Ecological environment data real-time monitoring method based on big data processing

Abstract

The invention provides an ecological environment data real-time monitoring method based on big data processing. The method comprises the following steps: determining a rectangular clearance area surrounding floating garbage according to patrol data; determining an extension direction corresponding to the rectangular clearing area as a clearing moving direction, and determining area contour lines which form the rectangular clearing area and are perpendicular to the clearing moving direction as a starting clearing line and a stopping clearing line; determining an end point location of the initial clearing line as a first clearing point location and a second clearing point location, and controlling the first ship and the second ship to move towards the first clearing point location and the second clearing point location; and the first ship and the second ship are controlled to clear and transport the floating garbage to the clearing and transporting stopping line in the clearing and transporting moving direction. The fishing efficiency is at least improved.

Description

Technical Field

[0001] The present invention relates to data processing technologies, and in particular to a real-time monitoring method for ecological environment data based on big data processing. Background Art

[0002] In ecological environment areas, garbage is like a lurking "killer", posing a serious threat to ecological balance and stability. Timely garbage cleaning is of great significance. Taking the wetland ecosystem around a city as an example, if the garbage in it can be regularly cleaned, the water quality of the wetland can be significantly improved. The originally turbid and odorous water body becomes clear, and the breeding of mosquitoes and flies in the surrounding area will also be greatly reduced, improving the living environment of residents. From an ecological perspective, this can prevent harmful substances in the garbage from seeping into the soil and water body, protect the living space of wetland animals and plants, maintain ecological balance and stability, avoid the decline of wetland ecological functions due to garbage accumulation, and reduce potential ecological damage such as biodiversity loss.

[0003] However, the current status of garbage cleaning in ecological environment areas is not optimistic. The inventor has deeply studied and found that most garbage cleaning work still relies on manual labor. In some river areas, workers need to manually pick up floating garbage. Every time they bend down to fish, it is accompanied by the consumption of time and energy. Moreover, in order to clean large areas, the cooperation of many workers is required, which not only consumes a large amount of manpower, but also requires the allocation of corresponding tools and other equipment, and the material cost invested is also extremely high. Over time, the contradiction between the high input of manpower and material resources and the low efficiency of cleaning work has become increasingly prominent.

[0004] Based on this, there is an urgent need for a real-time monitoring method for ecological environment data based on big data processing that can improve the fishing efficiency. Summary of the Invention

[0005] Based on the above problems, the present invention is proposed to provide a real-time monitoring method for ecological environment data based on big data processing that can overcome or at least partially solve the above problems.

[0006] According to one aspect of the present invention, there is provided a real-time monitoring method for ecological environment data based on big data processing, including the following steps:

[0007] Determine a rectangular cleaning area surrounding the floating garbage according to the inspection data;

[0008] Determine the extension direction corresponding to the rectangular cleaning area as the cleaning movement direction, and determine the area contour lines that form the rectangular cleaning area and are perpendicular to the cleaning movement direction as the starting cleaning line and the ending cleaning line;

[0009] Determine the extension direction corresponding to the rectangular cleaning area as the cleaning movement direction, and determine the area contour lines that make up the rectangular cleaning area and are perpendicular to the cleaning movement direction as the starting cleaning line and the ending cleaning line;

[0010] Determine the endpoint positions on the starting cleaning line as the first cleaning point and the second cleaning point, and control the first ship and the second ship to move towards the first cleaning point and the second cleaning point;

[0011] Control the first ship and the second ship to transport the floating garbage along the cleaning movement direction to the ending cleaning line.

[0012] Optionally, in the method according to the present invention, determining the rectangular cleaning area surrounding the floating garbage according to the inspection data includes:

[0013] Obtain the image acquisition size corresponding to the drone, and perform an array processing on the ecological area to obtain each ecological sub-area corresponding to the image acquisition size;

[0014] Obtain the center points of each area corresponding to each ecological sub-area respectively, and configure the inspection tasks of the drone based on the center points of each area;

[0015] Respond to the drone flying to any area center point, trigger the drone to perform a flow velocity measurement, and obtain the water flow velocity corresponding to the ecological area;

[0016] Determine the ecological area attributes based on the comparison result between the water flow velocity and the preset flow velocity retrieved, and configure different preset floating garbage recognition strategies based on different ecological area attributes, where the ecological area attributes include a stable attribute and a turbulent attribute;

[0017] Identify each ecological sub-area based on the preset floating garbage recognition strategy, and determine each ecological sub-area corresponding to the existence of floating garbage as a set to be cleaned;

[0018] Based on the mutual position relationship between the ecological sub-areas in the set to be cleaned, merge the ecological sub-areas to obtain the rectangular cleaning area.

[0019] Optionally, in the method according to the present invention, identifying each ecological sub-area based on the preset floating garbage recognition strategy, and determining each ecological sub-area corresponding to the existence of floating garbage as a set to be cleaned, includes:

[0020] Respond to the ecological area attribute being the stable attribute, control the drone to hover for a preset time, and take images of the ecological sub-area at preset frame numbers at intervals of the same time period within the preset time to obtain an ecological area image composed of image frames corresponding to different frame sequences;

[0021] Perform binarization processing on each image frame respectively, and perform pixel recognition on each obtained binarized image to obtain each image pixel point that composes the same binarized image;

[0022] Determine each water area pixel point corresponding to the water area pixel value among each image pixel point, and determine each other pixel point except each water area pixel point as each cleaning pixel point respectively;

[0023] Obtain the pixel ratio between each cleaning pixel point and each image pixel point located in the same binarized image, and perform ratio comparison on each pixel ratio corresponding to each binarized image;

[0024] Determine the pixel ratio with the smallest corresponding pixel ratio as the pixel ratio corresponding to the ecological area image, and determine each ecological sub-area with a corresponding pixel ratio greater than or equal to the preset ratio as the set to be cleaned.

[0025] Optionally, in the method according to the present invention, based on the preset floating garbage recognition strategy, identify each ecological sub-area, and determine each ecological sub-area corresponding to the existence of floating garbage as the set to be cleaned, including:

[0026] When the ecological area attribute of the ecological area is a turbulent attribute, control the drone to take an image of the ecological sub-area once to obtain an ecological area image;

[0027] Perform binarization processing on the ecological area image, and perform pixel recognition on the obtained binarized image to obtain each image pixel point that composes the binarized image;

[0028] Determine each water area pixel point corresponding to the water area pixel value among each image pixel point, and determine each other pixel point except each water area pixel point as each cleaning pixel point respectively;

[0029] Obtain the pixel ratio between each cleaning pixel point and each image pixel point located in the same binarized image, and determine each ecological sub-area with a corresponding pixel ratio greater than or equal to the preset ratio as the set to be cleaned.

[0030] Optionally, in the method according to the present invention, based on the mutual positional relationship between each ecological sub-area in the set to be cleaned, perform area merging on each ecological sub-area to obtain the rectangular cleaning area, including:

[0031] Determine the regional positions of each ecological sub-area in the set to be cleaned in the ecological area, and based on each regional position, divide each ecological sub-area with continuous connection into the same regional division group, and divide the remaining each ecological sub-area into different regional division groups respectively to obtain each regional division group;

[0032] Establish a regional coordinate system corresponding to the ecological region, and obtain the coordinates of each waste removal pixel point corresponding to each ecological sub-region located in the same regional division group, so as to obtain each waste removal coordinate point corresponding to each waste removal pixel point respectively;

[0033] Based on each waste removal coordinate point, determine the X-axis extreme coordinate point corresponding to the X-axis and the Y-axis extreme coordinate point corresponding to the Y-axis respectively, and form a rectangular waste removal area based on the minimum bounding rectangle algorithm, which at least covers any one of the X-axis extreme coordinate point and the Y-axis extreme coordinate point and encloses all the remaining waste removal coordinate points.

