Central target pollution risk analysis method and system based on distributed pollution monitoring

Abstract

The present invention discloses a central target pollution risk analysis method and system based on distributed pollution monitoring, which relates to the technical field of data analysis. The key points of its technical solution are as follows: obtaining the internal pollutant concentration and the external pollutant concentration set; screening out the effective data subset that has a pollution association with the internal pollutant concentration; performing polar coordinate space mapping on the effective data subset and dividing the azimuth interval; determining the azimuth contribution degree of the water source pollution risk; comparing the azimuth contribution degree with the risk contribution degree threshold to obtain the pollution risk classification and early warning result. The present invention effectively eliminates noise data to extract high-value pollution signals, and combines the polar coordinate space mapping technology to cluster and analyze the pollution sources according to the azimuth interval, which can improve the spatio-temporal analysis ability of the pollution diffusion path; at the same time, based on the pollutant flux, migration path resistance and meteorological parameters, it realizes the accurate quantification of the pollution risk contribution degree in different azimuths, and solves the problems of fuzzy spatial azimuth recognition and poor real-time performance of the traditional method.

Description

Technical Field

[0001] The present invention relates to the technical field of data analysis, and more specifically, it relates to a central target pollution risk analysis method and system based on distributed pollution monitoring. Background Art

[0002] With the rapid development of industry and agriculture and the acceleration of the urbanization process, surface and groundwater bodies are facing increasingly serious threats of compound pollution. The prevention and control of pollution risks in drinking water source areas have become the main research direction for ensuring public health and environmental safety. Traditional water quality monitoring methods mostly rely on static index evaluation, and it is difficult to cope with the dynamics, spatial heterogeneity, and multi-source interaction effects of pollution diffusion, especially in sudden pollution events, it is difficult to trace and warn in a timely manner.

[0003] Currently, existing pollution risk assessment methods mostly focus on the analysis of single pollution sources or static hydrological parameters, lacking the ability to synergistically analyze multi-dimensional dynamic pollution elements, resulting in insufficient risk warning accuracy and lagging prevention and control measures. In addition, some existing technologies adopt an evaluation model that couples the inherent vulnerability with the hazard level of pollution sources, and it conducts risk grading by superimposing groundwater dynamic parameters; however, this method relies on historical data modeling, cannot capture the spatio-temporal changes of pollution migration paths in real time, and has insufficient recognition of the spatial contribution degree of multi-source pollution; moreover, some existing technologies also use methods based on health statistical data or remote sensing monitoring cycles, although they introduce external parameters, there are problems such as large data processing delays and lack of analysis of spatial orientation correlation.

[0004] Therefore, how to research and design a central target pollution risk analysis method and system based on distributed pollution monitoring that can overcome the above defects is an urgent problem for us to solve currently. Summary of the Invention

[0005] To solve the deficiencies in the prior art, the purpose of the present invention is to provide a central target pollution risk analysis method and system based on distributed pollution monitoring. By establishing a screening mechanism between external monitoring data and internal monitoring data, noise data can be effectively eliminated to extract high-value pollution signals; and combined with polar coordinate space mapping technology, pollution sources are clustered and analyzed according to azimuth intervals, which can improve the spatio-temporal analysis ability of pollution diffusion paths; at the same time, based on pollutant flux, migration path resistance, and meteorological parameters, the accurate quantification of the pollution risk contribution degree in different directions is realized, solving the problems of fuzzy spatial orientation recognition and poor real-time performance of traditional methods.

[0006] The above technical purpose of the present invention is achieved through the following technical solutions:

[0007] In the first aspect, a central target pollution risk analysis method based on distributed pollution monitoring is provided, including the following steps:

[0008] Obtain the internal pollutant concentrations collected at the internal monitoring points of the target water source and the set of external pollutant concentrations collected at the external monitoring points of the target water source;

[0009] Screen out a subset of valid data from the set of external pollutant concentrations that has a pollution association with the internal pollutant concentrations;

[0010] Perform polar coordinate space mapping on the subset of valid data and divide it into multiple azimuth intervals;

[0011] Determine the azimuth contribution degree of the water source pollution risk based on the pollutant fluxes, migration path resistances, and meteorological parameters of all the external monitoring points in the azimuth interval;

[0012] Compare the azimuth contribution degree with the risk contribution degree thresholds for different grading early warnings to obtain the pollution risk grading early warning result of the target water source in the corresponding azimuth interval.

