An environmental pollution source detection and management system based on big data

Abstract

The invention relates to the field of environmental pollution monitoring, and discloses an environmental pollution source detection and management system based on big data, comprising an environmental data acquisition module, which is used to acquire various environmental data in real time; an environmental data processing module, which obtains a noise pollution index, a water pollution index and an atmospheric pollutant index respectively according to preset processing rules based on various environmental data acquired in real time; a pollution source analysis module, which compares the noise pollution index, the water pollution index and the atmospheric pollution index with corresponding pollution index thresholds respectively, and if at least one item exceeds the pollution index threshold, marks the detection area as a pollution area, acquires the position of each pollution area, and then establishes a pollution trend model according to the historical changes of each pollution area; and a pollution source management module, which performs corresponding management and control according to the results predicted by the pollution trend model of each pollution area.

Description

Technical Field

[0001] The present invention relates to the field of environmental pollution monitoring, and in particular to an environmental pollution source detection and management system based on big data. Background Art

[0002] Traditional environmental monitoring methods, such as manual sampling and laboratory analysis, have inherent defects such as long monitoring cycles, insufficient data accuracy, and limited coverage, and are no longer able to effectively respond to the current complex and changing environmental protection needs. Therefore, it has become an urgent task to develop an efficient, accurate, and comprehensive environmental pollution source detection and management system.

[0003] In recent years, the vigorous development of big data technology has brought about a revolutionary change in the field of environmental pollution monitoring. With its ability to process massive amounts of data, mine the deep value of data, and provide instant decision support, big data technology has significantly improved the efficiency and accuracy of environmental monitoring. The environmental pollution source detection and management system based on big data relies on these technical advantages to achieve real-time tracking, accurate identification and scientific management of environmental pollution sources, injecting new vitality into environmental protection work.

[0004] However, the existing environmental pollution source detection and management systems still have certain limitations. In practical applications, these systems usually rely on real-time pollution parameter detection to trigger the alarm mechanism, but ignore the evaluation and prediction of pollution trends. This results in managers often only being able to respond based on the alarm information after the pollution incident occurs, resulting in a certain management lag. This lag not only affects the timeliness of environmental management, but may also exacerbate the negative impact of environmental pollution to a certain extent.

[0005] Therefore, in order to overcome this defect of the existing system, we need to further explore the application of big data technology in the detection and management of environmental pollution sources, so as to achieve accurate prediction and evaluation of pollution trends, so as to take preventive measures in advance and reduce the risk of environmental pollution. Summary of the invention

[0006] The purpose of the present invention is to provide an environmental pollution source detection and management system based on big data to solve the above technical problems.

[0007] The purpose of the present invention can be achieved through the following technical solutions:

[0008] An environmental pollution source detection and management system based on big data, comprising:

[0009] The environmental data acquisition module divides the target area into multiple detection areas, and each detection area is provided with a group of environmental sensors for real-time collection of various environmental data, including noise, water quality and air pollutant data;

[0010] The environmental data processing module obtains the noise pollution index, water pollution index and air pollutant index respectively according to the preset processing rules based on the real-time collected environmental data;

[0011] The pollution source analysis module compares the noise pollution index, water pollution index and air pollution index with the corresponding pollution index thresholds. If at least one of them exceeds the pollution index threshold, the detection area is marked as a polluted area, the location of each polluted area is obtained, and then a pollution trend model is established based on the historical changes of each polluted area.

[0012] The pollution source management module performs corresponding management and control based on the results predicted by the pollution trend model of each pollution area.

