A monitoring method, device, electronic device and computer readable storage medium

Abstract

The embodiments of the present invention provide a monitoring method, device, electronic device and computer-readable storage medium, which relate to the field of remote sensing technology. By obtaining the heat island effect image map and temperature data in the meteorological data, and then determining the target area in the heat island effect image map according to the monitoring range, the average temperature value of the target area is greater than the average temperature value within the monitoring range. Further, a target correlation factor set corresponding to the target area is obtained, and the target correlation factor set includes multiple target correlation factors. The target correlation factor represents the correlation factor that meets the strong correlation condition of the heat island effect. Finally, the heat island effect contribution value of the target correlation factor is calculated, and the heat island effect contribution value represents the influence of the corresponding target correlation factor on the heat island effect of the target area, thereby realizing a multi-angle analysis of the causes of the urban heat island effect.

Description

Technical Field

[0001] The present invention relates to the field of remote sensing technology, and in particular to a monitoring method, device, electronic equipment and computer-readable storage medium. Background Art

[0002] Over the past half century, China has achieved urbanization at an unprecedented speed and scale. The rapid construction of cities and towns is certainly the core of China's rapid economic development, but the process of urbanization in China has inevitably brought about a series of problems, such as the reduction of cultivated land, soil erosion, air pollution, greenhouse effect, water pollution and other ecological and environmental problems. Relevant departments have taken a variety of measures to alleviate these problems, such as supervising urban sanitation, managing landscaping, managing environmental protection, and even combining the use of Internet of Things (IoT) technology. However, these methods are all to solve ecological and environmental problems after they occur, and require a lot of manpower, financial resources, and material resources, and they cannot achieve prevention before prevention.

[0003] In order to rationally plan urban construction and promote healthy urban development, existing technologies use remote sensing technology to analyze or monitor these issues.

[0004] However, existing technologies only use remote sensing images to conduct a single analysis or monitoring of a certain issue, such as monitoring forests, agriculture, or water and soil, but are unable to conduct a multi-angle analysis of the causes of urban development problems. Summary of the invention

[0005] The object of the present invention is to provide a monitoring method, device, electronic device and computer-readable storage medium to improve the problems existing in the prior art.

[0006] The technical solution adopted in the embodiment of the present invention is as follows:

[0007] In a first aspect, the present invention provides a monitoring method, comprising:

[0008] Obtain heat island effect images and temperature data from meteorological data;

[0009] Determine a target area in the heat island effect image according to the monitoring range, wherein the temperature data of the target area is greater than a temperature threshold within the monitoring range;

[0010] Acquire a target association factor set corresponding to the target area, wherein the target association factor set includes a plurality of target association factors, and the target association factors represent association factors that meet a strong correlation condition of the heat island effect;

[0011] The heat island effect contribution value of the target association factor is calculated, and the heat island effect contribution value represents the influence degree of the corresponding target association factor on the heat island effect of the target area.

[0012] The monitoring method provided by the embodiment of the present invention obtains the heat island effect image map and temperature data in the meteorological data, and then determines the target area in the heat island effect image map according to the monitoring range, and the average temperature value of the target area is greater than the average temperature value within the monitoring range. Further, the target correlation factor set corresponding to the target area is obtained, and the target correlation factor set includes multiple target correlation factors, and the target correlation factor represents the correlation factor that meets the strong correlation condition of the heat island effect. Finally, the heat island effect contribution value of the target correlation factor is calculated, and the heat island effect contribution value represents the influence of the corresponding target correlation factor on the heat island effect of the target area, thereby realizing a multi-angle analysis of the causes of the urban heat island effect. In an optional embodiment, the step of obtaining the target correlation factor set corresponding to the target area includes:

[0013] Acquire a set of correlation factors corresponding to the target area, wherein the set of correlation factors includes a plurality of the correlation factors, and the correlation factors represent influencing factors of the formation of the heat island effect corresponding to the target area;

[0014] The correlation factor set is screened to obtain a target correlation factor set.

[0015] By adopting this method, a target correlation factor set obtained by screening from the correlation factor set can be determined.

[0016] In an optional implementation, the step of screening the set of correlation factors to obtain a target set of correlation factors includes:

[0017] Performing correlation analysis on the data corresponding to any two of the correlation factors respectively to obtain a correlation coefficient, wherein the correlation coefficient represents the degree of correlation between the corresponding two correlation factors;

[0018] Determine a first association factor set according to all the association coefficients; the first association factor set includes a plurality of first association factors;

[0019] Performing regression analysis on the data corresponding to each of the first correlation factors and the temperature data of the target area to obtain regression analysis results;

[0020] According to all the regression analysis results, the target correlation factor set is determined.

[0021] By adopting this method, the final target correlation factor set is determined by performing correlation analysis and regression analysis on the correlation factor set.

