Smart park environment emergency management system based on big data analysis

Abstract

The invention relates to the technical field of environment treatment, and particularly discloses a smart park environment emergency treatment system based on big data analysis, which collects air quality data, water quality data and noise data of different areas in a park in real time through a data collection module, and obtains meteorological data in real time; then the data is transmitted to a data storage center in real time through a wireless transmission technology based on the data transmission module, then fusion analysis is carried out on the data based on the data analysis module, accurate prediction is carried out on environmental risks, and an emergency decision module can rapidly generate an emergency disposal scheme when an environmental event occurs. And automatic scheduling and on-site execution monitoring of emergency resources are realized, so that the emergency response efficiency is greatly improved, and the loss caused by environmental events is effectively reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of environmental governance, and particularly to a smart park environmental emergency governance system based on big data analysis. Background Art

[0002] With the rapid development of technology and the acceleration of the urbanization process, smart parks have emerged as a new type of industrial development model. In the operation of smart parks, environmental safety is of crucial importance.

[0003] However, there are many problems in the current park environmental management system: 1. The collection of environmental data is not comprehensive enough to accurately reflect the overall environmental situation of the park; 2. Most of them are based on comparing the collected data with thresholds to judge whether there is an environmental risk, without considering the data anomalies caused by temporary human activities, resulting in unnecessary resource allocation and personnel scheduling. Therefore, the present invention provides a smart park environmental emergency governance system based on big data analysis. Summary of the Invention

[0004] The purpose of the present invention is to provide a smart park environmental emergency governance system based on big data analysis to solve the above technical problems:

[0005] The purpose of the present invention can be achieved through the following technical solutions:

[0006] A smart park environmental emergency governance system based on big data analysis, the system includes: a data collection module, a data transmission module, a data analysis module, an emergency decision-making module, and an automatic control module;

[0007] The data collection module includes sensors distributed in various areas of the park, which are used to collect air quality data, water quality data, and noise data in the park in real time, and realize the comprehensive collection of multi-source environmental data;

[0008] The data transmission module is used to transmit the collected data to the data storage center in real time through wireless transmission technology;

[0009] The data analysis module is used to analyze the collected data and evaluate the environmental risks of the park according to the analysis results;

[0010] The emergency decision-making module is used to generate corresponding emergency warning decisions according to the evaluation results of the data analysis module on the park environment;

[0011] The automatic control module is used to automatically control the emergency equipment in the park according to the emergency warning decision.

[0012] As a further description of the solution of the present invention, the working process of the data collection module includes:

[0013] The park is divided into a production area, a residential living area, and a greening area, and these three areas are numbered respectively, and the numbers are in sequence: 1, 2, 3;

[0014] Air quality monitoring sensors and noise monitoring sensors are respectively arranged in the three areas to comprehensively monitor the air quality status and noise status in different areas of the park;

[0015] Water quality monitoring sensors are respectively arranged at the outlet of the park sewage treatment plant and the inlet of residential water use to comprehensively monitor the water quality status in different areas of the park;

[0016] The data acquisition module also accesses meteorological data from the meteorological department, including temperature, humidity, and wind speed.

[0017] As a further description of the solution of the present invention, the working process of the data transmission module includes: using 5G technology for data transmission in areas with good signal coverage, and using LoRa technology for data transmission in areas with weak signals;

[0018] The data storage center is built with mature distributed storage software to ensure the safe storage and fast access of data.

[0019] As a further description of the solution of the present invention, the working process of the data analysis module includes:

[0020] Step S1: Evaluate the air quality risk of the park according to the air quality data collected by the data acquisition module;

[0021] Step S2: Evaluate the noise risk of the park according to the noise data collected by the data acquisition module;

[0022] Step S3: Evaluate the water quality risk of the park according to the water quality data collected by the data acquisition module.

