Coast information evaluation method, device and equipment based on big data and medium

Abstract

The invention relates to a coast information evaluation method and device based on big data, equipment and a medium, and the method comprises the steps: obtaining historical remote sensing images corresponding to a plurality of vegetation blocks in a preset coast region, determining an area change curve and a first soil color value change diagram of each vegetation block based on the historical remote sensing images, obtaining a plurality of aerial images of each vegetation block in a preset historical time period, and determining a second soil color value change diagram of each vegetation block, garbage features in each aerial image of each vegetation block, personnel activity popularity, and a second soil color value change diagram of each vegetation block based on the plurality of aerial images; and determining the pollution degree of each vegetation block based on the first soil color value change diagram, the second soil color value change diagram, the garbage features and the personnel activity popularity, and determining the pollution degree of the preset coast area based on the area change curve of each vegetation block and the pollution degree of each vegetation block. The method has the effect of more conveniently and accurately evaluating the pollution degree of the coastal mud flat soil.

Description

Technical Field

[0001] The present application relates to the field of coastal assessment, and in particular to a coastal information assessment method, device, equipment and medium based on big data. Background Art

[0002] In recent years, alien invasive plants that have affected my country's coastal mudflats, such as Spartina alterniflora, have expanded rapidly in coastal areas due to the lack of natural enemies and other constraints, posing a serious threat to the safety of coastal wetland ecosystems. The crazy growth of these coastal invasive plants pollutes the soil and weakens the scouring effect of seawater on coastal mudflats. In addition, human production operations, garbage dumping and other activities on the mudflats have caused the mudflat soil to be polluted, and the inability of seawater to effectively scour has made coastal pollution even more serious.

[0003] At present, the degree of pollution of coastal invasive plants on coastal tidal flats is mainly assessed by relevant personnel through remote sensing images or field surveys. Assessing the degree of pollution of invasive plants on soil through remote sensing images requires analyzing a large amount of data, which is time-consuming, labor-intensive and has low accuracy. Summary of the invention

[0004] In order to more conveniently and accurately assess the degree of pollution of coastal tidal flat soil, the present application provides a coastal information assessment method, device, equipment and medium based on big data.

[0005] In the first aspect, the present application provides a coastal information assessment method based on big data, which adopts the following technical solutions: A coastal information assessment method based on big data, including: Obtain historical remote sensing images corresponding to multiple vegetation blocks in a preset coastal area; Determine an area change curve of each vegetation block and a first soil color value change graph based on the historical remote sensing image; Acquire multiple aerial images of each vegetation block within a preset historical time period, and determine a second soil color value change map of each vegetation block, garbage features in each aerial image of each vegetation block, and the heat of human activities based on the multiple aerial images; Determine the pollution degree of each vegetation block based on the first soil color value change map, the second soil color value change map, garbage characteristics and human activity heat; The pollution degree of the preset coastal area is determined based on the area change curve of each vegetation block and the pollution degree of each vegetation block.

[0006] By adopting the above technical solution, historical remote sensing images are obtained. The historical remote sensing images record the specific changes in the coast. Therefore, based on the historical remote sensing images, the area change curve of each vegetation block and the first soil color value change map can be determined. The vegetation block area change map represents the expansion or reduction of invasive plants. The color of the soil changes when it is polluted. Therefore, the first soil color value change map represents the changes in the soil pollution. Aerial images are obtained. Aerial images are closer than remote sensing images and can collect more details. Therefore, the second soil color value change map of each vegetation block is determined based on the aerial images. Since the remote sensing image and the aerial image are at different heights, the second soil color value change map There is a difference between the color value change map and the first soil color value change map. Combining the first soil color value change map and the second soil color value change map can more accurately assess the soil pollution and determine the garbage characteristics and human activity intensity of each vegetation block. The garbage characteristics also pollute the soil. The higher the human activity intensity, the greater the damage and pollution to the soil. Therefore, it is more accurate to determine the pollution degree of each vegetation block based on the first soil color value change map, the second soil color value change map, the garbage characteristics and the sum of the human activity intensity. Finally, combining the area change curve of each vegetation block and the pollution degree to comprehensively determine the pollution degree of the entire preset coastal area is more accurate and convenient than manual evaluation.

[0007] In another possible implementation, determining the pollution degree of each vegetation block based on the first soil color value change map, the second soil color value change map, garbage characteristics, and human activity heat includes: Determine a fitted soil color value change map for each vegetation block based on the first soil color value change map and the second soil color value change map; Determining a first average value of soil color values ​​from the fitted soil color value variation graph; Count the garbage features in each vegetation block to obtain the amount of garbage in each vegetation block; Determining a garbage purification difficulty score in each vegetation block based on the garbage characteristics in each vegetation block; The pollution degree of each vegetation block is determined based on the first average value, the amount of garbage, the garbage purification difficulty score and the intensity of human activities.

[0008] In another possible implementation, determining the pollution degree of the preset coastal area based on the area change curve of each vegetation block and the pollution degree of each vegetation block includes: Determine the area change trend of each vegetation block from the area change curve of each vegetation block, and obtain a first vegetation block whose area change trend is decreasing or unchanged, and a second vegetation block whose area change trend is increasing; determining a first ratio of the number of the second vegetation blocks to the number of the first vegetation blocks; Determining a first total area of ​​all first vegetation blocks and a second total area of ​​all second vegetation blocks; determining a second ratio of the second total area to the first total area; A score representing the pollution level of the preset coastal area is determined based on the pollution level of each vegetation block, the first ratio, the second ratio and the corresponding coefficients.

[0009] In another possible implementation, the determining the garbage purification difficulty score in each vegetation block based on the garbage characteristics in each vegetation block includes: Determine the outline of each vegetation block, and draw a plane rectangular coordinate system with the center point of the outline as the origin, wherein the two coordinate axes of the plane rectangular coordinate system form an angle of 45° with the coastline; Determine the coordinates of the garbage features in each quadrant of the plane rectangular coordinate system, and determine the coordinates of the center point representing the distribution of the garbage features in each quadrant based on the coordinates; Determine the distance from the center point coordinates to the origin of the plane rectangular coordinate system; determining the number of garbage features in each quadrant of the plane rectangular coordinate system; Determine a garbage purification difficulty score corresponding to each quadrant based on the distance, quantity, and garbage purification difficulty coefficient corresponding to each quadrant; The garbage purification difficulty score within each vegetation block is determined based on the garbage purification difficulty score corresponding to each quadrant.

[0010] In another possible implementation manner, determining the fitted soil color value change map of each vegetation block based on the first soil color value change map and the second soil color value change map includes: Determine a first time point corresponding to each color value in the first soil color value change graph, and determine a second time point corresponding to each color value in the second soil color value change graph; Determine a target color value group from the first soil color value change graph and the second soil color value change graph, wherein the target color value group is two color values ​​whose time interval between the first time point and the second time point is less than a preset interval; averaging the color values ​​of the target color value group; Mapping the obtained average value in the first soil color value change map according to the first time point in the target color value group to obtain a soil color value change map to be selected; Determine the remaining color values ​​in the second soil color value variation graph except the color values ​​in the target color value group; Determine an associated color value from the first soil color value variation diagram according to the second time point of each remaining color value, wherein the first time points of the associated color values ​​are the two color values ​​closest to the remaining color value, and the first time points of the associated color values ​​are respectively located before and after the second time points of the remaining color value; Calculate the average color value of the associated color value, and calculate the difference between the remaining color value and the average color value; The remaining color values ​​whose differences do not reach the preset difference threshold are mapped into the candidate soil color value change map according to the second time point of the remaining color values ​​to obtain the fitted soil color value change map of each vegetation block.