[0022] In an optional implementation, the step of calculating the heat island effect contribution value of the target association factor includes:

[0023] According to the temperature data, the heat island effect contribution value of each target correlation factor is calculated respectively by using the grey correlation analysis method.

[0024] Using this method, the contribution value of the urban heat island effect corresponding to each target correlation factor can be calculated through the grey correlation analysis method.

[0025] In an optional implementation manner, before the step of obtaining the heat island effect image and temperature data in the meteorological data, the method further includes:

[0026] Acquire multi-source remote sensing images;

[0027] Preprocessing the multi-source remote sensing images;

[0028] The slice map is obtained based on the preprocessed multi-source remote sensing images.

[0029] By adopting this method, by using the slice map as an aid, the selection of the target area can be made more accurate and monitorable.

[0030] In an optional implementation manner, when the multi-source remote sensing image is a satellite remote sensing image, the satellite remote sensing image includes a raster remote sensing image, and the step of preprocessing the multi-source remote sensing image includes:

[0031] Converting the raster remote sensing image into a TIFF format image;

[0032] If the TIFF format image meets the low-definition condition, image enhancement processing is performed;

[0033] If the TIFF format image meets the low-tone condition, an atmospheric correction process is performed.

[0034] By adopting this method, the preprocessing of raster remote sensing images in satellite remote sensing images is realized through steps such as format conversion and image processing.

[0035] In an optional implementation, when the multi-source remote sensing image is an unmanned aerial vehicle remote sensing image, the step of preprocessing the multi-source remote sensing image includes:

[0036] Positioning, orienting and geometrically correcting the UAV remote sensing image based on the UAV body posture and camera parameters;

[0037] Positioning and image projection are performed according to the coordinates of the UAV remote sensing image to generate a positioned TIFF format image.

[0038] By adopting this method, the raster remote sensing images in the UAV remote sensing images are preprocessed through positioning and orientation, geometric correction and image projection.

[0039] In an optional implementation manner, the multi-source remote sensing image includes a vector map and a meteorological satellite image, and the step of obtaining a slice map according to the pre-processed multi-source remote sensing image includes:

[0040] Stitching a plurality of preprocessed multi-source remote sensing images to obtain a first stitched image;

[0041] According to the monitoring range, using the vector graph to perform intersection processing on the first stitched image to obtain a first target image;

[0042] According to the monitoring range, the meteorological satellite image is subjected to intersection processing using the vector graph to obtain a second target image;

[0043] Slicing the first target image to obtain slice data;

[0044] Publishing a slice map according to the slice data;

[0045] The second target image is given transparency and then superimposed on the slice map.

[0046] By adopting this method, through image stitching, intersection processing and slicing processing of the stitched images using vector maps, a slice map and a slice map superimposed with meteorological satellite images were obtained.

[0047] In a second aspect, the present invention provides a monitoring device, comprising:

[0048] A data acquisition module is used to acquire a heat island effect image map and temperature data in meteorological data; it is also used to determine a target area in the heat island effect image map according to a monitoring range, and the temperature data of the target area is greater than a temperature threshold within the monitoring range; it is also used to acquire a target correlation factor set corresponding to the target area, the target correlation factor set includes multiple target correlation factors, and the target correlation factor represents a correlation factor that satisfies a strong correlation condition of the heat island effect;

[0049] The calculation module is used to calculate the heat island effect contribution value of the target association factor, and the heat island effect contribution value represents the influence degree of the corresponding target association factor on the heat island effect of the target area.

[0050] The monitoring method provided by the embodiment of the present invention obtains the heat island effect image map and temperature data in the meteorological data, and then determines the target area in the heat island effect image map according to the monitoring range, and the average temperature value of the target area is greater than the average temperature value in the monitoring range. Further, the target correlation factor set corresponding to the target area is obtained, and the target correlation factor set includes multiple target correlation factors, and the target correlation factor represents the correlation factor that meets the strong correlation condition of the heat island effect. Finally, the heat island effect contribution value of the target correlation factor is calculated, and the heat island effect contribution value represents the influence of the corresponding target correlation factor on the heat island effect of the target area, thereby realizing a multi-angle analysis of the causes of the urban heat island effect.

[0051] The device as described in the above embodiment further comprises:

[0052] A selection module is used to obtain multi-source remote sensing images; and is also used to pre-process the multi-source remote sensing images;

[0053] The slicing module is used to obtain slicing maps based on preprocessed multi-source remote sensing images.

[0054] By adopting this method, by using the slice map as an aid, the selection of the target area can be made more accurate and monitorable.

[0055] In a third aspect, the present invention provides an electronic device, comprising: a processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor, and when the electronic device is running, the processor communicates with the memory through the bus, and the processor executes the machine-readable instructions, which, when executed, performs the steps of the monitoring method described in any of the aforementioned embodiments.

