Green vision rate image acquisition method, storage medium and green vision rate acquisition equipment

Abstract

The invention provides a green vision rate acquisition method, a storage medium and green vision rate acquisition equipment. The method is applied to backpack type green vision rate acquisition equipment, the acquisition equipment comprises a display module, a positioning module, an inertial navigation module, an environment perception module and an image acquisition module, and the method comprises the following steps: presenting an initial path and acquisition points marked along the path on the display module; when a wearer of the acquisition equipment advances along the initial path, acquiring the surrounding environment condition in real time through an environment sensing module; judging whether the surrounding environment condition acquired in real time meets a path optimization condition or not; if the surrounding environment condition acquired in real time meets the path optimization condition, an optimized path and optimized acquisition points marked along the optimized path are displayed on a display module; in response to the wearer arriving at the optimized acquisition point, adjusting the pose of the image acquisition module to an optimized pose based on the surrounding environment condition at the optimized acquisition point acquired by the environment sensing module; and driving the image acquisition module to carry out shooting with the optimized pose.

Description

Technical Field

[0001] The present invention relates to the technical field of engineering surveying, and particularly to a green view rate image acquisition method, a storage medium, and a green view rate acquisition device. Background Art

[0002] The green view rate refers to the proportion of green plants among the objects seen by people's eyes, and is an important indicator for measuring the quality of the urban ecological environment and landscape beauty. In many fields such as urban planning and garden design, accurately collecting green view rate data is crucial for evaluating environmental quality, optimizing landscape layout, etc.

[0003] With the increasing attention of people to the quality of the urban ecological environment, the green view rate, as an important indicator for measuring the urban green space and residents' visual experience, its accurate collection and analysis have become increasingly critical.

[0004] The backpack-type green view rate acquisition device, with manual backpacking as the main carrying form, is more portable and flexible. The operator can carry it on the back and go deep into various corners of the city, including narrow alleys, inside parks, residential communities and other areas where vehicles are difficult to reach for data collection. This acquisition method can collect data more carefully for specific areas, ensuring that the collected data is more targeted and accurate.

[0005] The camera control methods of common backpack-type green view rate acquisition devices are mainly based on fixed angles or simple manual adjustment. In the fixed-angle control method, the camera is fixedly installed on the backpack device and always maintains the same shooting angle and field of view during the acquisition process. When the operator is walking, the camera takes pictures of the images along the way at the preset fixed angle. In the simple manual adjustment method, the operator relies on his own subjective judgment. During the acquisition process, the operator may stop and manually rotate the camera direction to try to take pictures from different angles, but this adjustment lacks systematicness and scientificity.

[0006] The above-described backpack-type green view rate acquisition devices have the following problems: (1) Traditional acquisition trajectory planning is often relatively simple and does not fully consider environmental factors (such as terrain undulation, building occlusion, etc.), resulting in a large amount of invalid information in the images collected by the camera, affecting the accuracy of green view rate calculation.

[0007] (2) Due to insufficient sensor cooperation and the irregularity of the collector's walking during the acquisition process, the disconnection between trajectory optimization and camera control occurs, and the data of GPS, IMU, camera, etc. are not deeply integrated, resulting in a large cumulative error of positioning drift. It is difficult to precisely control the movement trajectory of the camera in space, which in turn leads to deviations in the shooting angle and position of the camera, reducing the quality of image acquisition and ultimately affecting the reliability of the green view rate calculation result.

[0008] (3) Low efficiency and difficult to guarantee accuracy. Manually adjusting the camera angle relies on the subjective judgment of the operator, which not only affects the acquisition speed but also increases the work intensity of the operator. Moreover, there may be significant differences in the selection of shooting angles by different operators, and even the operations of the same operator at different times may not be consistent. This subjectivity leads to the lack of consistency and comparability of the collected image data, thus affecting the accuracy of the green view rate calculation. Summary of the Invention

[0009] In view of the deficiencies of the prior art, the present invention provides a method for collecting the green view rate.