[0013] A further technical solution is to establish the pollution trend model in the following process:

[0014] Obtain the time-varying curves of noise pollution index, water pollution index and air pollution index in each pollution area , , ;

[0015] Establish a coordinate system with time as the horizontal axis and various pollution indices as the vertical axis;

[0016] Draw the pollution index threshold straight lines corresponding to each pollution index on the coordinate system;

[0017] Determine whether the area where each pollution index changes over time intersects with the corresponding pollution index threshold straight line;

[0018] If there is an intersection, the sub-periods within a working cycle are divided according to the time points corresponding to the intersections. Otherwise, they are divided into sub-periods at random equal intervals. sub-period;

[0019] The pollution trend model of each pollution area is established according to the cumulative change of noise pollution index, water pollution index and air pollution index in each sub-period, and the expression is:

[0020] ; In the formula, , , represents the weight factor, Indicates Pollution trend indicators for each polluted area, , Indicates the left and right time endpoints of any sub-period. The reference variation curve of the noise pollution index is shown. The reference change curve of water pollution coefficient is shown. The reference change curve of the air pollution index is shown. Represents any sub-period.

[0021] A further technical solution is to obtain the noise pollution index as follows:

[0022] A group of noise sensors are arranged in each detection area;

[0023] The noise decibel value detected by each noise sensor Substitute the following formula: Calculate the noise pollution index ;

[0024] In the formula Indicates The weight coefficient corresponding to each noise sensor is: is the number of noise sensors, is the noise decibel reference value, represents the historical impact coefficient, , Indicates the reference value of the noise pollution index in the current detection area. is the maximum value of the noise pollution index of the current detection area in the previous monitoring cycle, It is the minimum value of the noise pollution index in the current detection area during the previous monitoring cycle.

[0025] According to a further technical solution, the process of obtaining the water pollution index is as follows:

[0026] Based on the water quality sensor group detection, various water quality parameters of the current detection area are obtained ;

[0027] The water quality parameters With the preset standard value Substitute the following formula:

[0028] Calculate the water pollution index of the current detection area ;

[0029] In the formula, , is the preset scale factor, , They are respectively the current detection area The maximum and minimum values ​​of water quality parameters, is the total number of water quality parameters.

[0030] According to a further technical solution, the process of obtaining the air pollution index is as follows:

[0031] The particle concentration in the current detection area is obtained based on the detection instrument , nitrogen oxide concentration , sulfur dioxide concentration and carbon monoxide concentration ;

[0032] Substituting into the formula: Calculate the air pollution index of the current detection area ;

[0033] In the formula, , , , They are the reference values ​​of particulate matter concentration, nitrogen oxide concentration, sulfur dioxide concentration and carbon monoxide concentration. is the jitter coefficient, is the exponential factor.

[0034] In a further technical solution, the jitter coefficient The acquisition process is:

[0035] The air pollution index of the current detection area The preset air pollution index threshold Make comparisons;

[0036] like , then the current time is , trace back a preset period to obtain , and then through the formula Calculate the jitter coefficient of the current detection area ;

[0037] like , then by the formula Calculate the jitter coefficient of the current detection area ;

[0038] In the formula, ~ For the detection period, is the curve of the exponential factor changing with time, The reference change curve.

[0039] The exponential factor The expression is:

[0040] ;

[0041] In the formula, is the total number of sampling times, For the The traffic volume at the time of sampling, For the The wind speed at the time of sampling, , is the reference coefficient.

[0042] Further technical solution, the working process of the pollution source management module is:

[0043] The pollution trend index of each pollution area The preset pollution trend indicator threshold range Make a comparison;

[0044] like , the current polluted area is classified as a severely polluted area and enters the first sequence of pollution control as a pollution source;

[0045] like , the current polluted area is classified as a moderately polluted area and enters the second sequence of pollution control as a pending pollution source;

[0046] like , the current polluted area is classified as a lightly polluted area and does not enter the pollution control sequence;

[0047] Among them, the first sequence>the second sequence.