[0023] As a further description of the solution of the present invention, the working process of evaluating the air quality risk of the park in step S1 includes:

[0024] Obtain the real-time temperature, humidity, and wind speed, and construct a mathematical model of the air quality risk coefficient, and the expression is:

[0025]

[0026] In the formula, n is the number of items of air pollutants monitored, i belongs to n, E i is the concentration of the i-th air pollutant, f(.) is a non-linear function of the pollutant concentration, k i is the weight coefficient of the i-th air pollutant, and g(T, H, V) is an adjustment function of environmental factors (temperature T, humidity H, wind speed V);

[0027] The mathematical model of the non - linear function of the pollutant concentration is as follows:

[0028]

[0029] The mathematical model of the adjustment function of the environmental factors (temperature T, humidity H, wind speed V) is as follows:

[0030]

[0031] In the formula, E i0 is the standard concentration of the i - th air pollutant, μ i is the non - linear coefficient of the i - th air pollutant, α, β, and γ respectively represent the weight coefficients corresponding to temperature T, humidity H, and wind speed V, T, H, and V respectively represent the real - time temperature, humidity, and wind speed, and T0, H0, and V0 respectively represent the standard values of temperature, humidity, and wind speed;

[0032] The non - linear coefficient μ i of the i - th air pollutant is related to the growth rate of the i - th air pollutant. Obtain the growth rate w i of the i - th air pollutant within the historical time period, and calculate the non - linear coefficient μ of the i - th air pollutant through the formula i ;

[0033] Calculate the air quality risk coefficients σ1, σ2, and σ3 of the production area, residential living area, and greening area respectively according to the air quality risk coefficient mathematical model;

[0034] Compare the air quality risk coefficients σ1, σ2, and σ3 of the production area, residential living area, and greening area with the corresponding thresholds set by the system respectively. If any air quality risk coefficient is greater than or equal to the corresponding threshold, it indicates that there is air pollution in the current area's air quality.

[0035] As a further description of the solution of the present invention, the working process of evaluating the noise risk in the park in step S2 includes:

[0036] Obtain the real - time noise, construct a noise risk coefficient mathematical model, and the expression is:

[0037]

[0038] In the formula, is the conversion coefficient, used to adjust the range of the risk value, h(t) is the function of the noise level changing with time within the set historical time period, t1 is the initial moment of the set historical time period, t2 is the end moment of the set historical time period, h0 is the reference noise level, and the unit of the noise level is decibel;

[0039] Calculate the air quality risk coefficients ρ1, ρ2, and ρ3 of the production area, residential area, and greening area respectively according to the noise risk coefficient mathematical model;

[0040] Compare the noise risk coefficients ρ1, ρ2, and ρ3 of the production area, residential area, and greening area with the corresponding thresholds set by the system respectively. If any of the noise risk coefficients is greater than or equal to the corresponding threshold, it indicates that there is noise pollution in the current area's noise quality.

[0041] As a further description of the solution of the present invention, the working process of evaluating the water quality risk of the park in step S3 includes:

[0042] Construct a water quality risk coefficient mathematical model, and the expression is:

[0043]

[0044] In the formula, m is the number of items of water quality pollutants monitored, j belongs to m, C j is the concentration of the j-th item of water quality pollutant, C j0 is the standard concentration of the j-th item of water quality pollutant, k j is the weight coefficient of the j-th item of water quality pollutant, μ j is the non-linear coefficient of the j-th item of water quality pollutant;

[0045] The non-linear coefficient μ of the j-th item of water quality pollutant j is related to the growth rate of the j-th item of water quality pollutant. Obtain the growth rate w j of the j-th item of water quality pollutant within the historical time period, and calculate the non-linear coefficient μ of the j-th item of water quality pollutant through the formula ; j ;

[0046] Calculate the water quality risk coefficients θ o 、θ i of the outlet of the sewage treatment plant and the inlet of domestic water respectively according to the water quality risk coefficient mathematical model;

[0047] Compare the water quality risk coefficients θ o 、θ i of the outlet of the sewage treatment plant and the inlet of domestic water with the corresponding thresholds set by the system respectively. If any of the water quality risk coefficients is greater than or equal to the corresponding threshold, it indicates that there is water quality pollution in the current area's water quality.