[0010] Specifically, Scheme 1: A method for collecting the green view rate, which is applied to a backpack-type green view rate collection device. The device is characterized in that it includes a display module, a positioning module, an inertial navigation module, an environmental perception module, and an image acquisition module. The method includes: S10. Present an initial path and collection points marked along the path on the display module. S20. When the wearer of the collection device travels along the initial path, obtain the surrounding environment situation in real time through the environmental perception module. S30. Judge whether the surrounding environment situation obtained in real time meets the path optimization condition. S40. If the surrounding environment situation obtained in real time meets the path optimization condition, present an optimized path and optimized collection points marked along the optimized path on the display module. S50. In response to the wearer reaching the optimized collection point, adjust the pose of the image acquisition module to the optimized pose based on the surrounding environment situation at the optimized collection point obtained by the environmental perception module. S60. Drive the image acquisition module to take a picture in the optimized pose.

[0011] Scheme 2: Based on Scheme 1, the method further includes: If the surrounding environment situation obtained in real time does not meet the path optimization condition, still present the current path and collection points marked along the current path on the display module.

[0012] Scheme 3: Based on Scheme 1, S10 includes: S100. Present a map of the area where the green view rate is to be collected on the display module. S102. Calculate the initial path based on the collection start point and end point input by the wearer and the geographical information in the area. S104. Calculate the collection points along the path based on the calculated initial path and the preset exposure interval. S106. Present the calculated initial path and the calculated collection points on the map.

[0013] Solution 4: Based on Solution 3, the geographic information includes one or more of road network information, building location information, and park green space information.

[0014] Solution 5: Based on Solution 3 or 4, S30 includes: Determine whether an obstacle appears in front of the wearer's movement. If it appears, the path optimization condition is met.

[0015] Solution 6: Based on Solution 5, S40 includes: S400. Calculate the optimized path, where the optimized path bypasses the obstacle; S402. Based on the calculated optimized path and the preset exposure interval, calculate the optimized collection points along the optimized path; S404. Present the calculated optimized path and the calculated optimized collection points on the map.

[0016] Solution 7: Based on Solution 6, S400 includes: S4000. Identify the type of the obstacle; S4002. Calculate the corresponding optimized path based on the type of the obstacle.

[0017] Solution 8: Based on Solution 7, S4000 includes: Invoke the target recognition algorithm to identify the type of the obstacle.

[0018] Solution 9: Based on Solution 7 or 8, S4002 includes: If the identified obstacle is a fixed obstacle, based on the geographic information on the map, invoke the first path planning algorithm to obtain the corresponding optimized path at one time.

[0019] Solution 10: Based on Solution 7 or 8, S4002 includes: If the identified obstacle is a single moving obstacle, according to the position and movement trend of the single moving obstacle, invoke the second path planning algorithm to preliminarily plan a path to avoid the single moving obstacle, where the movement speed and direction of the single moving obstacle are considered and a preset safety distance is reserved; Smooth the preliminarily planned path to obtain the optimized path; Monitor the movement state of the single moving obstacle at the first time interval and dynamically adjust the optimized path.

[0020] Solution 11: Based on Solution 10, the first time interval is 200 ms.

[0021] Solution 12: Based on Solution 10, S4002 includes: If it is recognized that the obstacle is a crowded area of multiple people, detect the positions and quantities of the human bodies. By statistically analyzing the distribution and density of human bodies in consecutive frame images, determine whether the area reaches a preset degree of crowding. Once it is determined whether the area reaches the preset degree of crowding, call the third path planning algorithm to preliminarily plan a path to avoid the crowded area of people. Smooth the preliminarily planned path to obtain an optimized path. Monitor the change of the crowded area of people at a second time interval, and dynamically adjust the optimized path.

[0022] Solution 13: Based on Solution 12, the second time interval is less than the first time interval.

[0023] Solution 14: Based on Solution 13, the second time interval is 50 ms.

