An emergency treatment analysis method and system for snowmelt surface source pollution

Abstract

The present invention discloses a method and system for emergency treatment and analysis of snowmelt non-point source pollution, relating to the field of data processing technology. The method comprises: collecting basic regional information of a target area; dividing the area based on the basic regional information; parsing the basic regional information to generate land use data, soil type data, soil property data, and historical meteorological data; using a SWAT model to obtain non-point source pollution simulation results; collecting snow cover data of the target area; and predicting snowmelt floods based on the snow cover data and the basic regional information using a WRF-driven two-layer grid overlay model to obtain prediction results; and conducting emergency management based on the prediction results and the non-point source pollution simulation results. The present invention solves the technical problems of low intelligence and low management accuracy in the prior art for emergency decision-making regarding snowmelt non-point source pollution, thereby achieving the technical effect of improving the efficiency and management quality of emergency treatment of snowmelt non-point source pollution.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to an emergency treatment analysis method and system for snowmelt surface source pollution. Background Art

[0002] With the rapid development of the economy, productivity has been greatly improved, and the pressure on the ecological environment has also increased dramatically. As water resources are essential natural resources for people's production and life, the protection of water resources has received close attention.

[0003] Snowmelt water is an extremely important water resource, especially the seasonal snow cover that can be preserved for several months. As a huge reservoir, it can have a delayed effect on precipitation and is very important for people's production and life. At present, with the gradual intensification of non-point source pollution, the traditional treatment of snowmelt non-point source pollution can no longer meet the needs. The traditional method is mainly to analyze pollution indicators by staff, and after discussion, form a reliable plan to deal with the pollution. However, the traditional treatment method has a long processing cycle and poor feedback timeliness, which cannot meet the needs of emergency treatment. The existing technology has the technical problems of low intelligence level and low management accuracy in emergency decision-making for snowmelt non-point source pollution. Summary of the Invention

[0004] The present application provides an emergency treatment analysis method and system for snowmelt surface source pollution, which is used to solve the technical problems of low intelligence level and low management accuracy in the existing technology for emergency decision-making on snowmelt surface source pollution.

[0005] In view of the above problems, the present application provides an emergency treatment analysis method and system for snowmelt surface source pollution.

[0006] The first aspect of the present application provides an emergency treatment analysis method for snowmelt non-point source pollution, the method comprising:

[0007] Collecting basic regional information of the target area, wherein the basic regional information includes historical usage information of each location in the area;

[0008] Performing regional division according to the regional basic information to generate a plurality of regional division results, wherein the plurality of regional division results have regional type identifiers;

[0009] Analyze the basic information of the region to generate land use data, soil type data, soil property data and historical meteorological data;

[0010] Performing non-point source pollution simulation based on the land use data, the soil type data, the soil property data, and the historical meteorological data using a SWAT model to obtain a non-point source pollution simulation result;

[0011] Collect snow data of the target area, and perform snowmelt flood prediction based on the snow data and the basic information of the area through a WRF-driven double-layer grid overlay model to obtain a prediction result;

[0012] Emergency management is carried out based on the prediction results and the non-point source pollution simulation results.

[0013] The second aspect of the present application provides an emergency treatment and analysis system for snowmelt non-point source pollution, the system comprising:

[0014] A basic information collection module, wherein the basic information collection module is used to collect regional basic information of the target area, wherein the regional basic information includes historical usage information of each location in the area;

[0015] A division result generating module, the division result generating module is used to perform regional division according to the regional basic information, and generate a plurality of regional division results, wherein the plurality of regional division results have regional type identifications;

[0016] An information analysis module, configured to analyze the regional basic information to generate land use data, soil type data, soil property data, and historical meteorological data;

[0017] A simulation result acquisition module, wherein the simulation result acquisition module is used to perform non-point source pollution simulation based on the land use data, the soil type data, the soil property data and the historical meteorological data through a SWAT model to obtain a non-point source pollution simulation result;

[0018] A prediction result acquisition module is used to collect snow data of the target area, and perform snowmelt flood prediction based on the snow data and the basic information of the area through a WRF-driven double-layer grid overlay model to obtain a prediction result;

[0019] An emergency management module is used to perform emergency management based on the prediction results and the non-point source pollution simulation results.

