Water area monitoring method, device, equipment, medium and product

Abstract

The invention discloses a water area monitoring method, device and equipment, a medium and a product. The method comprises the following steps: acquiring water quality monitoring section data of a to-be-monitored water area; inputting the water quality monitoring section data into a pollutant diffusion path model to obtain diffusion paths of pollutants in grids and pollution source information of the pollutants; and performing water quality prediction according to the water quality monitoring section data and the diffusion path to obtain a water quality prediction result. According to the technical scheme of the invention, a grid traceability regression method can be introduced, the diffusion path of the pollutant in the grid and the pollution source information of the pollutant can be obtained according to the water quality monitoring section data and the dynamic analysis of the river and lake water quality, and then the future change trend of the water quality can be predicted based on the diffusion path. And dynamic analysis and reasonable prediction of the water quality of the river and the lake are realized.

Description

TECHNICAL FIELD

[0001] Embodiments of the present application relate to the technical field of water area monitoring, and in particular to a water area monitoring method, device, equipment, medium and product. BACKGROUND

[0002] At present, the water quality of rivers and lakes is affected by multiple factors, such as geographical conditions, pollutant emission distribution, and ocean current conditions. The existing technology mainly analyzes and evaluates the current water quality based on the test results of sampled water quality, and predicts the water quality based on historical data of sampled water quality. However, the prediction results are often local, and it is difficult to truly and effectively reflect the water quality of large-area river and lake water areas. SUMMARY

[0003] Embodiments of the present application provide a water area monitoring method, device, equipment, medium and product to realize dynamic analysis and reasonable prediction of river and lake water quality.

[0004] According to an aspect of the present application, a water area monitoring method is provided, comprising:

[0005] Obtaining water quality monitoring section data of a water area to be monitored;

[0006] Inputting the water quality monitoring section data into a pollutant diffusion path model to obtain a diffusion path of a pollutant in a grid and pollution source information of the pollutant;

[0007] Performing water quality prediction according to the water quality monitoring section data and the diffusion path to obtain a water quality prediction result.

[0008] In some embodiments of the present application, before inputting the water quality monitoring section data into the pollutant diffusion path model, further comprising:

[0009] Obtaining a water quality pollution diffusion data set, and training a machine learning model based on the water quality pollution diffusion data set to obtain a pollutant diffusion path model; the water quality pollution diffusion data set includes input data parameters and output data parameters.

[0010] In some embodiments of the present application, training a machine learning model based on the water quality pollution diffusion data set to obtain a pollutant diffusion path model, comprising:

[0011] Inputting the input data parameters into the machine learning model to obtain predicted output parameters;

[0012] Training parameters of the machine learning model according to a target function constructed based on the predicted output parameters and the output data parameters;

[0013] Return to perform the operation of inputting the input data parameter into the machine learning model to obtain a predicted output parameter until a pollutant diffusion path model is obtained.

[0014] In some embodiments of the present application, the input data parameter comprises at least one of grid size, pH value, dissolved oxygen, permanganate index, chemical oxygen demand, ammonia nitrogen, total phosphorus, copper, zinc, fluoride, suspended solids and turbidity; and the output data parameter comprises pollutant concentration in each grid and pollutant concentration migration path.

[0015] In some embodiments of the present application, water quality prediction is performed according to the water quality monitoring section data and the diffusion path to obtain a water quality prediction result, which comprises:

[0016] A probability prediction model of pollutants in a grid is constructed according to the water quality monitoring section data and the diffusion path;

[0017] The water quality monitoring section data is input into the probability prediction model to obtain a water quality prediction result.

[0018] In some embodiments of the present application, before the water quality monitoring section data of the water area to be monitored is obtained, the method further comprises:

[0019] A three-dimensional coordinate system is constructed with the center point of the water area to be monitored as the origin;

[0020] The water area to be monitored is divided into a plurality of grids based on a preset size.

