Image transmission and processing system based on big data

Abstract

The present invention discloses an image transmission and processing system based on big data, comprising a data acquisition module, a camera unit, a video data processing module and a boundary data transmission module. The video data processing module is used to analyze and process the situation of tourists staying in the picture information obtained by the data acquisition module. The present invention focuses and filters the image of the central area shot by the camera through the video data processing module, and performs brightness analysis on the rock part in the picture, so that the monitoring and processing system can detect the safety of tourists shooting on the rocks in the scenic area more accurately, thereby improving the tourists' sightseeing safety; the method of dividing the camera shooting picture area through the touch area analysis submodule and extracting the key stepping area to predict the human body posture effectively avoids the problem of being blocked, simplifies the prediction steps, and increases the sustainability of the operation of the monitoring system. The present invention has the characteristics of strong image recognition ability and high humanization.

Description

Technical Field

[0001] The present invention relates to the field of image communication technology, and in particular to an image transmission and processing system based on big data. Background Art

[0002] With the advent of the Internet era, the application scope of computers is becoming more and more extensive, and they can better transmit and process various image data. With the increasing demand for video communication, the application scope of video communication is also expanding. Excellent image processing technology can well ensure that the original pixels can be retained to the greatest extent during the transmission process of the video. However, due to the limitations of existing channels, video images are prone to image content loss and degradation after data transmission.

[0003] During holidays, many tourists like to climb mountains. In the process of climbing mountains, there may be some hidden dangers, such as steep slopes or cliffs. Some tourists feel that the scenery is particularly good, and they will stand outside the mountain to take pictures of the scenery, especially in some foggy and humid weather. The friction of the rocks is small, and it is easy to fall due to instability when standing on them, causing unpredictable dangers. In the prior art, the scenic area monitoring system uses scenic area cameras to monitor passers-by for safety, and uses methods such as human joint point detection or human body detection to monitor human posture and determine whether there are safety risks for tourists in the target area. However, due to the complex environment of the scenic area and the high degree of confusion of the target image in the shooting picture, the shooting system often finds it difficult to achieve the recognition purpose; at the same time, the intelligent monitoring system installed in the scenic area has too high a processing load under long-term operation. Therefore, it is necessary to design an image transmission and processing system based on big data with strong image recognition capabilities and high humanization. Summary of the invention

[0004] The purpose of the present invention is to provide an image transmission and processing system based on big data to solve the problems raised in the above background technology.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions: an image transmission and processing system based on big data, including a data acquisition module, a camera unit, a video data processing module and a boundary data transmission module, wherein the data acquisition module is used to obtain mountain image information; the camera unit is used to shoot the regional image through the current regional camera; the video data processing module is used to analyze and process the situation of tourists staying in the image information obtained by the data acquisition module; the boundary data transmission module is used to output the processed mountain danger boundary image;

[0006] The video data processing module includes a recognition module, which is used to perform feature recognition on tourists in the acquired picture information;

[0007] The recognition module further includes a tourist posture processing submodule and a touch area analysis submodule. The tourist posture processing submodule is used to obtain the safety factor of the tourist's posture when he stays in the picture; the touch area analysis submodule is used to analyze the safety value of the rock or road at the tourist's staying position through the picture information.

[0008] According to the above technical solution, the data acquisition module includes a video data acquisition module and a terrain information acquisition module. The video data acquisition module is used to store the captured image information; the terrain information acquisition module is used to obtain relevant information of the current mountain.

[0009] According to the above technical solution, the video data processing module also includes a video data editing module, and the video data editing module is used to cut the picture area through the contour division of the recognition module.

[0010] According to the above technical solution, the data acquisition module, the video data processing module and the boundary data transmission module are connected to communicate with each other.

