Environment monitoring method and system based on artificial intelligence

Abstract

The embodiment of the invention provides an environment monitoring method and system based on artificial intelligence, and relates to the technical field of environment monitoring, and the method comprises the steps: obtaining target data based on a target grid, and the target data comprises target dynamic data and / or target static data; generating a target grid pollution diffusion prediction result according to the target data; and executing a target operation according to a target grid pollution diffusion prediction result. According to the invention, the perspectiveness of environmental monitoring can be improved, and the execution precision and execution efficiency of environmental protection measures are improved.

Description

Technical Field

[0001] Embodiments of the present application relate to the technical field of environmental monitoring, and in particular, to an environmental monitoring method and system based on artificial intelligence. Background Art

[0002] Environmental monitoring is mainly used to monitor and evaluate various factors in the environment to protect the ecosystem and human health from the impact of environmental pollution. In related technologies, there are problems such as strong hysteresis in environmental monitoring, low execution efficiency of environmental protection measures, and poor accuracy.

[0003] Therefore, there is an urgent need for a new technical solution to solve the above technical problems. Summary of the Invention

[0004] According to the embodiments of the present application, an environmental monitoring method and system based on artificial intelligence are provided, which can improve the foresight of environmental monitoring and the execution accuracy and efficiency of environmental protection measures.

[0005] In the first aspect of the present application, an environmental monitoring method based on artificial intelligence is proposed, including:

[0006] Based on a target grid, obtain target data, where the target data includes: target dynamic data, and / or target static data;

[0007] Generate a target grid pollution diffusion prediction result according to the target data;

[0008] Execute a target operation according to the target grid pollution diffusion prediction result.

[0009] In some feasible embodiments, before executing the above-mentioned obtaining target data based on the target grid, the method further includes:

[0010] Dynamically determine the target boundary corresponding to the target grid according to the target historical data within a preset time;

[0011] Wherein, the target historical data includes: target pollution source distribution data, and / or target population density data.

[0012] In some feasible embodiments, the above-mentioned generating a target environmental monitoring result according to the target data includes:

[0013] Construct a target digital twin model according to the target data;

[0014] Generate a target grid pollution diffusion prediction result according to the target digital twin model.

[0015] In some feasible embodiments, the above-mentioned executing a target operation according to the target grid pollution diffusion prediction result includes:

[0016] Determine the target pollution diffusion path according to the prediction result of the target grid pollution diffusion;

[0017] Execute the target operation according to the target pollution diffusion path.

[0018] In some feasible embodiments, when it is determined that the boundary corresponding to the target pollution diffusion path is less than or equal to the target boundary corresponding to the target grid, the above method further includes:

[0019] Determine the target regulation point, and / or the target regulation section according to the target pollution diffusion path;

[0020] Execute the target operation according to the target regulation point, and / or the target regulation section.

[0021] In some feasible embodiments, when it is determined that the boundary corresponding to the target pollution diffusion path is greater than the target boundary corresponding to the target grid, the above method further includes:

[0022] Determine a plurality of target grids according to the boundary corresponding to the target pollution diffusion path;

[0023] Determine the target pollution gradient according to the plurality of target grids;

[0024] Determine the target emission reduction ratio according to the target pollution gradient;

[0025] Determine the target regulation parameter according to the target emission reduction ratio;

[0026] Execute the target operation according to the target regulation parameter.

[0027] In some feasible embodiments, the above method further includes:

[0028] Determine the target pollution source point based on the prediction result of the target grid pollution diffusion.

[0029] In some feasible embodiments, the above target grid is provided with a target intelligent agent; the above method further includes:

[0030] Generate, and / or execute the target pollution control strategy based on the target intelligent agent.

[0031] In some feasible embodiments, the above method further includes:

[0032] When it is determined that the target pollution difference between the first target grid and the second target grid is less than the preset pollution difference, perform the target merging operation on the first target grid and the second target grid;

[0033] wherein, the first target grid and the second target grid are adjacent;

[0034] When it is determined that the target pollution difference between the first area and the second area of the target grid is greater than or equal to the preset pollution difference, perform a target splitting operation on the target grid to generate a target grid corresponding to the first area and a target grid corresponding to the second area.

[0035] In a second aspect of the present application, an environment monitoring system based on artificial intelligence is proposed, including:

[0036] An acquisition unit, configured to acquire target data based on the target grid, where the target data includes: target dynamic data, and / or, target static data;

[0037] A generation unit, configured to generate a target grid pollution diffusion prediction result according to the target data;

[0038] An execution unit, configured to perform a target operation according to the target grid pollution diffusion prediction result.

[0039] The environment monitoring method and system based on artificial intelligence provided by the embodiments of the present application, wherein the method includes: acquiring target data based on the target grid, where the target data includes: target dynamic data, and / or, target static data; generating a target grid pollution diffusion prediction result according to the target data; performing a target operation according to the target grid pollution diffusion prediction result. The present application can improve the forward-looking of environment monitoring and improve the execution accuracy and execution efficiency of environmental protection measures.