[0056] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed, implements the steps of the monitoring method as described in any of the aforementioned embodiments.

[0057] In summary, the monitoring method, device, electronic device and computer-readable storage medium provided by the embodiments of the present invention obtain the heat island effect image map and temperature data in the meteorological data, and then determine the target area in the heat island effect image map according to the monitoring range, and the average temperature value of the target area is greater than the average temperature value in the monitoring range. Further, the target correlation factor set corresponding to the target area is obtained, and the target correlation factor set includes multiple target correlation factors, and the target correlation factor represents the correlation factor that meets the strong correlation condition of the heat island effect. Finally, the heat island effect contribution value of the target correlation factor is calculated, and the heat island effect contribution value represents the influence of the corresponding target correlation factor on the heat island effect of the target area, thereby realizing a multi-angle analysis of the causes of the urban heat island effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0059] Figure 1 A flow chart of a monitoring method provided by an embodiment of the present invention.

[0060] Figure 2 One of the flow charts of another monitoring method provided by an embodiment of the present invention.

[0061] Figure 3 The second flowchart of another monitoring method provided by the embodiment of the present invention.

[0062] Figure 4 A third flow chart of another monitoring method provided by an embodiment of the present invention.

[0063] Figure 5 A fourth flow chart of another monitoring method provided by an embodiment of the present invention.

[0064] Figure 6 A fifth flow chart of another monitoring method provided by an embodiment of the present invention.

[0065] Figure 7 A sixth flow chart of another monitoring method provided in an embodiment of the present invention.

[0066] Figure 8 FIG7 is a flow chart of another monitoring method provided by an embodiment of the present invention.

[0067] Fig. 9 A schematic diagram of the principles of map tiles provided in an embodiment of the present invention.

[0068] Fig.10 A schematic diagram of functional modules of a monitoring device provided in an embodiment of the present invention.

[0069] Fig.11 A schematic diagram of functional modules of another monitoring device provided by an embodiment of the present invention.

[0070] Fig.12 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0071] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0072] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0073] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0074] In the description of the present invention, it should be noted that if the terms "upper", "lower", "inside", "outside", etc. appear to indicate an orientation or position relationship, they are based on the orientation or position relationship shown in the accompanying drawings, or are the orientation or position relationship in which the product of the invention is usually placed when used. They are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention.

[0075] In addition, the terms “first”, “second”, etc., if used, are merely used to distinguish between the descriptions and should not be understood as indicating or implying relative importance.

[0076] It should be noted that, in the absence of conflict, the features in the embodiments of the present invention may be combined with each other.

[0077] Over the past half century, China has achieved urbanization at an unprecedented speed and scale. The rapid construction of cities and towns is certainly the core of China's rapid economic development, but the process of China's urbanization expansion has inevitably brought about a series of problems, such as the reduction of arable land, soil erosion, air pollution, greenhouse effect, water pollution and other ecological and environmental problems.

[0078] For example, the urban heat island effect is a comprehensive manifestation of current urban development issues. The urban heat island effect is a phenomenon in which the temperature of a city rises due to factors such as a large amount of artificial heat generation, high heat storage bodies such as dense buildings, and a reduction in green space. The heat island effect causes the temperature in urban areas to be significantly higher than that in the periphery of the city. In meteorological satellite images, the temperature in the suburbs is lighter, while the temperature in the urban area is darker. This transition from low temperature to high temperature is like an island protruding from the sea. Since this island represents a high-temperature urban area, it is called an urban heat island. The main factors that form the urban heat island effect include urban underlying surface (the contact surface between the bottom of the atmosphere and the surface), artificial heat sources, water vapor influence, air pollution, reduction of green space, population migration and other factors.

[0079] Relevant departments have taken a variety of measures to alleviate these ecological problems, such as supervising urban sanitation, managing landscaping, managing environmental protection, and even combining the use of Internet of Things (IoT) technology. However, these methods are to solve ecological and environmental problems after they occur, and require a lot of manpower, financial resources, and material resources, and they cannot achieve prevention before prevention.

[0080] In order to rationally plan urban construction and promote healthy urban development, existing technologies also use remote sensing technology to analyze or monitor these problems. For different urban problem scenarios, different bands of remote sensing raster images are used, or various remote sensing indices are used for inversion, or machine learning models are used to build models for identification and prediction.

[0081] However, when using traditional remote sensing technology, it is necessary to have considerable experience in mathematical modeling of urban problems when selecting parameters; when using artificial intelligence technology, a high-configuration hardware environment is required. These technologies are either extensions of the mechanism, or have very high sample requirements, and also have high requirements for the temporal and spatial resolution of remote sensing images. However, remote sensing images with high temporal and spatial resolution are either inaccessible due to confidentiality regulations, or are charged per scene and are expensive, which results in huge financial costs. Moreover, existing technologies only use remote sensing images to conduct a single analysis or monitoring of a certain problem, such as monitoring forests, agriculture, or water and soil, but cannot conduct a multi-angle analysis of the causes of urban development problems.