[0011] According to the above technical solution, the operating method of the image monitoring and processing system further includes:

[0012] Step S1: Connecting an image monitoring and processing system to a scenic spot introduction device required to be brought into the scenic spot, and the tourist brings the device into the scenic spot;

[0013] Step S2: When the positioning system set in the scenic spot introduction device reaches the preset area, an indication signal is sent out, and the scenic spot camera is started and a panoramic picture of the scenic spot area is recorded;

[0014] Step S3: Recognize the human figure through the video image. When the human figure enters the specific area defined by the system, the system sends an electrical signal to start the recognition module, analyzes the area where the human figure is located, and defines the specific location where the tourist group stays on the rock;

[0015] Step S4: The tourist posture processing submodule further analyzes the specific position where the person steps by using image feature recognition technology, and calculates whether there is a safety risk when the tourist stays in the picture based on the analysis results;

[0016] Step S5: The system extracts the volume information of the target task's body on the rock that exceeds the safe area, obtains whether the tourist's staying position is safe through the picture information, divides the picture into boundaries, separates the safe staying area and the dangerous area, and stores the divided picture in the scenic area staff maintenance system.

[0017] According to the above technical solution, step S3 further includes:

[0018] Step S31: When the camera is shooting, the camera continuously changes the shooting direction according to the shooting direction trajectory route set by the internal system, and focuses on the central area images of the shooting pictures at different positions, and intercepts the focused central area. The specifically intercepted central area is proportionally reduced relative to the total shooting image, and the area size is 1 / 9 of the total shooting image area;

[0019] Step S32: the system performs grayscale processing on the regional image, obtains the grayscale value of the current regional image, and screens the regional image;

[0020] Step S33: Arrange the acquired target area images in descending order by the grayscale value of each central area of ​​the screened image, extract the camera orientation positions corresponding to the n central areas with the lowest grayscale values, where the specific value of n is determined by the maximum angle that the current camera can rotate, and after obtaining the relationship information between the rotation angle of the camera and the captured image, further process the central area image.

[0021] According to the above technical solution, in step S32, the specific method for screening the regional image is:

[0022] Through image feature analysis technology, the contour area of ​​the rock in the central image of the current area is obtained, and the proportion of the contour area occupied by the rock in the picture area is obtained, which is F%. According to the color of the rock, the type of current rock and the gray value range of the corresponding rock type are automatically retrieved in the database. In the obtained regional image gray value, all regional images within the gray value range of the target rock are screened, and the corresponding regional images with the rock proportion F%>θ% in all regional images are screened as target regional images, where θ is the proportion of the rock part of the current scenic area outside the scenic area road.

[0023] According to the above technical solution, in step S33, the method for processing the central area image further includes:

[0024] When the system recognizes through image analysis that a tourist has entered one of the n images selected by the camera, the positioning system set in the tourist's attraction introduction device communicates with the distance measurement system in the surveillance camera. The system obtains the size ratio of the tourist image captured by the camera and the relative height H of the camera set up on the ground in the current attraction area. Through the contour area of ​​the person in the image, the distance between the center image position and the shooting camera position is calculated as L, and the horizontal distance between the camera and the tourist is calculated as Camera and tourists’ shooting angles

[0025] According to the above technical solution, step S4 further includes:

[0026] Step S41: The system retrieves the visual image of the rock screen at the target time point, scans and identifies the contour nodes of the rock area, calibrates the upper convex point, the lower concave point, the supporting leg stepping point of the character and the non-supporting leg stepping point of the rock surface, connects the upper convex point and the most concave point, the lower convex point and the most concave point, respectively, measures the angles θ1 and θ2 between the connecting line and the horizontal plane, and calculates the volume of the character's body exceeding the safe area when on the rock through the formula Wherein, α represents the influence coefficient of angle θ1 on the volume outside the safe area crossed by the human body, β represents the influence coefficient of angle θ2 on the volume outside the safe area crossed by the human body, ε represents the influence coefficient of the current camera shooting angle θ3 on the volume outside the safe area crossed by the human body, T% is the reduction ratio of the shooting picture and the actual object obtained by the system at the current camera focal length, Q represents 1 / 2 of the length of the legs of the human body in the shooting picture, and S0 represents the area of ​​the total area of ​​the picture occupied by the person in the shooting picture;

[0027] Step S42: the recognition module determines the size relationship between V1 and the maximum offset safety volume V0 of the human body's center of gravity. When V1>V0, the recognition module stores the volume information of the target person's body on the rock that exceeds the safety area.