[0040] It should be understood that the content described in the summary of the invention section is not intended to limit the 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 easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Combined with the drawings and referring to the following detailed description, the above and other features, advantages and aspects of the embodiments of the present application will become more obvious. In the drawings, the same or similar reference numerals represent the same or similar elements, where:

[0042] Figure 1 It is a flowchart of an environment monitoring method based on artificial intelligence provided by an embodiment of the present application;

[0043] Figure 2 It is a structural diagram of an environment monitoring system based on artificial intelligence provided by an embodiment of the present application;

[0044] Figure 3 It is a structural diagram of an electronic device provided by an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0045] To make the objectives, technical solutions, and advantages of the embodiments of the present disclosure clearer, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present disclosure. Obviously, the described embodiments are some, but not all, of the embodiments of the present disclosure. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present disclosure without creative efforts fall within the scope of protection of the present disclosure.

[0046] In addition, the term "and / or" in this article is merely a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent three situations: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in this article generally represents an "or" relationship between the associated objects before and after.

[0047] In the first aspect of the embodiments of the present application, an environment monitoring method based on artificial intelligence is proposed. Figure 1 As shown in the flowchart of an environment monitoring method 100 based on artificial intelligence provided for the embodiments of the present application, Figure 1 as shown, the method 100 includes:

[0048] Step S1: Obtain target data based on a target grid, where the target data includes: target dynamic data, and / or, target static data.

[0049] It should be noted that the above-mentioned target grid corresponds to a target intelligent grid. Among them, the above-mentioned target intelligent grid may include: a target longitude and latitude grid, and / or, a target GIS grid.

[0050] Exemplarily, the above-mentioned target grid can be generated according to the target environmental monitoring area. Among them, the above-mentioned target grid granularity is negatively correlated with the environmental monitoring accuracy requirement, that is, the higher the environmental monitoring accuracy requirement, the smaller the grid granularity.

[0051] It should be noted that the above-mentioned target dynamic data may include: target meteorological data, and / or, target pollution source data, etc. Among them, the above-mentioned target meteorological data may include: wind speed data, wind direction data, temperature data, and / or, humidity data, etc.; the above-mentioned target pollution source data may include: mobile pollution source data, and / or, environmental medium response data, etc. Among them, the above-mentioned mobile pollution source data may include: mobile pollution source trajectory data, etc.; the above-mentioned environmental medium response data may include: atmospheric pollutant concentration response data, and / or, water pollutant concentration response data, etc.

[0052] It should be noted that the above-mentioned target static data may include: target industrial facility GIS coordinate data, target traffic road network density data, and / or, target vegetation coverage index data, etc.

[0053] Specifically, based on the above-mentioned target longitude and latitude grid, and / or target GIS grid, the target longitude and latitude grid, and / or the above-mentioned wind speed data, wind direction data, temperature data, humidity data, mobile pollution source data, environmental medium response data, target industrial facility GIS coordinate data, target traffic road network density data, and / or target vegetation coverage index data corresponding to the target GIS grid area can be retrieved.

[0054] In some feasible implementation manners, before performing the above-mentioned step S1: obtaining target data based on the target grid, the above method further includes:

[0055] Dynamically determining the target boundary corresponding to the target grid according to the target historical data within a preset time; wherein, the target historical data includes: target pollution source distribution data, and / or target population density data.

[0056] Exemplarily, the above-mentioned target pollution source distribution data may include: the longitude of the target pollution source, the latitude of the target pollution source, the emission level of the target pollution source, and / or target pollution source distribution density data, etc. The above-mentioned target population density data may include: target population heat map data, etc. The above-mentioned preset time may be negatively correlated with the determination accuracy requirement of the target boundary, that is, the higher the determination accuracy requirement of the target boundary, the shorter the preset time.

[0057] Exemplarily, the target boundary corresponding to the target grid can be dynamically determined according to the following formula:

[0058]

[0059] Among them, the above-mentioned G is used to represent the target grid set; d(g i , S) is used to represent the weighted distance from the target grid to the target pollution source; P(g j ) is used to represent the population exposure risk index within the target grid; Var(A(g)) is used to represent the variance of the target grid area; α is used to represent the first weight coefficient; β is used to represent the second weight coefficient; γ is used to represent the third weight coefficient.

[0060] It should be noted that the above-mentioned weighted distance d(g i , S) from the target grid to the target pollution source can be determined based on the Euclidean distance or Manhattan distance and corrected based on the dominant wind direction. The population exposure risk index P(g j ) within the target grid corresponds to a comprehensive index of the degree of pollution threat to the corresponding population within the target grid. The above-mentioned variance of the target grid area Var(A(g)) is used to control the regularity of the target grid shape and measure the balance of the target grid size.

[0061] It should be noted that the magnitude relationship among the above-mentioned first weight coefficient α, the second weight coefficient β, and the third weight coefficient γ can be determined according to the region type corresponding to the target grid.

