the first future time period is determined based on the water quality simulation model.
In step S330, third water quality prediction data of all grids in the first future time period is determined based on the first water quality prediction data and the second water quality prediction data.
In step S340, it is determined that the target period does not reach the predetermined value.
In the present embodiment, for example, the first future time period is set to 4 hours, and if the target time period is also 4 hours, that is, if 4 hours of third prediction data are required, the following iteration step is not required.
Conversely, if the first future time period is set to 4 hours and the target time period is set to 24 hours, that is, the third prediction data is required to be obtained for 24 hours, the following iteration steps are required. The second future time period is equal to the first future time period and is 4 hours, so that a plurality of iteration steps are required to obtain 24 hours of third prediction data.
And determining fourth water quality prediction data of the monitoring grid in a second future time period based on the water quality monitoring data of the target water area monitoring grid in the second preset time period and the fitting function. The second preset time period is a time period after the first preset time period, and the time periods are equal in length. The fourth water quality prediction data corresponds to prediction data of a second preset time period.
And determining fifth water quality prediction data of all grids in the second future time period based on the fourth water quality prediction data and the third water quality prediction data. The second preset time period is a time period after the first preset time period, and the time periods are equal in length. The fourth water quality prediction data corresponds to prediction data of a second preset time period. The third water quality prediction data is used as a correction value of the second water quality prediction data. And determining fifth water quality prediction data of all grids in the second future time period based on the fourth water quality prediction data and the third water quality prediction data.
In this embodiment, steps S310, S320, and S330 are the same as steps S110, S120, and S130, and are not described again.
The technical scheme provided by the embodiment provides an iteration method, and each iteration adds prediction by using the latest water quality monitoring data, so that high-precision hydrological water quality data closer to a real value is further obtained.
An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the method when executing the program.
A computer-readable storage medium, on which a processor program is stored, the processor program being adapted to carry out the above-mentioned method.
Fig. 4 is a schematic diagram illustrating a water quality quantitative simulation apparatus according to an embodiment of the present disclosure, where the water quality quantitative simulation apparatus includes a statistical prediction module 41, a model prediction module 42, and a data assimilation module 43.
The statistical prediction module 41 determines first water quality prediction data of the monitoring grid in a first future time period based on a fitting function, wherein the fitting function is a fitting function of water quality monitoring data determined based on the water quality monitoring data of the target water area monitoring grid in a first preset time period, wherein the fitting function changes along with time. The model prediction module 42 determines second water quality prediction data of all grids in the first future time period based on a preset water quality simulation model. The data assimilation module 43 determines third water quality prediction data of all grids in the first future time period based on the first water quality prediction data and the second water quality prediction data.
Fig. 5 is a schematic diagram illustrating a water quality quantitative simulation apparatus according to another embodiment of the present disclosure, where the water quality quantitative simulation apparatus includes a statistical prediction module 41, a model prediction module 42, a data assimilation module 43, and a time interval detection module 44.
The statistical prediction module 41 determines first water quality prediction data of the monitoring grid in a first future time period based on a fitting function, wherein the fitting function is a fitting function of water quality monitoring data determined based on the water quality monitoring data of the target water area monitoring grid in a first preset time period, wherein the fitting function changes along with time. The model prediction module 42 determines second water quality prediction data of all grids in the first future time period based on a preset water quality simulation model. The data assimilation module 43 determines third water quality prediction data of all grids in the first future time period based on the first water quality prediction data and the second water quality prediction data. The time interval monitoring module 44 sends an iteration instruction when the target time interval does not reach the predetermined value, the statistical prediction module 41 determines fourth water quality prediction data of the detection grid in the second future time interval based on the water quality detection data of the target water area detection grid in the second preset time interval and the fitting function according to the iteration instruction, and the data assimilation module 43 determines fifth water quality prediction data of all grids in the second future time interval based on the fourth water quality prediction data and the third water quality prediction data according to the iteration instruction.
The data assimilation module 43 first determines the influence weight of the monitoring grid on all grids, then determines the difference between the first water quality prediction data and the second prediction data of the monitoring grid, further determines the product of the row matrix formed by all the influence weights and the column matrix formed by the difference, and finally determines the sum of the second water quality prediction data and the product to obtain third water quality prediction data.
The data assimilation module 43 determines the influence weight of the monitoring grid on all grids, first determines the distance between the monitoring grid and all grids, and then determines the influence weight based on the distance and the preset influence radius.
It should be noted that the above-mentioned embodiments described with reference to the drawings are only intended to illustrate the present application and not to limit the scope of the present application, and those skilled in the art should understand that modifications or equivalent substitutions made on the present application without departing from the spirit and scope of the present application should be included in the scope of the present application. Furthermore, unless the context indicates otherwise, words that appear in the singular include the plural and vice versa. Additionally, all or a portion of any embodiment may be utilized with all or a portion of any other embodiment, unless stated otherwise.