[0034] Optionally, in the method according to the present invention, determine the extension direction corresponding to the rectangular waste removal area as the waste removal movement direction, and determine the regional contour lines that form the rectangular waste removal area and are perpendicular to the waste removal movement direction as the starting waste removal line and the ending waste removal line, including:

[0035] Obtain each regional contour line that forms the rectangular waste removal area, and obtain the distance between each regional contour line and the ship mooring line of the corresponding ship dock respectively, so as to obtain each movement distance;

[0036] Determine the extension direction perpendicular to the regional contour line with the minimum corresponding movement distance as the waste removal movement direction, and obtain the first contour dimension and the second contour dimension corresponding to the rectangular waste removal area contour in the directions perpendicular and parallel to the waste removal movement direction;

[0037] Compare the first contour dimension and the second contour dimension with the fence dimension of the corresponding trawl fence respectively, and determine the direction adjustment coefficient based on the comparison result;

[0038] In response to the direction adjustment coefficient being greater than or equal to the preset coefficient, determine the extension direction perpendicular to the waste removal movement direction as the updated waste removal movement direction;

[0039] Obtain each regional contour line perpendicular to the waste removal movement direction, and determine the regional contour line with the minimum corresponding movement distance as the ending waste removal line, and determine the regional contour line with the maximum corresponding movement distance as the starting waste removal line.

[0040] Optionally, in the method according to the present invention, compare the first contour dimension and the second contour dimension with the fence dimension of the corresponding trawl fence respectively, and determine the direction adjustment coefficient based on the comparison result, including:

[0041] When the first contour dimension is the same as the second contour dimension, determine the retrieved first reference coefficient as the direction adjustment coefficient, wherein the first reference coefficient is less than the preset coefficient;

[0042] Or,

[0043] When the first contour dimension is different from the second contour dimension, calculate the differences between the first contour dimension and the second contour dimension and the fence dimensions of the corresponding trawl fences respectively to obtain a first difference corresponding to the first contour dimension and a second difference corresponding to the second contour dimension;

[0044] When both the first difference and the second difference are non-positive numbers, determine the retrieved first reference coefficient as the direction adjustment coefficient;

[0045] When the first difference is a positive number and the second difference is a non-positive number, determine the retrieved second reference coefficient as the direction adjustment coefficient, where the second reference coefficient is greater than or equal to the preset coefficient;

[0046] When both the first difference and the second difference are positive numbers, determine the direction adjustment coefficient through the following formula:

[0047]

[0048] where C is the direction adjustment coefficient, k is the dimension normalization value, X1 is the first difference, and X2 is the second difference.

[0049] Optionally, in the method according to the present invention, controlling the first ship and the second ship to clear the floating garbage to the end clearing line along the clearing movement direction includes:

[0050] Respond to the real-time monitoring data to determine that the first ship and the second ship reach the first clearing point and the second clearing point respectively, obtain the line segment center point corresponding to the starting clearing line, and control the drone to fly to the line segment center point;

[0051] Respond to the drone flying to the line segment center point, trigger the drone to perform a flow measurement on the ecological area, and obtain the water area flow corresponding to the ecological area;

[0052] Determine the direction angle between the clearing movement direction and the water area flow, and determine the angle influence value based on the direction angle;

[0053] Determine the clearing ratio corresponding to the rectangular clearing area based on the number of pixels of each clearing pixel point located in the rectangular clearing area, and determine the ratio influence value based on the clearing ratio;

[0054] Determine the clearing buffer length based on the angle influence value and the ratio influence value, and extend the rectangular clearing area by the corresponding clearing buffer length along the direction away from the starting clearing line of the end clearing line to obtain an updated rectangular clearing area and an end clearing line;

[0055] Control the first ship and the second ship to transport the floating garbage in the rectangular garbage collection area to the end garbage collection line along the garbage collection moving direction.

[0056] Optionally, in the method according to the present invention, the garbage collection buffer length is determined by the following formula:

[0057]

[0058] Wherein, the L s is the garbage collection buffer length, the A z is the direction angle, the α1 is the retrieved angle weight, the k z is the angle normalization value, the P n is the number of pixels, the P a is the pixel area, the R a is the area of the region corresponding to the rectangular garbage collection area, the β1 is the retrieved ratio weight, the k a is the ratio normalization value.

[0059] Optionally, in the method according to the present invention,

[0060] The method further includes:

[0061] Obtain the actual buffer length sent by the management terminal based on the rectangular garbage collection area, and compare the actual buffer length with the garbage collection buffer length;

[0062] If the actual buffer length is greater than the garbage collection buffer length, increase the training of the angle weight and the ratio weight respectively;

[0063] If the actual buffer length is less than the garbage collection buffer length, decrease the training of the angle weight and the ratio weight respectively;

[0064] Obtain the trained angle weight and ratio weight through the following formula:

[0065]

[0066] Wherein, q + is the number of times of increasing the training of the angle weight α1, ∈ is the training constant value of the angle weight α1, q - is the number of times of decreasing the training of the angle weight α1, α2 is the trained angle weight, v + is the number of times of increasing the training of the ratio weight β1, δ is the training constant value of the ratio weight β1, v - is the number of times of decreasing the training of the ratio weight β1, β2 is the trained ratio weight.

[0067] Optionally, in the method according to the present invention,

[0068] The method further includes:

[0069] Determine an extension length corresponding to the rectangular cleaning area along the cleaning movement direction, and determine the cleaning attribute corresponding to the rectangular cleaning area based on the length comparison between the extension length and a preset length, where the cleaning attribute includes a one-way attribute and a two-way attribute;

[0070] In response to the extension length being greater than or equal to the preset length, determine the rectangular cleaning area as having a two-way attribute, and determine a region center line located at the center of the rectangular cleaning area and parallel to the termination cleaning line;

[0071] Determine the starting cleaning line and the termination cleaning line as the updated first starting cleaning line and the second starting cleaning line respectively, and determine the region center line as the updated termination cleaning line,

[0072] Control a first ship and a second ship located in different cleaning ship groups to move the ships towards the first starting cleaning line and the second starting cleaning line respectively, and determine the cleaning ship group located at the first starting cleaning line as the first ship group and the cleaning ship group located at the second starting cleaning line as the second ship group;

[0073] Control each ship in the first ship group to clean the floating garbage in the rectangular cleaning area to the termination cleaning line along the cleaning movement direction, and control each ship in the second ship group to clean the floating garbage in the rectangular cleaning area to the termination cleaning line along the direction opposite to the cleaning movement direction.

[0074] According to another aspect of the present invention, there is provided a real-time monitoring system for ecological environment data based on big data processing, including:

[0075] An ecological inspection module configured to determine a rectangular cleaning area surrounding floating garbage according to inspection data;

[0076] A direction determination module configured to determine the extension direction corresponding to the rectangular cleaning area as the cleaning movement direction, and determine the region contour lines that form the rectangular cleaning area and are perpendicular to the cleaning movement direction as the starting cleaning line and the termination cleaning line;

[0077] A movement control module configured to determine the endpoint positions of the starting cleaning line as the first cleaning point and the second cleaning point, and control the first ship and the second ship to move the ships towards the first cleaning point and the second cleaning point;

[0078] A cleaning control module configured to control the first ship and the second ship to clean the floating garbage to the termination cleaning line along the cleaning movement direction.