[0021] According to another aspect of the present application, a water area monitoring device is provided, which comprises:

[0022] An acquisition module is configured to acquire water quality monitoring section data of a water area to be monitored;

[0023] An input module is configured to input the water quality monitoring section data into a pollutant diffusion path model to obtain a diffusion path of pollutants in a grid and pollution source information of the pollutants;

[0024] A prediction module is configured to perform water quality prediction according to the water quality monitoring section data and the diffusion path to obtain a water quality prediction result.

[0025] According to another aspect of the present application, an electronic device is provided, which comprises:

[0026] At least one processor;

[0027] and a memory in communication connection with the at least one processor;

[0028] The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to execute the water area monitoring method according to any one of the embodiments of the present application.

[0029] According to another aspect of the present application, a computer readable storage medium is provided, which stores computer instructions for enabling a processor to implement the water area monitoring method according to any one of the embodiments of the present application when executed by the processor.

[0030] According to another aspect of the present application, the embodiments of the present application further provide a computer program product, which comprises a computer program, and the computer program implements the water area monitoring method according to any one of the embodiments of the present application when executed by a processor.

[0031] The embodiments of the present application obtain water quality monitoring section data of a water area to be monitored, input the water quality monitoring section data into a pollutant diffusion path model to obtain diffusion paths of the pollutant in a grid and pollution source information of the pollutant, and finally perform water quality prediction according to the water quality monitoring section data and the diffusion paths to obtain a water quality prediction result. Through the technical solution of the present application, the grid-based traceability regression method can be used to obtain diffusion paths of the pollutant in the grid and pollution source information of the pollutant according to the water quality monitoring section data, and then the future change trend of the water quality is predicted based on the diffusion paths, so that dynamic analysis of the water quality of the river and lake and reasonable prediction are realized.

[0032] It should be understood that the content described in this part is not intended to identify key or important features of the embodiments of the present application, nor is it used to limit the scope of the present application. Other features of the present application will become apparent from the following description. BRIEF DESCRIPTION OF DRAWINGS

[0033] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed in the embodiments. It should be understood that the following drawings only show some of the embodiments of the present application, and therefore should not be considered as limiting the scope. For those skilled in the art, other related drawings can also be obtained without creative labor.

[0034] Figure 1 is a flow chart of a water area monitoring method in the embodiments of the present application;

[0035] Figure 2 is a physical model schematic diagram of a water quality monitoring section-water body grid-pollution source in the embodiments of the present application;

[0036] Figure 3is a water quality monitoring section-water body grid-pollution source relationship network diagram in an embodiment of the present application;

[0037] Figure 4 is a structural diagram of a water area monitoring device in an embodiment of the present application;

[0038] Figure 5 is a structural diagram of an electronic device for implementing a water area monitoring method in an embodiment of the present application. DETAILED DESCRIPTION

[0039] In order to enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative efforts should fall within the protection scope of the present application.

[0040] It should be noted that the terms "first", "second", and the like in the specification and claims of the present application and the above-described drawings are used to distinguish similar objects, and do not necessarily indicate a specific order or a chronological sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "include" and "have" and the like are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device that includes a series of steps or units does not necessarily have to include only those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to the process, method, product or device.

[0041] It can be understood that, before using the technical solutions disclosed in the embodiments of the present disclosure, the type, use range, use scenario, etc. of the personal information involved in the present disclosure should be informed to the user and the authorization of the user should be obtained in accordance with relevant laws and regulations.

[0042] Embodiment one

[0043] Figure 1 is a flowchart of a water area monitoring method in an embodiment of the present application. The present embodiment can be applicable to the case of water area monitoring. The method can be executed by a water area monitoring device in an embodiment of the present application. The device can be realized in the form of software and / or hardware, as shown in Figure 1 The method specifically includes the following steps:

[0044] S101, obtaining water quality monitoring section data of a water area to be monitored.

