A smart park environmental emergency management system based on big data analysis

Abstract

The present invention relates to the field of environmental governance technology, and specifically discloses a smart park environmental emergency governance system based on big data analysis. The present invention uses a data acquisition module to collect air quality data, water quality data and noise data from different areas in the park in real time, and obtains meteorological data in real time. Then, based on a data transmission module, the data is transmitted to a data storage center in real time through wireless transmission technology. Then, based on a data analysis module, the data is integrated and analyzed to accurately predict environmental risks. The emergency decision-making module can quickly generate an emergency response plan when an environmental incident occurs, and realize automatic scheduling of emergency resources and on-site execution monitoring, which greatly improves the efficiency of emergency response and effectively reduces the losses caused by environmental incidents.

Description

Technical Field

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

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

[0003] However, the current park environmental management system has many problems: 1. The collection of environmental data is not comprehensive enough and cannot accurately reflect the overall environmental status of the park; 2. Most of the judgments on whether environmental risks occur are based on the comparison of collected data with thresholds, without considering data anomalies caused by temporary human activities, which leads to unnecessary resource allocation and personnel scheduling. Therefore, the present invention provides a smart park environmental emergency management system based on big data analysis. Summary of the Invention

[0004] The purpose of this invention is to provide a smart park environmental emergency management 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 management system based on big data analysis, the system includes: a data acquisition module, a data transmission module, a data analysis module, an emergency decision module and an automatic control module;

[0007] 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, realizing 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 assess the park environmental risks based on the analysis results;

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

[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 acquisition module includes:

[0013] The park is divided into production area, residential area and green area, and the three areas are numbered as follows: 1, 2, 3;

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

[0015] Water quality monitoring sensors are placed at the outlet of the park's sewage treatment plant and at the inlet of residents' water supply to comprehensively monitor the water quality 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 using mature distributed storage software to ensure secure storage and fast access to 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 based on the air quality data collected by the data acquisition module;

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

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

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

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

[0025]

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

[0027] The nonlinear function mathematical model of the pollutant concentration is:

[0028]

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

[0030]

[0031] Where, E i0 is the standard concentration of the i-th air pollutant, μ i is the nonlinear coefficient of the i-th air pollutant, 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. T0, H0, and V0 represent the standard values ​​of temperature, humidity, and wind speed, respectively.

[0032] The nonlinear coefficient μ of the i-th air pollutant i Related to the growth rate of the i-th air pollutant, obtain the growth rate w of the i-th air pollutant in the historical time period i , through the formula Calculate the nonlinear coefficient μ of the i-th air pollutant i ;

[0033] According to the mathematical model of air quality risk coefficient, the air quality risk coefficients σ1, σ2 and σ3 of production area, residential area and green area are calculated respectively;

[0034] The air quality risk coefficients σ1, σ2 and σ3 of the production area, residential area and green area are compared 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 air quality of the current area.

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

[0036] Obtain real-time noise and construct a mathematical model of noise risk coefficient, which is expressed as:

[0037]

[0038] Where, is a conversion coefficient used to adjust the range of the risk value, h(t) is the function of the noise level changing over time during the set historical time period, t1 is the starting time of the set historical time period, t2 is the ending time of the set historical time period, h0 is the reference noise level, and the noise level unit is decibel;

[0039] According to the mathematical model of noise risk coefficient, the air quality risk coefficients ρ1, ρ2 and ρ3 of the production area, residential area and green area are calculated respectively;

[0040] The noise risk coefficients ρ1, ρ2 and ρ3 of the production area, residential area and green area are compared with the corresponding thresholds set by the system respectively. If any noise risk coefficient is greater than or equal to the corresponding threshold, it means that there is noise pollution in the noise quality of the current area.