[0020] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0021] This application collects regional basic information of the target area, wherein the regional basic information includes the historical usage information of each location in the area, and then divides the area according to the regional basic information to generate multiple regional division results, and the multiple regional division results have regional type identifications. By parsing the regional basic information, land use data, soil type data, soil property data and historical meteorological data are generated. Then, based on the land use data, soil type data, soil property data and historical meteorological data, non-point source pollution simulation is performed using the SWAT model to obtain non-point source pollution simulation results; snow accumulation data of the target area is collected, and based on the snow accumulation data and regional basic information, snowmelt flood prediction is performed using the WRF-driven double-layer grid overlay model to obtain prediction results. Emergency management is then carried out based on the prediction results and the non-point source pollution simulation results. The technical effect of improving the accuracy of emergency management and enhancing the level of intelligent management is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0023] Figure 1 A schematic flow chart of an emergency treatment analysis method for snowmelt non-point source pollution provided in an embodiment of the present application;

[0024] Figure 2 A schematic diagram of a flow chart for outputting prediction results in an emergency treatment analysis method for snowmelt non-point source pollution provided in an embodiment of the present application;

[0025] Figure 3 A schematic diagram of a flow chart of emergency management of a target area in an emergency treatment analysis method for snowmelt non-point source pollution provided in an embodiment of the present application;

[0026] Figure 4 A schematic structural diagram of an emergency treatment and analysis system for snowmelt surface source pollution provided in an embodiment of the present application.

[0027] Explanation of the accompanying symbols: basic information collection module 11, division result generation module 12, information analysis module 13, simulation result acquisition module 14, prediction result acquisition module 15, emergency management module 16. Implementation Method

[0028] This application provides an emergency treatment analysis method and system for snowmelt surface source pollution, which is used to solve the technical problems of low intelligence level and low management accuracy in the existing technology for emergency decision-making on snowmelt surface source pollution.

[0029] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0030] It should be noted that the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or modules that are not clearly listed or are inherent to these processes, methods, products or devices. Example

[0031] like Figure 1 As shown, the present application provides an emergency treatment analysis method for snowmelt surface source pollution, characterized in that the method includes:

[0032] Step S100: collecting basic regional information of the target area, wherein the basic regional information includes historical usage information of each location in the area;

[0033] Specifically, the target area is any area that needs to be treated for snowmelt non-point source pollution. Non-point source pollution refers to the pollution caused by pollutants from non-specific locations, which are washed away by snowmelt and flow into the water body through the runoff process, resulting in eutrophication of the water body. The basic regional information is relevant information that can reflect the basic situation of the target area, including the area of the area, the geographical location of the area, regional planning information, information about enterprises in the area, etc. The historical usage information of each location in the area is information that describes the use of each location in the target area during the historical time period, including information such as the historical use object, purpose of use, and duration of use. Among them, the historical time period is set by the staff and is not restricted here.

[0034] Step S200: performing regional division according to the regional basic information to generate a plurality of regional division results, wherein the plurality of regional division results have regional type identifiers;

[0035] Specifically, the target area is divided into regions based on the information in the regional basic information, and multiple regional division results are generated based on the division results. Preferably, data is extracted from the regional basic information using historical use information as an index to obtain the historical use of each location in the target area, thereby dividing the target area into multiple regional division results based on different uses. Each regional division result is then identified based on the division basis to obtain a type identification for the corresponding area. Different uses correspond to different type identifications. Optionally, the type identifications include forest land, urban land, cultivated land, water area, and wasteland.

[0036] Step S300: parsing the regional basic information to generate land use data, soil type data, soil property data and historical meteorological data;

[0037] Specifically, data is extracted from regional basic information from different dimensions, and the data contained in the information is fully utilized to obtain the land use data, soil type data, soil property data and historical meteorological data. Among them, the land use data is data that describes the land use conditions corresponding to the multiple division results of the target area. The soil type data is data that describes the soil type of the area corresponding to the multiple division results. Optionally, the soil types include purple clay and gray-brown soil. The soil property data is data that describes the characteristics of the soil corresponding to the multiple division results, including nitrogen content, phosphorus content, etc. The historical meteorological data is data that describes the meteorological conditions corresponding to the time period far from the current time in the target area, including weather type, temperature data, humidity data, etc. Preferably, the historical meteorological data is the meteorological data obtained in a time period far from the current time, that is, the meteorological changes in the target area can be fully reflected to avoid accidental meteorological conditions. It can be meteorological data within one year or several years.