[0079] According to the solution of the present invention, the server controls the drone to fly and inspect along the extension direction of the ecological area to determine whether there is floating garbage in the ecological area, and then determines a rectangular cleaning area surrounding the floating garbage, so as to facilitate subsequent ships to accurately salvage the floating garbage. Then, the server obtains the rectangular center point corresponding to the rectangular cleaning area and controls the drone to move to the rectangular center point to monitor the rectangular cleaning area, so that the server can subsequently determine whether the ship has reached the corresponding point according to the obtained real-time monitoring data and perform corresponding cleaning on the floating garbage. Then, the server determines the extension direction corresponding to the rectangular cleaning area as the cleaning movement direction according to the actual situation, and then determines the two area contour lines that make up the rectangular cleaning area and are perpendicular to the cleaning movement direction as the starting cleaning line and the ending cleaning line respectively. Then, the server determines the two endpoint positions of the starting cleaning line as the first cleaning point and the second cleaning point respectively, and controls the first ship and the second ship in the same cleaning ship group to move towards the first cleaning point and the second cleaning point respectively. When the server determines through real-time monitoring data that the first ship and the second ship have reached the first cleaning point and the second cleaning point respectively, it controls the first ship and the second ship to clean the floating garbage located in the rectangular cleaning area along the cleaning movement direction. The present invention can reduce the time for salvaging floating garbage, thereby improving a certain salvage efficiency, and can play a certain role in environmental restoration of the ecological area. BRIEF DESCRIPTION OF THE DRAWINGS

[0080] Figure 1 FIG. shows a flowchart of a method for real-time monitoring of ecological environment data based on big data processing according to an embodiment of the present invention;

[0081] Figure 2 FIG. shows a schematic diagram of the cleaning movement direction in this embodiment;

[0082] Figure 3 FIG. shows a schematic diagram of a ship cleaning the floating garbage located in the rectangular cleaning area in this embodiment;

[0083] Figure 4 FIG. shows a block diagram of the structure of a real-time monitoring system for ecological environment data based on big data processing according to another embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0084] Hereinafter, exemplary embodiments of the present disclosure will be described in more detail with reference to the drawings. Although the exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided so that the present disclosure can be more thoroughly understood and the scope of the present disclosure can be completely conveyed to those skilled in the art.

[0085] To solve the problems existing in the above-mentioned background technology, the inventor proposed the solution of the present invention. An embodiment of the present invention provides a real-time monitoring method for ecological environment data based on big data processing, and this method can be executed in a computing device.

[0086] Figure 1 The flowchart of the real-time monitoring method for ecological environment data based on big data processing according to an embodiment of the present invention is shown, and this method is suitable for execution in a computing device.

[0087] As Figure 1 shown, the purpose of this embodiment is to implement a real-time monitoring method for ecological environment data based on big data processing, starting from step S101. In step S101, the following steps are included:

[0088] Determine the rectangular cleaning area surrounding the floating garbage according to the inspection data.

[0089] For example, in this embodiment, the server will control the drone to fly along the extension direction of the ecological area to inspect the ecological area to determine whether there is floating garbage such as garbage bags on the ecological area.

[0090] Since the subsequent ship uses a trawl fence to salvage the floating garbage, and the surface shape of the trawl fence is mostly rectangular, when the drone finds floating garbage on the ecological area during the inspection process, the server will determine a rectangular area surrounding the floating garbage, that is, the rectangular cleaning area, to facilitate the subsequent ship to accurately salvage the floating garbage.

[0091] It should be noted that when the ship uses a trawl fence to salvage the floating garbage, generally two ships will be set to move to two adjacent vertices of the rectangular cleaning area, and then move synchronously along the extension direction of the rectangular cleaning area. When the two ships move until the trawl fence surrounds all the floating garbage, the two ships will move crosswise towards each other, so as to ensure that the trawl fence is driven by the crosswise movement to switch from the "open state" to the corresponding "closed state" to complete the salvage work of the floating garbage.

[0092] Furthermore, the above-mentioned "determine the rectangular cleaning area surrounding the floating garbage according to the inspection data" further includes the following steps:

[0093] Obtain the image acquisition size corresponding to the drone, and perform array processing on the ecological area to obtain each ecological sub-area corresponding to the image acquisition size;

[0094] Obtain the central points of each region corresponding to each ecological sub-region respectively, and configure the inspection tasks of the drone based on the central points of each region;

[0095] Respond to the drone flying to any regional center point, trigger the drone to perform flow velocity measurement, and obtain the water flow velocity corresponding to the ecological region;

[0096] Determine the ecological region attributes based on the comparison result between the water flow velocity and the preset flow velocity retrieved, and configure different preset floating garbage recognition strategies based on different ecological region attributes, where the ecological region attributes include a stable attribute and a turbulent attribute;

[0097] Identify each ecological sub-region based on the preset floating garbage recognition strategy, and determine each ecological sub-region with corresponding floating garbage as the set to be cleared;

[0098] Based on the mutual positional relationship between the ecological sub-regions located in the set to be cleared, merge the ecological sub-regions to obtain the rectangular clearing region.

[0099] For example, in this embodiment, the server will first obtain the image acquisition size of the drone, and then array-process the ecological region along the extension direction of the ecological region according to the image acquisition size, so as to obtain each ecological sub-region corresponding to the image acquisition size of the drone. Then, the server will obtain the central points of each ecological sub-region and configure the inspection tasks of the drone based on the central points of each region, so that the subsequent drone can reach each regional center point in turn to perform the corresponding inspection tasks.

[0100] When the drone flies to any regional center point according to the inspection task, it will trigger the drone to measure the flow velocity of the ecological region, so as to obtain the water flow velocity of the ecological region. At this time, the server will retrieve the preset flow velocity and compare the water flow velocity with the preset flow velocity, so as to determine the ecological region attributes of the ecological region according to the comparison result. When the water flow velocity is greater than the preset flow velocity, it means that the water flow velocity of the current ecological region is too fast, and the corresponding ecological region attribute is the turbulent attribute; when the water flow velocity is less than or equal to the preset flow velocity, it means that the water flow velocity of the current ecological region is relatively slow, and the corresponding ecological region attribute is the stable attribute. Then, the server will configure different preset floating garbage recognition strategies for different ecological region attributes.

[0101] Then, the server will control the drone to identify whether there is floating garbage in each ecological sub-region based on the preset floating garbage recognition strategy, and determine each ecological sub-region with floating garbage as the set to be cleared.

[0102] In order to reduce the number of salvage operations and the salvage time to a certain extent, the server will merge and salvage adjacent floating garbage according to the mutual positional relationship between the ecological sub-regions in the set to be cleared, that is, merge the corresponding ecological sub-regions, so as to obtain a rectangular clearance area.

[0103] Furthermore, the above-mentioned "identifying each ecological sub-region based on the preset floating garbage recognition strategy and determining each ecological sub-region with corresponding floating garbage as the set to be cleared" further includes the following steps:

[0104] In response to the ecological region attribute being a stable attribute, control the drone to hover for a preset time, and within the preset time, take images of the ecological sub-region in a manner of the same time interval for a preset number of frames, so as to obtain an ecological region image composed of image frames with corresponding different frame sequences;

[0105] Perform binary processing on each image frame respectively, and perform pixel recognition on each obtained binary image respectively to obtain each image pixel point that composes the same binary image;

[0106] Determine each water area pixel point corresponding to the water area pixel value among each image pixel point, and determine each pixel point other than each water area pixel point as each clearance pixel point respectively;

[0107] Obtain the pixel ratio between each clearance pixel point and each image pixel point located in the same binary image, and compare the pixel ratios corresponding to each binary image;

[0108] Determine the pixel ratio with the smallest corresponding pixel ratio as the pixel ratio corresponding to the ecological region image, and determine each ecological sub-region with a corresponding pixel ratio greater than or equal to the preset ratio as the set to be cleared.

[0109] For example, in this embodiment, when the ecological region attribute of the ecological region is a stable attribute, it means that the water flow velocity in the current ecological region is small, so the floating garbage will not move to a far area in a short time. However, when collecting images of the ecological sub-region, it is possible to capture passing fish schools or bird flocks, which will have a certain impact on the subsequent identification of floating garbage. Therefore, the server will control the drone to hover at the center point of the region for a preset time, and within the preset time, take images of the ecological sub-region in a manner of the same time interval for a preset number of frames, so as to obtain an ecological region image composed of image frames with corresponding different frame sequences. For example, the preset time is 10 seconds and the preset number of frames is 5. Then the server will control the drone to hover at the center point of the region for 10 seconds, and take images of the ecological sub-region every two seconds, so as to obtain 5 ecological region images.