[0048] Beneficial effects of the present invention:

[0049] (1) Through the environmental data acquisition module, the target area is divided into multiple detection areas, and environmental sensors are set in each detection area. Key environmental data such as noise, water quality and atmospheric pollutants can be collected in real time and comprehensively. Compared with traditional manual monitoring or single-point monitoring, it not only improves the monitoring efficiency, but also ensures the accuracy and comprehensiveness of the data, providing a solid data foundation for subsequent environmental management and decision-making; through the environmental data processing module, the noise pollution index, water pollution index and atmospheric pollutant index are calculated according to the preset processing rules based on the real-time collected data. The quantitative processing enables the environmental conditions to be presented intuitively, which is convenient for managers to quickly understand the environmental conditions.

[0050] (2) By comparing the pollution index with the threshold, the polluted area can be quickly identified, providing important clues for subsequent pollution source tracking and management; the pollution source analysis module not only marks the polluted area, but also establishes a pollution trend model based on the historical changes of each polluted area, so as to predict future pollution trends and provide forward-looking guidance for environmental management. Through dynamic monitoring and trend analysis, managers can adjust management strategies in a timely manner and effectively respond to potential environmental risks; by taking preventive measures before a problem occurs, or responding quickly after a problem occurs, the impact of environmental pollution on the ecological environment and human health can be effectively reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] The present invention will be further described below in conjunction with the accompanying drawings.

[0052] Figure 1 It is a system logic block diagram of the present invention. DETAILED DESCRIPTION

[0053] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0054] See also Figure 1 As shown, the present invention is an environmental pollution source detection and management system based on big data, comprising:

[0055] The environmental data acquisition module divides the target area into multiple detection areas, and each detection area is provided with a group of environmental sensors for real-time collection of various environmental data, including noise, water quality and air pollutant data;

[0056] The environmental data processing module obtains the noise pollution index, water pollution index and air pollutant index respectively according to the preset processing rules based on the real-time collected environmental data;

[0057] The pollution source analysis module compares the noise pollution index, water pollution index and air pollution index with the corresponding pollution index thresholds. If at least one of them exceeds the pollution index threshold, the detection area is marked as a polluted area, the location of each polluted area is obtained, and then a pollution trend model is established based on the historical changes of each polluted area.

[0058] The pollution source management module performs corresponding management and control based on the results predicted by the pollution trend model of each pollution area.

[0059] The present invention divides the target area into a plurality of detection areas through an environmental data acquisition module, and sets an environmental sensor in each detection area, so that key environmental data such as noise, water quality and atmospheric pollutants can be collected in real time and comprehensively. Compared with traditional manual monitoring or single-point monitoring, the present invention not only improves the monitoring efficiency, but also ensures the accuracy and comprehensiveness of the data, and provides a solid data basis for subsequent environmental management and decision-making; at the same time, the environmental data processing module calculates the noise pollution index, the water pollution index and the atmospheric pollutant index according to the real-time collected data according to the preset processing rules, and the quantitative processing enables the environmental conditions to be intuitively presented, which is convenient for managers to quickly understand the environmental conditions; the pollution source analysis module can quickly identify the polluted area by comparing the pollution index with the threshold, and provides important clues for subsequent pollution source tracking and management; the pollution source analysis module not only marks the polluted area, but also establishes a pollution trend model according to the historical changes of each polluted area, so as to predict the future pollution trend, provide forward-looking guidance for environmental management, and through dynamic monitoring and trend analysis, managers can timely adjust the management strategy and effectively deal with potential environmental risks; by taking preventive measures before the problem occurs, or responding quickly after the problem occurs, the impact of environmental pollution on the ecological environment and human health is effectively reduced.