[0038] Step S400: performing non-point source pollution simulation based on the land use data, the soil type data, the soil property data and the historical meteorological data using a SWAT model to obtain a non-point source pollution simulation result;

[0039] Specifically, the SWAT model is a model that can simulate a watershed and establish the relationship between runoff, land use, soil type and pollutants. By inputting land use data, soil type data, soil property data and historical meteorological data into the SWAT model, the model's operational analysis and non-point source pollution simulation are performed to obtain the non-point source pollution simulation results. Among them, the non-point source pollution simulation results are obtained after temporal and spatial prediction simulations of the non-point source pollution caused by land use corresponding to multiple division results in the target area, including non-point source pollution time and non-point source pollution area. The non-point source pollution time is the time corresponding to the time when pollution will occur after the non-point source pollution simulation is performed on the target area. The non-point source pollution area is the area in the target area that will be polluted after the SWAT simulation. By using the SWAT model, the efficiency and accuracy of the non-point source pollution simulation of the target area can be improved.

[0040] Step S500: collecting snow data of the target area, and performing snowmelt flood prediction based on the snow data and the basic information of the area by using a WRF-driven double-layer grid overlay model to obtain a prediction result;

[0041] Further, such as Figure 2 As shown, step S500 in this embodiment of the application further includes:

[0042] Step S510: extracting regional environmental information from the regional basic information, wherein the regional environmental information includes regional terrain information;

[0043] Step S520: collecting and obtaining regional temperature data and sunlight exposure data of the target area;

[0044] Step S530: inputting the snow accumulation data, the regional environmental information, the regional temperature data and the sunlight exposure data into the WRF-driven double-layer grid overlay model;

[0045] Step S540: output the prediction result.

[0046] Furthermore, step S500 in the embodiment of the present application further includes:

[0047] Step S550: performing meteorological monitoring on the target area to obtain meteorological monitoring data;

[0048] Step S560: performing sampling image acquisition on each of the plurality of region division results to generate an image acquisition set;

[0049] Step S570: fitting the snow state of the target area according to the meteorological monitoring data and the image acquisition set to generate the snow data.

[0050] Specifically, the snow cover data is information describing regional snow cover conditions obtained by analyzing meteorological monitoring data for a target area within a period close to the current time, including data such as snow accumulation, snowmelt equivalent, and snow temperature. Based on the snow cover data and the regional basic information, a WRF-driven two-layer grid overlay model is used to predict the occurrence of snowmelt floods, thereby obtaining a prediction result. The prediction result is the result of predicting the occurrence of snowmelt floods.

[0051] Specifically, the WRF-driven double-layer grid overlay model is a functional model for intelligently simulating the snowmelt process. It uses the regional basic information as the initial boundary condition and combines the snow accumulation data to drive the WRF-driven double-layer grid overlay model to perform numerical simulation.

[0052] Specifically, the meteorological monitoring data is obtained by conducting recent meteorological monitoring of the target area, that is, monitoring meteorological changes in the target area within a period of time close to the current time. The meteorological monitoring data is obtained by using automatic observation instruments, such as small meteorological instruments, spectrometers, snow forks, steel rulers, snow scales, etc., to monitor the meteorological conditions in the area, and includes meteorological information, snow accumulation spectra, snow density, and other information.

[0053] Specifically, sampling images are acquired based on the plurality of region division results, that is, randomly sampling the plurality of region division results, and using an image acquisition device to acquire images based on the sampled region division results, thereby obtaining the image acquisition set. The image acquisition set reflects the actual image conditions corresponding to the sampled regions.