[0048] As a further description of the solution of the present invention, the working process of the emergency decision-making module includes:

[0049] When any type of environmental pollution event occurs in any area, immediately issue a warning;

[0050] After receiving the early warning information, obtain the area and type of the environmental pollution incident, and obtain the corresponding risk coefficient of the environmental pollution incident;

[0051] If air pollution and water pollution occur, compare the corresponding risk coefficient of the environmental pollution incident with the corresponding risk coefficient emergency interval set by the system, and start the corresponding number of purification equipment according to the size of the corresponding risk coefficient of the environmental pollution incident;

[0052] If noise pollution occurs, the staff will go to the corresponding area to control the noise.

[0053] Advantages of the present invention:

[0054] The present invention collects the air quality data, water quality data and noise data of different areas in the park in real time through the data collection module, and obtains the meteorological data in real time. Then, based on the data transmission module, it is transmitted to the data storage center in real time through wireless transmission technology. Then, based on the data analysis module, the data is fused and analyzed to accurately predict the environmental risk. The emergency decision-making module can quickly generate an emergency disposal plan when an environmental event occurs, and realize the automatic scheduling of emergency resources and on-site execution monitoring, greatly improving the emergency response efficiency and effectively reducing the losses caused by environmental events. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] The present invention will be further described below with reference to the accompanying drawings.

[0056] Figure 1 is a schematic structural diagram of the intelligent park environmental emergency governance system based on big data analysis of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0057] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0058] Please refer to Figure 1 As shown, the present invention provides an intelligent park environmental emergency governance system based on big data analysis, and the system includes: a data collection module, a data transmission module, a data analysis module, an emergency decision-making module and an automatic control module;

[0059] The data collection module includes sensors distributed in various areas of the park, and is used to collect the air quality data, water quality data and noise data in the park in real time, so as to realize the comprehensive collection of multi-source environmental data;

[0060] The data transmission module is used to transmit the collected data to the data storage center in real time through wireless transmission technology;

[0061] The data analysis module is used to analyze the collected data and evaluate the environmental risks in the park according to the analysis results;

[0062] The emergency decision-making module is used to generate corresponding emergency warning decisions according to the evaluation results of the park environment by the data analysis module;

[0063] The automatic control module is used to automatically control the emergency equipment in the park according to the emergency warning decision.

[0064] Through the above technical solutions, the present invention collects real-time air quality data, water quality data and noise data in different areas of the park through the data collection module, and obtains meteorological data in real time. Then, based on the data transmission module, it is transmitted to the data storage center in real time through wireless transmission technology. Then, based on the data analysis module, the data is fused and analyzed to accurately predict environmental risks. The emergency decision-making module can quickly generate emergency disposal plans when environmental events occur, and realize the automatic scheduling of emergency resources and on-site execution monitoring, greatly improving the emergency response efficiency and effectively reducing the losses caused by environmental events.

[0065] The working process of the data collection module includes:

[0066] The park is divided into a production area, a residential living area and a greening area, and these three areas are numbered respectively, and the numbers are in turn: 1, 2, 3;

[0067] Air quality monitoring sensors and noise monitoring sensors are respectively arranged in the three areas to comprehensively monitor the air quality status and noise status in different areas of the park;

[0068] Water quality monitoring sensors are respectively arranged at the outlet of the park sewage treatment plant and the inlet of residential water to comprehensively monitor the water quality status in different areas of the park;

[0069] The data collection module also accesses meteorological data from the meteorological department, including temperature, humidity and wind speed.

[0070] Through the above technical solutions, this embodiment provides a method for collecting park environmental data. The module includes air quality sensors, water quality monitoring equipment, and noise monitoring devices distributed in various areas of the park, which are used to collect real-time air quality data (such as concentrations of PM2.5, sulfur dioxide, nitrogen oxides, etc.), water quality data (such as chemical oxygen demand, ammonia nitrogen content, acidity and alkalinity, etc.) and noise data in the park. At the same time, it also accesses meteorological data from the meteorological department, including information such as temperature, humidity, wind speed, and wind direction, to achieve comprehensive collection of multi-source environmental data.

[0071] The working process of the data transmission module includes: using 5G technology for data transmission in areas with good signal coverage, and using LoRa technology for data transmission in areas with weak signals;

[0072] The data storage center is built with mature distributed storage software to ensure the safe storage and fast access of data.

