An intelligent management system for microplastic pollution areas for urban environmental protection

Abstract

The present invention discloses an intelligent management system for microplastic pollution areas for urban environmental protection, which belongs to the field of environmental protection technology, and specifically comprises: collecting the microplastic pollution density of a target river channel at a fixed period; obtaining the industrial output value and the commercial output value in adjacent areas on both sides of the target river channel, wherein the adjacent areas are defined as ranges of set widths on both sides of the river channel; monitoring the periodic changes in the microplastic pollution density of the river channel, and issuing an alarm when the microplastic pollution density shows an upward trend in consecutive periods; when the alarm is received, linearly fitting the relationship between the microplastic pollution density and the industrial output value / commercial output value in consecutive periods, and calculating the fitting coefficients of the microplastic pollution density and the industrial output value / commercial output value after the linear fitting; and processing industrial enterprises and commercial units respectively according to the values ​​of the fitting coefficients; the present invention can effectively analyze the main factors of pollution and provide support for urban management.

Description

Technical Field

[0001] The present invention relates to the technical field of environmental protection, and in particular to an intelligent management system for microplastic pollution areas for urban environmental protection. Background Art

[0002] With the acceleration of urbanization, industrial and commercial activities have become more and more frequent, which not only promotes economic development, but also brings a series of environmental problems. Among them, microplastic pollution is particularly concerning. Microplastics refer to plastic particles with a diameter of less than 5 mm. They can enter the water environment through various channels, including but not limited to industrial wastewater discharge, random disposal of plastic packaging, direct discharge of domestic sewage into rivers and lakes without adequate treatment, and rainwater washing away land containing microplastic waste. Once these tiny but large particles enter natural water bodies, they are difficult to be naturally degraded or removed, and thus exist in the water environment for a long time, causing a lasting impact on the ecosystem.

[0003] The presence of microplastics not only directly threatens water quality safety, but may also affect higher-level organisms and even human health through the cumulative amplification effect of the food chain. Therefore, the development of an effective intelligent management system for microplastic pollution areas is of great significance for analyzing the main causes of microplastic pollution and controlling microplastic pollution in urban environments. Summary of the invention

[0004] The purpose of the present invention is to provide an intelligent management system for microplastic pollution areas for urban environmental protection, solving the following technical problems:

[0005] Analyze the main causes of microplastic pollution and control microplastic pollution in urban environments.

[0006] The purpose of the present invention can be achieved through the following technical solutions:

[0007] An intelligent management system for microplastic pollution areas for urban environmental protection, comprising:

[0008] A pollution collection module is used to collect the microplastic pollution density of the target river at each fixed period, where the microplastic pollution density is the number of microplastic particles per unit volume of water;

[0009] The data acquisition module is used to obtain the industrial output value and commercial output value in the adjacent areas on both sides of the target river. The adjacent area is defined as a range of set width on both sides of the river.

[0010] The pollution monitoring module is used to monitor the periodic changes in the density of microplastic pollution in the river, and issue an alarm when the density of microplastic pollution shows an upward trend in consecutive cycles;

[0011] A data analysis module is used to perform linear fitting on the relationship between microplastic pollution density and industrial output value / commercial output value in consecutive periods when an alarm is received, and calculate the fitting coefficients of microplastic pollution density and industrial output value / commercial output value after linear fitting;

[0012] The result output module is used to calculate the fitting coefficient R1 between microplastic pollution density and industrial output value. 2 ≥0.7, while the fitting coefficient R2 of microplastic pollution density and commercial output value 2 When the fitting coefficient R1 of microplastic pollution density and industrial output value is less than 0.7, it is suggested to check the discharge of industrial enterprises in the adjacent areas on both sides of the river; 2 <0.7, the fitting coefficient R2 of microplastic pollution density and commercial output value 2 When ≥0.7, it is suggested to check the plastic waste disposal methods of commercial units in adjacent areas on both sides of the river.

[0013] As a further solution of the present invention: In the pollution collection module, the process of collecting microplastic pollution density is:

[0014] A set volume of polluted water is collected through a standard container, and light with a fixed incident angle is irradiated into the polluted water. The sum of the grayscale of the polluted water is obtained through image recognition, and the sum of the grayscale is substituted into the preset fitting model to obtain the corresponding order of magnitude of microplastic particles. The order of magnitude of microplastic particles is divided by the set volume to obtain the microplastic pollution density.

