CN116709037A - 5G camera-based method for transmitting monitoring key data of weather and rain - Google Patents
5G camera-based method for transmitting monitoring key data of weather and rain Download PDFInfo
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N23/00—Cameras or camera modules comprising electronic image sensors; Control thereof
- H04N23/80—Camera processing pipelines; Components thereof
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- H—ELECTRICITY
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- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L65/00—Network arrangements, protocols or services for supporting real-time applications in data packet communication
- H04L65/60—Network streaming of media packets
- H04L65/65—Network streaming protocols, e.g. real-time transport protocol [RTP] or real-time control protocol [RTCP]
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L65/00—Network arrangements, protocols or services for supporting real-time applications in data packet communication
- H04L65/80—Responding to QoS
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N23/00—Cameras or camera modules comprising electronic image sensors; Control thereof
- H04N23/80—Camera processing pipelines; Components thereof
- H04N23/81—Camera processing pipelines; Components thereof for suppressing or minimising disturbance in the image signal generation
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N7/00—Television systems
- H04N7/18—Closed-circuit television [CCTV] systems, i.e. systems in which the video signal is not broadcast
- H04N7/181—Closed-circuit television [CCTV] systems, i.e. systems in which the video signal is not broadcast for receiving images from a plurality of remote sources
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- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
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Abstract
The application discloses a 5G camera-based method for transmitting monitoring key data of weather and rain, which comprises the following steps: the main control CPU is connected with the camera through the CSI interface to acquire video data; the data processing module generates core data of the image or the video; and the AI pre-analysis module generates a key data stream uploading cloud according to the processed image and video information. According to the 5G camera-based method for transmitting the monitoring key data of the weather and the weather, the image processing frame and the video processing frame are integrated into a unified processing flow, so that modularized registration and dynamic loading are realized, and the processing efficiency is improved. The AI pre-analysis module performs advanced analysis to generate a key data stream, and the cloud can obtain clear data only by a small amount of video data. The application has better effects in the aspects of processing speed, bad weather resistance, data pertinence and the like.
Description
Technical Field
The application relates to the technical field of camera identification, in particular to a 5G camera-based method for transmitting monitoring key data of weather and wind.
Background
Along with the development of science and technology, the 5G technology provides faster and more stable network connection for the fields of Internet of things, intelligent cities, remote monitoring and the like. However, in practical applications, due to the influence of severe weather conditions such as wind and rain, the camera may be blocked when acquiring images and videos, resulting in degradation of image quality, thereby affecting the accuracy of target detection and tracking. In addition, fluctuations in the network signal strength may also cause delays or interruptions in data transmission, further reducing the reliability of the monitoring system.
Existing monitoring technologies face a number of challenges in weather conditions: the image and video quality is affected by natural factors such as rainwater, wind speed and the like, the network signal of the weather and the wind is poor, the sensor data is affected by the fluctuation of the network signal strength, the data transmission is possibly unstable, the data is transmitted in error, the transmitted data is too much and lack of pertinence, and the processing is too slow; the existing image processing frame and video processing frame are relatively independent and mainly distributed on the cloud, and a camera lacks a built-in preprocessing module, so that the data processing flow is not flexible and efficient.
In view of the above, there is a need for a method for transmitting monitoring key data based on weather of a 5G camera, which improves performance and reliability of a monitoring system under severe weather conditions by optimizing processing and transmission of image and video data under weather. By adopting a modularized design, the flexible configuration and the efficient operation of the image processing frame and the video processing frame are realized, and a new solution is provided for the development of future monitoring technology.
Disclosure of Invention
This section is intended to outline some aspects of embodiments of the application and to briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section as well as in the description of the application and in the title of the application, which may not be used to limit the scope of the application.
The present application has been made in view of the above-described problems.
Therefore, the technical problems solved by the application are as follows: the existing monitoring technology has the problems of serious weather influence, complete monitoring data transmission, lack of pertinence, slow processing and optimization of how to distribute the data processing flow on a camera and a cloud.
In order to solve the technical problems, the application provides the following technical scheme: A5G camera-based monitoring key data transmission method for weather and rain comprises the following steps:
the main control CPU is connected with the camera through the CSI interface to acquire video data;
the data processing module generates core data of the image or the video;
and the AI pre-analysis module generates a key data stream uploading cloud according to the processed image and video information.
