Water quality model concentration field correction method and system
By generating a set of water body traces and dividing them into correction units, the problem of multi-source data fusion was solved by using the inverse distance weighted difference method, which improved the simulation accuracy and prediction capability of the two-dimensional hydrodynamic water quality model.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-11-03
- Publication Date
- 2026-03-31
AI Technical Summary
How to integrate ground and remote sensing observation information from different sources, at different times, and with different spatial resolutions into a two-dimensional hydrodynamic and water quality model to improve the accuracy and reliability of simulation and forecasting.
By acquiring the velocity vector field of a two-dimensional hydrodynamic water quality model, a set of water body traces is generated, directional channel lines are extracted and divided into correction units, and based on the deviation between the model-calculated concentration field and the measured concentration data, inverse distance weighted difference is performed to generate a global concentration correction field, which replaces the model concentration field.
Dynamic correction of model data was achieved, which improved simulation accuracy and prediction ability, reduced simulation and forecast errors, and obtained higher accuracy simulation results.
Smart Images
Figure CN121765199A_ABST
Abstract
Description
Technical Field
[0001] This application belongs to the field of water environment data analysis and calculation correction technology, and relates to a water quality model concentration field correction method and system. Background Technology
[0002] In recent years, water environment monitoring technology has developed rapidly. Various types of online monitoring equipment, through flexible and diverse foundations such as shoreline base stations and water buoys, have enabled the rapid and high-frequency acquisition of point-like water environment data. At the same time, satellite remote sensing technology can also be used to directly or indirectly obtain water quality index information of inland rivers and water bodies, providing richer data for two-dimensional hydrodynamic and aquatic ecological models of rivers and lakes through multiple data models.
[0003] How to integrate ground and remote sensing observation information (i.e., multi-source data) from different sources, at different times, and with different spatial resolutions into a two-dimensional hydrodynamic and water quality model for simulation and forecasting, in order to improve the accuracy and reliability of the simulation and forecasting of the two-dimensional hydrodynamic and water quality model, is also one of the current problems in the field of simulation and forecasting of two-dimensional hydrodynamic and water quality models for rivers and lakes. Summary of the Invention
[0004] This application provides a water quality model concentration field correction method and system to solve the problem of fusion between multi-source monitoring data and calculated data, so as to improve the accuracy and reliability of simulation and forecasting of two-dimensional hydrodynamic water quality models.
[0005] Firstly, this application provides a method for correcting the concentration field of a water quality model. The method includes:
[0006] Obtain the velocity vector field of the target water area output by the two-dimensional hydrodynamic water quality model, and generate a two-dimensional water body trace set;
[0007] Directed channel lines are extracted from the set of two-dimensional water body traces. Based on the directed channel lines, the water body space corresponding to the target water body is divided into several correction units according to the spatial relationship between the water body units of the target water body and the directed channel lines.
[0008] Based on the deviation between the concentration field calculated by the model and the measured concentration data, an inverse distance weighted difference is performed at discrete points within each correction unit to obtain the unit concentration correction field.
[0009] The unit concentration correction fields corresponding to several correction units are merged to generate a global concentration correction field. The global concentration correction field is used to correct the global concentration data, and the corrected global concentration data correction field replaces the model concentration field of the two-dimensional hydrodynamic water quality model.
[0010] In one implementation of the first aspect, the velocity vector field of the target water area output by the two-dimensional hydrodynamic water quality model is obtained, and a two-dimensional water body trace set is generated, including:
[0011] The velocity vector field of the target water area is processed based on the vector tracking method to generate the water flow trajectory line;
[0012] The overlapping traces in the water flow trajectory are simplified by using a trace density threshold to generate a two-dimensional water trace set.
[0013] In one implementation of the first aspect, a directed channel line is extracted from the two-dimensional water body trace, and the water space corresponding to the target water area is divided into several correction units based on the directed channel line, including:
[0014] Calculate the minimum Euclidean distance from the discrete point to the set of two-dimensional water body traces;
[0015] Filter out the two-dimensional water body traces closest to the discrete points;
[0016] The centerline is extracted and tributary filtering is performed on the most recent two-dimensional water body trace to generate a directed channel line characterizing the main path of water flow;
[0017] Based on the directed channel line, the water space corresponding to the target water area is trimmed and optimized to divide the water space corresponding to the target water area into several correction units.
