Heavy metal load capacity analysis method and system based on urban environmental media

Abstract

The present invention discloses a heavy metal load capacity analysis method and system based on urban environmental media, by constructing a city map model and dividing multiple sub-areas, obtaining urban medium data and heavy metal monitoring data, combining urban medium data and heavy metal monitoring data to perform pollution analysis of sub-areas, and determine the pollution source area; according to the city map model, pollution source area and heavy metal monitoring data, combined with the ARIMA prediction model, heavy metal diffusion prediction is performed on each sub-area to obtain the predicted diffusion information of each sub-area; based on the predicted diffusion information, the target city is analyzed, and the heavy metal pollution assessment data and heavy metal load capacity assessment data of the target city are obtained. Through the present invention, it is possible to effectively combine urban environmental medium analysis with regional attribute analysis, realize information-based and precise analysis and evaluation of urban heavy metal pollution, realize precise regulation of the target city, and further realize sustainable urban development.

Description

Technical Field

[0001] The present invention relates to the field of data analysis, and more specifically, to a heavy metal load capacity analysis method and system based on urban environmental media. Background Art

[0002] Currently, there are few pollution analysis and capacity assessment technologies for heavy metals, and they are restricted by traditional technologies. Heavy metal analysis in cities is not well combined with comprehensive analysis of environmental media. Therefore, how to conduct information-based and accurate analysis of urban pollution is an important issue that needs to be solved urgently. Summary of the invention

[0003] The present invention overcomes the defects of the prior art and proposes a heavy metal load capacity analysis method and system based on urban environmental media.

[0004] The first aspect of the present invention provides a heavy metal load capacity analysis method based on urban environmental media, comprising:

[0005] Obtaining basic information of a target city, and constructing a three-dimensional city map model based on the basic information;

[0006] Based on the city map model, the region is divided into multiple sub-regions, the urban medium data and heavy metal monitoring data of the target city are obtained, and the pollution analysis of the sub-region is carried out by combining the urban medium data and heavy metal monitoring data to determine the pollution source area;

[0007] According to the city map model, pollution source area and heavy metal monitoring data, combined with the ARIMA prediction model, heavy metal diffusion prediction is performed for each sub-area to obtain predicted diffusion information for each sub-area;

[0008] Based on the predicted diffusion information, heavy metal pollution and capacity analysis is performed on the target city, and heavy metal pollution assessment data and heavy metal load capacity assessment data of the target city are obtained.

[0009] In this solution, the basic information of the target city is obtained, and a three-dimensional city map model is constructed based on the basic information, specifically:

[0010] Obtain information on the city area, map outline, industrial area, residential area, and agricultural area of ​​the target city;

[0011] Integrate the city area, map outline, industrial area, residential area, and agricultural area information to form basic information;

[0012] A three-dimensional visualized city map model is constructed based on the basic information.

[0013] In this solution, the region is divided based on the city map model to obtain multiple sub-regions, the urban medium data and heavy metal monitoring data of the target city are obtained, and the pollution analysis of the sub-region is performed in combination with the urban medium data and heavy metal monitoring data to determine the pollution source area, specifically:

[0014] Through the city map model, the regional grid is divided based on the area size of the target city, and multiple sub-regions are obtained, and each sub-region is guaranteed to meet the preset regional standards;

[0015] Acquire urban medium data of a target city, wherein the urban medium data includes soil areas, water areas, green areas, industrial areas, residential areas, and agricultural areas;

[0016] According to the urban medium data, the regional proportion of soil and water is calculated for each sub-region to obtain a soil and water medium proportion value;

[0017] According to the urban media data, the land use nature regional proportion analysis is performed on each sub-region to obtain the proportion values ​​of industrial, residential, and agricultural regions;

[0018] The heavy metal monitoring data of the target city is obtained within a monitoring period, and the heavy metal monitoring data is divided based on sub-regions to obtain sub-region monitoring data corresponding to each sub-region.

[0019] In this solution, the pollution analysis of sub-regions is carried out by combining urban media data with heavy metal monitoring data to determine the pollution source area, specifically:

[0020] The pollution source coefficient is calculated based on the proportion of industrial, residential, and agricultural areas;

[0021] Based on the sub-area monitoring data, obtain the metal pollution information of all sub-areas;

[0022] The metal pollution information includes the types and concentrations of heavy metals;

[0023] The sub-areas whose metal pollution information meets the preset pollution standard and whose pollution source coefficient is greater than the preset threshold are marked as pollution source areas.

[0024] In this solution, the heavy metal diffusion prediction is performed on each sub-region based on the city map model, pollution source area and heavy metal monitoring data in combination with the ARIMA prediction model to obtain the predicted diffusion information of each sub-region, specifically:

[0025] Obtain the heavy metal monitoring data of the target city within N periods, and obtain the sub-region monitoring data of each sub-region within the corresponding N periods;

[0026] generating time series data for each sub-region based on the sub-region monitoring data of each sub-region in the N periods;

[0027] Performing data cleaning and outlier detection preprocessing on the time series data;

[0028] Build an ARIMA-based forecasting model;

[0029] Generating an autocorrelation graph (ACF) and a partial autocorrelation graph (PACF) according to the time series data, and determining prediction model parameters based on the autocorrelation graph and the partial autocorrelation graph;

[0030] Dividing the time series data into a training set and a test set, and importing the training set and the test set into a prediction model for prediction training;

[0031] Based on the prediction model and time series data, the time series data of M periods are predicted and analyzed to obtain the prediction series data of each sub-region;

[0032] The predicted sequence data is analyzed to obtain predicted heavy metal monitoring data of M periods in each sub-region.

[0033] In this solution, the heavy metal diffusion prediction is performed on each sub-region based on the city map model, pollution source area and heavy metal monitoring data in combination with the ARIMA prediction model to obtain the predicted diffusion information of each sub-region, specifically:

[0034] Taking a sub-region as the analysis unit, the predicted heavy metal monitoring data corresponding to M periods are used to calculate the pollution diffusion rate of heavy metals to obtain the heavy metal diffusion rate;

[0035] By using the city map model, the direction from a sub-area to the pollution source area is analyzed, and the direction is marked as the diffusion direction;

[0036] The heavy metal diffusion rate and diffusion direction of each sub-region are integrated to form predicted diffusion information.

