CN117274827A - Intelligent environment-friendly remote real-time monitoring and early warning method and system - Google Patents
Intelligent environment-friendly remote real-time monitoring and early warning method and system Download PDFInfo
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Abstract
The invention discloses an intelligent environment-friendly remote real-time monitoring and early warning method and system, which relate to the field of environment-friendly monitoring and comprise the following steps: acquiring historical data of an environment, and establishing a reference judgment model according to the historical data; fuzzy preprocessing is adopted for the environmental electromagnetic radiation image characteristics, and a suspected abnormal area is judged according to a reference judgment model; for the suspected abnormal region, accurately processing the image characteristics of the environment electromagnetic radiation, and judging the suspected abnormal region as a pollution region or a normal region according to a reference judgment model; if the suspected abnormal area is a normal area, the processing is not performed; if the suspected abnormal area is a pollution area, the pollution degree of the pollution area is obtained, an early warning is sent out, and a treatment scheme corresponding to the pollution degree is generated. By arranging the model training module, the fuzzy processing module, the judging module, the accurate processing module and the sampling monitoring module, the accuracy of the final monitored result is not affected, and a large amount of unnecessary calculation can be avoided.
Description
Technical Field
The invention relates to the field of environmental protection monitoring, in particular to an intelligent environment-friendly remote real-time monitoring and early warning method and system.
Background
Environmental monitoring refers to the activity of an environmental monitoring agency in monitoring and measuring environmental quality conditions. Environmental monitoring is to monitor and measure indexes reflecting environmental quality to determine environmental pollution and environmental quality. The content of the environmental monitoring mainly comprises physical index monitoring, chemical index monitoring and ecological system monitoring. Environmental monitoring is the basis of scientific management environment and environment law enforcement supervision, and is an essential basic work for environmental protection. The core objective of the environment monitoring is to provide data of the current situation and the change trend of the environment quality, judge the environment quality, evaluate the current main environment problem and serve the environment management.
The existing environment monitoring method has insufficient detection intelligence, adopts indiscriminate accurate monitoring for environment monitoring, and can accurately respond to the position with problems, but when the coverage area of the monitoring range is huge, the indiscriminate accurate monitoring needs to occupy a large amount of calculation storage space to perform a large amount of unnecessary calculation, and the calculation consumes long time.
Disclosure of Invention
In order to solve the technical problems, the technical scheme provides an intelligent environment-friendly remote real-time monitoring and early warning method and system, which solve the problems that the detection intelligence of the existing environment monitoring method provided in the background art is insufficient, the environment monitoring is accurately monitored without difference, and the position where the problem occurs can accurately respond, but when the coverage area of the monitoring range is huge, the accurate monitoring without difference occupies a large amount of calculation storage space, a large amount of unnecessary calculation is performed, and the calculation consumes a long time.
In order to achieve the above purpose, the invention adopts the following technical scheme:
an intelligent environment-friendly remote real-time monitoring and early warning method comprises the following steps:
acquiring historical data of an environment, and establishing a reference judgment model according to the historical data;
dividing at least one monitoring area for a detection environment, deploying at least one remote sensing camera in the monitoring area, collecting environment electromagnetic radiation image characteristics by the remote sensing camera, performing fuzzy pretreatment on the environment electromagnetic radiation image characteristics, and judging a suspected abnormal area according to a reference judgment model;
for the suspected abnormal region, accurately processing the image characteristics of the environment electromagnetic radiation, and judging the suspected abnormal region as a pollution region or a normal region according to a reference judgment model;
if the suspected abnormal area is a normal area, no treatment is carried out;
if the suspected abnormal area is a pollution area, determining a key detection area of the pollution area according to a tracking algorithm, performing water quality detection, air detection and soil detection on the key detection area of the pollution area, comparing detection results in parallel to obtain the pollution degree of the pollution area, sending out early warning, and generating a treatment scheme corresponding to the pollution degree.