[0082] In order to solve the above problems, the embodiment of the present invention provides a possible implementation method to achieve multi-angle analysis of the causes of urban development problems. Figure 1 , Figure 1 A flow chart of a monitoring method provided by an embodiment of the present invention, the method steps include:

[0083] Step S103: Acquire the heat island effect image and temperature data in the meteorological data.

[0084] It can be understood that the heat source data of the meteorological data includes heat island effect data, that is, heat island effect image map and temperature data. For example, it can be observed from the heat island effect image map that the urban area or factory area of ​​the city will appear darker red, while the temperature color of the suburbs is lighter. Optionally, the meteorological data can be derived from a meteorological satellite.

[0085] Step S104: Determine the target area in the heat island effect image according to the monitoring range.

[0086] In this embodiment, the monitoring range can be preset as the range of an urban area of ​​a certain city. The temperature data of the target area is greater than the temperature threshold within the monitoring range.

[0087] It is understandable that in the heat island effect image, the monitoring range may have multiple dark red parts. The temperature data of the monitoring range can be obtained from the heat island effect image, and the temperature data of the monitoring range is compared with the temperature threshold of the monitoring range. The area corresponding to the temperature data greater than the temperature threshold is the target area. The temperature threshold can be set according to the actual situation of the monitored city and is not limited here.

[0088] Step S105: Obtain a target association factor set corresponding to the target area.

[0089] It can be understood that the target correlation factor set includes multiple target correlation factors, and the target correlation factor represents the correlation factor that meets the strong correlation condition of the heat island effect.

[0090] Optionally, the formation of urban heat island effect is caused by many factors, namely, correlation factors, such as urban energy consumption, population density, number of cars, building area, green area, etc. The factors closely related to the formation of heat island effect in the target area, namely, target correlation factors, are factors that meet the strong correlation conditions of heat island effect.

[0091] Step S106: Calculate the heat island effect contribution value of the target correlation factor.

[0092] In this embodiment, the heat island effect contribution of each target association factor may be calculated respectively, and the heat island effect contribution value may represent the influence degree of the corresponding target association factor on the heat island effect of the target area.

[0093] Optionally, if the heat island effect contribution value of a certain target correlation factor is higher, it means that the target correlation factor has a higher impact on the heat island effect of the target area, and for urban planning monitoring, the target correlation factor needs to be protected, managed or improved. It should be noted that this example is only an example and is not limited here.

[0094] The monitoring method provided by the embodiment of the present invention obtains the heat island effect image map and temperature data in the meteorological data, and then determines the target area in the heat island effect image map according to the monitoring range, and the average temperature value of the target area is greater than the average temperature value in the monitoring range. Further, the target correlation factor set corresponding to the target area is obtained, and the target correlation factor set includes multiple target correlation factors, and the target correlation factor represents the correlation factor that meets the strong correlation condition of the heat island effect. Finally, the heat island effect contribution value of the target correlation factor is calculated, and the heat island effect contribution value represents the influence of the corresponding target correlation factor on the heat island effect of the target area, thereby realizing a multi-angle analysis of the causes of the urban heat island effect.

[0095] Optionally, the target correlation factor is selected from the correlation factor set. Figure 1 In step S105, the embodiment of the present invention provides a possible implementation method to obtain the target correlation factor. Specifically, Figure 1 On the basis of Figure 2 A flow chart of another monitoring method provided by an embodiment of the present invention is shown in FIG. Figure 2 , the sub-steps of step S105 include:

[0096] Step S105-1: Obtain a set of correlation factors corresponding to the target area.

[0097] Step S105-2: Screen the correlation factor set to obtain a target correlation factor set.

[0098] Among them, the correlation factor set includes multiple correlation factors, and the correlation factors can characterize the influencing factors of the formation of the heat island effect corresponding to the target area.

[0099] It can be understood that the correlation factor set may include all correlation factors that have an impact on the heat island effect of the target area, that is, the set may include both correlation factors with a high degree of impact and correlation factors with a relatively low degree of impact.

[0100] Optionally, the target correlation factor can be obtained by filtering from the correlation factor set twice. Specifically, Figure 2 Based on this, for step S105-2, Figure 3 A flow chart of another monitoring method provided by an embodiment of the present invention is shown in FIG. Figure 3 , the sub-steps of step S105-2 include:

[0101] Step S105 - 2a: perform correlation analysis on the data corresponding to any two correlation factors to obtain a correlation coefficient, which represents the degree of correlation between the two corresponding correlation factors.

