CN114781823A - Urban ecological red line early warning method and device based on human activity feedback - Google Patents

Urban ecological red line early warning method and device based on human activity feedback Download PDF

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CN114781823A
CN114781823A CN202210339190.4A CN202210339190A CN114781823A CN 114781823 A CN114781823 A CN 114781823A CN 202210339190 A CN202210339190 A CN 202210339190A CN 114781823 A CN114781823 A CN 114781823A
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邓祥征
张帆
李志慧
程伟
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Abstract

The invention discloses an urban ecological red line early warning method and device based on human activity feedback, the technical scheme divides an urban construction planning layout map corresponding to a target urban area into an agricultural land area, a construction land area and an unused land area, and human activity points reported in the unused land area are actually illegal lands, which causes the largest influence on urban ecology; the method and the device solve the technical problem that the accuracy of urban ecological condition judgment is low due to the fact that some small-range polluted areas cannot be accurately identified in the prior art, realize accurate identification of the polluted areas, reduce the error rate of identifying the polluted areas as residential areas by mistake, improve the accuracy of urban ecological condition judgment, and provide powerful technical support for urban planning management and follow-up ecological environment improvement.

Description

Urban ecological red line early warning method and device based on human activity feedback
Technical Field
The invention relates to the technical field of urban planning management and monitoring, in particular to an urban ecological red line early warning method and device based on human activity feedback.
Background
With the development of economic society, the beauty of people on ecological environment is continuously improved, but driven by economic benefits, various human activities which destroy the ecological environment are always generated in large quantity. The urban ecological red line is a boundary control line in an environmental function division, and has important significance for ensuring the ecological safety of countries and regions and improving the ecological service function. The urban ecological red line mainly has the following five characteristics: the first is objectivity. Can embody the natural geographic characteristics and has important functions on maintaining ecological safety and improving ecological service functions. The second is legitimacy, once delimited, it has legal power and cannot be surged, otherwise it should be punished. The third is mandatory, maintaining, limiting or prohibiting development activities by taking strict environmental management measures. And fourthly, the red line limit is dynamic, can be changed due to the change of the external environment, and must be adjusted in time to meet the requirement of social change. And fifthly, the method is multidimensional and can be divided according to country levels, hydrologic drainage domains, geographic conditions and the like. The division of the urban ecological red line is a scientific problem and a management problem. Urban development depends on the usage of natural ecological resources and energy products in cities, regions, even countries or the world, and can bear and prevent natural disasters. The division of the ecological red line requires systematic study on the natural ecosystem in terms of "time, space, quantity, structure and order", so it is necessary to identify and warn the human activities that may damage the urban ecological red line, otherwise the planning and management of the ecological red line will become a paper theory.
In the prior art, the identification method for urban ecological red lines is to identify human activity points through remote sensing images, and in practical application, common human activity points comprise residential areas, polluted areas and the like. Because residential areas are used as main parts in urban layout, when the remote sensing images are identified, polluted areas in a small range cannot be identified accurately, or errors of identifying the polluted areas as the residential areas by mistake exist, so that the accuracy of judging urban ecological conditions is low, and powerful support cannot be provided for follow-up ecological environment improvement work.
Therefore, an urban ecological red line early warning strategy based on human activity feedback is urgently needed in the market at present to solve the technical problem that in the prior art, the accuracy of judging urban ecological conditions is not high due to the fact that some small-range polluted areas cannot be accurately identified or errors exist in identifying the polluted areas as residential areas by mistake.
Disclosure of Invention
The invention provides an urban ecological red line early warning method and device based on human activity feedback, which can accurately identify a polluted area, reduce the error rate of identifying the polluted area as a residential area by mistake, improve the accuracy of judging urban ecological conditions and provide powerful support for the subsequent ecological environment improvement work.
In order to solve the technical problem, an embodiment of the present invention provides an urban ecological red line early warning method based on human activity feedback, including:
obtaining a remote sensing image of a target city area and obtaining a city construction planning layout of the target city area;
dividing a planned land area in the remote sensing image according to the urban construction planning layout map, wherein the planned land comprises an agricultural land area, a construction land area and an unused land area; and, determining an ecological region in the unutilized region;
segmenting the divided remote sensing images according to various types of the planned land to obtain agricultural land remote sensing images, construction land remote sensing images and unused land remote sensing images;
inputting the remote sensing image of the unused area into a pre-established human activity recognition model to obtain human activity points in the unused area as violation lands;
and calculating an influence value of the illegal land on the ecological area, calculating a current ecological red line danger degree value of the target urban area according to the influence value, and sending out an early warning signal when the ecological red line danger degree value reaches a danger threshold value.