[0037] In this solution, the heavy metal pollution and capacity analysis is performed on the target city based on the predicted diffusion information, and the heavy metal pollution assessment data and heavy metal load capacity assessment data of the target city are obtained, specifically:

[0038] Obtaining a diffusion rate and a diffusion direction of a sub-region from the predicted diffusion information and marking them as a current diffusion rate and a current diffusion direction respectively;

[0039] Based on the sub-region monitoring data of the one sub-region and the current diffusion rate, current heavy metal pollution assessment is performed on the sub-region to obtain heavy metal pollution assessment data;

[0040] Through the city map model, the current diffusion direction is vectorized to obtain multiple direction vectors, and the average direction deviation of the multiple direction vectors is calculated;

[0041] Based on the average directional deviation degree of a sub-region, predicted diffusion information, and predicted heavy metal monitoring data of M periods, load capacity analysis and evaluation are performed to obtain heavy metal load capacity evaluation data;

[0042] All sub-regions are evaluated and analyzed to obtain heavy metal pollution assessment data and heavy metal load capacity assessment data for all sub-regions, and further generate pollution control plans.

[0043] The second aspect of the present invention further provides a heavy metal load capacity analysis system based on urban environmental media, the system comprising: a memory, a processor, the memory comprising a heavy metal load capacity analysis program based on urban environmental media, the heavy metal load capacity analysis program based on urban environmental media being executed by the processor to implement the following steps:

[0044] Obtaining basic information of a target city, and constructing a three-dimensional city map model based on the basic information;

[0045] Based on the city map model, the region is divided into multiple sub-regions, the urban medium data and heavy metal monitoring data of the target city are obtained, and the pollution analysis of the sub-region is carried out by combining the urban medium data and heavy metal monitoring data to determine the pollution source area;

[0046] According to the city map model, pollution source area and heavy metal monitoring data, combined with the ARIMA prediction model, heavy metal diffusion prediction is performed for each sub-area to obtain predicted diffusion information for each sub-area;

[0047] Based on the predicted diffusion information, heavy metal pollution and capacity analysis is performed on the target city, and heavy metal pollution assessment data and heavy metal load capacity assessment data of the target city are obtained.

[0048] In this solution, the basic information of the target city is obtained, and a three-dimensional city map model is constructed based on the basic information, specifically:

[0049] Obtain information on the city area, map outline, industrial area, residential area, and agricultural area of ​​the target city;

[0050] Integrate the city area, map outline, industrial area, residential area, and agricultural area information to form basic information;

[0051] A three-dimensional visualized city map model is constructed based on the basic information.

[0052] The third aspect of the present invention also provides a computer-readable storage medium, which includes a heavy metal load capacity analysis program based on urban environment media. When the heavy metal load capacity analysis program based on urban environment media is executed by a processor, the steps of the heavy metal load capacity analysis method based on urban environment media as described in any of the above items are implemented.

[0053] The present invention discloses a heavy metal load capacity analysis method and system based on urban environmental media, by constructing a city map model and dividing multiple sub-areas, obtaining urban medium data and heavy metal monitoring data, combining urban medium data and heavy metal monitoring data to perform pollution analysis of sub-areas, and determine the pollution source area; according to the city map model, pollution source area and heavy metal monitoring data, combined with the ARIMA prediction model, heavy metal diffusion prediction is performed on each sub-area to obtain the predicted diffusion information of each sub-area; based on the predicted diffusion information, the target city is analyzed, and the heavy metal pollution assessment data and heavy metal load capacity assessment data of the target city are obtained. Through the present invention, it is possible to effectively combine urban environmental medium analysis with regional attribute analysis, realize information-based and precise analysis and evaluation of urban heavy metal pollution, realize precise regulation of the target city, and further realize sustainable urban development. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 A flow chart showing a heavy metal load capacity analysis method based on urban environmental media according to the present invention is shown;

[0055] Figure 2 The flow chart of constructing a city map model according to the present invention is shown;

[0056] Figure 3 The pollution source area acquisition flow chart of the present invention is shown;

[0057] Figure 4 A block diagram of a heavy metal load capacity analysis system based on urban environmental media according to the present invention is shown. DETAILED DESCRIPTION

[0058] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0059] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0060] Figure 1 A flow chart of a heavy metal load capacity analysis method based on urban environmental media according to the present invention is shown.

[0061] like Figure 1 As shown, the first aspect of the present invention provides a heavy metal load capacity analysis method based on urban environmental media, comprising:

[0062] S102, obtaining basic information of a target city, and constructing a three-dimensional city map model based on the basic information;

[0063] S104, performing regional division based on the city map model to obtain multiple sub-regions, obtaining urban medium data and heavy metal monitoring data of the target city, combining the urban medium data and heavy metal monitoring data to perform pollution analysis on the sub-regions, and determining the pollution source area;

[0064] S106, predicting the diffusion of heavy metals in each sub-region based on the city map model, pollution source areas and heavy metal monitoring data in combination with the ARIMA prediction model, to obtain predicted diffusion information for each sub-region;

[0065] S108, performing heavy metal pollution and capacity analysis on the target city based on the predicted diffusion information, and obtaining heavy metal pollution assessment data and heavy metal load capacity assessment data of the target city.

[0066] Figure 2 The flowchart of constructing a city map model according to the present invention is shown.

[0067] According to an embodiment of the present invention, the basic information of the target city is obtained, and a three-dimensional city map model is constructed based on the basic information, specifically:

[0068] S202, obtaining the city area, map outline, industrial area, residential area, and agricultural area information of the target city;

[0069] S204, integrating the city area, map outline, industrial area, residential area, and agricultural area information to form basic information;

[0070] S206: constructing a three-dimensional visualized city map model based on the basic information.