Preferably, the establishing the reference judgment model according to the historical data includes the following steps:
classifying the acquired data according to the season nodes, and dividing the data into four seasons data of spring, summer, autumn and winter;
for spring data, acquiring the characteristics of an environment electromagnetic radiation image of a normal area, calculating the distribution ranges of different colors of the image, and acquiring the upper threshold value and the lower threshold value of the spring of different colors of the characteristics of the environment electromagnetic radiation image of the normal area;
for summer data, acquiring the environment electromagnetic radiation image characteristics of a normal area, calculating the distribution ranges of different colors of the image, and acquiring the upper threshold value and the lower threshold value of different colors of the environment electromagnetic radiation image characteristics of the normal area;
for autumn data, acquiring the environment electromagnetic radiation image characteristics of a normal area, calculating the distribution ranges of different colors of the image, and acquiring the autumn upper limit value and the autumn lower limit value of different colors of the environment electromagnetic radiation image characteristics of the normal area;
and for winter data, acquiring the environment electromagnetic radiation image characteristics of the normal area, calculating the distribution ranges of different chromaticities of the images, and acquiring the winter chromaticity upper threshold value and the winter chromaticity lower threshold value of different chromaticities of the environment electromagnetic radiation image characteristics of the normal area.
Preferably, the fuzzy preprocessing of the environmental electromagnetic radiation image features comprises the following steps:
randomly taking at least one sample point in the image characteristics of the environmental electromagnetic radiation;
the chromaticity of the electromagnetic radiation image at each sample point is identified, and the chromaticity value of each chromaticity is counted.
Preferably, the judging the suspected abnormal region according to the reference judgment model includes the following steps:
establishing the seasonality of the acquired data, wherein if the season is spring, an upper limit value of the spring chromaticity and a lower limit value of the spring chromaticity are used, if the season is summer, an upper limit value of the summer chromaticity and a lower limit value of the summer chromaticity are used, if the season is autumn, an upper limit value of the autumn chromaticity and a lower limit value of the autumn chromaticity are used, and if the season is autumn, an upper limit value of the autumn chromaticity and a lower limit value of the autumn chromaticity are used;
sequentially arranging at least one sample point and sequentially taking the sample points;
for each sample point, determining the chromaticity of the sample point, and calling an upper chromaticity threshold and a lower chromaticity threshold of the corresponding seasons of the corresponding chromaticity;
judging whether the chromaticity of the sample point is between an upper chromaticity threshold and a lower chromaticity threshold;
if yes, no feedback operation is performed;
if not, marking the sample points as suspected points;
and when the number of the suspected points exceeds a first preset value, judging the monitoring area as a suspected abnormal area, and otherwise, judging the monitoring area as a normal area.
Preferably, the precise processing of the environmental electromagnetic radiation image features comprises the following steps:
acquiring environmental electromagnetic radiation image characteristics, wherein the environmental electromagnetic radiation image characteristics are divided into at least one grid block by grids, the grid blocks are consistent in size, and the proportion of the area of each grid block occupying the environmental electromagnetic radiation image characteristics does not exceed a second preset value;
counting all the chromaticities in the grid block, counting the coverage area of each chromaticity, taking the chromaticity with the largest occupied area, and taking the chromaticity as the chromaticity of the grid block;
summarizing the grid blocks with the same chromaticity, counting the chromaticity value of each grid block with the same chromaticity, and calculating the average chromaticity value of the grid blocks with the same chromaticity to obtain the average chromaticity value of each chromaticity respectively.
Preferably, the judging the suspected abnormal area as the polluted area or the normal area according to the reference judgment model includes the following steps:
establishing the seasonality of the acquired data, wherein if the season is spring, an upper limit value of the spring chromaticity and a lower limit value of the spring chromaticity are used, if the season is summer, an upper limit value of the summer chromaticity and a lower limit value of the summer chromaticity are used, if the season is autumn, an upper limit value of the autumn chromaticity and a lower limit value of the autumn chromaticity are used, and if the season is autumn, an upper limit value of the autumn chromaticity and a lower limit value of the autumn chromaticity are used;
obtaining average chromaticity values of different chromaticities in a suspected abnormal area, sequentially arranging the average chromaticity values of the different chromaticities, and sequentially taking the average chromaticity value of each chromaticity;
calling an upper limit value and a lower limit value of the same chromaticity in the corresponding seasons;
judging whether the average chromaticity value of each chromaticity is between an upper chromaticity threshold and a lower chromaticity threshold;
if yes, no feedback operation is performed;
if not, marking the corresponding chromaticity as abnormal chromaticity;
and when the number of the abnormal chromaticities exceeds a third preset value, judging the suspected abnormal area as a polluted area, and otherwise, judging the monitoring area as a normal area.
Preferably, the determining the key detection area of the polluted area includes the following steps:
acquiring a chromaticity set with abnormal chromaticity in a pollution area;
taking one first chromaticity in the chromaticity set, and obtaining all first grid blocks with the chromaticity being the first chromaticity;
calculating an important grid block I with the chromatic aberration exceeding a fourth preset value in the grid block I according to a tracking algorithm;
and calculating the corresponding key grid blocks for each grid block in the chromaticity set, and summarizing all the key grid blocks to obtain a key detection area.