[0013] Further, screening out a subset of valid data from the set of external pollutant concentrations that has a pollution association with the internal pollutant concentrations includes:

[0014] Determine the fluctuation correlation degree between the external pollutant concentrations in the set of external pollutant concentrations and the internal pollutant concentrations;

[0015] If the fluctuation correlation degree is greater than the strong correlation fluctuation threshold, the corresponding external pollutant concentration is used as the valid data in the subset of valid data.

[0016] Further, the process of determining the fluctuation correlation degree includes:

[0017] Perform denoising processing and normalization processing on the external pollutant concentrations and the internal pollutant concentrations in the set of external pollutant concentrations to obtain the corresponding external concentration sequence and internal concentration sequence;

[0018] Perform Hilbert transform on the external concentration sequence and the internal concentration sequence respectively to obtain the corresponding external transformation result and internal transformation result;

[0019] Calculate the first instantaneous phase angle based on the external transformation result and calculate the second instantaneous phase angle based on the internal transformation result;

[0020] Calculate the integral phase difference cosine value within a preset period based on the first instantaneous phase angle and the second instantaneous phase angle, and use the integral phase difference cosine value as the fluctuation correlation degree.

[0021] Further, screening out a subset of valid data from the set of external pollutant concentrations that has a pollution association with the internal pollutant concentrations includes:

[0022] Determine the topographic infiltration path resistance between the external pollutant concentration where the external pollutants are concentrated and the internal pollutant concentration;

[0023] If the topographic infiltration path resistance is less than or equal to the maximum allowable infiltration resistance, the corresponding external pollutant concentration is used as the valid data in the valid data subset.

[0024] Furthermore, the determination process of the topographic infiltration path resistance includes:

[0025] Obtain the high-precision DEM data, geological data, and medium permeability between the external monitoring point corresponding to the external pollutant concentration and the internal monitoring point corresponding to the internal pollutant concentration;

[0026] Based on the high-precision DEM data, the geological data, and the medium permeability, calculate the topographic infiltration path resistance through a three-dimensional resistance model based on the Darcy - Forchheimer equation.

[0027] Furthermore, screening out the valid data subset with pollution correlation from the external pollutant concentration set includes:

[0028] Determine the distance attenuation effect value between the external pollutant concentration in the external pollutant concentration set and the internal pollutant concentration;

[0029] If the distance attenuation effect value is less than the effect threshold, the corresponding external pollutant concentration is used as the valid data in the valid data subset.

[0030] Furthermore, the determination process of the distance attenuation effect value includes:

[0031] Determine the straight-line distance between the external monitoring point corresponding to the external pollutant concentration and the internal monitoring point corresponding to the internal pollutant concentration;

[0032] Determine the ratio between the straight-line distance and the characteristic attenuation distance;

[0033] Input the ratio into the exponential attenuation model to calculate the distance attenuation effect value.

[0034] Furthermore, the determination of the azimuth contribution degree of the water source pollution risk includes:

[0035] Perform weighted averaging on the pollutant fluxes, migration path resistances, and meteorological parameters of all the external monitoring points in the azimuth interval respectively to obtain the aggregated pollutant flux, aggregated migration path resistance, and aggregated meteorological parameters;

[0036] Normalize the resistance of the aggregation migration path and determine a resistance correction parameter that is negatively correlated with the normalized value, where the value range of the resistance correction parameter is [0, 1];

[0037] Determine a meteorological correction parameter according to the aggregation meteorological parameters, where the value range of the meteorological correction parameter is [1, 3];

[0038] Determine the azimuth contribution degree for the water source pollution risk analysis corresponding to the azimuth interval by multiplying the aggregation pollutant flux, the resistance correction parameter, and the meteorological correction parameter.

[0039] Further, the meteorological parameter is at least one of a wind speed parameter and a rainfall parameter.

