Method and device for determining parameters of water environment model

Abstract

The invention provides a method and a device for determining parameters of a water environment model. The method comprises the following steps: acquiring pollutant data of a target river network, and constructing a water environment model of the target river network based on the pollutant data, water passing area data, hydraulic radius data, section flow data, water level data and flow distance data of the river network. And acquiring first-class water quality concentration data and second-class water quality concentration data at the water quality monitoring section. And with the minimum error between the water quality index simulation value and the field measured value as the target, continuously adjusting the parameters of the water environment model through the first type of water quality concentration data on the basis of the determined water level until a cut-off condition is reached. And verifying the parameter by using the second type of water quality concentration data to obtain a verification result. And if the verification result meets a preset condition, determining that the water environment model parameters are determined. According to the method provided by the invention, the parameters of the water environment model can be accurately determined.

Description

Technical Field

[0001] The present application relates to the technical field of electric vehicles, and in particular to a lithium battery temperature control method, a lithium battery temperature control system and a vehicle. Background Art

[0002] With the continuous development of science and technology, the deterioration of water quality and eutrophication in urban river networks are mainly attributed to the input of external pollutants such as agricultural non-point source pollution, domestic sewage, storm runoff, and industrial wastewater. These problems may lead to many problems in urban river networks, such as the reduction of aquatic plants and animals and the degradation of ecosystem services. Therefore, restoring the water quality of urban river networks to maintain the ecological water level of urban river networks, ensure human health and safety, and avoid eutrophication risks has become one of the urgent tasks facing most cities in the world.

[0003] Relevant personnel have made many efforts to improve the ecological environment of lakes. But in order to make these efforts need to be accurately quantified, it is necessary to build a good urban river network water environment model. However, building a good urban river network water environment model involves many parameters. It is impractical to actually measure and investigate each parameter on the spot. A method is needed to determine the best combination of parameter values. The relevant technology is not accurate in setting the parameters of the urban river network water environment model.

[0004] Therefore, how to accurately set the parameters of the urban river network water environment model has become a technical problem that technical personnel in this field urgently need to solve. Summary of the invention

[0005] Based on the above problems, the present application provides a method and device for determining water environment model parameters to solve the problem of inaccurate setting of related technical parameters.

[0006] The present application provides a method for determining water environment model parameters, the method comprising the following steps:

[0007] Obtain pollutant data of the target river network;

[0008] Constructing a water environment model of the target river network based on the pollutant data, the water flow area data, hydraulic radius data, cross-sectional flow data, water level data and flow distance data of the river network;

[0009] Acquire first-type water quality concentration data and second-type water quality concentration data at a water quality monitoring section, wherein the first-type water quality concentration data is used to determine parameters of the water environment model, and the second-type water quality concentration data is used to verify the parameters determined based on the first-type water quality concentration data;

[0010] With the goal of minimizing the error between the simulated value of the water quality index and the actual measured value on site, the parameters of the water environment model are continuously adjusted based on the determined water level through the first type of water quality concentration data until the cutoff condition is reached;

[0011] Using the second type of water quality concentration data to verify the parameter to obtain a verification result;

[0012] If the verification result meets the preset conditions, the water environment model parameters are determined.

[0013] In a possible implementation, the method further includes:

[0014] Obtaining geographic data and weather data of the city corresponding to the target river network;

[0015] Determine the water pollutant diffusion coefficient, attenuation coefficient, dry deposition flux, sediment release and river roughness of the water environment model based on the geographic data and the weather data;

[0016] The water pollutant diffusion coefficient, the attenuation coefficient, the dry deposition flux, the sediment release and the river channel roughness are used as initial values ​​corresponding to the water environment model.

[0017] The present application also provides a device for determining water environment model parameters, the device comprising the following modules:

[0018] The first acquisition module is used to obtain pollutant data of the target river network;

[0019] A water environment model module, used to construct a water environment model of the target river network based on the pollutant data, the water flow area data of the river network, the hydraulic radius data, the cross-sectional flow data, the water level data and the flow distance data;

[0020] A second acquisition module is used to acquire first-class water quality concentration data and second-class water quality concentration data at a water quality monitoring section, wherein the first-class water quality concentration data is used to determine the parameters of the water environment model, and the second-class water quality concentration data is used to verify the parameters determined based on the first-class water quality concentration data;

[0021] A parameter determination module is used to continuously adjust the parameters of the water environment model based on the determined water level and the first type of water quality concentration data with the goal of minimizing the error between the simulated value of the water quality index and the actual measured value on site until the cutoff condition is reached;

[0022] A verification module, used for verifying the parameter using the second type of water quality concentration data to obtain a verification result;

[0023] The verification result module is used to determine the water environment model parameters if the verification result meets the preset conditions.