[0011] In another possible implementation manner, determining the human activity heat of each vegetation block based on the multiple aerial images includes: Determine the number of people within each aerial image; Determine an average number of personnel and a total number of personnel based on the number of personnel; Based on the average number of personnel, the total number of personnel and their respective corresponding coefficients, a score representing the activity intensity of the personnel is determined.

[0012] In another possible implementation, the method further includes: Determine the remote sensing image of the location and time of the third vegetation block, wherein the third vegetation block is a vegetation block whose pollution degree reaches a preset degree threshold and whose area change trend is increasing; Output the remote sensing image of the location and time closest to the third vegetation block.

[0013] In the second aspect, the present application provides a coastal information assessment device based on big data, which adopts the following technical solution: A coastal information evaluation device based on big data, comprising: An image acquisition module is used to acquire historical remote sensing images corresponding to multiple vegetation blocks in a preset coastal area; A first determination module, used to determine an area change curve of each vegetation block and a first soil color value change graph based on the historical remote sensing image; A second determination module is used to obtain multiple aerial images of each vegetation block within a preset historical time period, and determine a second soil color value change map of each vegetation block, garbage features in each aerial image of each vegetation block, and human activity heat based on the multiple aerial images; a third determination module, configured to determine the pollution degree of each vegetation block based on the first soil color value change map, the second soil color value change map, garbage characteristics, and human activity heat; The fourth determination module is used to determine the pollution degree of the preset coastal area based on the area change curve of each vegetation block and the pollution degree of each vegetation block.

[0014] By adopting the above technical solution, the image acquisition module obtains historical remote sensing images, which record the specific changes of the coast. Therefore, the first determination module can determine the area change curve of each vegetation block and the first soil color value change map based on the historical remote sensing images. The vegetation block area change map represents the expansion or reduction of invasive plants. The color of the soil changes when it is polluted. Therefore, the first soil color value change map represents the change of soil pollution. The second determination module obtains aerial images. Aerial images are closer than remote sensing images and can collect more details. Therefore, the second determination module determines the second soil color value change map of each vegetation block based on the aerial images. Since the remote sensing image and the aerial image are at different heights, Therefore, there is a difference between the second soil color value change map and the first soil color value change map. Combining the first soil color value change map and the second soil color value change map can more accurately assess the soil pollution and determine the garbage characteristics and human activity intensity of each vegetation block. The garbage characteristics also pollute the soil. The higher the human activity intensity, the greater the damage and pollution to the soil. Therefore, the third determination module determines the pollution degree of each vegetation block more accurately based on the first soil color value change map, the second soil color value change map, the garbage characteristics and the sum of the human activity intensity. Finally, the fourth determination module combines the area change curve of each vegetation block and the pollution degree to comprehensively determine the pollution degree of the entire preset coastal area, which is more accurate and convenient than manual evaluation.

[0015] In another possible implementation, when the third determination module determines the pollution degree of each vegetation block based on the first soil color value change map, the second soil color value change map, garbage characteristics, and human activity heat, it is specifically used to: Determine a fitted soil color value change map for each vegetation block based on the first soil color value change map and the second soil color value change map; Determining a first average value of soil color values ​​from the fitted soil color value variation graph; Count the garbage features in each vegetation block to obtain the amount of garbage in each vegetation block; Determining a garbage purification difficulty score in each vegetation block based on the garbage characteristics in each vegetation block; The pollution degree of each vegetation block is determined based on the first average value, the amount of garbage, the garbage purification difficulty score and the intensity of human activities.

[0016] In another possible implementation, when the fourth determination module determines the pollution degree of the preset coastal area based on the area change curve of each vegetation block and the pollution degree of each vegetation block, it is specifically used to: Determine the area change trend of each vegetation block from the area change curve of each vegetation block, and obtain a first vegetation block whose area change trend is decreasing or unchanged, and a second vegetation block whose area change trend is increasing; determining a first ratio of the number of the second vegetation blocks to the number of the first vegetation blocks; Determining a first total area of ​​all first vegetation blocks and a second total area of ​​all second vegetation blocks; determining a second ratio of the second total area to the first total area; A score representing the pollution level of the preset coastal area is determined based on the pollution level of each vegetation block, the first ratio, the second ratio and the corresponding coefficients.

[0017] In another possible implementation, when determining the garbage purification difficulty score in each vegetation block based on the garbage characteristics in each vegetation block, the third determination module is specifically configured to: Determine the outline of each vegetation block, and draw a plane rectangular coordinate system with the center point of the outline as the origin, wherein the two coordinate axes of the plane rectangular coordinate system form an angle of 45° with the coastline; Determine the coordinates of the garbage features in each quadrant of the plane rectangular coordinate system, and determine the coordinates of the center point representing the distribution of the garbage features in each quadrant based on the coordinates; Determine the distance from the center point coordinates to the origin of the plane rectangular coordinate system; determining the number of garbage features in each quadrant of the plane rectangular coordinate system; Determine a garbage purification difficulty score corresponding to each quadrant based on the distance, quantity, and garbage purification difficulty coefficient corresponding to each quadrant; The garbage purification difficulty score within each vegetation block is determined based on the garbage purification difficulty score corresponding to each quadrant.

[0018] In another possible implementation, when the third determination module determines the fitted soil color value change map of each vegetation block based on the first soil color value change map and the second soil color value change map, it is specifically used to: Determine a first time point corresponding to each color value in the first soil color value change graph, and determine a second time point corresponding to each color value in the second soil color value change graph; Determine a target color value group from the first soil color value change graph and the second soil color value change graph, wherein the target color value group is two color values ​​whose time interval between the first time point and the second time point is less than a preset interval; averaging the color values ​​of the target color value group; Mapping the obtained average value in the first soil color value change map according to the first time point in the target color value group to obtain a soil color value change map to be selected; Determine the remaining color values ​​in the second soil color value variation graph except the color values ​​in the target color value group; Determine an associated color value from the first soil color value variation diagram according to the second time point of each remaining color value, wherein the first time points of the associated color values ​​are the two color values ​​closest to the remaining color value, and the first time points of the associated color values ​​are respectively located before and after the second time points of the remaining color value; Calculate the average color value of the associated color value, and calculate the difference between the remaining color value and the average color value; The remaining color values ​​whose differences do not reach the preset difference threshold are mapped into the candidate soil color value change map according to the second time point of the remaining color values ​​to obtain the fitted soil color value change map of each vegetation block.

[0019] In another possible implementation, when the second determination module determines the human activity heat of each vegetation block based on the multiple aerial images, it is specifically used to: Determine the number of people within each aerial image; Determine an average number of personnel and a total number of personnel based on the number of personnel; Based on the average number of personnel, the total number of personnel and their respective corresponding coefficients, a score representing the activity intensity of the personnel is determined.

[0020] In another possible implementation, the device further includes: A fifth determination module is used to determine the location of a third vegetation block and the most recent remote sensing image, wherein the third vegetation block is a vegetation block whose pollution degree reaches a preset degree threshold and whose area change trend is increasing; The output module is used to output the remote sensing image of the location and time closest to the third vegetation block.