[0071] It should be noted that the city map type based on three-dimensional visualization can intuitively and conveniently view the distribution and pollution of heavy metals in the target city, thereby improving the user's visualization experience.

[0072] According to an embodiment of the present invention, the region is divided based on the city map model to obtain multiple sub-regions, the urban medium data and heavy metal monitoring data of the target city are obtained, and the pollution analysis of the sub-region is performed in combination with the urban medium data and the heavy metal monitoring data to determine the pollution source area, specifically:

[0073] Through the city map model, the regional grid is divided based on the area size of the target city, and multiple sub-regions are obtained, and each sub-region is guaranteed to meet the preset regional standards;

[0074] Acquire urban medium data of a target city, wherein the urban medium data includes soil areas, water areas, green areas, industrial areas, residential areas, and agricultural areas;

[0075] According to the urban medium data, the regional proportion of soil and water is calculated for each sub-region to obtain a soil and water medium proportion value;

[0076] According to the urban media data, the land use nature regional proportion analysis is performed on each sub-region to obtain the proportion values ​​of industrial, residential, and agricultural regions;

[0077] The heavy metal monitoring data of the target city is obtained within a monitoring period, and the heavy metal monitoring data is divided based on sub-regions to obtain sub-region monitoring data corresponding to each sub-region.

[0078] It should be noted that due to the complex situation of heavy metal pollution and diffusion in cities, the present invention divides the sub-regions to conduct refined regional analysis, and there are differences in urban media, heavy metal distribution, and heavy metal migration rates in different regions. The preset regional standards include area and shape standards. After grid division, a certain degree of fusion of subdivided areas can be performed to form sub-regions that meet the standards. The urban medium data and heavy metal monitoring data can be visualized in the city map model. The monitoring cycle is the most recent cycle.

[0079] In the urban medium data, the soil area and the water area have different media, which plays an important role in the subsequent heavy metal capacity and pollution diffusion analysis. In addition, in areas with different attributes, the land media are also different, such as green areas, industrial areas, and residential areas. There are also differences in the soil media between areas with different attributes. The water area refers to the river area, etc. The soil and water medium ratio value is the ratio of soil and water to the total area of ​​the corresponding sub-area, which can effectively reflect the medium situation. The industrial, residential, and agricultural area ratio values ​​are the ratios of industrial, residential, and agricultural areas to the total area of ​​the corresponding sub-area.

[0080] The heavy metal monitoring data of the target city generally includes at least one monitoring point in each sub-region. The heavy metal monitoring data includes the concentration and type of preset heavy metals, and the preset heavy metals include Zn, Cd, Cr, Pb, Hg, Cu, Ni, As, etc.

[0081] Figure 3 The flowchart of obtaining the pollution source area of ​​the present invention is shown.

[0082] According to an embodiment of the present invention, the combination of urban medium data and heavy metal monitoring data to perform pollution analysis in sub-regions and determine the pollution source area is specifically as follows:

[0083] S302, calculating the pollution source coefficient based on the percentage values ​​of industrial, residential, and agricultural areas;

[0084] S304, obtaining metal pollution information of all sub-areas based on the sub-area monitoring data;

[0085] S306, the metal pollution information includes the type and concentration of heavy metals;

[0086] S308: Mark the sub-region whose metal pollution information meets the preset pollution standard and whose pollution source coefficient is greater than the preset threshold as a pollution source region.

[0087] It should be noted that the calculation formula for the pollution source coefficient is:

[0088] W k =P1×k1+P2×k2+P3×k3

[0089] Among them, W k is the pollution source coefficient, P1, P2, P3 are the proportions of industrial, residential and agricultural areas respectively, k1, k2, k3 are the correction coefficients of industrial, residential and agricultural areas respectively.

[0090] The preset pollution standards include preset standard heavy metal types and standard concentrations.

[0091] It is worth mentioning that the pollution source coefficient can effectively reflect the probability of the corresponding sub-region being a pollution source. The larger the value, the greater the probability. Generally speaking, k1>k2>k3.

[0092] According to an embodiment of the present invention, the heavy metal diffusion prediction is performed on each sub-region based on the city map model, the pollution source area and the heavy metal monitoring data in combination with the ARIMA prediction model to obtain the predicted diffusion information of each sub-region, specifically:

[0093] Obtain the heavy metal monitoring data of the target city within N periods, and obtain the sub-region monitoring data of each sub-region within the corresponding N periods;

[0094] generating time series data for each sub-region based on the sub-region monitoring data of each sub-region in the N periods;

[0095] Performing data cleaning and outlier detection preprocessing on the time series data;

[0096] Build an ARIMA-based forecasting model;

[0097] Generating an autocorrelation graph (ACF) and a partial autocorrelation graph (PACF) according to the time series data, and determining prediction model parameters based on the autocorrelation graph and the partial autocorrelation graph;

[0098] Dividing the time series data into a training set and a test set, and importing the training set and the test set into a prediction model for prediction training;

[0099] Based on the prediction model and time series data, the time series data of M periods are predicted and analyzed to obtain the prediction series data of each sub-region;

[0100] The predicted sequence data is analyzed to obtain predicted heavy metal monitoring data of M periods in each sub-region.

[0101] It should be noted that M is a relatively low value, which is limited by the prediction accuracy of the ARIMA model. The longer the prediction period, the lower the data reliability, so M cannot be too large. The N periods are the most recent N periods.

[0102] According to an embodiment of the present invention, the heavy metal diffusion prediction is performed on each sub-region based on the city map model, the pollution source area and the heavy metal monitoring data in combination with the ARIMA prediction model to obtain the predicted diffusion information of each sub-region, specifically:

[0103] Taking a sub-region as the analysis unit, the predicted heavy metal monitoring data corresponding to M periods are used to calculate the pollution diffusion rate of heavy metals to obtain the heavy metal diffusion rate;

[0104] By using the city map model, the direction from a sub-area to the pollution source area is analyzed, and the direction is marked as the diffusion direction;

[0105] The heavy metal diffusion rate and diffusion direction of each sub-region are integrated to form predicted diffusion information.