As a preferred scheme, the human activity recognition model is used for performing human activity recognition on an input remote sensing image and outputting human activity points existing in the remote sensing image;
wherein the human activity points comprise: farming land, residential and residential land, mining land, transportation land, water conservancy and hydropower facility land and sewage disposal land.
As a preferable scheme, the step of calculating an influence value of the illegal land on the ecological region and calculating a current ecological red line risk degree value of the target urban region according to the influence value further includes:
calculating an influence value of the illegal land on the ecological region to obtain a first influence value;
inputting the remote sensing image of the construction land to the human activity recognition model to obtain human activity points in the area for construction as the construction land, and calculating an influence value of the construction land on the ecological area to obtain a second influence value;
and calculating the current ecological red line danger degree value of the target city area according to the first influence value and the second influence value.
As a preferred scheme, the calculation formula of the ecological red line risk degree value is as follows:
Y＝y1+y2；
wherein Y is an ecological red line risk degree value; y is1Is a first impact value; y is2Is a second impact value;
Figure BDA0003578111900000031
wherein h isiThe linear distance between each violation area and the ecological area is in the unit of m; a is1Is a constant value; i is an illegal land, and n is a natural number;
Figure BDA0003578111900000032
wherein HiFor each construction land and said ecological areaLinear distance therebetween, in m; a is a2Is a constant value; i is construction land and n is a natural number.
As a preferable scheme, in the step of calculating the current ecological red line risk degree value of the target city area according to the first influence value and the second influence value, the method further includes:
inputting the agricultural land remote sensing image into the human activity recognition model to obtain human activity points in the agricultural area as agricultural land, and calculating an influence value of the agricultural land on the ecological area to obtain a third influence value;
and calculating the current ecological red line danger degree value of the target city area according to the first influence value, the second influence value and the third influence value.
As a preferred scheme, the calculation formula of the ecological red line risk degree value is as follows:
Y＝y1+y2+y3；
wherein Y is an ecological red line risk degree value; y is1Is a first impact value; y is2Is a second impact value; y is3Is a third impact value;
Figure BDA0003578111900000041
wherein h isiThe unit m is the linear distance between each violation land and the ecological region; a is1Is a constant value; i is an illegal land, and n is a natural number;
Figure BDA0003578111900000042
wherein HiThe linear distance between each construction land and the ecological region is in the unit of m; a is a2Is a constant value; i is a construction land, and n is a natural number;
Figure BDA0003578111900000043
wherein e isiThe unit of the straight line distance between each farming land and the ecological area is m; a is3Is a constant value; i is the farming land and n is a natural number.
Preferably, the human activity recognition model is constructed by a neural network algorithm model.
Preferably, the neural network algorithm model includes: the system comprises a front-end network used for extracting input image features and a back-end network used for carrying out human activity point feature positioning and various building identification in human activity points;
the head-end network includes: a first convolutional layer consisting of 2 3x3 convolutional kernels, a first pooling layer, a second convolutional layer consisting of 1 7x7 convolutional kernel and 2 1x1 convolutional kernels, a second pooling layer, and a third convolutional layer consisting of 1 7x7 convolutional kernel;
the backend network comprises: the system comprises a first branch for carrying out characteristic positioning of the human activity point and a second branch for carrying out various building identifications in the human activity point; wherein the first branch comprises: 1 7x7 convolution kernel, 2 3x3 convolution kernels and 2 1x1 convolution kernels are arranged in sequence; the second branch comprises: 3 of the 3x3 convolution kernels and 2 of the 1x1 convolution kernels are spaced apart from each other.
Correspondingly, another embodiment of the invention also provides an urban ecological red line early warning device based on human activity feedback, which comprises:
the image acquisition module is used for acquiring a remote sensing image of a target city area and acquiring a city construction planning layout of the target city area;
the region dividing module is used for dividing a planned land area in the remote sensing image according to the urban construction planning layout map, wherein the planned land comprises an agricultural land area, a construction land area and an unused land area; and, determining an ecological region in the unutilized region;
the image segmentation module is used for segmenting the divided remote sensing images according to various types of the planned land to obtain agricultural land remote sensing images, construction land remote sensing images and unused land remote sensing images;
the image recognition module is used for inputting the remote sensing image of the unused area into a pre-established human activity recognition model to obtain human activity points in the unused area as violation lands;
and the ecological early warning module is used for calculating an influence value of the illegal land on the ecological area, calculating a current ecological red line danger degree value of the target urban area according to the influence value, and sending an early warning signal when the ecological red line danger degree value reaches a danger threshold value.
