Water pollution prediction method and device, storage medium and electronic equipment

Abstract

The invention provides a water pollution prediction method and device, a storage medium and electronic equipment, and the method comprises the steps: obtaining target flow field data of a target region in a target time range, and obtaining target meteorological field data of the target region in the target time range; determining a particle influence calculation mode of each pollutant particle in the M pollutant particles, and determining particle influence data of the corresponding pollutant particles according to the particle influence calculation mode of each pollutant particle; updating the particle mass of each pollutant particle based on the particle influence data of each pollutant particle; and determining a target diffusion simulation result of each pollutant particle in the P pollutant particles based on the particle mass of each pollutant particle, the target flow field data and the target meteorological field data. According to the embodiment of the invention, water pollution prediction can be conveniently carried out, so that the water pollution prediction efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to a water pollution prediction method, device, storage medium and electronic equipment. Background Art

[0002] At present, the monitoring methods of surface water pollution accidents usually include online automatic water quality monitoring, manual mobile emergency monitoring, and laboratory fixed monitoring, etc. However, based on monitoring methods, it is impossible to quickly grasp the current situation and future development trend of water pollution accidents, and the conventional water quality numerical calculation model takes a long time to calculate, resulting in low efficiency. Based on this, how to conveniently predict water pollution to improve the efficiency of water pollution prediction has not yet had a good solution. Summary of the invention

[0003] In view of this, the embodiments of the present invention provide a water pollution prediction method, device, storage medium and electronic device to solve the problems that the related technology cannot quickly grasp the current status and future development trends of water pollution accidents based on monitoring means, and the conventional water quality numerical calculation model requires a long time to operate, resulting in low efficiency. In other words, the embodiments of the present invention can apply the acquired target flow field data to the diffusion simulation process of pollutant particles to decouple the hydrodynamic simulation process and the water quality simulation process, thereby conveniently performing water pollution prediction, so as to improve the efficiency of water pollution prediction, and thus quickly and comprehensively grasp the current status and future development trends of water pollution accidents.

[0004] According to one aspect of an embodiment of the present invention, a water pollution prediction method is provided, the method comprising: Acquire target flow field data of a target area within a target time range, and acquire target meteorological field data of the target area within the target time range; Determine a particle impact calculation method for each of the M pollutant particles, and determine particle impact data of the corresponding pollutant particles according to the particle impact calculation methods of each of the pollutant particles, wherein one particle impact data includes impact sub-data under each particle impact calculation method in the particle impact calculation method of the corresponding pollutant particle, and M is a positive integer; updating the particle mass of each of the pollutant particles based on the particle impact data of each of the pollutant particles; Based on the particle mass of each pollutant particle, the target flow field data and the target meteorological field data, the target diffusion simulation result of each pollutant particle among P pollutant particles is determined. A target diffusion simulation result is the diffusion simulation result of the corresponding pollutant particle in the target area and the target time range, and P is a positive integer.

[0005] According to another aspect of an embodiment of the present invention, a water pollution prediction device is provided, the device comprising: An acquisition unit, used for acquiring target flow field data of a target area within a target time range, and acquiring target meteorological field data of the target area within the target time range; A processing unit, used to determine a particle impact calculation method of each pollutant particle among the M pollutant particles, and determine particle impact data of the corresponding pollutant particles according to the particle impact calculation methods of each pollutant particle, wherein one particle impact data includes impact sub-data under each particle impact calculation method in the particle impact calculation method of the corresponding pollutant particle, and M is a positive integer; The processing unit is further used to update the particle mass of each pollutant particle based on the particle impact data of each pollutant particle; The processing unit is also used to determine the target diffusion simulation result of each pollutant particle among P pollutant particles based on the particle mass of each pollutant particle, the target flow field data and the target meteorological field data. A target diffusion simulation result is a diffusion simulation result of the corresponding pollutant particle in the target area and the target time range, and P is a positive integer.

[0006] According to another aspect of an embodiment of the present invention, an electronic device is provided, comprising a processor and a memory storing a program, wherein the program comprises instructions, which, when executed by the processor, enable the processor to perform the above-mentioned method.

[0007] According to another aspect of an embodiment of the present invention, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to enable a computer to execute the above-mentioned method.

[0008] The embodiment of the present invention can determine the particle impact calculation method of each pollutant particle among the M pollutant particles after obtaining the target flow field data of the target area within the target time range and the target meteorological field data of the target area within the target time range, and determine the particle impact data of the corresponding pollutant particles according to the particle impact calculation method of each pollutant particle, and one particle impact data includes the impact sub-data under each particle impact calculation method in the particle impact calculation method of the corresponding pollutant particle, and M is a positive integer. Further, based on the particle impact data of each pollutant particle, the particle mass of each pollutant particle can be updated; and based on the particle mass of each pollutant particle, the target flow field data and the target meteorological field data, the target diffusion simulation result of each pollutant particle among the P pollutant particles can be determined, and one target diffusion simulation result is the diffusion simulation result of the corresponding pollutant particle in the target area and target time range, and P is a positive integer. It can be seen that the embodiments of the present invention can apply the acquired target flow field data to the diffusion simulation process of pollutant particles to decouple the hydrodynamic simulation process and the water quality simulation process, thereby conveniently performing water pollution prediction to improve the efficiency of water pollution prediction. In other words, water pollution prediction can be quickly achieved through the target diffusion simulation results, so as to quickly grasp the current status and future development trends of water pollution accidents; and, the embodiments of the present invention can represent the changes of the corresponding pollutant particles in the drift diffusion simulation through the particle mass of each pollutant particle, so as to more accurately reflect the actual change trend of the pollutants indicated by each pollutant particle, thereby effectively improving the accuracy of the target diffusion simulation results. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Further details, features and advantages of the invention are disclosed in the following description of exemplary embodiments in conjunction with the accompanying drawings, in which: Figure 1 A schematic flow chart of a water pollution prediction method according to an exemplary embodiment of the present invention is shown; Figure 2 A schematic flow chart of another water pollution prediction method according to an exemplary embodiment of the present invention is shown; Figure 3 A schematic block diagram of a water pollution prediction device according to an exemplary embodiment of the present invention is shown; Figure 4 A block diagram of an exemplary electronic device that can be used to implement an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0010] Embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be construed as being limited to the embodiments described herein, which are instead provided for a more thorough and complete understanding of the present invention. It should be understood that the drawings and embodiments of the present invention are only for exemplary purposes and are not intended to limit the scope of protection of the present invention.

[0011] It should be understood that the various steps described in the method embodiments of the present invention may be performed in different orders and / or in parallel. In addition, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present invention is not limited in this respect.

[0012] The term "including" and its variations used in this document are open inclusions, that is, "including but not limited to". The term "based on" means "based at least in part on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one other embodiment"; the term "some embodiments" means "at least some embodiments". Relevant definitions of other terms will be given in the following description. It should be noted that the concepts of "first", "second", etc. mentioned in the present invention are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0013] It should be noted that the modifications of "one" and "plurality" mentioned in the present invention are illustrative rather than restrictive, and those skilled in the art should understand that, unless otherwise clearly indicated in the context, it should be understood as "one or more".