[0028] According to the above technical solution, in step S41, the specific method of calibrating the convex points, concave points, the supporting leg stepping points of the person and the non-supporting leg stepping points of the person on the rock surface further includes:

[0029] Step S411: extracting all the regional image information obtained in the step S3, arranging the grayscale values ​​except the central area in the same regional image in descending order, extracting the corresponding area with the highest grayscale value and the corresponding area with the lowest grayscale value, and setting them as the "lower concave point area" and the "upper convex point area" respectively;

[0030] Step S412: A reference point is calibrated at the location of the positioning system through the positioning system provided in the scenic spot introduction device on the tourist, and the specific area in the regional image where the reference point falls is detected. If the specific area where the object falls is located in the "lower concave point area" or the "upper convex point area", the stepping point of the object's supporting leg and the stepping point of the person's non-supporting leg are respectively calibrated in the adjacent area of ​​the specific area where the object falls; if the specific area where the object falls is not located in the "lower concave point area" or the "upper convex point area", a parallel line is drawn through the reference point parallel to the line connecting the center points of the "lower concave point area" and the "upper convex point area", and the stepping point of the object's supporting leg and the stepping point of the person's non-supporting leg are respectively calibrated in the area corresponding to the outermost end of the regional image where the parallel line passes.

[0031] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: the present invention focuses and filters the image of the central area captured by the camera through the video data processing module, thereby reducing the amount of camera calculation and further reducing the image analysis area, achieving more accurate positioning with the scenic area device carried by tourists, and performing brightness analysis on the rock part in the picture. By analyzing the smoothness of the rock surface, it is judged whether there is a safety risk when tourists step on the rock, so that the monitoring and processing system can detect the safety of tourists shooting on the rocks in the scenic area more accurately, thereby improving the tourists' tour safety; the method of dividing the camera shooting picture area through the touch area analysis submodule and extracting the key stepping area to predict the human body posture effectively avoids the problem of being blocked; at the same time, the prediction steps are simplified, the calculation difficulty is reduced, and the sustainability of the operation of the monitoring system is increased. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0033] Figure 1 It is a schematic diagram of the system module composition of the present invention. DETAILED DESCRIPTION

[0034] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0035] See also Figure 1 The present invention provides a technical solution: an image transmission and processing system based on big data, comprising:

[0036] The data acquisition module, the camera unit, the video data processing module and the boundary data transmission module are used to obtain the mountain image information; the camera unit is used to shoot the area image through the current area camera; the video data processing module is used to analyze and process the tourist stay situation in the image information obtained by the data acquisition module; the boundary data transmission module is used to output the processed mountain danger boundary image;

[0037] The video data processing module includes a recognition module, which is used to perform feature recognition on tourists in the acquired picture information;

[0038] The recognition module further includes a tourist posture processing submodule and a touch area analysis submodule. The tourist posture processing submodule is used to obtain the safety factor of the tourist's posture when he stays in the picture; the touch area analysis submodule is used to analyze the safety value of the rock or road where the tourist stays through the picture information.

[0039] The present invention focuses and filters the image of the central area shot by the camera through the video data processing module, while reducing the camera calculation amount, the image analysis area is further reduced, and a more accurate positioning is achieved with the scenic area device carried by the tourists. The brightness of the rock part in the picture is analyzed to effectively obtain the smoothness of the rock surface, so that the monitoring and processing system can detect the safety of tourists shooting on the rocks in the scenic area more accurately, thereby improving the tourists' sightseeing safety. The method of dividing the camera shooting picture area through the touch area analysis submodule and extracting the key stepping area to predict the human body posture effectively avoids the problem of being blocked. At the same time, the prediction steps are simplified, the difficulty of calculation is reduced, and the sustainability of the operation of the monitoring system is increased.