[0102] It can be understood that correlation analysis mainly analyzes whether the relationship between the correlation factors is close and whether there can be mutual influence. That is, correlation analysis is to screen out correlation factors with strong correlation between the two. For example, there is definitely a correlation between the number of cars and automobile exhaust emissions in the same city, while the correlation between the green area and energy consumption in the same city may be lower. It should be noted that this example is only an example and is not limited here.

[0103] Step S105-2b: Determine a first set of correlation factors according to all correlation coefficients.

[0104] The relevance factor set includes a plurality of first relevance factors.

[0105] Optionally, the correlation analysis between the data corresponding to any two correlation factors can be implemented by using SPSS (Statistical Product and Service Solutions) software or using statistical programming methods.

[0106] Step S105 - 2c: perform regression analysis on the data corresponding to each first correlation factor and the temperature data of the target area to obtain a regression analysis result.

[0107] It can be understood that the regression analysis of the data corresponding to each first correlation factor and the temperature data corresponding to the target area is to verify the correlation between the first correlation factor and the heat island effect of the target area. That is, the regression analysis is to screen out the first correlation factor with a high correlation with the heat island effect of the target area.

[0108] Step S105-2d: Determine the target correlation factor set based on all regression analysis results.

[0109] It is understandable that the urban heat island effect can be a comprehensive problem caused by various factors in the process of urban development. Solving the problem caused by one factor may not necessarily eliminate the urban heat island effect, and it may even become more serious. The internal structure of this comprehensive problem, the influence of each factor and its characteristics cannot be fully known and understood. The role and mechanism of this problem cannot be studied like a white box problem, but a gray system can be used to construct a model based on a certain thinking logic and inference.

[0110] Optionally, the contribution value of the heat island effect can be calculated using the grey correlation analysis method. Figure 1 Based on this, for step S106, Figure 4 A flow chart of another monitoring method provided by an embodiment of the present invention is shown in FIG. Figure 4 , the sub-steps of step S106 include:

[0111] Step S106-1: Calculate the heat island effect contribution value of each target correlation factor using the grey correlation analysis method according to the temperature data.

[0112] The measure of the correlation between the factors of two systems over time or between different objects is called correlation. In the process of system development, if the trends of the two factors are consistent, that is, the degree of synchronous change is high, then the correlation between the two is high; otherwise, it is low.

[0113] That is, the grey correlation analysis method is a method to measure the degree of correlation between factors based on the similarity or difference in the development trends between the factors.

[0114] The grey relational analysis process is as follows:

[0115] First, select the reference sequence as the original sequence:

[0116] x i ={x i (k)|k=1,2,…,n; i=0,1,2,…,m-1}

[0117] Where n is the number of temperature data samples, m is the number of target correlation factors, and x i (k) is the kth temperature data sample corresponding to the i-th target correlation factor, x i That is, the reference sequence corresponding to the target correlation factor;

[0118] For example, the meteorological data of a certain meteorological satellite can be updated 100 times a day, that is, the number of temperature data samples n=100, if the number of target correlation factors m=8, then x0(10) is the 10th temperature data sample corresponding to the first target correlation factor, and x0 is the reference sequence corresponding to the first target correlation factor. It can be understood that this example is only an example and is not limited here.

[0119] Then the correlation coefficient ξ i (k) is:

[0120]

[0121] Among them, ρ is the discrimination coefficient, ρ∈(0,1). The smaller ρ is, the greater the difference between the correlation coefficients is, and the stronger the discrimination ability is. Usually ρ is 0.5, ξ i (k) is the correlation coefficient corresponding to the target correlation factor;

[0122] Finally, the correlation coefficient ξ i (k) Calculate the grey relational degree γ i :

[0123]

[0124] Among them, n is also the number of temperature data samples, and the grey relational degree γ i The influence degree of the corresponding target association factor on the heat island effect of the target area can be characterized.

[0125] Optionally, in order to select the target area more accurately and with monitorability, a map service can also be used as an auxiliary. The embodiment of the present invention provides a possible implementation method to obtain a slice map. Specifically, Figure 1 On the basis of Figure 5 A flow chart of another monitoring method provided by an embodiment of the present invention is shown in FIG. Figure 5 , before step S103, the steps further include:

[0126] Step S100: Acquire multi-source remote sensing images.

[0127] It can be understood that remote sensing image data can be divided into raster remote sensing images, drone remote sensing images, vector map boundaries (vector images), meteorological satellite images, environmental satellite image data, microwave satellite image data, etc.

[0128] It should be noted that the multi-source remote sensing image can be obtained from multi-source remote sensing sensors, such as optics, meteorology, drones, cameras, etc. Especially for remote sensing optical images, unlike the prior art, which requires high temporal and spatial resolution to achieve monitoring and analysis, and high temporal and spatial resolution remote sensing images are expensive, the embodiments of the present invention use medium resolution or medium and high resolution images to meet monitoring needs, which reduces the monitoring cost to a certain extent. Among them, the Sentinel series satellites, Sunflower 8, LandSat data and my country's high-resolution series satellite data can be obtained free of charge from the official website.