[0014] The names of the messages or information exchanged between multiple devices in the embodiments of the present invention are only used for illustrative purposes, and are not used to limit the scope of these messages or information.

[0015] It should be noted that the execution subject of the water pollution prediction method provided in the embodiment of the present invention may be one or more electronic devices, and the present invention does not limit this; wherein, the electronic device may be a terminal (i.e., a client) or a server, then when the execution subject includes multiple electronic devices, and the multiple electronic devices include at least one terminal and at least one server, the water pollution prediction method provided in the embodiment of the present invention may be jointly executed by the terminal and the server. Accordingly, the terminal mentioned here may include but is not limited to: smart phones, tablet computers, laptop computers, desktop computers, intelligent voice interaction devices, smart home appliances, and the like. The server mentioned here may be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides cloud services, cloud databases, cloud computing (cloud computing), cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN (Content Delivery Network), and basic cloud computing services such as big data and artificial intelligence platforms, and the like.

[0016] Based on the above description, an embodiment of the present invention proposes a water pollution prediction method, which can be executed by the electronic device (terminal or server) mentioned above; or, the water pollution prediction method can be executed by the terminal and the server together. For the sake of convenience, the following description will be based on an example of an electronic device executing the water pollution prediction method; Figure 1 As shown, the water pollution prediction method may include the following steps S101-S104: S101, obtaining target flow field data of a target area within a target time range, and obtaining target meteorological field data of a target area within a target time range.

[0017] The target area may be any jurisdictional area, that is, any water area, which is not limited in the embodiment of the present invention; correspondingly, the target time range may be any time range, which is not limited in the embodiment of the present invention.

[0018] In an embodiment of the present invention, the target flow field data may include sub-flow field data of each flow field grid area in the target area within the target time range. One sub-flow field data may include flow field information of the corresponding flow field grid area at each flow field calculation time (i.e., flow field time step) within the target time range. One flow field information may include at least one of the following: the water level of the corresponding flow field grid area at the corresponding flow field calculation time, the flow velocity component (also called flow velocity) in each direction in at least one direction (such as the east direction and the north direction, etc.), salinity, water temperature and suspended sediment concentration, etc.; the embodiment of the present invention is not limited to this.

[0019] Correspondingly, the target meteorological field data may include sub-meteorological field data of each meteorological field grid area in the target area within the target time range, and one sub-meteorological field data may include meteorological field information of each meteorological field calculation time (i.e., meteorological field time step) of the corresponding meteorological field grid area within the target time range, and one meteorological field information may include at least one of the following: at least one wind speed, atmospheric pressure, and temperature of the corresponding meteorological field grid area at the corresponding meteorological field calculation time, etc.; the embodiment of the present invention does not limit this. Among them, one wind speed corresponds to one direction, that is, one meteorological field information may include the wind speed (i.e., wind speed component) under each wind direction in at least one wind direction.

[0020] Optionally, each flow field grid area and each meteorological field grid area in the target area may be the same or different, and the embodiment of the present invention does not limit this; that is, the size of a flow grid area and the size of a meteorological field grid area may be the same or different. Correspondingly, each flow field calculation time and each meteorological field calculation time in the target time range may be the same or different, and the embodiment of the present invention does not limit this; that is, the time interval between any two flow field calculation times may be the same as or different from the time interval between any two meteorological field calculation times, that is, the flow field time step and the meteorological field time step may be the same or different, and the embodiment of the present invention does not limit this. In other embodiments, a flow field information may also be the flow field information of a flow field grid area within a flow field sub-time range, and a meteorological field information may also be the meteorological field information of a meteorological field grid area within a meteorological field sub-time range, and the embodiment of the present invention does not limit this; for the convenience of explanation, the following description is taken as an example that a flow field information is the flow field information of a flow field grid area at a flow field calculation time, and a meteorological field information is the meteorological field information of a meteorological field grid area at a meteorological field calculation time.

[0021] It should be noted that each flow field grid area in the target area can be determined based on the shoreline direction and / or water terrain distribution of the target area, and the embodiment of the present invention does not limit the specific division method of the flow field grid area. Correspondingly, the embodiment of the present invention does not limit the specific division method of the meteorological field grid area.

[0022] Optionally, the target meteorological field data may be obtained in the following ways, including but not limited to: The first acquisition method: when acquiring the target meteorological field data of the target area within the target time range, the electronic device can download the meteorological field data file when detecting the start-up requirement of the target task. The meteorological field data file may include the forecast meteorological field data source of the target area within the target time range; and the meteorological field data file is formatted to obtain the target meteorological field data, and the target meteorological field data supports the network common data format (NetCDF format). It can be seen that the embodiment of the present invention can convert the acquired meteorological field data file into the target meteorological field data readable by the water pollution prediction model (also known as the water quality simulation model) through the developed file conversion program, and the target meteorological field data can be in the network common data format. Optionally, the electronic device can obtain a meteorological field data download link and use the meteorological field data download link to download the meteorological field data file.

[0023] Optionally, the target task may refer to a water pollution prediction task for a target area and a target time range. Optionally, the electronic device may include a system task planning tool, and trigger the start-up requirement of the target task through the system task planning tool, such as automatically triggering the start-up requirement of the target task at a specified time every day, thereby triggering the meteorological field data acquisition script to obtain the target meteorological field data, etc., or automatically triggering the start-up requirement of the target task at a preset interval; illustratively, the task can be planned based on the system task planning tool to perform daily timed triggering, daily timed download of meteorological field data for the next 10 days, and put it in a designated folder. Optionally, the designated folder, designated time, and preset interval duration can all be set according to experience or according to actual needs, and the embodiment of the present invention is not limited to this.

[0024] Optionally, the target meteorological field data can be obtained based on a forecast meteorological field data source of a global atmospheric model (Global Forecast System, GFS, also known as the global forecast system); wherein, when the meteorological field data only includes wind field data, it can also be obtained based on a forecast wind field data source.

[0025] The second acquisition method: The electronic device can obtain the meteorological data of the target area, and determine the target meteorological field data based on the meteorological data through the Weather Research and Forecasting Model (WRF), etc.

[0026] Optionally, the target flow field data may be obtained in the following ways, including but not limited to: The first acquisition method: when acquiring the target flow field data of the target area within the target time range, the electronic device can acquire the initial boundary data source, which may include at least one historical data to be processed; and preprocess the data of each historical data to be processed in the at least one historical data to be processed respectively, to obtain the preprocessed boundary data corresponding to each historical data to be processed, and one preprocessed boundary data includes a boundary data of the target area within the target time range; further, based on the preprocessed boundary data corresponding to each historical data to be processed, the target boundary data source can be determined, and the target hydrodynamic model can be called to determine the target flow field data based on the target boundary data source; wherein the target flow field data may include the sub-flow field data of each flow field grid area in the target area within the target time range. Specifically, when determining the target boundary data source based on the preprocessed boundary data corresponding to each historical data to be processed, the preprocessed boundary data corresponding to each historical data to be processed can be added to the target boundary data source, so that the target boundary data source includes the preprocessed boundary data corresponding to each historical data to be processed.