[0110] Next, the server will perform binarization processing on each image frame respectively to obtain each binarized image, and then perform pixel recognition on each binarized image to obtain each image pixel point that composes the same binarized image. At this time, the server will determine each water area pixel point corresponding to the water area pixel value among each image pixel point, and determine each of the other pixel points except each water area pixel point as each cleaning pixel point.

[0111] Then, the server will obtain the pixel ratio between each cleaning pixel point and each image pixel point located in the same binarized image, and compare the pixel ratios corresponding to each binarized image respectively. The smaller the pixel ratio indicates that the influence of bird flocks or fish schools in the area corresponding to this pixel ratio is smaller. Therefore, the server will determine the smallest pixel ratio as the pixel ratio corresponding to the ecological area image.

[0112] A preset ratio is preset in the server in advance. When the pixel ratio is greater than or equal to the preset ratio, it means that the range of floating garbage in the ecological sub-area corresponding to this pixel ratio is large. Therefore, the server will determine each ecological sub-area with a corresponding pixel ratio greater than or equal to the preset ratio as the set to be cleaned; when the pixel ratio is less than the preset ratio, it means that the range of floating garbage in the ecological sub-area corresponding to this pixel ratio is small. Therefore, there is no need to use a ship to clean the floating garbage, which can reduce a certain amount of manpower and material resources.

[0113] This embodiment can perform multi-frame image shooting on the ecological sub-areas of the ecological area with stable attributes, avoid being affected by bird flocks or fish schools when identifying floating garbage subsequently, and can determine the set to be cleaned through the pixel ratio, with a certain degree of accuracy.

[0114] Furthermore, the above "identifying each ecological sub-area based on the preset floating garbage recognition strategy and determining each ecological sub-area with corresponding floating garbage as the set to be cleaned" further includes the following steps:

[0115] When the ecological area attribute of the ecological area is a turbulent attribute, control the drone to perform one image shooting on the ecological sub-area to obtain an ecological area image;

[0116] Perform binarization processing on the ecological area image, and perform pixel recognition on the obtained binarized image to obtain each image pixel point that composes the binarized image;

[0117] Determine each water area pixel point corresponding to the water area pixel value among each image pixel point, and determine each of the other pixel points except each water area pixel point as each cleaning pixel point;

[0118] Obtain the pixel ratio between each cleaning pixel point and each image pixel point in the same binary image, and determine each ecological sub-region with a corresponding pixel ratio greater than or equal to the preset ratio as the set to be cleaned.

[0119] For example, in this embodiment, when the ecological region attribute of the ecological region is the turbulent attribute, it means that the water flow velocity in the current ecological region is relatively fast. When the server controls the drone to take the first image of the ecological sub-region at the first time and then takes the second image at a preset time interval, the floating garbage may have moved to other ecological sub-regions with the turbulent river water. Therefore, when the ecological region attribute of the ecological region is the turbulent attribute, there is no need to take multiple images of the ecological sub-region. So the server only needs to control the drone to take one image of the ecological sub-region to obtain the ecological region image.

[0120] Next, the server will perform binary processing on the ecological region image and perform pixel recognition on the obtained binary image to obtain each image pixel point that makes up the binary image. Then, determine each water pixel point corresponding to the water pixel value among each image pixel point, and determine each other pixel point except each water pixel point as each cleaning pixel point. Then, the server will obtain the pixel ratio between each cleaning pixel point and each image pixel point in the same binary image, and determine each ecological sub-region with a corresponding pixel ratio greater than or equal to the preset ratio as the set to be cleaned.

[0121] Furthermore, the above "merging each ecological sub-region based on the mutual position relationship between each ecological sub-region in the set to be cleaned to obtain the rectangular cleaning region" further includes the following steps:

[0122] Determine the regional positions of each ecological sub-region in the ecological region in the set to be cleaned, and divide each ecological sub-region showing continuous connection into the same regional division group based on each regional position, and divide the remaining ecological sub-regions into different regional division groups respectively to obtain each regional division group;

[0123] Establish a regional coordinate system corresponding to the ecological region, and obtain the coordinates of each cleaning pixel point corresponding to each ecological sub-region in the same regional division group to obtain each cleaning coordinate point corresponding to each cleaning pixel point;

[0124] Based on each cleaning coordinate point, determine the X-axis extreme coordinate point corresponding to the X-axis and the Y-axis extreme coordinate point corresponding to the Y-axis respectively, and form a rectangular cleaning region that at least covers any one of the X-axis extreme coordinate point and the Y-axis extreme coordinate point and encloses all the remaining cleaning coordinate points based on the minimum bounding rectangle algorithm.

[0125] For example, in this embodiment, the server will determine the respective regional positions of each ecological sub-region in the ecological region that is located in the set to be cleared, and divide each ecological sub-region with continuous connection into the same regional division group according to the respective regional positions, and then divide the remaining ecological sub-regions into different regional division groups respectively, so as to obtain each regional division group.

[0126] Next, the server will establish a regional coordinate system corresponding to the ecological region, and obtain the coordinates of each clearing pixel point corresponding to each ecological sub-region located in the same regional division group, so as to obtain each clearing coordinate point corresponding to each clearing pixel point respectively. Then, the X-axis extreme coordinate point corresponding to the X-axis and the Y-axis extreme coordinate point corresponding to the Y-axis are respectively determined among all the clearing coordinate points, and a rectangular clearing area that encloses all the clearing coordinate points except the X-axis extreme coordinate point and the Y-axis extreme coordinate point and at least covers the X-axis extreme coordinate point or the Y-axis extreme coordinate point is formed according to the existing technology of the minimum bounding rectangle algorithm.

[0127] This embodiment can divide each ecological sub-region with continuous connection into the same regional division group, can reduce the subsequent salvage times of the ship, and generate a rectangular clearing area according to the regional coordinate system, which is convenient for the subsequent ship to use a trawl to salvage floating garbage.

[0128] In step S102, the following contents are included:

[0129] Determine the extension direction corresponding to the rectangular clearing area as the clearing movement direction, and determine the regional contour lines that form the rectangular clearing area and are perpendicular to the clearing movement direction as the starting clearing line and the ending clearing line.

[0130] For example, in this embodiment, after the server determines the rectangular clearing area, it will first obtain the rectangular center point corresponding to the rectangular clearing area, and control the drone to move to the rectangular center point to perform regional monitoring on the rectangular clearing area, which is convenient for the subsequent server to determine whether the ship responsible for clearing reaches the corresponding point according to the obtained real-time monitoring data and perform corresponding clearing on the floating garbage.

[0131] In step S103, the following contents are included:

[0132] Determine the extension direction corresponding to the rectangular clearing area as the clearing movement direction, and determine the regional contour lines that form the rectangular clearing area and are perpendicular to the clearing movement direction as the starting clearing line and the ending clearing line.

[0133] For example, in this embodiment, if the shape of the rectangular cleaning area is a rectangle, there can be two moving directions for the ship when cleaning floating garbage, that is, two cleaning moving directions, which are respectively cleaning and moving along the extension direction of the longer regional contour line of the rectangular cleaning area, and cleaning and moving along the extension direction of the shorter regional contour line of the rectangular cleaning area.

[0134] Therefore, the server will determine the extension direction corresponding to the rectangular cleaning area as the cleaning moving direction according to the actual situation, and then determine the two regional contour lines that make up the rectangular cleaning area and are perpendicular to the cleaning moving direction as the starting cleaning line and the ending cleaning line respectively.