Claims (6)
1. A method for quantitative simulation of water quality, wherein a target water area is divided into a plurality of grids, the method comprising:
performing a data prediction step for a first preset time period and a first future time period, comprising:
determining first water quality prediction data of a monitoring grid in a first future time period based on a fitting function, wherein the fitting function is the fitting function of the water quality monitoring data which is determined based on the water quality monitoring data of the target water area monitoring grid in the first preset time period and changes along with time;
determining second water quality prediction data of all grids in the first future time period based on a preset water quality simulation model;
determining third water quality prediction data of all grids in the first future time period by combining a water quality simulation model and a fitting function based on the first water quality prediction data and the second water quality prediction data;
wherein the determining third water quality prediction data of all grids in the first future time period by combining a water quality simulation model and a fitting function based on the first water quality prediction data and the second water quality prediction data comprises:
determining an impact weight of the monitoring grid on each grid; the calculation formula of the influence weight of the monitoring grids on each grid is as follows:
wherein d isi，kThe distance between the kth monitoring grid and the ith grid is taken as the distance; r is a preset influence radius; by modifying di，kR to change the impact weight;
the calculation formula of the third water quality prediction data is as follows:
wherein, xbiSecond water quality prediction data for the ith grid; xaiThird water quality prediction data for the ith grid; w is ai，kThe influence weight of the kth monitoring grid on all grids; obvkFirst water quality prediction data for a kth monitoring grid; i is the number of grids; k is the number of monitoring grids;
determining a difference of the first water quality prediction data of a monitoring grid minus the second prediction data of the monitoring grid;
determining a product of a row matrix formed by the impact weights and a column matrix formed by the differences for all the monitoring grids on each grid;
determining the sum of the second water quality prediction data and the product of each grid to obtain third water quality prediction data of all grids;
the method further comprises the following steps: determining that the target time period does not reach a predetermined value;
performing iterative steps for a second preset time period and a second future time period, comprising:
determining fourth water quality prediction data of the monitoring grid in a second future time period based on the water quality monitoring data of the target water area monitoring grid in the second preset time period and the fitting function;
determining fifth water quality prediction data of all grids in the second future time period based on the fourth water quality prediction data and the third water quality prediction data;
the second preset time period is a time period after the first preset time period, and the time periods are equal in length; the fourth water quality prediction data corresponds to prediction data of the second preset time period; the third water quality prediction data is a correction value of the second water quality prediction data.
2. The method of claim 1, wherein determining the impact weight of the monitoring grid on each grid comprises:
determining a distance of the monitoring grid from each of the grids;
determining the influence weight based on the distance and a preset influence radius.
3. A water quality quantitative simulation device, characterized in that the device comprises:
the statistical prediction module is used for determining first water quality prediction data of the monitoring grids in a first future time period based on a fitting function, wherein the fitting function is the fitting function of the water quality monitoring data which is determined based on the water quality monitoring data of the target water area monitoring grids in the first preset time period and changes along with time;
the model prediction module is used for determining second water quality prediction data of all grids in the first future time period based on a preset water quality simulation model;
the data assimilation module is used for determining third water quality prediction data of all grids in the first future time period by combining a water quality simulation model and a fitting function based on the first water quality prediction data and the second water quality prediction data;
the data assimilation module is further used for determining the influence weight of the monitoring grids on each grid; the calculation formula of the influence weight of the monitoring grids on each grid is as follows:
wherein d isi，kThe distance between the kth monitoring grid and the ith grid is taken as the distance; r is a preset influence radius; by modifying di，kR to change the impact weight;
the calculation formula of the third water quality prediction data is as follows:
wherein, xbiSecond water quality prediction data for the ith grid; xaiThird water quality prediction data for the ith grid; w is ai，kThe influence weight of the kth monitoring grid on all grids; obvkFirst water quality prediction data for a kth monitoring grid; i is the number of grids; k is the number of monitoring grids;
determining a difference of the first water quality prediction data of a monitoring grid minus the second prediction data of the monitoring grid;
determining a product of a row matrix formed by the impact weights and a column matrix formed by the differences for all the monitoring grids on each grid;
determining the sum of the second water quality prediction data and the product of each grid to obtain third water quality prediction data of all grids;
determining that the target time period does not reach a predetermined value;
performing iterative steps for a second preset time period and a second future time period, comprising:
determining fourth water quality prediction data of the monitoring grid in a second future time period based on the water quality monitoring data of the target water area monitoring grid in the second preset time period and the fitting function;
determining fifth water quality prediction data of all grids in the second future time period based on the fourth water quality prediction data and the third water quality prediction data;
the second preset time period is a time period after the first preset time period, and the time periods are equal in length; the fourth water quality prediction data corresponds to the prediction data of the second preset time period, and the third water quality prediction data is a correction value of the second water quality prediction data.
4. The apparatus of claim 3, wherein the data assimilation module further determines distances of the monitoring grid from all of the grids, and determines the impact weight based on the distances and a preset impact radius.
5. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the method of any of claims 1 to 2 when executing the program.
6. A computer-readable storage medium, on which a processor program is stored, characterized in that the processor program is adapted to perform the method of any of claims 1 to 2.
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