[0027] Among them, at least one historical data to be processed may include historical data to be processed at the upstream boundary and / or historical data to be processed at the downstream boundary; optionally, one historical data to be processed may include hydrological data of each flow field grid area in any boundary (such as the upstream boundary or the downstream boundary) of the target area at each monitoring time among multiple monitoring times (that is, one historical data to be processed may correspond to a flow field grid area in any boundary), and one hydrological data may include at least one hydrological sub-information, and at least one hydrological sub-information may include but is not limited to: water level and flow velocity in each direction in at least one direction. Optionally, when performing data preprocessing on each of at least one of the historical data to be processed to obtain preprocessing boundary data corresponding to each of the historical data to be processed, for any of the at least one of the historical data to be processed, any of the flow field grid areas corresponding to any of the historical data to be processed, and any of the hydrological sub-information in at least one of the hydrological sub-information, the electronic device can determine any hydrological sub-information of any flow field grid area at each monitoring time from any of the historical data to be processed, and perform regression analysis on any of the hydrological sub-information of any of the flow field grid areas at each monitoring time, so as to obtain any of the hydrological sub-information of any of the flow field grid areas at each flow field calculation time, so as to realize data preprocessing of any of the hydrological sub-information of any of the flow field grid areas at each monitoring time, thereby realizing data preprocessing of any of the historical data to be processed; in this case, the preprocessing boundary data corresponding to any of the historical data to be processed may include any of the hydrological sub-information of any of the flow field grid areas at each flow field calculation time.

[0028] Optionally, when the flow field data of the upstream boundary and the downstream boundary of the target area are similar, the number of the historical data to be processed in at least one historical data to be processed may be one (such as only including the historical data to be processed of the upstream boundary or the historical data to be processed of the downstream boundary); illustratively, at least one historical data to be processed may include the historical data to be processed of the upstream boundary, in which case the pre-processed boundary data corresponding to the historical data to be processed of the downstream boundary may be the same as the pre-processed boundary data corresponding to the historical data to be processed of the upstream boundary. Optionally, when the downstream boundary is a tidal water area, at least one historical data to be processed may include the historical data to be processed of the upstream boundary; in this case, the initial boundary data source may also include the hydrological data in each flow field grid area in the downstream boundary of the target area, and the hydrological data in each flow field grid area in the downstream boundary of the target area may be added to the target boundary data source, so that the target boundary data source includes the hydrological data in each flow field grid area in the downstream boundary of the target area. It can be seen that the embodiment of the present invention can design the upstream and downstream boundaries of the hydrodynamic model based on the water characteristics of the target area, that is, design the flow field data of the upstream and downstream boundaries.

[0029] Optionally, the electronic device may include a data preprocessing module. In this case, the electronic device may perform data preprocessing through the data preprocessing module.

[0030] Optionally, when calling the target hydrodynamic model and determining the target flow field data based on the target boundary data source, the electronic device may call the target hydrodynamic model and determine the target flow field data based only on the target boundary data source, or may determine the target flow field data based on the target boundary data source and the target meteorological field data, and so on; the embodiment of the present invention is not limited to this. Optionally, when determining the target flow field data based on the target boundary data source, the intermediate flow field data may be determined based on the target boundary data source, and the intermediate flow field data may be converted into the target flow field data in the hydrodynamic flow field data format (such as the network general data format), such as the target flow field data may be supported as the network general data format, so as to facilitate the rapid calling of the water pollution prediction model.

[0031] In an embodiment of the present invention, the electronic device may also obtain an initial hydrodynamic model corresponding to the target area, and call the initial hydrodynamic model to predict the calibrated flow field data of the target area within the historical time range, that is, call the initial hydrodynamic model, and predict the calibrated flow field data of the target area within the historical time range based on the boundary data and / or meteorological field data of the target area within the historical time range; optionally, the historical time range may be any time range, which is not limited in the embodiment of the present invention. Then, the initial hydrodynamic model may be calibrated based on the calibrated flow field data and the benchmark data of the target area within the historical time range to obtain a calibrated hydrodynamic model; wherein the benchmark data may include the monitoring hydrological data of each monitoring point in the target area at each historical monitoring moment in the historical time range, in which case, the predicted hydrological data of each monitoring point (such as one monitoring point may correspond to one flow field grid area) at each historical monitoring moment may be determined from the calibrated flow field data, and based on the difference between the predicted hydrological data of each monitoring point at each historical monitoring moment and the monitored hydrological data of each monitoring point at each historical monitoring moment, the initial hydrodynamic model may be calibrated to obtain a calibrated hydrodynamic model.

[0032] For example, when the sum of the predicted flow velocities of each monitoring point at each historical monitoring moment is greater than the sum of the monitored flow velocities of each monitoring point at each historical monitoring moment, the bottom friction coefficient in the initial hydrodynamic model can be increased; or, when the sum of the predicted flow velocities of each monitoring point at each historical monitoring moment is less than the sum of the monitored flow velocities of each monitoring point at each historical monitoring moment, the above-mentioned bottom friction coefficient can be reduced, and so on; the embodiments of the present invention are not limited to this.

[0033] Furthermore, the target hydrodynamic model can be determined based on the calibrated hydrodynamic model; specifically, the electronic device can verify the calibrated hydrodynamic model. If the verification result meets the model determination conditions (such as the sum of the predicted flow rates of each monitoring point at each verification historical monitoring time, and the difference between the sum of the monitored flow rates of each monitoring point at each verification historical monitoring time is less than the preset verification difference threshold, etc.), the calibrated hydrodynamic model can be used as the target hydrodynamic model; if the verification result does not meet the model determination conditions, the calibrated hydrodynamic model can continue to be calibrated until the verification result meets the model determination conditions to obtain the target hydrodynamic model. Optionally, the preset verification difference threshold can be set according to experience or according to actual needs, and the embodiment of the present invention is not limited to this.

[0034] Optionally, a hydrodynamic model may be any one of hydrodynamic models such as POM, ECOMSED, ECOM-si, FVCOM, ROMS and DELFT3D, which is not limited in the embodiment of the present invention.

[0035] Optionally, the electronic device may also include an operation status judgment module. In this case, the operation status of the target hydrodynamic model can be monitored through the operation status judgment module; when an operation error or other operation status is detected, the target hydrodynamic model can be re-run to obtain target flow field data, and so on.

[0036] The second acquisition method: the electronic device can also obtain a flow field data download link, and use the flow field data downloaded based on the flow field data download link as the target flow field data, and so on.

[0037] S102, determining a particle impact calculation method for each of the M pollutant particles, and determining particle impact data of the corresponding pollutant particle according to the particle impact calculation method for each pollutant particle.

[0038] Among them, one particle impact data may include the impact sub-data under each particle impact calculation mode in the particle impact calculation mode of the corresponding pollutant particle, and M is a positive integer. In the embodiment of the present invention, the pollutants entering the surface water can be regarded as a collection of a certain number of pollutant particles, and each pollutant particle can have a certain particle mass.

[0039] Based on this, the particle mass of the pollutant particles can be updated at each drift diffusion simulation time step; optionally, the simulation time step corresponding to each drift diffusion simulation time step can be set according to experience or according to actual needs, and the embodiment of the present invention does not limit this. Among them, one drift diffusion simulation time step corresponds to one drift diffusion simulation process, that is, the particle mass of the pollutant particles can be updated at each drift diffusion simulation process.