[0045] In this embodiment, the water area to be monitored can be a water area of a river or lake to be monitored for water quality. For example, water quality monitoring can include pollution source tracing and predicting future trends of water quality.

[0046] It can be understood that the water quality monitoring section refers to a specific location or area set in a water body such as a river, lake, or reservoir for water quality monitoring. These sections are usually used for regular water sampling to analyze water quality parameters (such as pH, dissolved oxygen, chemical oxygen demand, ammonia nitrogen, etc.) to assess the pollution status and ecological health of the water body.

[0047] In this embodiment, the water quality monitoring section data may, for example, include pH, dissolved oxygen, permanganate index, chemical oxygen demand, ammonia nitrogen, total phosphorus, copper, zinc, fluoride, suspended solids, and turbidity.

[0048] Specifically, a plurality of water quality monitoring sections can be set in the water area to be monitored, and water quality monitoring section data can be regularly collected for water quality monitoring.

[0049] S102, input the water quality monitoring section data into the pollutant diffusion path model to obtain the diffusion path of the pollutant in the grid and the pollution source information of the pollutant.

[0050] In this embodiment, the pollutant diffusion path model can be a model for tracing the source of pollutants. The model is a relational model and is mainly used to illustrate the correlation between the water quality monitoring section, the water body grid, and the pollution source. In this model, the water quality monitoring section data is input, the water body grid is the analysis core, and the pollution source is the target output. The pollutant diffusion path identifies the correlation between the water quality monitoring section, the water body grid, and the pollution source.

[0051] The grid can be a grid divided according to a certain scale in the river and lake area.

[0052] It should be noted that the diffusion path can be the path of pollutant diffusion in the water area to be monitored.

[0053] In this embodiment, the pollution source information can be the location information of the pollution source in the water area to be monitored.

[0054] Specifically, the water quality monitoring section data is input into the pollutant diffusion path model. When the water quality monitoring section data shows that the pollutant is found in several grids, the migration and diffusion path of the pollutant between the grids can be inferred through the positional relationship between the grids. The overall diffusion path network of the pollutant can be obtained through the start and end point analysis, and the common initial point in the diffusion path network can be found, that is, the most probable source tracing path can be found to find the pollution source.

[0055] S103, water quality prediction is performed according to the water quality monitoring section data and the diffusion path, and water quality prediction results are obtained.

[0056] The water quality prediction results can be the possible diffusion path of the pollutant and the grid possibly invaded by the pollutant, which are predicted on the basis of the diffusion trend between adjacent grids and the diffusion path.

[0057] Specifically, on the basis of the diffusion path, the possible diffusion path of the pollutant in the future can be predicted according to the diffusion trend between adjacent grids, so that the grid possibly invaded by the pollutant in the future is predicted, and prediction data support for early pollutant interception is provided.

[0058] The embodiment of the application obtains water quality monitoring section data of a water area to be monitored, inputs the water quality monitoring section data into a pollutant diffusion path model, obtains a diffusion path of the pollutant in a grid and pollutant source information of the pollutant, and finally performs water quality prediction according to the water quality monitoring section data and the diffusion path to obtain water quality prediction results. Through the technical solution of the application, a gridding traceability regression method can be used to obtain the diffusion path of the pollutant in the grid and the pollutant source information of the pollutant according to the water quality monitoring section data, and then the future change trend of the water quality is predicted based on the diffusion path, so that dynamic analysis and reasonable prediction of the water quality of rivers and lakes are realized.

[0059] Optionally, before the water quality monitoring section data of the water area to be monitored is obtained, the method further includes:

[0060] A three-dimensional coordinate system is constructed with a center point of the water area to be monitored as an origin.

[0061] Specifically, the three-dimensional coordinate system can be constructed with a geographic center point of the water area to be monitored, such as a lake, as an origin.

[0062] The water area to be monitored is divided into a plurality of grids based on a preset size.