As a preferable scheme of the 5G camera-based weather and wind monitoring key data transmission method, the application comprises the following steps: the main control CPU is connected with the cameras through the CSI interfaces to obtain video data, wherein the main control CPU is connected with the multi-path digital cameras or the MIPI cameras through the CSI interfaces, and the cameras transmit images and videos to the image processing frame and the video processing frame.
As a preferable scheme of the 5G camera-based weather and wind monitoring key data transmission method, the image processing frame and the video processing frame comprise an image processing module, wherein after the image processing frame receives an image, the image processing module takes decoded image data as a core to generate hanging image filtering, morphological operation, feature segmentation, feature extraction, edge detection and image enhancement, and after the processing is finished, the image is encoded into jpeg and png formats and sent to an AI pre-analysis module;
after the video processing framework receives the video, the video is converted into video data blocks based on coding H.265, and the video data blocks are used as cores to generate video processing modules for hanging code stream conversion, video compression, video enhancement, video preloading, RTSP pushing and RTP pushing.
As a preferable scheme of the 5G camera-based weather and wind monitoring key data transmission method, the application comprises the following steps: the image processing frame and the video processing frame further comprise a video processing module and an image processing module which are registered to the frame in a modularized mode, dynamically load and preload the hooking function, and control to start or stop the hooking function through an open interface API.
As a preferable scheme of the 5G camera-based weather and wind monitoring key data transmission method, the application comprises the following steps: the AI pre-analysis module comprises that after the processing of the images and the videos is completed, serial data are used for receiving external wind speed and rainfall data, a sensor program is used for pushing the received data to a cloud end, the cloud end sends a control command to a control logic unit, and the logic unit judges the wind speed and the rainfall to operate;
when the wind speed is less than 6m/s and the rainfall is less than 8mm/h, judging that the camera has rainwater, and not affecting the camera to acquire images and videos, if the network signal strength is more than-70 dBm, directly uploading the videos and image data to a cloud end, if the network signal strength is less than-70 dBm, reducing the resolution of the images and the videos, improving the compression ratio, reducing the transmission pressure, uploading the cloud end, and if the network signal strength is less than-75 dBm, considering equipment failure or being shielded by network information, storing the data in a data cache, and encrypting;
when the wind speed is less than or equal to 6m/s and less than or equal to 14m/s, the rainfall is less than or equal to 8mm/h and less than or equal to 18mm/h, judging that the camera has rainwater, the camera is blocked by the rainwater, the acquired image and video are partially blocked, the image and video are transferred into an AI pre-analysis module, noise is reduced through median filtering, an SSD target detection algorithm is utilized to identify an object, an OpenPose algorithm is utilized to detect a human body, a TLD target tracking algorithm is utilized to track the target, after a preset dangerous event is identified, a pre-analysis result is sent to a cloud in a warning mode, if the network signal intensity is less than-75 dBm, the serial data of the same area are received to judge the network signal intensity of the 5G camera in the same area, if the network signal intensity is less than-75 dBm, the signals are regarded as being affected by weather, otherwise, the equipment failure or network information shielding is judged;
when the wind speed is more than 14m/s and the rainfall is more than 18mm/h, judging that the camera has rainwater, the camera is blocked by the rainwater, acquiring all the images and videos, transferring the images and videos to an AI pre-analysis module, processing the images and videos through a deep learning rain removing network, fusing the images and videos to perform multi-frame image fusion analysis, performing target detection, human body detection and target tracking after the analysis is completed, optimizing and correcting tracking results by combining historical tracking data and target movement characteristics, forwarding the identified preset dangerous event to a cloud in an alarm mode after the correction is completed, temporarily storing the video stream or serial data in a data cache if the signal is less than-80 dBm, and re-supplementing the data after the network is recovered.
As a preferable scheme of the 5G camera-based weather and wind monitoring key data transmission method, the application comprises the following steps: the AI pre-analysis module further comprises, when the wind speed is more than 16m/s, the camera shakes, the acquired images and videos have the blurring phenomenon, the images and videos are transferred to the AI pre-analysis module, the images are subjected to low-filtering noise reduction, the images are extracted into non-moving frames, the moving information is calculated according to the pixel change between the two frames of images, the images are subjected to motion compensation according to the calculated moving information, and the processed images and videos are subjected to dangerous event identification.