[0040] S103: updating the particle mass of each pollutant particle based on the particle impact data of each pollutant particle.

[0041] Among them, updating the particle mass of each pollutant particle can also be called performing a particle mass update operation on the particle mass of each pollutant particle; that is, the electronic device can perform a particle mass update operation on the particle mass of each pollutant particle based on the particle impact data of each pollutant particle.

[0042] Optionally, the electronic device may initialize the initial pollutant particles, such as assigning initial values ​​to the particle mass, area, volume, proportion, and movement weight coefficient of the initial pollutant particles. Optionally, the updating of the particle mass of each pollutant particle in the M pollutant particles may be a particle mass updating process at the f-th drift-diffusion simulation time step, that is, a particle mass updating operation may be performed on the particle mass of each pollutant particle at the f-th drift-diffusion simulation time step, f∈[1,F], F is a positive integer, and F may be equal to the number of drift-diffusion simulation time steps in the target time range.

[0043] Based on this, when the value of f is 1, before updating the particle mass of each pollutant particle, the particle mass of each pollutant particle can be the initialization particle mass, such as the particle mass of each pollutant particle can be determined based on the concentration monitoring data of the target pollutant, at which time each pollutant particle can be an initial pollutant particle; optionally, the target pollutant can be any pollutant, which is not limited in the embodiment of the present invention. Then correspondingly, when the value of f is greater than 1, before executing the particle mass update operation under the f-th drift diffusion simulation time step, the particle mass of each pollutant particle can be the updated particle mass of each pollutant particle under the f-1th drift diffusion simulation time step, that is, it can be the result after executing the particle mass update operation under the f-1th drift diffusion simulation time step.

[0044] S104, based on the particle mass of each pollutant particle, the target flow field data and the target meteorological field data, determine the target diffusion simulation result of each pollutant particle among P pollutant particles, a target diffusion simulation result is a diffusion simulation result of the corresponding pollutant particle in the target area and target time range, and P is a positive integer.

[0045] Among them, a target diffusion simulation result may be a diffusion simulation result of the corresponding pollutant particle at the Fth drift diffusion simulation time step, and a diffusion simulation result may include but is not limited to the particle mass and position information of a pollutant particle; it can be seen that at each drift diffusion simulation time step, the electronic device can perform a particle mass update operation and a drift diffusion simulation to achieve a particle tracking simulation (i.e., a drift diffusion simulation process) at each drift diffusion simulation time step. Optionally, a diffusion simulation result includes the particle mass and position information of a pollutant particle, which is not limited in the embodiment of the present invention.

[0046] It can be seen that the embodiment of the present invention provides a model calculation strategy based on the separation of the hydrodynamic model and the water quality numerical model, that is, a gridless Lagrangian particle tracing algorithm can be used to construct a water pollution prediction model, thereby using a Lagrangian particle tracing algorithm without a calculation grid to simulate the three-dimensional drift and diffusion simulation of the target pollutant in the surface water. In addition, the target pollutant can be any pollutant, which can realize the rapid simulation of different pollutants in the water area and improve the accuracy of the simulation.

[0047] The embodiment of the present invention can determine the particle impact calculation method of each pollutant particle among the M pollutant particles after obtaining the target flow field data of the target area within the target time range and the target meteorological field data of the target area within the target time range, and determine the particle impact data of the corresponding pollutant particles according to the particle impact calculation method of each pollutant particle. One particle impact data includes the impact sub-data under each particle impact calculation method in the particle impact calculation method of the corresponding pollutant particle. One pollutant particle corresponds to one particle mass, and M is a positive integer. Further, based on the particle impact data of each pollutant particle, the particle mass of each pollutant particle can be updated; and based on the particle mass of each pollutant particle, the target flow field data and the target meteorological field data, the target diffusion simulation result of each pollutant particle among the P pollutant particles can be determined. One target diffusion simulation result is the diffusion simulation result of the corresponding pollutant particle in the target area and target time range, and P is a positive integer. It can be seen that the embodiments of the present invention can apply the acquired target flow field data to the diffusion simulation process of pollutant particles to decouple the hydrodynamic simulation process and the water quality simulation process, thereby conveniently performing water pollution prediction to improve the efficiency of water pollution prediction. In other words, water pollution prediction can be quickly achieved through the target diffusion simulation results, so as to quickly grasp the current status and future development trends of water pollution accidents; and, the embodiments of the present invention can represent the changes of the corresponding pollutant particles in the drift diffusion simulation through the particle mass of each pollutant particle, so as to more accurately reflect the actual change trend of the pollutants indicated by each pollutant particle, thereby effectively improving the accuracy of the target diffusion simulation results.

[0048] Based on the above description, the embodiment of the present invention also proposes a more specific water pollution prediction method. Accordingly, the water pollution prediction method can be executed by the electronic device (terminal or server) mentioned above; or, the water pollution prediction method can be executed by the terminal and the server together. For the sake of convenience, the following description will be based on the example of an electronic device executing the water pollution prediction method; please refer to Figure 2 The water pollution prediction method may include the following steps S201-S205: S201, obtaining target flow field data of a target area within a target time range, and obtaining target meteorological field data of a target area within a target time range.

[0049] S202, determine the particle impact calculation method of each pollutant particle among M pollutant particles, and determine the particle impact data of the corresponding pollutant particles according to the particle impact calculation method of each pollutant particle. One particle impact data includes the impact sub-data under each particle impact calculation method in the particle impact calculation method of the corresponding pollutant particle, and M is a positive integer.

[0050] Among them, the particle impact calculation method of a pollutant particle may include at least one of the following: a volatilization calculation method, a degradation calculation method, and an evaporation calculation method, etc., which is not limited in the embodiment of the present invention. In the embodiment of the present invention, for any pollutant particle among the M pollutant particles, the particle impact calculation method of any pollutant particle can be determined based on the position information of any pollutant particle; specifically, a position information may include the depth (i.e., the vertical position) and horizontal position of the corresponding pollutant particle, etc. In this case, the particle impact calculation method of any pollutant particle can be determined based on the depth of any pollutant particle.

[0051] Optionally, the electronic device may determine that any pollutant particle is located on the water surface or in the water body based on the depth of any pollutant particle; if any pollutant particle is located on the water surface, the particle impact calculation method for determining any pollutant particle may include but is not limited to a volatilization calculation method, an evaporation calculation method, and a degradation calculation method; if any pollutant particle is located in the water body, the particle impact calculation method for determining any pollutant particle may include but is not limited to a degradation calculation method. Specifically, the electronic device may determine that any pollutant particle is located on the water surface or in the water body based on the depth of any pollutant particle and the water surface depth threshold; if the depth of any pollutant particle is less than or equal to the water surface depth threshold, it may be determined that any pollutant particle is located on the water surface; if the depth of any pollutant particle is greater than the water surface depth threshold, it may be determined that any pollutant particle is located in the water body. Optionally, the water surface depth threshold may be determined based on the simulation time step and the wave height, or may be set according to experience, or may be set according to actual needs, and the embodiment of the present invention does not limit this; optionally, the wave height may be set according to experience, or may be set according to actual needs, and the embodiment of the present invention does not limit this. Optionally, if the water meter depth threshold is determined based on the simulation time step and wave height, the electronic device may use formula 1.1 to calculate the water meter depth threshold: Formula 1.1 Among them, D z can refer to the wave height and ∆t can refer to the simulation time step.