[0135] Furthermore, the above "determining the extension direction corresponding to the rectangular cleaning area as the cleaning moving direction, and determining the regional contour lines that make up the rectangular cleaning area and are perpendicular to the cleaning moving direction as the starting cleaning line and the ending cleaning line" further includes the following steps:

[0136] Obtain each regional contour line that makes up the rectangular cleaning area, and obtain the distance between each regional contour line and the ship mooring line of the corresponding ship dock respectively to obtain each moving distance;

[0137] Determine the extension direction perpendicular to the regional contour line with the smallest corresponding moving distance as the cleaning moving direction, and obtain the first contour size and the second contour size corresponding to the rectangular cleaning area contour in the directions perpendicular and parallel to the cleaning moving direction;

[0138] Compare the first contour size and the second contour size with the fence size of the corresponding trawl fence respectively, and determine the direction adjustment coefficient based on the comparison result;

[0139] In response to the direction adjustment coefficient being greater than or equal to the preset coefficient, determine the extension direction perpendicular to the cleaning moving direction as the updated cleaning moving direction;

[0140] Obtain each regional contour line perpendicular to the cleaning moving direction, and determine the regional contour line with the smallest corresponding moving distance as the ending cleaning line, and determine the regional contour line with the largest corresponding moving distance as the starting cleaning line.

[0141] For example, in this embodiment, the server will obtain each regional contour line that makes up the rectangular cleaning area, and obtain the distance between each regional contour line and the ship mooring line of the corresponding ship dock respectively, so as to obtain each moving distance. Then, the server will determine the extension direction perpendicular to the regional contour line with the smallest corresponding moving distance as the cleaning moving direction, as Figure 2 shown, so that the ship can save a certain amount of time and reduce the corresponding fuel consumption when cleaning the floating garbage according to the determined cleaning moving direction.

[0142] In order to minimize the number of retrievals as much as possible when the ship clears floating garbage, the server will obtain the first contour dimension and the second contour dimension corresponding to the outline of the rectangular cleaning area in the direction perpendicular to the cleaning movement direction and the direction parallel to the cleaning movement direction respectively, and then compare the first contour dimension and the second contour dimension with the fence dimension of the trawl fence respectively, so as to determine the direction adjustment coefficient according to the comparison result.

[0143] When the direction adjustment coefficient is greater than or equal to the preset coefficient, it means that the server needs to change the cleaning movement direction. Therefore, the server will determine the extended direction perpendicular to the cleaning movement direction as the updated cleaning movement direction. At this time, the server will obtain the contour lines of each area perpendicular to the cleaning movement direction, and determine the contour line with the smallest corresponding movement distance as the end cleaning line, and determine the contour line with the largest corresponding movement distance as the start cleaning line.

[0144] Furthermore, the above "comparing the first contour dimension and the second contour dimension with the fence dimension of the corresponding trawl fence respectively, and determining the direction adjustment coefficient based on the comparison result" further includes the following steps:

[0145] When the first contour dimension is the same as the second contour dimension, the retrieved first reference coefficient is determined as the direction adjustment coefficient, where the first reference coefficient is less than the preset coefficient;

[0146] Or,

[0147] When the first contour dimension is different from the second contour dimension, the first contour dimension and the second contour dimension are respectively subjected to a difference calculation with the fence dimension of the corresponding trawl fence to obtain a first difference corresponding to the first contour dimension and a second difference corresponding to the second contour dimension;

[0148] When both the first difference and the second difference are non-positive numbers, the retrieved first reference coefficient is determined as the direction adjustment coefficient;

[0149] When the first difference is a positive number and the second difference is a non-positive number, the retrieved second reference coefficient is determined as the direction adjustment coefficient, where the second reference coefficient is greater than or equal to the preset coefficient;

[0150] When both the first difference and the second difference are positive numbers, the direction adjustment coefficient is determined by the following formula:

[0151]

[0152] where C is the direction adjustment coefficient, k is the dimension normalization value, X1 is the first difference, and X2 is the second difference.

[0153] For example, in this embodiment, when the first contour dimension is the same as the second contour dimension, that is, there is no need to adjust the waste collection and transportation moving direction, so the server will determine the first reference coefficient less than the preset coefficient retrieved as the direction adjustment coefficient.

[0154] When the first contour dimension is different from the second contour dimension, the server will calculate the differences between the first contour dimension and the second contour dimension and the fence dimension respectively, so as to obtain the first difference corresponding to the first contour dimension and the second difference corresponding to the second contour dimension. When both the first difference and the second difference are non-positive numbers, it means that both the first contour dimension and the second contour dimension are smaller than the fence dimension, so there is no need to adjust the waste collection and transportation moving direction. Furthermore, the server will determine the first reference coefficient retrieved as the direction adjustment coefficient; when the first difference is a positive number and the second difference is a non-positive number, it means that the first contour dimension is larger than the fence dimension, then the ship cannot complete the salvage of floating garbage in one go according to the original waste collection and transportation moving direction, but needs to conduct multiple salvages. However, the second contour dimension is smaller than the fence dimension, so the server will determine the second reference coefficient greater than or equal to the preset coefficient retrieved as the direction adjustment coefficient, so that the ship can complete the salvage of floating garbage in one go according to the updated waste collection and transportation moving direction; when both the first difference and the second difference are positive numbers, it means that both the first contour dimension and the second contour dimension are larger than the fence dimension, then the ship cannot complete the salvage of floating garbage in one go whether according to the original waste collection and transportation moving direction or the updated waste collection and transportation moving direction. However, the server can determine the direction adjustment coefficient through a formula to minimize the number of salvages when the ship salvages floating garbage according to the updated waste collection and transportation moving direction.

[0155] This embodiment can determine different direction adjustment coefficients according to different difference situations of the first contour dimension and the second contour dimension, can reduce the number of salvages of the ship, thereby reducing certain costs and improving the corresponding salvage efficiency.

[0156] In step S104, the following contents are included:

[0157] Determine the endpoint positions of the starting waste collection and transportation line as the first waste collection and transportation point and the second waste collection and transportation point, and control the first ship and the second ship to move the ships towards the first waste collection and transportation point and the second waste collection and transportation point.

[0158] For example, in this embodiment, the server will respectively determine the two endpoint positions of the starting waste collection and transportation line as the first waste collection and transportation point and the second waste collection and transportation point, and control the two ships in the same waste collection and transportation ship group to move towards the two waste collection and transportation points respectively, that is, control the first ship and the second ship to move the ships towards the first waste collection and transportation point and the second waste collection and transportation point respectively.

[0159] In step S105, the following is included:

[0160] Control the first ship and the second ship to transport the floating garbage along the garbage collection moving direction to the end garbage collection line.

[0161] For example, in this embodiment, when the server determines that the first ship and the second ship reach the first garbage collection point and the second garbage collection point respectively through real-time monitoring data, it will control the first ship and the second ship to transport the floating garbage in the rectangular garbage collection area along the garbage collection moving direction.

[0162] Furthermore, the above "control the first ship and the second ship to transport the floating garbage along the garbage collection moving direction to the end garbage collection line" further includes the following steps:

[0163] Respond to the real-time monitoring data to determine that the first ship and the second ship reach the first garbage collection point and the second garbage collection point respectively, obtain the line segment center point corresponding to the starting garbage collection line, and control the drone to fly to the line segment center point;

[0164] Respond to the drone flying to the line segment center point, trigger the drone to perform the flow measurement of the ecological area, and obtain the water area flow corresponding to the ecological area;

[0165] Determine the direction angle between the garbage collection moving direction and the water area flow, and determine the angle influence value based on the direction angle;

[0166] Determine the garbage collection ratio corresponding to the rectangular garbage collection area based on the pixel quantity of each garbage collection pixel point in the rectangular garbage collection area, and determine the ratio influence value based on the garbage collection ratio;

[0167] Determine the garbage collection buffer length based on the angle influence value and the ratio influence value, and extend the area corresponding to the garbage collection buffer length of the rectangular garbage collection area along the way that the end garbage collection line is far from the starting garbage collection line, to obtain the updated rectangular garbage collection area and the end garbage collection line;

[0168] Control the first ship and the second ship to transport the floating garbage in the rectangular garbage collection area along the garbage collection moving direction to the end garbage collection line.