[0018] In one implementation of the first aspect, based on the deviation between the concentration field calculated by the model and the measured concentration data, inverse distance weighted interpolation is performed at discrete points within the correction unit to obtain the unit concentration correction field, including:
[0019] Obtain the discrete point set and monitoring point set within the correction unit, wherein each discrete point includes the spatial coordinates of the discrete point and the model concentration value, and each monitoring point includes the spatial coordinates of the monitoring point and the measured concentration data;
[0020] Using monitoring points as sample points and discrete points as interpolation points, inverse distance weighted interpolation is performed to obtain the interpolation results;
[0021] The corrected concentration values of discrete points are calculated and aggregated based on the interpolation results to generate a unit concentration correction field.
[0022] In one implementation of the first aspect, the concentration deviation value of the monitoring point is either the absolute concentration deviation value or the relative concentration deviation value of the monitoring point.
[0023] In one implementation of the first aspect, calculating and aggregating the corrected concentration values of discrete points based on the interpolation results to generate a cell concentration correction field includes:
[0024] Calculate the corrected concentration values for discrete points based on the interpolation results and the model concentration values;
[0025] For the discrete points located at the monitoring point locations, the measured concentration data of the monitoring point is forcibly used as the corrected concentration value of the discrete point;
[0026] The corrected concentration values of all the discrete points are organized into a spatial point dataset and filtered to obtain the cell concentration correction field.
[0027] In one implementation of the first aspect, the measured concentration data includes: probe data and probe-remote sensing data; wherein,
[0028] The probe data is point-scale concentration field data of the in-situ water quality probe real-time monitoring point.
[0029] The probe-remote sensing data is remote sensing inversion concentration field data calibrated from the probe data.
[0030] Secondly, this application provides a water quality model concentration field correction system. The system includes:
[0031] The acquisition module is used to acquire the velocity vector field of the target water area output by the two-dimensional hydrodynamic water quality model and generate a two-dimensional water body trace set;
[0032] The correction unit division module is used to extract directed channel lines from the two-dimensional water body trace set, and based on the directed channel lines, divide the water body space corresponding to the target water body into several correction units according to the spatial relationship between the water body units of the target water body and the directed channel lines.
[0033] The concentration field correction module is used to calculate the deviation between the concentration field and the measured concentration data based on the model, and to perform inverse distance weighted difference at discrete points within each correction unit to obtain the unit concentration correction field.
[0034] The correction module is used to merge the unit concentration correction fields corresponding to several correction units to generate a global concentration correction field, use the global concentration correction field to correct the global concentration data, and replace the model concentration field of the two-dimensional hydrodynamic water quality model with the corrected global concentration data.
[0035] Thirdly, this application provides an electronic device, which includes: a processor and a memory, wherein the memory is used to store a computer program; the processor is communicatively connected to the memory, and when the computer program is invoked, it executes the water quality model concentration field correction method according to any one of the first aspects of this application.
[0036] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the water quality model concentration field correction method according to any one of the first aspects of this application.
[0037] As described above, the water quality model concentration field correction method and system of this application have the following beneficial effects:
[0038] This application integrates probe and remote sensing observation data into a two-dimensional hydrodynamic water quality model, dynamically updates the sensitive parameters of water level and water quality simulation, realizes dynamic correction of model state variables, and improves model simulation accuracy and prediction capability.
[0039] The method described in this application can integrate direct and indirect observation information from different sources and at different resolutions to adjust the model's trajectory, thereby reducing model simulation and forecasting errors and obtaining more accurate simulation and forecasting results. Attached Figure Description
[0040] Figure 1 The diagram shown is a scene illustration of the water quality model concentration field correction method described in this application.
[0041] Figure 2 The flowchart shown is a water quality model concentration field correction method according to an embodiment of this application.
[0042] Figure 3a The diagram shown is a schematic representation of the velocity vector field described in an embodiment of this application.
[0043] Figure 3b The diagram shown is a schematic diagram of the water flow trajectory line described in the embodiments of this application.