[0021] In a third aspect, the present application provides an electronic device, which adopts the following technical solution: An electronic device, comprising: at least one processor; Memory; At least one application, wherein at least one application is stored in a memory and configured to be executed by at least one processor, and at least one is configured to: execute a coastal information assessment method based on big data as shown in any possible implementation of the first aspect.

[0022] In a fourth aspect, the present application provides a computer-readable storage medium, which adopts the following technical solution: A computer-readable storage medium, when the computer program is executed in a computer, enables the computer to execute a coastal information assessment method based on big data as described in any one of the first aspects.

[0023] In summary, the present application includes at least one of the following beneficial technical effects: Obtain historical remote sensing images. Historical remote sensing images record the specific changes in the coast. Therefore, based on historical remote sensing images, the area change curve of each vegetation block and the first soil color value change map can be determined. The vegetation block area change map represents the expansion or reduction of invasive plants. The color of the soil changes when it is polluted. Therefore, the first soil color value change map represents the changes in soil pollution. Obtain aerial images. Aerial images are closer than remote sensing images and can collect more details. Therefore, the second soil color value change map of each vegetation block is determined based on the aerial images. Since the remote sensing image and the aerial image are at different heights, the second soil color value change map There is a difference between the first soil color value change map and the first soil color value change map. Combining the first soil color value change map and the second soil color value change map can more accurately assess the soil pollution and determine the garbage characteristics and human activity intensity of each vegetation block. The garbage characteristics also pollute the soil. The higher the human activity intensity, the greater the damage and pollution to the soil. Therefore, it is more accurate to determine the pollution degree of each vegetation block based on the first soil color value change map, the second soil color value change map, the garbage characteristics and the sum of the human activity intensity. Finally, combining the area change curve of each vegetation block and the pollution degree to comprehensively determine the pollution degree of the entire preset coastal area is more accurate and convenient than manual evaluation. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 It is a flow chart of a coastal information assessment method based on big data in an embodiment of the present application.

[0025] Figure 2 It is an example diagram of the coastline, vegetation blocks, garbage features and the establishment of a plane rectangular coordinate system in the embodiments of the present application.

[0026] Figure 3 It is a structural schematic diagram of a coastal information assessment device based on big data in an embodiment of the present application.

[0027] Figure 4 It is a structural schematic diagram of an electronic device according to an embodiment of the present application.

[0028] Figure numerals: 21, coastline; 22, vegetation block; 23, garbage characteristics; 30, a coastal information evaluation device based on big data; 301, image acquisition module; 302, first determination module; 303, second determination module; 304, third determination module; 305, fourth determination module; 40, electronic device; 401, processor; 402, bus; 403, memory; 404, transceiver. DETAILED DESCRIPTION

[0029] The present application is further described in detail below in conjunction with the accompanying drawings.

[0030] After reading this specification, those skilled in the art may make modifications to this embodiment without any creative contribution as needed, but such modifications are protected by patent law as long as they are within the scope of the claims of this application.

[0031] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0032] In addition, the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article, unless otherwise specified, generally means that the associated objects before and after are in an "or" relationship.

[0033] The embodiments of the present application are further described in detail below in conjunction with the drawings in the specification.

[0034] The embodiment of the present application provides a coastal information assessment method based on big data, which is executed by an electronic device, which can be a server or a terminal device, wherein the server can be an independent physical server, a server cluster or a distributed system composed of multiple physical servers, or a cloud server that provides cloud computing services. The terminal device can be a smart phone, a tablet computer, a laptop computer, a desktop computer, etc., but is not limited to this. The terminal device and the server can be directly or indirectly connected via wired or wireless communication, and the embodiment of the present application does not limit this. Figure 1As shown, the method includes step S101, step S102, step S103, step S104 and step S105, wherein: S101, obtaining historical remote sensing images corresponding to a plurality of vegetation blocks in a preset coastal area.

[0035] For the embodiments of the present application, the preset coastal area can be a coastal area manually set on a visual operation interface, for example, the preset coastal area is delineated according to longitude and latitude, or the preset coastal area is framed out by an input device such as a mouse. The electronic device can obtain historical remote sensing images of multiple vegetation blocks in the preset coastal area from relevant surveying and mapping websites, or can obtain historical remote sensing images corresponding to multiple vegetation blocks through the relevant remote sensing mapping database API interface, or the electronic device can directly obtain historical remote sensing images through remote sensing satellites. The historical remote sensing image can be a plurality of remote sensing images at different historical time points. Vegetation blocks are blocks formed by the reproduction and growth of invasive species or other plants that cause damage to the coastal ecological environment, such as Spartina alterniflora. Vegetation blocks also include areas of a preset range around the outline formed by the vegetation.

[0036] S102: Determine an area change curve of each vegetation block and a first soil color value change graph based on historical remote sensing images.

[0037] For the embodiment of the present application, since the historical remote sensing images record the area size of each vegetation block, after the electronic device acquires the historical remote sensing images, it can obtain the coordinate points according to the time point of each historical remote sensing image and the area size of the corresponding vegetation block, and map the coordinate points corresponding to each historical remote sensing image in a preset coordinate system in which the area changes with time and connect them in sequence to obtain the area change curve of each vegetation block. The area change curve can be used to more intuitively and conveniently observe the area change of each vegetation block.

[0038] Historical remote sensing images record the color of the soil in the vegetation blocks and the preset areas near the vegetation blocks. The greater the deviation between the color value and the color value of the soil when the soil is not polluted, the more serious the soil pollution is. Therefore, the electronic equipment can perform grayscale transformation on the historical remote sensing images to obtain the grayscale value of the remote sensing images, that is, use the grayscale value to represent the color value, map the coordinate points corresponding to each historical remote sensing image in the preset coordinate system where the grayscale value changes with time, and connect them in sequence to obtain the first soil color value change curve of each vegetation block. The first soil color value change curve can be used to more intuitively and conveniently observe the soil color change of each vegetation block, that is, the degree of soil pollution.

[0039] Specifically, taking Spartina alterniflora as an example, the rampant growth of Spartina alterniflora hinders the scouring of coastal mudflats by seawater, resulting in the soil, garbage, and dead animals and plants on the coastal mudflats being unable to be carried back into the ocean by the water current, thereby reducing the seawater exchange capacity. Garbage and other pollutants accumulate, ferment, and rot on the soil, causing the soil color to change, which in turn indicates that the soil pollution is more serious.

[0040] S103, obtaining multiple aerial images of each vegetation block within a preset historical time period, and determining a second soil color value change map of each vegetation block, garbage features in each aerial image of each vegetation block, and the activity intensity of personnel based on the multiple aerial images.

[0041] For the embodiment of the present application, the aerial image is an image of the vegetation block taken by a drone, and is also an aerial image collected at multiple different time points in history. Compared with the remote sensing image, the aerial image is closer to the vegetation block, and can collect more detailed features of the vegetation block and the surrounding area, and can determine the soil color value of each vegetation block and the surrounding area according to the aerial image, and then determine the second soil color value change map by the method recorded in step S102. The second soil color value change map is different from the first soil color value change map. Since the two color value change maps are determined based on images collected at different heights, there are differences between the two color value change maps, but both can characterize the soil color value changes of the vegetation block to a certain extent.

[0042] The electronic device inputs each aerial image into a trained network model for feature recognition, thereby identifying the garbage features in each aerial image. The garbage features can also characterize the soil pollution situation to a certain extent. Specifically, the network model can be a convolutional neural network model, a recurrent neural network model, or other types of network models. The electronic device also analyzes each aerial image to obtain the activity intensity of personnel in each vegetation block. Since production and other activities of personnel on coastal mudflats will also cause soil pollution, the higher the activity intensity of personnel, the greater the degree of soil pollution, and vice versa.