[0024] In a possible implementation manner, the device further includes an initial value determination module, and the initial value determination module is used to:

[0025] Obtaining geographic data and weather data of the city corresponding to the target river network;

[0026] Determine the water pollutant diffusion coefficient, attenuation coefficient, dry deposition flux, sediment release and river roughness of the water environment model based on the geographic data and the weather data;

[0027] The water pollutant diffusion coefficient, the attenuation coefficient, the dry deposition flux, the sediment release and the river channel roughness are used as initial values ​​corresponding to the water environment model.

[0028] The present application also provides an electronic device, the electronic device comprising a processor and a memory:

[0029] The memory is used to store a computer program and transmit the computer program to the processor;

[0030] The processor is used to execute the steps of the above-mentioned method for determining water environment model parameters according to the instructions in the computer program.

[0031] The present application also provides a computer-readable storage medium, which is used to store a computer program. When the computer program is executed by an electronic device, the steps of the method for determining the water environment model parameters are implemented.

[0032] Compared with the prior art, this application has the following beneficial effects:

[0033] The method for determining the parameters of the water environment model proposed in this application builds a water environment model based on a variety of water quality and flow data, and comprehensively considers multiple factors such as water flow area, hydraulic radius, cross-sectional flow, water level and flow distance. Compared with the traditional single parameter dependence in the background technology, this multi-dimensional data input can more comprehensively describe the water environment process and water quality changes of the river network, making the model more scientific and reasonable. By continuously adjusting the parameters of the water environment model, dynamic optimization is carried out with the goal of minimizing the error between the simulated value of the water quality index and the actual measured value on site. This process ensures that the model can adapt to different water quality changes and timely reflect the actual water environment state of the river network. Compared with the more static parameter settings in the background technology, it has stronger adaptability. This application uses the second type of water quality concentration data to verify the determination of parameters to ensure the rationality and effectiveness of the selected parameters. This verification mechanism enhances the reliability of the model and avoids the problem of inaccurate model caused by the lack of effective verification means in the background technology. This application provides an accurate basis for the parameters of the water environment model by obtaining the pollutant data of the target river network and the concentration data of the water quality monitoring section. Compared with the problem of inaccurate parameter determination in the background technology, the precise data collection and verification process of the present application can effectively improve the accuracy of the model parameters and ensure that the model better reflects the actual water environment conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0035] Figure 1 A flow chart of a method for determining water environment model parameters provided in an embodiment of the present application;

[0036] Figure 2 A schematic diagram of the location of a river network water quality section provided in an embodiment of the present application;

[0037] Figure 3 A schematic diagram of the corresponding relationship between the simulated values ​​and the measured values ​​of the chemical oxygen demand concentrations of the six water quality sections provided in the embodiment of the present application;

[0038] Figure 4 A schematic diagram of the corresponding relationship between the simulated values ​​and the measured values ​​of the ammonia nitrogen concentrations of the six water quality sections provided in the embodiment of the present application;

[0039] Figure 5 A schematic diagram of the corresponding relationship between the simulated values ​​and the measured values ​​of the total phosphorus concentrations of the six water quality sections provided in the embodiment of the present application;

[0040] Figure 6 A schematic diagram of the corresponding relationship between the simulated values ​​and the measured values ​​of the chemical oxygen demand concentrations of the four water quality sections provided in the embodiment of the present application;

[0041] Figure 7 A schematic diagram of the corresponding relationship between the simulated values ​​and the measured values ​​of the ammonia nitrogen concentrations of the four water quality sections provided in the embodiment of the present application;

[0042] Figure 8 A schematic diagram of the corresponding relationship between the simulated values ​​and the measured values ​​of the total phosphorus concentrations of the four water quality sections provided in the embodiment of the present application;

[0043] Fig. 9 A schematic diagram of the structure of a device for determining water environment model parameters provided in an embodiment of the present application. DETAILED DESCRIPTION

[0044] As described above, relevant personnel have made many efforts to improve the ecological environment of lakes. But in order to accurately quantify these efforts, a good urban river network water environment model needs to be constructed. However, building a good urban river network water environment model involves many parameters. It is impractical to actually measure and investigate each parameter on site. A method is needed to determine the best combination of parameter values. The relevant technology is not accurate in setting the parameters of the urban river network water environment model.