[0073] The working process of the data analysis module includes:

[0074] Step S1: Evaluate the air quality risk of the park according to the air quality data collected by the data acquisition module;

[0075] Step S2: Evaluate the noise risk of the park according to the noise data collected by the data acquisition module;

[0076] Step S3: Evaluate the water quality risk of the park according to the water quality data collected by the data acquisition module.

[0077] The working process of evaluating the air quality risk of the park in Step S1 includes:

[0078] Obtain the real-time temperature, humidity and wind speed, and construct a mathematical model for the air quality risk coefficient, with the expression:

[0079]

[0080] In the formula, n is the number of items of air pollutants monitored, i belongs to n, E i is the concentration of the i-th air pollutant, f(.) is the non-linear function of the pollutant concentration, k i is the weight coefficient of the i-th air pollutant, and g(T, H, V) is the adjustment function of environmental factors (temperature T, humidity H, wind speed V);

[0081] The non-linear function mathematical model of the pollutant concentration is:

[0082]

[0083] The adjustment function mathematical model of environmental factors (temperature T, humidity H, wind speed V) is:

[0084]

[0085] In the formula, E i0 is the standard concentration of the i-th air pollutant, μ iis the non - linear coefficient of the i - th air pollutant, where α, β, and γ represent the weight coefficients corresponding to temperature T, humidity H, and wind speed V respectively, T, H, and V represent the real - time temperature, humidity, and wind speed respectively, and T0, H0, and V0 represent the standard values of temperature, humidity, and wind speed respectively;

[0086] The non - linear coefficient μ of the i - th air pollutant i is related to the growth rate of the i - th air pollutant. Obtain the growth rate w of the i - th air pollutant within a historical time period i , and calculate the non - linear coefficient μ of the i - th air pollutant through the formula ; i ;

[0087] Calculate the air quality risk coefficients σ1, σ2, and σ3 of the production area, residential living area, and greening area respectively according to the air quality risk coefficient mathematical model;

[0088] Compare the air quality risk coefficients σ1, σ2, and σ3 of the production area, residential living area, and greening area with the corresponding thresholds set by the system respectively. If any of the air quality risk coefficients is greater than or equal to the corresponding threshold, it indicates that there is air pollution in the current area's air quality.

[0089] Through the above - mentioned technical solution, this embodiment provides a method for predicting and evaluating the air quality risk of each area in the park, obtaining the real - time temperature, humidity, and wind speed, and constructing an air quality risk coefficient mathematical model in combination with meteorological data where, is the standardized concentration of the i - th air pollutant, f(E i ) is a non - linear function of the pollutant concentration, and g(T, H, V) is an adjustment function of environmental factors (temperature T, humidity H, wind speed V), which is used to smooth the risk contribution at low pollutant concentrations, reflects the non - linear risk growth at high pollutant concentrations. The non - linear coefficient μ of the i - th air pollutant i is related to the growth rate of the i - th air pollutant. Calculate the air quality risk coefficients σ1, σ2, and σ3 of the production area, residential living area, and greening area respectively according to the air quality risk coefficient mathematical model; Compare the air quality risk coefficients σ1, σ2, and σ3 of the production area, residential living area, and greening area with the corresponding thresholds set by the system respectively. If any of the air quality risk coefficients is greater than or equal to the corresponding threshold, it indicates that there is air pollution in the current area's air quality.

[0090] Among them, the process of obtaining the growth rate of the $i$-th air pollutant is as follows: obtain the function of the $i$-th air pollutant changing with time, and then take the derivative of the function of the $i$-th air pollutant changing with time to obtain the growth rate of the $i$-th air pollutant.