[0129] Step S101: pre-process multi-source remote sensing images.

[0130] Step S102: Obtain a slice map based on the preprocessed multi-source remote sensing image.

[0131] Optionally, when the multi-source remote sensing image is a satellite remote sensing image, the satellite remote sensing image can contain a raster remote sensing image. Figure 5 In step S101, the embodiment of the present invention provides a possible implementation method to implement preprocessing of raster remote sensing images. Specifically, Figure 5 On the basis of Figure 6 A flow chart of another monitoring method provided by an embodiment of the present invention is shown in FIG. Figure 6 The sub-steps of step S101 may include:

[0132] Step S101 - 1 , converting the raster remote sensing image into a TIFF format image.

[0133] TIFF format, or Tag Image File Format, is a flexible bitmap format used to store images including photographs and artwork. It is flexible in storing image information, can support many color systems, and is independent of the operating system.

[0134] It should be noted that non-raster format remote sensing images, such as vector images, remain as they are.

[0135] Step S101 - 2 : If the TIFF format image meets the low-definition condition, image enhancement processing is performed.

[0136] Step S101 - 3 : If the TIFF format image meets the low-tone condition, an atmospheric correction process is performed.

[0137] It is understandable that if the converted TIFF format image is not clear enough or the color tone affects the interpretation, it can be processed by image enhancement or atmospheric correction using software such as ArcGIS and SNAP.

[0138] Optional: When the multi-source remote sensing image is a drone remote sensing image, Figure 5 In step S101, the embodiment of the present invention provides another possible implementation method to implement the preprocessing of the UAV remote sensing image. Specifically, in Figure 5 On the basis of Figure 7 A flow chart of another monitoring method provided by an embodiment of the present invention is shown in FIG. Figure 7 The sub-steps of step S101 may include:

[0139] Step S101-a: positioning, orienting and geometrically correcting the UAV remote sensing image based on the UAV body posture and camera parameters.

[0140] Each UAV remote sensing image contains coordinate information, which can be combined with the UAV body posture and camera parameters to perform positioning and orientation processing on the UAV remote sensing image based on the coordinate information.

[0141] Correspondingly, in the field of remote sensing, geometric correction refers to the use of a series of mathematical models to correct and eliminate deformation caused by factors such as deformation of photographic materials, distortion of objective lenses, atmospheric refraction, earth curvature, earth rotation, terrain undulations, etc. when remote sensing images are imaged, resulting in the geometric position, shape, size, orientation and other characteristics of various objects on the original image being inconsistent with the expression requirements in the reference system.

[0142] Step S101 - b, performing positioning and image projection according to the coordinates of the UAV remote sensing image, and generating a positioned TIFF format image.

[0143] It can be understood that image projection can also be called projection transformation, which includes the mutual conversion between geographic coordinate system and projection coordinate system.

[0144] Optionally, the acquired multi-source remote sensing images may include vector graphics and meteorological satellite images. Figure 5 Based on this, for step S102, Figure 8 A flow chart of another monitoring method provided by an embodiment of the present invention is shown in FIG. Figure 8 The sub-steps of step S102 may include:

[0145] Step S102 - 1 , stitching a plurality of pre-processed multi-source remote sensing images to obtain a first stitched image.

[0146] It can be understood that the first stitched image is obtained by performing stitching processing according to the coordinates in the plurality of pre-processed multi-source remote sensing images.

[0147] Step S102-2: According to the monitoring range, the first stitched image is subjected to intersection processing using a vector diagram to obtain a first target image.

[0148] Among them, the map composed of points, lines and surfaces is called a vector map in the field of remote sensing. The first target image is the image map corresponding to the monitoring range.

[0149] Step S102-3: Based on the monitoring range, the meteorological satellite image is subjected to intersection processing using a vector diagram to obtain a second target image.

[0150] The second target image is a cropped meteorological satellite image corresponding to the monitoring range.

[0151] Step S102 - 4 , slicing the first target image to obtain slice data.

[0152] Step S102-5: Publish the slice map according to the slice data.

[0153] Optionally, the first target image may be sliced ​​according to TMS (Tile Map Service) specifications to obtain slice data.

[0154] As you can understand, sliced ​​data, also called map tiles, can be stored on the server or in any database, and then sent to the client for display based on different requests submitted by the user. Map tiles are a multi-resolution hierarchical model. From the bottom to the top of the tile pyramid, the resolution is getting lower and lower, but the geographical range remains unchanged. The principle is roughly as follows: Fig. 9 shown.

[0155] Step S102-6: Add transparency to the second target image and then overlay it on the slice map.