[0052] It should be noted that the particle impact data of a pollutant particle may be the particle impact data of the corresponding pollutant particle at the current drift diffusion simulation time step (i.e., the fth drift diffusion simulation time step), that is, the electronic device may respectively determine the particle impact data of each pollutant particle at the current drift diffusion simulation time step. Based on this, the particle impact data of a pollutant particle may include but is not limited to at least one of the following: the volatilization mass, degradation mass, and evaporation mass of the corresponding pollutant particle.

[0053] Specifically, when determining the particle impact data of the corresponding pollutant particles according to the particle impact calculation method of each pollutant particle, for any pollutant particle among the M pollutant particles, and any particle impact calculation method in the particle impact calculation method of any pollutant particle, if any particle impact calculation method is a volatilization calculation method, the impact sub-data of any pollutant particle under the volatilization calculation method (i.e., the volatilization mass of any pollutant particle) can be determined according to the volatilization calculation method, and the particle impact data of any pollutant particle includes the impact sub-data of any pollutant particle under any particle impact calculation method. Optionally, the electronic device can use formula 1.2 to calculate the volatilization mass of any pollutant particle: Formula 1.2 Among them, d m is the volatile mass of any pollutant particle in the current drift diffusion simulation time step, M0 is the particle mass of any pollutant particle, d is the above-mentioned water surface depth threshold, and K7 can be the mass transfer coefficient; optionally, one pollutant can correspond to one mass transfer coefficient, and the mass transfer coefficients corresponding to different pollutants can be set according to experience or according to actual needs; based on this, K7 can be the mass transfer coefficient corresponding to the target pollutant.

[0054] Correspondingly, if the impact calculation method of any particle is a degradation calculation method, then the electronic device can determine the impact sub-data of any pollutant particle under the degradation calculation method according to the degradation calculation method, that is, the degradation mass of any pollutant particle; optionally, the electronic device can use formula 1.3 to calculate the degradation result of any pollutant particle on the tth day: Formula 1.3 Among them, M tIt can be the degradation result of any pollutant particle on the tth day, k can be the decay rate of the substance, and t can be the number of days in the current drift diffusion simulation time step (for example, if the current drift diffusion simulation time step is a drift diffusion simulation time step within the first day of the target time range, then the value of t can be 1). Optionally, the degradation mass of any pollutant particle (i.e., the degradation mass of any pollutant particle under the current drift diffusion simulation time step) can be (the particle mass of any pollutant particle - the degradation result of any pollutant particle on the tth day) / the total number of drift diffusion simulation time steps in one day, where the particle mass of any pollutant particle refers to the updated particle mass of any pollutant particle under the f-1th drift diffusion simulation time step when the current drift diffusion simulation time step is the fth drift diffusion simulation time step; or, it can be (the updated particle mass or the initialization particle mass of any pollutant particle under the last drift diffusion simulation time step on the t-1th day - the degradation result of any pollutant particle on the tth day) / the total number of drift diffusion simulation time steps in one day, etc., which is not limited in the embodiments of the present invention. Optionally, one pollutant may correspond to one decay rate, and the decay rates of different pollutants may be set based on experience or actual needs, which is not limited in the embodiment of the present invention.

[0055] Then, accordingly, if the impact calculation method of any particle is the evaporation calculation method, then the electronic device can determine the impact sub-data of any pollutant particle under the evaporation calculation method according to the evaporation calculation method, that is, the evaporation mass of any pollutant particle; optionally, the electronic device can calculate the evaporation rate of any pollutant particle, and then use the evaporation rate of any pollutant particle to calculate the evaporation mass of any pollutant particle, that is, the multiplication result between the evaporation rate of any pollutant particle and the simulation time step can be used as the evaporation mass of any pollutant particle. Specifically, the electronic device can use formula 1.4 to calculate the evaporation rate of any pollutant particle: Formula 1.4 Where E is the evaporation rate of any pollutant particle (g / m 2 h, i.e. grams per square meter per hour), K e M can be the evaporation rate (m / h), W It can be expressed as molar mass (g / mole), P VP K may be the saturated vapor pressure corresponding to the target pollutant, R may be the gas constant, and T may be the water temperature. Optionally, the electronic device may use formula 1.5 to calculate K e : Formula 1.5 Among them, U can be the wind speed at 10m (m / h, i.e. meters per hour), that is, the wind speed 10 meters above the water surface; L can be the diameter of the cylinder or sphere corresponding to any pollutant particle in any mass state (m); S C It can be the Schmidt number (i.e. critical point value) of the target pollutant in the air; optionally, one pollutant can correspond to one S C , S of different pollutants C It can be set according to experience or according to actual needs, and the embodiment of the present invention does not limit this.

[0056] S203: updating the particle mass of each pollutant particle based on the particle impact data of each pollutant particle.

[0057] Specifically, for any pollutant particle among the M pollutant particles, if the particle impact data of any pollutant particle includes the volatilization mass of any pollutant particle, the particle mass of any pollutant particle may be subtracted from the volatilization mass of any pollutant particle to update the particle mass of any pollutant particle. If the particle impact data of any pollutant particle includes the evaporation mass of any pollutant particle, the particle mass of any pollutant particle may be subtracted from the evaporation mass of any pollutant particle to update the particle mass of any pollutant particle. If the particle impact data of any pollutant particle includes the degradation mass of any pollutant particle, the particle mass of any pollutant particle may be subtracted from the degradation mass of any pollutant particle to update the particle mass of any pollutant particle. In other words, the particle mass of any pollutant particle may be subtracted from each influencing sub-data in the particle impact data of any pollutant particle.

[0058] In other embodiments, the electronic device may also update the particle mass of any pollutant particle immediately according to the influence sub-data of any pollutant particle under any particle impact calculation mode after obtaining the influence sub-data of any pollutant particle under any particle impact calculation mode (such as the volatile mass of any pollutant particle), thereby continuing to calculate the influence sub-data of any pollutant particle under the next particle impact calculation mode according to the updated particle mass of any pollutant particle, thereby further updating the particle mass of any pollutant particle, and so on; the present invention is not limited to this.

[0059] S204 , based on the particle mass of each pollutant particle, determine Q pollutant particles from the M pollutant particles, where Q is a positive integer.

[0060] Among them, the particle mass of each pollutant particle in the Q pollutant particles is greater than the active mass threshold; in the embodiment of the present invention, the electronic device can determine the active mass threshold, and based on the particle mass of each pollutant particle, determine Q pollutant particles whose particle mass is greater than the active mass threshold from the M pollutant particles, so as to determine Q pollutant particles from the M pollutant particles. Optionally, the active mass threshold can be set according to experience or according to actual needs, and the embodiment of the present invention does not limit this.

[0061] S205, based on the target flow field data and the target meteorological field data, respectively perform drift diffusion simulation on each of the Q pollutant particles to obtain the diffusion simulation result of each of the Q pollutant particles, and determine the target diffusion simulation result of each of the P pollutant particles based on the diffusion simulation result of each of the Q pollutant particles.