[0062] Exemplarily, when the region type corresponding to the above-mentioned target grid is an industrial area, the first weight coefficient α is controlled to be greater than the second weight coefficient β, and the second weight coefficient β is greater than the third weight coefficient γ.

[0063] Exemplarily, when the region type corresponding to the above-mentioned target grid is a residential area, the second weight coefficient β is controlled to be greater than the first weight coefficient α, and the first weight coefficient α is greater than the third weight coefficient γ.

[0064] It should be noted that the constraint conditions corresponding to the above formula (1) may include: the overlap rate of the target adjacent grids is greater than or equal to a preset overlap rate threshold, and / or, the difference degree of the number of grids of each computing node is less than or equal to a preset difference degree threshold. Among them, the above-mentioned preset overlap rate threshold can be set to 20%. The above-mentioned preset difference degree threshold can be set to 15%.

[0065] Thus, the above method can dynamically determine the target boundary corresponding to the target grid according to the target historical data within a preset time, which is beneficial to improving the determination accuracy and efficiency of the target boundary corresponding to the target grid, thereby improving the retrieval accuracy and efficiency of the target data.

[0066] Step S2; Generate a target grid pollution diffusion prediction result according to the target data.

[0067] Exemplarily, according to the above-mentioned wind speed data, wind direction data, temperature data, humidity data, mobile pollution source data, environmental medium response data, GIS coordinates data of target industrial facilities, target traffic road network density data, and / or, target vegetation coverage index data, etc., a target grid pollution diffusion prediction result, that is, a pollution diffusion prediction result corresponding to the target grid, can be generated. Among them, the above-mentioned target grid pollution diffusion prediction result may include: an air pollution diffusion prediction result, a water pollution diffusion prediction result, and / or, a soil pollution diffusion prediction result, etc., corresponding to the target grid.

[0068] In some feasible implementation manners, the above step S2; generating a target environmental monitoring result according to the target data includes:

[0069] Step S21; Construct a target digital twin model according to the target data.

[0070] Exemplarily, a target digital twin model can be constructed based on the above wind speed data, wind direction data, temperature data, humidity data, mobile pollution source data, environmental medium response data, GIS coordinates data of the target industrial facility, target traffic road network density data, and / or target vegetation coverage index data, etc.

[0071] Step S22: Generate a target grid pollution diffusion prediction result according to the target digital twin model.

[0072] Exemplarily, according to the above target digital twin model, the above target grid pollution diffusion prediction result, that is, the pollution diffusion prediction result corresponding to the target grid, can be generated. Among them, the above target grid pollution diffusion prediction result may include: the air pollution diffusion prediction result corresponding to the target grid, the water pollution diffusion prediction result, and / or the soil pollution diffusion prediction result, etc.

[0073] It should be noted that among them, the above air pollution diffusion prediction result can be generated based on the following formula:

[0074] Among them, C(x, y, z) is used to represent the pollutant concentration at the target point corresponding to the spatial coordinates (x, y, z); Q is used to represent the emission rate of the target pollution source; μ is used to represent the average wind speed; σ y is used to represent the diffusion parameter corresponding to the horizontal direction; σ z is used to represent the diffusion parameter corresponding to the vertical direction; H is used to represent the effective emission height of the target pollution source; NDVI_factor is used to represent the target vegetation index.

[0075] It should be noted that the pollutant concentration C(x, y, z) at the target point corresponding to the above spatial coordinates (x, y, z) is used to determine the diffusion concentration of the target pollution source at a specific location, that is, the target point. The emission rate Q of the above target pollution source is used to reflect the intensity of the target pollution source. Among them, the greater the above emission rate Q, the higher the downstream concentration. The above average wind speed μ is used to determine the influence of the wind speed on the horizontal diffusion speed of the target pollutant. Among them, the greater the average wind speed μ, the wider the diffusion range and the lower the local concentration. The diffusion parameter σ y corresponding to the above horizontal direction and the diffusion parameter σ z corresponding to the above vertical direction are used to characterize the diffusion range of the target pollutant in the horizontal and vertical directions and are related to the atmospheric stability. The effective emission height H of the above target pollution source is used to reflect the actual release position of the target pollution source; the (z - H) 2 is the vertical diffusion term. Among them, the pollutant concentration can be symmetrically distributed with H as the center. The above target vegetation index NDVI_factor is used to determine the amount of target pollutants that can be reduced by vegetation through interception and sedimentation.

[0076] It should be noted that the above-mentioned target vegetation index NDVI_factor can be determined based on the following formula:

[0077] NDVI factor = 1 - 0.3×NDVI#3

[0078] where NDVI is used to represent the normalized difference vegetation index.

[0079] It should be noted that the above-mentioned prediction result of water pollution diffusion can be determined based on the following formula:

[0080]

[0081] where C is used to represent the concentration of the target pollutant; t is used to represent the unit time; u is used to represent the velocity vector of the fluid; D is used to represent the target diffusion coefficient; S is used to represent the source term; k is used to represent the degradation rate of the target pollutant.