[0040] In a second aspect, a central target pollution risk analysis system based on distributed pollution monitoring is provided. This system is used to implement the central target pollution risk analysis method based on distributed pollution monitoring as described in any one of the first aspects, including:

[0041] A data acquisition module for acquiring the internal pollutant concentration collected by the internal monitoring points of the target water source and the external pollutant concentration set collected by the external monitoring points of the target water source;

[0042] A data screening module for screening out a valid data subset that has a pollution association with the internal pollutant concentration from the external pollutant concentration set;

[0043] An azimuth division module for performing polar coordinate space mapping on the valid data subset and dividing it into multiple azimuth intervals;

[0044] A contribution analysis module for determining the azimuth contribution degree of the water source pollution risk according to the pollutant flux, migration path resistance, and meteorological parameters of all the external monitoring points in the azimuth interval;

[0045] A grading and early warning module for comparing the azimuth contribution degree with the risk contribution degree thresholds of different grading early warnings to obtain the pollution risk grading early warning result of the target water source in the corresponding azimuth interval.

[0046] Compared with the prior art, the present invention has the following beneficial effects:

[0047] 1. The method for analyzing the pollution risk of the central target based on distributed pollution monitoring provided by the present invention effectively eliminates noise data to extract high-value pollution signals by establishing a screening mechanism between external monitoring data and internal monitoring data; and combines the polar coordinate space mapping technology to cluster and analyze the pollution sources according to azimuth intervals, which can improve the spatio-temporal analysis ability of the pollution diffusion path; at the same time, based on pollutant flux, migration path resistance and meteorological parameters, it realizes the accurate quantification of the pollution risk contribution degree in different directions, and solves the problems of fuzzy spatial azimuth recognition and poor real-time performance of traditional methods.

[0048] 2. The present invention extracts the instantaneous phase characteristics of the pollutant concentration signal through Hilbert transform, calculates the integral phase difference cosine value as the fluctuation correlation index, and effectively captures the time-frequency correlation of pollution migration. This method breaks through the frequency domain limitation of the traditional correlation coefficient method and can still accurately identify the phase synchronization characteristics of pollution conduction in a strong noise environment, improving the signal-to-noise ratio and anti-interference ability of pollution source tracing.

[0049] 3. The present invention integrates the Darcy-Forschheimer equation to construct a three-dimensional resistance model, couples multi-physical field parameters such as medium permeability, elevation difference and fluid inertia effect, and accurately quantifies the pollutant migration path resistance; compared with the traditional two-dimensional Darcy model, this method can analyze the non-linear seepage characteristics in complex geological structures and solve the problem of inaccurate pollution diffusion analysis in steep slope areas.

[0050] 4. The present invention uses an exponential decay model to quantify the distance effect of pollution propagation, and dynamically adjusts the characteristic decay distance parameter according to rainfall intensity and evaporation, which can break through the limitation of the fixed decay coefficient and significantly improve the evaluation accuracy of the pollution influence range in the dry-wet alternating area.

[0051] 5. The present invention generates a negative correlation correction coefficient by normalizing the migration resistance, and combines the non-linear meteorological correction factors of wind speed and rainfall to construct an azimuth contribution degree model with multi-dimensional parameter coupling, which can effectively integrate the dynamic interaction effects of environmental elements and solve the evaluation deviation problem caused by the parameter solidification of the traditional weighted average method. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] The drawings described herein are used to provide a further understanding of the embodiments of the present invention, form a part of this application, and do not limit the embodiments of the present invention. In the drawings:

[0053] Figure 1 is the flowchart in Embodiment 1 of the present invention;

[0054] Figure 2 is the system block diagram in Embodiment 2 of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0055] To make the objectives, technical solutions, and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below in conjunction with embodiments and drawings. The illustrative embodiments of the present invention and their descriptions are only used to explain the present invention and do not limit the present invention.

[0056] It should be noted that the terms "first" and "second" are only used for descriptive purposes and cannot be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of such features. In the description of the present invention, "a plurality of" means two or more unless otherwise specifically defined.

[0057] Embodiment 1: A method for analyzing the pollution risk of a central target based on distributed pollution monitoring, as Figure 1 shown, includes the following steps:

[0058] S1: Obtain the internal pollutant concentration collected by the internal monitoring points of the target water source and the set of external pollutant concentrations collected by the external monitoring points of the target water source;

[0059] S2: Screen out the effective data subset that has a pollution association with the internal pollutant concentration from the set of external pollutant concentrations;

[0060] S3: Perform polar coordinate space mapping on the effective data subset and divide it into multiple azimuth intervals;

[0061] S4: Determine the azimuth contribution degree of the water source pollution risk according to the pollutant flux, migration path resistance, and meteorological parameters of all external monitoring points in the azimuth interval;

[0062] S5: Compare the azimuth contribution degree with the risk contribution degree thresholds of different classification early warnings to obtain the pollution risk classification early warning result of the target water source in the corresponding azimuth interval.