[0062] In an embodiment of the present invention, if the current drift diffusion simulation time step is the last drift diffusion simulation time step in the target time range, the P pollutant particles may be Q pollutant particles, and the target diffusion simulation result of each pollutant particle can be obtained; if the current drift diffusion simulation time step is not the last drift diffusion simulation time step, the particle mass update operation and drift diffusion simulation can be continued for each of the Q pollutant particles until the drift diffusion simulation process under the last drift diffusion simulation time step is completed. A drift diffusion simulation process may include a particle mass update operation and a drift diffusion simulation; optionally, a drift diffusion simulation can be performed on each pollutant particle based on the target flow field data, the target meteorological field data and the particle mass of each pollutant particle.

[0063] Specifically, when drift diffusion simulation is performed on each of the Q pollutant particles based on the target flow field data and the target meteorological field data to obtain the diffusion simulation result of each of the Q pollutant particles, for any of the Q pollutant particles, the electronic device can capture the surrounding flow velocity data and wind speed data of any pollutant particle in the current drift diffusion simulation time step in real time based on the target flow field data and the target meteorological field data, so as to obtain the flow field information of each adjacent flow field grid area in at least one adjacent flow field grid area corresponding to any pollutant particle, and obtain the meteorological field information (such as wind field information) of each adjacent meteorological field grid area in at least one adjacent meteorological field grid area corresponding to any pollutant particle, so as to perform weighted summation on the flow field information of each adjacent flow field grid area to obtain the flow field information of any pollutant particle (such as the flow velocity in each direction in at least one direction), and perform weighted summation on the meteorological field information of each adjacent meteorological field grid area to obtain the meteorological field information of any pollutant particle (such as the wind speed in each direction in at least one direction). Based on this, drift diffusion simulation can be performed on any pollutant particle based on the flow field information and meteorological field information of any pollutant particle. It should be understood that the electronic device can also determine the particle impact data of any pollutant particle based on the flow field information and meteorological field information of any pollutant particle and in accordance with the particle impact calculation method of any pollutant particle.

[0064] Optionally, an adjacent flow field grid area of ​​any pollutant particle may be: a flow field grid area whose distance to any pollutant particle is less than a preset flow field distance threshold, or the first Y flow field grid areas closest to any pollutant particle, Y is a positive integer, and so on; this is not limited in the embodiment of the present invention. Correspondingly, an adjacent meteorological field grid area of ​​any pollutant particle may be: a meteorological field grid area whose distance to any pollutant particle is less than a preset meteorological field distance threshold, or the first V meteorological field grid areas closest to any pollutant particle, V is a positive integer, and so on; this is not limited in the embodiment of the present invention. Optionally, the preset flow field distance threshold and the preset meteorological field distance threshold may be set according to experience or according to actual needs, and this is not limited in the embodiment of the present invention.

[0065] Optionally, the flow field information of any adjacent flow field grid area can be determined based on the flow field information of any adjacent flow field grid area at the first flow field time step (i.e., the previous flow field time step) and the second flow field time step (i.e., the next flow field time step) corresponding to the current drift-diffusion simulation time step; specifically, the flow field information of any adjacent flow field grid area at the first flow field time step and the flow field information at the second flow field time step can be weighted summed according to the spatial distance to obtain the flow field information of any adjacent flow field grid area. Optionally, the weight of the flow field information of any adjacent flow field grid area at the first flow field time step can be: the ratio between the first distance and the time step distance, the first distance can be the distance between the current drift-diffusion simulation time step and the first flow field time step, and the time step distance can be the distance between the first flow field time step and the second flow field time step; correspondingly, the weight of the flow field information of any adjacent flow field grid area at the second flow field time step can be: the ratio between the second distance and the time step distance, and the second distance can be the distance between the current drift-diffusion simulation time step and the second flow field time step. Similarly, the meteorological field information of any adjacent meteorological field grid area can be determined based on the meteorological field information of any adjacent meteorological field grid area at the first meteorological field time step and the second meteorological field time step corresponding to the current drift diffusion simulation time step. The method for determining the meteorological field information of any adjacent meteorological field grid area can be the same as the method for determining the flow field information of any adjacent flow field grid area, and the embodiments of the present invention will not be repeated here.

[0066] Optionally, when weighted summing is performed on the flow field information of each adjacent flow field grid area to obtain the flow field information of any pollutant particle, the weights of the flow field information of each adjacent flow field grid area may be the same (such as mean operation) or different, and the embodiment of the present invention does not limit this. Exemplarily, the electronic device may also determine the weights of the flow field information of each adjacent flow field grid area based on the distance between each adjacent flow field grid area and any pollutant particle; taking any adjacent flow field grid area in at least one adjacent flow field grid area as an example, the weight of the flow field information of any adjacent flow field grid area may be the ratio between the third distance and the flow field grid distance, the third distance may be the distance between any adjacent flow field grid area and any pollutant particle, and the flow field grid distance may be the sum of the distances between each adjacent flow field grid area and any pollutant particle. Correspondingly, the weights of the meteorological field information of each adjacent meteorological field grid area may be the same or different, and the embodiment of the present invention does not limit this; wherein, the specific determination method of the weights of the meteorological field information of each adjacent meteorological field grid area may be the same as the specific determination method of the weights of the flow field information of each adjacent flow field grid area, and the embodiment of the present invention will not be repeated here.

[0067] Optionally, the electronic device may also be assisted by a fixed shoreline to limit the drift and diffusion simulation of pollutant particles to always be within the target area, so as to realize the drift and diffusion simulation process of pollutant particles.

[0068] In summary, it can be seen that the embodiments of the present invention can decouple the hydrodynamic simulation process and the water quality simulation process, and regularly generate target flow field data and target meteorological field data of the target area. After a water pollution accident occurs, the water pollution prediction model based on the particle algorithm can be started again. The drift, diffusion and destination process of the pollutant particles can be completed within a few minutes, which greatly improves the calculation efficiency and thus greatly enhances the support for emergency management work.

[0069] The embodiment of the present invention can determine the particle impact calculation method of each pollutant particle in the M pollutant particles after obtaining the target flow field data of the target area within the target time range and the target meteorological field data of the target area within the target time range, and determine the particle impact data of the corresponding pollutant particles according to the particle impact calculation method of each pollutant particle, one particle impact data includes the impact sub-data under each particle impact calculation method in the particle impact calculation method of the corresponding pollutant particle; and based on the particle impact data of each pollutant particle, update the particle mass of each pollutant particle. Further, based on the particle mass of each pollutant particle, Q pollutant particles can be determined from the M pollutant particles, where Q is a positive integer; and based on the target flow field data and the target meteorological field data, drift diffusion simulation is performed on each pollutant particle in the Q pollutant particles, respectively, to obtain the diffusion simulation result of each pollutant particle in the Q pollutant particles, so as to determine the target diffusion simulation result of each pollutant particle in the P pollutant particles based on the diffusion simulation result of each pollutant particle in the Q pollutant particles. It can be seen that the embodiments of the present invention can quickly simulate the position information of pollutant particles in the target area, and can simulate the particle mass change process of pollutant particles in water, which can improve the accuracy of target diffusion simulation results; based on this, the embodiments of the present invention can conveniently perform water pollution prediction, thereby quickly obtaining the target diffusion simulation results of the corresponding pollutant particles.