[0091] The working process of evaluating the noise risk of the park in step S2 includes:

[0092] Obtain the real-time noise, and construct a mathematical model of the noise risk coefficient, and the expression is:

[0093]

[0094] In the formula, is a conversion coefficient, which is used to adjust the range of the risk value. $h(t)$ is a function of the noise level changing with time within a set historical time period. $t_1$ is the initial moment of the set historical time period, $t_2$ is the end moment of the set historical time period, and $h_0$ is the reference noise level, and the unit of the noise level is decibel;

[0095] Calculate the air quality risk coefficients $\rho_1$, $\rho_2$ and $\rho_3$ of the production area, the residential living area and the greening area respectively according to the mathematical model of the noise risk coefficient;

[0096] Compare the noise risk coefficients $\rho_1$, $\rho_2$ and $\rho_3$ of the production area, the residential living area and the greening area with the corresponding thresholds set by the system respectively. If any of the noise risk coefficients is greater than or equal to the corresponding threshold, it means that there is noise pollution in the current area's noise quality.

[0097] Through the above technical solution, this embodiment provides a method for evaluating and predicting the noise pollution risk, obtaining the data of the noise changing with time, and constructing a mathematical model of the noise risk coefficient, is a normalization coefficient, which is used to adjust the range of $\rho$, and $\ln(1 + h(t))$ is used to smooth the risk contribution at low noise levels, is used to reflect the non-linear risk growth at high noise levels. Calculate the air quality risk coefficients $\rho_1$, $\rho_2$ and $\rho_3$ of the production area, the residential living area and the greening area respectively according to the mathematical model of the noise risk coefficient; compare the noise risk coefficients $\rho_1$, $\rho_2$ and $\rho_3$ of the production area, the residential living area and the greening area with the corresponding thresholds set by the system respectively. If any of the noise risk coefficients is greater than or equal to the corresponding threshold, it means that there is noise pollution in the current area's noise quality.

[0098] The working process of evaluating the water quality risk of the park in step S3 includes:

[0099] Construct a mathematical model of the water quality risk coefficient, and the expression is:

[0100]

[0101] Wherein, m is the number of items of water quality pollutants to be monitored, j belongs to m, C j is the concentration of the j-th water quality pollutant, C j0 is the standard concentration of the j-th water quality pollutant, k j is the weight coefficient of the j-th water quality pollutant, μ j is the non-linear coefficient of the j-th water quality pollutant;

[0102] The non-linear coefficient μ j of the j-th water quality pollutant is related to the growth rate of the j-th water quality pollutant. Obtain the growth rate w j of the j-th water quality pollutant within the historical time period, and through the formula calculate the non-linear coefficient μ j of the j-th water quality pollutant;

[0103] Calculate the water quality risk coefficients θ o and θ i of the outlet of the sewage treatment plant and the inlet of the domestic water respectively according to the water quality risk coefficient mathematical model;

[0104] Compare the water quality risk coefficients θ o and θ i of the outlet of the sewage treatment plant and the inlet of the domestic water with the corresponding thresholds set by the system respectively. If any of the water quality risk coefficients is greater than or equal to the corresponding threshold, it indicates that there is water pollution in the current area.

[0105] Through the above technical solution, this embodiment provides a method for evaluating and predicting water pollution risk, obtains the parameters of various water quality pollutants, and then constructs a water quality risk coefficient mathematical model, which is used to smooth the risk contribution at low concentrations, which is used to reflect the non-linear risk growth at high concentrations. Calculate the water quality risk coefficients θ o and θ i of the outlet of the sewage treatment plant and the inlet of the domestic water respectively according to the water quality risk coefficient mathematical model; Compare the water quality risk coefficients θ o and θ i of the outlet of the sewage treatment plant and the inlet of the domestic water with the corresponding thresholds set by the system respectively. If any of the water quality risk coefficients is greater than or equal to the corresponding threshold, it indicates that there is water pollution in the current area.

[0106] Among them, the process of obtaining the growth rate of the j-th water quality pollutant is as follows: obtain the function of the j-th water quality pollutant changing with time, and then take the derivative of the function of the j-th water quality pollutant changing with time to obtain the growth rate of the j-th water quality pollutant.