[0015] As a further solution of the present invention: the construction process of the fitting model is:

[0016] A set volume of water sample is injected into a standard container, and light with a fixed incident angle is irradiated into the standard container. Different orders of magnitude of microplastic particles are sequentially added into the water sample until the microplastic particles are evenly dispersed, and a water image of the corresponding water sample is collected;

[0017] Grayscale the water body image to generate a grayscale image, obtain the grayscale histogram of the grayscale image, and obtain the number of grayscale steps n and the number of pixels mi of each grayscale step Gi according to the grayscale histogram, i=1,...,n. According to the formula Calculate the grayscale sum to obtain the grayscale sum corresponding to the order of magnitude N of each microplastic particle; based on the power function A fitting model was established to fit the relationship between the total grayscale and the order of magnitude N of microplastic particles, where exp represents the natural exponential function. The values ​​of parameters a and b were calculated by the least squares method to obtain the constructed fitting model.

[0018] As a further solution of the present invention: in the data analysis module, the calculation process of the fitting coefficient is:

[0019] With industrial output value or commercial output value as the horizontal axis and microplastic pollution density as the vertical axis, a plane rectangular coordinate system is constructed, and the coordinate data in n consecutive periods are plotted in the coordinate system to obtain the best fitting straight line function of microplastic pollution density and industrial output value / commercial output value, and the fitting coefficients of the two best fitting straight line functions are calculated in turn:

[0020] ;

[0021] Among them, in any best-fit straight line function, y i The horizontal axis x i The actual value of microplastic pollution density in the period, The horizontal axis x i The predicted value of the best fitting straight line function, is the average value of microplastic pollution density in all periods.

[0022] As a further solution of the present invention: in the result output module, when the fitting coefficients of microplastic pollution density and industrial output value and commercial output value are both ≥ 0.7, it is simultaneously prompted to check the emissions of industrial enterprises in the area and to check the plastic waste treatment methods of commercial units;

[0023] When the fitting coefficients of microplastic pollution density with industrial output value and commercial output value are both less than 0.7, monitoring will continue.

[0024] As a further solution of the present invention: in the result output module, when the fitting coefficients of the microplastic pollution density with the industrial output value and the commercial output value are all less than 0.7, it also includes obtaining meteorological data within n consecutive periods through the data acquisition module. If the meteorological data shows that heavy rainfall and strong winds occur within n consecutive periods, it indicates that the reason for the increasing trend of the microplastic pollution density is the weather.

[0025] As a further solution of the present invention: the pollution monitoring module also includes an alarm when the density of microplastic pollution in a certain period exceeds a preset safety threshold.

[0026] As a further solution of the present invention: In the data acquisition module, the methods of obtaining the industrial output value and commercial output value in the adjacent areas on both sides of the target river include but are not limited to government public data, enterprise self-reported data and third-party market research data.

[0027] Beneficial effects of the present invention:

[0028] The present invention can effectively identify the main sources of pollution by real-time monitoring of the microplastic pollution density of the target river and combining industrial output value and commercial output value data for linear fitting analysis. The system has flexible data processing capabilities and can process multiple information sources from government public data, corporate self-reported data, and third-party market research data. Its data analysis module provides data-based decision support, which helps to scientifically determine whether specific industries need to be inspected and promote the balance between economic development and environmental protection. In addition, the system also takes into account the impact of meteorological factors on microplastic pollution, and immediately issues an alarm when the pollution reaches a preset safety threshold, providing managers with clear action indicators. As an integrated management platform, the system integrates multiple functions such as data collection, monitoring, analysis, and result output, providing urban environmental protection departments with a comprehensive tool to more effectively manage and control microplastic pollution problems, thereby reducing environmental pollution and protecting ecosystems and human health. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] The present invention will be further described below in conjunction with the accompanying drawings.

[0030] Figure 1 It is a module schematic diagram of the present invention. DETAILED DESCRIPTION

[0031] 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.

[0032] See also Figure 1 As shown, the present invention is an intelligent management system for microplastic pollution areas for urban environmental protection, comprising:

[0033] Pollution collection module: The pollution collection module is the basic information collection part of the system. It collects river water samples regularly through sensors deployed in the river or regular sampling. These samples are then sent to the laboratory for analysis to determine the number of microplastic particles per unit volume, that is, the microplastic pollution density. This data is crucial to understanding the pollution level of the river and can serve as the basis for assessing pollution trends and sources.

[0034] Data collection module: The data collection module is responsible for obtaining relevant economic data from local governments, business registration agencies and industrial enterprises. These data include industrial and commercial output values ​​within a set width range on both sides of the river. By analyzing these data, the potential links between industrial and commercial activities and microplastic pollution can be explored.