Preferably, the water quality detection, air detection and soil detection for the key detection area of the polluted area comprise the following steps:
acquiring at least one sampling point of a key detection area of a pollution area, and acquiring water quality data, air quality data and soil index data for each sampling point;
the water quality data comprise physicochemical pollution indexes and biological indexes, the air quality data comprise inhalable particulate matter content and nitrogen oxide content, and the soil index data comprise heavy metal element content, harmful nonmetallic element content and residual organic pesticide content;
and calculating the average value of the water quality data, the air quality data and the soil index data of all the sampling points, and obtaining a first water quality detection value, a first air quality detection value and a first soil index detection value of a key detection area of the polluted area to obtain the results of water quality detection, air detection and soil detection.
Preferably, the parallel comparison detection result comprises the following steps:
carrying out water quality detection, air detection and soil detection on normal areas adjacent to the pollution areas;
acquiring at least one detection point of a normal area adjacent to a polluted area, and acquiring water quality data, air quality data and soil index data for each detection point;
calculating the average value of the water quality data, the air quality data and the soil index data of all detection points to obtain a second water quality detection value, a second air quality detection value and a second soil index detection value of a normal area which are compared in parallel;
calculating the deviation degree of the first water quality detection value and the second water quality detection value, calculating the deviation degree of the first air quality detection value and the second air quality detection value, and calculating the deviation degree of the first soil index detection value and the second soil index detection value;
and obtaining the pollution degree of the polluted area according to the deviation degree.
An intelligent environment environmental protection remote real-time monitoring and early warning system for realizing the intelligent environment environmental protection remote real-time monitoring and early warning method, comprising:
the data acquisition module is used for acquiring historical data of the environment and electromagnetic radiation image characteristics of the environment;
the model training module is used for establishing a reference judgment model according to the historical data;
the environment monitoring module is used for collecting environment electromagnetic radiation image characteristics;
the blurring processing module is used for carrying out blurring preprocessing on the environmental electromagnetic radiation image characteristics;
the judging module is used for judging the suspected abnormal area, judging the suspected abnormal area as a pollution area or a normal area and obtaining the pollution degree of the pollution area;
the precise processing module is used for precisely processing the environmental electromagnetic radiation image characteristics;
the sampling monitoring module is used for carrying out water quality detection, air detection and soil detection on key detection areas of the polluted area and carrying out water quality detection, air detection and soil detection on normal areas adjacent to the polluted area;
and the early warning module is used for sending out early warning.
Compared with the prior art, the invention has the beneficial effects that:
the method has the advantages that the monitoring area which is possibly problematic is screened out through the model training module, the fuzzy processing module, the judging module, the accurate processing module and the sampling monitoring module, the monitoring area which is possibly problematic is accurately processed, the determined pollution area is sampled to obtain the determined pollution degree, a coping scheme can be made according to the pollution degree, the calculation force used by the fuzzy processing is low, the occupied storage space is small, the calculation result is fast, the instantaneity is strong, meanwhile, the accurate processing is carried out on the monitoring area with a reduced range, the effect on the final monitoring result precision is avoided, and a large amount of unnecessary calculation can be avoided.
Drawings
FIG. 1 is a schematic flow chart of an intelligent environment protection remote real-time monitoring and early warning method of the invention;
FIG. 2 is a schematic flow chart of a reference judgment model established by historical data according to the invention;
FIG. 3 is a schematic flow chart of judging suspected abnormal areas according to a reference judgment model;
FIG. 4 is a schematic diagram of a precise processing flow for ambient electromagnetic radiation image features in accordance with the present invention;
FIG. 5 is a flow chart of the method for judging a suspected abnormal area as a polluted area or a normal area according to a reference judgment model;
FIG. 6 is a schematic diagram of a process for determining a focus detection area of a contaminated area according to the present invention;
FIG. 7 is a schematic diagram of a flow of water quality detection, air detection and soil detection for a key detection area of a polluted area according to the present invention;
FIG. 8 is a flow chart of the parallel comparison test result of the present invention.
Detailed Description
The following description is presented to enable one of ordinary skill in the art to make and use the invention. The preferred embodiments in the following description are by way of example only and other obvious variations will occur to those skilled in the art.