[0107] The working process of the emergency decision-making module includes:

[0108] When any type of environmental pollution event occurs in any area, an early warning is immediately issued;

[0109] After receiving the early warning information, obtain the area and type of the environmental pollution event, and obtain the corresponding risk coefficient of the environmental pollution event;

[0110] If air pollution and water pollution occur, compare the corresponding risk coefficient of the environmental pollution event with the corresponding risk coefficient emergency range set by the system, and start the corresponding number of purification devices according to the size of the corresponding risk coefficient of the environmental pollution event;

[0111] If noise pollution occurs, the staff will go to the corresponding area to control the noise.

[0112] Through the above technical solutions, this embodiment provides emergency plans for different types of environmental events (air pollution events, water pollution events, noise pollution events), including resource allocation and personnel scheduling. After receiving the early warning information, the system generates corresponding emergency plans according to the type of environmental event. If air pollution and water pollution occur, compare the corresponding risk coefficient of the environmental pollution event with the corresponding risk coefficient emergency range set by the system, and start the corresponding number of purification devices according to the size of the corresponding risk coefficient of the environmental pollution event; if noise pollution occurs, the staff will go to the corresponding area to control the noise.

[0113] It should be noted that the calculations in the present invention are all simple numerical calculations, which have been dimensionless processed, and the standard data, threshold ranges, and weight coefficients set in the present invention are all empirical data and will not be elaborated.

[0114] The above has described an embodiment of the present invention in detail, but the content described is only the preferred embodiment of the present invention and cannot be considered as limiting the scope of implementation of the present invention. All equivalent changes and improvements made according to the scope of the present invention application should still fall within the scope covered by the patent of the present invention.

Claims

1. A smart park environmental emergency governance system based on big data analysis, characterized in that, The system includes: a data acquisition module, a data transmission module, a data analysis module, an emergency decision-making module, and an automatic control module; The data acquisition module includes sensors distributed in various areas of the park, which are used to collect air quality data, water quality data, and noise data in the park in real time, so as to comprehensively collect multi-source environmental data; The data transmission module is used to transmit the collected data to the data storage center in real time through wireless transmission technology; The data analysis module is used to analyze the collected data and evaluate the environmental risks in the park according to the analysis results; The emergency decision-making module is used to generate corresponding emergency warning decisions according to the evaluation results of the data analysis module on the park environment; The automatic control module is used to automatically control the emergency equipment in the park according to the emergency warning decision; 2. The intelligent park environmental emergency governance system based on big data analysis according to claim 1, wherein The working process of the data acquisition module includes: The park is divided into a production area, a residential living area, and a greening area, and these three areas are numbered respectively, and the numbers are in turn: 1, 2, 3; Air quality monitoring sensors and noise monitoring sensors are respectively arranged in the three areas to comprehensively monitor the air quality status and noise status in different areas of the park; Water quality monitoring sensors are respectively arranged at the outlet of the park sewage treatment plant and the inlet of residential water to comprehensively monitor the water quality status in different areas of the park; The data acquisition module also accesses meteorological data from the meteorological department, including temperature, humidity, and wind speed.