[0054] Specifically, meteorological monitoring data corresponding to a sampling area is extracted based on the meteorological monitoring data, and snow cover state data is extracted to obtain information such as snow spectrum and snow density in the sampling area. Then, a comparison is made with the snow spectrum and snow density information in the image collection set to obtain a difference value. The difference values of multiple sampling areas are averaged to obtain a monitoring difference. Data correction is performed based on the monitoring difference and combined with the meteorological monitoring data to fit the snow cover state of the target area and generate the snow cover data.

[0055] Specifically, the regional environment information is obtained by indexing the regional basic information using the regional environment. The regional environment information includes regional terrain information. The regional terrain information is obtained by collecting the slope and slope direction of the target area using a terrain analysis tool, reflecting the terrain changes in the target area.

[0056] Specifically, a temperature measuring instrument is used to collect the temperature in the target area to obtain the regional temperature data. The regional temperature data reflects the temperature changes in the target area during the monitoring period. The solar radiation flux due to water vapor absorption, cloud reflection, clean air scattering, and absorption index is accumulated as the sunlight exposure data. The snow accumulation data, the regional environmental information, the regional temperature data, and the sunlight exposure data are input into the WRF-driven two-layer grid overlay model. After model calculation, the possible degree of flooding caused by snowmelt in the target area and the trajectory of the flood flow are predicted, i.e., the prediction result.

[0057] Step S600: performing emergency management according to the prediction results and the non-point source pollution simulation results.

[0058] Further, such as Figure 3 As shown, step S600 in this embodiment of the application further includes:

[0059] Step S610: generating region levels of the plurality of region division results according to the type identifier;

[0060] Step S620: constructing a two-level early warning model according to the regional level;

[0061] Step S630: inputting the prediction result and the non-point source pollution simulation result into the dual-level early warning model, and outputting a dual-level early warning result, wherein the dual-level early warning result includes a pollution early warning result and a meltwater early warning result;

[0062] Step S640: Perform emergency management of the target area based on the dual-level warning results.

[0063] Specifically, snowmelt floods and non-point source pollution are analyzed based on the prediction results and non-point source pollution simulation results, respectively, so that emergency management of emergencies in the target area can be carried out based on the analysis results. The regional levels of the multiple regional division results are obtained based on the type identification. Preferably, the corresponding levels are matched from the regional level library based on the type identification. For example, if the type identification is urban land, the corresponding level is level one; if the type identification is wasteland, the corresponding level is level six. Among them, level one is the highest level. The regional level library is set by the staff and is not limited here.

[0064] By setting corresponding dual-level warning standards according to the regional grade library and constructing the dual-level warning model based on the dual-level warning standards, the dual-level warning model is an intelligent model for dual-level warning analysis of snowmelt floods and non-point source pollution in the target area. The model is based on a BP neural network framework, uses the prediction results and the non-point source pollution simulation results as input data, and uses pollution warning results and meltwater warning results as output data.

[0065] Specifically, the dual-level early warning model is obtained by obtaining multiple sample pollution warning results, multiple meltwater warning results, multiple sample prediction results, and multiple sample non-point source pollution simulation results from the target area as a training dataset. The training dataset is used to conduct supervised training on the dual-level early warning model until the training reaches convergence. The meltwater warning results are obtained by analyzing the prediction results and issuing a warning if the amount of meltwater after snowmelt will cause flooding. The pollution warning results are obtained by issuing a warning if non-point source pollution will affect the target area.

[0066] Furthermore, step S600 in the embodiment of the present application further includes:

[0067] Step S650: performing pollution assessment based on the pollution warning result in the dual-level warning result;

[0068] Step S660: If the pollution warning result cannot meet the preset pollution threshold, an emergency treatment plan is generated according to the melt water warning result and the regional level.

[0069] Furthermore, step S600 in the embodiment of the present application further includes:

[0070] Step S670: Recording emergency management results, wherein the emergency management results include emergency management plans and actual emergency status;

[0071] Step S680: Counting multiple emergency management results and generating prediction feedback information based on the statistical results;

[0072] Step S690: Optimizing the SWAT model and the WRF-driven double-layer grid overlay model according to the prediction feedback information.