[0063] In step S1, the internal monitoring points refer to the monitoring points located within the target water source, which can be one or more; if there is only one, it is generally set at the center of the target water source; if there are multiple, the internal pollutant concentration can be the average of the concentrations of multiple monitoring points.

[0064] The external monitoring points are the monitoring points located outside the target water source. The external monitoring points can be arranged in a non-uniform radial pattern around the target water source, and each monitoring point is equipped with multiple parameter sensors to monitor information such as heavy metals, organic substances, and hydrological parameters.

[0065] In some examples, the minimum distance between the external monitoring points and the edge of the target water source does not exceed the set monitoring distance. By setting the monitoring distance, irrelevant or weakly relevant monitoring points can be initially excluded.

[0066] In addition, both the internal pollutant concentration and the external pollutant concentration concentrated in the external pollutant concentration are time-series data, which can be transmitted to the data processing center through a wireless network for processing.

[0067] In step S2, the screening of the effective data subset can be based on one or more of the fluctuation correlation degree, the terrain penetration path resistance, and the distance attenuation effect as the screening conditions.

[0068] In some examples, screening out the effective data subset with pollution correlation from the external pollutant concentration includes: determining the fluctuation correlation degree between the external pollutant concentration in the external pollutant concentration and the internal pollutant concentration; if the fluctuation correlation degree is greater than the strong correlation fluctuation threshold, the corresponding external pollutant concentration is used as the effective data in the effective data subset.

[0069] Specifically, first use the wavelet threshold denoising method to denoise the external pollutant concentration and the internal pollutant concentration in the external pollutant concentration respectively, and normalize the denoised concentration data to obtain the corresponding external concentration sequence and the internal concentration sequence , refers to the internal concentration sequence corresponding to the th external monitoring point.

[0070] Then perform Hilbert transform (integral transform) on the external concentration sequence and the internal concentration sequence respectively to obtain the corresponding external transform result and internal transform result. The expression of the Hilbert transform is:

[0071] ;

[0072] where, represents the Hilbert transform result of the pollutant concentration corresponding to the current time point ; represents the Cauchy principal value integral; represents the half window of the time shift; represents the time shift; represents the time offset and the pollutant concentration corresponding to the place.

[0073] Then calculate the first instantaneous phase angle according to the external transform result and calculate the second instantaneous phase angle according to the internal transform result. The calculation expression of the instantaneous phase angle is:

[0074] ;

[0075] where, represents the instantaneous phase angle of the current time point ; Represents the real part of the Hilbert transform result ; Represents the imaginary part of the Hilbert transform result ; Represents the arctangent function

[0076] Finally, calculate the cosine value of the integral phase difference within the preset period based on the first instantaneous phase angle and the second instantaneous phase angle, and use the cosine value of the integral phase difference as the fluctuation correlation degree. The calculation expression of the cosine value of the integral phase difference is:

[0077] ;

[0078] Wherein, Represents the fluctuation correlation degree of the th external monitoring point; Represents the integration period, which can be 24 hours; Represents the instantaneous phase angle at the current time point in the internal concentration sequence ; Represents the th external monitoring point corresponding to the instantaneous phase angle at the current time point in the external concentration sequence ;

[0079] It should be noted that the output range of the fluctuation correlation degree is generally [-1, 1]. When the fluctuation correlation degree is greater than 0.7, it can be determined as a strong correlation.

[0080] The advantage of using the Hilbert transform in the present invention is that it can accurately extract the instantaneous phase of the non-steady signal. Through cosine integral processing, when the phases of the two signals are synchronized, it can still maintain stability under noise interference and can cover the daily change cycle to eliminate the influence of short-term disturbances.