[0082] It should be noted that the above-mentioned concentration C of the target pollutant is used to determine the mass of the pollutant in a unit volume of water; the above-mentioned velocity vector u of the fluid is used to represent the advection of the target pollutant with the water flow, or convective transport; the above-mentioned target diffusion coefficient D is used to characterize the molecular diffusion or turbulent diffusion ability of the target pollutant in water, which can be determined according to the water system velocity; the above-mentioned source term S is used to represent the generation or input rate of the pollutant per unit volume per unit time; the above-mentioned degradation rate k of the target pollutant is used to represent the attenuation rate of the target pollutant. Among them, the above is used to determine the change of the concentration of the target pollutant with time; the above-mentioned convective term is used to determine the migration of the target pollutant with the fluid movement; the above-mentioned diffusion term is used to determine the diffusion caused by the concentration gradient.

[0083] It should be noted that the above-mentioned prediction result of soil pollution diffusion can be determined based on the following formula:

[0084]

[0085] where C is used to represent the concentration of the target pollutant; t is used to represent the unit time; D is used to represent the target diffusion coefficient; v is used to represent the infiltration rate; λ is used to represent the adsorption coefficient; ρ b is used to represent the soil capacity; z is used to represent the vertical spatial coordinate.

[0086] It should be noted that the above target pollutant concentration C is used to determine the mass of pollutants in the soil per unit volume; the above target diffusion coefficient D is used to characterize the molecular diffusion of the target pollutant in the soil, or the mechanical dispersion ability; the seepage rate v is used to characterize the convective migration of the target pollutant with the groundwater flow; the adsorption coefficient λ is used to represent the attenuation rate of the target pollutant; the soil capacity ρ b is used to characterize the dry weight of the soil per unit volume and affects the distribution of the target pollutant under adsorption; the vertical spatial coordinate z is used to characterize the migration of the target pollutant in the vertical direction. Among them, the above dispersion term is used to determine the diffusion and mechanical dispersion caused by the concentration gradient; the convective term is used to represent the advective migration of the target pollutant with the groundwater flow; the adsorption term is related to the solid-phase concentration.

[0087] Thus, the above method can accurately and automatically construct a target digital twin model according to the target data; according to the target digital twin model, dynamically and automatically and accurately generate the target grid pollution diffusion prediction result, and provide accurate data support for accurately executing the target operation according to the target grid pollution diffusion prediction result to treat the pollution in the target grid area, thereby improving the execution accuracy and execution efficiency of the target operation and improving the response speed and response efficiency of the pollution treatment in the target grid area.

[0088] Step S3; Execute the target operation according to the target grid pollution diffusion prediction result.

[0089] Exemplarily, the target operation can be executed according to the air pollution diffusion prediction result, water pollution diffusion prediction result, and / or soil pollution diffusion prediction result corresponding to the target grid to block and / or alleviate the target pollution, thereby realizing the pollution treatment of the area corresponding to the target grid.

[0090] In some feasible embodiments, the above step S3; Execute the target operation according to the target grid pollution diffusion prediction result, including:

[0091] Step S31; Determine the target pollution diffusion path according to the target grid pollution diffusion prediction result.

[0092] Exemplarily, the target air pollution diffusion path, target water pollution diffusion path, and / or target soil pollution diffusion path can be determined according to the air pollution diffusion prediction result, water pollution diffusion prediction result, and / or soil pollution diffusion prediction result corresponding to the target grid.

[0093] Step S32; Execute the target operation according to the target pollution diffusion path.

[0094] It should be noted that, according to the target air pollution diffusion path, the target pollution control device can be controlled to perform a target action, and / or, the target traffic signal system can be automatically regulated.

[0095] Exemplarily, the above-mentioned target pollution control device may include: a sprinkler fog cannon vehicle, an air purification tower, and / or an adsorbent spraying device, etc.

[0096] Exemplarily, the above-mentioned target grid can be docked with the traffic management system in real time to achieve automatic regulation of the target traffic signal system.

[0097] It should be noted that, according to the target water pollution diffusion path, the target pollution control device can be controlled to perform a target action.

[0098] Exemplarily, the above-mentioned target pollution control device may include: a target pumping device, and / or a target floating trash interception device, etc.

[0099] It should be noted that, according to the target soil pollution diffusion path, the target pollution control device can be controlled to perform a target action. Among them, the above-mentioned target pollution control device may include: a target anti-seepage device, and / or a target high-pressure jet grouting device.

[0100] Thus, the above method can achieve automatic and accurate prediction of the target pollution diffusion path according to the target grid pollution diffusion prediction result; according to the target pollution diffusion path, automatically and accurately perform the target operation in advance to prevent the further diffusion of the target pollution, which is beneficial to accurately limit the pollution range of the target pollution and improve the treatment efficiency of the target pollution.

