An LED screen play control security management system and method, and an LED screen

Abstract

The application relates to the field of screen broadcasting, and specifically discloses an LED screen broadcasting control safety management system and method and an LED screen. A main control unit receives original video data, sends the original video data to a storage unit for storage, extracts each frame of image from the stored original video data and sends the frame of image to a computing power unit for safety information identification, sends the image after safety information identification to the storage unit for storage, and restores all the images after safety information identification into video data for playing. Based on the edge side data real-time detection and management technology, the edge side data monitoring and management are realized, high bandwidth is not needed, and the network transmission risk is avoided, the product deployment is flexible, the product is highly expandable, the spread risk of the illegal information is effectively reduced under the premise of not affecting real-time performance.

Description

Technical Field

[0001] This invention relates to the field of screen broadcasting, specifically to an LED screen broadcasting control safety management system and method, and an LED screen. Background Technology

[0002] With the rapid development of information technology, LED screens have become an important channel for information dissemination, and secure broadcast control systems are crucial technical means to ensure the safety of broadcast content and prevent broadcast accidents. Existing secure broadcast control technologies mainly employ pre-encryption and automatic identification and filtering technologies. The former uses encryption algorithms such as AES and RSA to encrypt transmitted signals, preventing unauthorized interception or tampering of broadcast content during transmission. The latter is primarily deployed on the server side, relying on powerful algorithm processing capabilities and high network bandwidth; however, the risk of data interception and tampering still exists during layered network transmission.

[0003] Existing technologies primarily rely on an architecture of endpoint, network, and cloud, all three being indispensable, while also demanding high network bandwidth and cloud processing capabilities. This presents significant disadvantages in edge computing scenarios with high real-time requirements, and the deployment costs are high, making them unsuitable for LED screen display equipment. Summary of the Invention

[0004] To address the aforementioned issues, this invention provides an LED screen broadcast control security management system and method, and an LED screen based on edge-side data real-time detection and management technology. This enables edge-side data monitoring and management without requiring high bandwidth and avoiding network transmission risks. Furthermore, the product is flexible in deployment and highly scalable, effectively reducing the risk of spreading illegal information without affecting real-time performance.

[0005] In a first aspect, the technical solution of the present invention provides an LED screen broadcast control safety management system, comprising,

[0006] Storage unit: Used to store raw video data and image data after security information identification;

[0007] Computing power unit: used for pre-training the security information recognition neural network model, and using the security information recognition neural network model to detect and process security information in images;

[0008] Main control unit: Used to receive raw video data, send the raw video data to the storage unit for storage, extract each frame of image from the stored raw video data and send it to the computing unit for security information identification, send the images after security information identification to the storage unit for storage, and restore all the images after security information identification into video data for playback.

[0009] In one optional implementation, the main control unit adopts an ARM architecture RK35XX platform and LPDDR5 memory, integrates an NPU and an expanded computing card as a computing unit, and integrates an SSD solid-state drive as a storage unit.

[0010] In an optional implementation, the main control unit also integrates multiple data interfaces to enable multi-node network communication for receiving raw video data.

[0011] Secondly, the technical solution of the present invention provides an LED screen broadcast control security management method, implemented based on any of the above-mentioned systems, the method comprising the following steps:

[0012] Receive raw video data;

[0013] The raw video data is sent to the storage unit for storage;

[0014] Each frame of the image is extracted from the stored raw video data and sent to the computing unit for security information identification;

[0015] The image after security information recognition is sent to the storage unit for storage;

[0016] The images, after all security information has been identified, are restored as video data for playback.

[0017] In one optional implementation, each frame of image is extracted from the stored raw video data and sent to the computing unit for security information identification, specifically including:

[0018] Decode the raw video data and extract images frame by frame in sequence;

[0019] Each extracted frame image is sequentially labeled;

[0020] Each frame of image, after being sequentially marked, is sent to the computing unit for security information identification.