[0045] Relevant technologies generally adopt forward design. When forward design cannot accurately obtain parameters, reverse design provides a way of thinking. Through evidence design based on water quality results, a set of model parameter values ​​that can best reflect the river water environment conditions is determined through repeated trials, reducing the difficulty of collecting parameters required to build the model and improving the universality of the model building method.

[0046] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0047] It is understandable that the method provided in the present application can be applied to a processing device, which is a processing device that can obtain pollutant data of a target river network, such as a terminal device or a server that can obtain pollutant data of a target river network. The method provided in the present application can be executed independently by a terminal device or a server, or can be applied to a network scenario in which a terminal device and a server communicate, and is executed by the cooperation of a terminal device and a server. Among them, the terminal device can be a computer, a mobile phone or other device. The server can be understood as an application server or a Web server. In actual deployment, the server can be an independent server or a cluster server.

[0048] Figure 1 A flow chart of a method for determining water environment model parameters provided in this application, the method comprising the following steps:

[0049] S101: Obtain pollutant data of the target river network.

[0050] The processing equipment can obtain pollutant data of the target river network. The pollutant data may include the main controlled pollutants of the target river network and the corresponding concentrations.

[0051] S102: Construct a water environment model of the target river network based on pollutant data, water flow area data of the river network, hydraulic radius data, cross-sectional flow data, water level data and flow distance data.

[0052] The processing equipment can construct a water environment model of the target river network based on pollutant data, water flow area data of the river network, hydraulic radius data, cross-sectional flow data, water level data and flow distance data.

[0053] In a possible implementation, since design drawings are required when building a river network, the connectivity layout of the river network and the location and scale of the gate and pump engineering will be recorded. In the target river network, there will be corresponding water flow and pollutant migration and transformation rules, which can be obtained by measuring the water quality. Based on the connectivity layout of the river network, the location and scale of the gate and pump engineering, the measured water quality data and other information, according to the flow and pollutant migration and transformation rules, the processing equipment can obtain pollutant data, river network water area data, hydraulic radius data, cross-sectional flow data, water level data and flow distance data and other information under the condition of determining reasonable boundary conditions and initial conditions, and then construct the water environment model of the target river network based on pollutant data, river network water area data, hydraulic radius data, cross-sectional flow data, water level data and flow distance data. The water environment model can be used to predict the water flowing into the water treatment plant bypass.

[0054] Specifically, the processing equipment can extract the geometric information corresponding to the river network and generalize the river network structure, size and position through the geometric information. Then, the river section is reasonably generalized based on the measured terrain and water conservancy project data. Then, according to the design parameters, location and scheduling rules of the pump gate, it is generalized into the water environment model, including gate width, number of gate openings and closings, opening height, pump station drainage flow and time. Then, the measured water quality concentration and water level flow values ​​at the initial and boundary of the river network are input into the model as initial conditions and boundary conditions. Finally, the pollutants discharged into the river network in the study area are generalized and input into the model in the form of point sources. The required data include drainage and pollutant emission concentration based on time series. The river roughness, diffusion coefficient and pollutant attenuation coefficient in the water environment calculation model and water quality calculation model are calibrated and verified.