[0070] Based on the description of the above-mentioned water pollution prediction method, the present invention also provides a water pollution prediction device, which can be a computer program (including program code) running in an electronic device; Figure 3 As shown, the water pollution prediction device may include an acquisition unit 301 and a processing unit 302. The water pollution prediction device may execute Figure 1 or Figure 2 The water pollution prediction method shown, that is, the water pollution prediction device can run the above units: An acquisition unit 301 is used to acquire target flow field data of a target area within a target time range, and to acquire target meteorological field data of the target area within the target time range; The processing unit 302 is used to determine the particle impact calculation method of each pollutant particle among the M pollutant particles, and determine the particle impact data of the corresponding pollutant particle according to the particle impact calculation method of each pollutant particle, wherein one particle impact data includes the impact sub-data under each particle impact calculation method in the particle impact calculation method of the corresponding pollutant particle, and M is a positive integer; The processing unit 302 is further configured to update the particle mass of each pollutant particle based on the particle impact data of each pollutant particle; The processing unit 302 is also used to determine a target diffusion simulation result for each of P pollutant particles based on the particle mass of each pollutant particle, the target flow field data and the target meteorological field data. A target diffusion simulation result is a diffusion simulation result of the corresponding pollutant particle in the target area and the target time range, and P is a positive integer.

[0071] In one embodiment, when the processing unit 302 determines the target diffusion simulation result of each of the P pollutant particles based on the particle mass of each pollutant particle, the target flow field data, and the target meteorological field data, it can be specifically used to: Based on the particle mass of each pollutant particle, determining Q pollutant particles from the M pollutant particles, where Q is a positive integer; Based on the target flow field data and the target meteorological field data, drift diffusion simulation is performed on each of the Q pollutant particles to obtain a diffusion simulation result of each of the Q pollutant particles, so as to determine a target diffusion simulation result of each of the P pollutant particles based on the diffusion simulation result of each of the Q pollutant particles.

[0072] In another embodiment, when the processing unit 302 determines the Q pollutant particles from the M pollutant particles based on the particle mass of each pollutant particle, it can be specifically configured to: Determine activity quality thresholds; Based on the particle mass of each pollutant particle, Q pollutant particles having a particle mass greater than the active mass threshold are determined from the M pollutant particles, so as to determine the Q pollutant particles from the M pollutant particles.

[0073] In another implementation manner, when acquiring the target meteorological field data of the target area within the target time range, the acquisition unit 301 may be specifically used to: When the start requirement of the target task is detected, a meteorological field data file is downloaded, wherein the meteorological field data file includes a forecast meteorological field data source of the target area within the target time range; The meteorological field data file is format-converted to obtain the target meteorological field data, and the target meteorological field data supports a universal network data format.

[0074] In another embodiment, when acquiring the target flow field data of the target area within the target time range, the acquisition unit 301 may be specifically used to: Acquire an initial boundary data source, wherein the initial boundary data source includes at least one historical data to be processed; Preprocessing each of the at least one historical data to be processed to obtain preprocessed boundary data corresponding to each of the historical data to be processed, wherein one preprocessed boundary data includes boundary data of the target area within the target time range; Based on the preprocessed boundary data corresponding to each of the historical data to be processed, the target boundary data source is determined, and the target hydrodynamic model is called to determine the target flow field data based on the target boundary data source; wherein the target flow field data includes the sub-flow field data of each flow field grid area in the target area within the target time range.

[0075] In another implementation, the acquisition unit 301 may also be used to: Acquire an initial hydrodynamic model corresponding to the target area; The processing unit 302 may also be used to: Calling the initial hydrodynamic model to predict the rated flow field data of the target area within a historical time range; The initial hydrodynamic model is calibrated based on the calibrated flow field data and the benchmark data of the target area within the historical time range to obtain a calibrated hydrodynamic model, and the target hydrodynamic model is determined based on the calibrated hydrodynamic model.

[0076] In another embodiment, the particle impact data of a pollutant particle includes at least one of the following: a volatilization mass, a degradation mass, and an evaporation mass of the corresponding pollutant particle.

[0077] According to one embodiment of the present invention, Figure 1 or Figure 2 Each step involved in the method shown can be Figure 3 The water pollution prediction device shown in the figure is executed by each unit. For example, Figure 1The step S101 shown in FIG. 1 can be performed by Figure 3 The acquisition unit 301 shown in FIG. 1 is executed, and steps S102-S104 can be performed by Figure 3 The processing unit 302 shown in is executed. For example, Figure 2 The step S201 shown in FIG. 1 can be performed by Figure 3 The acquisition unit 301 shown in FIG. 1 is executed, and steps S202-S205 can be performed by Figure 3 The processing unit 302 shown in is executed, and so on.

[0078] According to another embodiment of the present invention, Figure 3 Each unit in the water pollution prediction device shown can be separately or completely combined into one or several other units to constitute, or one (some) of the units can be further divided into multiple smaller units in function to constitute, which can achieve the same operation without affecting the realization of the technical effects of the embodiments of the present invention. The above-mentioned units are divided based on logical functions. In practical applications, the function of one unit can also be realized by multiple units, or the functions of multiple units can be realized by one unit. In other embodiments of the present invention, any water pollution prediction device may also include other units. In practical applications, these functions can also be implemented with the assistance of other units, and can be implemented by the collaboration of multiple units.

[0079] According to another embodiment of the present invention, the program can be executed by running a program on a general electronic device such as a computer including a central processing unit (CPU), a random access memory medium (RAM), a read-only memory medium (ROM), and other processing elements and storage elements. Figure 1 or Figure 2 A computer program (including program code) for each step involved in the corresponding method shown in Figure 3 The water pollution prediction device shown in the embodiment of the present invention is used to implement the water pollution prediction method of the embodiment of the present invention. The computer program can be recorded on a computer storage medium, for example, and loaded into the above-mentioned electronic device through the computer storage medium and run therein.

[0080] Based on the description of the above method embodiment and device embodiment, the exemplary embodiment of the present invention further provides an electronic device, including: at least one processor; and a memory connected to the at least one processor in communication. The memory stores a computer program that can be executed by the at least one processor, and the computer program is used to enable the electronic device to perform the method according to the embodiment of the present invention when executed by the at least one processor.

[0081] Exemplary embodiments of the present invention also provide a non-transitory computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor of a computer, is used to cause the computer to perform a method according to an embodiment of the present invention.

[0082] An exemplary embodiment of the present invention further provides a computer program product, comprising a computer program, wherein when the computer program is executed by a processor of a computer, the computer is used to enable the computer to perform a method according to an embodiment of the present invention.

[0083] refer to Figure 4 , a block diagram of an electronic device 400 that can be used as a server or client of the present invention will now be described, which is an example of a hardware device that can be applied to various aspects of the present invention. The electronic device is intended to represent various forms of digital electronic computer devices, such as laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0084] like Figure 4 As shown, the electronic device 400 includes a computing unit 401, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 402 or a computer program loaded from a storage unit 408 into a random access memory (RAM) 403. In the RAM 403, various programs and data required for the operation of the electronic device 400 can also be stored. The computing unit 401, the ROM 402, and the RAM 403 are connected to each other via a bus 404. An input / output (I / O) interface 405 is also connected to the bus 404.