[0156] It can be understood that the second target image after being given transparency is a layer, and the slice map is a layer, and the superposition of the two is the superposition of layers.

[0157] Optionally, the above-mentioned target correlation factors and their corresponding heat island effect contribution values ​​and slice data may be stored in a server.

[0158] In a possible implementation, the heat island effect contribution value obtained above can also be inverted and verified. For example, the heat island effect contribution value obtained above is obtained by analyzing the heat island effect data of Fengyun satellites. Then, the heat island effect data of other meteorological satellites can be obtained to invert and verify the heat island effect contribution value calculated above. For another example, the heat island effect data of Fengyun satellites is used to finally analyze that the heat island effect contribution value of the target correlation factor of automobile exhaust emissions in a certain area is the highest. Then, the pollution data of other meteorological satellites can be used to judge whether the automobile exhaust pollution in the area is high, thereby indirectly verifying the accuracy of the result. It should be noted that this example is only an example and is not limited here.

[0159] Optionally, an interface can be designed based on a RESTful network structure, and then the web page can call the heat island effect contribution value and slice data in the server through the interface and display them on the web page. It should be noted that this step can be performed after step S106 or after the inversion verification, which is not limited here.

[0160] Based on the above monitoring methods, please see Fig.10 , Fig.10 A monitoring device 200 provided in an embodiment of the present invention includes a data acquisition module 230 and a calculation module 240 .

[0161] The data acquisition module 230 is used to obtain the heat island effect image map and temperature data in the meteorological data; it is also used to determine the target area in the heat island effect image map according to the monitoring range, and the average temperature value of the target area is greater than the average temperature value within the monitoring range; it is also used to obtain the target correlation factor set corresponding to the target area, the target correlation factor set includes multiple target correlation factors, and the target correlation factor represents the correlation factor that meets the strong correlation condition of the heat island effect;

[0162] The calculation module 240 is used to calculate the heat island effect contribution value of the target correlation factor, and the heat island effect contribution value represents the influence degree of the corresponding target correlation factor on the heat island effect of the target area.

[0163] For further information, see Fig.11The device may also include a selection module 210 and a slicing module 220.

[0164] The selection module 210 is used to obtain multi-source remote sensing images; and is also used to pre-process the multi-source remote sensing images;

[0165] The slicing module 220 is used to obtain a slicing map based on the pre-processed multi-source remote sensing images.

[0166] The above-mentioned device is used to execute the method provided by the aforementioned embodiment, and its implementation principle and technical effect are similar, which will not be repeated here.

[0167] The above modules may be one or more integrated circuits configured to implement the above methods, such as one or more application specific integrated circuits (ASIC), or one or more digital dingnal processors (DSP), or one or more field programmable gate arrays (FPGA). For another example, when a module is implemented in the form of a processing element scheduling program code, the processing element may be a general-purpose processor, such as a central processing unit (CPU) or other processor that can call program code. For another example, these modules may be integrated together and implemented in the form of a system-on-a-chip (SoC).

[0168] Further, based on the above monitoring method, please refer to Fig.12 , Fig.12 The present invention provides a schematic diagram of the structure of an electronic device 300 provided in an embodiment of the present invention. The electronic device 300 includes a processor 310, a memory 320 and a bus 330. The memory 320 stores machine-readable instructions executable by the processor 310. When the electronic device is running, the processor 310 communicates with the memory 320 via the bus 330, and the processor 310 executes the machine-readable instructions, which implements the above method embodiment when executed.

[0169] Furthermore, based on the above monitoring method, an embodiment of the present invention also provides a computer-readable storage medium, on which a computer program is stored, and the above method embodiment is implemented when the program is executed.

[0170] In summary, the embodiments of the present invention provide a monitoring method, device, electronic device and computer-readable storage medium, which obtains the heat island effect image map and temperature data in the meteorological data, and then determines the target area in the heat island effect image map according to the monitoring range, and the average temperature value of the target area is greater than the average temperature value in the monitoring range. Further, the target correlation factor set corresponding to the target area is obtained, and the target correlation factor set includes multiple target correlation factors, and the target correlation factor represents the correlation factor that meets the strong correlation condition of the heat island effect. Finally, the heat island effect contribution value of the target correlation factor is calculated, and the heat island effect contribution value represents the influence of the corresponding target correlation factor on the heat island effect of the target area, thereby realizing a multi-angle analysis of the causes of the urban heat island effect. Further, in urban monitoring planning, the above analysis results can be used as an auxiliary to better make urban development planning decisions.