[0063] The preset size can be a grid division size set according to the size of the water area to be monitored or the computing resource, and the embodiment is not limited in this regard. For example, the preset size can be 1m*1m*1m.

[0064] Specifically, the three-dimensional grid can be divided according to the size of 1m*1m*1m (the size of the grid can be adjusted according to the size of the lake or the computing resource), and the water area to be monitored, such as a lake, is equivalent to n 1m³ cubes. The water bodies between adjacent river and lake cubes can flow and interact with each other.

[0065] Optionally, before the water quality monitoring section data is input into the pollutant diffusion path model, the method further includes:

[0066] An water quality pollution diffusion dataset is acquired, and a machine learning model is trained based on the water quality pollution diffusion dataset to obtain a pollutant diffusion path model.

[0067] The technical scheme of the embodiment of the present application innovatively quotes a gridding traceability regression method on the basis of traditional macroscopic prediction, microscopic detection and other technical means to perform water quality monitoring. Specifically, a river or lake is divided into a plurality of grids according to a certain specification, and initial water quality data in each grid is detected with a certain time node as an initial node. Assuming that an external pollution source invades, it will inevitably fall into a certain water grid. Due to the fluidity of lake water, pollutants are extremely easy to invade adjacent water grids. By marking an initial pollution point, a pollution path and a pollution termination point, a set of water quality pollution diffusion datasets is obtained, and a plurality of sets of water quality pollution traceability data are obtained through multi-component simulation or experiments, based on which a machine learning model is trained.

[0068] The water quality pollution diffusion dataset includes input data parameters and output data parameters.

[0069] Optionally, the input data parameters include at least one of a grid size, a pH value, dissolved oxygen, a permanganate index, a chemical oxygen demand, ammonia nitrogen, total phosphorus, copper, zinc, fluoride, suspended solids and turbidity; and the output data parameters include a pollutant concentration in each grid and a pollutant concentration migration path.

[0070] In a specific implementation, the training dataset includes input data parameters such as a lake water grid size, a pH value, dissolved oxygen, a permanganate index, a chemical oxygen demand, ammonia nitrogen, total phosphorus, copper, zinc, fluoride, suspended solids and turbidity, and output parameters include a pollutant concentration in a unit grid and a pollutant concentration migration path.

[0071] Specifically, based on historical data, a plurality of key characteristic variables and dependent variables (i.e., target values) affecting water quality and pollutant migration are analyzed and found, a water quality pollution diffusion dataset is constructed, a model is trained using a machine learning method, and then real monitoring data is input to continuously verify and improve the model accuracy, and finally a pollutant diffusion path model is obtained. After the model is trained, it can be used to predict which grids will have pollutants and how they migrate and diffuse, so as to perform pollutant traceability.

[0072] Optionally, the machine learning model is trained based on the water quality pollution diffusion dataset to obtain the pollutant diffusion path model, including:

[0073] The input data parameters are input into the machine learning model to obtain predicted output parameters.

[0074] The predicted output parameters can be output data parameters output by the machine learning model after predicting the input input data parameters.

[0075] The parameters of the machine learning model are trained according to a target function constructed based on the predicted output parameters and the output data parameters.

[0076] The operation of inputting the input data parameters into the machine learning model to obtain the predicted output parameters is returned to be performed until the pollutant diffusion path model is obtained.

[0077] In actual operation, it is assumed that the position of the pollution source is P, the coordinates of P in the coordinate system with the geographical center point of the water area to be monitored such as a lake as the origin can be recorded as P(x, y, z), and P falls in a certain water grid, which is recorded as Uo. The water grids near Uo can be recorded as {U1, U2, U3, …, Ui}.

[0078] When the pollutant diffuses from a certain grid to another grid, it must pass through at least one adjacent grid, that is, there is a pollutant diffusion path between adjacent contaminated grids. It is assumed that the adjacent pollutant grids are Ui and Ui+1, and the diffusion path between the two adjacent grids is recorded as li, that is, the diffusion path can be expressed as , the transformation formula is . Wherein, the arrow mark is a vector path, "diff" means "diffusion", and "*" represents "coupling", that is, the grid is bound to the path. In calculation, the center point coordinates of the grid can be taken as the starting point of the path.