3. An intelligent park environmental emergency governance system based on big data analysis according to claim 1, characterized in that, The working process of the data transmission module includes: using 5G technology for data transmission in areas with good signal coverage, and using LoRa technology for data transmission in areas with weak signals; The data storage center is built with mature distributed storage software to ensure the safe storage and fast access of data; 4. The intelligent park environmental emergency governance system based on big data analysis according to claim 2, wherein, The working process of the data analysis module includes: Step S1: Evaluate the air quality risk in the park according to the air quality data collected by the data acquisition module; Step S2: Evaluate the noise risk in the park according to the noise data collected by the data acquisition module; Step S3: Evaluate the water quality risk in the park according to the water quality data collected by the data acquisition module; 5. The intelligent park environmental emergency governance system based on big data analysis according to claim 4, characterized in that, The working process of evaluating the air quality risk in the park in step S1 includes: Obtain real-time temperature, humidity, and wind speed, and construct a mathematical model for the air quality risk coefficient, and the expression is: Where n is the number of items of air pollutants monitored, i belongs to n, and E i is the concentration of the i-th air pollutant, f(.) is a non-linear function of the pollutant concentration, and k i is the weight coefficient of the i-th air pollutant, and g(T, H, V) is an adjustment function of environmental factors (temperature T, humidity H, wind speed V); The mathematical model of the non-linear function of the pollutant concentration is: The mathematical model of the adjustment function of the environmental factors (temperature T, humidity H, wind speed V) is: where, E i0 is the standard concentration of the i-th air pollutant, μ i is the non-linear coefficient of the i-th air pollutant, α, β and γ respectively represent the weight coefficients corresponding to the temperature T, humidity H, and wind speed V, T, H and V respectively represent the real-time temperature, humidity and wind speed, and T0, H0 and V0 respectively represent the standard values of temperature, humidity and wind speed; The non - linear coefficient μ of the i - th air pollutant i is related to the growth rate of the i - th air pollutant. Obtain the growth rate w of the i - th air pollutant within the historical time period i , and through the formula calculate the non - linear coefficient μ of the i - th air pollutant i ; Calculate the air quality risk coefficients σ1, σ2, and σ3 of the production area, the residential living area, and the greening area respectively according to the mathematical model of the air quality risk coefficient; Compare the air quality risk coefficients σ1, σ2, and σ3 of the production area, the residential living area, and the greening area with the corresponding thresholds set by the system respectively. If any air quality risk coefficient is greater than or equal to the corresponding threshold, it means that there is air pollution in the current area's air quality.

6. The intelligent park environmental emergency governance system based on big data analysis according to claim 5, characterized in that, The working process of evaluating the noise risk in the park in step S2 includes: Obtain real-time noise, and construct a mathematical model for the noise risk coefficient, and the expression is: Wherein, is a conversion coefficient for adjusting the range of risk values, h(t) is a function of the noise level varying with time within a set historical time period, t1 is the initial moment of the set historical time period, t2 is the end moment of the set historical time period, h0 is the reference noise level, and the unit of the noise level is decibel; Calculate the air quality risk coefficients ρ1, ρ2, and ρ3 of the production area, residential living area, and greening area respectively according to the noise risk coefficient mathematical model; Compare the noise risk coefficients ρ1, ρ2, and ρ3 of the production area, residential living area, and greening area with the corresponding thresholds set by the system respectively. If any of the noise risk coefficients is greater than or equal to the corresponding threshold, it indicates that there is noise pollution in the current area's noise quality.

7. The intelligent park environmental emergency governance system based on big data analysis according to claim 4, wherein, The working process of evaluating the water quality risk of the park in step S3 includes: Construct a water quality risk coefficient mathematical model, and the expression is: where m is the number of items of water quality pollutants monitored, j belongs to m, C j is the concentration of the j-th item of water quality pollutant, C j0 is the standard concentration of the j-th item of water quality pollutant, k j is the weight coefficient of the j-th item of water quality pollutant, μ j is the non-linear coefficient of the j-th item of water quality pollutant; The non-linear coefficient μ of the j-th water quality pollutant j is related to the growth rate of the j-th water quality pollutant. Obtain the growth rate w of the j-th water quality pollutant within the historical time period j , and through the formula calculate the non-linear coefficient μ of the j-th water quality pollutant j ; Calculate the water quality risk coefficients θ o and θ i at the outlet of the sewage treatment plant and the inlet of domestic water respectively according to the water quality risk coefficient mathematical model; o 、θ i ; Respectively compare the water quality risk coefficients θ o and θ i with the corresponding thresholds set by the system. If any of the water quality risk coefficients is greater than or equal to the corresponding threshold, it indicates that there is water pollution in the current regional water quality.

8. An intelligent park environmental emergency governance system based on big data analysis according to claim 1, characterized in that, The working process of the emergency decision-making module includes: When any type of environmental pollution event occurs in any area, immediately issue a warning; When receiving the warning information, obtain the area and type of the environmental pollution event, and obtain the corresponding environmental pollution event risk coefficient; If air pollution and water pollution occur, compare the corresponding environmental pollution event risk coefficients with the corresponding risk coefficient emergency intervals set by the system, and start the corresponding number of purification devices according to the magnitude of the corresponding environmental pollution event risk coefficients; If noise pollution occurs, the staff will go to the corresponding area to control the noise.

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