[0021] In an optional implementation, each frame of image after sequential marking is sent to the computing unit for security information identification, specifically including:

[0022] The current image is input into a pre-trained security information recognition neural network model for feature recognition.

[0023] The system detects whether there is any illegal information based on the feature recognition results.

[0024] If any illegal information is found, the current image will be processed for security purposes.

[0025] In one optional implementation, the security information recognition neural network model includes convolutional layers, activation functions, and fully connected layers. The convolutional layers extract local features from the input image through convolutional kernel filters, the activation functions recognize and classify local image features, and output feedback results, and the fully connected layers perform multiple fitting on local image features to enhance feature information.

[0026] When training a security information recognition neural network model, the first step is to classify the violation information and create labeled data samples. Then, based on the data classification and samples, the size and number of CNN convolutional kernels are created, the activation function is confirmed, the Adam optimizer is selected to train the CNN network, and training diversity is increased by scaling, rotating, and flipping the images.

[0027] In one alternative implementation, security processing of the current image includes blurring or deleting frames from the current image.

[0028] In one optional implementation, the images after all security information has been identified are restored to video data for playback, specifically including:

[0029] The images are encoded according to their sequential markings, and all security information is identified and the images are then restored into video data for playback.

[0030] Thirdly, the technical solution of the present invention provides an LED screen configured with any of the above-described systems and executing any of the above-described methods.

[0031] This invention provides an LED screen broadcast control security management system and method, and an LED screen that, compared to existing technologies, have the following advantages: A system comprising a storage unit, a computing unit, and a main control unit is constructed on the edge-side LED screen, and a security information recognition neural network model is trained. Data storage is achieved based on the storage unit, security information recognition using the model is achieved based on the computing unit, and edge-side security management is achieved based on the main control unit. This invention utilizes edge-side real-time data detection and management technology to achieve edge-side data monitoring and management, eliminating the need for high bandwidth and avoiding network transmission risks. Furthermore, the product is flexible in deployment and highly scalable, effectively reducing the risk of spreading illegal information without affecting real-time performance. Attached Figure Description

[0032] To more clearly illustrate the technical solutions of the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0033] Figure 1This is a schematic diagram of the structure of an LED screen broadcast control and safety management system provided in an embodiment of the present invention.

[0034] Figure 2 This is a schematic diagram of a method for LED screen broadcast control safety management provided in an embodiment of the present invention. Detailed Implementation

[0035] To enable those skilled in the art to better understand the present invention, the invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. Obviously, the described embodiments are merely some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0036] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention.

[0037] Figure 1 This is a schematic diagram of the structure of an LED screen broadcast control and safety management system provided in an embodiment of the present invention. It is used in the construction of LED screens. Figure 1 The system described enables real-time data detection and management based on the edge.

[0038] like Figure 1 As shown, the system includes a storage unit, a computing unit, and a main control unit.

[0039] Storage unit: Used to store raw video data and image data after security information identification.

[0040] Computing unit: Used to pre-train a security information recognition neural network model, and to use the security information recognition neural network model to detect and process security information in images.

[0041] Main control unit: Used to receive raw video data, send the raw video data to the storage unit for storage, extract each frame of image from the stored raw video data and send it to the computing unit for security information identification, send the image data after security information identification to the storage unit for storage, and restore all the images after security information identification into video data for playback.

[0042] In this embodiment, the main control unit adopts the ARM architecture RK35XX platform and a high-bandwidth LPDDR5 memory solution. It integrates a high-performance NPU and a scalable computing card as computing units, and a high-bandwidth SSD as a storage unit. The SSD uses an M.2 interface. In edge application scenarios, network communication modules are mostly concentrated in the main control unit. The main control unit also integrates multiple data interfaces to support multi-node network communication for receiving raw video data. In addition, the main control unit provides a terminal device system-level communication API interface protocol to support the management and control of the broadcast system on the application side.

[0043] On the software side, it can be based on the Linux operating system and a runtime environment that supports the deployment of CNN model architecture. In addition, the main control unit also provides a system-level API interface protocol to support data communication and system management at the application end.