[0043] S104, determining the pollution degree of each vegetation block based on the first soil color value change map, the second soil color value change map, garbage characteristics, and human activity heat.

[0044] For the embodiments of the present application, in summary, the first soil color value change graph, the second soil color value change graph, garbage characteristics and human activity heat are all important factors affecting the assessment of the pollution level of each vegetation block. Therefore, the electronic device combines the above four factors to comprehensively determine the pollution level of each vegetation block more accurately.

[0045] S105: Determine the pollution degree of the preset coastal area based on the area change curve of each vegetation block and the pollution degree of each vegetation block.

[0046] For the embodiment of the present application, the electronic device can determine the pollution level of the entire preset coastal area after determining the pollution level of each vegetation block. By first determining the pollution level of each vegetation block and then determining the pollution level of the entire preset coastal area, it is more convenient and accurate than manually judging the pollution level of the coastal area.

[0047] In a possible implementation of the embodiment of the present application, in step S104, the pollution degree of each vegetation block is determined based on the first soil color value change map, the second soil color value change map, the garbage characteristics, and the heat of human activities, specifically including step S1041 (not shown in the figure), step S1042 (not shown in the figure), step S1043 (not shown in the figure), step S1044 (not shown in the figure), and step S1045 (not shown in the figure), wherein: S1041: Determine a fitted soil color value change map for each vegetation block based on the first soil color value change map and the second soil color value change map.

[0048] For the embodiment of the present application, since the first soil color value change map and the second soil color value change map are determined based on images collected at different heights, although both can characterize the changes in soil color values ​​and soil pollution conditions, there are still differences. Therefore, the electronic device comprehensively analyzes the first soil color value change map and the second soil color value change map to determine a fitted soil color value change map for each vegetation block. The determined fitted soil color value change map characterizes the soil pollution condition more accurately.

[0049] S1042: Determine a first average value of the soil color value from the fitted soil color value variation graph.

[0050] For the embodiment of the present application, after the electronic device determines the fitted soil color value change graph, it calculates the first average value of the soil grayscale value using the average value calculation formula based on the grayscale value of each coordinate point in the change graph. The first average value is used to more accurately characterize the specific color depth of the overall soil in the historical time period corresponding to the historical remote sensing image, that is, the first average value is used to more accurately characterize the soil pollution situation.

[0051] S1043, counting the garbage features in each vegetation block to obtain the amount of garbage in each vegetation block.

[0052] For the embodiment of the present application, after the electronic device identifies the garbage features in each vegetation block, it accumulates and counts the garbage features to obtain the amount of garbage. The more garbage there is in a vegetation block, the more serious the soil pollution in the vegetation block.

[0053] S1044, determining a garbage purification difficulty score in each vegetation block based on the garbage characteristics in each vegetation block.

[0054] For the embodiment of the present application, after the electronic device identifies the garbage characteristics in each vegetation block, it can further analyze the garbage characteristics, such as determining the location of the garbage characteristics, etc. to analyze the garbage purification difficulty score in each vegetation block. The garbage purification difficulty can characterize the difficulty of purifying garbage on the soil due to the scouring effect of seawater, that is, the difficulty of seawater bringing garbage back into the ocean. The higher the score, the more serious the soil pollution in the vegetation block.

[0055] S1045, determining the pollution degree of each vegetation block based on the first average value, the amount of garbage, the garbage purification difficulty score, and the heat of human activities.

[0056] For the embodiment of the present application, in summary, the first average value, the amount of garbage, the garbage purification difficulty score and the activity intensity of personnel are all important factors that affect the degree of pollution of the vegetation block. After the electronic device determines the first average value, it can calculate the difference between the first average value and the preset gray value. The preset gray value represents the gray value of the soil when the soil is not polluted. The larger the difference, the more serious the soil pollution. Therefore, the staff can set different coefficients for the above-mentioned first average value and other four elements, and then perform weighted calculations on the difference, the amount of garbage, the garbage purification difficulty score and the activity intensity of personnel in combination with their respective corresponding coefficients to obtain a score representing the degree of pollution of each vegetation block. The score is more accurate in representing the degree of pollution of each vegetation block. And it is more accurate and convenient to comprehensively calculate the score representing the degree of pollution through the first average value, the amount of garbage and other relevant important parameters.

[0057] In a possible implementation of the embodiment of the present application, in step S105, the pollution degree of the preset coastal area is determined based on the area change curve of each vegetation block and the pollution degree of each vegetation block, specifically including step S1051 (not shown in the figure), step S1052 (not shown in the figure), step S1053 (not shown in the figure), step S1054 (not shown in the figure) and step S1055 (not shown in the figure), wherein: S1051, determining the area change trend of each vegetation block from the area change curve of each vegetation block, obtaining a first vegetation block with a decreasing or unchanged area change trend, and a second vegetation block with an increasing area change trend.

[0058] For the embodiment of the present application, the electronic device performs linear fitting on the area change curve of each vegetation block to obtain an approximate linear function, and then the electronic device determines the slope of the linear function. If the slope is positive, it means that the area change trend is increasing, if the slope is negative, it means that the area change trend is decreasing, and if the slope is 0, it means that the area change trend is unchanged. The electronic device determines the first vegetation block and the second vegetation block in the above manner.

[0059] S1052: Determine a first ratio of the number of second vegetation blocks to the number of first vegetation blocks.

[0060] For the embodiment of the present application, the electronic device determines the number of second vegetation blocks whose area change trend is increasing, and the number of first vegetation blocks whose area change trend is decreasing or unchanged, and then the electronic device calculates a first ratio of the two numbers, with the number of second vegetation blocks as the numerator and the number of first vegetation blocks as the denominator. The larger the first ratio, the more vegetation blocks in the preset coastal area are in an area increase, and the preset coastal area as a whole is in a state of invasive plant expansion. Moreover, since there are a large number of vegetation blocks in an area increase, it means that the faster the expansion speed of plants in the preset coastal area, the more serious the pollution.

[0061] S1053: Determine a first total area of ​​all first vegetation blocks and a second total area of ​​all second vegetation blocks.

[0062] For the embodiment of the present application, the electronic device sums up the areas of all first vegetation blocks to obtain a first total area, and sums up the areas of all second vegetation blocks to obtain a second total area.

[0063] S1054: Determine a second ratio of the second total area to the first total area.

[0064] For the embodiment of the present application, the electronic device calculates the second ratio of the two total areas, with the second total area as the numerator and the first total area as the denominator. The larger the second ratio is, the larger the proportion of vegetation blocks in the preset coastal area that are expanding is in the preset coastal area, and the preset coastal area as a whole is in a state of expansion of invasive plants and the expansion trend is serious, which also indicates that the pollution in the preset coastal area is more serious.

[0065] S1055: Determine a score representing the pollution level of a preset coastal area based on the pollution level of each vegetation block, the first ratio, the second ratio, and the corresponding coefficients.

[0066] For the embodiment of the present application, in summary, the pollution degree of each vegetation block, the first ratio and the second ratio are all key factors affecting the integrated preset coastal area pollution degree, and the degree of influence is different, so the staff can set the corresponding coefficients through the visual operation interface, and then the electronic device calls the corresponding coefficients to perform weighted calculation on the pollution degree, the first ratio and the second ratio of each block to obtain a score representing the pollution degree of the preset coastal area. By combining the pollution degree of each vegetation block, the first ratio and the second ratio, the pollution degree of the preset coastal area can be comprehensively evaluated more accurately.