As a preferable scheme of the 5G camera-based weather and wind monitoring key data transmission method, the key data flow comprises processed image and video data, wind speed and rain amount data, AI pre-analysis dangerous event types and a camera steering request instruction.
As a preferable scheme of the 5G camera-based weather and wind monitoring key data transmission method, the application comprises the following steps: the request camera steering instruction comprises the steps of sending a target tracking request to a cloud after the AI pre-analysis module identifies preset dangerous time, converting target tracking of recorded video into target tracking of a camera, and recording dangerous time states in real time.
As a preferable scheme of the 5G camera-based weather and wind monitoring key data transmission method, the application comprises the following steps: a computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements a method of monitoring critical data transfer based on weather and wind of a 5G camera when executing the computer program.
As a preferable scheme of the 5G camera-based weather and wind monitoring key data transmission method, the application comprises the following steps: a computer-readable storage medium, on which a computer program is stored,
the method is characterized in that the computer program is executed by a processor to realize a monitoring key data transmission method based on weather and wind of the 5G camera.
The application has the beneficial effects that: according to the 5G camera-based method for transmitting the monitoring key data of the weather and the weather, the image processing frame and the video processing frame are integrated into a unified processing flow, so that modularized registration and dynamic loading are realized, and the processing efficiency is improved. The AI pre-analysis module performs advanced analysis to generate a key data stream, and the cloud can obtain clear data only by a small amount of video data. The application has better effects in the aspects of processing speed, bad weather resistance, data pertinence and the like.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the description of the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art. Wherein:
fig. 1 is an overall flowchart of a method for transmitting monitoring key data based on weather and rain of a 5G camera according to an embodiment of the present application.
Detailed Description
So that the manner in which the above recited objects, features and advantages of the present application can be understood in detail, a more particular description of the application, briefly summarized above, may be had by reference to the embodiments, some of which are illustrated in the appended drawings. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments of the present application without making any inventive effort, shall fall within the scope of the present application.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present application, but the present application may be practiced in other ways other than those described herein, and persons skilled in the art will readily appreciate that the present application is not limited to the specific embodiments disclosed below.
Further, reference herein to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic can be included in at least one implementation of the application. The appearances of the phrase "in one embodiment" in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments.
While the embodiments of the present application have been illustrated and described in detail in the drawings, the cross-sectional view of the device structure is not to scale in the general sense for ease of illustration, and the drawings are merely exemplary and should not be construed as limiting the scope of the application. In addition, the three-dimensional dimensions of length, width and depth should be included in actual fabrication.
Also in the description of the present application, it should be noted that the orientation or positional relationship indicated by the terms "upper, lower, inner and outer", etc. are based on the orientation or positional relationship shown in the drawings, are merely for convenience of describing the present application and simplifying the description, and do not indicate or imply that the apparatus or elements referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as limiting the present application. Furthermore, the terms "first, second, or third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
The terms "mounted, connected, and coupled" should be construed broadly in this disclosure unless otherwise specifically indicated and defined, such as: can be fixed connection, detachable connection or integral connection; it may also be a mechanical connection, an electrical connection, or a direct connection, or may be indirectly connected through an intermediate medium, or may be a communication between two elements. The specific meaning of the above terms in the present application will be understood in specific cases by those of ordinary skill in the art.
Example 1
Referring to fig. 1, for one embodiment of the present application, there is provided a method for transmitting monitoring key data of weather and wind based on a 5G camera, including:
s1: the main control CPU is connected with the camera through the CSI interface to acquire video data.
Furthermore, the main control CPU is connected with the multi-path digital cameras or the MIPI cameras through the CSI interface, and the cameras transmit images and videos to the image processing frame and the video processing frame.
It should be noted that, through CSI interface connection multichannel digital camera or MIPI camera, can acquire the image or the video stream that a plurality of cameras gathered simultaneously, realize that single CPU acquires multiple items of data to send the data unification to image processing frame and video processing frame, guarantee the processing efficiency of equipment.
S2: the data processing module generates core data of an image or video.
Still further, an image processing framework: after the image processing frame receives the image, the decoded image data is used as a core to generate an image processing module for hooking image filtering, morphological operation, feature segmentation, feature extraction, edge detection and image enhancement, and after the processing is finished, the image is encoded into jpeg and png formats and sent to an AI pre-analysis module.