[0040] The data acquisition module includes a video data acquisition module and a terrain information acquisition module. The video data acquisition module is used to store the captured image information; the terrain information acquisition module is used to obtain relevant information of the current mountain.

[0041] The video data processing module also includes a video data editing module, which is used to cut the picture area through the contour division of the recognition module.

[0042] The data acquisition module, the video data processing module and the boundary data transmission module are connected to communicate with each other.

[0043] In a preferred embodiment, the operating method of the image monitoring and processing system further comprises:

[0044] Step S1: Connecting an image monitoring and processing system to a scenic spot introduction device required to be brought into the scenic spot, and the tourist brings the device into the scenic spot;

[0045] Step S2: When the positioning system set in the scenic spot introduction device reaches the preset area, an indication signal is sent out, and the scenic spot camera is started and a panoramic picture of the scenic spot area is recorded;

[0046] Step S3: Recognize the human figure through the video image. When the human figure enters the specific area defined by the system, the system sends an electrical signal to start the recognition module, analyzes the area where the human figure is located, and defines the specific location where the tourist group stays on the rock;

[0047] Step S4: The tourist posture processing submodule further analyzes the specific position where the person steps by using image feature recognition technology, and calculates whether there is a safety risk when the tourist stays in the picture based on the analysis results;

[0048] Step S5: The system extracts the volume information of the target task's body on the rock that exceeds the safe area, obtains whether the tourist's staying position is safe through the picture information, divides the picture into boundaries, and separates the safe area and the dangerous area, where the safe area is black and the dangerous area is white, and stores the divided picture in the scenic area staff maintenance system.

[0049] In this embodiment, step S3 further includes:

[0050] Step S31: When the camera is shooting, the camera continuously changes the shooting direction according to the shooting direction trajectory route set by the internal system, and focuses on the central area images of the shooting pictures at different positions, and intercepts the focused central area. The specifically intercepted central area is proportionally reduced relative to the total shooting image, and the area size is 1 / 9 of the total shooting image area;

[0051] Through this technical solution, by focusing the image of the central area taken by the camera, the camera's calculation amount can be reduced while the image analysis area can be further reduced, achieving more accurate positioning with the scenic area devices carried by tourists, making the monitoring and processing system more accurate in detecting the safety of tourists shooting on the rocks in the scenic area, thereby improving the safety of tourists' tours.

[0052] Step S32: the system performs grayscale processing on the regional image, obtains the grayscale value of the current regional image, and screens the regional image;

[0053] Step S33: Arrange the acquired target area images in descending order by the grayscale value of the central area of ​​each image after screening, and extract the camera orientation positions corresponding to the n central areas with the lowest grayscale values, where the specific value of n is determined by the maximum angle that the current camera can rotate. The larger the maximum angle that the camera can rotate, the larger the value of n. When the camera can rotate to 360°, the value of n is 10. After obtaining the relationship information between the rotation angle of the camera and the captured image, the central area image is further processed.

[0054] The grayscale value represents the color depth of the point in the black and white image. The grayscale value range is [0, 255]. A grayscale value of 255 corresponds to white, and a grayscale value of 0 corresponds to black. The higher the grayscale value, the higher the brightness of the current image.

[0055] When tourists are walking and sightseeing in the scenic area, the environment of the scenic area will be greatly different at different locations on the scenic area roads. Some scenic area scenes have low terrain and low brightness. These locations have low safety risk factors for tourists due to the high frequency of tourists passing by and complete construction facilities; however, some scenic area scenes have high terrain and high brightness. These locations are steep. Due to the low frequency of tourists passing by and the low completeness of scenic area protection facilities compared to low-lying facilities, the safety risk factors for tourists are high. Therefore, the grayscale value of the current camera shot can be used as an effective criterion for identifying the risk factor of tourists playing at the current location.