[0067] In a possible implementation of the embodiment of the present application, in step S1044, based on the garbage characteristics in each vegetation block, the garbage purification difficulty score in each vegetation block is determined, specifically including step S1 (not shown in the figure), step S2 (not shown in the figure), step S3 (not shown in the figure), step S4 (not shown in the figure), step S5 (not shown in the figure) and step S6 (not shown in the figure), wherein: S1, determine the outline of each vegetation block and draw a plane rectangular coordinate system with the center point of the outline as the origin.

[0068] Among them, the angle between the two coordinate axes of the plane rectangular coordinate system and the coastline is 45°.

[0069] For the embodiment of the present application, the electronic device determines the outline of each vegetation block based on the most recent aerial image, performs denoising on the most recent aerial image and performs grayscale transformation on the denoised aerial image, then finds the grayscale value step to determine the outline of the vegetation block, and then determines the minimum circumscribed rotation rectangle of the outline, determines the center coordinates of the rectangle, and the center coordinates are the center point of the outline. The electronic device can draw a plane rectangular coordinate system with the center point as the origin.

[0070] The electronic device determines the coastline of the preset coastal area and approximates the coastline into a straight line, with the direction of seawater scouring perpendicular to the coastline. After the electronic device determines the approximate straight line of the coastline, it draws a plane rectangular coordinate system with the two coordinate axes at an angle of 45° to the coastline and obtains four quadrants, such as Figure 2 As shown, the electronic device draws a coordinate system according to the direction of the coastline 21 and the position of the vegetation block 22. The seawater scours the area on the coast in a direction perpendicular to the coastline 21. The vegetation on the vegetation block 22 blocks the further flow and scouring of the seawater, and the invasive plants have a blocking effect on garbage, etc., resulting in the soil and garbage features 23 being unable to be easily moved back to the ocean, thereby causing the soil pollution to worsen.

[0071] S2, determining the coordinates of the garbage features in each quadrant of the plane rectangular coordinate system, and determining the coordinates of the center point representing the distribution of the garbage features in each quadrant based on the coordinates.

[0072] For the embodiment of the present application, after the electronic device identifies the garbage feature, it determines the garbage feature as a coordinate point on a plane rectangular coordinate system and determines the coordinates of each garbage feature, thereby determining the coordinates of the garbage feature in each quadrant, and then the electronic device calculates the arithmetic mean of the X coordinates and Y coordinates of all scattered points, and uses these two average values ​​as the coordinates of the center point of the garbage feature distribution in each quadrant. Specifically, the electronic device first adds all X coordinates and divides them by the total number of scattered points to obtain the average value of the X coordinates, and then adds all Y coordinates and divides them by the total number of scattered points to obtain the average value of the Y coordinates.

[0073] S3, determine the distance from the center point coordinates to the origin of the plane rectangular coordinate system.

[0074] For the embodiment of the present application, after the electronic device determines the coordinates of the center point of the garbage characteristic distribution in each quadrant, it calculates the distance from the center point coordinates to the origin, and the electronic device can obtain the distance using the distance formula between two points. The closer to the origin, that is, the smaller the distance, the closer the garbage is to the center of the vegetation block, the greater the impact of the vegetation barrier, and the more difficult it is to be washed away by sea water. For the quadrant farthest from the coastline, due to the separation of sea water and garbage by the vegetation block, the farther the center point of the garbage characteristic distribution is from the coastline, the less likely it is to be washed away by sea water. However, since it is more obvious and may move due to external factors such as windy weather, it is also analyzed according to the smaller the distance, the more difficult it is to be washed away by sea water.

[0075] S4, determining the number of garbage features in each quadrant of the plane rectangular coordinate system.

[0076] For the embodiment of the present application, the electronic device counts the garbage features in each quadrant to obtain the number of garbage features in each quadrant. The greater the number of garbage features, the greater the difficulty of purification, and vice versa.

[0077] S5, determining the garbage purification difficulty score corresponding to each quadrant based on the distance, quantity, and the garbage purification difficulty coefficient corresponding to each quadrant.

[0078] For the embodiments of the present application, in summary, distance and quantity are both key factors regarding the difficulty of garbage purification corresponding to each quadrant, and the degree of influence is different. Therefore, the staff sets corresponding coefficients for the distance and quantity, takes the reciprocal of the distance, and uses the reciprocal of the distance and the quantity to call the corresponding coefficients to calculate an intermediate score. Since the position of each quadrant is different, the corresponding garbage purification difficulty is also different. Therefore, the staff sets a different garbage purification difficulty coefficient for each quadrant. The electronic device calls the garbage purification difficulty coefficient corresponding to each quadrant and multiplies it by the above-mentioned intermediate score to obtain the garbage purification difficulty score corresponding to each quadrant.

[0079] In other embodiments, the electronic device can also determine the soil color grayscale value in each quadrant from the aerial image, and then calculate the difference between the soil color grayscale value in each quadrant and the preset grayscale value, and comprehensively calculate the garbage purification difficulty score in each quadrant by combining the distance, quantity and grayscale value difference.

[0080] S6, determining the garbage purification difficulty score in each vegetation block based on the garbage purification difficulty score corresponding to each quadrant.

[0081] For the embodiment of the present application, the electronic device determines the garbage purification difficulty score corresponding to each quadrant and then sums the garbage purification scores of the four quadrants to obtain the garbage purification difficulty score of each vegetation block. By analyzing the garbage distribution and amount of garbage in each quadrant, the garbage purification difficulty score of each vegetation block can be determined more accurately in combination with the location of each quadrant.

[0082] In other embodiments, the electronic device can also calculate the garbage purification difficulty score of each vegetation area for each aerial image in the above manner, and then average the garbage purification difficulty scores of all aerial images, and determine the obtained average value as the garbage purification difficulty score of each vegetation block.

[0083] In a possible implementation of the embodiment of the present application, in step S1041, a fitting soil color value change map of each vegetation block is determined based on the first soil color value change map and the second soil color value change map, specifically including step 1, step 2, step 3, step 4, step 5, step 6, step 7 and step 8, wherein: Step 1: determine a first time point corresponding to each color value in the first soil color value change graph, and determine a second time point corresponding to each color value in the second soil color value change graph.

[0084] For the embodiment of the present application, the electronic device retrieves the time point of each color value on the horizontal time axis in the first soil color value change graph, that is, the first time point, and determines the second time point in the same manner.

[0085] Step 2: determining a target color value group from the first soil color value change map and the second soil color value change map.

[0086] The target color value group is two color values ​​whose time interval between the first time point and the second time point is less than a preset interval.

[0087] For the embodiment of the present application, the historical remote sensing images may be collected at a specified time interval, and the aerial images may be collected at a specified time interval or not. The preset interval is much smaller than the time interval for collecting remote sensing images. The preset interval serves as a dividing point for whether the first time point and the second time point are close in time. The electronic device calculates the difference between the first time point and each second time point of each color value, thereby obtaining two color values ​​with a time interval less than the preset interval, i.e., the target color value group. The two color values ​​in the target color value group are close in time, so they can represent the soil color value at the same time. The color values ​​in the target color value group facilitate subsequent correction to obtain more realistic and accurate color values.

[0088] Step three, find the average value of the color value of the target color value group.