[0024] Solution 15: Based on Solution 12, the detecting the positions and quantities of the human bodies includes: calling a deep learning-based human body detection algorithm to detect the positions and quantities of the human bodies.

[0025] Solution 16: Based on Solution 12, the smoothing process is implemented by a Bezier curve or a spline curve.

[0026] Solution 17: Based on Solution 1, the method further includes: obtaining the position of the wearer in real time based on the positioning module to determine whether the wearer reaches the optimized acquisition point.

[0027] Solution 18: Based on Solution 1, adjusting the pose of the image acquisition module to an optimized pose according to the surrounding environment conditions at the optimized acquisition point obtained based on the environment perception module includes: Calling a pose control algorithm to analyze the characteristics of the shooting scene according to the surrounding environment conditions. Based on the characteristics, adjust the pose of the image acquisition module to the optimized pose.

[0028] Solution 19: Based on Solution 18, the adjusting the pose of the image acquisition module to the optimized pose based on the characteristics includes: If multiple subjects are detected in the shooting scene, determine the range that the image acquisition module needs to cover and the objects to be focused on according to the importance of the subjects and the spatial relationship between them. Obtain the current position and attitude information of the image acquisition module through the inertial navigation module. Based on the current position and attitude information of the image acquisition module, as well as the range to be covered and the objects of key concern by the image acquisition module, calculate the optimal angle to which the image acquisition module needs to be adjusted.

[0029] Solution 20: Based on Solution 18, adjusting the pose of the image acquisition module to the optimized pose according to the characteristics includes: if the shooting scene is detected to be a narrow street, tilt the image acquisition module to both sides to obtain the greening information on both sides of the street. When the shooting angle is too large and directly facing the sunlight, the camera will automatically adjust the pitch angle to collect the shooting subject.

[0030] Solution 21: Based on Solution 19 or 20, the method further includes: Detect the adjusted shooting angle; If the adjusted shooting angle is directly facing the sunlight, further adjust the pitch angle of the image acquisition module to avoid direct sunlight.

[0031] Solution 22: Based on Solution 1 or 18, the device further includes a motor, and the pose of the image acquisition module is adjusted to the optimized pose through the motor.

[0032] Solution 23: The present invention also provides a storage medium, in which a program is stored, and when the program is executed by a processor, the method described in any one of Solutions 1 - 22 is implemented.

[0033] Solution 24: The present invention also provides a green view rate acquisition device, characterized in that the device includes a display module, a positioning module, an inertial navigation module, an environment perception module, an image acquisition module, a memory, and a processor, wherein a program is stored in the memory, and when the program is executed by the processor, the method described in any one of Solutions 1 - 22 is implemented.

[0034] Compared with the prior art, the image acquisition method provided by the present invention has the following beneficial effects: (1) The comprehensiveness of information acquisition is improved. Through trajectory optimization and intelligent adjustment of the camera angle, this solution can acquire image information more comprehensively in a complex environment, avoiding information omission caused by a fixed angle or untimely manual adjustment. Whether it is a narrow street or a terrain undulation area, it can be more accurately photographed and recorded, improving the accuracy of green view rate calculation.

[0035] (2) The operation efficiency is improved. This solution realizes the automatic optimization and control of the acquisition trajectory and the camera angle. The operator does not need to manually adjust the camera frequently and can continuously and efficiently collect data during walking. Compared with the manual adjustment method, it greatly saves the acquisition time, improves the work efficiency, and reduces the work intensity of the operator.

[0036] (3)Enhanced data accuracy. Based on objective environmental perception and precise algorithm calculation, the camera angle and acquisition trajectory are controlled, avoiding the subjective influence in the manual adjustment method. The collected image data has better consistency and comparability, providing a reliable data basis for the accurate calculation of the green view rate and improving the credibility of the green view rate analysis results.

