CN116151683A - Real-time monitoring environment artificial intelligent detection method - Google Patents
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Abstract
The invention discloses an environment artificial intelligent detection method for real-time monitoring, which relates to the technical field of environment monitoring, and solves the technical problems that an abnormal period with excessive emission of a designated area is not found in time, and meanwhile, whether the designated area is managed or not cannot be judged according to the purification parameters of the surrounding environment, whether the purification parameters of the surrounding area reach the standard is checked, if the designated area does not reach the designated standard, the designated area is marked, the designated early warning period of the area to be managed is managed, different areas to be managed are managed according to the partition management mode, the emission parameters are limited, the abnormal emission period of the corresponding area can be quickly found by adopting the detection mode through artificial intelligence to automatically manage the emission parameters, whether the designated area needs to be managed or not is checked according to the purification values of the surrounding environment, and the adjacent areas to be managed are distinguished and managed, so that the environment management effect of the whole smart city can be improved.
Description
Technical Field
The invention belongs to the technical field of environment monitoring, and particularly relates to an environment artificial intelligent detection method for real-time monitoring.
Background
The environment monitoring module realizes real-time environment data display of air temperature, air humidity, soil temperature, soil humidity, illuminance, carbon dioxide concentration, oxygen concentration, soil pH value and the like of the greenhouse, and can also check the historical data of various sensors so as to be convenient for analyzing the crop growth condition; the sensor position map can be checked, so that environmental data of each position of the greenhouse can be analyzed conveniently.
The invention discloses an artificial intelligence-based marine ecological environment monitoring method, which comprises the following steps of: step one: marine data acquisition: detecting various parameters of the seawater, and knowing the environmental index of the marine ecological environment in real time; step two: data analysis: analyzing the measured marine data by combining the meteorological data, and knowing the association condition of the marine data and the meteorological data; step three: and (3) data warehouse entry comparison: uploading the measured data into a marine monitoring database, and comparing the measured data with the past date data to know the change trend of the marine ecological environment; step four: monitoring report output: all measured data are arranged, and a detailed report is generated, so that personnel can quickly know marine ecological environment information and then make corresponding processing; according to the marine ecological environment monitoring method based on artificial intelligence, marine data can be acquired through various acquisition modes, accuracy of measured data is ensured, and effective monitoring of the marine ecological environment is achieved.
In the smart city environment monitoring process, the pollution emission amount of a certain area is generally controlled, but the control mode still has the following defects to be improved:
the abnormal time period with excessive emission of the designated area is not found in time, and meanwhile, whether the designated area is managed or not cannot be judged according to the purification parameters of the surrounding environment.
Disclosure of Invention
The present invention aims to solve at least one of the technical problems existing in the prior art; therefore, the invention provides a real-time monitoring environment artificial intelligent detection method, which is used for solving the technical problems that an abnormal period with excessive emission of a designated area is not found in time and whether the designated area is managed or not can not be judged according to the purification parameters of the surrounding environment.
To achieve the above object, an embodiment according to a first aspect of the present invention provides an artificial intelligence detection method for real-time monitoring environment, including the following steps:
s1, acquiring environmental parameter data of different areas of a smart city in advance, wherein the acquired environmental parameter data comprise PM2.5 content parameters, pollutant gas content parameters and pollutant emission content of the area;
s2, processing environmental parameter data of different areas according to a time interval division mode, acquiring early warning time intervals of the areas through processing results, determining the early warning time intervals of the different areas, acquiring specific pollution parameter values of the early warning time intervals, and extracting a single pollution parameter value maximum value from a plurality of pollution parameter values of the early warning time intervals;
s3, determining a designated time period and a designated area according to the maximum value of the single-set pollution parameter value, acquiring environmental parameter data of a designated range around the designated area in the designated time period from big data, determining purification parameters of the peripheral range according to the environmental parameter data and the corresponding maximum value of the single-set pollution parameter value, checking whether the purification parameters of the peripheral range reach standards, and if the purification parameters of the peripheral range do not reach the standards, marking the designated area for external environmental protection personnel to manage and control;
s4, controlling the early warning time period appointed by the area to be controlled, controlling different areas to be controlled according to a partition control mode, limiting the emission parameters, and automatically controlling the emission parameters through artificial intelligence.