[0060] The process of establishing the pollution trend model is:

[0061] Obtain the time-varying curves of noise pollution index, water pollution index and air pollution index in each pollution area , , ;

[0062] Establish a coordinate system with time as the horizontal axis and various pollution indices as the vertical axis;

[0063] Draw the pollution index threshold straight lines corresponding to each pollution index on the coordinate system;

[0064] Determine whether the area where each pollution index changes over time intersects with the corresponding pollution index threshold straight line;

[0065] If there is an intersection, the sub-periods within a working cycle are divided according to the time points corresponding to the intersections. Otherwise, they are divided into sub-periods at random equal intervals. sub-period;

[0066] The pollution trend model of each pollution area is established according to the cumulative change of noise pollution index, water pollution index and air pollution index in each sub-period, and the expression is:

[0067] ; In the formula, , , Represents the weight factor, determined based on historical data analysis, Indicates Pollution trend indicators for each polluted area, , Indicates the left and right time endpoints of any sub-period. The reference variation curve of the noise pollution index is shown. The reference change curve of water pollution coefficient is shown. The reference change curve of the air pollution index is shown. Represents any sub-period.

[0068] The present invention provides a method for establishing a pollution trend model, by comprehensively analyzing the noise pollution, water pollution and air pollution in each detection area, the expression is: The pollution trend index of each pollution area is calculated, in which the changes of the three parameters are compared with the reference changes obtained based on historical data analysis, and then the changes of noise pollution, water pollution and air pollution in a working cycle are accumulated in an integral way. Obviously, The larger the value is, the higher the noise pollution level of the current pollutant exceeds the reference level. and By the same token, the larger the value is, the higher the degree of water pollution and air pollution in the current polluted area. By comprehensively analyzing the pollution changes in these three aspects, we can obtain more accurate pollution trend indicators, which comprehensively reflect the pollution trend of each polluted area and provide accurate data support for subsequent pollution source control.

[0069] The process of obtaining the noise pollution index is:

[0070] A group of noise sensors are arranged in each detection area;

[0071] The noise decibel value detected by each noise sensor Substitute the following formula: Calculate the noise pollution index ;

[0072] In the formula Indicates The weight coefficients corresponding to the noise sensors are determined based on historical data analysis. is the number of noise sensors, is the noise decibel reference value, represents the historical impact coefficient, , Indicates the reference value of the noise pollution index in the current detection area. is the maximum value of the noise pollution index of the current detection area in the previous monitoring cycle, It is the minimum value of the noise pollution index in the current detection area during the previous monitoring cycle.

[0073] The present invention provides a method for obtaining a noise pollution index by measuring the noise decibel value detected by each noise sensor. Substitute the following formula: Calculate the noise pollution index , obviously The average value of each noise decibel is obtained by the mean calculation method, which can more accurately reflect the noise decibel value of the current detection area. Then the calculated noise decibel average is compared with the noise decibel reference value. By comparison, it is obvious The larger the value, the higher the noise pollution level in the current detection area. Conversely, the lower the noise pollution level in the current detection area. In order to further improve the accuracy of the noise pollution index, The historical impact coefficient is calculated in an extreme way to reflect the noise decibel fluctuation range in the current detection area. Obviously, the larger the value, the more abnormal the detection area is, and the smaller the value, the closer the noise decibel fluctuation range in the detection area is to being stable.

[0074] The process of obtaining the water pollution index is:

[0075] Based on the water quality sensor group detection, various water quality parameters of the current detection area are obtained ;

[0076] The water quality parameters With the preset standard value Substitute the following formula:

[0077] Calculate the water pollution index of the current detection area ;

[0078] In the formula, , To preset the proportional coefficient, it is formulated based on historical data and experimental data. , They are respectively the current detection area The maximum and minimum values ​​of water quality parameters, is the total number of water quality parameters.

[0079] The present invention uses existing water quality sensors or detection instruments to obtain various water quality parameters in the current detection area, including pH value, dissolved oxygen, heavy metal content, total phosphorus content and turbidity, and then uses Calculate the ratio between each water quality parameter and the preset standard value. Obviously, the larger the ratio, the more likely the water quality parameter is to be abnormal. By making reference to the extreme value difference of each water quality parameter in the current detection area, an accurate water quality pollution assessment can be comprehensively established to improve the accuracy of the determination of polluted areas and reduce the cases of missed reports and false alarms.

