CN113155191A - Urban area ecological environment monitoring method - Google Patents

Urban area ecological environment monitoring method Download PDF

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CN113155191A
CN113155191A CN202110408825.7A CN202110408825A CN113155191A CN 113155191 A CN113155191 A CN 113155191A CN 202110408825 A CN202110408825 A CN 202110408825A CN 113155191 A CN113155191 A CN 113155191A
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宁景苑
孙雨玘
梅正昊
蒋晨豪
叶海芬
惠国华
易晓梅
郜园园
张建锋
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Zhejiang A&F University ZAFU
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Abstract

The invention discloses a method for monitoring ecological environment in an urban area. It comprises the following steps: converting the detection data of a negative oxygen ion sensor, a dust sensor, a temperature sensor, a humidity sensor, a wind speed sensor, a wind direction sensor, a rainfall sensor, an air pressure sensor, a noise sensor and an ultraviolet sensor into adjustment data; calculating an ecological environment evaluation index LAP every N according to the adjustment data; and judging the health state of the current ecological environment according to the ecological environment evaluation index LAP. The invention can collect ecological environment data on line in real time, and evaluate the health state of the ecological environment according to the calculated ecological environment evaluation index, thereby realizing effective monitoring of the ecological environment in urban areas.

Description

Urban area ecological environment monitoring method
Technical Field
The invention relates to the technical field of environmental monitoring, in particular to an urban area ecological environment monitoring method.
Background
The urban ecological environment is a special artificial ecological environment established by human beings on the basis of transformation and adaptation to the natural environment, the automatic purification capacity of the urban ecological environment to environmental pollution is far inferior to that of the natural ecological environment, and as the urbanization process is intensified, the rushing population brings unprecedented development of various industries, simultaneously the urban ecological environment is also subjected to huge impact, and the dynamic monitoring and trend prediction of the urban ecological environment are important. The traditional urban area ecological environment monitoring uses artificial measurement and analysis, not only has low efficiency and high cost, but also is difficult to effectively monitor the whole situation in the detected area in real time according to a theoretical optimal model.
Disclosure of Invention
In order to solve the technical problems, the invention provides an urban area ecological environment monitoring method which can collect ecological environment data in real time on line and evaluate the health state of the ecological environment according to the calculated ecological environment evaluation index, thereby realizing effective monitoring of the urban area ecological environment.
In order to solve the problems, the invention adopts the following technical scheme:
the invention discloses a method for monitoring ecological environment of urban areas, which comprises the following steps:
detection data D output by the computer to the negative oxygen ion sensor S1 in the urban areas1(t) processing to obtain adjustment data Vs1(t) detection data D outputted from the dust sensor S2 in the urban areas2(t) processing to obtain adjustment data Vs2(t) detection data D outputted from the urban area temperature sensor S3s3(t) processing to obtain adjustment data Vs3(t) to the humidity in urban areasDetection data D output from the sensor S4s4(t) processing to obtain adjustment data Vs4xt), detected data D output from the wind velocity sensor S5 in the urban areas5(t) processing to obtain adjustment data Vs5(t) detection data D outputted from the wind direction sensor S6 in the urban areas6(t) processing to obtain adjustment data Vs6(t) detection data D outputted from the urban rainfall sensor S7s7(t) processing to obtain adjustment data Vs7(t) detection data D outputted from the in-urban-area barometric sensor S8s8(t) processing to obtain adjustment data Vs8(t) the detection data D outputted from the noise sensor S9 in the urban areas9(t) processing to obtain adjustment data Vs9(t) detection data D outputted from the ultraviolet sensor S10 in the urban areas10(t) processing to obtain adjustment data Vs10(t), t is time;
the computer calculates the ecological environment evaluation index LAP once every N seconds, when the LAP is not less than nm1 and not more than nm2, the ecological environment is judged to be healthy, when the LAP is not less than th1 and not more than nm1 or the LAP is not less than nm2 and not more than th2, the ecological environment is judged to be sub-healthy, when the lad is not less than bod1 and not more than th1 or the LAP is not less than th2 and not more than bod2, the bod1 and not more than th1 and not more than nm1 and not more than nm2 and not more than th2 and not more than bod2, and the ecological environment is judged to be unhealthy.