[0073] Specifically, a pollution evaluation is performed based on the pollution warning results in the dual-level warning results, that is, the pollution level and scope are evaluated, and the evaluation results are weighted according to a preset weight ratio to obtain a pollution evaluation value. The pollution evaluation value is compared with the preset pollution threshold. If it does not meet the threshold, it indicates that although the non-point source pollution has caused an impact, the impact is relatively minor and no treatment is required. In this case, the emergency treatment plan can be generated based on the Rongshui warning results and the regional level. The emergency treatment plan is a specific measure for early warning treatment of the target area.

[0074] Specifically, the emergency management results are recorded, wherein the emergency management results include the emergency treatment plan and the actual emergency status. The actual emergency status describes the actual pollution level and the actual amount of snowmelt water in the target area. By summarizing multiple emergency treatment situations, the multiple emergency management results are obtained, and based on this, predictive feedback information is obtained. The preset feedback information is the degree of difference between the predicted emergency management situation and the actual situation. The preset feedback information is used as the target to be eliminated in the model optimization, and the SWAT model and the WRF-driven two-layer grid overlay model are optimized.

[0075] Furthermore, step S690 in this embodiment of the present application further includes:

[0076] Step S691: Setting the matching tolerance value for emergency plan matching;

[0077] Step S692: matching a control solution library with a solution based on the prediction result, the nonpoint source pollution simulation result, and the matching tolerance value;

[0078] Step S693: Perform emergency management of the target area based on the solution matching results.

[0079] Specifically, the matching tolerance value of the emergency plan matching is the allowable fitness deviation between the plan and the situation that needs to be governed during the matching process of the emergency plan. It is set by the staff and is not restricted here. After obtaining the matching tolerance value, the predicted results and non-point source pollution results are used as index basis, and the index range is limited by the matching tolerance value in the governance plan library, and the corresponding plan matching result is obtained through plan matching. Among them, the plan matching result is a plan for corresponding governance of the predicted situation of snowmelt floods and the situation of non-point source pollution. By conducting emergency management of the target area according to the governance plan in the plan matching result, the technical effect of improving emergency management of the early warning situation of the target area and improving management efficiency is achieved.

[0080] In summary, the embodiments of the present application have at least the following technical effects:

[0081] This application collects regional basic information of the target area, and at the same time collects the usage of each location in the historical time period, and then extracts the historical usage information in the regional basic information to divide the area, thereby obtaining multiple regional division results of different types. Dividing the area by usage can provide a differentiated analysis of the impact of subsequent snowmelt non-point source pollution, achieving the goal of improving the accuracy of pollution treatment, and then analyzing the regional basic information from multiple different dimensions to generate land use data, soil type data, soil property data and historical meteorological data. The SWAT model is then used to simulate non-point source pollution. By collecting snow accumulation data in the target area and combining the regional basic information with the WRF-driven double-layer grid overlay model, snowmelt flood prediction is performed, and emergency management is carried out based on the prediction results and the non-point source pollution simulation results. The technical effect of improving the efficiency and accuracy of emergency management is achieved. Example

[0082] Based on the same inventive concept as the emergency treatment analysis method for snowmelt surface source pollution in the above embodiment, Figure 4 As shown, the present application provides an emergency treatment and analysis system for snowmelt surface source pollution. The system and method embodiments in the present application are based on the same inventive concept. The system includes:

[0083] A basic information collection module 11 is used to collect basic regional information of a target area, wherein the basic regional information includes historical usage information of each location in the area;

[0084] A division result generating module 12 is configured to perform regional division according to the regional basic information and generate a plurality of regional division results, wherein the plurality of regional division results have regional type identifiers;

[0085] An information analysis module 13 is used to analyze the regional basic information to generate land use data, soil type data, soil property data and historical meteorological data;

[0086] A simulation result obtaining module 14 is configured to perform non-point source pollution simulation based on the land use data, the soil type data, the soil property data, and the historical meteorological data using a SWAT model to obtain a non-point source pollution simulation result;

[0087] A prediction result acquisition module 15 is used to collect snow data of the target area, and perform snowmelt flood prediction based on the snow data and the basic information of the area through a WRF-driven double-layer grid overlay model to obtain a prediction result;

[0088] The emergency management module 16 is used to perform emergency management according to the prediction results and the non-point source pollution simulation results.