[0037] (4)Enhanced adaptability and versatility. The device can automatically adjust the acquisition strategy, including trajectory planning and camera angle control, according to the environmental characteristics of different acquisition areas (such as different functional areas of the city, different topographies). Compared with the fixed-angle control method, it has stronger adaptability and versatility and can be applied to various complex and diverse urban environmental green view rate acquisition tasks. Description of the Drawings

[0038] Figure 1 It is a schematic flowchart of the green view rate image acquisition method according to an embodiment of the present invention; Figure 2 It is a schematic flowchart of displaying the path and acquisition points according to an embodiment of the present invention; Figure 3 It is a schematic diagram of path optimization for a single moving obstacle according to an embodiment of the present invention; Figure 4 It is a schematic diagram of path optimization when the crowd is dense according to an embodiment of the present invention; Figure 5 It is a schematic flowchart of optimizing the pose of the computational image acquisition module according to an embodiment of the present invention; Figure 6 It is a schematic structural diagram of the green view rate acquisition device according to an embodiment of the present invention. Detailed Embodiments

[0039] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0040] In the first aspect of the present invention, as Figure 1 shown, a green view rate image acquisition method is provided, which is applied to a backpack-type green view rate acquisition device. The backpack-type green view rate acquisition device includes a display module, a positioning module, an inertial navigation module, an environmental perception module, and an image acquisition module.

[0041] The green view rate image acquisition method includes: S10. Present the initial path and acquisition points marked along the path on the display module; S20. When the wearer of the acquisition device travels along the initial path, the surrounding environment situation is obtained in real time through the environmental perception module; S30. Determine whether the surrounding environment situation obtained in real time meets the path optimization conditions; S40. If the surrounding environment situation obtained in real time meets the path optimization conditions, present the optimized path and the optimized acquisition points marked along the optimized path on the display module; S50. In response to the wearer reaching the optimized acquisition point, adjust the pose of the image acquisition module to the optimized pose based on the surrounding environment situation at the optimized acquisition point obtained by the environmental perception module; S60. Drive the image acquisition module to take pictures in the optimized pose.

[0042] In this method, the angle and acquisition trajectory of the image acquisition module are controlled based on objective environmental perception and precise algorithm calculation, and the acquisition strategy can be automatically adjusted according to the environmental characteristics of different acquisition areas (such as different functional areas of the city, different topographies and landforms), so it has stronger adaptability and versatility and can be applied to various complex and diverse urban environment green view rate acquisition tasks.

[0043] In the above solution, if the surrounding environment situation obtained in real time does not meet the path optimization conditions, the current path and the acquisition points marked along the current path are still presented on the display module.

[0044] In S10, according to the geographical data of the target area, such as a three-dimensional digital model, combined with the requirements of green view rate acquisition (such as the distribution density of sampling points, the coverage range, etc.) and the current geographical location information, etc., a path planning algorithm is used to plan the initial acquisition trajectory. When planning the initial trajectory, obstacles such as buildings are avoided, and at the same time, the uniformity of the distribution of acquisition points under different terrain conditions is ensured to ensure the comprehensiveness and representativeness of the acquired data. As Figure 2 shown, it specifically includes: S100. Present the map of the area (target area) where the green view rate is to be acquired on the display module; S102. Calculate the initial path based on the acquisition start point and end point input by the wearer and the geographical information in the target area; S104. Calculate the acquisition points along the path based on the calculated initial path and the preset exposure interval; S106. Present the calculated initial path and the calculated acquisition points on the map.

[0045] The geographical data of the target area can be downloaded from the official website of Open Street Map or Baidu Map, etc., and includes one or more of road network information, building location information, and park green space information.

[0046] The current geographical location information can be obtained through the positioning module.

[0047] In S20, the environmental perception module may include a high-definition camera, which can continuously capture images of the surrounding environment at an extremely high frame rate.