[0079] Due to the influence of environmental factors, the water flow direction is anisotropic, that is, the pollutants in the same grid are prone to simultaneously invading and diffusing into multiple adjacent grids, that is, in a group of contaminated water grids, a grid forms a pollutant diffusion path with multiple adjacent grids. It can be found that when the starting grids of multiple paths are the same grid, the starting grid is the uppermost one in the group of water grids, and thus it can be recursively deduced that when the pollutants in the grid Uo only diffuse to i adjacent grids at t0, the grids affected at t0 can be expressed as . On this basis, when the pollutants further diffuse to outer grids at t1, each grid at t0 diffuses to at least adjacent grids in the direction (respectively, the x, y, and z axis directions), and the grids affected at t1 can be expressed as . In this way, when the pollutants further diffuse to outer grids at tj, the grids affected at tj can be expressed as .

[0080] Optionally, water quality prediction is performed according to the water quality monitoring section data and the diffusion path to obtain a water quality prediction result, including:

[0081] A probability prediction model of the pollutant in the grid is constructed according to the water quality monitoring section data and the diffusion path.

[0082] Specifically, based on the derived logical relationship of the diffusion of pollutants in the water body grid, combined with the data input of the water quality monitoring section, a probability prediction model of the diffusion and tracing of pollutants in the water body grid is constructed.

[0083] Specifically, the diffusion probability Pt of the pollutants at time t can be calculated by the following formula: .

[0084] The water quality monitoring section data is input into the probability prediction model to obtain the water quality prediction result.

[0085] Specifically, the water quality monitoring section data is , the water quality monitoring section data is input into the probability prediction model to obtain the water quality prediction result, .

[0086] The embodiment of the present application constructs a three-dimensional coordinate system with the center point of the water area to be monitored as the origin, divides the water area to be monitored into a plurality of grids based on a preset size, obtains water quality monitoring section data of the water area to be monitored, obtains a water quality pollution diffusion data set, trains a machine learning model based on the water quality pollution diffusion data set, obtains a pollutant diffusion path model, then inputs the water quality monitoring section data into the pollutant diffusion path model to obtain the diffusion path of the pollutants in the grid and the pollution source information of the pollutants, and finally constructs a probability prediction model of the pollutants in the grid according to the water quality monitoring section data and the diffusion path, inputs the water quality monitoring section data into the probability prediction model to obtain the water quality prediction result. Through the technical scheme of the present application, the river and lake are divided into a plurality of grids according to a certain specification, the grid-based tracing regression method is used, the water quality of the river and lake is dynamically analyzed according to the water quality monitoring section data, the diffusion path of the pollutants in the grid and the pollution source information of the pollutants are obtained, and then the future change trend of the water quality is predicted based on the diffusion path, realizing dynamic analysis and reasonable prediction of the water quality of the river and lake.

[0087] As an exemplary description of an embodiment of the present application, Figure 2 is a physical model diagram of a water quality monitoring section-water body grid-pollution source in an embodiment of the present application. As Figure 2 shown, the lake water area is divided into grids, it is assumed that the flow direction of the lake water is from right to left, the pollution source is marked with stars, there are multiple pollutant diffusion paths, the water quality monitoring section is set, and the water quality monitoring section data is collected regularly for water quality monitoring.