[0089] Furthermore, the system further comprises:

[0090] A regional environmental information extraction unit, configured to extract regional environmental information from the regional basic information, wherein the regional environmental information includes regional terrain information;

[0091] a temperature data acquisition unit, the temperature data acquisition unit being used to acquire regional temperature data and sunlight exposure data of the target area;

[0092] An overlay model input unit, the overlay model input unit being used to input the snow accumulation data, the regional environmental information, the regional temperature data and the sunlight exposure data into the WRF-driven double-layer grid overlay model;

[0093] The prediction result output unit is used to output the prediction result.

[0094] Furthermore, the system further comprises:

[0095] A meteorological monitoring unit, configured to perform meteorological monitoring on the target area and obtain meteorological monitoring data;

[0096] an image acquisition set generation unit, configured to perform sampling image acquisition on each of the plurality of region division results to generate an image acquisition set;

[0097] A snow data generation unit is used to perform snow state fitting of the target area based on the meteorological monitoring data and the image acquisition set to generate the snow data.

[0098] Furthermore, the system further comprises:

[0099] a region level generating unit, configured to generate region levels for the plurality of region division results according to the type identifier;

[0100] A dual-level early warning model construction unit, the dual-level early warning model construction unit is used to construct a dual-level early warning model according to the regional level;

[0101] A dual-level warning result output unit, the dual-level warning result output unit is used to input the prediction result and the non-point source pollution simulation result into the dual-level warning model, and output a dual-level warning result, wherein the dual-level warning result includes a pollution warning result and a meltwater warning result;

[0102] A regional management unit is used to perform emergency management of the target area through the dual-level warning results.

[0103] Furthermore, the system further comprises:

[0104] a pollution evaluation unit, configured to perform pollution evaluation based on the pollution warning result in the dual-level warning result;

[0105] An emergency treatment plan generating unit is used to generate an emergency treatment plan according to the melt water warning result and the regional level if the pollution warning result fails to meet the preset pollution threshold.

[0106] Furthermore, the system further comprises:

[0107] An emergency management result recording unit, the emergency management result recording unit is used to record emergency management results, wherein the emergency management results include emergency governance plans and actual emergency status;

[0108] A feedback information generating unit, the feedback information generating unit being used to collect statistics of a plurality of emergency management results and generate prediction feedback information according to the statistical results;

[0109] A model optimization unit is used to optimize the SWAT model and the WRF-driven double-layer grid superposition model according to the prediction feedback information.

[0110] Furthermore, the system further comprises:

[0111] a tolerance value matching unit, the tolerance value matching unit being used to set a matching tolerance value for emergency plan matching;

[0112] A solution matching unit, configured to match a control solution library according to the prediction result, the non-point source pollution simulation result, and the matching tolerance value;

[0113] A target area emergency management unit is used to perform emergency management of the target area based on the solution matching results.

[0114] It should be noted that the above-mentioned order of the embodiments of the present application is for descriptive purposes only and does not represent the superiority or inferiority of the embodiments. The above description is of specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps described in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0115] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should be included in the scope of protection of the present application.

[0116] This specification and drawings are merely illustrative of the present application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Obviously, those skilled in the art may make various modifications and variations to this application without departing from the scope of this application. Thus, this application is intended to include such modifications and variations as fall within the scope of this application and its equivalents.