[0048] In one embodiment, S30 includes: determining whether an obstacle appears in front of the wearer during movement. If an obstacle appears, it meets the path optimization condition. During the process of the wearer going to the collection point, images are collected in real time through the camera, and the collected images are analyzed by an image recognition algorithm to identify the factors affecting the collection, then path optimization is required. For example, if the image recognition finds a temporary building ahead and there is no such information in the map, then this area needs to be temporarily marked as an impassable area so that it can be avoided during path optimization, thereby intelligently completing path movement and image collection.

[0049] In one embodiment, S40 includes: S400. Calculate an optimized path to bypass the obstacle; S402. Based on the calculated optimized path and a preset exposure interval, calculate optimized collection points along the optimized path; S404. Present the calculated optimized path and the calculated optimized collection points on the map.

[0050] There are various types of obstacles, such as fixed obstacles like roadblocks, and moving obstacles like moving cars and walking people. These different obstacles will have different impacts on path optimization. For example, for a fixed obstacle, if it blocks the moving route, it needs to be bypassed; while for a moving vehicle, if it will quickly drive away from the planned path, it has no impact on the collector, and the existing planned path can still be used.

[0051] Therefore, in one embodiment, S400 includes: S4000. Identify the type of the obstacle; S4002. Calculate a corresponding optimized path based on the type of the obstacle.

[0052] The identification of the obstacle type can be completed by invoking a target recognition algorithm.

[0053] In S4002 of one embodiment, if the identified obstacle is a fixed obstacle, based on the geographical information on the map, the first path planning algorithm is called to obtain the corresponding optimized path at one time. For example, the position of the obstacle in the map is represented in the form of coordinates, and the occupied area range is determined according to its shape and size. The edge detection algorithm can also be used to identify the object contour in the scene, and the color analysis algorithm is used to distinguish the color characteristics of different objects, so as to identify various objects in the scene, judge their positional relationships, and obtain the overall layout information of the environment. Then, an obstacle constraint model is constructed, the area where the obstacle is located is marked as an impassable area, and it is incorporated into the map data to correct the passable area in the map. When optimizing the path, avoid the obstacle area marked as impassable in the map.

[0054] The first path planning algorithm can be the artificial potential field method, the dynamic window method, etc., which can avoid obstacles for path planning. Since the obstacle is fixed, therefore, the path planning algorithm can be called once to obtain the planned path without adjustment.

[0055] In S4002 of one embodiment, as Figure 3 shown, if the identified obstacle is a single moving obstacle (for example, a single moving vehicle, a single running pedestrian, etc.), according to the position and movement trend of the single moving obstacle, the second path planning algorithm is called to preliminarily plan a path to avoid the single moving obstacle, where the movement speed and direction of the single moving obstacle are considered, and a preset safety distance is reserved to avoid the wearer colliding with the single moving obstacle; The preliminarily planned path is smoothed to obtain an optimized path to reduce path mutations and jitters.

[0056] The motion state of the single moving obstacle is monitored at the first time interval, and the optimized path is dynamically adjusted.

[0057] Preferably, the preset safety distance, that is, the standard detour radius, is 1.5 times the size of the obstacle; the first time interval is 200 ms.

[0058] The second path planning algorithm can be the RRT series algorithm or the DWA algorithm, which is suitable for real-time obstacle avoidance.

[0059] In S4002 of one embodiment, as Figure 4 shown, if the identified obstacle is a gathering area of multiple people, information such as the position and number of people is detected; By statistically analyzing the distribution and density of people in consecutive frame images, it is judged whether the area reaches the preset degree of crowd density; Once it is judged that the area reaches the preset degree of crowd density, the third path planning algorithm is called to preliminarily plan a path to avoid the gathering area of people; Smooth the initially planned path to reduce path mutations and jitters, and finally obtain an optimized path; Monitor the changes in the personnel gathering area at a second time interval and dynamically adjust the optimized path.

[0060] For the personnel gathering area, it is necessary to monitor the movement and density changes in real time so as to dynamically adjust the optimized path in real time. Therefore, the monitoring time should be less than the first time interval. Preferably, the second time interval is 50 ms.