[0106] It should be noted that the predicted heavy metal monitoring data includes preset heavy metal types and concentrations, and the diffusion rate is the concentration change rate of the heavy metal calculated within M cycles, which can directly reflect the diffusion rate. The pollution source area includes at least one, and the diffusion direction includes at least one.

[0107] According to an embodiment of the present invention, the heavy metal pollution and capacity analysis is performed on the target city based on the predicted diffusion information, and the heavy metal pollution assessment data and heavy metal load capacity assessment data of the target city are obtained, specifically:

[0108] Obtaining a diffusion rate and a diffusion direction of a sub-region from the predicted diffusion information and marking them as a current diffusion rate and a current diffusion direction respectively;

[0109] Based on the sub-region monitoring data of the one sub-region and the current diffusion rate, current heavy metal pollution assessment is performed on the sub-region to obtain heavy metal pollution assessment data;

[0110] Through the city map model, the current diffusion direction is vectorized to obtain multiple direction vectors, and the average direction deviation of the multiple direction vectors is calculated;

[0111] Based on the average directional deviation degree of a sub-region, predicted diffusion information, and predicted heavy metal monitoring data of M periods, load capacity analysis and evaluation are performed to obtain heavy metal load capacity evaluation data;

[0112] All sub-regions are evaluated and analyzed to obtain heavy metal pollution assessment data and heavy metal load capacity assessment data for all sub-regions, and further generate pollution control plans.

[0113] It should be noted that the direction vector is mainly used to analyze the direction value. The average directional deviation, i.e., the average value of the angles between the directional vectors, can reflect the overall pollution diffusion direction of the pollution source to the current sub-region. The larger the deviation, the wider the range of the direction of pollution diffusion in the current sub-region. On the contrary, the more single the direction of pollution diffusion, for example, in a sub-region, there is a pollution source in each of the left and right (or east-west) directions, then the pollution diffusion direction is wider, and the average directional deviation is also larger. For example, in a sub-region, there are two pollution sources in each of the right (or east) directions, then the pollution diffusion direction is relatively single, and the diffusion range is narrower (all in the same direction). The heavy metal load capacity assessment data includes information such as the load capacity value, current capacity value, and expected time to reach the load of multiple heavy metals, and there are differences in the multiple heavy metals analyzed in each sub-region. The pollution control scheme includes heavy metal pollution control information for each sub-region.

[0114] It is worth mentioning that the smaller the heavy metal load capacity value is, the more its sub-region is affected by the current environment, the more serious the spread of various pollution sources and heavy metals to its sub-region is, its load capacity becomes smaller, and its bearing capacity is also weakening. Therefore, the corresponding subsequent pollution control priority is also higher.

[0115] In a sub-region, the corresponding average directional deviation and diffusion rate are both negatively correlated with the loading capacity value.

[0116] According to an embodiment of the present invention, it also includes:

[0117] Based on the predicted diffusion information, the diffusion rates of all sub-regions in the target city are obtained;

[0118] Based on the heavy metal load capacity assessment data, obtain the heavy metal load capacity values ​​of all sub-areas in the target city;

[0119] Judging all sub-regions, marking sub-regions with a diffusion rate higher than a first preset value and a heavy metal loading capacity value lower than the first preset value as low-demand regions, and marking other non-low-demand regions as high-demand regions;

[0120] In the next monitoring cycle, heavy metal monitoring will be conducted in high-demand areas, and the corresponding heavy metal monitoring data of high-demand areas will be obtained;

[0121] Select a low-demand area, and obtain multiple adjacent sub-areas of the low-demand area in combination with the city map model, and ensure that the adjacent sub-areas are high-demand areas;

[0122] Extracting monitoring data of multiple adjacent sub-areas from the heavy metal monitoring data of the high-demand area to obtain the adjacent area monitoring data;

[0123] According to the monitoring data of the adjacent area, data prediction based on a linear regression model is performed on the one low-demand area to obtain corresponding linear prediction data;

[0124] Analyze all low-demand areas and obtain corresponding linear prediction data;

[0125] All linear prediction data are integrated with the heavy metal monitoring data in high-demand areas to form complete monitoring data for the target city.

[0126] It should be noted that the heavy metal load capacity value is generally taken as the average load value of multiple heavy metals. In the present invention, in the sub-region where the diffusion rate is higher than the first preset value and the heavy metal load capacity value is less than the first preset value, the characteristics are that the heavy metal pollution in the region is less affected by the environment and has a certain capacity load, and the monitoring data is highly correlated and highly linear with the adjacent area. Therefore, it is marked as a low demand area, and the data is filled by linear regression prediction of the adjacent area, which can reduce unnecessary monitoring points and reduce the manpower and material costs of monitoring while ensuring the accuracy and integrity of the data in advance, thereby conducting a more scientific and efficient heavy metal capacity load analysis, and providing high-accuracy data support for the follow-up.

[0127] According to an embodiment of the present invention, the

[0128] Figure 4 A block diagram of a heavy metal load capacity analysis system based on urban environmental media according to the present invention is shown.

[0129] The second aspect of the present invention further provides a heavy metal load capacity analysis system 4 based on urban environmental media, the system comprising: a memory 41, a processor 42, the memory comprising a heavy metal load capacity analysis program based on urban environmental media, the heavy metal load capacity analysis program based on urban environmental media being executed by the processor to implement the following steps:

[0130] Obtaining basic information of a target city, and constructing a three-dimensional city map model based on the basic information;

[0131] Based on the city map model, the region is divided into multiple sub-regions, the urban medium data and heavy metal monitoring data of the target city are obtained, and the pollution analysis of the sub-region is carried out by combining the urban medium data and heavy metal monitoring data to determine the pollution source area;

[0132] According to the city map model, pollution source area and heavy metal monitoring data, combined with the ARIMA prediction model, heavy metal diffusion prediction is performed for each sub-area to obtain predicted diffusion information for each sub-area;

[0133] Based on the predicted diffusion information, heavy metal pollution and capacity analysis is performed on the target city, and heavy metal pollution assessment data and heavy metal load capacity assessment data of the target city are obtained.