Preferably, the method for calculating the ecological environment assessment index LAP by the computer every N seconds comprises the following steps:
m1: the method for calculating the ecological environment evaluation parameter PJ (t) of each moment t in N seconds by the computer comprises the following steps:
will adjust the data Vs1(t)、Vs2(t)、Vs3(t)、Vs4(t)、Vs5(t)、Vs6(t)、Vs7(t)、Vs8(t)、Vs9(t)、Vs10(t) normalization to [1, 10 ] respectively]Within the interval, corresponding normalized data L is obtaineds1(t)、Ls2(t)、Ls3(t)、Ls4(t)、Ls5(t)、Ls6(t)、Ls7(t)、Ls8(t)、Ls9(t)、Ls10(t)，
Figure BDA0003023726600000031
PJ1(t)＝a1×log(Ls1(t))+a4×Ls4(t)+a7×log(Ls7(t))
PJ2(t)＝a2×Ls2(t)+a9×Ls9(t)+a10×Ls10(t)，
PJ3(t)＝a3×Ls3(t)，
PJ4(t)＝a5×Ls5(t)+a6×Ls6(t)-a8×Ls8(t)，
Wherein a1, a2, a3, a4, a5, a6, a7, a8, a9 and a10 are weight coefficients;
m2: inputting the ecological environment evaluation parameters PJ (t) of each time t in N seconds into a nonlinear collaborative model:
Figure BDA0003023726600000032
Figure BDA0003023726600000033
Figure BDA0003023726600000034
wherein B (x) is a load system, k (x) is a load excitation signal, x is a dynamic parameter of the nonlinear collaborative model, P is a regulation real parameter, cos (2 π ft) is a frequency component of the input signal, f is a frequency, Q is an intensity of the load excitation signal, a, b are real parameters,
when x is equal to xjThen, the nonlinear collaborative model generates a step, and the characteristic value of the step state is calculated
Figure BDA0003023726600000035
Preferably, the computer detects data D of the sensor Sisi(t) treatmentObtaining adjustment data Vsi(t) the process comprising the steps of, i ═ 1 to 10:
n1: the computer calculates the detection data D from the t-delta t moment to the t momentsi(t) amplitude mean ssu (t), amplitude maximum sma (t) and amplitude minimum smi (t);
n2: calculating a sensor characteristic angle mapping function K1(t), a mutation suppression function K2(t), a signal amplitude adjustment function K3(t),
Figure BDA0003023726600000041
Figure BDA0003023726600000042
Figure BDA0003023726600000043
n3: calculating adjustment data Vsi(t)，
Figure BDA0003023726600000044
Preferably, the negative oxygen ion sensor is AN AN-400 sensor, the dust sensor is a PM1003 sensor, the temperature sensor is AN NCT75DMR2G sensor, the humidity sensor is a GWSD50-100 sensor, the wind speed sensor is a GD51 sensor, the wind direction sensor is a GFD5X sensor, the rainfall sensor is a Mini-RL sensor, the air pressure sensor is AN MIK-P300 sensor, the noise sensor is a WS600A sensor, and the ultraviolet sensor is AN AGM2401B sensor. The sensor parameters are as in table one:
Figure BDA0003023726600000045
watch 1
Preferably, said V iss1When the value of (t) is in different ranges, a1 is different; vs2(t) a2 is different when the value is in different ranges; vs3(t) a3 is different when the value is in different ranges; vs4(t) a4 is different when the value is in different ranges; vs5(t) a5 is different when the value is in different ranges; vs6(t) a6 is different when the value is in different ranges; vs7(t) a7 is different when the value is in different ranges; vs8(t) a8 is different when the value is in different ranges; vs9(t) a9 is different when the value is in different ranges; vs10When the value of (t) is in a different range, the value of a10 is different.