As a preferred scheme, the human activity recognition model is used for performing human activity recognition on an input remote sensing image and outputting human activity points existing in the remote sensing image;
wherein the human activity points comprise: farming land, residential land, mining land, transportation land, water conservancy and hydropower facility land and pollution discharge land.
As a preferred scheme, the ecological early warning module is further configured to: calculating an influence value of the illegal land on the ecological region to obtain a first influence value; inputting the remote sensing image of the construction land to the human activity recognition model to obtain human activity points in the area for construction as the construction land, and calculating an influence value of the construction land on the ecological area to obtain a second influence value; and calculating the current ecological red line danger degree value of the target city area according to the first influence value and the second influence value.
As a preferred scheme, the calculation formula of the ecological red line risk degree value is as follows:
Y＝y1+y2；
wherein Y is an ecological red line risk degree value; y is1Is a first impact value; y is2Is a second impact value;
Figure BDA0003578111900000051
wherein h isiThe unit m is the linear distance between each violation land and the ecological region; a is a1Is a constant value; i is illegal land, and n is a natural number;
Figure BDA0003578111900000061
wherein HiThe linear distance between each construction land and the ecological region is in the unit of m; a is2Is a constant value; i is construction land and n is a natural number.
As a preferred scheme, the ecological early warning module is configured to, in the step of calculating the current ecological red line risk degree value of the target city area according to the first influence value and the second influence value, further: inputting the agricultural land remote sensing image into the human activity recognition model to obtain human activity points in the agricultural area as agricultural land, and calculating an influence value of the agricultural land on the ecological area to obtain a third influence value; and calculating the current ecological red line risk degree value of the target city area according to the first influence value, the second influence value and the third influence value.
As a preferred scheme, the calculation formula of the ecological red line risk degree value is as follows:
Y＝y1+y2+y3；
wherein Y is an ecological red line risk degree value; y is1Is a first impact value; y is2Is a second impact value; y is3Is a third impact value;
Figure BDA0003578111900000062
wherein h isiThe unit m is the linear distance between each violation land and the ecological region; a is1Is a constant value; i is illegal land, and n is a natural number;
Figure BDA0003578111900000063
wherein HiThe unit of the linear distance between each construction land and the ecological area is m; a is a2Is a constant value; i is a construction land, and n is a natural number;
Figure BDA0003578111900000064
wherein e isiThe linear distance between each farming land and the ecological region is in the unit of m; a is3Is a constant value; i is the farming land and n is a natural number.
Preferably, the human activity recognition model is constructed by a neural network algorithm model.
Preferably, the neural network algorithm model includes: the system comprises a front-end network used for extracting input image features and a back-end network used for carrying out human activity point feature positioning and various building identification in human activity points;
the head-end network includes: a first convolutional layer consisting of 2 convolution kernels of 3x3, a first pooling layer, a second convolutional layer consisting of 1 convolution kernel of 7x7 and 2 convolution kernels of 1x1, a second pooling layer, and a third convolutional layer consisting of 1 convolution kernel of 7x 7;
the backend network comprises: a first branch for performing feature localization of human activity points and a second branch for performing building identification of classes in human activity points; wherein the first branch comprises: 1 7x7 convolution kernel, 2 3x3 convolution kernels and 2 1x1 convolution kernels are arranged in sequence; the second branch comprises: 3 of the 3x3 convolution kernels and 2 of the 1x1 convolution kernels are spaced apart from each other.
An embodiment of the present invention further provides a computer-readable storage medium, where the computer-readable storage medium includes a stored computer program; wherein the computer program, when running, controls the device on which the computer readable storage medium is located to execute the city ecological red line warning method based on human activity feedback according to any one of the above items.
The embodiment of the present invention further provides a terminal device, which includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, where the processor, when executing the computer program, implements the city ecological red line warning method based on human activity feedback as described in any one of the above.