[0035] Pollution Monitoring Module: The Pollution Monitoring Module uses the data provided by the Pollution Collection Module to continuously track changes in the density of microplastic pollution in the river. If the density of microplastic pollution is detected to be increasing over several consecutive cycles, the module will trigger an alarm, indicating that further analysis and action may be needed.

[0036] Data Analysis Module: When the pollution monitoring module sounds an alarm, the data analysis module starts working. It uses statistical software to perform linear fitting analysis on the relationship between microplastic pollution density and industrial and commercial output values ​​in consecutive periods. By calculating the fitting coefficient (R square value), the impact of industrial and commercial activities on microplastic pollution can be quantified.

[0037] Result output module: The result output module generates specific suggestions and reports based on the fitting coefficients provided by the data analysis module. If the fitting coefficient between microplastic pollution density and industrial output value is high (for example, R1 2 ≥0.7), while the fitting coefficient with commercial output value is low (e.g. R2 2 <0.7), it indicates that industrial activities may be the main source of pollution, and the government or relevant departments will be prompted to check the emissions of industrial enterprises. On the contrary, if the fitting coefficient of microplastic pollution density and commercial output value is high (for example, R2 2 ≥0.7), while the fitting coefficient with industrial output value is low (e.g. R1 2 <0.7), it may indicate that there are problems with the way commercial units handle plastic waste, and the waste disposal processes of these units need to be checked and improved.

[0038] Through the collaborative work of this series of modules, the system can not only promptly detect the problem of microplastic pollution in rivers, but also provide scientific basis for decision makers to help them formulate targeted measures to effectively reduce and control microplastic pollution in rivers.

[0039] In another preferred embodiment of the present invention, in the pollution collection module, the process of collecting microplastic pollution density is:

[0040] Collection of polluted water: Use standard containers to collect a set volume of polluted water. This step ensures the consistency and repeatability of the sample, providing a stable basis for subsequent analysis.

[0041] Light irradiation and image recognition: Irradiate the collected polluted water with a fixed incident angle of light, and capture the image of the water through a high-resolution camera or other image recognition equipment. Use advanced image recognition technology, such as machine learning algorithms, to grayscale the image, thereby converting the color image into a grayscale image.

[0042] Calculation of grayscale sum: Further analyze the grayscale image to obtain its grayscale histogram. The grayscale histogram shows the number of pixels at each grayscale level in the image. By calculating the sum of the number of pixels at all grayscale levels (i.e., the grayscale sum), the grayscale sum reflects the impact of the microplastic order of magnitude on the reflectivity of the water body, and a value related to the number of microplastic particles in the water body can be obtained.

[0043] It is worth noting that the process of building the fitting model is as follows: In order to convert the grayscale sum into the specific number of microplastic particles, an accurate fitting model needs to be built. This process includes the following key steps:

[0044] Sample preparation: Pour a set volume of water sample into a standard container and ensure that the conditions are consistent for each experiment.

[0045] Microplastic particle placement: Different quantities of microplastic particles are placed into water samples in sequence. After each placement, ensure that the microplastic particles are fully dispersed and evenly distributed in the water.

[0046] Image acquisition and processing: Use the same method as the polluted water collection method to collect the water image of each sample, and perform grayscale processing and grayscale sum calculation.

[0047] Model fitting: A fitting model was established based on the power function to describe the relationship between the total grayscale and the change in the order of magnitude N of microplastic particles. The values ​​of the model parameters a and b were calculated using the least squares method to complete the construction of the fitting model.

[0048] Calculation of microplastic pollution density: Using the constructed fitting model, the total grayscale of the polluted water body is substituted into the model to obtain the corresponding microplastic particle magnitude N. Finally, the microplastic particle magnitude N is divided by the set volume to obtain the microplastic pollution density.

[0049] Through such detailed steps and precise calculation methods, the embodiments of the present invention can provide an efficient, accurate and repeatable way to measure the density of microplastic pollution in rivers. This not only helps to better understand and manage river pollution problems, but also provides a scientific basis for relevant policy formulation and environmental protection measures.

[0050] In another preferred embodiment of the present invention, in the data analysis module, the calculation process of the fitting coefficient is:

[0051] Data preparation: First, collect data on industrial output value, commercial output value and corresponding microplastic pollution density over n consecutive periods. These data will serve as the basis for analysis.

[0052] Construct a rectangular coordinate system: Use industrial output value and commercial output value as the horizontal axis (x-axis) and microplastic pollution density as the vertical axis (y-axis) to construct two rectangular coordinate systems. This can intuitively show the relationship between variables.