[0169] For example, in this embodiment, during the actual operation process, floating garbage is transported through a trawl fence arranged between the first ship and the second ship to achieve the corresponding cleaning process. During the transportation process, the trawl fence will exert a certain surface thrust on the floating garbage, causing the floating garbage to be gathered in the trawl fence. Due to the generation of the surface thrust, the floating garbage will correspondingly have a moving speed in the direction of the cleaning movement. Therefore, just by controlling the first ship and the second ship to move to the end cleaning line of the corresponding rectangular cleaning area, there may be a situation where the floating garbage cannot be fully gathered, resulting in corresponding omission phenomena.

[0170] To solve the above problems, it is necessary to extend the corresponding area of the rectangular cleaning area and obtain an updated end cleaning line based on the updated rectangular cleaning area. The corresponding method steps are as follows:

[0171] First, it can be known that the influencing dimensions of the moving speed of floating garbage include the water area flow direction and the corresponding area ratio of the floating garbage. Therefore, in order to obtain different data corresponding to the two dimensions, after the first ship and the second ship respectively reach the corresponding first cleaning point and the second cleaning point in response to the real-time monitoring data, the line segment center point corresponding to the starting cleaning line is obtained, and the drone is further controlled to fly to the line segment center point;

[0172] Next, the drone can be controlled to perform a flow measurement of the ecological area to obtain the water area flow direction of the corresponding ecological area. At the same time, when cleaning the floating garbage, the trawl fence will drive the floating garbage to move in the direction of the cleaning movement. In the natural scenario, the floating garbage will move according to the naturally generated water area flow direction. Therefore, in order to judge the influence of the water area flow direction on the moving speed of the floating garbage, the direction angle between the water area flow direction and the cleaning movement direction can be determined, and the corresponding angle influence value can be further determined based on the direction angle;

[0173] Then, in the above process, since each cleaning pixel point corresponding to each ecological sub-area in the set of ecological areas to be cleaned is determined, the pixel number of all cleaning pixel points in the rectangular cleaning area can be obtained by adding the values. By multiplying the pixel number by the pixel area occupied by a corresponding pixel point, the cleaning area corresponding to all cleaning pixel points is obtained, and the cleaning area is divided by the area of the corresponding rectangular cleaning area to obtain the corresponding cleaning ratio. Further, the ratio influence value is determined based on the cleaning ratio;

[0174] Then, based on the obtained included angle influence value and ratio influence value above, the corresponding waste collection buffer length can be determined, and the rectangular waste collection area is extended by the corresponding waste collection buffer length in the direction away from the starting waste collection line along the original termination line, so as to obtain the updated rectangular waste collection area including the buffer extension area and the termination waste collection line, where, as Figure 3 shown, Figure 3 shows a schematic diagram of the corresponding ship collecting floating garbage in the rectangular waste collection area. It can be seen from Figure 3 that the corresponding rectangular waste collection area includes the buffer extension area.

[0175] Finally, the first ship and the second ship can be controlled to collect the floating garbage along the waste collection moving direction, that is, by setting the corresponding waste collection buffer length to ensure that all floating garbage can be collected as much as possible, and the acquisition of the waste collection buffer length is based on multi-dimensional data and also has a certain data accuracy.

[0176] Furthermore, in this embodiment, the above-mentioned waste collection buffer length can be specifically obtained by the following formula:

[0177]

[0178] where, the L s is the waste collection buffer length, the A z is the direction included angle, the α1 is the retrieved included angle weight, the k z is the included angle normalization value, the P n is the number of pixels, the P a is the pixel point area, the R a is the area of the corresponding rectangular waste collection area, the β1 is the retrieved ratio weight, and the k a is the ratio normalization value.

[0179] It should be noted that in this embodiment, the above-mentioned included angle weight, pixel point area, ratio weight, included angle normalization value and ratio normalization value are all set in advance and stored in the corresponding storage device, such as stored in the server.

[0180] Here, from the above content, it can be known that the above-mentioned cleaning buffer length is obtained based on the angle influence value and the proportion influence value. The angle influence value is obtained by multiplying the direction angle by the angle weight, and the proportion influence value is obtained by multiplying the cleaning proportion by the proportion weight. Based on this, since the morphological attributes determined by the numerical comparison results may deviate from the actual situation to a certain extent, in order to improve the corresponding determination accuracy, the corresponding weights can be updated and trained in real time to achieve the process of updating and iterating the weights, and thus the corresponding determination accuracy can also be continuously improved.

[0181] Here, the update and iteration of each weight can be achieved through the following steps:

[0182] The method further includes:

[0183] Obtain the actual buffer length sent by the management end based on the rectangular cleaning area, and compare the actual buffer length with the cleaning buffer length;

[0184] If the actual buffer length is greater than the cleaning buffer length, then increase the training of the angle weight and the proportion weight respectively;

[0185] If the actual buffer length is less than the cleaning buffer length, then decrease the training of the angle weight and the proportion weight respectively;

[0186] Obtain the trained angle weight and proportion weight through the following formula:

[0187]

[0188] where, q + is the number of times of increasing the training of the angle weight α1, ∈ is the training constant value of the angle weight α1, q - is the number of times of decreasing the training of the angle weight α1, α2 is the trained angle weight, v + is the number of times of increasing the training of the proportion weight β1, δ is the training constant value of the proportion weight β1, v - is the number of times of decreasing the training of the proportion weight β1, β2 is the trained proportion weight.

[0189] It should be noted that in this embodiment, based on the above steps, the floating garbage can be cleaned correspondingly based on the first ship and the second ship in the same cleaning ship group. If the extension length of the corresponding rectangular cleaning area is too long, it will result in a long cleaning time, thus reducing the corresponding cleaning efficiency. Based on this, the following method steps can be used to further improve the cleaning efficiency:

[0190] Determine an extension length corresponding to the rectangular cleaning area along the cleaning movement direction, and determine the cleaning attribute corresponding to the rectangular cleaning area based on the length comparison between the extension length and a preset length, where the cleaning attribute includes a one-way attribute and a two-way attribute;

[0191] In response to the extension length being greater than or equal to the preset length, determine the rectangular cleaning area as having a two-way attribute, and determine a center line of the area that is located at the center of the rectangular cleaning area and parallel to the end cleaning line;

[0192] Determine the start cleaning line and the end cleaning line as the updated first start cleaning line and the second start cleaning line respectively, and determine the center line of the area as the updated end cleaning line;

[0193] Control the first ship and the second ship located in different cleaning ship groups to move the ships towards the first start cleaning line and the second start cleaning line respectively, and determine the cleaning ship group located on the first start cleaning line as the first ship group and the cleaning ship group located on the second start cleaning line as the second ship group;

[0194] Control each ship in the first ship group to clean the floating garbage in the rectangular cleaning area to the end cleaning line along the cleaning movement direction, and control each ship in the second ship group to clean the floating garbage in the rectangular cleaning area to the end cleaning line along the direction opposite to the cleaning movement direction.

[0195] For example, in this embodiment, when the obtained extension length is less than the preset length, it indicates that the extension length of the rectangular cleaning area is short, that is, using one cleaning ship group can meet the corresponding efficiency conditions. Therefore, the cleaning attribute of the rectangular cleaning area can be determined as a one-way attribute; and if the obtained extension length is greater than or equal to the preset length, it indicates that the extension length of the rectangular cleaning area is too long, that is, when using one cleaning ship group to clean the floating garbage, it will take a long cleaning time. At this time, the cleaning attribute can be determined as a two-way attribute, and two cleaning ship groups can be used to jointly perform the cleaning of the floating garbage in the rectangular cleaning area.