[0055] In a possible implementation, the processing device can construct the Saint-Venant equations of the target river network based on pollutant data, water flow area data of the river network, hydraulic radius data, cross-sectional flow data, water level data and flow distance data, and the Saint-Venant equations include a water flow continuity equation, a water flow momentum equation and a one-dimensional convection equation;

[0056] The water flow continuity equation can be expressed by the following formula:

[0057]

[0058] Among them, X represents the flow distance, t represents the flow time, A represents the water flow area, and Q represents the cross-sectional flow rate;

[0059] The water flow momentum equation can be expressed as follows:

[0060]

[0061] Wherein, X represents the flow distance, t represents the flow time, A represents the water flow area, Q represents the cross-sectional flow rate, α represents the momentum correction coefficient, R represents the hydraulic radius, and n represents the roughness coefficient of the target river network;

[0062] The one-dimensional convection equation can be expressed as follows:

[0063]

[0064] Wherein, A represents the water flow area, C represents the pollutant data in the target river network, t represents the flow time, X represents the flow distance, Q represents the cross-sectional flow, D represents the water flow diffusion coefficient of the target river network, and K represents the attenuation coefficient.

[0065] The treatment equipment can simulate the concentration change of the main controlled pollutants based on the various state variables and interrelationships related to the three main controlled pollutants and the control equations of the water quality state variables. Based on the various state variables and interrelationships related to the three main controlled pollutants and the control equations of the water quality state variables, the treatment equipment can simulate the concentration change of the main controlled pollutants. In a possible implementation, the concentration simulation of the main controlled pollutant data of the river can be performed in the form of a generalized equation. The main controlled pollutant data can be chemical oxygen demand data, ammonia nitrogen data, dissolved oxygen data and total phosphorus data.

[0066] Chemical oxygen demand data can be expressed by the following formula:

[0067]

[0068] Among them, C COD represents the chemical oxygen demand concentration, t represents time, k COD represents the first-order degradation rate of chemical oxygen demand at a preset temperature, C DO represents the concentration of dissolved oxygen, HS_COD represents the half-saturated oxygen concentration corresponding to the chemical oxygen demand, The amount of sediment released to represent the chemical oxygen demand, θ COD represents the Arrhenius temperature coefficient, T represents temperature, and T-20 represents that the preset temperature is 20°C;

[0069] The ammonia nitrogen content data can be expressed by the following formula:

[0070]

[0071] in, represents the concentration of ammonia nitrogen, t represents time, UN P Indicates the total amount of ammonia nitrogen taken up by plants in the target river network, UN B represents the total amount of ammonia nitrogen taken up by bacteria in the target river network, Indicates the first-order decay rate of ammonia nitrogen at a preset temperature, represents the temperature coefficient of the decay rate of ammonia conversion to nitrite, HS_NH4 represents the half-saturated oxygen concentration corresponding to ammonia nitrogen, Indicates the amount of ammonia nitrogen released from the sediment, T indicates the temperature, and T-20 indicates that the preset temperature is 20°C;

[0072] Dissolved oxygen data can be expressed by the following formula:

[0073]

[0074]

[0075] Among them, CDO represents the concentration of dissolved oxygen, t represents time, k2 represents the atmospheric reoxygenation rate, C S represents the saturated dissolved oxygen concentration, phtsyn represents the oxygen production of the plants in the target river network through photosynthesis, and respt represents the oxygen consumption of the plants in the target river network through plant respiration. Indicates the first-order decay rate of ammonia nitrogen at a preset temperature, The temperature coefficient representing the decay rate of ammonia conversion to nitrite, Indicates the concentration of ammonia nitrogen, C DO represents the concentration of dissolved oxygen, HS_NH4 represents the half-saturated oxygen concentration corresponding to ammonia nitrogen, k COD represents the first-order degradation rate of chemical oxygen demand at a preset temperature, θ COD represents the Arrhenius temperature coefficient, C COD represents the chemical oxygen demand concentration, HS_COD represents the semi-saturated oxygen concentration corresponding to the chemical oxygen demand, θ SOD represents the temperature coefficient of SOD, HS_SOD represents the half-saturated oxygen concentration corresponding to SOD, T represents temperature, and T-20 represents that the preset temperature is 20°C;

[0076] Total phosphorus data can be expressed by the following formula:

[0077]

[0078] Among them, C TP represents the concentration of phosphorus, k TP represents the yield of phosphorus released during the release of chemical oxygen demand, k COD represents the first-order degradation rate of chemical oxygen demand at a preset temperature, θ COD represents the Arrhenius temperature coefficient, C COD represents the chemical oxygen demand concentration, C DO represents the concentration of dissolved oxygen, HS_COD represents the half-saturated oxygen concentration corresponding to the chemical oxygen demand, Indicates the release of total phosphorus from sediment, UP P It represents the total amount of ammonia nitrogen taken up by plants in the target river network, and z is the total phosphorus coefficient.