[0089] For the embodiment of the present application, the electronic device calculates the average value of each target color value group through the average value calculation formula, and uses the average value to more accurately represent the soil color values ​​corresponding to the first time point and the second time point (the same time point).

[0090] Step 4: Map the obtained average value into the first soil color value change map according to the first time point in the target color value group to obtain a candidate soil color value change map.

[0091] For the embodiment of the present application, after the electronic device determines the average value in step three, since the target color value group represents the soil color value at the same time point, the electronic device maps the average value into the first soil color value change graph according to the first time point of the target color value group and deletes the soil color value originally corresponding to the first time point of the target color value group, thereby obtaining the soil color value change graph to be selected.

[0092] Step 5: Determine the remaining color values ​​in the second soil color value variation graph except the color values ​​in the target color value group.

[0093] According to the embodiment of the present application, the electronic device removes the color values ​​in the target color value group from the second soil color value change map, thereby obtaining the remaining color values.

[0094] Step six: determining the associated color value from the first soil color value variation diagram according to the second time point of each remaining color value.

[0095] The first time points of the associated color values ​​are the two color values ​​closest to the remaining color values, and the first time points of the associated color values ​​are respectively located before and after the second time points of the remaining color values.

[0096] In the embodiment of the present application, the electronic device determines the associated color value from the first soil color value variation diagram according to the second time point of the remaining color value. The associated color value is the color value that has a time-progressive relationship with the remaining color value.

[0097] Step 7: Calculate the average color value of the associated color values, and calculate the difference between the remaining color values ​​and the average color value.

[0098] For the embodiment of the present application, in order to improve the data richness and accuracy of the color value, the electronic device determines the associated color value and calculates the color value average of the associated color value, and uses the color value average to represent the overall level of the associated color value. Then, the difference between the remaining color value and the color value average is calculated. The size of the difference represents the credibility of the remaining color value.

[0099] Step eight, mapping the remaining color values ​​whose differences do not reach the preset difference threshold value into the candidate soil color value change map according to the second time point of the remaining color values, and obtaining the fitted soil color value change map of each vegetation block.

[0100] For the embodiment of the present application, the preset difference threshold is used as the demarcation point of whether the difference is too large. If the difference reaches the preset difference threshold, it means that the difference between the remaining color value and the associated color value is too large, which is unreliable data. If the difference does not reach the preset difference threshold, it means that the difference between the remaining color value and the associated color value is small, which is reliable data. Therefore, the electronic device maps the remaining color value whose difference does not reach the preset difference threshold to the above-mentioned candidate soil color value change map according to the second time point of the remaining color value, thereby obtaining a fitted soil color value change map. The fitted soil color value change map combines the first soil color value change map and the second soil color value change map, improves the richness of the data and retains data with higher accuracy and higher credibility, so that the first average value determined later according to the fitted soil color value change map is more accurate.

[0101] In a possible implementation of the embodiment of the present application, in step S103, the activity heat of personnel in each vegetation block is determined based on multiple aerial images, specifically including step S1031 (not shown in the figure), step S1032 (not shown in the figure), and step S1033 (not shown in the figure), wherein: S1031, determining the number of people in each aerial image.

[0102] In the embodiment of the present application, the electronic device inputs each aerial image into the trained network model to perform personnel recognition to obtain the personnel features in each aerial image, and then counts the personnel features to determine the number of personnel in each aerial image. The more personnel there are, the higher the popularity of personnel activities.

[0103] S1032, determining an average number of personnel and a total number of personnel based on the number of personnel.

[0104] For the embodiment of the present application, after the electronic device determines the number of people in each aerial image, it sums the number of people in all aerial images to obtain the total number, and calculates the average number of people by the average value calculation formula. The larger the average number of people, the higher the continuous personnel heat in the time period corresponding to all aerial images, the larger the total number of people, the higher the personnel activity heat, and the higher the personnel activity heat, the more serious the soil pollution.

[0105] S1033, determining a score representing the activity intensity of personnel based on the average number of personnel, the total number of personnel, and their respective corresponding coefficients.

[0106] For the embodiment of the present application, in summary, the average number of personnel and the total number of personnel are both key factors regarding the enthusiasm of personnel activities, so the staff sets the coefficients of the average number of personnel and the total number of personnel in advance, and then the electronic device calls the coefficient to perform weighted calculation on the average number of personnel and the total number of personnel to obtain a score, which can characterize the enthusiasm of personnel activities. It is more accurate to determine the enthusiasm of personnel activities by comprehensively considering the average number of personnel and the total number of personnel.

[0107] In a possible implementation of the embodiment of the present application, step S105 further includes step S106 (not shown in the figure) and step S107 (not shown in the figure), wherein: S106, determining the location of the third vegetation block and the remote sensing image that is closest in time.

[0108] Among them, the third vegetation block is a vegetation block whose pollution level reaches a preset threshold and whose area change trend is increasing.

[0109] S107, outputting the remote sensing image of the third vegetation block that is closest in position and time.

[0110] For the embodiment of the present application, the pollution level of the third vegetation block reaches the preset pollution level, indicating that the pollution is more serious, and the area change trend is increasing, so the third vegetation block is the vegetation block that needs to be treated first and has a higher priority. Therefore, the electronic device determines the location and time of the third vegetation block and the most recent remote sensing image, and sends the most recent remote sensing image to the terminal device of the staff, so that the staff can understand and grasp the specific situation of the third vegetation block in time and make corresponding preparations. For example, based on the most recent remote sensing image, the staff can know the area size of the third vegetation block and prepare the appropriate amount of materials.

[0111] The above-mentioned embodiment introduces a coastal information assessment method based on big data from the perspective of method flow. The following embodiment introduces a coastal information assessment device based on big data from the perspective of a virtual module or a virtual unit. For details, please refer to the following embodiment.

[0112] The present application embodiment provides a coastal information evaluation device 20 based on big data, such as Figure 3 As shown, the coastal information evaluation device 30 based on big data may specifically include: The image acquisition module 301 is used to acquire historical remote sensing images corresponding to multiple vegetation blocks in a preset coastal area; A first determination module 302 is used to determine an area change curve of each vegetation block and a first soil color value change graph based on historical remote sensing images; The second determination module 303 is used to obtain multiple aerial images of each vegetation block within a preset historical time period, and determine the second soil color value change map of each vegetation block, garbage features in each aerial image of each vegetation block, and the activity heat of personnel based on the multiple aerial images; The third determination module 304 is used to determine the pollution degree of each vegetation block based on the first soil color value change map, the second soil color value change map, garbage characteristics and human activity heat; The fourth determination module 305 is used to determine the pollution degree of the preset coastal area based on the area change curve of each vegetation block and the pollution degree of each vegetation block.

[0113] The embodiment of the present application discloses a coastal information evaluation device 20 based on big data, wherein the image acquisition module 301 acquires historical remote sensing images, which record the specific changes of the coast. Therefore, the first determination module 302 can determine the area change curve of each vegetation block and the first soil color value change diagram based on the historical remote sensing images. The vegetation block area change diagram represents the expansion or reduction of invasive plants. The color of the soil changes when it is polluted. Therefore, the first soil color value change diagram represents the change of soil pollution. The second determination module 303 acquires aerial images. Aerial images are closer than remote sensing images and can collect more details. Therefore, the second determination module 303 determines the second soil color value change diagram of each vegetation block based on the aerial images. The image is at a different height from the aerial image, so there is a difference between the second soil color value change map and the first soil color value change map. Combining the first soil color value change map and the second soil color value change map, the soil pollution can be evaluated more accurately in the future, and the garbage characteristics and human activity intensity of each vegetation block can be determined. The garbage characteristics also pollute the soil. The higher the human activity intensity, the greater the damage and pollution to the soil. Therefore, the third determination module 304 determines the pollution degree of each vegetation block more accurately based on the first soil color value change map, the second soil color value change map, the garbage characteristics and the sum of the human activity intensity. Finally, the fourth determination module 305 combines the area change curve of each vegetation block and the pollution degree to comprehensively determine the pollution degree of the entire preset coastal area, which is more accurate and convenient than manual evaluation.