[0056] Similarly, in the same location, the density of trees and rock-like obstructions will also affect the brightness of the camera image. The smaller the grayscale value detected, the higher the density of trees and rock-like obstructions, and the smaller the coefficient of safety risk for tourists; the larger the grayscale value detected, the lower the density of trees and rock-like obstructions, and the greater the coefficient of safety risk for tourists.

[0057] Through this technical solution, the problem of excessive processing load due to long-term operation of the intelligent photography and monitoring system installed in the scenic area is solved. By monitoring the grayscale of the image, the safety factor of tourists visiting different locations in the scenic area can be effectively identified, and the sampled images at different locations can be screened. While improving the monitoring accuracy, the operating load of the monitoring system is greatly reduced.

[0058] In step S32 of this embodiment, the specific method for screening the regional image is:

[0059] Through image feature analysis technology, the contour area of ​​the rock in the central image of the current area is obtained, and the proportion of the contour area occupied by the rock in the picture area is obtained, which is F%. According to the color of the rock, the type of current rock and the gray value range of the corresponding rock type are automatically retrieved in the database. In the obtained regional image gray value, all regional images within the gray value range of the target rock are screened, and the corresponding regional images with the rock proportion F%>θ% in all regional images are screened as target regional images, where θ is the proportion of the rock part of the current scenic area outside the scenic area road.

[0060] When light hits the rock, it will produce different degrees of diffuse reflection. The smoothness of the rock determines the intensity of the light that hits the rock and reflects toward the camera.

[0061] On the other hand, the smoothness of the rock also determines the possibility of safety risks for tourists after stepping on the rock. The smoother the rock, the higher the possibility of safety risks for tourists; the rougher the rock, the lower the possibility of safety risks for tourists.

[0062] By analyzing the brightness of the rock part in the picture, the smoothness of the rock surface can be effectively obtained, which serves as a strong basis for judging whether there is a safety risk when tourists step on the rocks.

[0063] In step S33 of this embodiment, the method for processing the central area image further includes:

[0064] When the system recognizes through image analysis that a tourist has entered one of the n images selected by the camera, the positioning system set in the tourist's attraction introduction device communicates with the distance measurement system in the surveillance camera. The system obtains the size ratio of the tourist image captured by the camera and the relative height H of the camera set up on the ground in the current attraction area. Through the contour area of ​​the person in the image, the distance between the center image position and the shooting camera position is calculated as L, and the horizontal distance between the camera and the tourist is calculated as Camera and tourists’ shooting angles

[0065] In this embodiment, step S4 further includes:

[0066] Step S41: The system retrieves the visual image of the rock screen at the target time point, scans and identifies the contour nodes of the rock area, calibrates the upper convex point, the lower concave point, the supporting leg stepping point of the character and the non-supporting leg stepping point of the rock surface, connects the upper convex point and the most concave point, the lower convex point and the most concave point, respectively, measures the angles θ1 and θ2 between the connecting line and the horizontal plane, and calculates the volume of the character's body exceeding the safe area when on the rock through the formula Wherein, α represents the influence coefficient of angle θ1 on the volume outside the safe area crossed by the human body, β represents the influence coefficient of angle θ2 on the volume outside the safe area crossed by the human body, ε represents the influence coefficient of the current camera shooting angle θ3 on the volume outside the safe area crossed by the human body, T% is the reduction ratio of the shooting picture and the actual object obtained by the system at the current camera focal length, Q represents 1 / 2 of the length of the legs of the human body in the shooting picture, and S0 represents the area of ​​the total area of ​​the picture occupied by the person in the shooting picture;

[0067] Step S42: the recognition module determines the size relationship between V1 and the maximum offset safety volume V0 of the human body's center of gravity. When V1>V0, the recognition module stores the volume information of the target person's body on the rock that exceeds the safety area.