[0053] Plot data points: In each coordinate system, plot the data points of n consecutive cycles. Each data point represents the industrial output value or commercial output value of a cycle and the corresponding microplastic pollution density.

[0054] Calculate the best fit line: Use statistical methods to calculate the best fit line in each coordinate system. This line should be as close as possible to all data points, minimizing the difference between all observed values ​​and predicted values.

[0055] Calculation of fitting coefficient: For any best fitting straight line function, calculate its fitting coefficient. This coefficient reflects the degree to which the independent variable (industrial output value or commercial output value) explains the change in the dependent variable (microplastic pollution density):

[0056] ;

[0057] Among them, in any best-fit straight line function, y i The horizontal axis x i The actual value of microplastic pollution density in the period, The horizontal axis x i The predicted value of the best fitting straight line function, is the average value of microplastic pollution density in all periods.

[0058] By comparing the fitting coefficients in the two coordinate systems, it can be determined whether industrial output value or commercial output value has a stronger correlation with microplastic pollution density. If the value of one coordinate system is close to 1, it means that the economic activity indicator has a strong linear relationship with microplastic pollution density; if the value is lower, it means that the linear relationship is not obvious.

[0059] Conclusion and application: Based on the analysis results, it can be concluded which economic activities contribute more to microplastic pollution in rivers, thus providing a basis for formulating targeted pollution reduction measures. For example, if it is found that the correlation between industrial output value and microplastic pollution density is high, then it may be necessary to strengthen the supervision and control of industrial emissions.

[0060] In another preferred embodiment of the present invention, in the result output module, when the fitting coefficients of microplastic pollution density and industrial output value and commercial output value are both ≥ 0.7, it is simultaneously prompted to check the emissions of industrial enterprises in the area and to check the plastic waste treatment methods of commercial units;

[0061] When the fitting coefficients of microplastic pollution density with industrial output value and commercial output value are both less than 0.7, monitoring will continue.

[0062] In another preferred embodiment of the present invention, in the result output module:

[0063] On the basis of the original data collection module, the function of collecting meteorological data is added. This includes but is not limited to meteorological parameters such as rainfall, wind speed, wind direction, temperature, humidity, etc. within n consecutive cycles. These data can be obtained through cooperation with the meteorological department, or collected in real time through meteorological monitoring equipment installed on site.

[0064] Analysis of meteorological data: The collected meteorological data will be used to analyze its relationship with changes in microplastic pollution density. In particular, it will be analyzed whether heavy rainfall and strong winds are associated with the rising trend of microplastic pollution density. This analysis can help determine the role of natural factors in microplastic pollution.

[0065] Decision logic of the result output module: When the fitting coefficients of microplastic pollution density and industrial output value and commercial output value are less than 0.7, the result output module will start the meteorological data analysis program. If the meteorological data shows that heavy rain and strong winds have occurred in n consecutive cycles, the system will prompt that the reason for the rising trend of microplastic pollution density may be weather factors.

[0066] Comprehensive analysis and recommendations: Combined with the analysis results of meteorological data, the result output module can also provide more comprehensive recommendations. For example, if it is determined that weather is the main cause of the increase in microplastic pollution density, then relevant departments can be advised to take preventive measures under specific weather conditions, such as increasing the cleaning of rivers, or temporarily restricting certain activities that may lead to increased microplastic pollution.

[0067] In another preferred embodiment of the present invention, in the pollution monitoring module, in order to enhance the practicality and responsiveness of the system, the pollution monitoring module can not only analyze the relationship between the microplastic pollution density and the industrial output value and commercial output value, but also can immediately issue an alarm when the microplastic pollution density exceeds a preset safety threshold.

[0068] In another preferred embodiment of the present invention, in the data acquisition module, the method of obtaining the industrial output value and commercial output value in the adjacent areas on both sides of the target river includes but is not limited to government public data, enterprise self-reported data and third-party market research data.