[0101] In some feasible implementation manners, when it is determined that the boundary corresponding to the target pollution diffusion path is less than or equal to the target boundary corresponding to the target grid, the above method further includes:

[0102] Step S321; determining a target regulation point and / or a target regulation section according to the target pollution diffusion path.

[0103] Step S322; performing a target operation according to the target regulation point and / or the target regulation section.

[0104] It should be noted that when it is determined that the boundary corresponding to the target pollution diffusion path is less than or equal to the target boundary corresponding to the target grid according to the above-mentioned target pollution diffusion path, that is, when it is determined that the range of the target pollution is limited within the target grid, a target regulation point within the target grid can be determined according to the target pollution diffusion path to start the target pollution control device, and / or a target regulation section within the target grid can be determined according to the target pollution diffusion path to regulate the target traffic signal system corresponding to the target regulation section to limit the flow of the target regulation section, so as to achieve the pollution treatment of the target pollution diffusion path.

[0105] Thus, the above method can accurately and automatically determine the target regulation points and / or the target regulation sections according to the target pollution diffusion path; and accurately and automatically execute the target operations according to the target regulation points and / or the target regulation sections, so as to accurately execute the target pollution control operation inside the target grid when the boundary corresponding to the target pollution diffusion path is less than or equal to the target boundary corresponding to the target grid, thereby improving the response accuracy and response efficiency of pollution control inside the target grid.

[0106] In some feasible implementation manners, when it is determined that the boundary corresponding to the target pollution diffusion path is greater than the target boundary corresponding to the target grid, the above method further includes:

[0107] Step S323; determining a plurality of target grids according to the boundary corresponding to the target pollution diffusion path.

[0108] It should be noted that when it is determined according to the above target pollution diffusion path that the boundary corresponding to the target pollution diffusion path is greater than the target boundary corresponding to the target grid, a plurality of target grids spanned by the target pollution diffusion path can be determined according to the boundary corresponding to the target pollution diffusion path.

[0109] Exemplarily, based on a quadtree structure and / or an octree structure, the above-mentioned plurality of target grids can be recursively determined according to the above target pollution diffusion path, so that the coverage rate of the above-mentioned plurality of target grids for the above target pollution diffusion path is greater than or equal to a preset coverage rate. Wherein, the above preset coverage rate can be determined according to the accuracy requirement of pollution control. The above preset coverage rate is positively correlated with the accuracy requirement of pollution control, that is, the higher the accuracy requirement of pollution control, the greater the above preset coverage rate.

[0110] Step S324; determining a target pollution gradient according to the plurality of target grids.

[0111] Exemplarily, the above target pollution gradient can be determined according to the area corresponding to the target pollution diffusion path covered by the above plurality of target grids.

[0112] Step S325; determining a target emission reduction ratio according to the target pollution gradient.

[0113] It should be noted that the above target emission reduction ratio can be determined based on the following formula:

[0114]

[0115] Among them, the above η is the target emission reduction ratio; α is the first sensitivity coefficient; β is the second sensitivity coefficient; ΔC is the target pollution concentration increment; C0 is the target pollution concentration baseline value; S is the area corresponding to the target pollution diffusion path covered by the target grid; S0 is the area corresponding to the target grid.

[0116] It should be noted that the ratio of the above target pollution concentration increment ΔC to the above target pollution concentration baseline value C0 is used to represent the above target pollution gradient. The value range of the above first sensitivity coefficient α can be greater than or equal to 0.35 and less than or equal to 0.55. The value range of the above second sensitivity coefficient β can be greater than or equal to 0.25 and less than or equal to 0.45.

[0117] Step S326; Determine the target regulation parameter according to the target emission reduction ratio.

[0118] Exemplarily, the target pollution control equipment and / or the regulation parameter of the target traffic signal system can be determined according to the above target emission reduction ratio.

[0119] Specifically, the regulation parameters of the above target pollution control equipment can include: the operating power of the target pollution control equipment, and / or the operating time, etc. Among them, the operating power of the above target pollution control equipment and / or the operating time are positively correlated with the above target emission reduction ratio, that is, the higher the target emission reduction ratio, the greater the operating power of the target pollution control equipment and / or the longer the operating time.

[0120] Specifically, the regulation parameters of the above target traffic signal system can include: the flow limit, the green light duration, and / or the red light duration, etc. Among them, the flow limit and / or the green light duration are negatively correlated with the above target emission reduction ratio, that is, the higher the above target emission reduction ratio, the shorter the above flow limit and / or the green light duration. The red light duration is positively correlated with the above target emission reduction ratio, that is, the higher the above target emission reduction ratio, the longer the above red light duration.

[0121] Step S327; Perform the target operation according to the target regulation parameter.

[0122] Exemplarily, the target pollution control equipment and / or the target traffic signal system can be regulated according to the regulation parameters of the above target pollution control equipment and / or the target traffic signal system, so that the target pollution control equipment and / or the target traffic signal system reach the target operating state, thereby realizing the joint response of the target pollution control equipment and / or the target traffic signal system corresponding to multiple above target grid areas according to the above target emission reduction ratio to perform the target pollution control operation.