Referring to fig. 1, an intelligent environment environmental protection remote real-time monitoring and early warning method includes:
acquiring historical data of an environment, and establishing a reference judgment model according to the historical data;
dividing at least one monitoring area for a detection environment, deploying at least one remote sensing camera in the monitoring area, collecting environment electromagnetic radiation image characteristics by the remote sensing camera, performing fuzzy pretreatment on the environment electromagnetic radiation image characteristics, and judging a suspected abnormal area according to a reference judgment model;
for the suspected abnormal region, accurately processing the image characteristics of the environment electromagnetic radiation, and judging the suspected abnormal region as a pollution region or a normal region according to a reference judgment model;
if the suspected abnormal area is a normal area, no treatment is carried out;
if the suspected abnormal area is a pollution area, determining a key detection area of the pollution area according to a tracking algorithm, performing water quality detection, air detection and soil detection on the key detection area of the pollution area, comparing detection results in parallel to obtain the pollution degree of the pollution area, sending out early warning, and generating a treatment scheme corresponding to the pollution degree;
the principle of the scheme is as follows: the color of the plants in the polluted place in the environment can change, and the chromaticity of the plants is different from that of the normal area, so that the suspected abnormal area is screened out by using fuzzy pretreatment, and the suspected abnormal area is accurately treated to obtain the polluted area.
Referring to fig. 2, establishing a reference judgment model from historical data includes the steps of:
classifying the acquired data according to the season nodes, and dividing the data into four seasons data of spring, summer, autumn and winter;
for spring data, acquiring the characteristics of an environment electromagnetic radiation image of a normal area, calculating the distribution ranges of different colors of the image, and acquiring the upper threshold value and the lower threshold value of the spring of different colors of the characteristics of the environment electromagnetic radiation image of the normal area;
for summer data, acquiring the environment electromagnetic radiation image characteristics of a normal area, calculating the distribution ranges of different colors of the image, and acquiring the upper threshold value and the lower threshold value of different colors of the environment electromagnetic radiation image characteristics of the normal area;
for autumn data, acquiring the environment electromagnetic radiation image characteristics of a normal area, calculating the distribution ranges of different colors of the image, and acquiring the autumn upper limit value and the autumn lower limit value of different colors of the environment electromagnetic radiation image characteristics of the normal area;
for winter data, acquiring the environment electromagnetic radiation image characteristics of a normal area, calculating the distribution ranges of different chromaticities of the image, and acquiring winter chromaticity upper threshold values and winter chromaticity lower threshold values of different chromaticities of the environment electromagnetic radiation image characteristics of the normal area;
the chromaticity ranges for different seasons are different and therefore need to be distinguished.
The method for performing fuzzy preprocessing on the image characteristics of the environmental electromagnetic radiation comprises the following steps:
randomly taking at least one sample point in the image characteristics of the environmental electromagnetic radiation;
the chromaticity of the electromagnetic radiation image at each sample point is identified, and the chromaticity value of each chromaticity is counted.
Referring to fig. 3, the determination of the suspected abnormal region according to the reference determination model includes the steps of:
establishing the seasonality of the acquired data, wherein if the season is spring, an upper limit value of the spring chromaticity and a lower limit value of the spring chromaticity are used, if the season is summer, an upper limit value of the summer chromaticity and a lower limit value of the summer chromaticity are used, if the season is autumn, an upper limit value of the autumn chromaticity and a lower limit value of the autumn chromaticity are used, and if the season is autumn, an upper limit value of the autumn chromaticity and a lower limit value of the autumn chromaticity are used;
sequentially arranging at least one sample point and sequentially taking the sample points;
for each sample point, determining the chromaticity of the sample point, and calling an upper chromaticity threshold and a lower chromaticity threshold of the corresponding seasons of the corresponding chromaticity;
judging whether the chromaticity of the sample point is between an upper chromaticity threshold and a lower chromaticity threshold;
if yes, no feedback operation is performed;
if not, marking the sample points as suspected points;
and when the number of the suspected points exceeds a first preset value, judging the monitoring area as a suspected abnormal area, and otherwise, judging the monitoring area as a normal area.