Compared with the prior art, the embodiment of the invention has the following beneficial effects:
according to the technical scheme, the city construction planning layout map corresponding to the target city area is divided into three areas, namely a farming land area, a construction land area and an unused area, and the city ecology area belongs to the unused area, so that the human activity points reported in the unused area are actually illegal lands, the city ecology is influenced to the maximum extent, the current ecological red line risk degree value can be determined by calculating the influence value of the illegal lands on the ecology area, and early warning is given; the method solves the technical problem that the judgment accuracy of the urban ecological situation is not high due to the fact that the polluted areas in a small range cannot be accurately identified or the polluted areas are mistakenly identified as residential areas in the prior art, realizes accurate identification of the polluted areas, reduces the error rate of mistakenly identifying the polluted areas as the residential areas, improves the judgment accuracy of the urban ecological situation, and provides powerful support for follow-up ecological environment improvement work.
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FIG. 1: the steps of the urban ecological red line early warning method based on human activity feedback in the embodiment of the invention are as a flow chart;
FIG. 2 is a schematic diagram: the schematic diagram of the remote sensing image of the target city area in the embodiment of the invention;
FIG. 3: the method is a structural schematic diagram of a neural network algorithm model in the embodiment of the invention;
FIG. 4 is a schematic view of: the urban ecological red line early warning device based on human activity feedback in the embodiment of the invention is a schematic structural diagram;
FIG. 5: the structure diagram of an embodiment of the terminal device provided by the embodiment of the invention is shown.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without inventive step based on the embodiments of the present invention, are within the scope of protection of the present invention.
Example one
Referring to fig. 1, a flow chart of steps of a city ecological red line warning method based on human activity feedback according to an embodiment of the present invention includes steps S1 to S5, and the steps are as follows:
and step S1, obtaining a remote sensing image of the target city area and obtaining a city construction planning layout of the target city area.
Specifically, a remote sensing image of a city area to be researched needs to be acquired, and since the building reporting in the city is very strict, as shown in fig. 2, the image is a schematic diagram of the remote sensing image of the target city area in the embodiment of the present invention. The construction condition of the current scale of the target city can be obtained in the city construction planning layout.
Step S2, dividing a planning land area in the remote sensing image according to the city construction planning layout, wherein the planning land comprises an agricultural land area, a construction land area and an unused land area; and determining an ecological region in the unutilized region.
Specifically, agricultural areas include arable land, garden land, woodland, grassland, and other agricultural lands; the construction land area comprises cities, construction towns, rural residential sites, mining land, transportation and water conservancy land and other construction land; the unused land includes ecosphere, water area, natural reserve and illegal buildings.
And step S3, segmenting the divided remote sensing images according to various types of the planned land to obtain agricultural land remote sensing images, construction land remote sensing images and unused land remote sensing images.
Specifically, the remote sensing image is divided into three areas divided in step S2, and corresponding agricultural land remote sensing images, construction land remote sensing images and unused land remote sensing images are obtained.
And step S4, inputting the remote sensing image of the unused land into a human activity recognition model established in advance, and obtaining human activity points in the unused land as the illegal land.
In this embodiment, the human activity recognition model is configured to perform human activity recognition on an input remote sensing image, and output a human activity point existing in the remote sensing image; wherein the human activity points comprise: farming land, residential and residential land, mining land, transportation land, water conservancy and hydropower facility land and sewage disposal land.
In this embodiment, the human activity recognition model is constructed by a neural network algorithm model. Fig. 3 is a schematic structural diagram of a neural network algorithm model in the embodiment of the present invention; the neural network algorithm model comprises: the system comprises a front-end network used for extracting features of an input image and a back-end network used for carrying out feature positioning on human activity points and identification on various buildings in the human activity points; the head-end network includes: a first convolutional layer consisting of 2 3x3 convolutional kernels, a first pooling layer, a second convolutional layer consisting of 1 7x7 convolutional kernel and 2 1x1 convolutional kernels, a second pooling layer, and a third convolutional layer consisting of 1 7x7 convolutional kernel; the backend network includes: a first branch for performing feature localization of human activity points and a second branch for performing building identification of classes in human activity points; wherein the first branch comprises: 1 7x7 convolution kernel, 2 3x3 convolution kernels and 2 1x1 convolution kernels are arranged in sequence; the second branch comprises: the 3x3 convolution kernels and the 2 x1 convolution kernels are spaced apart from each other.