[0044] Figure 3c The diagram shown is a simplified two-dimensional water trace set as described in the embodiments of this application.
[0045] Figure 4a The diagram shown is a schematic diagram of the directed slot line described in the embodiments of this application.
[0046] Figure 4b The diagram shown is a schematic representation of the correction unit described in an embodiment of this application.
[0047] Figure 5 The flowchart shown is a process for obtaining the unit concentration correction field as described in an embodiment of this application.
[0048] Figure 6 The diagram shown is a structural schematic of the water quality model concentration field correction system described in this application embodiment.
[0049] Figure 7 The diagram shown is a structural schematic of the electronic device described in an embodiment of this application.
[0050] Component designation explanation
[0051] 1. Water quality model concentration field correction system
[0052] 11. Probe Device
[0053] 12 Remote sensing devices
[0054] 13 Two-dimensional hydrodynamic water quality model
[0055] 14 Processor devices
[0056] 6. Water quality model concentration field correction system
[0057] 61 Acquisition Module
[0058] 62. Correction Unit Division Module
[0059] 63 Concentration Field Correction Module
[0060] 64 Calibration Module
[0061] 7 Electronic devices
[0062] 71 Memory
[0063] 72 processor
[0064] 73 Monitors
[0065] Steps S21 to S24
[0066] Steps S231~S233 Detailed Implementation
[0067] The following specific examples illustrate the implementation of this application. Those skilled in the art can easily understand other advantages and effects of this application from the content disclosed in this specification. This application can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of this application. It should be noted that, unless otherwise specified, the following embodiments and features in the embodiments can be combined with each other.
[0068] It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of this application. Therefore, the drawings only show the components related to this application and are not drawn according to the actual number, shape and size of the components in the actual implementation. In the actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.
[0069] Two-dimensional hydrodynamic water quality models are tools used to simulate water flow and pollutant transport and diffusion processes in water bodies. The core of a hydrodynamic model is to simulate the flow state of a water body, including changes in water level, velocity, and direction, by solving hydrodynamic equations such as the continuity equation and momentum equation.
[0070] Figure 1 This is a schematic diagram illustrating an application scenario of the water quality model concentration field correction system according to an embodiment of this application. For example... Figure 1 As shown, it includes: a probe device 11, a remote sensing device 12, a two-dimensional hydrodynamic water quality model 13, and a processor device 14.
[0071] The probe device 11 is used to acquire probe data, which refers to the data obtained by directly contacting the water body with a physical sensor (probe). These probes are typically deployed at fixed sites (such as buoys or shore stations), mobile platforms (such as unmanned vessels or mobile equipment), or measured manually by hand or by deployment (such as portable water quality meters or COD meters). The probe data acquired by the probe device 11 represents the instantaneous or short-term average state at the location of the probe device (a point or a very small area), but its measurement may be affected by local factors such as water flow, biological attachment, and sediment disturbance near the probe.
[0072] Remote sensing device 12 is used to acquire remote sensing data. This refers to the use of sensors mounted on satellites, aircraft, drones, or ground-based towers to detect the optical properties (reflected and emitted electromagnetic radiation) of water bodies in a non-contact manner, and then derive water environment parameters through inversion algorithms. It employs a non-contact measurement method, using electromagnetic waves to detect water data, and a single observation can cover a large area (from a few square kilometers to the entire globe), providing spatial distribution information.
[0073] The two-dimensional hydrodynamic water quality model 13 is used to simulate and predict the concentration field of water bodies.
[0074] The processor device 14 is used to receive the velocity vector field of the target water area output by the two-dimensional hydrodynamic water quality model, the probe data obtained by the probe device 11, and the remote sensing data obtained by the remote sensing device 12, and to perform model concentration field correction based on the water quality model concentration field correction method described in the embodiments of this application to generate a global concentration correction field, and to send the generated global concentration correction field to the two-dimensional hydrodynamic water quality model 13 to replace the model concentration field of the two-dimensional hydrodynamic water quality model 13.
[0075] The technical solutions in the embodiments of this application will be described in detail below with reference to the accompanying drawings.