[0080] The process of obtaining the air pollution index is:

[0081] The particle concentration in the current detection area is obtained based on the detection instrument , nitrogen oxide concentration , sulfur dioxide concentration and carbon monoxide concentration ;

[0082] Substituting into the formula: Calculate the air pollution index of the current detection area ;

[0083] In the formula, , , , They are the reference values ​​of particulate matter concentration, nitrogen oxide concentration, sulfur dioxide concentration and carbon monoxide concentration. is the jitter coefficient, is the exponential factor.

[0084] The jitter factor The acquisition process is:

[0085] The air pollution index of the current detection area The preset air pollution index threshold Make comparisons;

[0086] like , then the current time is , trace back a preset period to obtain , and then through the formula Calculate the jitter coefficient of the current detection area ;

[0087] like , then by the formula Calculate the jitter coefficient of the current detection area ;

[0088] In the formula, ~ For the detection period, is the curve of the exponential factor changing with time, The reference change curve.

[0089] By calculating the jitter coefficient, it is possible to react to the cumulative change of the exponential factor during the detection period. Obviously, the larger the cumulative change, the greater the deviation between the actual change curve of the exponential factor and the reference change curve, and therefore the more unstable the exponential factor. As a result, there is a higher possibility of crossing the peak traffic flow or gusts, resulting in an abnormal increase in the atmospheric pollution index in the current detection area. However, it cannot represent the actual situation in the detection area. The abnormal increase can be corrected and compensated by means of the jitter coefficient to reduce the probability of misjudgment of the pollution area based on atmospheric pollution.

[0090] The exponential factor The expression is:

[0091] ;

[0092] In the formula, is the total number of sampling times, For the The traffic volume at the time of sampling, For the The wind speed at the time of sampling, , is the reference coefficient.

[0093] The present invention first obtains the particle concentration of the current detection area , nitrogen oxide concentration , sulfur dioxide concentration and carbon monoxide concentration , and then compared with their respective reference values Obviously, the larger the ratio is, the higher the concentration of the air pollution parameter is, and the greater the air pollution index of the current detection area is. In order to improve the accuracy of the air pollution index and reflect the actual situation of the detection area, it is expressed in the form of an index. Increase exponential factor Obviously, the larger the index factor is, the greater the impact on the overall air pollution index is. Therefore, the more serious the air pollution level in the current detection area is, ; Obviously, the greater the wind speed in the current detection area, the faster the spread of air pollution and the higher the degree of harm, and the greater the traffic volume, the greater the exhaust emissions and the higher the degree of air pollution; therefore, the index factor is obtained by comprehensively processing the vehicle flow and wind speed, and then while considering the various parameters of air pollutant concentrations, the impact of vehicle flow and wind speed is also evaluated to improve the accuracy of the assessment of the air pollution index.

[0094] The working process of the pollution source management module is as follows:

[0095] The pollution trend index of each pollution area The preset pollution trend indicator threshold range Make a comparison;

[0096] like , the current polluted area is classified as a severely polluted area and enters the first sequence of pollution control as a pollution source;

[0097] like , the current polluted area is classified as a moderately polluted area and enters the second sequence of pollution control as a pending pollution source;

[0098] like , the current polluted area is classified as a lightly polluted area and does not enter the pollution control sequence; among them, the first sequence > the second sequence.

[0099] It should be noted that the calculation formulas and various parameters involved in the calculation in the present invention have been dimensionally processed in advance, and the process of dimensionless processing is well known in the industry and will not be described here.