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

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

[0043]

[0044] In the formula, m is the number of water pollutants monitored, j belongs to m, C j is the concentration of the jth water pollutant, C j0 is the standard concentration of the jth water pollutant, k j is the weight coefficient of the jth water pollutant, μ j is the nonlinear coefficient of the jth water pollutant;

[0045] The nonlinear coefficient μ of the j-th water pollutant j Related to the growth rate of the jth water quality pollutant, obtain the growth rate w of the jth water quality pollutant in the historical time period j , through the formula Calculate the nonlinear coefficient μ of the jth water pollutant j ;

[0046] According to the water quality risk coefficient mathematical model, the water quality risk coefficient θ of the sewage treatment plant outlet and the residential water inlet is calculated respectively. o ,θ i ;

[0047] The water quality risk coefficients θ at the outlet of the sewage treatment plant and the inlet of residential water are respectively o ,θ i Compared with the corresponding threshold set by the system, if any water quality risk coefficient is greater than or equal to the corresponding threshold, it means that the water quality in the current area is polluted.

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

[0049] When any type of environmental pollution incident occurs in any area, an early warning will be issued immediately;

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

[0051] If air pollution or water pollution occurs, the corresponding environmental pollution event risk coefficient will be compared with the corresponding risk coefficient emergency interval set by the system, and the corresponding number of purification equipment will be activated according to the size of the corresponding environmental pollution event risk coefficient;

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

[0053] Beneficial effects of the present invention:

[0054] The present invention uses a data acquisition module to collect air quality data, water quality data and noise data from different areas in the park in real time, and obtains meteorological data in real time. Then, based on the data transmission module, it transmits the data to the data storage center in real time through wireless transmission technology. Then, based on the data analysis module, the data is integrated and analyzed to accurately predict environmental risks. The emergency decision-making module can quickly generate emergency response plans when environmental events occur, and realize automatic scheduling of emergency resources and on-site execution monitoring, which greatly improves the efficiency of emergency response and effectively reduces 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 It is a structural diagram of the smart park environmental emergency management system based on big data analysis of the present invention. DETAILED DESCRIPTION

[0057] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making any creative efforts shall fall within the scope of protection of the present invention.

[0058] See also Figure 1 As shown, the present invention provides a smart park environmental emergency management system based on big data analysis, the system includes: a data acquisition module, a data transmission module, a data analysis module, an emergency decision module and an automatic control module;

[0059] 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, realizing 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 assess the park environmental risks based on the analysis results;

[0062] The emergency decision-making module is used to generate corresponding emergency warning decisions based on 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 solution, the present invention collects air quality data, water quality data and noise data in different areas of the park in real time through the data acquisition module, and obtains meteorological data in real time, and then transmits it to the data storage center in real time through wireless transmission technology based on the data transmission module. Then, based on the data analysis module, the data is integrated and analyzed to accurately predict environmental risks. The emergency decision-making module can quickly generate emergency response plans when environmental events occur, and realize automatic scheduling of emergency resources and on-site execution monitoring, which greatly improves the efficiency of emergency response and effectively reduces the losses caused by environmental events.

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

[0066] The park is divided into production area, residential area and green area, and the three areas are numbered as follows: 1, 2, 3;

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

[0068] Water quality monitoring sensors are placed at the outlet of the park's sewage treatment plant and at the inlet of residents' water supply to comprehensively monitor the water quality in different areas of the park;

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

[0070] Through the above technical solution, this embodiment provides a method for collecting park environmental data. This module includes air quality sensors, water quality monitoring equipment, and noise monitoring devices distributed throughout the park. It is used to collect real-time air quality data (such as PM2.5, sulfur dioxide, and nitrogen oxide concentrations), water quality data (such as chemical oxygen demand, ammonia nitrogen content, and pH), and noise data within the park. 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 using mature distributed storage software to ensure secure storage and fast access to data.

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

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

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

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

[0077] The process of assessing the air quality risk of the park in step S1 includes:

[0078] Obtain real-time temperature, humidity, and wind speed to construct a mathematical model of air quality risk coefficient, which is expressed as:

[0079]

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

[0081] The nonlinear function mathematical model of the pollutant concentration is:

[0082]

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

[0084]

[0085] Where, E i0 is the standard concentration of the i-th air pollutant, μ iis the nonlinear coefficient of the i-th air pollutant, 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. T0, H0, and V0 represent the standard values ​​of temperature, humidity, and wind speed, respectively.