[0069] 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 intelligent management system for microplastic pollution areas for urban environmental protection, characterized in that: include: A pollution collection module is used to collect the microplastic pollution density of the target river at each fixed period, where the microplastic pollution density is the number of microplastic particles per unit volume of water; The data acquisition module is used to obtain the industrial output value and commercial output value in the adjacent areas on both sides of the target river. The adjacent area is defined as a range of set width on both sides of the river. The pollution monitoring module is used to monitor the periodic changes in the density of microplastic pollution in the river, and issue an alarm when the density of microplastic pollution shows an upward trend in consecutive cycles; A data analysis module is used to perform linear fitting on the relationship between microplastic pollution density and industrial output value / commercial output value in consecutive periods when an alarm is received, and calculate the fitting coefficients of microplastic pollution density and industrial output value / commercial output value after linear fitting; The result output module is used to calculate the fitting coefficient R1 between microplastic pollution density and industrial output value. 2 ≥0.7, while the fitting coefficient R2 of microplastic pollution density and commercial output value 2 When the fitting coefficient R1 of microplastic pollution density and industrial output value is less than 0.7, it is suggested to check the discharge of industrial enterprises in the adjacent areas on both sides of the river; 2 <0.7, the fitting coefficient R2 of microplastic pollution density and commercial output value 2 When ≥0.7, it is suggested to check the plastic waste disposal methods of commercial units in adjacent areas on both sides of the river.

2. According to claim 1, an intelligent management system for microplastic pollution areas for urban environmental protection is characterized in that: In the pollution collection module, the process of collecting microplastic pollution density is as follows: A set volume of polluted water is collected through a standard container, and light with a fixed incident angle is irradiated into the polluted water. The sum of the grayscale of the polluted water is obtained through image recognition, and the sum of the grayscale is substituted into the preset fitting model to obtain the corresponding order of magnitude of microplastic particles. The order of magnitude of microplastic particles is divided by the set volume to obtain the microplastic pollution density.

3. According to claim 2, an intelligent management system for microplastic pollution areas for urban environmental protection is characterized in that: The construction process of the fitting model is: A set volume of water sample is injected into a standard container, and light with a fixed incident angle is irradiated into the standard container. Different orders of magnitude of microplastic particles are sequentially added into the water sample until the microplastic particles are evenly dispersed, and a water image of the corresponding water sample is collected; Grayscale the water body image to generate a grayscale image, obtain the grayscale histogram of the grayscale image, and obtain the number of grayscale steps n and the number of pixels mi of each grayscale step Gi according to the grayscale histogram, i=1,...,n. According to the formula Calculate the grayscale sum to obtain the grayscale sum corresponding to the order of magnitude N of each microplastic particle; based on the power function A fitting model was established to fit the relationship between the total grayscale and the order of magnitude N of microplastic particles, where exp represents the natural exponential function. The values ​​of parameters a and b were calculated by the least squares method to obtain the constructed fitting model.

4. The intelligent management system for microplastic pollution areas for urban environmental protection according to claim 1 is characterized in that: In the data analysis module, the calculation process of the fitting coefficient is: With industrial output value or commercial output value as the horizontal axis and microplastic pollution density as the vertical axis, a plane rectangular coordinate system is constructed, and the coordinate data in n consecutive periods are plotted in the coordinate system to obtain the best fitting straight line function of microplastic pollution density and industrial output value / commercial output value, and the fitting coefficients of the two best fitting straight line functions are calculated in turn: ； Among them, in any best-fit straight line function, y i The horizontal axis x i The actual value of microplastic pollution density in the period, The horizontal axis x i The predicted value of the best fitting straight line function, is the average value of microplastic pollution density in all periods.

5. The intelligent management system for microplastic pollution areas for urban environmental protection according to claim 1 is characterized in that: In the result output module, when the fitting coefficients of microplastic pollution density and industrial output value and commercial output value are both ≥ 0.7, it is prompted to check the emissions of industrial enterprises in the area and the plastic waste disposal methods of commercial units; When the fitting coefficients of microplastic pollution density with industrial output value and commercial output value are both less than 0.7, monitoring will continue.

6. The intelligent management system for microplastic pollution areas for urban environmental protection according to claim 1 is characterized in that: In the result output module, when the fitting coefficients of microplastic pollution density with industrial output value and commercial output value are both less than 0.7, it also includes obtaining meteorological data within n consecutive periods through the data acquisition module. If the meteorological data shows that heavy rainfall and strong winds occur within n consecutive periods, it indicates that the reason for the increasing trend of microplastic pollution density is weather.

7. The intelligent management system for microplastic pollution areas for urban environmental protection according to claim 1 is characterized in that: The pollution monitoring module also includes an alarm that sounds when the density of microplastic pollution within a certain period exceeds a preset safety threshold.

8. The intelligent management system for microplastic pollution areas for urban environmental protection according to claim 1 is characterized in that: In the data collection module, the methods for obtaining the industrial output value and commercial output value in the adjacent areas on both sides of the target river include but are not limited to government public data, enterprise self-reported data and third-party market research data.

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