[0088] As an exemplary description of an embodiment of the present application, Figure 3 is a relationship network diagram of a water quality monitoring section-water body grid-pollution source in an embodiment of the present application. As Figure 3As shown, multiple water quality monitoring sections (water quality monitoring section 1, water quality monitoring section 2, ..., water quality monitoring section j) are set up. Monitoring data at time T1 (including pH, dissolved oxygen, permanganate index, chemical oxygen demand, ammonia nitrogen, total phosphorus, copper, zinc, fluoride, suspended solids, turbidity, etc.) is collected as input into the pollutant diffusion path model. The grids (grid 1, grid 2, grid 3, ..., grid i) that have been invaded by pollutants at time T1 are identified. Source tracing training is performed based on the monitoring data at time T1 and the grids that have been invaded by pollutants at time T1, resulting in source tracing paths and pollution sources. Specifically, when a pollutant is found in several grids, the positional relationship between the grids can be used to infer the migration and diffusion paths of the pollutant between them. By analyzing the end and starting points of the paths, the overall diffusion path network of the pollutant can be derived. By finding the common starting point in the diffusion path network, the most likely source tracing path is inferred to locate the pollutant. Subsequently, data validation can be performed using real-world pollution sources and the monitoring data at time T1 to continuously verify and improve the model accuracy. On this basis, the diffusion trends between adjacent grids can be used to predict the possible diffusion paths of pollutants at the next moment, thereby predicting the grids that may be invaded by pollutants at the next moment, providing predictive data support for early pollutant interception. In other words, based on the grids that have been invaded by pollutants at time T1 and the predicted paths (which can be inferred from the source path), the grids that may be invaded by pollutants at time T2 (grid i+1, grid i+2, grid i+3, ..., grid i→n) can be inferred.

[0089] The technical solution of the embodiment of the present invention designs a dynamic assessment method for river and lake water quality, and predicts future changes in water quality based on the dynamic assessment results, thereby realizing dynamic analysis and prediction of river and lake water quality.

[0090] Example 2

[0091] Figure 4 This is a schematic diagram of the structure of a water area monitoring device in an embodiment of the present invention. This embodiment is applicable to water area monitoring. The device can be implemented in software and / or hardware. The device can be integrated into any device that provides water area monitoring functions, such as Figure 4 As shown, the water area monitoring device specifically includes: an acquisition module 201, an input module 202 and a prediction module 203.

[0092] The acquisition module 201 is used to obtain water quality monitoring section data of the water area to be monitored;

[0093] An input module 202 is used to input the water quality monitoring section data into a pollutant diffusion path model to obtain the diffusion path of pollutants in the grid and the pollution source information of the pollutants;

[0094] The prediction module 203 is configured to perform water quality prediction according to the water quality monitoring section data and the diffusion path, and obtain a water quality prediction result.

[0095] Optionally, the device further comprises:

[0096] The training module is configured to obtain a water quality pollution diffusion data set, and train a machine learning model based on the water quality pollution diffusion data set to obtain a pollutant diffusion path model; the water quality pollution diffusion data set comprises input data parameters and output data parameters.

[0097] Optionally, the training module is specifically configured to:

[0098] input the input data parameters into the machine learning model to obtain predicted output parameters;

[0099] train parameters of the machine learning model according to a target function constructed based on the predicted output parameters and the output data parameters;

[0100] return to perform the operation of inputting the input data parameters into the machine learning model to obtain predicted output parameters until the pollutant diffusion path model is obtained.

[0101] Optionally, the input data parameters comprise at least one of grid size, pH value, dissolved oxygen, permanganate index, chemical oxygen demand, ammonia nitrogen, total phosphorus, copper, zinc, fluoride, suspended solids and turbidity; and the output data parameters comprise pollutant concentration in each grid and a pollutant concentration migration path.

[0102] Optionally, the prediction module 203 is specifically configured to:

[0103] construct a probability prediction model of pollutants in a grid according to the water quality monitoring section data and the diffusion path;

[0104] input the water quality monitoring section data into the probability prediction model to obtain the water quality prediction result.

[0105] Optionally, the device further comprises:

[0106] The construction module is configured to construct a three-dimensional coordinate system with a center point of the water area to be monitored as an origin;

[0107] The division module is configured to divide the water area to be monitored into a plurality of grids based on a preset size.