[0086] The nonlinear coefficient μ of the i-th air pollutant i Related to the growth rate of the i-th air pollutant, obtain the growth rate w of the i-th air pollutant in the historical time period i , through the formula Calculate the nonlinear coefficient μ of the i-th air pollutant i ;

[0087] According to the mathematical model of air quality risk coefficient, the air quality risk coefficients σ1, σ2 and σ3 of production area, residential area and green area are calculated respectively;

[0088] The air quality risk coefficients σ1, σ2 and σ3 of the production area, residential area and green area are compared 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 air quality of the current area.

[0089] Through the above technical solution, this embodiment provides a method for predicting and evaluating the air quality risk in each area of ​​the park, obtaining real-time temperature, humidity and wind speed, and combining meteorological data to build an air quality risk coefficient mathematical model in, is the standardized concentration of the i-th air pollutant, f(E i ) is a nonlinear function of pollutant concentration, g(T, H, V) is a regulating function of environmental factors (temperature T, humidity H, wind speed V), Used to smooth the risk contribution of pollutants at low concentrations, The nonlinear risk growth at high concentrations of the reaction pollutant, the nonlinear coefficient μ of the i-th air pollutant i It is related to the growth rate of the i-th air pollutant. The air quality risk coefficients σ1, σ2 and σ3 of the production area, residential area and green area are calculated respectively according to the mathematical model of the air quality risk coefficient; the air quality risk coefficients σ1, σ2 and σ3 of the production area, residential area and green area are compared with the corresponding thresholds set by the system. If any air quality risk coefficient is greater than or equal to the corresponding threshold, it means that there is air pollution in the air quality of the current area.

[0090] The process of obtaining the growth rate of the i-th air pollutant is as follows: obtaining the time-varying function of the i-th air pollutant, and then deriving the time-varying function of the i-th air pollutant to obtain the growth rate of the i-th air pollutant.

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

[0092] Obtain real-time noise and construct a mathematical model of noise risk coefficient, which is expressed as:

[0093]

[0094] Where, is a conversion coefficient used to adjust the range of the risk value, h(t) is the function of the noise level changing over time during the set historical time period, t1 is the starting time of the set historical time period, t2 is the ending time of the set historical time period, h0 is the reference noise level, and the noise level unit is decibel;

[0095] According to the mathematical model of noise risk coefficient, the air quality risk coefficients ρ1, ρ2 and ρ3 of the production area, residential area and green area are calculated respectively;

[0096] The noise risk coefficients ρ1, ρ2 and ρ3 of the production area, residential area and green area are compared with the corresponding thresholds set by the system respectively. If any noise risk coefficient is greater than or equal to the corresponding threshold, it means that there is noise pollution in the noise quality of the current area.

[0097] Through the above technical solution, this embodiment provides a noise pollution risk assessment and prediction method, which obtains noise change data over time and constructs a noise risk coefficient mathematical model. is a normalization coefficient used to adjust the range of ρ, ln(1+h(t)) is used to smooth the risk contribution at low noise levels, It is used to reflect the nonlinear risk growth at high noise levels. The air quality risk coefficients ρ1, ρ2 and ρ3 of the production area, residential area and green area are calculated respectively according to the mathematical model of the noise risk coefficient. The noise risk coefficients ρ1, ρ2 and ρ3 of the production area, residential area and green area are compared with the corresponding thresholds set by the system. If any noise risk coefficient is greater than or equal to the corresponding threshold, it means that there is noise pollution in the noise quality of the current area.