Referring to fig. 4, the precise processing of the ambient electromagnetic radiation image features includes the steps of:
acquiring environmental electromagnetic radiation image characteristics, wherein the environmental electromagnetic radiation image characteristics are divided into at least one grid block by grids, the grid blocks are consistent in size, and the proportion of the area of each grid block occupying the environmental electromagnetic radiation image characteristics does not exceed a second preset value;
counting all the chromaticities in the grid block, counting the coverage area of each chromaticity, taking the chromaticity with the largest occupied area, and taking the chromaticity as the chromaticity of the grid block;
summarizing grid blocks with the same chromaticity, and counting the chromaticity value of each grid block with the same chromaticity, wherein the calculation process is as follows: multiplying the chromaticity value by the area of the corresponding grid block, accumulating to obtain a sum value, obtaining the total area of the grid block with the same chromaticity, and dividing the sum value by the total area to obtain the average chromaticity value of the grid block with the same chromaticity;
and calculating the average chromaticity value of the grid blocks with the same chromaticity to obtain the average chromaticity value of each chromaticity respectively.
Referring to fig. 5, according to the reference judgment model, judging that the suspected abnormal region is a contaminated region or a normal region includes the steps of:
establishing the seasonality of the acquired data, wherein if the season is spring, an upper limit value of the spring chromaticity and a lower limit value of the spring chromaticity are used, if the season is summer, an upper limit value of the summer chromaticity and a lower limit value of the summer chromaticity are used, if the season is autumn, an upper limit value of the autumn chromaticity and a lower limit value of the autumn chromaticity are used, and if the season is autumn, an upper limit value of the autumn chromaticity and a lower limit value of the autumn chromaticity are used;
obtaining average chromaticity values of different chromaticities in a suspected abnormal area, sequentially arranging the average chromaticity values of the different chromaticities, and sequentially taking the average chromaticity value of each chromaticity;
calling an upper limit value and a lower limit value of the same chromaticity in the corresponding seasons;
judging whether the average chromaticity value of each chromaticity is between an upper chromaticity threshold and a lower chromaticity threshold;
if yes, no feedback operation is performed;
if not, marking the corresponding chromaticity as abnormal chromaticity;
when the number of the abnormal chromaticities exceeds a third preset value, judging the suspected abnormal area as a pollution area, otherwise, judging the monitoring area as a normal area;
the third preset value is set according to historical experience.
Referring to fig. 6, determining an important detection area of a contaminated area includes the steps of:
acquiring a chromaticity set with abnormal chromaticity in a pollution area;
taking one first chromaticity in the chromaticity set, and obtaining all first grid blocks with the chromaticity being the first chromaticity;
calculating an important grid block I with the chromatic aberration exceeding a fourth preset value in the grid block I according to a tracking algorithm;
the tracking algorithm is as follows: taking a first grid block as an example, wherein at least one grid block is arranged, the chromaticity of the first grid block is the chromaticity first, the first grid block is provided with a corresponding average chromaticity value first, the absolute value of the difference between each first grid block and the average chromaticity value first is calculated, and the absolute value is compared with the average chromaticity value first to obtain the chromatic aberration;
and calculating the corresponding key grid blocks for each grid block in the chromaticity set, and summarizing all the key grid blocks to obtain a key detection area.
Referring to fig. 7, performing water quality detection, air detection and soil detection on a key detection area of a contaminated area includes the steps of:
acquiring at least one sampling point of a key detection area of a pollution area, and acquiring water quality data, air quality data and soil index data for each sampling point;
the water quality data comprise physicochemical pollution indexes and biological indexes, the air quality data comprise inhalable particulate matter content and nitrogen oxide content, and the soil index data comprise heavy metal element content, harmful nonmetallic element content and residual organic pesticide content;
calculating the average value of water quality data, air quality data and soil index data of all sampling points;
taking water quality data as an example, obtaining water quality data of all sampling points, superposing the water quality data of all sampling points to obtain superposition values, counting the number of all sampling points, dividing the superposition values by the number of the sampling points to obtain the average value of the water quality data of all sampling points, wherein the average value of the water quality data of all sampling points is a first water quality detection value;
and obtaining a first water quality detection value, a first air quality detection value and a first soil index detection value of a key detection area of the polluted area, and obtaining results of water quality detection, air detection and soil detection.