[0079] S103: Acquire the first type of water quality concentration data and the second type of water quality concentration data at the water quality monitoring section.

[0080] The processing equipment obtains the first type of water quality concentration data and the second type of water quality concentration data at the water quality monitoring section. The first type of water quality concentration data is used to determine the parameters of the water environment model, and the second type of water quality concentration data is used to verify the parameters determined based on the first type of water quality concentration data.

[0081] S104: With the goal of minimizing the error between the simulated value of the water quality index and the actual measured value on site, the parameters of the water environment model are continuously adjusted based on the determined water level through the first type of water quality concentration data until the cutoff condition is reached.

[0082] The processing equipment aims to minimize the error between the simulated value of the water quality index and the actual measured value on site, and continuously adjusts the parameters of the water environment model based on the determined water level through the first type of water quality concentration data until the cutoff condition is reached. The cutoff condition can be that the difference between the simulated value of the water quality index and the actual measured value on site is less than a preset value.

[0083] S105: Using the second type of water quality concentration data to verify the parameters to obtain a verification result.

[0084] The processing equipment verifies the parameters using the second type of water quality concentration data to obtain a verification result.

[0085] S106: If the verification result meets the preset conditions, the water environment model parameters are determined.

[0086] The method for determining the parameters of the water environment model proposed in this application builds a water environment model based on a variety of water quality and flow data, and comprehensively considers multiple factors such as water flow area, hydraulic radius, cross-sectional flow, water level and flow distance. Compared with the traditional single parameter dependence in the background technology, this multi-dimensional data input can more comprehensively describe the water environment process and water quality changes of the river network, making the model more scientific and reasonable. By continuously adjusting the parameters of the water environment model, dynamic optimization is carried out with the goal of minimizing the error between the simulated value of the water quality index and the actual measured value on site. This process ensures that the model can adapt to different water quality changes and timely reflect the actual water environment state of the river network. Compared with the more static parameter settings in the background technology, it has stronger adaptability. This application uses the second type of water quality concentration data to verify the determination of parameters to ensure the rationality and effectiveness of the selected parameters. This verification mechanism enhances the reliability of the model and avoids the problem of inaccurate model caused by the lack of effective verification means in the background technology. This application provides an accurate basis for the parameters of the water environment model by obtaining the pollutant data of the target river network and the concentration data of the water quality monitoring section. Compared with the problem of inaccurate parameter determination in the background technology, the precise data collection and verification process of the present application can effectively improve the accuracy of the model parameters and ensure that the model better reflects the actual water environment conditions.

[0087] Next, we will explain and illustrate with actual cases. Taking the urban river network as an example, we will determine the parameters of the urban river network water environment model and verify the model. Figure 2A schematic diagram of the location of a river network water quality section provided in the embodiment of this application. Taking into account the freezing in the north in winter, the simulation period is from March 1, 2020 to November 30, 2020, including the three seasons of spring, summer and autumn. In order to improve the accuracy and reliability of model prediction, the monitoring points for determining the water quality model parameters are selected as widely distributed, evenly distributed and representative river network monitoring points. Figure 2 There are 6 monitoring points, namely T1, T2, T3, T4, T5 and T6. The other 4 monitoring points (Y1, Y2, Y3 and Y4) are selected as verification points to verify the accuracy and reliability of the water quality model. The monitoring point data are determined using the model water quality parameters, and the measured concentrations of CODCr, NH3-N and TP at the water quality model parameter points are determined to calibrate the river network model parameters. The water quality model parameters are determined by the minimum error between the simulated value and the measured value. The measured concentrations of CODCr, NH3-N and TP at the verification model water quality points are used to verify the accuracy and reliability of the model. The model water quality parameters are adjusted according to the flood season and non-flood season. March to May and October to November are the non-flood seasons, and June to September are the flood seasons.

[0088] Figure 3 A schematic diagram of the correspondence between the simulated values ​​and the measured values ​​of the chemical oxygen demand concentrations of the six water quality sections provided in the embodiment of the present application.