Preferably, in the step S2, the specific manner of processing the environmental parameter data of different areas is as follows:
s21, taking the current moment as a calibration moment, acquiring a plurality of time periods 24h before the calibration moment, and marking the time periods as i, wherein i=1, 2, … …, n and n=24, and i is a single integer time period;
s22, obtaining PM2.5 contents with different time periodsParameters, pollutant gas content parameters, and pollutant emission content of the zone, and marks the PM2.5 content parameter as PM i-k Marking a contaminant gas content parameter as WR i-k Marking a pollutant emission content parameter as PF i-k Where k represents different regions, WRC is used i-k ＝PM i-k ×C1+WR i-k ×C2+PF i-k Obtaining pollution parameter values WRC of different areas and different time periods by using the X C3 i-k Wherein k represents different regions;
s23, pollution parameter WRC i-k Comparing with a preset parameter Y1, and in a certain period of time, the pollution parameter WRC i-k There are multiple groups, when WRC i-k When the number is less than Y1, no processing is carried out, otherwise, the time period is marked as an early warning time period, an ultra-warning signal is generated, and the number of times and the duration of the existence of the ultra-warning signal are obtained;
s24, marking the existence times of the super-alarm signal as CS i-k Marking the time length of existence of the super-alarm signal as SC i-k BD is adopted i-k ＝CS i-k ×X1+SC i-k X2 obtaining comparison parameter BD belonging to different time periods i-k 24 groups of comparison parameters BD belonging to the same area are compared i-k Arranging, and extracting the comparison parameter BD with the largest value i-k And by the comparison of the reference value BD i-k Acquiring a corresponding period of time and a plurality of groups of pollution parameter values WRC corresponding to the period of time i-k And from multiple sets of pollution parameters WRC i-k The internal extraction maximum is marked as WRC i-kmax 。
Preferably, in the step S3, the specific way of marking the designated area is as follows:
s31, for the maximum value WRC of the pollution parameter value of a single group i-kmax Receiving, determining a corresponding period and a corresponding region according to the marks i and k, and processing PM2.5 content parameters, pollutant gas content parameters and pollutant emission content in the environmental parameter data by adopting the same processing mode of the step S22 to obtain a plurality of groups of different pollution parameter values WRC i-k ；
S32, obtaining a plurality of groups of different pollution parameter values WRC i-k Performing mean processing to obtain corresponding processing mean CL i-k By JH i-k ＝WRC i-k -CL i-k Obtaining the purification parameter JH i-k ；
S33, purifying parameters JH obtained by processing i-k The specific comparison method is as follows: when JH i-k When Y2 is less than, generating a control signal, otherwise, generating no signal;
s34, marking the designated area according to the generated control signal, and setting the marked area as the area to be controlled.
Preferably, in the step S4, the specific manner of controlling the different areas to be controlled is as follows:
s41, acquiring to-be-controlled areas belonging to the same early warning period, sequentially acquiring distance parameters between two adjacent groups of to-be-controlled areas, and marking different distance parameters as JLt, wherein t represents different distance parameters;
s42, comparing the distance parameter JLt with a preset parameter Y3, and generating a partition control signal when JLt is smaller than Y3, otherwise, not generating any signal;
s43, carrying out partition control on the two groups of areas to be controlled according to the partition control signals, wherein the specific control mode is that the high-yield time periods of the two groups of areas to be controlled are staggered.