The video processing framework includes: after the video processing framework receives the video, the video is converted into video data blocks based on coding H.265, and the video data blocks are used as cores to generate video processing modules for hanging code stream conversion, video compression, video enhancement, video preloading, RTSP pushing and RTP pushing.
It should be noted that, the video processing module and the image processing module are registered to the framework in a modularized manner, and dynamically load and preload the hooking function, and control to enable or disable the hooking function through the open interface API.
It should also be noted that the hooking function can be dynamically loaded and preloaded according to actual requirements, thereby reducing unnecessary computing resource consumption. For example, in a scenario where edge detection is not required, no loading is performed, saving computing resources. Through the functional design, new functions can be conveniently added for the image processing frame and the video processing frame or the existing functions can be conveniently upgraded, the maintainability and the upgradeability of the system can be improved, and the requirements of future technical development are met.
Furthermore, the H.265 has the characteristic of high compression ratio, is suitable for transmitting data to the requirement under the condition that network environments are crossed by my application, can reduce the pressure of data transmission, and can locally store more videos under the condition that network signals do not meet the transmission. The H.265 has lower coding and decoding delay and is suitable for video application scenes which need real-time processing and transmission by a camera. The h.265 can reduce the code rate by half and the resource occupation under the same image quality compared with the h.264.
S3: and the AI pre-analysis module generates a key data stream uploading cloud according to the processed image and video information.
Still further, the AI pre-analysis module includes: after the processing of the images and the videos is completed, the serial data receive external wind speed and rainfall data, the sensor program pushes the received data to the cloud, the cloud sends a control command to the control logic unit, and the logic unit judges the wind speed and the rainfall to operate.
When the wind speed is less than 6m/s and the rainfall is less than 8mm/h, judging that the camera has rainwater, and not affecting the camera to acquire images and videos, if the network signal intensity is more than-70 dBm, directly uploading the videos and image data to a cloud end, if the network signal intensity is less than-70 dBm, reducing the resolution of the images and the videos, improving the compression ratio, reducing the transmission pressure, uploading the cloud end, and if the network signal intensity is less than-75 dBm, considering equipment failure or being shielded by network information, storing the data in a data cache, and encrypting.
When the wind speed is less than or equal to 6m/s and less than or equal to 14m/s, the rainfall is less than or equal to 8mm/h and less than or equal to 18mm/h, judging that the camera is in rainwater shielding, the acquired image and video are partially shielded by the rainwater, transferring the image and video to an AI pre-analysis module, reducing noise through median filtering, identifying an object by using an SSD target detection algorithm, detecting a human body by using an OpenPose algorithm, carrying out target tracking by using a TLD target tracking algorithm, sending a pre-analysis result to a cloud in an alarm mode after a preset dangerous event is identified, judging the network signal strength of a 5G camera in the same area if the network signal strength is less than-75 dBm, and if the network signal strength is less than-75 dBm, judging that the signal is affected by weather, otherwise, judging that the equipment is faulty or is shielded by network information.
When the wind speed is more than 14m/s and the rainfall is more than 18mm/h, judging that the camera has rainwater, the camera is blocked by the rainwater, acquiring all the images and videos, transferring the images and videos to an AI pre-analysis module, processing the images and videos through a deep learning rain removing network, fusing the images and videos to perform multi-frame image fusion analysis, performing target detection, human body detection and target tracking after the analysis is completed, optimizing and correcting tracking results by combining historical tracking data and target movement characteristics, forwarding the identified preset dangerous event to a cloud in an alarm mode after the correction is completed, temporarily storing the video stream or serial data in a data cache if the signal is less than-80 dBm, and re-supplementing the data after the network is recovered.
It should be noted that when the wind speed is greater than 16m/s, the camera shakes, the acquired images and videos have the blurring phenomenon, the images and videos are transferred to an AI pre-analysis module, the images are subjected to low-filtering noise reduction, the images are extracted into non-moving frames, the motion information is calculated according to the pixel change between the two frames of images, the images are subjected to motion compensation according to the calculated motion information, and the processed images and videos are subjected to dangerous event identification.