[0076] Figure 2 This is a flowchart illustrating the water quality model concentration field correction method provided in this application embodiment. This water quality model concentration field correction method can be applied to mobile phone processors, tablet processors, computer processors, etc. Figure 2 As shown, the water quality model concentration field correction method provided in this application embodiment includes the following steps S21 to S24.
[0077] Step S21: Obtain the velocity vector field of the target water area output by the two-dimensional hydrodynamic water quality model, and generate a two-dimensional water body trace set.
[0078] In this embodiment, step S21 includes: processing the obtained flow velocity vector field of the target water area based on the vector tracking method to generate water flow trajectory lines; simplifying the overlapping traces in the water flow trajectory lines by using a trace density threshold to generate a two-dimensional water trace set.
[0079] Among them, the water flow trajectory line is the flow trajectory of fluid particles over a continuous time process, which is only related to the fluid particles, and different particles may have different trajectory shapes. The two-dimensional water trajectory set is a collection of fluid particle trajectories in a lake over time, obtained through numerical simulation or measured data. The trajectory set can show the path, direction, and velocity changes of water flow, and is an important tool for studying lake flow fields, pollutant transport, and diffusion.
[0080] The velocity vector field of the target water area output by the hydrodynamic model includes: the magnitude and direction components of the velocity at the calculated grid nodes, as well as the time step sequence of the hydrodynamic model. When performing calculations using particle tracing, virtual particle swarms need to be deployed at key locations in the target water area, with the particle density dynamically adjusted according to hydrological characteristics. It should be noted that a high density of 1-5 particles / km² is used in river areas, while a low density of 0.2-1 particles / km² is used in open lakes. Each particle carries its position coordinates (x, y, z) and timestamp information. Starting from the inlet and outlet of the water body with the obtained velocity vector field, the particle tracing algorithm is used to calculate the particle motion integral to generate the water flow trajectory line, such as... Figure 3a As shown, this is the velocity vector field of the target area, which includes two inlets and one outlet, as follows. Figure 3b As shown, this is the water flow trajectory line of the target water area obtained by calculating the particle motion integral using the particle tracking algorithm.
[0081] The trace density factor can be determined comprehensively based on factors such as the concentration and distribution density of traces, as well as hydrodynamic characteristics. When analyzing the flow trajectories of lakes, it may be found that some areas may have a large number of overlapping traces. These overlapping traces usually appear in areas with slower flow velocities and relatively stable flow fields, or are caused by the high similarity of the trajectories of adjacent fluid particles over a certain period of time.
[0082] Simplifying overlapping water flow trajectories using a trace density threshold to generate a two-dimensional water trace set involves: dividing the target water area into regular grid cells, counting the number of water flow trajectories traversed by each grid cell, generating a trace spatial density field, and using kernel density estimation to obtain high-density core regions and low-density edge regions in the trace spatial density field. The high-density core region corresponds to the main flow path of the target water area, and the low-density edge region corresponds to eddies or tributaries of the target water area. A trace density factor is set, and traces within a certain distance are merged based on the set trace density factor, ensuring that the direction of all traces is consistent with the direction of the main flow path, thereby simplifying overlapping traces and generating a two-dimensional water trace set. Figure 3c As shown, this is a simplified set of two-dimensional water body traces.
[0083] Step S22: Extract directed channel lines from the two-dimensional water body trace set, and based on the directed channel lines, divide the water body space corresponding to the target water body into several correction units according to the spatial relationship between the water body units of the target water body and the directed channel lines.
[0084] In this embodiment, step S22 includes: calculating the minimum Euclidean distance from the discrete point to the set of two-dimensional water body traces; selecting the two-dimensional water body trace closest to the discrete point; extracting the centerline and filtering the tributaries of the closest two-dimensional water body trace to generate a directed channel line representing the main path of the water flow; and performing water body trimming and optimization on the water body space corresponding to the target water area based on the directed channel line to divide the water body space corresponding to the target water area into several correction units.