Compared with the prior art, the invention has the beneficial effects that: determining early warning periods of different areas, acquiring specific pollution parameter values of the early warning periods, extracting a single set of pollution parameter value maximum value from a plurality of sets of pollution parameter values existing in the early warning periods, determining a designated period and a designated area according to the single set of pollution parameter value maximum value, acquiring environmental parameter data of a designated range around the designated area in the designated period from big data, determining purification parameters of the peripheral range according to the environmental parameter data and the corresponding single set of pollution parameter value maximum value, checking whether the purification parameters of the peripheral range reach standards, if the purification parameters of the peripheral range do not reach the designated standard, marking the designated area, managing and controlling the early warning periods designated by the area to be managed, limiting the emission parameters in a partition management mode, automatically managing and controlling the emission parameters through artificial intelligence, adopting the detection mode, checking whether the designated area needs to be managed or not according to the purification values of the peripheral environment, distinguishing and controlling the adjacent area to be managed, and improving the management and control effect of the environment of the whole smart city.
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FIG. 1 is a schematic flow chart of the method of the invention.
Detailed Description
The technical solutions of the present invention will be clearly and completely described in connection with the embodiments, and it is obvious that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, the application provides an artificial intelligence detection method for real-time monitoring environment, which comprises the following steps:
s1, acquiring environmental parameter data of different areas of a smart city in advance, wherein the acquired environmental parameter data comprise PM2.5 content parameters, pollutant gas content parameters and pollutant emission content of the area;
s2, processing environmental parameter data of different areas according to a time interval division mode, acquiring early warning time intervals of the areas through processing results, determining the early warning time intervals of the different areas, acquiring specific pollution parameter values of the early warning time intervals, and extracting a single pollution parameter value maximum value from a plurality of pollution parameter values in the early warning time intervals, wherein the specific processing mode is as follows:
s21, taking the current moment as a calibration moment, acquiring a plurality of time periods 24h before the calibration moment, and marking the plurality of time periods as i, wherein i=1, 2, … …, n and n=24, wherein i is a single integer time period (can be understood as a group of time periods between 1 point and 2 points);
s22, obtaining a plurality ofPM2.5 content parameters, pollutant gas content parameters, and pollutant emission content of the region, which are different in each time period, and marking the PM2.5 content parameters as PM i-k Marking a contaminant gas content parameter as WR i-k Marking a pollutant emission content parameter as PF i-k Where k represents different regions, WRC is used i-k ＝PM i-k ×C1+WR i-k ×C2+PF i-k Obtaining pollution parameter values WRC of different areas and different time periods by using the X C3 i-k Wherein k represents different regions;
s23, pollution parameter WRC i-k Is compared with a preset parameter Y1 (the pollution parameter WRC is used in a certain period of time) i-k There are multiple groups, and multiple groups of different pollution parameter values WRC i-k Sequentially comparing with a preset parameter Y1, wherein the specific value of Y1 is determined by an operator according to experience), and when the WRC is obtained i-k When the time is less than Y1, no processing is carried out, otherwise, the time is marked as an early warning time, an ultra-warning signal is generated, and the number of times of existence of the ultra-warning signal and the duration of existence (the duration unit is minutes) are obtained;
s24, marking the existence times of the super-alarm signal as CS i-k Marking the time length of existence of the super-alarm signal as SC i-k BD is adopted i-k ＝CS i-k ×X1+SC i-k X2 obtaining comparison parameter BD belonging to different time periods i-k 24 groups of comparison parameters BD belonging to the same area are compared i-k Arranging, and extracting the comparison parameter BD with the largest value i-k And by the comparison of the reference value BD i-k Acquiring a corresponding period of time and a plurality of groups of pollution parameter values WRC corresponding to the period of time i-k And from multiple sets of pollution parameters WRC i-k The internal extraction maximum is marked as WRC i-kmax ；
S3, determining a designated time period and a designated area according to the maximum value of the single-group pollution parameter value, acquiring environmental parameter data of a designated range around the designated area in the designated time period from big data (the designated range around is generally limited to 5 km), determining a purification parameter of the peripheral range according to the environmental parameter data and the corresponding maximum value of the single-group pollution parameter value, checking whether the purification parameter of the peripheral range meets the standard, if the purification parameter of the peripheral range does not meet the designated standard, marking the designated area, controlling the designated area by external environmental protection personnel, and marking the designated area by the following modes:
s31, for the maximum value WRC of the pollution parameter value of a single group i-kmax Receiving, determining a corresponding period and a corresponding region according to the marks i and k, and processing PM2.5 content parameters, pollutant gas content parameters and pollutant emission content in the environmental parameter data by adopting the same processing mode of the step S22 to obtain a plurality of groups of different pollution parameter values WRC i-k ；