[0044] The above text provides a detailed description of an embodiment of an LED screen broadcast control security management system. Based on the LED screen broadcast control security management system described in the above embodiment, this invention also provides an LED screen broadcast control security management method corresponding to the method.

[0045] Figure 2 This is a schematic diagram of a method for LED screen broadcast control safety management provided in an embodiment of the present invention. Figure 2 The implementing entity can be the LED screen broadcast control and safety management system described in the above embodiment. Depending on different needs, the order of the steps in this flowchart can be changed, and some steps can be omitted.

[0046] like Figure 2 As shown, the method includes the following steps.

[0047] S1 receives raw video data.

[0048] In this embodiment, the main control unit receives video image processing tasks and receives the raw video data to be processed. It should be noted that the main control unit refers to the main controller of the screen broadcasting device. Data can be transmitted via networks or other common methods. This embodiment primarily focuses on risk management of the data received by the screen broadcasting terminal device; risks during transmission do not affect this solution.

[0049] S2 sends the raw video data to the storage unit for storage.

[0050] In this embodiment, the main control unit sends the received raw video data to an SSD solid-state drive for caching. High-bandwidth SSD hard drives can be developed for caching video images and other data in edge computing devices.

[0051] S3 extracts each frame of image from the stored raw video data and sends it to the computing unit for security information identification.

[0052] In this embodiment, when the main control unit receives a data task, it stores the pre-processed and processed data through an integrated large-capacity SSD solid-state drive, performs CNN model training and image feature recognition through the high-performance NPU and extended computing cards integrated in the scheduling system, and performs specific processing on the identified problematic image frames.

[0053] The computing unit pre-trains a security information recognition neural network model, which performs security information recognition on images, including the detection and processing of security information. The main control unit extracts images from the stored raw video data and transmits them to the computing unit, which then executes the security information recognition neural network model to perform security information recognition, specifically including the following steps.

[0054] S3.1, Decode the raw video data and extract images frame by frame in sequence.

[0055] In this embodiment, the original video data is extracted into images of all frames, and security information is identified for each frame.

[0056] S3.2, sequentially label each extracted frame of image.

[0057] The purpose of this step is to sequentially mark each frame of the image so that it can be restored to video format after the subsequent security information identification and processing is completed. The restoration is performed according to the sequential markings to ensure the correctness of the restored video.

[0058] S3.3, each frame of image after sequential marking is sent to the computing unit for security information identification.

[0059] S3.3.1 Input the current image into a pre-trained security information recognition neural network model for feature recognition.

[0060] S3.3.2, detect whether there is any illegal information based on the feature recognition results.

[0061] S3.3.3 If there is any violation information, perform security processing on the current image.

[0062] The computing unit uses a security information recognition neural network model to identify security information in each frame of the image. When illegal information is detected, the image undergoes security processing. This embodiment's security information recognition neural network model consists of convolutional layers, activation functions, and fully connected layers. The convolutional layers extract local features from the input image using convolutional kernel filters. The activation function identifies and classifies local image features and outputs feedback results. The fully connected layers perform multiple fitting on the local image features to enhance feature information. The activation function can be a mathematical function with the form f(x) = Max(ax,x).

[0063] During model training, appropriate training parameters (including the number of data samples q, the upper limit of training u, and the proportion coefficient of data samples with different features s; the numerical range needs to be configured according to different scenarios) are configured for model training and optimization. First, pre-defined classified and labeled data samples are created for confidential, sensitive, and other illegal information. Then, based on the data classification and samples, the size and number of CNN convolutional kernels are created, and the activation function f(x) = Max(ax,x) is confirmed. The Adam optimizer is selected to train the CNN network, and training diversity is increased by performing operations such as image scaling, rotation, and flipping. The CNN network outputs the type of illegal information identified and the processing result for each frame of image recognition. In an optional implementation, a model optimization and upgrade interface can be provided at the system layer.