Claims

1. An emergency treatment analysis method for snowmelt non-point source pollution, characterized in that: The method comprises: Collecting basic regional information of the target area, wherein the basic regional information includes historical usage information of each location in the area; Performing regional division according to the regional basic information to generate a plurality of regional division results, wherein the plurality of regional division results have regional type identifiers; Analyze the basic information of the region to generate land use data, soil type data, soil property data and historical meteorological data; Performing non-point source pollution simulation based on the land use data, the soil type data, the soil property data, and the historical meteorological data using a SWAT model to obtain a non-point source pollution simulation result; Collect snow data of the target area, and perform snowmelt flood prediction based on the snow data and the basic information of the area through a WRF-driven double-layer grid overlay model to obtain a prediction result; Conduct emergency management based on the prediction results and the non-point source pollution simulation results; The method further comprises: Extracting regional environmental information from the regional basic information, wherein the regional environmental information includes regional terrain information; Collecting and obtaining regional temperature data and sunlight exposure data of the target area; Inputting the snow accumulation data, the regional environmental information, the regional temperature data and the sunlight exposure data into the WRF driven double-layer grid overlay model; outputting the prediction result; The method further comprises: Performing meteorological monitoring on the target area to obtain meteorological monitoring data; Performing sampling image acquisition on the plurality of region division results respectively to generate an image acquisition set; Fitting the snow cover state of the target area according to the meteorological monitoring data and the image acquisition set to generate the snow cover data; The method further comprises: generating regional levels of the plurality of regional division results according to the type identifier; Constructing a two-level early warning model based on the regional levels; Inputting the prediction result and the non-point source pollution simulation result into the dual-level early warning model, and outputting a dual-level early warning result, wherein the dual-level early warning result includes a pollution early warning result and a meltwater early warning result; Emergency management of the target area is carried out through the dual-level warning results.

2. The method according to claim 1, wherein The method further comprises: Performing pollution assessment based on the pollution warning result in the dual-level warning result; If the pollution warning result cannot meet the preset pollution threshold, an emergency treatment plan is generated according to the melt water warning result and the regional level.

3. The method according to claim 1, wherein The method further comprises: Record emergency management results, including emergency management plans and actual emergency status; Count multiple emergency management results and generate forecast feedback information based on the statistical results; The SWAT model and the WRF-driven double-layer grid superposition model are optimized according to the prediction feedback information.

4. The method according to claim 1, wherein The method further comprises: Set the matching tolerance value for emergency plan matching; Matching the treatment solution library according to the prediction result, the non-point source pollution simulation result and the matching tolerance value; Emergency management of the target area is carried out based on the solution matching results.

5. An emergency treatment and analysis system for snowmelt non-point source pollution, characterized in that: The system comprises: A basic information collection module, wherein the basic information collection module is used to collect regional basic information of the target area, wherein the regional basic information includes historical usage information of each location in the area; A division result generating module, the division result generating module is used to perform regional division according to the regional basic information, and generate a plurality of regional division results, wherein the plurality of regional division results have regional type identifications; An information analysis module, configured to analyze the regional basic information to generate land use data, soil type data, soil property data, and historical meteorological data; A simulation result acquisition module, wherein the simulation result acquisition module is used to perform non-point source pollution simulation based on the land use data, the soil type data, the soil property data and the historical meteorological data through a SWAT model to obtain a non-point source pollution simulation result; A prediction result acquisition module is used to collect snow data of the target area, and perform snowmelt flood prediction based on the snow data and the basic information of the area through a WRF-driven double-layer grid overlay model to obtain a prediction result; An emergency management module, configured to perform emergency management based on the prediction results and the non-point source pollution simulation results; A regional environmental information extraction unit, configured to extract regional environmental information from the regional basic information, wherein the regional environmental information includes regional terrain information; a temperature data acquisition unit, the temperature data acquisition unit being used to acquire regional temperature data and sunlight exposure data of the target area; An overlay model input unit, the overlay model input unit being used to input the snow accumulation data, the regional environmental information, the regional temperature data and the sunlight exposure data into the WRF-driven double-layer grid overlay model; A prediction result output unit, configured to output the prediction result; A meteorological monitoring unit, configured to perform meteorological monitoring on the target area and obtain meteorological monitoring data; an image acquisition set generation unit, configured to perform sampling image acquisition on each of the plurality of region division results to generate an image acquisition set; a snow data generating unit, configured to perform snow state fitting on the target area according to the meteorological monitoring data and the image acquisition set to generate the snow data; a region level generating unit, configured to generate region levels for the plurality of region division results according to the type identifier; A dual-level early warning model construction unit, the dual-level early warning model construction unit is used to construct a dual-level early warning model according to the regional level; A dual-level warning result output unit, the dual-level warning result output unit is used to input the prediction result and the non-point source pollution simulation result into the dual-level warning model, and output a dual-level warning result, wherein the dual-level warning result includes a pollution warning result and a meltwater warning result; A regional management unit is used to perform emergency management of the target area through the dual-level warning results.

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