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

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

[0100]

[0101] In the formula, m is the number of water pollutants monitored, j belongs to m, C j is the concentration of the jth water pollutant, C j0 is the standard concentration of the jth water pollutant, k j is the weight coefficient of the jth water pollutant, μ j is the nonlinear coefficient of the jth water pollutant;

[0102] The nonlinear coefficient μ of the j-th water pollutant j Related to the growth rate of the jth water quality pollutant, obtain the growth rate w of the jth water quality pollutant in the historical time period j , through the formula Calculate the nonlinear coefficient μ of the jth water pollutant j ;

[0103] According to the water quality risk coefficient mathematical model, the water quality risk coefficient θ of the sewage treatment plant outlet and the residential water inlet is calculated respectively. o ,θ i ;

[0104] The water quality risk coefficients θ at the outlet of the sewage treatment plant and the inlet of residential water are respectively o ,θ i Compared with the corresponding threshold set by the system, if any water quality risk coefficient is greater than or equal to the corresponding threshold, it means that the water quality in the current area is polluted.

[0105] Through the above technical solution, this embodiment provides a method for water pollution risk assessment and prediction, obtains the parameters of various water pollutants, and then constructs a water quality risk coefficient mathematical model. Used to smooth the risk contribution at low concentrations, It is used to reflect the nonlinear risk growth at high concentrations. The water quality risk coefficients θ are calculated for the outlet of the sewage treatment plant and the inlet of residential water use according to the water quality risk coefficient mathematical model. o ,θ i ; The water quality risk coefficients θ at the outlet of the sewage treatment plant and the inlet of residential water are respectively o ,θ i Compared with the corresponding threshold set by the system, if any water quality risk coefficient is greater than or equal to the corresponding threshold, it means that the water quality in the current area is polluted.

[0106] Among them, the process of obtaining the growth rate of the j-th water quality pollutant is: obtain the time-varying function of the j-th water quality pollutant, and then derive the time-varying function of the j-th water quality pollutant 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 incident occurs in any area, an early warning will be issued immediately;

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

[0110] If air pollution or water pollution occurs, the corresponding environmental pollution event risk coefficient will be compared with the corresponding risk coefficient emergency interval set by the system, and the corresponding number of purification equipment will be activated according to the size of the corresponding environmental pollution event risk coefficient;

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

[0112] Through the above technical solution, 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. When the early warning information is received, the system generates a corresponding emergency plan based on the type of environmental event. If air pollution and water pollution occur, the corresponding environmental pollution event risk coefficient will be compared with the corresponding risk coefficient emergency range set by the system. According to the size of the corresponding environmental pollution event risk coefficient, the corresponding number of purification equipment will be activated; 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 dimensionlessly processed, and the standard data, threshold intervals, and weight coefficients set in the present invention are all empirical data and need not be elaborated.