According to the evaluation standards provided by national standards such as DGTJ08-2253-2018 green ecological urban area evaluation standard, HJ168-2020, environmental monitoring and analysis method standard formulation technical guide and the like, the regional ecological system characteristic indexes are roughly divided into three levels:
providing an added item index for the evaluation of an ecological environment system, wherein the added item index comprises negative oxygen ions, environmental humidity, rainfall and the like, and is defined as a primary influence index;
providing indexes of the subtractive terms for the evaluation of the ecological environment system, wherein the indexes comprise dust particles, noise influence, ultraviolet radiation and the like, and are defined as secondary influence indexes;
and (3) providing an added index for evaluation of the ecological environment system, wherein the added index comprises environmental temperature, air speed and wind direction, atmospheric pressure and the like, and is defined as a three-level influence index.
The first order impact indicator weights are as follows:
Figure BDA0003023726600000051
the secondary influence index weights are as follows:
Figure BDA0003023726600000061
the three levels of influence index weights are as follows:
Figure BDA0003023726600000062
the first-order influence indexes play a role in improving and maintaining the ecological environment systematically, for example, proper rainfall can increase the moisture content and the air humidity, and the negative oxygen ion indexes are obviously increased, so that the urban ecological environment is obviously improved; the secondary influence indexes are indexes having negative influence on the ecological environment, such as emission of dust and noise entangled in urban construction and transportation processes, not only influence the normal life of urban residents, but also have weakening effect on indexes such as negative oxygen ions and the like, and in addition, the excessively strong ultraviolet irradiation also has certain destructive effect on the ecological environment; the three-level influence indexes are widely available, but the influence is limited, the temperature has complex influence on the ecological environment, the excessively low or high temperature can generate adverse influence on the growth of residents and plants and the circulation of the ecological environment, but the proper temperature can actively promote ecological enhancement and restoration, so that the influence of the temperature indexes on the ecology is mostly positive, the action degree is adjusted through the weight of the temperature indexes, and the wind speed, the wind direction and the air pressure generate weak influence.
The invention has the beneficial effects that: the ecological environment data can be collected on line in real time, and the health state of the ecological environment is evaluated according to the calculated ecological environment evaluation index, so that the ecological environment in the urban area can be effectively monitored.
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FIG. 1 is a flow chart of an embodiment;
fig. 2 is a range diagram of the ecological environment assessment index LAP of the example.
Detailed Description
The technical scheme of the invention is further specifically described by the following embodiments and the accompanying drawings.
Example (b): the method for monitoring the ecological environment of the urban area in the embodiment, as shown in fig. 1, includes the following steps:
computer to urban areaDetection data D output by in-domain negative oxygen ion sensor S1s1(t) processing to obtain adjustment data Vs1(t) detection data D outputted from the dust sensor S2 in the urban areas2(t) processing to obtain adjustment data Vs2(t) detection data D outputted from the urban area temperature sensor S3s3(t) processing to obtain adjustment data Vs3(t) detection data D outputted from the humidity sensor S4 in the urban areas4(t) processing to obtain adjustment data Vs4(t) the detected data D outputted from the wind velocity sensor S5 in the urban areas5(t) processing to obtain adjustment data Vs5(t) detection data D outputted from the wind direction sensor S6 in the urban areas6(t) processing to obtain adjustment data Vs6(t) detection data D outputted from the urban rainfall sensor S7s7(t) processing to obtain adjustment data Vs7(t) detection data D outputted from the in-urban-area barometric sensor S8s8(t) processing to obtain adjustment data Vs8(t) the detection data D outputted from the noise sensor S9 in the urban areas9(t) processing to obtain adjustment data Vs9(t) detection data D outputted from the ultraviolet sensor S10 in the urban areas10(t) processing to obtain adjustment data Vs10(t), t is time;
the computer calculates the ecological environment evaluation index LAP once every N seconds, when the LAP is not less than nm1 and not more than nm2, the ecological environment is judged to be healthy, when the LAP is not less than th1 and not more than nm1 or the LAP is not less than nm2 and not more than th2, the ecological environment is judged to be sub-healthy, when the lad is not less than bod1 and not more than th1 or the LAP is not less than th2 and not more than bod2, the bod1 and not more than th1 and not more than nm1 and not more than nm2 and not more than th2 and not more than bod2, and the ecological environment is judged to be unhealthy.