[0061] The third path planning algorithm can be sampling-based RRT or optimization algorithms (such as GA, PSO), etc.

[0062] By calling the deep learning-based human detection algorithm (YOLO) to analyze the images obtained by the environmental perception module, human body information such as position and quantity can be obtained. By statistically analyzing the distribution and density of humans in consecutive frame images, it is judged whether the area is crowded with people. When it is detected that the area is crowded with people, the area where the humans are located is marked as an impassable area and incorporated into the map data, and the passable areas in the map are corrected to avoid this area when optimizing the path.

[0063] When smoothing the above-mentioned planned route, it can be achieved by Bezier curves or spline curves.

[0064] In the green view rate acquisition scenario, during path planning, the complex movement and distribution of people in crowded areas are highly uncertain, which will frequently interfere with the acquisition system. Moreover, the crowd density, movement direction, and speed in crowded areas change in real time, requiring the system to respond in real time to adjust the path, which has the greatest impact on the acquisition accuracy; ordinary single-entity moving obstacles such as moving vehicles have a lower movement change frequency and complexity than people in crowded areas and are predictable, with the second greatest impact on the acquisition accuracy; the positions and shapes of fixed obstacles are stable, and the impact on the acquisition accuracy is relatively fixed and the smallest.

[0065] By the method of this patent, allocating computing resources according to different situations (such as allocating 70% of the computing resources for crowded area detection and adjustment) can effectively improve the computing efficiency of the system.

[0066] In S4002 of an embodiment, if environmental changes are recognized, including changes in light intensity, contrast, vegetation growth status, etc., the degree of impact on the acquisition effect is evaluated. For example, changes in light intensity may affect the clarity and color accuracy of an image, thereby affecting the accuracy of green vision rate calculation; adverse weather (such as heavy fog) may cause the field of view of the acquisition device to be limited and effective image data cannot be obtained. Therefore, for the acquired image data, analyze its parameters such as brightness and contrast, calculate the average brightness value of the image. If this value changes significantly within a short period of time and exceeds a certain threshold range, it indicates that the lighting conditions have changed suddenly. At the same time, analyze the histogram distribution of the image and observe whether there is an obvious shift to further confirm the lighting change situation.

[0067] Light intensity analysis: Calculate the grayscale histogram of the image, which reflects the distribution of the number of pixels at different grayscale levels in the image. By analyzing the shape and position of the histogram, the light intensity can be judged. If the histogram is mainly concentrated in the high grayscale area, it indicates that the overall image is brighter and the light intensity is large; if it is concentrated in the low grayscale area, the image is darker and the light intensity is small.

[0068] Contrast analysis: Using the gray value range method, calculate the difference between the maximum and minimum gray values of the pixels in the image. The larger the difference, the higher the contrast of the image.

[0069] When the real-time environment transformation exceeds the set threshold and affects image acquisition, the area where the environmental information changes is set as an impassable area and integrated into the map data, and the passable areas in the map are corrected to avoid this area when optimizing the path.

[0070] In an embodiment, the acquisition method further includes: based on the positioning module, the position of the wearer is obtained in real time to determine whether the wearer has reached the optimized acquisition point. The positioning module can be a GNSS module, which can obtain the position information of the wearer and display the position of the wearer on the map in the display module. Similar to Baidu Map, the wearer can see their travel trajectory.

[0071] In an embodiment, when optimizing the path, the trajectory that the wearer has walked, such as the trajectory from the previous acquisition point to the current position, will be input as a parameter into the path planning algorithm. That is, at the current position, the path planning is cumulative rather than starting from scratch.

[0072] In S50, after reaching the acquisition point, calculate the current pose position information of the image acquisition module according to the previous pose and travel trajectory of the image acquisition module, and then calculate the best shooting angle of the image acquisition module according to the environment and image acquisition requirements, and adjust the image acquisition module.

[0073] The requirements for image acquisition include: whether the height of the camera is level with the human eye, and whether the posture conforms to the perspective of a person standing upright and looking straight ahead.