[0085] Specifically, a discrete point set is obtained from the two-dimensional water body trace set. The minimum Euclidean distance from each discrete point to the two-dimensional water body trace set is calculated, and the number of discrete points belonging to each two-dimensional water body trace is calculated. Two-dimensional water body traces with a number of belongings exceeding a threshold are selected as candidate channel lines. The obtained candidate channel lines are then subjected to spatial clustering and simplification processing to generate directed channel lines representing the main path of water flow. Preferably, the spatial clustering uses the Douglas-Peucker algorithm to simplify the geometry, with a clustering tolerance of 10%-20% of the average width of the lake.
[0086] A fixed-width buffer zone is generated using the obtained directed channel line as the axis, and the spatial intersection of the generated buffer zone and the water space of the target water area is calculated. The intersection area is defined as the initial correction unit. The overlapping area of the spatially adjacent initial correction units is processed to obtain correction units. Each correction unit has a unique identifier and each correction unit is associated with a directed channel line.
[0087] like Figure 4a As shown, this is a directed channel line extracted from a two-dimensional water body trace set, such as... Figure 4bAs shown, the correction units are divided, where each color block represents a correction unit, and each correction unit is associated with a directed slot line.
[0088] Preferably, the overlapping region processing of spatially adjacent initial correction units includes: merging the overlapping region into one of the initial correction units or dividing the overlapping region by using the perpendicular bisector of the spacing of the directed slot lines as the boundary.
[0089] Step S23: Based on the deviation between the concentration field calculated by the model and the measured concentration data, perform inverse distance weighted difference at discrete points within each correction unit to obtain the unit concentration correction field.
[0090] In this embodiment, as Figure 5 As shown, step S23 includes steps S231 to S233:
[0091] Step S231: Obtain the discrete point set and monitoring point set within the calibration unit. Each discrete point includes the spatial coordinates of the discrete point and the model concentration value, and each monitoring point includes the spatial coordinates of the monitoring point and the measured concentration data.
[0092] In this embodiment, the measured concentration data includes: probe data and probe-remote sensing data; wherein, the probe data is the point-scale concentration field data of the real-time monitoring point of the in-situ water quality probe; and the probe-remote sensing data is the remote sensing inversion concentration field data calibrated by the probe data.
[0093] Step S232: Calculate the concentration deviation value of the monitoring point, and combine the concentration deviation value with the monitoring point as the sample point and the discrete point as the interpolation point to be interpolated, perform inverse distance weighted interpolation to obtain the interpolation result.
[0094] The concentration deviation at a monitoring point is the deviation between the model concentration value and the measured concentration data at that monitoring point. This concentration deviation can be either an absolute or relative concentration deviation.
[0095] In this embodiment, a power parameter of 0.5 to 3 yields reasonable results. The inverse distance weighting method relies primarily on the power value of the inverse distance, and the power parameter controls the influence of known points on the interpolation value based on the distance from the output point. As the power increases, the interpolation value gradually approaches the value of the nearest sampling point; a smaller power value will have a greater impact on surrounding points that are farther away, resulting in a smoother plane.
[0096] The interpolation formula for inverse distance weighted interpolation is:
[0097]
[0098] Where x is the point to be interpolated, f(x) is the interpolation result of the point to be interpolated, and w i(x) represents the weight of sample point i with respect to the point to be interpolated, f i The value at sample point i;
[0099] The weight formula for calculating the weights of the interpolation points is:
[0100]
[0101] Among them, (x i y i ) represents the coordinates of the point to be interpolated, and j represents the sample point.
[0102] In this embodiment, the inverse distance weighted interpolation adopts a directional constraint strategy, including: obtaining the two-dimensional flow direction corresponding to the correction unit as the dominant direction, setting a first search radius in the direction parallel to the directed slot line, setting a second search radius in the direction perpendicular to the directed slot line, wherein the length of the first search radius is at least twice the length of the second search radius, delineating an elliptical search area based on the first search radius and the second search radius, and selecting monitoring points located within the elliptical search area to participate in the difference calculation.
[0103] Step S233: Calculate and aggregate the corrected concentration values of discrete points based on the interpolation results to generate a cell concentration correction field.
[0104] In this embodiment, step S233 includes: calculating the corrected concentration value of discrete points based on the interpolation results and model concentration values; for discrete points located at monitoring point locations, forcing the measured concentration data of the monitoring point to be used as the corrected concentration value of the discrete point; organizing the corrected concentration values of all discrete points into a spatial point dataset and performing filtering processing to obtain the unit concentration correction field.