[0114] In a possible implementation of the embodiment of the present application, when the third determination module 304 determines the pollution degree of each vegetation block based on the first soil color value change map, the second soil color value change map, the garbage characteristics, and the heat of human activities, it is specifically used to: Determine a fitted soil color value change map for each vegetation block based on the first soil color value change map and the second soil color value change map; Determine a first average value of the soil color value from the fitted soil color value variation graph; Count the garbage features in each vegetation block to obtain the amount of garbage in each vegetation block; Determine the garbage purification difficulty score in each vegetation block based on the garbage characteristics in each vegetation block; The pollution degree of each vegetation block is determined based on the first average value, the amount of garbage, the garbage purification difficulty score, and the heat of human activities.

[0115] In a possible implementation of the embodiment of the present application, when the fourth determination module 305 determines the pollution degree of the preset coastal area based on the area change curve of each vegetation block and the pollution degree of each vegetation block, it is specifically used to: Determine the area change trend of each vegetation block from the area change curve of each vegetation block, and obtain a first vegetation block whose area change trend is decreasing or unchanged, and a second vegetation block whose area change trend is increasing; determining a first ratio of the number of the second vegetation blocks to the number of the first vegetation blocks; Determining a first total area of ​​all first vegetation blocks and a second total area of ​​all second vegetation blocks; determining a second ratio of the second total area to the first total area; A score representing the pollution level of a preset coastal area is determined based on the pollution level of each vegetation block, the first ratio, the second ratio, and the corresponding coefficients.

[0116] In a possible implementation of the embodiment of the present application, when the third determination module 304 determines the garbage purification difficulty score in each vegetation block based on the garbage characteristics in each vegetation block, it is specifically used to: Determine the outline of each vegetation block and draw a rectangular coordinate system with the center point of the outline as the origin. The two coordinate axes of the rectangular coordinate system form an angle of 45° with the coastline. Determine the coordinates of the garbage feature in each quadrant of the plane rectangular coordinate system, and determine the coordinates of the center point representing the distribution of the garbage feature in each quadrant based on the coordinates; Determine the distance from the center point coordinates to the origin of the plane rectangular coordinate system; Determine the number of garbage features in each quadrant of the plane rectangular coordinate system; Determine the garbage purification difficulty score corresponding to each quadrant based on the distance, quantity and garbage purification difficulty coefficient corresponding to each quadrant; The garbage purification difficulty score within each vegetation block is determined based on the garbage purification difficulty score corresponding to each quadrant.

[0117] In a possible implementation of the embodiment of the present application, when the third determination module 304 determines the fitted soil color value change map of each vegetation block based on the first soil color value change map and the second soil color value change map, it is specifically used to: Determine a first time point corresponding to each color value in the first soil color value change graph, and determine a second time point corresponding to each color value in the second soil color value change graph; Determine a target color value group from the first soil color value change graph and the second soil color value change graph, wherein the target color value group is two color values ​​whose time interval between the first time point and the second time point is less than a preset interval; Calculate the average color value of the target color value group; Mapping the obtained average value in the first soil color value change map according to the first time point in the target color value group to obtain a soil color value change map to be selected; Determine the remaining color values ​​in the second soil color value variation graph except the color values ​​in the target color value group; Determine the associated color value from the first soil color value variation diagram according to the second time point of each remaining color value, wherein the first time points of the associated color values ​​are the two color values ​​closest to the remaining color value, and the first time points of the associated color values ​​are respectively located before and after the second time points of the remaining color value; Calculate the average color value of the associated color value, and calculate the difference between the remaining color value and the average color value; The remaining color values ​​whose differences do not reach the preset difference threshold are mapped in the candidate soil color value change map according to the second time point of the remaining color values ​​to obtain a fitted soil color value change map for each vegetation block.

[0118] In a possible implementation of the embodiment of the present application, when the second determination module 303 determines the activity heat of personnel in each vegetation block based on multiple aerial images, it is specifically used to: Determine the number of people within each aerial image; Determine the average number of personnel and the total number of personnel based on the number of personnel; The score representing the activity intensity of personnel is determined based on the average number of personnel, the total number of personnel and their respective corresponding coefficients.

[0119] In a possible implementation of the embodiment of the present application, the device 30 further includes: A fifth determination module is used to determine the location of the third vegetation block and the most recent remote sensing image, where the third vegetation block is a vegetation block whose pollution level reaches a preset level threshold and whose area change trend is increasing; The output module is used to output the remote sensing image of the location and time of the third vegetation block.

[0120] In the embodiment of the present application, the first determination module 302, the second determination module 303, the third determination module 304, the fourth determination module 305 and the fifth determination module may be the same determination module, or different determination modules, or partially the same determination modules, which is not limited here.

[0121] Technical personnel in the relevant field can clearly understand that, for the convenience and brevity of description, the specific working process of the coastal information assessment device 30 based on big data described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0122] An electronic device is provided in an embodiment of the present application, such as Figure 4As shown, Figure 4 The electronic device 40 shown includes: a processor 401 and a memory 403. The processor 401 and the memory 403 are connected, such as through a bus 402. Optionally, the electronic device 40 may also include a transceiver 404. It should be noted that in actual applications, the transceiver 404 is not limited to one, and the structure of the electronic device 40 does not constitute a limitation on the embodiments of the present application.

[0123] Processor 401 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array) or other programmable logic devices, transistor logic devices, hardware components or any combination thereof. It may implement or execute various exemplary logic blocks, modules and circuits described in conjunction with the disclosure of this application. Processor 401 may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0124] The bus 402 may include a path to transmit information between the above components. The bus 402 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. The bus 402 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 4 Only one thick line is used in the diagram, but it does not mean that there is only one bus or only one type of bus.

[0125] The memory 403 may be a ROM (Read Only Memory) or other types of static storage devices that can store static information and instructions, a RAM (Random Access Memory) or other types of dynamic storage devices that can store information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), a CD-ROM (Compact Disc Read Only Memory) or other optical disk storage, optical disk storage (including compressed optical disk, laser disk, optical disk, digital versatile disk, Blu-ray disk, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto.

[0126] The memory 403 is used to store the application code for executing the solution of the present application, and the execution is controlled by the processor 401. The processor 401 is used to execute the application code stored in the memory 403 to implement the contents shown in the above method embodiment.