[0123] Thus, the above method can achieve accurate and automated determination of multiple target grids based on the boundary corresponding to the target pollution diffusion path, accurate and automated determination of the target pollution gradient based on the above target grids, accurate and automated determination of the target emission reduction ratio based on the target pollution gradient, accurate and automated determination of the target regulation parameter based on the target emission reduction ratio, and accurate and automated execution of the target operation based on the target regulation parameter, so as to achieve accurate and automated scheduling of multiple target grids to jointly respond and execute the target pollution control operation when it is determined that the boundary corresponding to the target pollution diffusion path is greater than the target boundary corresponding to the target grid, thereby improving the accuracy and efficiency of multiple target grids in collaborative pollution control.

[0124] In some feasible embodiments, the above method further includes:

[0125] Step S328: When it is determined that the target pollution difference between the first target grid and the second target grid is less than the preset pollution difference, perform a target merging operation on the first target grid and the second target grid; wherein, the first target grid and the second target grid are adjacent.

[0126] It should be noted that the above target pollution difference may include: pollution gradient difference, pollution concentration difference, and / or target pollution source type difference, etc.

[0127] Exemplarily, when it is determined that the pollution gradient difference between the first target grid and the second target grid is less than 5 μg / m 3 / k, a target merging operation can be performed on the adjacent first target grid and second target grid.

[0128] Exemplarily, when it is determined that the pollution concentration difference between the first target grid and the second target grid is less than 5%, a target merging operation can be performed on the adjacent first target grid and second target grid.

[0129] Exemplarily, when it is determined that the similarity of the target pollution source types between the first target grid and the second target grid is greater than 70%, that is, when the target pollution source type difference between the first target grid and the second target grid is less than 30%, a target merging operation can be performed on the adjacent first target grid and second target grid.

[0130] Thus, the above method can achieve accurate and automated execution of the target merging operation on the first target grid and the second target grid when it is determined that the target pollution difference between the adjacent first target grid and the second target grid is less than the preset pollution difference, which is beneficial to accurately merge redundant target grids, simplify the grid architecture, reduce resource occupancy and consumption, and improve the calculation and operation efficiency.

[0131] It should be noted that in some feasible embodiments, the above method further includes:

[0132] Step S329; when it is determined that the target pollution difference between the first region and the second region of the target grid is greater than or equal to the preset pollution difference, perform a target splitting operation on the target grid to generate a target grid corresponding to the first region and a target grid corresponding to the second region.

[0133] It should be noted that the above target pollution difference may include: pollution gradient difference, pollution concentration difference, and / or target pollution source type difference, etc.

[0134] Exemplarily, when it is determined that the pollution gradient difference between the first region and the second region of the target grid is greater than or equal to 5 μg / m 3 / k, a target splitting operation can be performed on the target grid to generate a target grid corresponding to the first region and a target grid corresponding to the second region.

[0135] Exemplarily, when it is determined that the pollution concentration difference between the first region and the second region of the target grid is greater than or equal to 5%, a target splitting operation can be performed on the target grid to generate a target grid corresponding to the first region and a target grid corresponding to the second region.

[0136] Exemplarily, when it is determined that the similarity of the target pollution source types between the first region and the second region of the target grid is less than or equal to 70%, that is, when the target pollution source type difference between the first target grid and the second target grid is greater than or equal to 30%, a target splitting operation can be performed on the target grid to generate a target grid corresponding to the first region and a target grid corresponding to the second region.

[0137] Thus, the above method can achieve automatic and precise execution of the target splitting operation on the target grid when it is determined that the target pollution difference between the first region and the second region of the target grid is greater than or equal to the preset pollution difference, so as to automatically and precisely generate a target grid corresponding to the first region and a target grid corresponding to the second region, thereby further improving the accuracy of the target grid architecture, further improving the operation accuracy of the target grid, further improving the capture accuracy of the target data, further improving the generation accuracy of the target grid pollution diffusion prediction result, and further improving the execution accuracy of the target operation, so as to further improve the response accuracy of pollution control.

[0138] In some feasible embodiments, the above method further includes:

[0139] Step S33; determine the target pollution source point based on the target grid pollution diffusion prediction result.

[0140] Exemplarily, based on the above-mentioned target grid pollution diffusion prediction results, a backward trajectory analysis operation can be performed to determine the above-mentioned target pollution source location.

[0141] Specifically, based on the HYSPLIT model and wind direction data, the possible source paths of the target pollutants can be traced backward from the target grid where the pollution concentration is greater than the preset concentration threshold to determine the longitude and latitude corresponding to the above-mentioned target pollution source location. Among them, the above-mentioned preset concentration threshold is negatively correlated with the positioning accuracy requirement of the target pollution source location, that is, the higher the positioning accuracy requirement of the target pollution source location, the lower the preset concentration threshold.