[0114] The above is a detailed description of an embodiment of the present invention. However, the content described is only a preferred embodiment of the present invention and should not be considered to limit the scope 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. A smart park environmental emergency management system based on big data analysis, characterized by: The system includes: a data acquisition module, a data transmission module, a data analysis module, an emergency decision 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, realizing comprehensive collection of 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 assess the park environmental risks based on the analysis results; The emergency decision-making module is used to generate corresponding emergency warning decisions based on the evaluation results of the park environment by the data analysis module; The automatic control module is used to automatically control the emergency equipment in the park according to the emergency warning decision; The working process of the data acquisition module includes: The park is divided into production area, residential area and green area, and the three areas are numbered as follows: 1, 2, 3; Air quality monitoring sensors and noise monitoring sensors are deployed in three areas to comprehensively monitor the air quality and noise conditions in different areas of the park; Water quality monitoring sensors are placed at the outlet of the park's sewage treatment plant and at the inlet of residents' water supply to comprehensively monitor the water quality in different areas of the park; The data acquisition module also accesses meteorological data from the meteorological department, including temperature, humidity and wind speed; The working process of the data analysis module includes: Step S1: Evaluate the air quality risk of the park based on the air quality data collected by the data acquisition module; Step S2: Evaluate the noise risk of the park based on the noise data collected by the data acquisition module; Step S3: Evaluate the water quality risk of the park based on the water quality data collected by the data acquisition module; The process of assessing the air quality risk of the park in step S1 includes: Obtain real-time temperature, humidity, and wind speed to construct a mathematical model of air quality risk coefficient, which is expressed as: ； Where n is the number of monitored air pollutants, Belongs to n, is the concentration of the ith air pollutant, is a nonlinear function of pollutant concentration, is the weight coefficient of the i-th air pollutant, It is a regulating function of environmental factors (temperature T, humidity H, wind speed V); The nonlinear function mathematical model of the pollutant concentration is: ； The mathematical model of the regulation function of the environmental factors (temperature T, humidity H, wind speed V) is: ; Where, is the standard concentration of the i-th air pollutant, is the nonlinear coefficient of the ith air pollutant, 、 and Respectively represent the weight coefficients corresponding to temperature T, humidity H, and wind speed V, 、 and Respectively represent real-time temperature, humidity and wind speed, 、 and Represent the standard values ​​of temperature, humidity and wind speed respectively; The nonlinear coefficient of the i-th air pollutant Related to the growth rate of the i-th air pollutant, obtain the growth rate of the i-th air pollutant in the historical period , through the formula Calculate the nonlinear coefficient of the i-th air pollutant ; According to the mathematical model of air quality risk coefficient, the air quality risk coefficients of production area, residential area and green area are calculated respectively. 、 and ; The air quality risk coefficients of production areas, residential areas and green areas are respectively 、 and Compared with the corresponding threshold set by the system, if any air quality risk coefficient is greater than or equal to the corresponding threshold, it means that the air quality in the current area is polluted; The process of evaluating the noise risk of the park in step S2 includes: Obtain real-time noise and construct a mathematical model of noise risk coefficient, which is expressed as: ； Where, is the conversion factor, which is used to adjust the range of risk value. It is a function of the noise level changing over time within a set historical time period. It is the initial moment of setting the historical time period. It sets the end time of the historical time period. is the reference noise level, and the noise level is in decibels; According to the noise risk coefficient mathematical model, the air quality risk coefficients of the production area, residential area and green area are calculated respectively. 、 and ; The noise risk coefficients of production areas, residential areas and green areas are respectively 、 and Compared with the corresponding threshold set by the system, if any noise risk coefficient is greater than or equal to the corresponding threshold, it means that the noise quality in the current area is subject to noise pollution; The process of assessing the water quality risk of the park in step S3 includes: Construct a mathematical model of water quality risk coefficient, the expression is: ； In the formula, m is the number of water pollutants monitored, Belongs to m, is the concentration of the jth water pollutant, is the standard concentration of the jth water pollutant, is the weight coefficient of the j-th water quality pollutant, is the nonlinear coefficient of the jth water pollutant; The nonlinear coefficient of the j-th water pollutant Related to the growth rate of the jth water quality pollutant, obtain the growth rate of the jth water quality pollutant in the historical time period , through the formula Calculate the nonlinear coefficient of the jth water pollutant ; According to the water quality risk coefficient mathematical model, the water quality risk coefficients of the sewage treatment plant outlet and the residential water inlet are calculated respectively. 、 ; The water quality risk coefficients at the outlet of the sewage treatment plant and the inlet of residential water are respectively 、 Compared with the corresponding threshold set by the system, if any water quality risk coefficient is greater than or equal to the corresponding threshold, it means that the water quality in the current area is polluted.

2. The smart park environmental emergency management system based on big data analysis according to claim 1 is 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 using mature distributed storage software to ensure secure storage and fast access to data.

3. The smart park environmental emergency management system based on big data analysis according to claim 2 is characterized in that: The working process of the emergency decision-making module includes: When any type of environmental pollution incident occurs in any area, an early warning will be issued immediately; When receiving the early warning information, obtain the area and type of the environmental pollution event, and obtain the corresponding environmental pollution event risk coefficient; If air pollution or water pollution occurs, the corresponding environmental pollution event risk coefficient will be compared with the corresponding risk coefficient emergency interval set by the system, and the corresponding number of purification equipment will be activated according to the size of the corresponding environmental pollution event risk coefficient; If noise pollution occurs, staff will go to the corresponding area to control the noise.

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