[0127] The electronic devices include, but are not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), and fixed terminals such as digital TVs, desktop computers, etc. It can also be a server, etc. Figure 4 The electronic device shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0128] The embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon. When the computer-readable storage medium is run on a computer, the computer can execute the corresponding contents of the aforementioned method embodiment. Compared with the related art, the embodiment of the present application obtains historical remote sensing images. The historical remote sensing images record the specific changes in the coast. Therefore, the area change curve of each vegetation block and the first soil color value change map can be determined based on the historical remote sensing images. The vegetation block area change map represents the expansion or reduction of invasive plants. The color of the soil changes when it is polluted. Therefore, the first soil color value change map represents the changes in the soil pollution. Aerial images are obtained. Aerial images are closer than remote sensing images and can collect more details. Therefore, the second soil color value change map of each vegetation block is determined based on the aerial images. Since the remote sensing image and the aerial image are at different heights, the second There is a difference between the soil color value change map and the first soil color value change map. Combining the first soil color value change map and the second soil color value change map can more accurately assess the soil pollution and determine the garbage characteristics and human activity intensity of each vegetation block. The garbage characteristics also pollute the soil. The higher the human activity intensity, the greater the damage and pollution to the soil. Therefore, it is more accurate to determine the pollution degree of each vegetation block based on the first soil color value change map, the second soil color value change map, the garbage characteristics and the sum of human activity intensity. Finally, combining the area change curve of each vegetation block and the pollution degree to comprehensively determine the pollution degree of the entire preset coastal area is more accurate and convenient than manual evaluation.

[0129] It should be understood that, although the steps in the flowchart of the accompanying drawings are displayed in sequence as indicated by the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least a part of the steps in the flowchart of the accompanying drawings may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a part of the sub-steps or stages of other steps.

[0130] The above description is only a partial implementation method of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A coastal information assessment method based on big data, characterized in that: include: Obtain historical remote sensing images corresponding to multiple vegetation blocks in a preset coastal area; Determine an area change curve of each vegetation block and a first soil color value change graph based on the historical remote sensing image; Acquire multiple aerial images of each vegetation block within a preset historical time period, and determine a second soil color value change map of each vegetation block, garbage features in each aerial image of each vegetation block, and the heat of human activities based on the multiple aerial images; Determine the pollution degree of each vegetation block based on the first soil color value change map, the second soil color value change map, garbage characteristics and human activity heat; The pollution degree of the preset coastal area is determined based on the area change curve of each vegetation block and the pollution degree of each vegetation block.

2. The coastal information evaluation method based on big data according to claim 1 is characterized in that: The determining the pollution degree of each vegetation block based on the first soil color value change map, the second soil color value change map, garbage characteristics, and human activity heat includes: Determine a fitted soil color value change map for each vegetation block based on the first soil color value change map and the second soil color value change map; Determining a first average value of soil color values ​​from the fitted soil color value variation graph; Count the garbage features in each vegetation block to obtain the amount of garbage in each vegetation block; Determining a garbage purification difficulty score in each vegetation block based on the garbage characteristics in each vegetation block; The pollution degree of each vegetation block is determined based on the first average value, the amount of garbage, the garbage purification difficulty score and the intensity of human activities.

3. A coastal information assessment method based on big data according to claim 2, characterized in that: Determining the pollution degree of the preset coastal area based on the area change curve of each vegetation block and the pollution degree of each vegetation block includes: Determine the area change trend of each vegetation block from the area change curve of each vegetation block, and obtain a first vegetation block whose area change trend is decreasing or unchanged, and a second vegetation block whose area change trend is increasing; determining a first ratio of the number of the second vegetation blocks to the number of the first vegetation blocks; Determining a first total area of ​​all first vegetation blocks and a second total area of ​​all second vegetation blocks; determining a second ratio of the second total area to the first total area; A score representing the pollution level of the preset coastal area is determined based on the pollution level of each vegetation block, the first ratio, the second ratio and the corresponding coefficients.

4. The coastal information evaluation method based on big data according to claim 2 is characterized in that: The determining the garbage purification difficulty score in each vegetation block based on the garbage characteristics in each vegetation block includes: Determine the outline of each vegetation block, and draw a plane rectangular coordinate system with the center point of the outline as the origin, wherein the two coordinate axes of the plane rectangular coordinate system form an angle of 45° with the coastline; Determine the coordinates of the garbage features in each quadrant of the plane rectangular coordinate system, and determine the coordinates of the center point representing the distribution of the garbage features in each quadrant based on the coordinates; Determine the distance from the center point coordinates to the origin of the plane rectangular coordinate system; determining the number of garbage features in each quadrant of the plane rectangular coordinate system; Determine a garbage purification difficulty score corresponding to each quadrant based on the distance, quantity, and garbage purification difficulty coefficient corresponding to each quadrant; The garbage purification difficulty score within each vegetation block is determined based on the garbage purification difficulty score corresponding to each quadrant.

5. The coastal information evaluation method based on big data according to claim 2 is characterized in that: The step of determining a fitted soil color value change map for each vegetation block based on the first soil color value change map and the second soil color value change map includes: Determine a first time point corresponding to each color value in the first soil color value change graph, and determine a second time point corresponding to each color value in the second soil color value change graph; Determine a target color value group from the first soil color value change graph and the second soil color value change graph, wherein the target color value group is two color values ​​whose time interval between the first time point and the second time point is less than a preset interval; averaging the color values ​​of the target color value group; Mapping the obtained average value in the first soil color value change map according to the first time point in the target color value group to obtain a soil color value change map to be selected; Determine the remaining color values ​​in the second soil color value variation graph except the color values ​​in the target color value group; Determine an associated color value from the first soil color value variation diagram according to the second time point of each remaining color value, wherein the first time points of the associated color values ​​are the two color values ​​closest to the remaining color value, and the first time points of the associated color values ​​are respectively located before and after the second time points of the remaining color value; Calculate the average color value of the associated color value, and calculate the difference between the remaining color value and the average color value; The remaining color values ​​whose differences do not reach the preset difference threshold are mapped into the candidate soil color value change map according to the second time point of the remaining color values ​​to obtain the fitted soil color value change map of each vegetation block.

6. The coastal information assessment method based on big data according to claim 1 is characterized in that: The determining the human activity heat of each vegetation block based on the multiple aerial images includes: Determine the number of people within each aerial image; Determine an average number of personnel and a total number of personnel based on the number of personnel; Based on the average number of personnel, the total number of personnel and their respective corresponding coefficients, a score representing the activity intensity of the personnel is determined.

7. The coastal information evaluation method based on big data according to claim 2 is characterized in that: The method further comprises: Determine the remote sensing image of the location and time of the third vegetation block, wherein the third vegetation block is a vegetation block whose pollution degree reaches a preset degree threshold and whose area change trend is increasing; Output the remote sensing image of the location and time closest to the third vegetation block.

8. A coastal information evaluation device based on big data, characterized in that: include: An image acquisition module is used to acquire historical remote sensing images corresponding to multiple vegetation blocks in a preset coastal area; A first determination module, used to determine an area change curve of each vegetation block and a first soil color value change graph based on the historical remote sensing image; A second determination module is used to obtain multiple aerial images of each vegetation block within a preset historical time period, and determine a second soil color value change map of each vegetation block, garbage features in each aerial image of each vegetation block, and human activity heat based on the multiple aerial images; a third determination module, configured to determine the pollution degree of each vegetation block based on the first soil color value change map, the second soil color value change map, garbage characteristics, and human activity heat; The fourth determination module is used to determine the pollution degree of the preset coastal area based on the area change curve of each vegetation block and the pollution degree of each vegetation block.

9. An electronic device, characterized in that: It includes: at least one processor; Memory; At least one application, wherein the at least one application is stored in the memory and is configured to be executed by the at least one processor, and the at least one application is used to execute a coastal information assessment method based on big data according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed in a computer, the computer is caused to execute a coastal information assessment method based on big data as described in any one of claims 1 to 7.

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