[0105] If the concentration deviation value of the monitoring point is the absolute concentration deviation value, and the interpolation result obtained in step S232 is the absolute deviation of the discrete point, then the corrected concentration value of the discrete point is calculated based on the interpolation result and the model concentration value, including: adding the obtained interpolation result to the model concentration value of the discrete point to obtain the corrected concentration value.
[0106] If the concentration deviation value of the monitoring point is a relative concentration deviation value, and the interpolation result obtained in step S232 is the relative deviation of the discrete point, then the corrected concentration value of the discrete point is calculated based on the interpolation result and the model concentration value, including: multiplying the obtained interpolation result by the model concentration value of the discrete point to obtain the corrected concentration value.
[0107] In this process, a non-negative constraint is applied to the obtained corrected concentration value. If the corrected concentration value is less than 0, the corrected concentration value of the discrete point is forcibly set to 0.
[0108] Step S24: Merge the unit concentration correction fields corresponding to several correction units to generate a global concentration correction field, use the global concentration correction field to correct the global concentration data, and replace the model concentration field of the two-dimensional hydrodynamic water quality model with the corrected global concentration data.
[0109] The scope of protection of the water quality model concentration field correction method in this application is not limited to the execution order of the steps listed in this embodiment. Any solution implemented by adding, subtracting, or replacing steps in the prior art based on the principles of this application is included within the scope of protection of this application.
[0110] This application also provides a water quality model concentration field correction system, which can implement the water quality model concentration field correction method described in this application. However, the implementation device of the water quality model concentration field correction method described in this application includes, but is not limited to, the structure of the water quality model concentration field correction system listed in this embodiment. All structural modifications and substitutions of the prior art made based on the principles of this application are included within the protection scope of this application.
[0111] like Figure 6 As shown, this embodiment provides a water quality model concentration field correction system 6, including:
[0112] The acquisition module 61 is used to acquire the velocity vector field of the target water area output by the two-dimensional hydrodynamic water quality model and generate a two-dimensional water body trace set;
[0113] The correction unit division module 62 is used to extract directed channel lines from the two-dimensional water body trace set, and based on the directed channel lines, divide the water body space corresponding to the target water body into several correction units according to the spatial relationship between the water body units of the target water body and the directed channel lines.
[0114] The concentration field correction module 63 is used to calculate the deviation between the concentration field and the measured concentration data based on the model, and to perform inverse distance weighted difference at discrete points within each correction unit to obtain the unit concentration correction field.
[0115] The correction module 64 is used to merge the unit concentration correction fields corresponding to several correction units to generate a global concentration correction field, use the global concentration correction field to correct the global concentration data, and replace the model concentration field of the two-dimensional hydrodynamic water quality model with the corrected global concentration data.
[0116] Since the specific implementation of this embodiment corresponds to the aforementioned method embodiment, the same details will not be repeated here.
[0117] It should be noted that the above division of modules is merely a logical functional division. In actual implementation, they can be fully or partially integrated into a single physical entity, or they can be physically separated. Furthermore, these modules can be implemented entirely in software via processing element calls; they can be fully implemented in hardware; or some modules can be implemented by processing element calls to software, while others are implemented in hardware. For example, module x can be a separate processing element, or it can be integrated into a chip in the aforementioned device. Alternatively, it can be stored as program code in the memory of the aforementioned device, and its function can be called and executed by a processing element of the device. The implementation of other modules is similar. Moreover, these modules can be fully or partially integrated together, or they can be implemented independently. The processing element mentioned here can be an integrated circuit with signal processing capabilities. In the implementation process, each step of the above method or each of the above modules can be completed through integrated logic circuits in the hardware of the processor element or through software instructions.
[0118] For example, these modules can be one or more integrated circuits configured to implement the above methods, such as one or more Application Specific Integrated Circuits (ASICs), one or more Digital Signal Processors (DSPs), or one or more Field Programmable Gate Arrays (FPGAs). As another example, when a module is implemented using processing element scheduler code, the processing element can be a general-purpose processor, such as a Central Processing Unit (CPU) or other processor capable of calling program code. Furthermore, these modules can be integrated together to form a system-on-a-chip (SOC).