The method for calculating the ecological environment evaluation index LAP every N seconds by the computer comprises the following steps:
m1: the method for calculating the ecological environment evaluation parameter PJ (t) of each moment t in N seconds by the computer comprises the following steps:
will adjust the data Vs1(t)、Vs2(t)、Vs3(t)、Vs4(t)、Vs5(t)、Vs6(t)、Vs7(t)、Vs8(t)、Vs9(t)、Vs10(t) normalization to [1, 10 ] respectively]Within the interval, corresponding normalized data L is obtaineds1(t)、Ls2(t)、Ls3(t)、Ls4(t)、Ls5(t)、Ls6(t)、Ls7(t)、Ls8(t)、Ls9(t)、Ls10(t)，
Figure BDA0003023726600000081
PJ1(t)＝a1×log(Ls1(t))+a4×Ls4(t)+a7×log(Ls7(t))
PJ2(t)＝a2×Ls2(t)+a9×Ls9(t)+a10×Ls10(t)，
PJ3(t)＝a3×Ls3(t)，
PJ4(t)＝a5×Ls5(t)+a6×Ls6(t)-a8×Ls8(t)，
Wherein a1, a2, a3, a4, a5, a6, a7, a8, a9 and a10 are weight coefficients;
m2: inputting the ecological environment evaluation parameters PJ (t) of each time t in N seconds into a nonlinear collaborative model:
Figure BDA0003023726600000091
Figure BDA0003023726600000092
Figure BDA0003023726600000093
wherein B (x) is a load system, k (x) is a load excitation signal, x is a dynamic parameter of the nonlinear collaborative model, P is a regulation real parameter, cos (2 π ft) is a frequency component of the input signal, f is a frequency, Q is an intensity of the load excitation signal, a, b are real parameters,
when x is equal to xjThen, the nonlinear collaborative model generates a step, and the characteristic value of the step state is calculated
Figure BDA0003023726600000094
Computer detection data D of sensor Sisi(t) processing to obtain adjustment data Vsi(t) the process comprising the steps of, i ═ 1 to 10:
n1: the computer calculates the detection data D from the t-delta t moment to the t momentsi(t) amplitude mean ssu (t), amplitude maximum sma (t) and amplitude minimum smi (t);
n2: calculating a sensor characteristic angle mapping function K1(t), a mutation suppression function K2(t), a signal amplitude adjustment function K3(t),
Figure BDA0003023726600000095
Figure BDA0003023726600000096
Figure BDA0003023726600000097
n3: calculating adjustment data Vsi(t)，
Figure BDA0003023726600000101
The negative oxygen ion sensor is AN AN-400 sensor, the dust sensor is a PM1003 sensor, the temperature sensor is AN NCT75DMR2G sensor, the humidity sensor is a GWSD50-100 sensor, the wind speed sensor is a GD51 sensor, the wind direction sensor is a GFD5X sensor, the rainfall sensor is a Mini-RL sensor, the air pressure sensor is AN MIK-P300 sensor, the noise sensor is a WS600A sensor, and the ultraviolet sensor is AN AGM2401B sensor. The sensor parameters are as in table one:
Figure BDA0003023726600000102
watch 1
Vs1(t) a1 is different when the value is in different ranges; vs2(t) a2 is different when the value is in different ranges; vs3(t) a3 is different when the value is in different ranges; vs4(t) a4 is different when the value is in different ranges; vs5(t) a5 is different when the value is in different ranges; vs6(t) a6 is different when the value is in different ranges; vs7(t) a7 is different when the value is in different ranges; vs8(t) a8 is different when the value is in different ranges; vs9(t) a9 is different when the value is in different ranges; vs10When the value of (t) is in a different range, the value of a10 is different.