[0068] The volume detection method for human bodies crossing safe areas through this technical solution solves the problem of high reliance on the accuracy of human posture recognition, which leads to difficulty in monitoring and recognition in actual applications and low recognition efficiency, making the safety prediction of tourists more accurate.

[0069] In step S41 of this embodiment, the specific method of calibrating the convex points, concave points, the supporting leg stepping points of the person, and the non-supporting leg stepping points of the person on the rock surface further includes:

[0070] Step S411: extracting all the regional image information obtained in step S3, arranging the grayscale values ​​except the central area in the same regional image in descending order, extracting the corresponding area with the highest grayscale value and the corresponding area with the lowest grayscale value, and setting them as the "lower concave point area" and the "upper convex point area" respectively;

[0071] Step S412: A reference point is calibrated at the location of the positioning system through the positioning system provided in the scenic spot introduction device on the tourist, and the specific area in the regional image where the reference point falls is detected. If the specific area where the object falls is located in the "lower concave point area" or the "upper convex point area", the stepping point of the object's supporting leg and the stepping point of the person's non-supporting leg are respectively calibrated in the adjacent area of ​​the specific area where the object falls; if the specific area where the object falls is not located in the "lower concave point area" or the "upper convex point area", a parallel line is drawn through the reference point parallel to the line connecting the center points of the "lower concave point area" and the "upper convex point area", and the stepping point of the object's supporting leg and the stepping point of the person's non-supporting leg are respectively calibrated in the area corresponding to the outermost end of the regional image where the parallel line passes.

[0072] In the prior art, current human posture recognition methods estimate and track human posture by analyzing the joints of the target human body in the image or by human body detection. However, due to the single location of the camera in the scenic area, the shooting angle is greatly limited, especially when part of the picture is blocked, it is difficult to identify all the joints of the target human body in the shooting picture. This technical solution divides the camera shooting picture area and extracts the key stepping area to predict the human posture, which effectively avoids the problem of being blocked; at the same time, it simplifies the prediction steps, reduces the difficulty of calculation, and increases the sustainability of the monitoring system operation.

[0073] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0074] Finally, it should be noted that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. An image transmission and processing system based on big data, characterized by: It includes a data acquisition module, a camera unit, a video data processing module and a boundary data transmission module. The data acquisition module is used to obtain mountain image information; the camera unit is used to shoot the area image through the current area camera; the video data processing module is used to analyze and process the situation of tourists staying in the image information obtained by the data acquisition module; the boundary data transmission module is used to output the processed mountain danger boundary image; The video data processing module includes a recognition module, which is used to perform feature recognition on tourists in the acquired picture information; The recognition module further includes a tourist posture processing submodule and a touch area analysis submodule, wherein the tourist posture processing submodule is used to obtain the safety factor of the tourist's posture when the tourist stays in the picture; the touch area analysis submodule is used to analyze the safety value of the rock or road where the tourist stays through the picture information; The data acquisition module includes a video data acquisition module and a terrain information acquisition module. The video data acquisition module is used to store the captured image information; the terrain information acquisition module is used to acquire relevant information of the current mountain; The video data processing module further includes a video data editing module, which is used to cut the picture area by the contour division of the recognition module; The data acquisition module, the video data processing module and the boundary data transmission module are connected to each other for communication; The operating method of the image transmission and processing system further comprises: Step S1: installing an image transmission and processing system in a scenic spot introduction device required to be carried when entering a scenic spot, and the tourist carries the device with him / her into the scenic spot; Step S2: When the positioning system set in the scenic spot introduction device reaches the preset area, an indication signal is sent out, and the scenic spot camera is started and a panoramic picture of the scenic spot area is recorded; Step S3: Recognize the human figure through the video image. When the human figure enters the specific area defined by the system, the system sends an electrical signal to start the recognition module, analyzes the area where the human figure is located, and defines the specific location where the tourist group stays on the rock; Step S4: The tourist posture processing submodule further analyzes the specific position where the person steps by using image feature recognition technology, and calculates whether there is a safety risk when the tourist stays in the picture based on the analysis results; Step S5: The system extracts the volume information of the target person's body on the rock that exceeds the safe area, obtains whether the tourist's stay position is safe through the picture information, divides the picture into a safe stay area and a dangerous area, and stores the divided picture in the scenic area staff maintenance system; The step S3 further comprises: Step S31: When the camera is shooting, the camera continuously changes the shooting direction according to the shooting direction trajectory route set by the internal system, and focuses on the central area images of the shooting pictures at different positions, and intercepts the focused central area. The specifically intercepted central area is proportionally reduced relative to the total shooting image, and the area size is 1 / 9 of the total shooting image area; Step S32: the system performs grayscale processing on the regional image, obtains the grayscale value of the current regional image, and screens the regional image; Step S33: Arrange the acquired target area images in descending order by the grayscale value of each central area of ​​the screened image, extract the camera orientation positions corresponding to the n central areas with the lowest grayscale values, where the specific value of n is determined by the maximum angle that the current camera can rotate, and after obtaining the relationship information between the rotation angle of the camera and the captured image, further process the central area image.