[0085] A plurality of components in the electronic device 400 are connected to the I / O interface 405, including: an input unit 406, an output unit 407, a storage unit 408, and a communication unit 409. The input unit 406 may be any type of device capable of inputting information to the electronic device 400, and the input unit 406 may receive input digital or character information, and generate key signal inputs related to user settings and / or function control of the electronic device. The output unit 407 may be any type of device capable of presenting information, and may include, but is not limited to, a display, a speaker, a video / audio output terminal, a vibrator, and / or a printer. The storage unit 408 may include, but is not limited to, a disk, an optical disk. The communication unit 409 allows the electronic device 400 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks, and may include, but is not limited to, a modem, a network card, an infrared communication device, a wireless communication transceiver, and / or a chipset, such as a Bluetooth™ device, a WiFi device, a WiMax device, a cellular communication device, and / or the like.

[0086] The computing unit 401 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 401 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 401 performs the various methods and processes described above. For example, in some embodiments, the water pollution prediction method may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 408. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 400 via ROM 402 and / or a communication unit 409. In some embodiments, the computing unit 401 may be configured to perform the water pollution prediction method in any other appropriate manner (e.g., by means of firmware).

[0087] The program code for implementing the method of the present invention can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code can be executed entirely on the machine, partially on the machine, partially on the machine as a stand-alone software package and partially on a remote machine, or entirely on a remote machine or server.

[0088] In the context of the present invention, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0089] As used herein, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, apparatus, and / or device (e.g., disk, optical disk, memory, programmable logic device (PLD)) for providing machine instructions and / or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term "machine-readable signal" refers to any signal for providing machine instructions and / or data to a programmable processor.

[0090] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0091] The systems and techniques described herein may be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0092] A computer system may include clients and servers. Clients and servers are generally remote from each other and usually interact through a communication network. The relationship of client and server is generated by computer programs running on respective computers and having a client-server relationship to each other.

[0093] Furthermore, it should be understood that what is disclosed above is only a preferred embodiment of the present invention, and certainly cannot be used to limit the scope of rights of the present invention. Therefore, equivalent changes made according to the claims of the present invention are still within the scope of the present invention.

Claims

1. A water pollution prediction method, characterized in that: include: Acquire target flow field data of a target area within a target time range, and acquire target meteorological field data of the target area within the target time range; Determine a particle impact calculation method for each of the M pollutant particles, and determine particle impact data of the corresponding pollutant particles according to the particle impact calculation methods of each of the pollutant particles, wherein one particle impact data includes impact sub-data under each particle impact calculation method in the particle impact calculation method of the corresponding pollutant particle, and M is a positive integer; updating the particle mass of each of the pollutant particles based on the particle impact data of each of the pollutant particles; Based on the particle mass of each pollutant particle, the target flow field data and the target meteorological field data, the target diffusion simulation result of each pollutant particle among P pollutant particles is determined. A target diffusion simulation result is the diffusion simulation result of the corresponding pollutant particle in the target area and the target time range, and P is a positive integer.

2. The method according to claim 1, characterized in that: The step of determining a target diffusion simulation result for each of the P pollutant particles based on the particle mass of each pollutant particle, the target flow field data, and the target meteorological field data includes: Based on the particle mass of each pollutant particle, determining Q pollutant particles from the M pollutant particles, where Q is a positive integer; Based on the target flow field data and the target meteorological field data, drift diffusion simulation is performed on each of the Q pollutant particles to obtain a diffusion simulation result of each of the Q pollutant particles, so as to determine a target diffusion simulation result of each of the P pollutant particles based on the diffusion simulation result of each of the Q pollutant particles.

3. The method according to claim 2, characterized in that The step of determining Q pollutant particles from the M pollutant particles based on the particle mass of each pollutant particle includes: Determine activity quality thresholds; Based on the particle mass of each pollutant particle, Q pollutant particles having a particle mass greater than the active mass threshold are determined from the M pollutant particles, so as to determine the Q pollutant particles from the M pollutant particles.

4. The method according to any one of claims 1 to 3, characterized in that: The acquiring target meteorological field data of the target area within the target time range comprises: When the start-up requirement of the target task is detected, a meteorological field data file is downloaded, wherein the meteorological field data file includes a forecast meteorological field data source of the target area within the target time range; The meteorological field data file is format-converted to obtain the target meteorological field data, and the target meteorological field data supports a universal network data format.

5. The method according to any one of claims 1 to 3, characterized in that: The step of obtaining target flow field data of a target area within a target time range includes: Acquire an initial boundary data source, wherein the initial boundary data source includes at least one historical data to be processed; Preprocessing each of the at least one historical data to be processed to obtain preprocessed boundary data corresponding to each of the historical data to be processed, wherein one preprocessed boundary data includes boundary data of the target area within the target time range; Based on the preprocessed boundary data corresponding to each of the historical data to be processed, the target boundary data source is determined, and the target hydrodynamic model is called to determine the target flow field data based on the target boundary data source; wherein the target flow field data includes the sub-flow field data of each flow field grid area in the target area within the target time range.

6. The method according to claim 5, characterized in that The method further comprises: Acquire an initial hydrodynamic model corresponding to the target area; Calling the initial hydrodynamic model to predict the rated flow field data of the target area within a historical time range; The initial hydrodynamic model is calibrated based on the calibrated flow field data and the benchmark data of the target area within the historical time range to obtain a calibrated hydrodynamic model, and the target hydrodynamic model is determined based on the calibrated hydrodynamic model.

7. The method according to any one of claims 1 to 3, characterized in that: The particle impact data of a pollutant particle includes at least one of the following: a volatilization mass, a degradation mass, and an evaporation mass of the corresponding pollutant particle.

8. A water pollution prediction device, characterized in that: The device comprises: An acquisition unit, used for acquiring target flow field data of a target area within a target time range, and acquiring target meteorological field data of the target area within the target time range; A processing unit, used to determine a particle impact calculation method of each pollutant particle among the M pollutant particles, and determine particle impact data of the corresponding pollutant particles according to the particle impact calculation methods of each pollutant particle, wherein one particle impact data includes impact sub-data under each particle impact calculation method in the particle impact calculation method of the corresponding pollutant particle, and M is a positive integer; The processing unit is further used to update the particle mass of each pollutant particle based on the particle impact data of each pollutant particle; The processing unit is also used to determine the target diffusion simulation result of each pollutant particle among P pollutant particles based on the particle mass of each pollutant particle, the target flow field data and the target meteorological field data. A target diffusion simulation result is a diffusion simulation result of the corresponding pollutant particle in the target area and the target time range, and P is a positive integer.

9. An electronic device, characterized in that: include: processor; as well as Memory for storing programs, The program includes instructions, which, when executed by the processor, cause the processor to perform the method according to any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium storing computer instructions, wherein: The computer instructions are used to make a computer execute the method according to any one of claims 1-7.

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