S32, obtaining a plurality of groups of different pollution parameter values WRC i-k Performing mean processing to obtain corresponding processing mean CL i-k By JH i-k ＝WRC i-k -CL i-k Obtaining the purification parameter JH i-k ；
S33, purifying parameters JH obtained by processing i-k The specific comparison method is as follows: when JH i-k When Y2 is less than, generating a control signal, otherwise, generating no signal;
s34, marking the designated area according to the generated control signal, and setting the marked area as the area to be controlled;
s4, controlling the early warning time period appointed by the area to be controlled, controlling different areas to be controlled according to a partition control mode, limiting the emission parameters, and automatically controlling the emission parameters through artificial intelligence, wherein the specific mode for controlling is as follows:
s41, acquiring to-be-controlled areas belonging to the same early warning period, sequentially acquiring distance parameters between two adjacent groups of to-be-controlled areas, and marking different distance parameters as JLt, wherein t represents different distance parameters;
s42, comparing the distance parameter JLt with a preset parameter Y3, and generating a partition control signal when JLt is smaller than Y3, otherwise, not generating any signal;
s43, carrying out partition control on the two groups of areas to be controlled according to partition control signals, wherein the specific control mode is that the high-yield time periods of the two groups of areas to be controlled are staggered, and the condition that large-area pollution is caused by simultaneous generation is avoided.
The partial data in the formula are all obtained by removing dimension and taking the numerical value for calculation, and the formula is a formula closest to the real situation obtained by simulating a large amount of collected data through software; the preset parameters and the preset threshold values in the formula are set by those skilled in the art according to actual conditions or are obtained through mass data simulation.
The working principle of the invention is as follows: the method comprises the steps of collecting environmental parameter data of different areas of a smart city in advance, processing the environmental parameter data of the different areas according to a time interval division mode, obtaining an early warning time interval of the area through a processing result, determining the early warning time interval of the different areas, obtaining specific pollution parameter values of the early warning time interval, extracting a single group of pollution parameter value maximum values from a plurality of groups of pollution parameter values existing in the early warning time interval, determining a designated time interval and a designated area according to the single group of pollution parameter value maximum values, obtaining environmental parameter data of a designated range around the designated area of the designated time interval from big data, determining purification parameters of the peripheral range according to the environmental parameter data and the corresponding single group of pollution parameter value maximum values, checking whether the purification parameters of the peripheral range reach the standard, if the purification parameters of the peripheral range do not reach the designated standard, marking the designated area, controlling the environmental protection personnel outside, controlling the different areas to be controlled according to a partition control mode, limiting the emission parameters, automatically controlling the emission parameters, rapidly finding the corresponding area, and controlling the peripheral environment values of the adjacent areas according to the special environmental protection areas, and checking whether the peripheral areas need to be controlled by the special protection areas.
The above embodiments are only for illustrating the technical method of the present invention and not for limiting the same, and it should be understood by those skilled in the art that the technical method of the present invention may be modified or substituted without departing from the spirit and scope of the technical method of the present invention.
Claims (4)
1. The real-time monitoring environment artificial intelligence detection method is characterized by comprising the following steps of:
s1, acquiring environmental parameter data of different areas of a smart city in advance, wherein the acquired environmental parameter data comprise PM2.5 content parameters, pollutant gas content parameters and pollutant emission content of the area;
s2, processing environmental parameter data of different areas according to a time interval division mode, acquiring early warning time intervals of the areas through processing results, determining the early warning time intervals of the different areas, acquiring specific pollution parameter values of the early warning time intervals, and extracting a single pollution parameter value maximum value from a plurality of pollution parameter values of the early warning time intervals;
s3, determining a designated time period and a designated area according to the maximum value of the single-set pollution parameter value, acquiring environmental parameter data of a designated range around the designated area in the designated time period from big data, determining purification parameters of the peripheral range according to the environmental parameter data and the corresponding maximum value of the single-set pollution parameter value, checking whether the purification parameters of the peripheral range reach standards, and if the purification parameters of the peripheral range do not reach the standards, marking the designated area for external environmental protection personnel to manage and control;
s4, controlling the early warning time period appointed by the area to be controlled, controlling different areas to be controlled according to a partition control mode, limiting the emission parameters, and automatically controlling the emission parameters through artificial intelligence.