[0089] Figure 4 A schematic diagram of the correspondence between the simulated values ​​and the measured values ​​of ammonia nitrogen concentrations in six water quality sections provided in an embodiment of the present application.

[0090] Figure 5 A schematic diagram of the correspondence between the simulated values ​​and the measured values ​​of the total phosphorus concentration in the six water quality sections provided in the embodiment of the present application.

[0091] In order to facilitate data processing, we can Figure 3-Figure 5 The data in generate Table 1, which shows the error between the simulated and measured values ​​of the water quality parameters of the water quality section.

[0092]

[0093] Figure 6 A schematic diagram of the correspondence between the simulated values ​​and the measured values ​​of the chemical oxygen demand concentrations of the four water quality sections provided in the embodiment of the present application.

[0094] Figure 7 A schematic diagram of the correspondence between the simulated values ​​and the measured values ​​of ammonia nitrogen concentrations in four water quality sections provided in an embodiment of the present application.

[0095] Figure 8 A schematic diagram of the correspondence between the simulated values ​​and the measured values ​​of the total phosphorus concentration in the four water quality sections provided in the embodiment of the present application.

[0096] Figure 6-Figure 8The data in is used to validate the parameters.

[0097] The present application also provides a Fig. 9 The structure diagram of the device for determining water environment model parameters shown in FIG. 9 is a schematic diagram of the structure of the device for determining water environment model parameters, wherein the device 900 for determining water environment model parameters comprises the following modules:

[0098] The first acquisition module 901 is used to acquire pollutant data of the target river network;

[0099] A water environment model module 902 is used to construct a water environment model of the target river network based on the pollutant data, the water flow area data of the river network, the hydraulic radius data, the cross-sectional flow data, the water level data and the flow distance data;

[0100] The second acquisition module 903 is used to acquire first-type water quality concentration data and second-type water quality concentration data at the water quality monitoring section, wherein the first-type water quality concentration data is used to determine the parameters of the water environment model, and the second-type water quality concentration data is used to verify the parameters determined based on the first-type water quality concentration data;

[0101] The parameter determination module 904 is used to continuously adjust the parameters of the water environment model based on the determined water level and the first type of water quality concentration data with the goal of minimizing the error between the simulated value of the water quality index and the actual measured value on site until the cutoff condition is reached;

[0102] Verification module 905, used to verify the parameter using the second type of water quality concentration data to obtain a verification result;

[0103] The verification result module 906 is used to complete the determination of the water environment model parameters if the verification result meets the preset conditions.

[0104] In a possible implementation manner, the device further includes an initial value determination module, and the initial value determination module is used to:

[0105] Obtaining geographic data and weather data of the city corresponding to the target river network;

[0106] Determine the water pollutant diffusion coefficient, attenuation coefficient, dry deposition flux, sediment release and river roughness of the water environment model based on the geographic data and the weather data;

[0107] The water pollutant diffusion coefficient, the attenuation coefficient, the dry deposition flux, the sediment release and the river channel roughness are used as initial values ​​corresponding to the water environment model.

[0108] The water environment model parameter determination device proposed in this application builds a water environment model based on a variety of water quality and flow data, and comprehensively considers multiple factors such as water flow area, hydraulic radius, cross-sectional flow, water level and flow distance. Compared with the traditional single parameter dependence in the background technology, this multi-dimensional data input can more comprehensively describe the water environment process and water quality changes of the river network, making the model more scientific and reasonable. By continuously adjusting the parameters of the water environment model, dynamic optimization is carried out with the goal of minimizing the error between the simulated value of the water quality index and the actual measured value on site. This process ensures that the model can adapt to different water quality changes and timely reflect the actual water environment state of the river network. Compared with the more static parameter settings in the background technology, it has stronger adaptability. This application uses the second type of water quality concentration data to verify the determination of parameters to ensure the rationality and effectiveness of the selected parameters. This verification mechanism enhances the reliability of the model and avoids the problem of inaccurate model caused by the lack of effective verification means in the background technology. This application provides an accurate basis for the parameters of the water environment model by obtaining the pollutant data of the target river network and the concentration data of the water quality monitoring section. Compared with the problem of inaccurate parameter determination in the background technology, the precise data collection and verification process of the present application can effectively improve the accuracy of the model parameters and ensure that the model better reflects the actual water environment conditions.