[0142] Specifically, based on the above-mentioned possible source paths, through multi-trajectory superposition, a probability distribution map of the target pollution source location can be generated, so as to achieve precise positioning of the target pollution source.

[0143] Thus, the above method can achieve automatic and precise tracing of the target pollution source to achieve precise treatment of the target pollution source, thereby improving the pollution treatment accuracy and treatment efficiency.

[0144] In some feasible implementation manners, the above-mentioned target grid is provided with a target intelligent agent; the above method further includes: S4; generating and / or executing a target pollution treatment strategy based on the target intelligent agent.

[0145] Exemplarily, a target intelligent agent can be set in each target grid to generate a target pollution treatment strategy corresponding to each target grid area according to the target data corresponding to each target grid.

[0146] It should be noted that the above-mentioned target intelligent agent can include: a target perception intelligent agent, a target decision-making intelligent agent, and / or a target execution intelligent agent.

[0147] Among them, the above-mentioned target perception intelligent agent is used to collect the above-mentioned target data in real time; the above-mentioned target decision-making intelligent agent is used to automatically generate a corresponding target pollution treatment strategy based on the target AI model according to the above-mentioned target data; the above-mentioned target execution intelligent agent is used to automatically control the target pollution treatment equipment to execute target actions and / or automatically regulate the target traffic signal system according to the above-mentioned target pollution treatment strategy.

[0148] Exemplarily, the above-mentioned target pollution treatment equipment can include: a sprinkler fog cannon vehicle, an air purification tower, an adsorbent spraying device, a target pumping device, a target floating trash interception device, an adsorbent spraying device, a target anti-seepage device, and / or a target high-pressure jet grouting device, etc.

[0149] Exemplarily, the above-mentioned target grid can be connected to the traffic management system in real time to achieve automatic regulation of the target traffic signal system.

[0150] Thus, the above method can achieve the automatic generation of the target agent based on the above, and / or execute the target pollution control strategy corresponding to the target grid area, which is beneficial to improving the response speed and response efficiency of pollution control in the target grid area.

[0151] Based on this, the environmental monitoring method based on artificial intelligence provided by this application can automatically and accurately capture target data based on the target grid, where the target data includes: target dynamic data, and / or target static data, which is beneficial to improving the acquisition accuracy and acquisition efficiency of the target data corresponding to the target grid; according to the target data, automatically and accurately generate the target grid pollution diffusion prediction result, which is beneficial to improving the generation accuracy and generation efficiency of the target grid pollution diffusion prediction result; according to the target grid pollution diffusion prediction result, automatically and accurately execute the target operation to achieve the pollution control of the target grid area, which is beneficial to improving the execution accuracy and execution efficiency of the target operation, thereby improving the response speed and response efficiency of pollution control in the target grid area.

[0152] It should be noted that for the foregoing method embodiments, for the sake of simple description, they are all expressed as a series of action combinations. However, those skilled in the art should know that this application is not limited by the described action sequence, because according to this application, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily essential to this application.

[0153] The above is the introduction of the method embodiments. The following further illustrates the solution of this application through device embodiments.

[0154] In the second aspect of the embodiments of this application, an environmental monitoring system based on artificial intelligence is proposed. Figure 2 It is a structural schematic diagram of an environmental monitoring system 200 provided by the embodiments of this application. As Figure 2 shown, the system 200 includes: an acquisition unit 210, a generation unit 220, and an execution unit 230.

[0155] The acquisition unit 210 is used to acquire target data based on the target grid, where the target data includes: target dynamic data, and / or target static data;

[0156] The generation unit 220 is used to generate a target grid pollution diffusion prediction result according to the target data;

[0157] The execution unit 230 is used to execute a target operation according to the target grid pollution diffusion prediction result.

[0158] Figure 3The following is a schematic structural diagram of an electronic device 300 provided by an embodiment of the present application. As Figure 3 shown, the electronic device 300 includes a central processing unit (CPU) 301, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 302 or a program loaded from a storage section 308 into a random access memory (RAM) 303. In the RAM 303, various programs and data required for the operation of the terminal device or the server are also stored. The CPU 301, the ROM 302, and the RAM 303 are connected to each other via a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0159] The following components are connected to the I / O interface 305: an input section 306 including a keyboard, a mouse, etc.; an output section 307 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc. and a speaker, etc.; a storage section 308 including a hard disk, etc.; and a communication section 309 including a network interface card such as a LAN card, a modem, etc. The communication section 309 performs communication processing via a network such as the Internet. A drive 310 is also connected to the I / O interface 305 as needed. A removable medium 311, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 310 as needed so that a computer program read from it can be installed into the storage section 308 as needed.

[0160] Specifically, according to an embodiment of the present application, the above method flow steps can be implemented as a computer software program. For example, an embodiment of the present application includes a computer program product, which includes a computer program carried on a machine-readable medium, and the computer program includes program codes for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through the communication section 309, and / or installed from the removable medium 311. When the computer program is executed by a central processing unit (CPU) 301, the above functions defined in the system of the present application are executed.