According to the evaluation standards provided by national standards such as DGTJ08-2253-2018 green ecological urban area evaluation standard, HJ168-2020, environmental monitoring and analysis method standard formulation technical guide and the like, the regional ecological system characteristic indexes are roughly divided into three levels:
providing an added item index for the evaluation of an ecological environment system, wherein the added item index comprises negative oxygen ions, environmental humidity, rainfall and the like, and is defined as a primary influence index;
providing indexes of the subtractive terms for the evaluation of the ecological environment system, wherein the indexes comprise dust particles, noise influence, ultraviolet radiation and the like, and are defined as secondary influence indexes;
and (3) providing an added index for evaluation of the ecological environment system, wherein the added index comprises environmental temperature, air speed and wind direction, atmospheric pressure and the like, and is defined as a three-level influence index.
The first order impact indicator weights are as follows:
Figure BDA0003023726600000111
the secondary influence index weights are as follows:
Figure BDA0003023726600000112
the three levels of influence index weights are as follows:
Figure BDA0003023726600000121
the first-order influence indexes play a role in improving and maintaining the ecological environment systematically, for example, proper rainfall can increase the moisture content and the air humidity, and the negative oxygen ion indexes are obviously increased, so that the urban ecological environment is obviously improved; the secondary influence indexes are indexes having negative influence on the ecological environment, such as emission of dust and noise entangled in urban construction and transportation processes, not only influence the normal life of urban residents, but also have weakening effect on indexes such as negative oxygen ions and the like, and in addition, the excessively strong ultraviolet irradiation also has certain destructive effect on the ecological environment; the three-level influence indexes are widely available, but the influence is limited, the temperature has complex influence on the ecological environment, the excessively low or high temperature can generate adverse influence on the growth of residents and plants and the circulation of the ecological environment, but the proper temperature can actively promote ecological enhancement and restoration, so that the influence of the temperature indexes on the ecology is mostly positive, the action degree is adjusted through the weight of the temperature indexes, and the wind speed, the wind direction and the air pressure generate weak influence.
In this scenario, the range of the ecological environment assessment index LAP is shown schematically, and as shown in fig. 2, when LAP is between nm1 and nm2, the ecological environment is healthy.

Claims (5)

1. A method for monitoring ecological environment of urban areas is characterized by comprising the following steps:
detection data D output by the computer to the negative oxygen ion sensor S1 in the urban areas1(t) processing to obtain adjustment data Vs1(t) to cityDetection data D output by dust sensor S2 in urban areas2(t) processing to obtain adjustment data Vs2(t) detection data D outputted from the urban area temperature sensor S3s3(t) processing to obtain adjustment data Vs3(t) detection data D outputted from the humidity sensor S4 in the urban areas4(t) processing to obtain adjustment data Vs4(t) the detected data D outputted from the wind velocity sensor S5 in the urban areas5(t) processing to obtain adjustment data Vs5(t) detection data D outputted from the wind direction sensor S6 in the urban areas6(t) processing to obtain adjustment data Vs6(t) detection data D outputted from the urban rainfall sensor S7s7(t) processing to obtain adjustment data Vs7(t) detection data D outputted from the in-urban-area barometric sensor S8s8(t) processing to obtain adjustment data Vs8(t) the detection data D outputted from the noise sensor S9 in the urban areas9(t) processing to obtain adjustment data Vs9(t) detection data D outputted from the ultraviolet sensor S10 in the urban areas10(t) processing to obtain adjustment data Vs10(t), t is time;
the computer calculates the ecological environment evaluation index LAP once every N seconds, when the LAP is not less than nm1 and not more than nm2, the ecological environment is judged to be healthy, when the LAP is not less than th1 and not more than nm1 or the LAP is not less than nm2 and not more than th2, the ecological environment is judged to be sub-healthy, when the lad is not less than bod1 and not more than th1 or the LAP is not less than th2 and not more than bod2, the bod1 and not more than th1 and not more than nm1 and not more than nm2 and not more than th2 and not more than bod2, and the ecological environment is judged to be unhealthy.