[0109] An embodiment of the present application also provides a device for determining water environment model parameters, wherein the device includes a memory and a processor, the memory is used to store instructions or codes, and the processor is used to execute the instructions or codes so that the device performs the steps of the method for determining water environment model parameters described in any embodiment of the present application.

[0110] In practical applications, the computer-readable storage medium may be any combination of one or more computer-readable media, which may be a computer-readable signal medium or a computer-readable storage medium.

[0111] The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection with one or more wires, 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 thereof. In the present embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in conjunction with an instruction execution system, device, or device.

[0112] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, which carry computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0113] The program code embodied on the computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0114] Computer program code for performing the operations of the present invention may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0115] It should be noted that each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment. The device embodiment described above is merely schematic, in which the unit described as a separate component may or may not be physically separated, and the component prompted as a unit may or may not be a physical unit, that is, it may be located in one place, or it may be distributed on multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative work.

[0116] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed in the present application should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A method for determining water environment model parameters, characterized in that: include: Obtain pollutant data of the target river network; Constructing a water environment model of the target river network based on the pollutant data, the water flow area data, hydraulic radius data, cross-sectional flow data, water level data and flow distance data of the river network; Acquire first-type water quality concentration data and second-type water quality concentration data at a water quality monitoring section, wherein the first-type water quality concentration data is used to determine parameters of the water environment model, and the second-type water quality concentration data is used to verify the parameters determined based on the first-type water quality concentration data; With the goal of minimizing the error between the simulated value of the water quality index and the actual measured value on site, the parameters of the water environment model are continuously adjusted based on the determined water level through the first type of water quality concentration data until the cutoff condition is reached; Using the second type of water quality concentration data to verify the parameter to obtain a verification result; If the verification result meets the preset conditions, the water environment model parameters are determined.

2. The method according to claim 1, characterized in that The method further comprises: Obtaining geographic data and weather data of the city corresponding to the target river network; Determine the water pollutant diffusion coefficient, attenuation coefficient, dry deposition flux, sediment release and river roughness of the water environment model based on the geographic data and the weather data; The water pollutant diffusion coefficient, the attenuation coefficient, the dry deposition flux, the sediment release and the river channel roughness are used as initial values ​​corresponding to the water environment model.

3. A device for determining water environment model parameters, characterized in that: include: The first acquisition module is used to obtain pollutant data of the target river network; A water environment model module, used to construct a water environment model of the target river network based on the pollutant data, the water flow area data of the river network, the hydraulic radius data, the cross-sectional flow data, the water level data and the flow distance data; A second acquisition module is used to acquire first-type water quality concentration data and second-type water quality concentration data at a water quality monitoring section, wherein the first-type water quality concentration data is used to determine the parameters of the water environment model, and the second-type water quality concentration data is used to verify the parameters determined based on the first-type water quality concentration data; A parameter determination module is used to continuously adjust the parameters of the water environment model based on the determined water level and the first type of water quality concentration data with the goal of minimizing the error between the simulated value of the water quality index and the actual measured value on site until the cutoff condition is reached; A verification module, used for verifying the parameter using the second type of water quality concentration data to obtain a verification result; The verification result module is used to determine the water environment model parameters if the verification result meets the preset conditions.

4. The device according to claim 3, characterized in that The device further comprises an initial value determination module, wherein the initial value determination module is used to: Obtaining geographic data and weather data of the city corresponding to the target river network; Determine the water pollutant diffusion coefficient, attenuation coefficient, dry deposition flux, sediment release and river roughness of the water environment model based on the geographic data and the weather data; The water pollutant diffusion coefficient, the attenuation coefficient, the dry deposition flux, the sediment release and the river channel roughness are used as initial values ​​corresponding to the water environment model.

5. An electronic device, characterized in that: comprising a memory and a processor, wherein: The memory is used to store the computer program; The processor is used to execute the computer program to implement the method for determining the water environment model parameters as described in any one of claims 1-2.

6. A computer-readable storage medium, characterized in that: Used to store a computer program, wherein when the computer program is executed by a processor, the method for determining water environment model parameters as described in any one of claims 1-2 is implemented.

Images