Specifically, an algorithm model constructed in a manner that convolutional layers and pooling layers are combined with each other may enable recognition to be more accurate. In order to adapt to the scene of a target area, a unique model structure is adopted when the model structure is adjusted, and the human activity point identification result is more accurate.
And step S5, calculating an influence value of the illegal land on the ecological area, calculating the current ecological red line danger degree value of the target urban area according to the influence value, and sending out an early warning signal when the ecological red line danger degree value reaches a danger threshold value.
According to the technical scheme, the city construction planning layout map corresponding to the target city area is divided into three areas, namely a farming land area, a construction land area and an unused area, and the city ecology area belongs to the unused area, so that the human activity points reported in the unused area are actually illegal lands, the city ecology is influenced to the maximum extent, the current ecological red line risk degree value can be determined by calculating the influence value of the illegal lands on the ecology area, and early warning is given; the method solves the technical problem that the judgment accuracy of the urban ecological situation is not high due to the fact that the polluted areas in a small range cannot be accurately identified or the polluted areas are mistakenly identified as residential areas in the prior art, realizes accurate identification of the polluted areas, reduces the error rate of mistakenly identifying the polluted areas as the residential areas, improves the judgment accuracy of the urban ecological situation, and provides powerful support for follow-up ecological environment improvement work.
In another embodiment, an improvement is made to any of the above embodiments, the step of calculating an influence value of the violation site on the ecological region in step S5, and the step of calculating the current ecological red line risk degree value of the target city region according to the influence value further includes step S51: calculating an influence value of the illegal land on the ecological region to obtain a first influence value; inputting a remote sensing image of the construction land into the human activity recognition model to obtain human activity points in the area for construction as the construction land, and calculating an influence value of the construction land on the ecological area to obtain a second influence value; and calculating the current ecological red line danger degree value of the target city area according to the first influence value and the second influence value.
Specifically, the calculation formula of the ecological red line risk degree value is as follows:
Y＝y1+y2；
wherein Y is an ecological red line risk degree value; y is1Is a first impact value; y is2Is a second impact value;
Figure BDA0003578111900000101
wherein h isiThe linear distance between each violation area and the ecological area is in the unit of m; a is a1Is a constant value; i is an illegal land, and n is a natural number;
Figure BDA0003578111900000102
wherein HiThe linear distance between each construction land and the ecological region is in the unit of m; a is a2Is a constant value; i is the construction land and n is a natural number.
Specifically, in order to further improve the practicability of the technical scheme, the technical scheme can also consider the influence value of the construction land on the ecological area, and since the construction land and the illegal land belong to the more serious pollution area, the influence is actually greater when the construction land and the illegal land are closer to the ecological area. Calculating the ecological red line affected value of the ecological area by combining the type of each human activity point and the linear distance between each human activity point and the ecological area; it is understood that any kind of calculable algorithm can be used as the application of the scheme, and the substitution and the adjustment can be carried out according to the actual situation. And finally, obtaining the danger degree value of the ecological red line according to calculation, thereby sending out an early warning signal and further improving the practicability of the scheme.
In another embodiment, an improvement is made on any of the above embodiments, and the step of calculating the current ecological red line risk degree value of the target urban area according to the first influence value and the second influence value further includes step S52: inputting the agricultural land remote sensing image into the human activity recognition model to obtain human activity points in the agricultural area as agricultural land, and calculating an influence value of the agricultural land on the ecological area to obtain a third influence value; and calculating the current ecological red line risk degree value of the target city area according to the first influence value, the second influence value and the third influence value.
Specifically, the calculation formula of the ecological red line risk degree value is as follows:
Y＝y1+y2+y3；
wherein Y is an ecological red line risk degree value; y is1Is a first impact value; y is2Is a second impact value; y is3Is a third impact value;
Figure BDA0003578111900000111
wherein h isiThe linear distance between each violation area and the ecological area is in the unit of m; a is1Is a constant value; i is illegal land, and n is a natural number;
Figure BDA0003578111900000112
wherein HiThe linear distance between each construction land and the ecological region is in the unit of m; a is2Is a constant value; i is a construction land, and n is a natural number;
Figure BDA0003578111900000113
wherein e isiThe linear distance between each farming land and the ecological region is in the unit of m; a is3Is a constant value; i is the farming land and n is a natural number.