[0119] This application also provides a computer-readable storage medium having a computer program stored thereon. When executed by a processor, the computer program implements the water quality model concentration field correction method provided in the embodiments of the present invention.
[0120] This application embodiment may also provide a computer program product comprising one or more computer instructions. When the computer instructions are loaded and executed on a computing device, all or part of the processes or functions described in this application embodiment are generated.
[0121] When the computer program product is executed by a computer, the computer performs the method described in the foregoing method embodiments. The computer program product can be a software installation package; when the foregoing method is required, the computer program product can be downloaded and executed on the computer.
[0122] This application also provides an electronic device, please refer to... Figure 7 The image shown is a schematic diagram of the structure of an electronic device 7 in one embodiment of this application. Figure 7 As shown, electronic device 7 includes: at least one processor 71, memory 72, at least one network interface 73, and user interface 75. The various components in electronic device 7 are coupled together via a bus system 74. It is understood that the bus system 74 is used to implement communication between these components. In addition to a data bus, the bus system 74 also includes a power bus, a control bus, and a status signal bus. However, for clarity, in… Figure 7 The general will label all buses as bus systems.
[0123] User interface 75 may include a monitor, keyboard, mouse, trackball, clicker, button, touchpad, or touchscreen.
[0124] It is understood that memory 72 can be volatile memory or non-volatile memory, or both. Non-volatile memory can be read-only memory (ROM) or programmable read-only memory (PROM), which serves as an external cache. By way of example, but not limitation, many forms of RAM are available, such as static random access memory (SRAM) and synchronous static random access memory (SSRAM). The memories described in the embodiments of this application are intended to include, but are not limited to, these and any other suitable categories of memory.
[0125] In this embodiment, the memory 72 is used to store various types of data to support the operation of the electronic device 7. Examples of this data include: any executable program for operation on the electronic device 7, such as the operating system 721 and application programs 722; the operating system 721 contains various system programs, such as the framework layer, core library layer, driver layer, etc., for implementing various basic services and handling hardware-based tasks. The application program 722 may contain various applications, such as a media player, browser, etc., for implementing various application services. The video decoding and reconstruction method provided in this embodiment may be included in the application program 722.
[0126] In an exemplary embodiment, the electronic device 7 may be used by one or more application-specific integrated circuits (ASICs), DSPs, programmable logic devices (PLDs), or complex programmable logic devices (CPLDs) to perform the aforementioned method.
[0127] The descriptions of the processes or structures corresponding to the above figures each have their own emphasis. For parts of a process or structure that are not described in detail, please refer to the relevant descriptions of other processes or structures.
[0128] In summary, this application integrates probe and remote sensing observation data into a two-dimensional hydrodynamic and water quality model, dynamically updates sensitive parameters for water level and water quality simulation, and achieves dynamic correction of model state variables, thereby improving model simulation accuracy and predictive capability. The method of this application can fuse direct and indirect observation information from different sources and at different resolutions to adjust the model's trajectory, thus reducing model simulation and forecasting errors and obtaining more accurate simulation and forecasting results. Therefore, this application effectively overcomes the various shortcomings of existing technologies and has high industrial application value.
[0129] The above embodiments are merely illustrative of the principles and effects of this application and are not intended to limit this application. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of this application. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in this application should still be covered by the claims of this application.
Claims
1. A method for correcting the concentration field of a water quality model, characterized in that, include: Obtain the velocity vector field of the target water area output by the two-dimensional hydrodynamic water quality model, and generate a two-dimensional water body trace set; Directed channel lines are extracted from the set of two-dimensional water body traces. Based on the directed channel lines, the water body space corresponding to the target water body is divided into several correction units according to the spatial relationship between the water body units of the target water body and the directed channel lines. Based on the deviation between the concentration field calculated by the model and the measured concentration data, an inverse distance weighted difference is performed at discrete points within each correction unit to obtain the unit concentration correction field. The unit concentration correction fields corresponding to several correction units are merged to generate a global concentration correction field. The global concentration correction field is used to correct the global concentration data, and the corrected global concentration data replaces the model concentration field of the two-dimensional hydrodynamic water quality model.