[0081] In some examples, an effective data subset with a pollution correlation between the external pollutant concentration and the internal pollutant concentration is screened out from the external pollutant concentration set, including: determining the topographic infiltration path resistance between the external pollutant concentration in the external pollutant concentration set and the internal pollutant concentration; if the topographic infiltration path resistance is less than or equal to the maximum allowable infiltration resistance, the corresponding external pollutant concentration is used as the effective data in the effective data subset.

[0082] Specifically, first obtain the high-precision DEM (Digital Elevation Model) data, geological data, and medium permeability between the external monitoring points corresponding to the external pollutant concentration and the internal monitoring points corresponding to the internal pollutant concentration. Among them, the medium permeability can be experimentally determined after drilling and sampling.

[0083] Based on high-precision DEM data, geological data, and medium permeability, the terrain infiltration path resistance is calculated through a three-dimensional resistance model based on the Darcy-Forscheimer equation. The calculation expression of the three-dimensional resistance model based on the Darcy-Forscheimer equation is as follows:

[0084] ;

[0085] Among them, represents the terrain infiltration path resistance, with the unit of Pa; represents the infiltration path length, with the unit of m; represents the hydrodynamic viscosity coefficient, with the unit of Pa·s; represents the medium permeability, with the unit of m 2 ; represents the seepage velocity, with the unit of m / s; represents the fluid density, with the unit of kg / m 3 ; represents the acceleration due to gravity, with the unit of m / s 2 ; represents the water level difference, with the unit of m; 、 respectively represent the first empirical coefficient and the second empirical coefficient. The specific values can be obtained through calibration of hydrological experiments, and the values are different for different geological environments.

[0086] Based on Darcy's law, the present invention describes the viscous resistance of fluid flow in porous media through Darcy resistance. The resistance is proportional to the fluid viscosity, seepage path length, and flow velocity, and inversely proportional to the medium permeability; in addition, the potential energy is used to reflect the hydrostatic pressure difference caused by the water level difference, driving the fluid flow, which is directly related to the fluid density, acceleration due to gravity, and water level difference; furthermore, the present invention describes the contribution of the fluid kinetic energy to the resistance in high-speed seepage through kinetic energy, reflecting the inertial effect, which is proportional to the fluid density and the square of the flow velocity.

[0087] By coupling multiple physical fields including the viscous resistance dominated by low-speed seepage, the gravitational potential energy driven by the hydrostatic pressure difference, and the inertial effect dominated by high-speed seepage, the present invention can achieve accurate analysis of the terrain infiltration path resistance under complex geological conditions.

[0088] In some examples, an effective data subset related to the internal pollutant concentration is screened out from the external pollutant concentration set, including: determining the distance attenuation effect value between the external pollutant concentration in the external pollutant concentration set and the internal pollutant concentration; if the distance attenuation effect value is less than the effect threshold, the corresponding external pollutant concentration is used as the effective data in the effective data subset.

[0089] Specifically, determine the straight-line distance between the external monitoring point corresponding to the external pollutant concentration and the internal monitoring point corresponding to the internal pollutant concentration, and determine the ratio between the straight-line distance and the characteristic attenuation distance; input the ratio into the exponential decay model to calculate the distance attenuation effect value, and the specific expression is:

[0090] ;

[0091] Among them, represents the distance attenuation effect value of the th external monitoring point, and its value range is [0, 1], and it decays exponentially with distance; represents the straight-line distance from the th external monitoring point to the internal monitoring point; represents the characteristic attenuation distance.

[0092] For the characteristic attenuation distance, a dynamic correction mechanism can be adopted for adjustment. For example, during the rainy season, the characteristic attenuation distance can be adjusted larger according to the daily rainfall, while during the dry season, the characteristic attenuation distance can be adjusted smaller according to the evaporation rate.

[0093] The present invention establishes an exponential decay model based on Fick's diffusion law, aiming to eliminate the external monitoring points at long distances and dynamically correct the characteristic attenuation distance considering the effects of rainfall scouring and evaporation concentration, which can improve the adaptability of the model.

[0094] Through the above method, the present invention can efficiently extract valuable data from a large amount of monitoring data, reduce the subsequent analysis and calculation amount while ensuring the accuracy, and provide a reliable data basis for real-time risk assessment.

[0095] In step S3, a polar coordinate system can be established with the internal monitoring point or the center of the target water source as the origin: ; among them, is the radial distance of the polar coordinate; is the azimuth angle of the polar coordinate; is the maximum coverage radius of the monitoring network.