Specifically, in order to further improve the practicability of the present technical solution, the present technical solution may also take into account the value of the effect of the farming land on the formation of the ecological area, and since the farming land is not a contaminated area, the effect is actually smaller as the distance between the farming land and the ecological area is closer. Calculating the ecological red line affected value of the ecological area by combining the type of each human activity point and the linear distance between each human activity point and the ecological area; it is understood that any kind of calculable algorithm can be used as the application of the present solution, and the substitution and adjustment can be performed according to the actual situation. And finally, obtaining the danger degree value of the ecological red line according to calculation, thereby sending out an early warning signal and further improving the practicability of the scheme.
Example two
As shown in fig. 4, a schematic structural diagram of an urban ecological red line warning device based on human activity feedback according to another embodiment of the present invention includes an image acquisition module, an area division module, an image segmentation module, an image recognition module, and an ecological warning module, where each module is specifically as follows:
and the image acquisition module is used for acquiring a remote sensing image of the target city area and acquiring a city construction planning layout of the target city area.
The region division module is used for dividing a planning land area in the remote sensing image according to the urban construction planning layout map, wherein the planning land area comprises an agricultural land area, a construction land area and an unused land area; and determining an ecological region in the unutilized region.
And the image segmentation module is used for segmenting the divided remote sensing images according to various types of the planned land to obtain agricultural land remote sensing images, construction land remote sensing images and unused land remote sensing images.
And the image identification module is used for inputting the remote sensing image of the unused area into a pre-established human activity identification model to obtain the human activity points in the unused area as the illegal land.
In this embodiment, the human activity recognition model is configured to perform human activity recognition on an input remote sensing image, and output a human activity point existing in the remote sensing image; wherein the human activity points comprise: farming land, residential and residential land, mining land, transportation land, water conservancy and hydropower facility land and sewage disposal land.
In this embodiment, the human activity recognition model is constructed by a neural network algorithm model. The neural network algorithm model comprises: the system comprises a front-end network used for extracting features of an input image and a back-end network used for carrying out feature positioning on human activity points and identification on various buildings in the human activity points; the head-end network includes: a first convolutional layer consisting of 2 3x3 convolutional kernels, a first pooling layer, a second convolutional layer consisting of 1 7x7 convolutional kernel and 2 1x1 convolutional kernels, a second pooling layer, and a third convolutional layer consisting of 1 7x7 convolutional kernel; the backend network comprises: the system comprises a first branch for carrying out characteristic positioning of the human activity point and a second branch for carrying out various building identifications in the human activity point; wherein the first branch comprises: 1 7x7 convolution kernel, 2 3x3 convolution kernels and 2 1x1 convolution kernels are arranged in sequence; the second branch comprises: 3 of the 3x3 convolution kernels and 2 of the 1x1 convolution kernels are spaced apart from each other.
And the ecological early warning module is used for calculating an influence value of the illegal land on the ecological area, calculating a current ecological red line danger degree value of the target urban area according to the influence value, and sending an early warning signal when the ecological red line danger degree value reaches a danger threshold value.
According to the technical scheme, the city construction planning layout map corresponding to the target city area is divided into three areas, namely a farming land area, a construction land area and an unused area, and the city ecology area belongs to the unused area, so that the human activity points reported in the unused area are actually illegal lands, the city ecology is influenced to the maximum extent, the current ecological red line risk degree value can be determined by calculating the influence value of the illegal lands on the ecology area, and early warning is given; the method and the device solve the technical problem that in the prior art, the accuracy of judging urban ecological conditions is low due to the fact that polluted areas in a small range cannot be accurately identified or the polluted areas are mistakenly identified as residential areas, realize accurate identification of the polluted areas, reduce the error rate of mistakenly identifying the polluted areas as the residential areas, improve the accuracy of judging the urban ecological conditions, and provide powerful support for follow-up ecological environment improvement work.
In another embodiment, an improvement is made on any of the above embodiments, and the ecology warning module is further configured to: calculating an influence value of the illegal land on the ecological region to obtain a first influence value; inputting the remote sensing image of the construction land to the human activity recognition model to obtain human activity points in the area for construction as the construction land, and calculating an influence value of the construction land on the ecological area to obtain a second influence value; and calculating the current ecological red line danger degree value of the target city area according to the first influence value and the second influence value.