Referring to fig. 8, the parallel contrast detection result includes the following steps:
carrying out water quality detection, air detection and soil detection on normal areas adjacent to the pollution areas;
acquiring at least one detection point of a normal area adjacent to a polluted area, and acquiring water quality data, air quality data and soil index data for each detection point;
calculating the average value of the water quality data, the air quality data and the soil index data of all detection points to obtain a second water quality detection value, a second air quality detection value and a second soil index detection value of a normal area which are compared in parallel;
calculating the deviation degree of the first water quality detection value and the second water quality detection value, calculating the deviation degree of the first air quality detection value and the second air quality detection value, and calculating the deviation degree of the first soil index detection value and the second soil index detection value;
the degree of deviation is calculated as follows: taking the deviation degree of the first air quality detection value and the second air quality detection value as an example, calculating the absolute value of the difference between the first air quality detection value and the second air quality detection value, and dividing the absolute value by the second air quality detection value to obtain the deviation degree of the first air quality detection value and the second air quality detection value;
and according to the deviation degree, obtaining the pollution degree of the pollution area, wherein the larger the deviation degree is, the larger the pollution degree is.
An intelligent environment environmental protection remote real-time monitoring and early warning system for realizing the intelligent environment environmental protection remote real-time monitoring and early warning method, comprising:
the data acquisition module is used for acquiring historical data of the environment and electromagnetic radiation image characteristics of the environment;
the model training module is used for establishing a reference judgment model according to the historical data;
the environment monitoring module is used for collecting environment electromagnetic radiation image characteristics;
the blurring processing module is used for carrying out blurring preprocessing on the environmental electromagnetic radiation image characteristics;
the judging module is used for judging the suspected abnormal area, judging the suspected abnormal area as a pollution area or a normal area and obtaining the pollution degree of the pollution area;
the precise processing module is used for precisely processing the environmental electromagnetic radiation image characteristics;
the sampling monitoring module is used for carrying out water quality detection, air detection and soil detection on key detection areas of the polluted area and carrying out water quality detection, air detection and soil detection on normal areas adjacent to the polluted area;
and the early warning module is used for sending out early warning.
The working process of the intelligent environment-friendly remote real-time monitoring and early warning system is as follows:
step one: the data acquisition module acquires historical data of the environment;
step two: the model training module establishes a reference judgment model according to the historical data;
step three: the environment monitoring module collects the image characteristics of the environment electromagnetic radiation;
step four: the fuzzy processing module adopts fuzzy preprocessing to the environment electromagnetic radiation image characteristics, and the judging module judges a suspected abnormal area;
step five: for the suspected abnormal region, the precise processing module adopts precise processing to the environmental electromagnetic radiation image characteristics, the judging module judges the pollution region, and the key detection region of the pollution region is determined;
step six: the sampling monitoring module performs water quality detection, air detection and soil detection on the key detection area of the pollution area, and the judging module compares the detection results in parallel to obtain the pollution degree of the pollution area;
step seven: the early warning module sends out early warning and generates a treatment scheme corresponding to the pollution degree.
Still further, the present solution also proposes a storage medium, on which a computer readable program is stored, and when the computer readable program is called, the above intelligent environmental protection remote real-time monitoring and early warning method is executed.
It is understood that the storage medium may be a magnetic medium, e.g., floppy disk, hard disk, magnetic tape; optical media such as DVD; or a semiconductor medium such as a solid state disk SolidStateDisk, SSD, etc.
In summary, the invention has the advantages that: the method has the advantages that the monitoring area which is possibly problematic is screened out through the model training module, the fuzzy processing module, the judging module, the accurate processing module and the sampling monitoring module, the monitoring area which is possibly problematic is accurately processed, the determined pollution area is sampled to obtain the determined pollution degree, a coping scheme can be made according to the pollution degree, the calculation force used by the fuzzy processing is low, the occupied storage space is small, the calculation result is fast, the instantaneity is strong, meanwhile, the accurate processing is carried out on the monitoring area with a reduced range, the effect on the final monitoring result precision is avoided, and a large amount of unnecessary calculation can be avoided.
The foregoing has shown and described the basic principles, principal features and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, and that the above embodiments and descriptions are merely illustrative of the principles of the present invention, and various changes and modifications may be made therein without departing from the spirit and scope of the invention, which is defined by the appended claims. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (10)
1. An intelligent environment-friendly remote real-time monitoring and early warning method is characterized by comprising the following steps of:
acquiring historical data of an environment, and establishing a reference judgment model according to the historical data;
dividing at least one monitoring area for a detection environment, deploying at least one remote sensing camera in the monitoring area, collecting environment electromagnetic radiation image characteristics by the remote sensing camera, performing fuzzy pretreatment on the environment electromagnetic radiation image characteristics, and judging a suspected abnormal area according to a reference judgment model;
for the suspected abnormal region, accurately processing the image characteristics of the environment electromagnetic radiation, and judging the suspected abnormal region as a pollution region or a normal region according to a reference judgment model;
if the suspected abnormal area is a normal area, no treatment is carried out;
if the suspected abnormal area is a pollution area, determining a key detection area of the pollution area according to a tracking algorithm, performing water quality detection, air detection and soil detection on the key detection area of the pollution area, comparing detection results in parallel to obtain the pollution degree of the pollution area, sending out early warning, and generating a treatment scheme corresponding to the pollution degree.