It should be further noted that, the selected threshold value is an experimental result, the wind speed threshold value is divided into three levels, namely 6m/s, 14m/s and 16m/s, the setting can fully consider the falling deviation capability of different wind forces to the rainwater, the real environment can be simulated, the rainwater quantity of the rainwater blown by the wind on the screen of the camera in the weather of wind and rain can be simulated, and the 16m/s is the jitter threshold value which accords with the measurement in the open environment after the camera is arranged. The network signal strength is set to be-70 dBm as an extreme value of stable transmission, when the network signal is lower than the threshold value, the transmission of a common camera is interrupted, but the application is set to be-80 dBm, because the application selects to convert images and videos into key data streams, the codes of the videos are H.265, the compression ratio is high, and the data stream transmission can be carried out only by short network smoothness.
It should be noted that the critical data streams include: processed image and video data, wind speed and rain amount data, AI pre-analysis dangerous event types and request camera steering instructions.
It should be further noted that, after the AI pre-analysis module identifies the preset dangerous time, a tracking target request is sent to the cloud end, the target tracking of the recorded video is converted into the target tracking of the camera, and the dangerous time state is recorded in real time.
Example 2
In order to verify the beneficial effects of the application, scientific demonstration is carried out through economic benefit calculation and simulation experiments.
Different weather conditions are set.
The same monitoring area adopts cameras of my application and traditional technical scheme respectively.
And respectively collecting images, videos and sensor data of two sets of monitoring systems under various weather and rain environments, and simultaneously recording the network signal intensity.
And uniformly processing and analyzing the collected data, and comparing the image and video quality of the two sets of monitoring systems under different weather environments with the data transmission effect of the sensor.
And analyzing the data processing speed and the data processing efficiency of the two sets of monitoring systems in different weather environments, and evaluating the real-time response capability of the systems to preset dangerous events.
As shown in the risk coping ability comparison table of table 1, the my application has lower signal requirements than the conventional technical scheme in any severe case, because the my application generates critical data streams for video data and image data in the case of sudden dangerous events, reduces the requirement for network signals, and thus, the my application is easier to avoid network interruption in case of network signal proximity. Since the image is processed before the key data stream is generated by my application, the image quality is remarkably improved because the image quality is prevented from being degraded due to the transmission problem. The dangerous event information is converted into the key data stream and sent to the cloud, so that the problem of alarm delay caused by data redundancy is effectively avoided, and the capability of coping with the emergency crisis is greatly improved.
Table 1 comparison table of risk coping capacities
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
Logic and/or steps represented in the flowcharts or otherwise described herein, e.g., a ordered listing of executable instructions for implementing logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.
More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). In addition, the computer readable medium may even be paper or other suitable medium on which the program is printed, as the program may be electronically captured, via, for instance, optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory.
It is to be understood that portions of the present application may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, may be implemented using any one or combination of the following techniques, as is well known in the art: discrete logic circuits having logic gates for implementing logic functions on data signals, application specific integrated circuits having suitable combinational logic gates, programmable Gate Arrays (PGAs), field Programmable Gate Arrays (FPGAs), and the like.
It should be noted that the above embodiments are only for illustrating the technical solution of the present application and not for limiting the same, and although the present application has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present application may be modified or substituted without departing from the spirit and scope of the technical solution of the present application, which is intended to be covered in the scope of the claims of the present application.
Claims (10)
1. The utility model provides a monitoring key data transmission method based on weather and rain of 5G camera which characterized in that includes:
the main control CPU is connected with the camera through the CSI interface to acquire video data;
the data processing module generates core data of the image or the video;
and the AI pre-analysis module generates a key data stream uploading cloud according to the processed image and video information.
2. The 5G camera-based method for transmitting monitoring key data of weather and rain according to claim 1, wherein: the main control CPU is connected with the cameras through the CSI interfaces to obtain video data, wherein the main control CPU is connected with the multi-path digital cameras or the MIPI cameras through the CSI interfaces, and the cameras transmit images and videos to the image processing frame and the video processing frame.
3. The 5G camera-based method for transmitting monitoring key data of weather and rain according to claim 1 or 2, wherein: the image processing frame and the video processing frame comprise an image processing module for hanging image filtering, morphological operation, feature segmentation, feature extraction, edge detection and image enhancement, wherein after the image processing frame receives an image, the image processing module takes decoded image data as a core, and encodes the image into jpeg and png formats and sends the jpeg and png formats to an AI pre-analysis module after processing is completed;
after the video processing framework receives the video, the video is converted into video data blocks based on coding H.265, and the video data blocks are used as cores to generate video processing modules for hanging code stream conversion, video compression, video enhancement, video preloading, RTSP pushing and RTP pushing.