2. The method for monitoring ecological environment of urban area according to claim 1, wherein said method for calculating ecological environment evaluation index LAP by computer every N seconds comprises the following steps:
m1: the method for calculating the ecological environment evaluation parameter PJ (t) of each moment t in N seconds by the computer comprises the following steps:
will adjust the data Vs1(t)、Vs2(t)、Vs3(t)、Vs4(t)、Vs5(t)、Vs6(t)、Vs7(t)、Vs8(t)、Vs9(t)、Vs10(t) normalization to [1, 10 ] respectively]Within the interval, corresponding normalized data L is obtaineds1(t)、Ls2(t)、Ls3(t)、Ls4(t)、Ls5(t)、Ls6(t)、Ls7(t)、Ls8(t)、Ls9(t)、Ls10(t)，
Figure FDA0003023726590000021
PJ1(t)＝a1×log(Ls1(t))+a4×Ls4(t)+a7×log(Ls7(t))，
PJ2(t)＝a2×Ls2(t)+a9×Ls9(t)+a10×Ls10(t)，
PJ3(t)＝a3×Ls3(t)，
PJ4(t)＝a5×Ls5(t)+a6×Ls6(t)-a8×Ls8(t)，
Wherein a1, a2, a3, a4, a5, a6, a7, a8, a9 and a10 are weight coefficients;
m2: inputting the ecological environment evaluation parameters PJ (t) of each time t in N seconds into a nonlinear collaborative model:
Figure FDA0003023726590000022
Figure FDA0003023726590000023
Figure FDA0003023726590000024
wherein B (x) is a load system, k (x) is a load excitation signal, x is a dynamic parameter of the nonlinear collaborative model, P is a regulation real parameter, cos (2 π ft) is a frequency component of the input signal, f is a frequency, Q is an intensity of the load excitation signal, a, b are real parameters,
when x is equal to xjThen, the nonlinear collaborative model generates a step, and the characteristic value of the step state is calculated
Figure FDA0003023726590000031
3. The method as claimed in claim 1, wherein the computer detects data D of sensor Sisi(t) processing to obtain adjustment data Vsi(t) the process comprising the steps of, i ═ 1 to 10:
n1: the computer calculates the detection data D from the t-delta t moment to the t momentsi(t) amplitude mean ssu (t), amplitude maximum sma (t) and amplitude minimum smi (t);
n2: calculating a sensor characteristic angle mapping function K1(t), a mutation suppression function K2(t), a signal amplitude adjustment function K3(t),
Figure FDA0003023726590000032
Figure FDA0003023726590000033
Figure FDA0003023726590000034
n3: calculating adjustment data Vsi(t)，
Figure FDA0003023726590000035
4. The method for monitoring the ecological environment of AN urban area according to claim 1, wherein the negative oxygen ion sensor is AN-400 sensor, the dust sensor is a PM1003 sensor, the temperature sensor is AN NCT75DMR2G sensor, the humidity sensor is a GWSD50-100 sensor, the wind speed sensor is a GD51 sensor, the wind direction sensor is a GFD5X sensor, the rainfall sensor is a Mini-RL sensor, the air pressure sensor is AN MIK-P300 sensor, the noise sensor is a WS600A sensor, and the ultraviolet sensor is AN AGM2401B sensor.
5. The method as claimed in claim 1, wherein V is a natural gas, and V is a natural gas, or a natural gas, and is a gas, and is a gass1(t) a1 is different when the value is in different ranges; vs2(t) a2 is different when the value is in different ranges; vs3(t) a3 is different when the value is in different ranges; vs4(t) a4 is different when the value is in different ranges; vs5(t) a5 is different when the value is in different ranges; vs6(t) a6 is different when the value is in different ranges; vs7(t) a7 is different when the value is in different ranges; vs8(t) a8 is different when the value is in different ranges; vs9(t) a9 is different when the value is in different ranges; vs10When the value of (t) is in a different range, the value of a10 is different.
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CN113686395A (en) * 2021-09-24 2021-11-23 浙江农林大学 Mountain forest land ecological environment monitoring device and method
CN117974077A (en) * 2024-04-01 2024-05-03 潍坊宏图环保设备有限公司 Ecological environment monitoring system based on big data

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