In this embodiment, the calculation formula of the ecological red line risk degree value is as follows:
Y＝y1+y2；
wherein Y is an ecological red line risk degree value; y is1Is a first impact value; y is2Is a second impact value;
Figure BDA0003578111900000141
wherein h isiThe linear distance between each violation area and the ecological area is in the unit of m; a is a1Is a constant value; i is an illegal land, and n is a natural number;
Figure BDA0003578111900000142
wherein HiThe unit of the linear distance between each construction land and the ecological area is m; a is a2Is a constant value; i is construction land and n is a natural number.
In another embodiment, an improvement is made on any of the above embodiments, wherein the ecological early warning module is configured to, in the step of calculating the current ecological red line risk degree value of the target urban area according to the first influence value and the second influence value, further: inputting the agricultural land remote sensing image into the human activity recognition model to obtain human activity points in the agricultural area as agricultural land, and calculating an influence value of the agricultural land on the ecological area to obtain a third influence value; and calculating the current ecological red line danger degree value of the target city area according to the first influence value, the second influence value and the third influence value.
In this embodiment, the calculation formula of the ecological red line risk degree value is:
Y＝y1+y2+y3；
wherein Y is an ecological red line risk degree value; y is1Is a first impact value; y is2Is a second impact value; y is3Is a third impact value;
Figure BDA0003578111900000143
wherein h isiThe linear distance between each violation area and the ecological area is in the unit of m; a is1Is a constant value; i is illegal land, and n is a natural number;
Figure BDA0003578111900000144
wherein HiThe linear distance between each construction land and the ecological region is in the unit of m; a is2Is a constant value; i is a construction land, and n is a natural number;
Figure BDA0003578111900000145
wherein e isiThe linear distance between each farming land and the ecological region is in the unit of m; a is a3Is a constant value; i is the farming land and n is a natural number.
EXAMPLE III
An embodiment of the present invention further provides a computer-readable storage medium, where the computer-readable storage medium includes a stored computer program; wherein the computer program controls, when running, a device where the computer-readable storage medium is located to execute the urban ecological red line warning method based on human activity feedback according to any of the above embodiments.
Example four
Referring to fig. 5, which is a schematic structural diagram of an embodiment of a terminal device according to an embodiment of the present invention, the terminal device includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and the processor, when executing the computer program, implements the city ecological red line warning method based on human activity feedback according to any of the embodiments.
Preferably, the computer program may be divided into one or more modules/units (e.g., computer program) that are stored in the memory and executed by the processor to implement the invention. The one or more modules/units may be a series of computer program instruction segments capable of performing specific functions, which are used for describing the execution process of the computer program in the terminal device.
The Processor may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA) or other Programmable logic device, a discrete Gate or transistor logic device, a discrete hardware component, etc., the general purpose Processor may be a microprocessor, or the Processor may be any conventional Processor, the Processor is a control center of the terminal device, and various interfaces and lines are used to connect various parts of the terminal device.
The memory mainly includes a program storage area and a data storage area, wherein the program storage area may store an operating system, an application program required for at least one function, and the like, and the data storage area may store related data and the like. In addition, the memory may be a high speed random access memory, may also be a non-volatile memory such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, or may also be other volatile solid state memory devices.
It should be noted that the terminal device may include, but is not limited to, a processor and a memory, and those skilled in the art will understand that the terminal device is only an example and does not constitute a limitation of the terminal device, and may include more or less components, or combine some components, or different components.
The above-mentioned embodiments are provided to further explain the objects, technical solutions and advantages of the present invention in detail, and it should be understood that the above-mentioned embodiments are only examples of the present invention and are not intended to limit the scope of the present invention. It should be understood that various changes, substitutions and alterations can be made herein without departing from the spirit and scope of the invention as defined by the appended claims.

Claims (10)

1. A city ecological red line early warning method based on human activity feedback is characterized by comprising the following steps:
acquiring a remote sensing image of a target city area and acquiring a city construction planning layout of the target city area;
dividing a planning land area in the remote sensing image according to the urban construction planning layout, wherein the planning land area comprises an agricultural land area, a construction land area and an unused land area; and, determining an ecological region in the unutilized region;
segmenting the divided remote sensing images according to various types of the planned land to obtain agricultural land remote sensing images, construction land remote sensing images and unused land remote sensing images;
inputting the remote sensing image of the unused area into a pre-established human activity recognition model to obtain human activity points in the unused area as violation lands;
and calculating an influence value of the illegal land on the ecological area, calculating a current ecological red line danger degree value of the target urban area according to the influence value, and sending out an early warning signal when the ecological red line danger degree value reaches a danger threshold value.