[0074] In S50, adjusting the pose of the image acquisition module to the optimized pose based on the surrounding environment conditions at the optimized acquisition point obtained by the environment perception module includes: Analyzing the characteristics of the shooting scene according to the surrounding environment conditions; Based on the characteristics of the scene, calling the pose control algorithm to calculate the optimized pose of the image acquisition module, and adjusting the image acquisition module to the optimized pose.

[0075] In one embodiment, calling the pose control algorithm based on the characteristics of the scene to calculate the optimized pose of the image acquisition module, as Figure 5 shown, includes: If multiple subjects are detected in the shooting scene, determining the range to be covered by the image acquisition module and the objects of key concern according to the importance of the subjects and the spatial relationship between them; Obtaining the current position and pose information of the image acquisition module through the inertial navigation module; Combining the current position and pose information of the image acquisition module with the range to be covered by the image acquisition module and the objects of key concern, calculating the best angle to which the image acquisition module needs to be adjusted.

[0076] In another embodiment, calling the pose control algorithm based on the characteristics of the scene to calculate the optimized pose of the image acquisition module includes: If the shooting scene is detected to be a narrow street, tilting the image acquisition module to both sides to obtain the greening information on both sides of the street. When the shooting angle is too large and directly facing the sunlight, the camera will automatically adjust the pitch angle to face the shooting subject for acquisition.

[0077] In one embodiment, it further includes: Detecting the adjusted shooting angle; If the adjusted shooting angle is directly facing the sunlight, further adjusting the pitch angle of the image acquisition module to avoid direct sunlight.

[0078] Adjusting the pose of the image acquisition module to the optimized pose according to the surrounding environment conditions not only avoids the uncertainty of manually adjusting the pose of the image acquisition module, but also optimizes the acquisition strategy and improves the accuracy of green view rate calculation.

[0079] In S50, the device further includes a motor, and the pose (including rotation and pitch) of the image acquisition module is adjusted to the optimized pose through the motor.

[0080] In one embodiment, the image acquisition module acquires images at the optimal shooting angle, and associates and stores the acquired image data with information such as the corresponding geographical location, acquisition time, and the attitude of the image acquisition module. The stored data format facilitates subsequent reading and processing by the green view rate analysis software.

[0081] In a second aspect of the present invention, the process described in the above flowchart can be implemented as a computer software program. That is, a non-volatile computer storage medium is provided, and the non-volatile computer storage medium stores one or more programs. When the one or more programs are executed by a device, the device executes the method of the first aspect of the present invention. In such an embodiment, the computer program can be downloaded and installed from the network through the communication part, and / or installed from a removable medium.

[0082] In a third aspect of the present invention, an electronic device is provided, as Figure 6 shown, including a display module, a positioning module, an inertial navigation module, an environment perception module, an image acquisition module, a memory, and a processor. A program is stored in the memory, and when the program is executed by the processor, the method of the first aspect of the present invention is implemented.

[0083] In a specific example, a backpack-type green view rate acquisition device suitable for implementing the present embodiment includes a central processing module (CPU), which can execute the method of the first aspect of the present application according to a program stored in a read-only memory (ROM) or a program loaded from a storage part into a random access memory (RAM). The CPU, ROM, and RAM are connected to each other through a bus. An input / output (I / O) interface is also connected to the bus.

[0084] The following components are connected to the I / O interface: an input part including a keyboard, a mouse, etc.; an output part including a liquid crystal display (LCD), a speaker, etc.; a storage part including a hard disk, etc.; and a communication part including a network interface card such as a LAN card, a modem, etc. The communication part performs communication processing via a network such as the Internet. A drive is also connected to the I / O interface as needed. A removable medium, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive as needed, so that a computer program read from it can be installed into the storage part as needed.