[0196] That is, when the extension length is greater than or equal to the preset length, the center line of the area located at the center of the rectangular cleaning area and parallel to the termination cleaning line can be determined. Thus, the starting cleaning line and the termination cleaning line are respectively determined as the updated first starting cleaning line and the second starting cleaning line, and the center line of the area is determined as the updated termination cleaning line. After the update of the starting cleaning line and the termination cleaning line is completed, two different cleaning ship groups can be controlled to move to the first starting cleaning line and the second starting cleaning line respectively, obtaining the first ship group located at the first starting cleaning line and the second ship group located at the second starting cleaning line. Finally, each ship in the first ship group and the second ship group can be controlled to move towards the termination cleaning line while using different trawl fences, so as to clean the floating garbage in the rectangular cleaning area to the termination cleaning line, enabling the trawl fences to surround the floating garbage and completing the cleaning of the floating garbage, thereby improving the corresponding cleaning efficiency.

[0197] In summary, in this embodiment, the server controls the drone to fly and inspect along the extension direction of the ecological area to determine whether there is floating garbage on the ecological area, and then determines a rectangular cleaning area surrounding the floating garbage, facilitating subsequent ships to accurately salvage the floating garbage. Then, the server obtains the rectangular center point corresponding to the rectangular cleaning area and controls the drone to move to the rectangular center point to monitor the rectangular cleaning area, facilitating the subsequent server to determine whether the ship reaches the corresponding point and clean the floating garbage according to the obtained real-time monitoring data. Then, the server determines the extension direction corresponding to the rectangular cleaning area as the cleaning movement direction according to the actual situation, and then determines the two area contour lines forming the rectangular cleaning area and perpendicular to the cleaning movement direction as the starting cleaning line and the termination cleaning line respectively. Then, the server determines the two endpoint positions of the starting cleaning line as the first cleaning point and the second cleaning point respectively, and controls the first ship and the second ship in the same cleaning ship group to move towards the first cleaning point and the second cleaning point respectively. When the server determines through the real-time monitoring data that the first ship and the second ship reach the first cleaning point and the second cleaning point respectively, it controls the first ship and the second ship to clean the floating garbage in the rectangular cleaning area along the cleaning movement direction. The present invention can reduce the time for salvaging floating garbage, thereby improving a certain salvage efficiency, and can play a certain role in environmental restoration of the ecological area.

[0198] Another embodiment of the present invention provides a real-time monitoring system for ecological environment data based on big data processing. Figure 4 For its corresponding system block diagram, the system includes:

[0199] An ecological inspection module, configured to determine a rectangular cleaning area surrounding floating garbage according to inspection data;

[0200] A direction determination module, configured to determine the extension direction corresponding to the rectangular cleaning area as the cleaning movement direction, and determine the area contour lines that form the rectangular cleaning area and are perpendicular to the cleaning movement direction as the starting cleaning line and the ending cleaning line;

[0201] A movement control module, configured to determine the endpoint positions of the starting cleaning line as the first cleaning point and the second cleaning point, and control the first ship and the second ship to move towards the first cleaning point and the second cleaning point;

[0202] A cleaning control module, configured to control the first ship and the second ship to clean the floating garbage along the cleaning movement direction to the ending cleaning line.

[0203] In the specification provided herein, the algorithms and displays are not inherently related to any particular computer, virtual system, or other device. Various general-purpose systems can also be used in conjunction with the examples of the present invention. The structure required to construct such a system will be apparent from the above description. Additionally, the present invention is not directed to any particular programming language. It should be understood that the content of the present invention described herein can be implemented using various programming languages, and the descriptions of specific languages above are for the purpose of disclosing the preferred embodiments of the present invention.

[0204] In the specification provided herein, a large number of specific details are set forth. However, it can be understood that the embodiments of the present invention can be practiced without these specific details. In some instances, well-known methods, structures, and technologies have not been shown in detail so as not to obscure the understanding of this specification.

[0205] Similarly, it should be understood that, in order to streamline the present disclosure and assist in understanding one or more of the various inventive aspects, in the above description of the exemplary embodiments of the present invention, the various features of the present invention are sometimes grouped together into a single embodiment, figure, or description thereof.

[0206] Those skilled in the art should understand that the modules or units or components of the devices in the examples disclosed herein can be arranged in the devices as described in the embodiments, or alternatively can be located in one or more devices different from the devices in the examples. The modules in the foregoing examples can be combined into one module or further divided into multiple sub-modules.

[0207] Those skilled in the art can understand that the modules in the devices in the embodiments can be adaptively changed and arranged in one or more devices different from the embodiments. The modules or units or components in the embodiments can be combined into one module or unit or component, and in addition, they can be divided into multiple sub-modules or sub-units or sub-components.

[0208] In addition, those skilled in the art can understand that although some of the embodiments described herein include certain features included in other embodiments rather than other features, the combination of the features of different embodiments means that it is within the scope of the present invention and forms different embodiments.

[0209] In addition, some of the embodiments herein are described as a combination of methods or method elements that can be implemented by a processor of a computer system or by other devices performing the functions. Therefore, a processor having the necessary instructions for implementing the method or method elements forms a device for implementing the method or method elements. In addition, the elements described herein in the device embodiments are examples of the following devices: the device is used to implement the functions performed by the elements for the purpose of implementing the invention.

[0210] As used herein, unless otherwise specified, the use of ordinal numbers "first", "second", "third", etc. to describe ordinary objects only represents different instances of similar objects, and does not intend to imply that the objects so described must have a given order in terms of time, space, sorting, or in any other way.

[0211] Although the present invention is described in terms of a limited number of embodiments, those skilled in the art in this technical field will understand, based on the above description, that other embodiments can be envisaged within the scope of the present invention thus described. In addition, it should be noted that the language used in this specification is mainly selected for the purpose of readability and teaching, rather than for the purpose of explaining or limiting the subject matter of the present invention.

Claims

1. A real-time monitoring method for ecological environment data based on big data processing, characterized in that: The following steps are involved: Determine the rectangular removal area surrounding the floating garbage based on the inspection data; The extending direction corresponding to the rectangular cleaning area is determined as the cleaning movement direction, and the area contour lines constituting the rectangular cleaning area and perpendicular to the cleaning movement direction are determined as the starting cleaning line and the ending cleaning line; Determine the endpoints of the starting removal line as the first removal point and the second removal point, and control the first ship and the second ship to move toward the first removal point and the second removal point; The first ship and the second ship are controlled to remove the floating garbage to the end removal line along the removal direction.

2. The real-time monitoring method of ecological environment data based on big data processing according to claim 1 is characterized in that: Based on the inspection data, a rectangular removal area surrounding floating garbage is determined, including: Acquire the image acquisition size corresponding to the drone, and perform array processing on the ecological area to obtain ecological sub-areas corresponding to the image acquisition size; Obtaining the center points of each region corresponding to each ecological sub-region, and configuring the drone to perform inspection tasks based on the center points of each region; In response to the drone flying to the center point of any area, the drone is triggered to perform flow velocity measurement to obtain the water area flow velocity corresponding to the ecological area; Determine the ecological region attribute based on the comparison result between the water area flow velocity and the retrieved preset flow velocity, and configure different preset floating garbage identification strategies based on different ecological region attributes, wherein the ecological region attributes include a stable attribute and a turbulent attribute; Based on the preset floating garbage identification strategy, each ecological sub-area is identified, and each ecological sub-area corresponding to floating garbage is determined as a collection to be removed; Based on the mutual positional relationship between the ecological sub-regions in the to-be-removed set, the ecological sub-regions are merged to obtain the rectangular removal area.

3. The real-time monitoring method of ecological environment data based on big data processing according to claim 2 is characterized in that: Based on the preset floating garbage identification strategy, each ecological sub-area is identified, and each ecological sub-area corresponding to floating garbage is determined as a collection to be removed, including: In response to the ecological region attribute being a stable attribute, the drone is controlled to hover for a preset time, and a preset number of frames of images of the ecological sub-region are captured at equal intervals within the preset time, so as to obtain an ecological region image composed of image frames corresponding to different frame sequences; Binarization is performed on each image frame, and pixel recognition is performed on each obtained binary image to obtain each image pixel point that constitutes the same binary image; Determine each water area pixel point corresponding to the water area pixel value in each image pixel point, and determine each pixel point other than each water area pixel point as each cleaning pixel point; Obtain the pixel ratio between each cleaning pixel point and each image pixel point in the same binary image, and compare the pixel ratios corresponding to each binary image; The pixel ratio with the smallest corresponding pixel ratio is determined as the pixel ratio corresponding to the ecological area image, and each ecological sub-area with a corresponding pixel ratio greater than or equal to a preset ratio is determined as a set to be cleared.