[0064] In this embodiment, security processing of the current image includes blurring or deleting frames from the current image, which can be done on specific areas (areas containing illegal information).

[0065] S4 sends the image after security information recognition to the storage unit for storage.

[0066] In this embodiment, images containing illegal information are sent to the storage unit for storage after being blurred or having frames deleted; images without illegal information are sent directly to the storage unit for storage.

[0067] S5 restores all security information from the images into video data for playback.

[0068] In this embodiment, images are encoded according to their sequence markings, and all images after security information recognition are restored into video data for playback. The main control unit encodes all images after security information recognition in their original order. The image order is marked during frame extraction. The encoding here mainly refers to the restoration of the original video after frame extraction, and then outputs it to the display terminal for playback.

[0069] This embodiment is based on edge-side data real-time detection and management technology to achieve edge-side data monitoring and management. It does not require high bandwidth and avoids network transmission risks. Moreover, the product is flexible in deployment and highly scalable, effectively reducing the risk of the spread of illegal information without affecting real-time performance.

[0070] This embodiment provides an LED screen, configured with the LED screen broadcast control security management system described above, and executing the LED screen broadcast control security management method described above.

[0071] The above-disclosed embodiments are merely preferred embodiments of the present invention, but the present invention is not limited thereto. Any non-creative variations that can be conceived by those skilled in the art, as well as any improvements and modifications made without departing from the principles of the present invention, should fall within the protection scope of the present invention.

Claims

1. An LED screen play control security management system, characterized in that, The system is arranged in an LED screen, comprising: a storage unit: for storing original video data and image data after security information identification; a computing unit: for pre-training a security information identification neural network model, and using the security information identification neural network model to detect and process security information of images; a main control unit: for receiving original video data, sending the original video data to the storage unit for storage, extracting each frame of image from the stored original video data and sending it to the computing unit for security information identification, sending the image after security information identification to the storage unit for storage, and restoring all the images after security information identification to video data for playing; The main control unit adopts ARM architecture RK35XX platform and LPDDR5 memory, integrates NPU and extended computing card as the computing unit, and integrates SSD solid state disk as the storage unit; The main control unit also integrates multiple data interfaces to realize multi-node network communication for receiving original video data.

2. A LED screen play control security management method, characterized in that, The system of claim 1 is implemented, and the method comprises the following steps: receiving original video data; sending the original video data to the storage unit for storage; extracting each frame of image from the stored original video data and sending it to the computing unit for security information identification; sending the image after security information identification to the storage unit for storage; restoring all the images after security information identification to video data for playing; sending each frame of image after sequential marking to the computing unit for security information identification, specifically including: inputting the current image into the pre-trained security information identification neural network model for feature identification; detecting whether there is illegal information according to the feature identification result; if there is illegal information, performing security processing on the current image; The security information identification neural network model comprises a convolution layer, an activation function and a full connection layer, the convolution layer extracts local features of the input image through a convolution kernel filter, the activation function realizes identification and classification of the local features of the image and outputs feedback results, and the full connection layer performs multiple fitting on the local features of the image to strengthen the feature information; When training the security information identification neural network model, first, classify the illegal information and create data labeling samples, determine the size and number of CNN convolution kernel according to the data classification and sample creation, confirm the activation function, select the Adam optimizer to train the CNN network, and increase the training diversity by scaling, rotating and flipping the image; The security processing on the current image includes blurring or frame deletion processing on the current image; restoring all the images after security information identification to video data for playing, specifically including: encoding according to the sequential marking of the images, and restoring all the images after security information identification to video data for playing.

3. The LED screen play control security management method of claim 2, wherein, extracting each frame of image from the stored original video data and sending it to the computing unit for security information identification, specifically including: decoding the original video data and extracting each frame of image in sequence; sequentially marking each frame of image extracted; sending each frame of image after sequential marking to the computing unit for security information identification.

4. An LED screen, characterized in that The LED screen is built-in The system of claim 1, performing the method of any of claims 2-3.

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