[0134] According to an embodiment of the present invention, the basic information of the target city is obtained, and a three-dimensional city map model is constructed based on the basic information, specifically:

[0135] Obtain information on the city area, map outline, industrial area, residential area, and agricultural area of ​​the target city;

[0136] Integrate the city area, map outline, industrial area, residential area, and agricultural area information to form basic information;

[0137] A three-dimensional visualized city map model is constructed based on the basic information.

[0138] It should be noted that the city map type based on three-dimensional visualization can intuitively and conveniently view the distribution and pollution of heavy metals in the target city, thereby improving the user's visualization experience.

[0139] According to an embodiment of the present invention, the region is divided based on the city map model to obtain multiple sub-regions, the urban medium data and heavy metal monitoring data of the target city are obtained, and the pollution analysis of the sub-region is performed in combination with the urban medium data and the heavy metal monitoring data to determine the pollution source area, specifically:

[0140] Through the city map model, the regional grid is divided based on the area size of the target city, and multiple sub-regions are obtained, and each sub-region is guaranteed to meet the preset regional standards;

[0141] Acquire urban medium data of a target city, wherein the urban medium data includes soil areas, water areas, green areas, industrial areas, residential areas, and agricultural areas;

[0142] According to the urban medium data, the regional proportion of soil and water is calculated for each sub-region to obtain a soil and water medium proportion value;

[0143] According to the urban media data, the land use nature regional proportion analysis is performed on each sub-region to obtain the proportion values ​​of industrial, residential, and agricultural regions;

[0144] The heavy metal monitoring data of the target city is obtained within a monitoring period, and the heavy metal monitoring data is divided based on sub-regions to obtain sub-region monitoring data corresponding to each sub-region.

[0145] It should be noted that due to the complex situation of heavy metal pollution and diffusion in cities, the present invention divides the sub-regions to conduct refined regional analysis, and there are differences in urban media, heavy metal distribution, and heavy metal migration rates in different regions. The preset regional standards include area and shape standards. After grid division, a certain degree of fusion of subdivided areas can be performed to form sub-regions that meet the standards. The urban medium data and heavy metal monitoring data can be visualized in the city map model. The monitoring cycle is the most recent cycle.

[0146] In the urban medium data, the soil area and the water area have different media, which plays an important role in the subsequent heavy metal capacity and pollution diffusion analysis. In addition, in areas with different attributes, the land media are also different, such as green areas, industrial areas, and residential areas. There are also differences in the soil media between areas with different attributes. The water area refers to the river area, etc. The soil and water medium ratio value is the ratio of soil and water to the total area of ​​the corresponding sub-area, which can effectively reflect the medium situation. The industrial, residential, and agricultural area ratio values ​​are the ratios of industrial, residential, and agricultural areas to the total area of ​​the corresponding sub-area.

[0147] The heavy metal monitoring data of the target city generally includes at least one monitoring point in each sub-region. The heavy metal monitoring data includes the concentration and type of preset heavy metals, and the preset heavy metals include Zn, Cd, Cr, Pb, Hg, Cu, Ni, As, etc.

[0148] According to an embodiment of the present invention, the combination of urban medium data and heavy metal monitoring data to perform pollution analysis in sub-regions and determine the pollution source area is specifically as follows:

[0149] The pollution source coefficient is calculated based on the proportion of industrial, residential, and agricultural areas;

[0150] Based on the sub-area monitoring data, obtain the metal pollution information of all sub-areas;

[0151] The metal pollution information includes the types and concentrations of heavy metals;

[0152] The sub-areas whose metal pollution information meets the preset pollution standard and whose pollution source coefficient is greater than the preset threshold are marked as pollution source areas.

[0153] It should be noted that the calculation formula for the pollution source coefficient is:

[0154] W k =P1×k1+P2×k2+P3×k3

[0155] Among them, W k is the pollution source coefficient, P1, P2, P3 are the proportions of industrial, residential and agricultural areas respectively, k1, k2, k3 are the correction coefficients of industrial, residential and agricultural areas respectively.

[0156] The preset pollution standards include preset standard heavy metal types and standard concentrations.

[0157] It is worth mentioning that the pollution source coefficient can effectively reflect the probability of the corresponding sub-region being a pollution source. The larger the value, the greater the probability. Generally speaking, k1>k2>k3.

[0158] According to an embodiment of the present invention, the heavy metal diffusion prediction is performed on each sub-region based on the city map model, the pollution source area and the heavy metal monitoring data in combination with the ARIMA prediction model to obtain the predicted diffusion information of each sub-region, specifically:

[0159] Obtain the heavy metal monitoring data of the target city within N periods, and obtain the sub-region monitoring data of each sub-region within the corresponding N periods;

[0160] generating time series data for each sub-region based on the sub-region monitoring data of each sub-region in the N periods;

[0161] Performing data cleaning and outlier detection preprocessing on the time series data;

[0162] Build an ARIMA-based forecasting model;

[0163] Generating an autocorrelation graph (ACF) and a partial autocorrelation graph (PACF) according to the time series data, and determining prediction model parameters based on the autocorrelation graph and the partial autocorrelation graph;

[0164] Dividing the time series data into a training set and a test set, and importing the training set and the test set into a prediction model for prediction training;

[0165] Based on the prediction model and time series data, the time series data of M periods are predicted and analyzed to obtain the prediction series data of each sub-region;

[0166] The predicted sequence data is analyzed to obtain predicted heavy metal monitoring data of M periods in each sub-region.

[0167] It should be noted that M is a relatively low value, which is limited by the prediction accuracy of the ARIMA model. The longer the prediction period, the lower the data reliability, so M cannot be too large. The N periods are the most recent N periods.