[0108] The product can perform the water area monitoring method provided by any embodiment of the application, and has the corresponding functional modules and beneficial effects of the execution method.

[0109] Embodiment three

[0110] Figure 5 A structural diagram of an electronic device 30 that can be used to implement embodiments of the present application is shown. The electronic device is intended to represent various forms of digital computers, such as laptops, desktops, tablets, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device can also represent various forms of mobile devices such as personal digital assistants, cellular telephones, smartphones, wearable devices (e.g., headsets, glasses, watches, etc.), and other similar computing devices. The components shown here, their connections and relationships, and their functions, are meant to be examples only, and are not intended to limit the implementations of the present application described and / or claimed in this document.

[0111] As shown, the electronic device 30 includes at least one processor 31, and memory, such as read-only memory (ROM) 32, random access memory (RAM) 33, etc., communicatively connected to the at least one processor 31, where the memory stores computer programs executable by the at least one processor. The processor 31 can perform various appropriate actions and processes according to the computer programs stored in the read-only memory (ROM) 32 or loaded into the random access memory (RAM) 33 from the storage unit 38. In the RAM 33, various programs and data required for the operation of the electronic device 30 can also be stored. The processor 31, the ROM 32, and the RAM 33 are connected to each other through a bus 34. An input / output (I / O) interface 35 is also connected to the bus 34. Figure 5

[0112] Various components in the electronic device 30 are connected to the I / O interface 35, including an input unit 36, such as a keyboard, a mouse, etc., an output unit 37, such as various types of displays, speakers, etc., a storage unit 38, such as a magnetic disk, an optical disk, etc., and a communication unit 39, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 39 allows the electronic device 30 to exchange information / data with other devices through a computer network, such as the Internet, and / or various telecommunication networks.

[0113] The processor 31 can be various general and / or special purpose processing components with processing and computing capabilities. Some examples of the processor 31 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 31 performs various methods and processes described above, such as the water area monitoring method:

[0114] obtaining water quality monitoring section data of a water area to be monitored;

[0115] ​The water quality monitoring section data is input into a pollutant diffusion path model to obtain a diffusion path of the pollutant in a grid and source information of the pollutant;

[0116] Water quality is predicted according to the water quality monitoring section data and the diffusion path to obtain a water quality prediction result.

[0117] In some embodiments, the water area monitoring method can be implemented as a computer program tangibly embodied in a computer readable storage medium, such as storage unit 38. In some embodiments, portions or all of the computer program can be loaded and / or installed onto electronic device 30 via ROM 32 and / or communication unit 39. When the computer program is loaded onto RAM 33 and executed by processor 31, one or more steps of the water area monitoring method described above can be performed. Alternatively, in other embodiments, processor 31 can be configured to perform the water area monitoring method by any other suitable means, such as by way of firmware.

[0118] Various implementations of the systems and techniques described above can be realized in digital electronic circuitry, integrated circuitry, a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), a system on a chip (SOC), a programmable logic device (PLD), a computer hardware, firmware, software, and / or combinations thereof. These various implementations can include implementation in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which can be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.

[0119] Computer programs used to implement the methods of the present application can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the computer program, when executed, implements the functions / acts specified in the flowcharts and / or block diagrams. The computer program can be executed entirely on a machine, partially on a machine, partially on a machine and partially on a remote machine or entirely on a remote machine or server.

[0120] In the context of the present application, a computer-readable storage medium can be a tangible medium that can contain or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. A computer-readable storage medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium can be a machine-readable signal medium. More specific examples of a machine-readable storage medium will include one or more lines of a program of instructions in a transitory signal, a portable computer diskette, 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 disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0121] To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the electronic device. Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form, including acoustic, speech, or tactile input.