2. The method for detecting the environment artificial intelligence of the real-time monitoring according to claim 1, wherein in the step S2, the specific way of processing the environment parameter data of different areas is as follows:
s21, taking the current moment as a calibration moment, acquiring a plurality of time periods 24h before the calibration moment, and marking the time periods as i, wherein i=1, 2, … …, n and n=24, and i is a single integer time period;
s22, acquiring PM2.5 content parameters, pollutant gas content parameters and pollutant emission content of the area with different time periods, and marking the PM2.5 content parameters as PM i-k Marking a contaminant gas content parameter as WR i-k Marking a pollutant emission content parameter as PF i-k Where k represents different regions, WRC is used i-k ＝PM i-k ×C1+WR i-k ×C2+PF i-k Obtaining pollution parameter values WRC of different areas and different time periods by using the X C3 i-k Wherein k represents different regions;
s23, pollution parameter WRC i-k Comparing with a preset parameter Y1, and in a certain period of time, the pollution parameter WRC i-k There are multiple groups, when WRC i-k When the number is less than Y1, no processing is carried out, otherwise, the time period is marked as an early warning time period, an ultra-warning signal is generated, and the number of times and the duration of the existence of the ultra-warning signal are obtained;
s24, marking the existence times of the super-alarm signal as CS i-k Marking the time length of existence of the super-alarm signal as SC i-k BD is adopted i-k ＝CS i-k ×X1+SC i-k X2 obtaining comparison parameter BD belonging to different time periods i-k 24 groups of comparison parameters BD belonging to the same area are compared i-k Arranging, and extracting the comparison parameter BD with the largest value i-k And by the comparison of the reference value BD i-k Acquiring a corresponding period of time and a plurality of groups of pollution parameter values WRC corresponding to the period of time i-k And from multiple sets of pollution parameters WRC i-k The internal extraction maximum is marked as WRC i-kmax 。
3. The method for detecting the environment artificial intelligence by real-time monitoring according to claim 2, wherein in the step S3, the specific way of marking the designated area is as follows:
s31, for the maximum value WRC of the pollution parameter value of a single group i-kmax Receiving, determining a corresponding time period and a corresponding region according to the marks i and k, and adopting steps for PM2.5 content parameters, pollutant gas content parameters and pollutant emission content in the environmental parameter dataStep S22, processing in the same processing mode to obtain a plurality of groups of different pollution parameter values WRC i-k ；
S32, obtaining a plurality of groups of different pollution parameter values WRC i-k Performing mean processing to obtain corresponding processing mean CL i-k By JH i-k ＝WRC i-k -CL i-k Obtaining the purification parameter JH i-k ；
S33, purifying parameters JH obtained by processing i-k The specific comparison method is as follows: when JH i-k When Y2 is less than, generating a control signal, otherwise, generating no signal;
s34, marking the designated area according to the generated control signal, and setting the marked area as the area to be controlled.
4. The method for detecting the environment artificial intelligence of the real-time monitoring according to claim 3, wherein in the step S4, the specific ways of controlling the different areas to be controlled are as follows:
s41, acquiring to-be-controlled areas belonging to the same early warning period, sequentially acquiring distance parameters between two adjacent groups of to-be-controlled areas, and marking different distance parameters as JLt, wherein t represents different distance parameters;
s42, comparing the distance parameter JLt with a preset parameter Y3, and generating a partition control signal when JLt is smaller than Y3, otherwise, not generating any signal;
s43, carrying out partition control on the two groups of areas to be controlled according to the partition control signals, wherein the specific control mode is that the high-yield time periods of the two groups of areas to be controlled are staggered.
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