[0168] According to an embodiment of the present invention, the heavy metal diffusion prediction is performed on each sub-region based on the city map model, the pollution source area and the heavy metal monitoring data in combination with the ARIMA prediction model to obtain the predicted diffusion information of each sub-region, specifically:

[0169] Taking a sub-region as the analysis unit, the predicted heavy metal monitoring data corresponding to M periods are used to calculate the pollution diffusion rate of heavy metals to obtain the heavy metal diffusion rate;

[0170] By using the city map model, the direction from a sub-area to the pollution source area is analyzed, and the direction is marked as the diffusion direction;

[0171] The heavy metal diffusion rate and diffusion direction of each sub-region are integrated to form predicted diffusion information.

[0172] It should be noted that the predicted heavy metal monitoring data includes preset heavy metal types and concentrations, and the diffusion rate is the concentration change rate of the heavy metal calculated within M cycles, which can directly reflect the diffusion rate. The pollution source area includes at least one, and the diffusion direction includes at least one.

[0173] According to an embodiment of the present invention, the heavy metal pollution and capacity analysis is performed on the target city based on the predicted diffusion information, and the heavy metal pollution assessment data and heavy metal load capacity assessment data of the target city are obtained, specifically:

[0174] Obtaining a diffusion rate and a diffusion direction of a sub-region from the predicted diffusion information and marking them as a current diffusion rate and a current diffusion direction respectively;

[0175] Based on the sub-region monitoring data of the one sub-region and the current diffusion rate, current heavy metal pollution assessment is performed on the sub-region to obtain heavy metal pollution assessment data;

[0176] Through the city map model, the current diffusion direction is vectorized to obtain multiple direction vectors, and the average direction deviation of the multiple direction vectors is calculated;

[0177] Based on the average directional deviation degree of a sub-region, predicted diffusion information, and predicted heavy metal monitoring data of M periods, load capacity analysis and evaluation are performed to obtain heavy metal load capacity evaluation data;

[0178] All sub-regions are evaluated and analyzed to obtain heavy metal pollution assessment data and heavy metal load capacity assessment data for all sub-regions, and further generate pollution control plans.

[0179] It should be noted that the direction vector is mainly used to analyze the direction value. The average directional deviation, i.e., the average value of the angles between the directional vectors, can reflect the overall pollution diffusion direction of the pollution source to the current sub-region. The larger the deviation, the wider the range of the direction of pollution diffusion in the current sub-region. On the contrary, the more single the direction of pollution diffusion, for example, in a sub-region, there is a pollution source in each of the left and right (or east-west) directions, then the pollution diffusion direction is wider, and the average directional deviation is also larger. For example, in a sub-region, there are two pollution sources in each of the right (or east) directions, then the pollution diffusion direction is relatively single, and the diffusion range is narrower (all in the same direction). The heavy metal load capacity assessment data includes information such as the load capacity value, current capacity value, and expected time to reach the load of multiple heavy metals, and there are differences in the multiple heavy metals analyzed in each sub-region. The pollution control scheme includes heavy metal pollution control information for each sub-region.

[0180] It is worth mentioning that the smaller the heavy metal load capacity value is, the more its sub-region is affected by the current environment, the more serious the spread of various pollution sources and heavy metals to its sub-region is, its load capacity becomes smaller, and its bearing capacity is also weakening. Therefore, the corresponding subsequent pollution control priority is also higher.

[0181] In a sub-region, the corresponding average directional deviation and diffusion rate are both negatively correlated with the loading capacity value.

[0182] According to an embodiment of the present invention, it also includes:

[0183] Based on the predicted diffusion information, the diffusion rates of all sub-regions in the target city are obtained;

[0184] Based on the heavy metal load capacity assessment data, obtain the heavy metal load capacity values ​​of all sub-areas in the target city;

[0185] Judging all sub-regions, marking sub-regions with a diffusion rate higher than a first preset value and a heavy metal loading capacity value lower than the first preset value as low-demand regions, and marking other non-low-demand regions as high-demand regions;

[0186] In the next monitoring cycle, heavy metal monitoring will be conducted in high-demand areas, and the corresponding heavy metal monitoring data of high-demand areas will be obtained;

[0187] Select a low-demand area, and obtain multiple adjacent sub-areas of the low-demand area in combination with the city map model, and ensure that the adjacent sub-areas are high-demand areas;

[0188] Extracting monitoring data of multiple adjacent sub-areas from the heavy metal monitoring data of the high-demand area to obtain the adjacent area monitoring data;

[0189] According to the monitoring data of the adjacent area, data prediction based on a linear regression model is performed on the one low-demand area to obtain corresponding linear prediction data;

[0190] Analyze all low-demand areas and obtain corresponding linear prediction data;

[0191] All linear prediction data are integrated with the heavy metal monitoring data in high-demand areas to form complete monitoring data for the target city.

[0192] It should be noted that the heavy metal load capacity value is generally taken as the average load value of multiple heavy metals. In the present invention, in the sub-region where the diffusion rate is higher than the first preset value and the heavy metal load capacity value is less than the first preset value, the characteristics are that the heavy metal pollution in the region is less affected by the environment and has a certain capacity load, and the monitoring data is highly correlated and highly linear with the adjacent area. Therefore, it is marked as a low demand area, and the data is filled by linear regression prediction of the adjacent area, which can reduce unnecessary monitoring points and reduce the manpower and material costs of monitoring while ensuring the accuracy and integrity of the data in advance, thereby conducting a more scientific and efficient heavy metal capacity load analysis, and providing high-accuracy data support for the follow-up.

[0193] The third aspect of the present invention also provides a computer-readable storage medium, which includes a heavy metal load capacity analysis program based on urban environment media. When the heavy metal load capacity analysis program based on urban environment media is executed by a processor, the steps of the heavy metal load capacity analysis method based on urban environment media as described in any of the above items are implemented.