2. The intelligent environmental protection remote real-time monitoring and early warning method according to claim 1, wherein the building of the reference judgment model according to the historical data comprises the following steps:
classifying the acquired data according to the season nodes, and dividing the data into four seasons data of spring, summer, autumn and winter;
for spring data, acquiring the characteristics of an environment electromagnetic radiation image of a normal area, calculating the distribution ranges of different colors of the image, and acquiring the upper threshold value and the lower threshold value of the spring of different colors of the characteristics of the environment electromagnetic radiation image of the normal area;
for summer data, acquiring the environment electromagnetic radiation image characteristics of a normal area, calculating the distribution ranges of different colors of the image, and acquiring the upper threshold value and the lower threshold value of different colors of the environment electromagnetic radiation image characteristics of the normal area;
for autumn data, acquiring the environment electromagnetic radiation image characteristics of a normal area, calculating the distribution ranges of different colors of the image, and acquiring the autumn upper limit value and the autumn lower limit value of different colors of the environment electromagnetic radiation image characteristics of the normal area;
and for winter data, acquiring the environment electromagnetic radiation image characteristics of the normal area, calculating the distribution ranges of different chromaticities of the images, and acquiring the winter chromaticity upper threshold value and the winter chromaticity lower threshold value of different chromaticities of the environment electromagnetic radiation image characteristics of the normal area.
3. The intelligent environment-friendly remote real-time monitoring and early warning method according to claim 2, wherein the fuzzy preprocessing is adopted for the image characteristics of the environment electromagnetic radiation, and the method comprises the following steps:
randomly taking at least one sample point in the image characteristics of the environmental electromagnetic radiation;
the chromaticity of the electromagnetic radiation image at each sample point is identified, and the chromaticity value of each chromaticity is counted.
4. The intelligent environment-friendly remote real-time monitoring and early warning method according to claim 3, wherein the judging of the suspected abnormal area according to the reference judgment model comprises the following steps:
establishing the seasonality of the acquired data, wherein if the season is spring, an upper limit value of the spring chromaticity and a lower limit value of the spring chromaticity are used, if the season is summer, an upper limit value of the summer chromaticity and a lower limit value of the summer chromaticity are used, if the season is autumn, an upper limit value of the autumn chromaticity and a lower limit value of the autumn chromaticity are used, and if the season is autumn, an upper limit value of the autumn chromaticity and a lower limit value of the autumn chromaticity are used;
sequentially arranging at least one sample point and sequentially taking the sample points;
for each sample point, determining the chromaticity of the sample point, and calling an upper chromaticity threshold and a lower chromaticity threshold of the corresponding seasons of the corresponding chromaticity;
judging whether the chromaticity of the sample point is between an upper chromaticity threshold and a lower chromaticity threshold;
if yes, no feedback operation is performed;
if not, marking the sample points as suspected points;
and when the number of the suspected points exceeds a first preset value, judging the monitoring area as a suspected abnormal area, and otherwise, judging the monitoring area as a normal area.
5. The intelligent environment-friendly remote real-time monitoring and early warning method according to claim 4, wherein the precise processing of the environment electromagnetic radiation image features comprises the following steps:
acquiring environmental electromagnetic radiation image characteristics, wherein the environmental electromagnetic radiation image characteristics are divided into at least one grid block by grids, the grid blocks are consistent in size, and the proportion of the area of each grid block occupying the environmental electromagnetic radiation image characteristics does not exceed a second preset value;
counting all the chromaticities in the grid block, counting the coverage area of each chromaticity, taking the chromaticity with the largest occupied area, and taking the chromaticity as the chromaticity of the grid block;
summarizing the grid blocks with the same chromaticity, counting the chromaticity value of each grid block with the same chromaticity, and calculating the average chromaticity value of the grid blocks with the same chromaticity to obtain the average chromaticity value of each chromaticity respectively.