[0100] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. An environmental pollution source detection and management system based on big data, characterized in that: include: The environmental data acquisition module divides the target area into multiple detection areas, and each detection area is provided with a group of environmental sensors for real-time collection of various environmental data, including noise, water quality and air pollutant data; The environmental data processing module obtains the noise pollution index, water pollution index and air pollutant index respectively according to the preset processing rules based on the real-time collected environmental data; The pollution source analysis module compares the noise pollution index, water pollution index and air pollution index with the corresponding pollution index thresholds. If at least one of them exceeds the pollution index threshold, the detection area is marked as a polluted area, the location of each polluted area is obtained, and then a pollution trend model is established based on the historical changes of each polluted area. The pollution source management module performs corresponding management and control based on the results predicted by the pollution trend model of each pollution area; The process of establishing the pollution trend model is: Obtain the time-varying curves of noise pollution index, water pollution index and air pollution index in each pollution area , , ; Establish a coordinate system with time as the horizontal axis and various pollution indices as the vertical axis; Draw the pollution index threshold straight lines corresponding to each pollution index on the coordinate system; Determine whether the area where each pollution index changes over time intersects with the corresponding pollution index threshold straight line; If there is an intersection, the sub-periods within a working cycle are divided according to the time points corresponding to the intersections. Otherwise, they are divided into sub-periods at random equal intervals. sub-period; The pollution trend model of each pollution area is established according to the cumulative change of noise pollution index, water pollution index and air pollution index in each sub-period, and the expression is: ; In the formula, , , represents the weight factor, Indicates Pollution trend indicators for each polluted area, , Indicates the left and right time endpoints of any sub-period. The reference variation curve of the noise pollution index is shown. The reference change curve of water pollution coefficient is shown. The reference change curve of the air pollution index is shown. represents any sub-period; The process of obtaining the noise pollution index is: A group of noise sensors are arranged in each detection area; The noise decibel value detected by each noise sensor Substitute the following formula: Calculate the noise pollution index ; In the formula Indicates The weight coefficient corresponding to each noise sensor is: is the number of noise sensors, is the noise decibel reference value, represents the historical impact coefficient, , Indicates the reference value of the noise pollution index in the current detection area. is the maximum value of the noise pollution index of the current detection area in the previous monitoring cycle, It is the minimum value of the noise pollution index of the current detection area in the previous monitoring cycle; The process of obtaining the water pollution index is: Based on the water quality sensor group detection, various water quality parameters of the current detection area are obtained ; The water quality parameters With the preset standard value Substitute the following formula: Calculate the water pollution index of the current detection area ; In the formula, , is the preset scale factor, , They are respectively the current detection area The maximum and minimum values ​​of water quality parameters, is the total number of water quality parameters; The process of obtaining the air pollution index is: The particle concentration in the current detection area is obtained based on the detection instrument , nitrogen oxide concentration , sulfur dioxide concentration and carbon monoxide concentration ; Substituting into the formula: Calculate the air pollution index of the current detection area ; In the formula, , , , They are the reference values ​​of particulate matter concentration, nitrogen oxide concentration, sulfur dioxide concentration and carbon monoxide concentration. is the jitter coefficient, is the exponential factor; The working process of the pollution source management module is as follows: The pollution trend index of each pollution area The preset pollution trend indicator threshold range Make a comparison; like , the current polluted area is classified as a severely polluted area and enters the first sequence of pollution control as a pollution source; like , the current polluted area is classified as a moderately polluted area and enters the second sequence of pollution control as a pending pollution source; like , the current polluted area is classified as a lightly polluted area and does not enter the pollution control sequence; Among them, the first sequence>the second sequence.

2. The environmental pollution source detection and management system based on big data according to claim 1 is characterized in that: The jitter factor The acquisition process is: The air pollution index of the current detection area The preset air pollution index threshold Make comparisons; like , then the current time is , trace back a preset period to obtain , and then through the formula Calculate the jitter coefficient of the current detection area ; like , then by the formula Calculate the jitter coefficient of the current detection area ; In the formula, ~ For the detection period, is the curve of the exponential factor changing with time, The reference change curve.

3. The environmental pollution source detection and management system based on big data according to claim 1 is characterized in that: The exponential factor The expression is: ; In the formula, is the total number of sampling times, For the The traffic volume at the time of sampling, For the The wind speed at the time of sampling, , is the reference coefficient.

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