4. The real-time monitoring method of ecological environment data based on big data processing according to claim 2 is characterized in that: Based on the preset floating garbage identification strategy, each ecological sub-area is identified, and each ecological sub-area corresponding to floating garbage is determined as a collection to be removed, including: When the ecological region attribute of the ecological region is a turbulent attribute, controlling the drone to take an image of the ecological sub-region to obtain an ecological region image; Binarizing the ecological region image, and performing pixel recognition on the obtained binary image to obtain each image pixel constituting the binary image; Determine each water area pixel point corresponding to the water area pixel value in each image pixel point, and determine each pixel point other than each water area pixel point as each cleaning pixel point; The pixel ratio between each cleaning pixel point and each image pixel point in the same binary image is obtained, and each ecological sub-area whose corresponding pixel ratio is greater than or equal to a preset ratio is determined as a set to be cleaned.

5. The real-time monitoring method of ecological environment data based on big data processing according to claim 3 or 4 is characterized in that: Based on the mutual positional relationship between the ecological sub-regions in the to-be-removed set, the ecological sub-regions are merged to obtain the rectangular removal area, including: Determine the regional positions of each ecological sub-region in the to-be-removed set in the ecological region, and divide the ecological sub-regions that are continuously connected into the same regional division group based on the regional positions, and divide the remaining ecological sub-regions into different regional division groups, to obtain regional division groups; Establishing a regional coordinate system corresponding to the ecological region, and acquiring coordinates of each cleaning pixel point corresponding to each ecological sub-region located in the same regional division group, to obtain each cleaning coordinate point corresponding to each cleaning pixel point; Based on each cleaning coordinate point, the X-axis extreme value coordinate point corresponding to the X-axis and the Y-axis extreme value coordinate point corresponding to the Y-axis are determined respectively, and based on the minimum circumscribed rectangle algorithm, a rectangular cleaning area is formed that covers at least any one of the X-axis extreme value coordinate point and the Y-axis extreme value coordinate point and surrounds all the remaining cleaning coordinate points.

6. The real-time monitoring method of ecological environment data based on big data processing according to claim 5 is characterized in that: The extension direction corresponding to the rectangular removal area is determined as the removal movement direction, and the area contour lines that constitute the rectangular removal area and are perpendicular to the removal movement direction are determined as the starting removal line and the ending removal line, including: Acquire the contour lines of each area constituting the rectangular removal area, and acquire the distance between each area contour line and the ship berthing line of the corresponding ship wharf to obtain each moving distance; Determine the extension direction perpendicular to the area contour line with the smallest corresponding moving distance as the cleaning and transporting moving direction, and obtain the first contour size and the second contour size corresponding to the rectangular cleaning and transporting area contour in directions perpendicular and parallel to the cleaning and transporting moving direction; Comparing the first outline size and the second outline size with the fence size of the corresponding trawl fence, and determining a direction adjustment coefficient based on the comparison result; In response to the direction adjustment coefficient being greater than or equal to a preset coefficient, determining an extension direction perpendicular to the cleaning and transporting moving direction as an updated cleaning and transporting moving direction; The contour lines of each area perpendicular to the cleaning and transporting moving direction are obtained, and the contour line of the area corresponding to the smallest moving distance is determined as the ending cleaning and transporting line, and the contour line of the area corresponding to the largest moving distance is determined as the starting cleaning and transporting line.

7. The real-time monitoring method of ecological environment data based on big data processing according to claim 6 is characterized in that: Comparing the first outline size and the second outline size with the fence size of the corresponding trawl fence, and determining the direction adjustment coefficient based on the comparison result, including: When the first outline size is the same as the second outline size, determining the retrieved first reference coefficient as the direction adjustment coefficient, wherein the first reference coefficient is smaller than the preset coefficient; or, When the first outline size is different from the second outline size, performing difference calculation between the first outline size and the second outline size and the fence size of the corresponding trawl fence, respectively, to obtain a first difference value corresponding to the first outline size and a second difference value corresponding to the second outline size; When both the first difference and the second difference are non-positive numbers, determining the retrieved first reference coefficient as the direction adjustment coefficient; When the first difference is a positive number and the second difference is a non-positive number, determining the retrieved second reference coefficient as the direction adjustment coefficient, wherein the second reference coefficient is greater than or equal to the preset coefficient; When the first difference and the second difference are both positive numbers, the direction adjustment coefficient is determined by the following formula: Wherein, C is the direction adjustment coefficient, k is the size normalization value, X1 is the first difference, and X2 is the second difference.

8. The real-time monitoring method of ecological environment data based on big data processing according to claim 4 is characterized in that: Controlling the first ship and the second ship to remove the floating garbage to the end removal line along the removal direction, including: In response to the real-time monitoring data, determining that the first ship and the second ship have arrived at the first removal point and the second removal point respectively, obtaining the center point of the line segment corresponding to the starting removal line, and controlling the drone to fly to the center point of the line segment; In response to the drone flying to the center point of the line segment, the drone is triggered to perform flow direction measurement of the ecological area to obtain the water flow direction corresponding to the ecological area; Determine the direction angle between the cleaning and transporting moving direction and the flow direction of the water area, and determine the angle influence value based on the direction angle; Determine a transportation ratio corresponding to the rectangular transportation area based on the number of pixels of each transportation pixel point located in the rectangular transportation area, and determine a ratio influence value based on the transportation ratio; Determine the cleaning buffer length based on the angle influence value and the ratio influence value, and extend the rectangular cleaning area corresponding to the cleaning buffer length along the termination cleaning line away from the start cleaning line to obtain an updated rectangular cleaning area and termination cleaning line; The first ship and the second ship are controlled to transport the floating garbage in the rectangular transport area to the termination transport line along the transport movement direction.

9. The real-time monitoring method of ecological environment data based on big data processing according to claim 8 is characterized in that: The cleaning buffer length is determined by the following formula: Among them, the L s is the length of the cleaning buffer, z is the direction angle, α1 is the angle weight, and k z is the normalized value of the angle, the P n is the number of pixels, the P a is the pixel area, the R a is the area corresponding to the rectangular cleaning area, β1 is the proportional weight to be retrieved, and k a is the ratio normalized value.

10. The real-time monitoring method of ecological environment data based on big data processing according to claim 1 is characterized in that: The method further comprises: Determine an extension length corresponding to the rectangular cleaning area along the cleaning movement direction, and determine a cleaning attribute corresponding to the rectangular cleaning area based on a length comparison between the extension length and a preset length, wherein the cleaning attribute includes a unidirectional attribute and a bidirectional attribute; In response to the extension length being greater than or equal to the preset length, the rectangular clearing area is determined to be a bidirectional attribute, and a center line of the area located at the center of the rectangular clearing area and parallel to the termination clearing line is determined; The starting removal line and the ending removal line are respectively determined as the updated first starting removal line and the second starting removal line, and the center line of the area is determined as the updated ending removal line. Respectively control the first ship and the second ship located in different cleaning and transportation ship groups to move toward the first starting cleaning and transportation line and the second starting cleaning and transportation line, and determine the cleaning and transportation ship group located at the first starting cleaning and transportation line as the first ship group, and determine the cleaning and transportation ship group located at the second starting cleaning and transportation line as the second ship group; The ships in the first ship group are controlled to move along the removal direction, and the ships in the second ship group are controlled to move along the direction opposite to the removal direction to remove the floating garbage in the rectangular removal area to the termination removal line.

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