[0171] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed by the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A monitoring method, characterized in that: include: Obtain heat island effect images and temperature data from meteorological data; Determine a target area in the heat island effect image according to the monitoring range, wherein the temperature data of the target area is greater than a temperature threshold within the monitoring range; Acquire a set of correlation factors corresponding to the target area, wherein the set of correlation factors includes a plurality of the correlation factors, and the correlation factors represent influencing factors of the formation of the heat island effect corresponding to the target area; Performing correlation analysis on the data corresponding to any two of the correlation factors respectively to obtain a correlation coefficient, wherein the correlation coefficient represents the degree of correlation between the corresponding two correlation factors; Determine a first correlation factor set according to all the correlation coefficients; the first correlation factor set includes a plurality of first correlation factors, and any two of the first correlation factors are strongly correlated; Performing regression analysis on the data corresponding to each of the first correlation factors and the temperature data of the target area to obtain regression analysis results; According to all the regression analysis results, a target correlation factor set is determined; the target correlation factor set includes a plurality of target correlation factors, and the target correlation factors represent correlation factors that meet the strong correlation condition of the heat island effect; The heat island effect contribution value of the target association factor is calculated, and the heat island effect contribution value represents the influence degree of the corresponding target association factor on the heat island effect of the target area.

2. The method according to claim 1, characterized in that The step of calculating the heat island effect contribution value of the target association factor comprises: According to the temperature data, the heat island effect contribution value of each target correlation factor is calculated respectively by using the grey correlation analysis method.

3. The method according to claim 1, characterized in that Before the step of obtaining the heat island effect image and temperature data in the meteorological data, the method further includes: Acquire multi-source remote sensing images; Preprocessing the multi-source remote sensing images; The slice map is obtained based on the preprocessed multi-source remote sensing images.

4. The method according to claim 3, characterized in that When the multi-source remote sensing image is a satellite remote sensing image, the satellite remote sensing image includes a raster remote sensing image, and the step of preprocessing the multi-source remote sensing image includes: Converting the raster remote sensing image into a TIFF format image; If the TIFF format image meets the low-definition condition, image enhancement processing is performed; If the TIFF format image meets the low-tone condition, an atmospheric correction process is performed.

5. The method according to claim 3, characterized in that When the multi-source remote sensing image is an unmanned aerial vehicle remote sensing image, the step of preprocessing the multi-source remote sensing image includes: Positioning, orienting and geometrically correcting the UAV remote sensing image based on the UAV body posture and camera parameters; Positioning and image projection are performed according to the coordinates of the UAV remote sensing image to generate a positioned TIFF format image.

6. The method according to claim 3, characterized in that The multi-source remote sensing images include vector maps and meteorological satellite images, and the step of obtaining a slice map according to the pre-processed multi-source remote sensing images includes: Stitching a plurality of preprocessed multi-source remote sensing images to obtain a first stitched image; According to the monitoring range, performing intersection processing on the first stitched image using the vector graph to obtain a first target image; According to the monitoring range, the meteorological satellite image is subjected to intersection processing using the vector graph to obtain a second target image; Slicing the first target image to obtain slice data; Publishing a slice map according to the slice data; The second target image is given transparency and then superimposed on the slice map.

7. A monitoring device, characterized in that: include: The data acquisition module is used to obtain the heat island effect image map and temperature data in the meteorological data; it is also used to determine the target area in the heat island effect image map according to the monitoring range, and the temperature data of the target area is greater than the temperature threshold value within the monitoring range; it is also used to: Acquire a set of correlation factors corresponding to the target area, wherein the set of correlation factors includes a plurality of the correlation factors, and the correlation factors represent the influencing factors of the formation of the heat island effect corresponding to the target area; perform correlation analysis on the data corresponding to any two of the correlation factors respectively to obtain a correlation coefficient, and the correlation coefficient represents the degree of correlation between the corresponding two correlation factors; determine a first set of correlation factors according to all the correlation coefficients; the first set of correlation factors includes a plurality of first correlation factors, and any two of the first correlation factors are strongly correlated; perform regression analysis on the data corresponding to each of the first correlation factors and the temperature data of the target area respectively to obtain a regression analysis result; determine a target set of correlation factors according to all the regression analysis results, the target set of correlation factors includes a plurality of target correlation factors, and the target correlation factors represent correlation factors that meet the strong correlation condition of the heat island effect; The calculation module is used to calculate the heat island effect contribution value of the target association factor, and the heat island effect contribution value represents the influence degree of the corresponding target association factor on the heat island effect of the target area.

8. The device according to claim 7, characterized in that Also includes: A selection module is used to obtain multi-source remote sensing images; and is also used to pre-process the multi-source remote sensing images; The slicing module is used to obtain slicing maps based on preprocessed multi-source remote sensing images.

9. An electronic device, characterized in that: include: A processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor, and when the electronic device is running, the processor communicates with the memory via the bus, and the processor executes the machine-readable instructions, which, when executed, performs the steps of the monitoring method as described in any one of claims 1 to 6.

10. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the program is executed, the steps of the monitoring method according to any one of claims 1 to 6 are implemented.

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