[0194] The present invention discloses a heavy metal load capacity analysis method and system based on urban environmental media, by constructing a city map model and dividing multiple sub-areas, obtaining urban medium data and heavy metal monitoring data, combining urban medium data and heavy metal monitoring data to perform pollution analysis of sub-areas, and determine the pollution source area; according to the city map model, pollution source area and heavy metal monitoring data, combined with the ARIMA prediction model, heavy metal diffusion prediction is performed on each sub-area to obtain the predicted diffusion information of each sub-area; based on the predicted diffusion information, the target city is analyzed, and the heavy metal pollution assessment data and heavy metal load capacity assessment data of the target city are obtained. Through the present invention, it is possible to effectively combine urban environmental medium analysis with regional attribute analysis, realize information-based and precise analysis and evaluation of urban heavy metal pollution, realize precise regulation of the target city, and further realize sustainable urban development.

[0195] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0196] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed on multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0197] In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0198] A person of ordinary skill in the art can understand that: all or part of the steps of implementing the above method embodiments can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above method embodiments; and the aforementioned storage medium includes: mobile storage devices, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks or optical disks, and other media that can store program codes.

[0199] Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention can be essentially or partly reflected in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, magnetic disks or optical disks.

[0200] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A heavy metal load capacity analysis method based on urban environmental media, characterized in that: include: Obtaining basic information of a target city, and constructing a three-dimensional city map model based on the basic information; Based on the city map model, the region is divided into multiple sub-regions, the urban medium data and heavy metal monitoring data of the target city are obtained, and the pollution analysis of the sub-region is carried out by combining the urban medium data and heavy metal monitoring data to determine the pollution source area; According to the city map model, pollution source area and heavy metal monitoring data, combined with the ARIMA prediction model, heavy metal diffusion prediction is performed for each sub-area to obtain predicted diffusion information for each sub-area; Based on the predicted diffusion information, heavy metal pollution and capacity analysis is performed on the target city, and heavy metal pollution assessment data and heavy metal load capacity assessment data of the target city are obtained; The regional division is performed based on the city map model to obtain multiple sub-regions, obtain the urban medium data and heavy metal monitoring data of the target city, and perform pollution analysis on the sub-regions in combination with the urban medium data and heavy metal monitoring data to determine the pollution source area, specifically: Through the city map model, the regional grid is divided based on the area size of the target city, and multiple sub-regions are obtained, and each sub-region is guaranteed to meet the preset regional standards; Acquire urban medium data of a target city, wherein the urban medium data includes soil areas, water areas, green areas, industrial areas, residential areas, and agricultural areas; According to the urban medium data, the regional proportion of soil and water is calculated for each sub-region to obtain a soil and water medium proportion value; According to the urban media data, the land use nature regional proportion analysis is performed on each sub-region to obtain the proportion values ​​of industrial, residential, and agricultural regions; Obtain the heavy metal monitoring data of the target city within a monitoring period, divide the heavy metal monitoring data based on sub-regions, and obtain the sub-region monitoring data corresponding to each sub-region; Among them, the heavy metal diffusion prediction is performed on each sub-region according to the city map model, pollution source area and heavy metal monitoring data in combination with the ARIMA prediction model to obtain the predicted diffusion information of each sub-region, specifically: Obtain the heavy metal monitoring data of the target city within N periods, and obtain the sub-region monitoring data of each sub-region within the corresponding N periods; generating time series data for each sub-region based on the sub-region monitoring data of each sub-region in the N periods; Performing data cleaning and outlier detection preprocessing on the time series data; Build an ARIMA-based forecasting model; Generate an autocorrelation graph and a partial autocorrelation graph according to the time series data, and determine prediction model parameters based on the autocorrelation graph and the partial autocorrelation graph; Dividing the time series data into a training set and a test set, and importing the training set and the test set into a prediction model for prediction training; Based on the prediction model and time series data, the time series data of M periods are predicted and analyzed to obtain the prediction series data of each sub-region; The predicted sequence data is analyzed to obtain predicted heavy metal monitoring data of M periods in each sub-region; Among them, the heavy metal diffusion prediction is performed on each sub-region according to the city map model, pollution source area and heavy metal monitoring data in combination with the ARIMA prediction model to obtain the predicted diffusion information of each sub-region, specifically: Taking a sub-region as the analysis unit, the predicted heavy metal monitoring data corresponding to M periods are used to calculate the pollution diffusion rate of heavy metals to obtain the heavy metal diffusion rate; By using the city map model, the direction from a sub-area to the pollution source area is analyzed, and the direction is marked as the diffusion direction; Integrate the information of the heavy metal diffusion rate and diffusion direction in each sub-region to form predicted diffusion information; The heavy metal pollution and capacity analysis of the target city based on the predicted diffusion information is performed to obtain the heavy metal pollution assessment data and heavy metal load capacity assessment data of the target city, specifically: Obtaining a diffusion rate and a diffusion direction of a sub-region from the predicted diffusion information and marking them as a current diffusion rate and a current diffusion direction respectively; Based on the sub-region monitoring data and the current diffusion rate of the one sub-region, current heavy metal pollution assessment is performed on the sub-region to obtain heavy metal pollution assessment data; Through the city map model, the current diffusion direction is vectorized to obtain multiple direction vectors, and the average direction deviation of the multiple direction vectors is calculated; Based on the average directional deviation degree of a sub-region, predicted diffusion information, and predicted heavy metal monitoring data of M periods, load capacity analysis and evaluation are performed to obtain heavy metal load capacity evaluation data; All sub-regions are evaluated and analyzed to obtain heavy metal pollution assessment data and heavy metal load capacity assessment data for all sub-regions, and further generate pollution control plans.

2. A heavy metal load capacity analysis method based on urban environmental media according to claim 1, characterized in that: The acquiring of basic information of the target city and the building of a three-dimensional city map model based on the basic information are specifically as follows: Obtain information on the city area, map outline, industrial area, residential area, and agricultural area of ​​the target city; Integrate the city area, map outline, industrial area, residential area, and agricultural area information to form basic information; A three-dimensional visualized city map model is constructed based on the basic information.