[0161] It should be noted that the computer-readable medium described in this application can be a computer-readable signal medium, a computer-readable storage medium, or any combination of the two. A computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples of a computer-readable storage medium can include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this application, a computer-readable storage medium can be any tangible medium that contains or stores a program, which can be used by or in conjunction with an instruction execution system, apparatus, or device. And in this application, a computer-readable signal medium can include a data signal propagated in a baseband or as part of a carrier wave, which carries computer-readable program code. Such a propagated data signal can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on a computer-readable medium can be transmitted using any appropriate medium, including but not limited to: wireless, wire, optical cable, RF, etc., or any suitable combination of the above.

[0162] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram can represent a module, a program segment, or a part of code, and the foregoing module, program segment, or part of code contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks can occur in a different order than marked in the accompanying drawings. For example, two consecutive blocks shown can actually be executed substantially in parallel, and they can sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and the combination of blocks in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system for performing the specified functions or operations, or can be implemented by a combination of dedicated hardware and computer instructions.

[0163] The units or modules involved in the embodiments of the present application can be implemented in software or in hardware. The described units or modules can also be provided in a processor. Among them, the names of these units or modules do not, in some cases, constitute a limitation on the units or modules themselves.

[0164] The above description is only a preferred embodiment of the present application and an explanation of the technical principles applied. Those skilled in the art should understand that the scope of the application involved in the present application is not limited to the technical solutions formed by the specific combination of the above technical features, and should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the foregoing application concept. For example, the technical solutions formed by mutually replacing the above features with the technical features (but not limited to) having similar functions applied in the present application.

Claims

1. An environmental monitoring method based on artificial intelligence, characterized in that, Including: Based on the target grid, obtain target data, where the target data includes: target dynamic data, and / or target static data; Generate a target grid pollution diffusion prediction result according to the target data; Execute a target operation according to the target grid pollution diffusion prediction result.

2. The environmental monitoring method based on artificial intelligence according to claim 1, characterized in that, Before performing the step of obtaining target data based on the target grid, the method further includes: Dynamically determine the target boundary corresponding to the target grid according to the target historical data within a preset time; Wherein, the target historical data includes: target pollution source distribution data, and / or target population density data.

3. The environmental monitoring method based on artificial intelligence according to claim 1, characterized in that, The step of generating a target environmental monitoring result according to the target data includes: Construct a target digital twin model according to the target data; Generate a target grid pollution diffusion prediction result according to the target digital twin model.

4. The environmental monitoring method based on artificial intelligence according to claim 3, characterized in that, The step of executing a target operation according to the target grid pollution diffusion prediction result includes: Determine a target pollution diffusion path according to the target grid pollution diffusion prediction result; Execute the target operation according to the target pollution diffusion path.

5. The environmental monitoring method based on artificial intelligence according to claim 4, wherein When it is determined that the boundary corresponding to the target pollution diffusion path is less than or equal to the target boundary corresponding to the target grid, the method further includes: Determine a target regulation point, and / or a target regulation section according to the target pollution diffusion path; Execute the target operation according to the target regulation point, and / or the target regulation section.

6. The environmental monitoring method based on artificial intelligence according to claim 5, characterized in that When it is determined that the boundary corresponding to the target pollution diffusion path is greater than the target boundary corresponding to the target grid, the method further includes: Determine multiple target grids according to the boundary corresponding to the target pollution diffusion path; Determine a target pollution gradient according to the multiple target grids; Determine a target emission reduction ratio according to the target pollution gradient; Determine target regulation parameters according to the target emission reduction ratio; Execute the target operation according to the target regulation parameters.

7. The environmental monitoring method based on artificial intelligence according to any one of claims 1 to 6, characterized in that, It further includes: Determine a target pollution source point based on the target grid pollution diffusion prediction result.

8. The environmental monitoring method based on artificial intelligence according to claim 7, wherein The target grid is provided with a target intelligent agent; The method further includes: Generate, and / or execute a target pollution control strategy based on the target intelligent agent.

9. The environmental monitoring method based on artificial intelligence according to claim 8, characterized in that, It further includes: When it is determined that the target pollution difference between the first target grid and the second target grid is less than a preset pollution difference, perform a target merging operation on the first target grid and the second target grid; Wherein, the first target grid and the second target grid are adjacent; When it is determined that the target pollution difference between the first area and the second area of the target grid is greater than or equal to the preset pollution difference, perform a target splitting operation on the target grid to generate a target grid corresponding to the first area and a target grid corresponding to the second area.

10. An environment monitoring system based on artificial intelligence, characterized in that, Including: An acquisition unit, configured to obtain target data based on a target grid, where the target data includes: target dynamic data, and / or target static data; A generation unit, configured to generate a target grid pollution diffusion prediction result according to the target data; An execution unit, configured to execute a target operation according to the target grid pollution diffusion prediction result.

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