6. The intelligent environment-friendly remote real-time monitoring and early warning method according to claim 5, wherein the judging of the suspected abnormal area as the polluted area or the normal area according to the reference judgment model comprises the following steps:
establishing the seasonality of the acquired data, wherein if the season is spring, an upper limit value of the spring chromaticity and a lower limit value of the spring chromaticity are used, if the season is summer, an upper limit value of the summer chromaticity and a lower limit value of the summer chromaticity are used, if the season is autumn, an upper limit value of the autumn chromaticity and a lower limit value of the autumn chromaticity are used, and if the season is autumn, an upper limit value of the autumn chromaticity and a lower limit value of the autumn chromaticity are used;
obtaining average chromaticity values of different chromaticities in a suspected abnormal area, sequentially arranging the average chromaticity values of the different chromaticities, and sequentially taking the average chromaticity value of each chromaticity;
calling an upper limit value and a lower limit value of the same chromaticity in the corresponding seasons;
judging whether the average chromaticity value of each chromaticity is between an upper chromaticity threshold and a lower chromaticity threshold;
if yes, no feedback operation is performed;
if not, marking the corresponding chromaticity as abnormal chromaticity;
and when the number of the abnormal chromaticities exceeds a third preset value, judging the suspected abnormal area as a polluted area, and otherwise, judging the monitoring area as a normal area.
7. The intelligent environmental protection remote real-time monitoring and early warning method according to claim 6, wherein the step of determining the key detection area of the polluted area comprises the following steps:
acquiring a chromaticity set with abnormal chromaticity in a pollution area;
taking one first chromaticity in the chromaticity set, and obtaining all first grid blocks with the chromaticity being the first chromaticity;
calculating an important grid block I with the chromatic aberration exceeding a fourth preset value in the grid block I according to a tracking algorithm;
and calculating the corresponding key grid blocks for each grid block in the chromaticity set, and summarizing all the key grid blocks to obtain a key detection area.
8. The intelligent environment-friendly remote real-time monitoring and early warning method according to claim 7, wherein the water quality detection, air detection and soil detection of the key detection area of the polluted area comprises the following steps:
acquiring at least one sampling point of a key detection area of a pollution area, and acquiring water quality data, air quality data and soil index data for each sampling point;
the water quality data comprise physicochemical pollution indexes and biological indexes, the air quality data comprise inhalable particulate matter content and nitrogen oxide content, and the soil index data comprise heavy metal element content, harmful nonmetallic element content and residual organic pesticide content;
and calculating the average value of the water quality data, the air quality data and the soil index data of all the sampling points, and obtaining a first water quality detection value, a first air quality detection value and a first soil index detection value of a key detection area of the polluted area to obtain the results of water quality detection, air detection and soil detection.
9. The intelligent environment-friendly remote real-time monitoring and early warning method according to claim 8, wherein the parallel comparison detection result comprises the following steps:
carrying out water quality detection, air detection and soil detection on normal areas adjacent to the pollution areas;
acquiring at least one detection point of a normal area adjacent to a polluted area, and acquiring water quality data, air quality data and soil index data for each detection point;
calculating the average value of the water quality data, the air quality data and the soil index data of all detection points to obtain a second water quality detection value, a second air quality detection value and a second soil index detection value of a normal area which are compared in parallel;
calculating the deviation degree of the first water quality detection value and the second water quality detection value, calculating the deviation degree of the first air quality detection value and the second air quality detection value, and calculating the deviation degree of the first soil index detection value and the second soil index detection value;
and obtaining the pollution degree of the polluted area according to the deviation degree.
10. An intelligent environmental protection remote real-time monitoring and early warning system for realizing the intelligent environmental protection remote real-time monitoring and early warning method according to any one of claims 1-9, which is characterized by comprising the following steps:
the data acquisition module is used for acquiring historical data of the environment and electromagnetic radiation image characteristics of the environment;
the model training module is used for establishing a reference judgment model according to the historical data;
the environment monitoring module is used for collecting environment electromagnetic radiation image characteristics;
the blurring processing module is used for carrying out blurring preprocessing on the environmental electromagnetic radiation image characteristics;
the judging module is used for judging the suspected abnormal area, judging the suspected abnormal area as a pollution area or a normal area and obtaining the pollution degree of the pollution area;
the precise processing module is used for precisely processing the environmental electromagnetic radiation image characteristics;
the sampling monitoring module is used for carrying out water quality detection, air detection and soil detection on key detection areas of the polluted area and carrying out water quality detection, air detection and soil detection on normal areas adjacent to the polluted area;
and the early warning module is used for sending out early warning.
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