2. The image transmission and processing system based on big data according to claim 1, characterized in that: In step S32, the specific method for screening the regional image is: Through image feature analysis technology, the outline area of ​​the rock in the current central area image is obtained, and the proportion of the outline area occupied by the rock in the picture area is obtained. According to the color of the rock, the type of the current rock and the gray value range of the corresponding rock type are automatically retrieved in the database. In the obtained regional image gray value, all regional images within the gray value range of the target rock are screened, and the proportion of rock in all regional images is calculated. The corresponding area image is screened as the target area image, where θ% is the proportion of the rock part of the current scenic area outside the scenic area road.

3. The image transmission and processing system based on big data according to claim 2, characterized in that: In step S33, the method for processing the central area image further includes: When the system recognizes through image analysis that a tourist has entered one of the n images selected by the camera, the positioning system set in the tourist's attraction introduction device communicates with the distance measurement system in the surveillance camera. The system obtains the size ratio of the tourist image captured by the camera and the relative height H of the camera set up on the ground in the current scenic area. Through the contour area of ​​the person in the image, it is inferred that the distance between the image position of the center area and the shooting camera position is L, and the horizontal distance between the camera and the tourist is , camera and tourists’ shooting angles .

4. The image transmission and processing system based on big data according to claim 3, characterized in that: The step S4 further comprises: Step S41: The system retrieves the visual image of the rock screen at the target time point, scans and identifies the contour nodes of the rock area, calibrates the upper convex point, the lower concave point, the supporting leg stepping point of the person and the non-supporting leg stepping point of the rock surface, connects the upper convex point and the most concave point, the lower convex point and the most concave point, and measures the angle between the connecting line and the horizontal plane. , the volume of the character's body exceeding the safe area when on the rock is calculated by the formula , where Indicates the angle The influence coefficient of the angle on the volume outside the safety area crossed by the human body, Indicates the angle The influence coefficient of the angle on the volume outside the safety area crossed by the human body, Indicates the current camera shooting angle The coefficient of influence on the volume of the human body crossing the safe area, T% is the reduction ratio of the shooting picture and the actual object obtained by the system at the current camera focal length, Q represents 1 / 2 of the length of the legs of the human body in the shooting picture, and S0 represents the area of ​​the person in the shooting picture; Step S42: the recognition module determines the size relationship between V1 and the maximum offset safety volume V0 of the human body's center of gravity. When V1>V0, the recognition module stores the volume information of the target person's body on the rock that exceeds the safety area.

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