[0096] Then, the space is divided into multiple azimuth intervals according to the set azimuth angle size (such as 36 azimuth intervals, each azimuth interval is 10°), and each azimuth interval is marked as , such as if the azimuth angle of the th external monitoring point is 25°, then it belongs to the azimuth interval , and the azimuth angle range of is

[0097] In step S4, determine the azimuth contribution degree of the water source pollution risk, including: respectively performing weighted averaging on the pollutant fluxes, migration path resistances, and meteorological parameters of all external monitoring points in the azimuth interval to obtain the aggregated pollutant flux, aggregated migration path resistance, and aggregated meteorological parameter; performing normalization processing on the aggregated migration path resistance, and determining a resistance correction parameter that is negatively correlated with the normalized value, where the value range of the resistance correction parameter is [0, 1]; determining a meteorological correction parameter according to the aggregated meteorological parameter, where the value range of the meteorological correction parameter is [1, 3]; using the product of the aggregated pollutant flux, resistance correction parameter, and meteorological correction parameter to determine the azimuth contribution degree for water source pollution risk analysis in the corresponding azimuth interval.

[0098] In some examples, the weight coefficients of the weighted averaging can be proportionally divided according to the distance attenuation effect values of all external monitoring points in an azimuth interval; in addition, the weight coefficients of the weighted averaging can also be proportionally determined according to the straight-line distance from the external monitoring point to the internal monitoring point.

[0099] The above-mentioned pollutant flux refers to the mass of pollutants passing through the cross-section of the water flow per unit time. In some examples, the pollutant mass can be determined by the product of the groundwater flow velocity, pollutant concentration, and cross-sectional area of the water flow. In some examples, the pollutant mass can also be determined by the product of the groundwater flow velocity, pollutant concentration, cross-sectional area of the water flow, and fluctuation correlation degree.

[0100] In some examples, the migration path resistance can directly adopt the above-mentioned terrain penetration path resistance, and then after weighted averaging to obtain the aggregated migration path resistance, and dividing the aggregated migration path resistance by the resistance upper limit value, the aggregated migration path resistance can be normalized to [0, 1]. The smaller the normalized value, the smoother the path, so use 1 minus the normalized value as the resistance correction parameter.

[0101] In some examples, the meteorological parameter can be a wind speed parameter, or a rainfall parameter, or can also include both a wind speed parameter and a rainfall parameter at the same time.

[0102] The wind speed parameter shows a promoting effect on diffusion, so first determine the ratio of the wind speed parameter to the wind speed upper limit value, and then add 1 to the ratio as the meteorological correction parameter corresponding to the wind speed parameter.

[0103] While the rainfall parameter shows an enhancing effect on infiltration, so first determine the ratio of the rainfall parameter to the rainfall upper limit value, and then add 1 to the ratio as the meteorological correction parameter corresponding to the rainfall parameter.

[0104] If the meteorological parameter includes both a wind speed parameter and a rainfall parameter at the same time, then add the meteorological correction parameter corresponding to the wind speed parameter and the meteorological correction parameter corresponding to the rainfall parameter to obtain the final meteorological correction parameter.

[0105] Through polar coordinate space mapping and multi-parameter dynamic fusion, the present invention achieves precise quantification of the azimuth contribution degree of pollution risk, and solves the deficiencies of traditional methods in terms of direction recognition accuracy, complex environment adaptability, etc.

[0106] In step S5, the risk contribution degree thresholds for different classification early warnings are different. The azimuth contribution degree of each azimuth interval is compared with the risk contribution degree thresholds for different classification early warnings. If the azimuth contribution degree of the azimuth interval is greater than the risk contribution degree threshold for the nth classification early warning and less than the risk contribution degree threshold for the (n + 1)th classification early warning, then the pollution risk classification early warning result corresponding to the nth classification early warning is output for the azimuth interval.

[0107] Embodiment 2: A central target pollution risk analysis system based on distributed pollution monitoring, which is used to implement the central target pollution risk analysis method based on distributed pollution monitoring as described in Embodiment 1, as Figure 2 shown, including a data acquisition module, a data screening module, an azimuth division module, a contribution analysis module, and a classification early warning module.