2. The city ecological red line early warning method based on human activity feedback as claimed in claim 1, wherein the human activity recognition model is used for performing human activity recognition on the input remote sensing image and outputting human activity points existing in the remote sensing image;
wherein the human activity points comprise: farming land, residential and residential land, mining land, transportation land, water conservancy and hydropower facility land and sewage disposal land.
3. The city ecological red line early warning method based on human activity feedback as claimed in claim 1 or 2, wherein the step of calculating the impact value of the violation land on the ecological region and calculating the current ecological red line risk degree value of the target urban region according to the impact value further comprises:
calculating an influence value of the illegal land on the ecological region to obtain a first influence value;
inputting the remote sensing image of the construction land to the human activity recognition model to obtain human activity points in the area for construction as the construction land, and calculating an influence value of the construction land on the ecological area to obtain a second influence value;
and calculating the current ecological red line danger degree value of the target city area according to the first influence value and the second influence value.
4. The city ecological red line early warning method based on human activity feedback as claimed in claim 3, wherein the calculation formula of the ecological red line risk degree value is:
Y＝y1+y2；
wherein Y is an ecological red line risk degree value; y is1Is a first impact value; y is2Is a second impact value;
Figure FDA0003578111890000021
wherein h isiThe unit m is the linear distance between each violation land and the ecological region; a is a1Is a constant value; i is an illegal land, and n is a natural number;
Figure FDA0003578111890000022
wherein HiThe unit of the linear distance between each construction land and the ecological area is m; a is2Is a constant value; i is the construction land and n is a natural number.
5. The city ecological red line warning method based on human activity feedback as claimed in claim 3, wherein the step of calculating the current ecological red line risk degree value of the target city area according to the first and second influence values further comprises:
inputting the agricultural land remote sensing image into the human activity recognition model to obtain human activity points in the agricultural area as agricultural land, and calculating an influence value of the agricultural land on the ecological area to obtain a third influence value;
and calculating the current ecological red line danger degree value of the target city area according to the first influence value, the second influence value and the third influence value.
6. The city ecological red line early warning method based on human activity feedback as claimed in claim 5, wherein the calculation formula of the ecological red line risk degree value is:
Y＝y1+y2+y3；
wherein Y is an ecological red line risk degree value; y is1Is a first impact value; y is2Is a second impact value; y is3Is a third impact value;
Figure FDA0003578111890000031
wherein h isiThe linear distance between each violation area and the ecological area is in the unit of m; a is1Is a constant value; i is illegal land, and n is a natural number;
Figure FDA0003578111890000032
wherein HiThe linear distance between each construction land and the ecological region is in the unit of m; a is a2Is a constant value; i is a construction land, and n is a natural number;
Figure FDA0003578111890000033
wherein e isiThe unit of the straight line distance between each farming land and the ecological area is m; a is3Is a constant value; i is the farming land and n is a natural number.
7. The city ecological red line warning method based on human activity feedback as claimed in claim 1, characterized in that the human activity recognition model is constructed by a neural network algorithm model.
8. The utility model provides an ecological red line early warning device in city based on human activity feedback which characterized in that includes:
the image acquisition module is used for acquiring a remote sensing image of the target city area and acquiring a city construction planning layout map of the target city area;
the region dividing module is used for dividing a planned land area in the remote sensing image according to the urban construction planning layout map, wherein the planned land comprises an agricultural land area, a construction land area and an unused land area; and, determining an ecological region in the unutilized region;
the image segmentation module is used for segmenting the divided remote sensing images according to various types of the planned land to obtain agricultural land remote sensing images, construction land remote sensing images and unused land remote sensing images;
the image identification module is used for inputting the remote sensing image of the unused area into a human activity identification model which is established in advance to obtain a human activity point in the unused area as an illegal land;
and the ecological early warning module is used for calculating an influence value of the illegal land on the ecological area, calculating the current ecological red line danger degree value of the target urban area according to the influence value, and sending an early warning signal when the ecological red line danger degree value reaches a danger threshold value.
9. A computer-readable storage medium, characterized in that the computer-readable storage medium comprises a stored computer program; wherein the computer program, when executed, controls an apparatus in which the computer-readable storage medium is located to perform the city ecological red line warning method based on human activity feedback according to any one of claims 1 to 7.
10. A terminal device comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, when executing the computer program, implementing the city ecological red line warning method based on human activity feedback according to any one of claims 1-7.
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