[0085] The flowcharts and schematic diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of the system, method, and computer program product of this embodiment. In this regard, each block in the flowchart or schematic diagram can represent a module, a program segment, or a part of code, and the above-mentioned module, program segment, or part of code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than that marked in the accompanying drawings. For example, two consecutive blocks shown may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the schematic diagram and / or flowchart, as well as the combination of blocks in the schematic and / or flowchart, can be implemented by a dedicated hardware-based system that performs the specified function or operation, or can be implemented by a combination of dedicated hardware and computer instructions.

[0086] The modules described in this embodiment can be implemented in software. The described modules can also be provided in a processor.

[0087] It should be noted that in the description of the present invention, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including", or any other variation thereof is intended to cover non-exclusive inclusion.

[0088] Obviously, the above-mentioned embodiments of the present invention are merely examples for clearly illustrating the present invention, rather than limitations on the implementation manners of the present invention. For those of ordinary skill in the art, other different forms of changes or modifications can be made based on the above description. It is impossible to list all the implementation manners here. Any obvious changes or modifications derived from the technical solutions of the present invention still fall within the protection scope of the present invention.

Claims

1. A green view rate acquisition method, applied to a backpack green view rate acquisition device, characterized in that: The device includes a display module, a positioning module, an inertial navigation module, an environment perception module and an image acquisition module. The method comprises: S10, presenting an initial path and collection points marked along the path on the display module; S20, when the wearer of the acquisition device moves along the initial path, the surrounding environment is acquired in real time through the environment perception module; S30, determining whether the surrounding environment conditions obtained in real time meet the path optimization conditions; S40, if the surrounding environment conditions acquired in real time meet the path optimization conditions, presenting the optimized path and the optimized collection points marked along the optimized path on the display module; S50, in response to the wearer arriving at the optimized acquisition point, adjusting the posture of the image acquisition module to an optimized posture based on the surrounding environment at the optimized acquisition point acquired by the environment perception module; S60: driving the image acquisition module to shoot in the optimized posture.

2. The method according to claim 1, characterized in that The method further includes: if the surrounding environment conditions acquired in real time do not meet the path optimization conditions, the current path and the collection points marked along the current path are still presented on the display module.

3. The method according to claim 1, characterized in that S10 includes: S100, presenting a map of an area where green viewing rate collection is to be performed on the display module; S102, calculating an initial path based on the collection starting point and end point input by the wearer and the geographic information in the area; S104, calculating acquisition points along the path based on the calculated initial path and the preset exposure interval; S106: Present the calculated initial path and the calculated collection points on the map.

4. The method according to claim 3, characterized in that The geographic information includes one or more of road network information, building location information, and park green space information.

5. The method according to claim 3 or 4, characterized in that: S30 includes: Determine whether there is an obstacle in front of the wearer. If so, it meets the path optimization conditions.

6. The method according to claim 5, characterized in that S40 includes: S400, calculating an optimized path, wherein the optimized path bypasses the obstacle; S402, calculating optimized acquisition points along the optimized path based on the calculated optimized path and the preset exposure interval; S404: Present the calculated optimized path and the calculated optimized collection points on the map.

7. The method according to claim 1, characterized in that The adjusting the posture of the image acquisition module to the optimized posture based on the surrounding environment at the optimized acquisition point acquired by the environment perception module includes: Calling the posture control algorithm to analyze the characteristics of the shooting scene according to the surrounding environment; Based on the characteristics, the posture of the image acquisition module is adjusted to an optimized posture.

8. The method according to claim 1, characterized in that: The device also includes a motor, through which the posture of the image acquisition module is adjusted to the optimized posture.

9. A storage medium, characterized in that: The storage medium stores a program, and when the program is executed by the processor, the method according to any one of claims 1 to 8 is implemented.

10. A green viewing rate acquisition device, characterized in that: The device includes a display module, a positioning module, an inertial navigation module, an environment perception module, an image acquisition module, a memory and a processor, wherein the memory stores a program, and when the program is executed by the processor, the method according to any one of claims 1-8 is implemented.

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