[0108] Among them, the data acquisition module is used to acquire the internal pollutant concentration collected by the internal monitoring points of the target water source and the external pollutant concentration set collected by the external monitoring points of the target water source; the data screening module is used to screen out the effective data subset that has a pollution association with the internal pollutant concentration from the external pollutant concentration set; the azimuth division module is used to perform polar coordinate space mapping on the effective data subset and divide it into multiple azimuth intervals; the contribution analysis module is used to determine the azimuth contribution degree of the water source pollution risk according to the pollutant flux, migration path resistance, and meteorological parameters of all external monitoring points in the azimuth interval; the classification early warning module is used to compare the azimuth contribution degree with the risk contribution degree thresholds for different classification early warnings to obtain the pollution risk classification early warning result of the target water source in the corresponding azimuth interval.

[0109] Working principle: The present invention establishes a screening mechanism between external monitoring data and internal monitoring data to effectively eliminate noise data and extract high-value pollution signals. And combined with the polar coordinate space mapping technology, the pollution sources are clustered and analyzed according to azimuth intervals, which can improve the spatio-temporal analysis ability of the pollution diffusion path. At the same time, based on the pollutant flux, migration path resistance, and meteorological parameters, precise quantification of the pollution risk contribution degree in different azimuths is achieved, solving the problems of fuzzy spatial azimuth recognition and poor real-time performance of traditional methods.

[0110] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0111] The present application is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram can be implemented by computer program instructions, and the combination of the flows and / or blocks in the flowchart and / or block diagram can also be implemented. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in Figure 1 one or more of the flows Figure 1 or blocks or the combination of blocks.

[0112] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing devices to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured article including instruction means, and the instruction means implement the functions specified in Figure 1 one or more of the flows Figure 1 or blocks or the combination of blocks.

[0113] These computer program instructions can also be loaded onto a computer or other programmable data processing devices, so that a series of operation steps are executed on the computer or other programmable devices to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable devices provide steps for implementing the functions specified in Figure 1 one or more of the flows Figure 1 or blocks or the combination of blocks.

[0114] The specific embodiments described above further elaborate on the purpose, technical solutions, and beneficial effects of the present invention. It should be understood that the above description is only the specific embodiments of the present invention and is not used to limit the protection scope of the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A central target pollution risk analysis method based on distributed pollution monitoring, characterized in that Including the following steps: Obtain the internal pollutant concentrations collected at the internal monitoring points of the target water source and the set of external pollutant concentrations collected at the external monitoring points of the target water source; Screen out a subset of valid data from the set of external pollutant concentrations that has a pollution association with the internal pollutant concentrations; Perform polar coordinate space mapping on the subset of valid data and divide it into multiple azimuth intervals; Establish a polar coordinate system with the internal monitoring point or the center of the target water source as the origin, divide the space into multiple azimuth intervals according to the set azimuth angle size, and classify the azimuth angles of the external monitoring points into the azimuth intervals; Determine the azimuth contribution degree of the water source pollution risk based on the pollutant fluxes, migration path resistances, and meteorological parameters of all the external monitoring points in the azimuth interval; Compare the azimuth contribution degree with the risk contribution degree thresholds for different classification early warnings to obtain the pollution risk classification early warning result of the target water source in the corresponding azimuth interval; The determination of the azimuth contribution degree of the water source pollution risk includes: Perform weighted averaging on the pollutant fluxes, migration path resistances, and meteorological parameters of all the external monitoring points in the azimuth interval respectively to obtain the aggregated pollutant flux, aggregated migration path resistance, and aggregated meteorological parameter; the meteorological parameter is at least one of the wind speed parameter and the rainfall parameter; Perform normalization processing on the aggregated migration path resistance and determine a resistance correction parameter that is negatively correlated with the normalized value, and the value range of the resistance correction parameter is [0,1]; divide the aggregated migration path resistance by the resistance upper limit to obtain the normalized value, and use 1 minus the normalized value as the resistance correction parameter; Determine a meteorological correction parameter according to the aggregated meteorological parameter, and the value range of the meteorological correction parameter is [1,3]; determine the ratio of the wind speed parameter to the wind speed upper limit value, and then add 1 to the ratio as the meteorological correction parameter corresponding to the wind speed parameter; Determine the azimuth contribution degree for analyzing the water source pollution risk in the corresponding azimuth interval by multiplying the aggregated pollutant flux, the resistance correction parameter, and the meteorological correction parameter.