3. A heavy metal load capacity analysis method based on urban environmental media according to claim 2, characterized in that: The pollution analysis of sub-regions is performed by combining urban medium data with heavy metal monitoring data to determine the pollution source area, specifically: The pollution source coefficient is calculated based on the proportion of industrial, residential, and agricultural areas; Based on the sub-area monitoring data, obtain the metal pollution information of all sub-areas; The metal pollution information includes the types and concentrations of heavy metals; The sub-areas whose metal pollution information meets the preset pollution standard and whose pollution source coefficient is greater than the preset threshold are marked as pollution source areas.

4. A heavy metal load capacity analysis system based on urban environmental media, characterized in that: The system includes: a memory and a processor, wherein the memory includes a heavy metal load capacity analysis program based on an urban environment medium, and when the heavy metal load capacity analysis program based on an urban environment medium is executed by the processor, the following steps are implemented: Obtaining basic information of a target city, and constructing a three-dimensional city map model based on the basic information; Based on the city map model, the region is divided into multiple sub-regions, the urban medium data and heavy metal monitoring data of the target city are obtained, and the pollution analysis of the sub-region is carried out by combining the urban medium data and heavy metal monitoring data to determine the pollution source area; According to the city map model, pollution source area and heavy metal monitoring data, combined with the ARIMA prediction model, heavy metal diffusion prediction is performed for each sub-area to obtain predicted diffusion information for each sub-area; Based on the predicted diffusion information, heavy metal pollution and capacity analysis is performed on the target city, and heavy metal pollution assessment data and heavy metal load capacity assessment data of the target city are obtained; The regional division is performed based on the city map model to obtain multiple sub-regions, obtain the urban medium data and heavy metal monitoring data of the target city, and perform pollution analysis on the sub-regions in combination with the urban medium data and heavy metal monitoring data to determine the pollution source area, specifically: Through the city map model, the regional grid is divided based on the area size of the target city, and multiple sub-regions are obtained, and each sub-region is guaranteed to meet the preset regional standards; Acquire urban medium data of a target city, wherein the urban medium data includes soil areas, water areas, green areas, industrial areas, residential areas, and agricultural areas; According to the urban medium data, the regional proportion of soil and water is calculated for each sub-region to obtain a soil and water medium proportion value; According to the urban media data, the land use nature regional proportion analysis is performed on each sub-region to obtain the proportion values ​​of industrial, residential, and agricultural regions; Obtain the heavy metal monitoring data of the target city within a monitoring period, divide the heavy metal monitoring data based on sub-regions, and obtain the sub-region monitoring data corresponding to each sub-region; Among them, the heavy metal diffusion prediction is performed on each sub-region according to the city map model, pollution source area and heavy metal monitoring data in combination with the ARIMA prediction model to obtain the predicted diffusion information of each sub-region, specifically: Obtain the heavy metal monitoring data of the target city within N periods, and obtain the sub-region monitoring data of each sub-region within the corresponding N periods; generating time series data for each sub-region based on the sub-region monitoring data of each sub-region in the N periods; Performing data cleaning and outlier detection preprocessing on the time series data; Build an ARIMA-based forecasting model; Generate an autocorrelation graph and a partial autocorrelation graph according to the time series data, and determine prediction model parameters based on the autocorrelation graph and the partial autocorrelation graph; Dividing the time series data into a training set and a test set, and importing the training set and the test set into a prediction model for prediction training; Based on the prediction model and time series data, the time series data of M periods are predicted and analyzed to obtain the prediction series data of each sub-region; The predicted sequence data is analyzed to obtain predicted heavy metal monitoring data of M periods in each sub-region; Among them, the heavy metal diffusion prediction is performed on each sub-region according to the city map model, pollution source area and heavy metal monitoring data in combination with the ARIMA prediction model to obtain the predicted diffusion information of each sub-region, specifically: Taking a sub-region as the analysis unit, the predicted heavy metal monitoring data corresponding to M periods are used to calculate the pollution diffusion rate of heavy metals to obtain the heavy metal diffusion rate; By using the city map model, the direction from a sub-area to the pollution source area is analyzed, and the direction is marked as the diffusion direction; Integrate the information of the heavy metal diffusion rate and diffusion direction in each sub-region to form predicted diffusion information; The heavy metal pollution and capacity analysis of the target city based on the predicted diffusion information is performed to obtain the heavy metal pollution assessment data and heavy metal load capacity assessment data of the target city, specifically: Obtaining a diffusion rate and a diffusion direction of a sub-region from the predicted diffusion information and marking them as a current diffusion rate and a current diffusion direction respectively; Based on the sub-region monitoring data and the current diffusion rate of the one sub-region, current heavy metal pollution assessment is performed on the sub-region to obtain heavy metal pollution assessment data; Through the city map model, the current diffusion direction is vectorized to obtain multiple direction vectors, and the average direction deviation of the multiple direction vectors is calculated; Based on the average directional deviation degree of a sub-region, predicted diffusion information, and predicted heavy metal monitoring data of M periods, load capacity analysis and evaluation are performed to obtain heavy metal load capacity evaluation data; All sub-regions are evaluated and analyzed to obtain heavy metal pollution assessment data and heavy metal load capacity assessment data for all sub-regions, and further generate pollution control plans.

5. A heavy metal load capacity analysis system based on urban environmental media according to claim 4, characterized in that: The acquiring of basic information of the target city and the building of a three-dimensional city map model based on the basic information are specifically as follows: Obtain information on the city area, map outline, industrial area, residential area, and agricultural area of ​​the target city; Integrate the city area, map outline, industrial area, residential area, and agricultural area information to form basic information; A three-dimensional visualized city map model is constructed based on the basic information.

6. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a heavy metal load capacity analysis program based on urban environmental media. When the heavy metal load capacity analysis program based on urban environmental media is executed by a processor, the steps of the heavy metal load capacity analysis method based on urban environmental media as described in any one of claims 1 to 3 are implemented.

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