4. The 5G camera-based weather and wind monitoring key data transmission method of claim 3, wherein: the image processing frame and the video processing frame further comprise a video processing module and an image processing module which are registered to the frame in a modularized mode, dynamically load and preload the hooking function, and control to start or stop the hooking function through an open interface API.
5. The 5G camera-based method for transmitting monitoring key data of weather and rain according to claim 4, wherein: the AI pre-analysis module comprises that after the processing of the images and the videos is completed, serial data are used for receiving external wind speed and rainfall data, a sensor program is used for pushing the received data to a cloud end, the cloud end sends a control command to a control logic unit, and the logic unit judges the wind speed and the rainfall to operate;
when the wind speed is less than 6m/s and the rainfall is less than 8mm/h, judging that the camera has rainwater, and not affecting the camera to acquire images and videos, if the network signal strength is more than-70 dBm, directly uploading the videos and image data to a cloud end, if the network signal strength is less than-70 dBm, reducing the resolution of the images and the videos, improving the compression ratio, reducing the transmission pressure, uploading the cloud end, and if the network signal strength is less than-75 dBm, considering equipment failure or being shielded by network information, storing the data in a data cache, and encrypting;
when the wind speed is less than or equal to 6m/s and less than or equal to 14m/s, the rainfall is less than or equal to 8mm/h and less than or equal to 18mm/h, judging that the camera has rainwater, the camera is blocked by the rainwater, the acquired image and video are partially blocked, the image and video are transferred into an AI pre-analysis module, noise is reduced through median filtering, an SSD target detection algorithm is utilized to identify an object, an OpenPose algorithm is utilized to detect a human body, a TLD target tracking algorithm is utilized to track the target, after a preset dangerous event is identified, a pre-analysis result is sent to a cloud in a warning mode, if the network signal intensity is less than-75 dBm, the serial data of the same area are received to judge the network signal intensity of the 5G camera in the same area, if the network signal intensity is less than-75 dBm, the signals are regarded as being affected by weather, otherwise, the equipment failure or network information shielding is judged;
when the wind speed is more than 14m/s and the rainfall is more than 18mm/h, judging that the camera has rainwater, the camera is blocked by the rainwater, acquiring all the images and videos, transferring the images and videos to an AI pre-analysis module, processing the images and videos through a deep learning rain removing network, fusing the images and videos to perform multi-frame image fusion analysis, performing target detection, human body detection and target tracking after the analysis is completed, optimizing and correcting tracking results by combining historical tracking data and target movement characteristics, forwarding the identified preset dangerous event to a cloud in an alarm mode after the correction is completed, temporarily storing the video stream or serial data in a data cache if the signal is less than-80 dBm, and re-supplementing the data after the network is recovered.
6. The 5G camera-based method for transmitting monitoring key data of weather and rain according to claim 5, wherein: the AI pre-analysis module further comprises, when the wind speed is more than 16m/s, the camera shakes, the acquired images and videos have the blurring phenomenon, the images and videos are transferred to the AI pre-analysis module, the images are subjected to low-filtering noise reduction, the images are extracted into non-moving frames, the moving information is calculated according to the pixel change between the two frames of images, the images are subjected to motion compensation according to the calculated moving information, and the processed images and videos are subjected to dangerous event identification.
7. The 5G camera-based method for transmitting monitoring key data of weather and rain according to claim 6, wherein: the key data stream comprises processed image and video data, wind speed and rain amount data, AI pre-analysis dangerous event types and a request camera steering instruction.
8. The 5G camera-based method for transmitting monitoring key data of weather and rain according to claim 7, wherein: the request camera steering instruction comprises the steps of sending a target tracking request to a cloud after the AI pre-analysis module identifies preset dangerous time, converting target tracking of recorded video into target tracking of a camera, and recording dangerous time states in real time.
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that: the processor, when executing the computer program, implements the steps of the method of any one of claims 1 to 8.
10. A computer-readable storage medium having stored thereon a computer program, characterized by: the computer program, when executed by a processor, implements the method for monitoring critical data transfer based on weather and wind of a 5G camera according to any one of claims 1 to 8.
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