2. The central target pollution risk analysis method based on distributed pollution monitoring according to claim 1, wherein Screening out a subset of valid data from the set of external pollutant concentrations that has a pollution association with the internal pollutant concentrations includes: Determine the fluctuation association degree between the external pollutant concentrations in the set of external pollutant concentrations and the internal pollutant concentrations; If the fluctuation association degree is greater than the strong association fluctuation threshold, the corresponding external pollutant concentration is used as the valid data in the subset of valid data.

3. The method for analyzing the pollution risk of the central target based on distributed pollution monitoring according to claim 2, wherein, The determination process of the fluctuation association degree includes: Perform denoising processing and normalization processing on the external pollutant concentrations and the internal pollutant concentrations in the set of external pollutant concentrations to obtain the corresponding external concentration sequence and internal concentration sequence; Perform Hilbert transform on the external concentration sequence and the internal concentration sequence respectively to obtain the corresponding external transformation result and internal transformation result; Calculate the first instantaneous phase angle according to the external transformation result and calculate the second instantaneous phase angle according to the internal transformation result; Calculate the integral of the cosine value of the phase difference within a preset period according to the first instantaneous phase angle and the second instantaneous phase angle, and use the integral of the cosine value of the phase difference as the fluctuation correlation degree.

4. The central target pollution risk analysis method based on distributed pollution monitoring according to claim 1, characterized in that Screen out a subset of valid data with pollution correlation with the internal pollutant concentration from the external pollutant concentration set, including: Determine the topographic infiltration path resistance between the external pollutant concentration in the external pollutant concentration set and the internal pollutant concentration; If the topographic infiltration path resistance is less than or equal to the maximum allowable infiltration resistance, the corresponding external pollutant concentration is used as the valid data in the subset of valid data.

5. The method for analyzing the pollution risk of a central target based on distributed pollution monitoring according to claim 4, wherein The determination process of the topographic infiltration path resistance includes: Obtain the high-precision DEM data, geological data, and medium permeability between the external monitoring point corresponding to the external pollutant concentration and the internal monitoring point corresponding to the internal pollutant concentration; Based on the high-precision DEM data, the geological data, and the medium permeability, calculate the topographic infiltration path resistance through a three-dimensional resistance model based on the Darcy-Forschheimer equation.

6. The method for analyzing the pollution risk of a central target based on distributed pollution monitoring according to claim 1, wherein Screen out a subset of valid data with pollution correlation with the internal pollutant concentration from the external pollutant concentration set, including: Determine the distance attenuation effect value between the external pollutant concentration in the external pollutant concentration set and the internal pollutant concentration; If the distance attenuation effect value is less than the effect threshold, the corresponding external pollutant concentration is used as the valid data in the subset of valid data.

7. The method for analyzing the pollution risk of the central target based on distributed pollution monitoring according to claim 6, wherein The determination process of the distance attenuation effect value includes: Determine the straight-line distance between the external monitoring point corresponding to the external pollutant concentration and the internal monitoring point corresponding to the internal pollutant concentration; Determine the ratio between the straight-line distance and the characteristic attenuation distance; Input the ratio into an exponential attenuation model to calculate the distance attenuation effect value.

8. The central target pollution risk analysis system based on distributed pollution monitoring is characterized in that This system is used to implement the central target pollution risk analysis method based on distributed pollution monitoring as described in any one of claims 1-7, including: A data acquisition module for acquiring the internal pollutant concentration collected at the internal monitoring points of the target water source and the set of external pollutant concentrations collected at the external monitoring points of the target water source; A data screening module for screening out a subset of valid data with pollution correlation with the internal pollutant concentration from the external pollutant concentration set; An azimuth division module for performing polar coordinate space mapping on the subset of valid data and dividing it into multiple azimuth intervals; A contribution analysis module for determining the azimuth contribution degree of the water source pollution risk according to the pollutant flux, migration path resistance, and meteorological parameters of all the external monitoring points in the azimuth interval; A grading early warning module for comparing the azimuth contribution degree with the risk contribution degree thresholds of different grading early warnings to obtain the pollution risk grading early warning result of the target water source in the corresponding azimuth interval.

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