2. The water quality model concentration field correction method according to claim 1, characterized in that, Obtain the velocity vector field of the target water area output by the two-dimensional hydrodynamic water quality model, and generate a two-dimensional water body trace set, including: The velocity vector field of the target water area is processed based on the vector tracking method to generate the water flow trajectory line; The overlapping traces in the water flow trajectory are simplified by using a trace density threshold to generate a two-dimensional water trace set.
3. The water quality model concentration field correction method according to claim 1, characterized in that, Directed channel lines are extracted from the two-dimensional water body traces, and the water space corresponding to the target water area is divided into several correction units based on the directed channel lines, including: Calculate the minimum Euclidean distance from the discrete point to the set of two-dimensional water body traces; Filter out the two-dimensional water body traces closest to the discrete points; The centerline is extracted and tributary filtering is performed on the most recent two-dimensional water body trace to generate a directed channel line characterizing the main path of water flow; Based on the directed channel line, the water space corresponding to the target water area is trimmed and optimized to divide the water space corresponding to the target water area into several correction units.
4. The water quality model concentration field correction method according to claim 1, characterized in that, Based on the deviation between the model-calculated concentration field and the measured concentration data, inverse distance weighted interpolation is performed at discrete points within each correction unit to obtain the unit concentration correction field, including: Obtain the discrete point set and monitoring point set within the correction unit, wherein each discrete point includes the spatial coordinates of the discrete point and the model concentration value, and each monitoring point includes the spatial coordinates of the monitoring point and the measured concentration data; Calculate the concentration deviation value of the monitoring points, and combine the concentration deviation value with the monitoring points as sample points and the discrete points as interpolation points to be interpolated, perform inverse distance weighted interpolation to obtain the interpolation result; The corrected concentration values of discrete points are calculated and aggregated based on the interpolation results to generate a unit concentration correction field.
5. The water quality model concentration field correction method according to claim 4, characterized in that, The concentration deviation value of the monitoring point is either the absolute concentration deviation value or the relative concentration deviation value of the monitoring point.
6. The water quality model concentration field correction method according to claim 4, characterized in that, The process involves calculating and aggregating the corrected concentration values of discrete points based on the interpolation results to generate a cell concentration correction field, including: Calculate the corrected concentration values for discrete points based on the interpolation results and the model concentration values; For the discrete points located at the monitoring point locations, the measured concentration data of the monitoring point is forcibly used as the corrected concentration value of the discrete point; The corrected concentration values of all the discrete points are organized into a spatial point dataset and filtered to obtain the cell concentration correction field.
7. The water quality model concentration field correction method according to claim 1, characterized in that, The measured concentration data includes: probe data and probe-remote sensing data; wherein... The probe data is point-scale concentration field data of the in-situ water quality probe real-time monitoring point. The probe-remote sensing data is remote sensing inversion concentration field data calibrated from the probe data.
8. A water quality model concentration field correction system, characterized in that, include: The acquisition module is used to acquire the velocity vector field of the target water area output by the two-dimensional hydrodynamic water quality model and generate a two-dimensional water body trace set; The correction unit division module is used to extract directed channel lines from the two-dimensional water body trace set, and based on the directed channel lines, divide the water body space corresponding to the target water body into several correction units according to the spatial relationship between the water body units of the target water body and the directed channel lines. The concentration field correction module is used to calculate the deviation between the concentration field and the measured concentration data based on the model, and to perform inverse distance weighted difference at discrete points within each correction unit to obtain the unit concentration correction field. The correction module is used to merge the unit concentration correction fields corresponding to several correction units to generate a global concentration correction field, use the global concentration correction field to correct the global concentration data, and replace the model concentration field of the two-dimensional hydrodynamic water quality model with the corrected global concentration data.
9. An electronic device, characterized in that, The electronic device includes: A memory that stores a computer program; The processor, which is communicatively connected to the memory, executes the water quality model concentration field correction method according to any one of claims 1 to 7 when calling each of the computer programs.
10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the water quality model concentration field correction method according to any one of claims 1 to 7.