[0122] The systems and techniques described here can be implemented in a computing system that includes a back end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front end component (e.g., a user computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back end, middleware, or front end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0123] The computing system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. Servers can be cloud servers, also known as cloud computing servers or cloud hosts, which are a host product in the cloud computing service system to solve the defects of great management difficulty and weak business scalability in traditional physical hosts and VPS services.

[0124] In an embodiment, the present embodiment further includes a computer program product comprising a computer program which, when executed by a processor, implements the water area monitoring method of any of the embodiments of the present application.

[0125] In implementing the computer program product, computer program code for carrying out operations of the present application can be written in one or more programming languages or combinations of languages including object oriented programming languages such as Java, Smalltalk, C++ and conventional procedural programming languages such as "C" or the like. The program code can execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer can 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 the connection can be made to an external computer (for example, through the Internet using an Internet Service Provider).

[0126] It should be understood that the various forms of flow shown above can be re-ordered, added to, or deleted from without departing from the scope of the present application. For example, the steps recited in the present application can be performed in parallel, in series, or in a different order, without departing from the desired results of the technical solutions of the present application, and this is not limited herein.

[0127] The above detailed description does not constitute a limitation on the scope of protection of the present application. Those skilled in the art should understand that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent replacements and improvements made within the spirit and principles of the present application shall be included in the scope of protection of the present application.

Claims

1. A water area monitoring method, characterized in that: include: Obtain water quality monitoring section data of the water area to be monitored; Inputting the water quality monitoring section data into the pollutant diffusion path model to obtain the diffusion path of the pollutants in the grid and the pollution source information of the pollutants; Water quality prediction is performed based on the water quality monitoring section data and the diffusion path to obtain a water quality prediction result.

2. The method according to claim 1, characterized in that Before inputting the water quality monitoring section data into the pollutant diffusion path model, the method further includes: A water pollution diffusion dataset is obtained, and a machine learning model is trained based on the water pollution diffusion dataset to obtain a pollutant diffusion path model; the water pollution diffusion dataset includes input data parameters and output data parameters.

3. The method according to claim 2, characterized in that The machine learning model is trained based on the water pollution diffusion dataset to obtain a pollutant diffusion path model, including: Inputting the input data parameters into the machine learning model to obtain predicted output parameters; Training the parameters of the machine learning model according to the objective function constructed based on the predicted output parameters and the output data parameters; Return to executing the operation of inputting the input data parameters into the machine learning model to obtain the predicted output parameters until the pollutant diffusion path model is obtained.

4. The method according to claim 2, characterized in that The input data parameters include: at least one of: grid size, pH value, dissolved oxygen, permanganate index, chemical oxygen demand, ammonia nitrogen, total phosphorus, copper, zinc, fluoride, suspended solids and turbidity; the output data parameters include pollutant concentration and pollutant concentration migration path within each grid.

5. The method according to claim 1, wherein Water quality prediction is performed based on the water quality monitoring section data and the diffusion path to obtain a water quality prediction result, including: Constructing a probability prediction model of pollutants in a grid based on the water quality monitoring section data and the diffusion path; The water quality monitoring section data is input into the probability prediction model to obtain a water quality prediction result.

6. The method according to claim 1, characterized in that Before obtaining the water quality monitoring section data of the water area to be monitored, it also includes: Construct a three-dimensional coordinate system with the center point of the water area to be monitored as the origin; The water area to be monitored is divided into a plurality of grids based on a preset size.

7. A water area monitoring device, characterized in that: include: An acquisition module is used to obtain water quality monitoring section data of the water area to be monitored; An input module, used to input the water quality monitoring section data into a pollutant diffusion path model to obtain the diffusion path of pollutants in the grid and the pollution source information of the pollutants; The prediction module is used to perform water quality prediction based on the water quality monitoring section data and the diffusion path to obtain a water quality prediction result.

8. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively coupled to the at least one processor; The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the water area monitoring method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the water area monitoring method according to any one of claims 1 to 6 when executed.

10. A computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the computer program implements the water area monitoring method according to any one of claims 1 to 6.

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