Quantum-Safe Enhanced Broadcasting Methods and Systems for Smart Communities

Abstract

This invention proposes a quantum-safe enhanced broadcasting method and system for smart communities. The quantum-safe enhanced broadcasting method includes: establishing a quantum key distribution network for the community, with the network covering the broadcasting terminal devices corresponding to each building; using the broadcasting terminal devices corresponding to each building as node devices, and dynamically selecting master node devices through a video management platform; the video management platform sending video data to the master node devices using quantum encryption; and the master node devices segmenting the broadcast video and publishing the segmented video data packets to slave node devices using quantum encryption. The system includes modules corresponding to the steps of the method.

Description

Technical Field

[0001] This invention proposes a quantum-safe enhanced broadcasting method and system for smart communities, belonging to the field of community video broadcasting control technology. Background Technology

[0002] With the rapid development of the digital economy and the accelerating pace of smart city construction, smart communities, as a crucial component of smart cities, are increasingly informatized. Smart communities involve a large amount of sensitive resident information, such as personal identification information, home addresses, and consumption records. Simultaneously, various business systems within the community, such as security monitoring, access control systems, and property management, also generate substantial amounts of critical business data. The security and confidentiality of this data are paramount; leakage or tampering could not only infringe upon residents' privacy but also seriously impact the security and stability of the community. However, data security in smart communities currently faces numerous challenges.

[0003] Traditional encryption technologies primarily rely on the complexity of mathematical calculations to ensure security. However, traditional video conferencing systems face several problems and challenges when dealing with the large-scale and complex application scenarios of smart communities. For example, as the community expands and the number of conferencing terminal devices increases, achieving efficient and stable transmission of video data becomes a pressing issue. Furthermore, ensuring the security and privacy of video data during transmission, and preventing data leaks and malicious attacks, are also key problems that smart community video conferencing systems need to address. Summary of the Invention

[0004] This invention provides a quantum-safe enhanced broadcasting method and system for smart communities to address the problems existing in the prior art. The technical solution adopted is as follows:

[0005] A quantum-safe enhanced broadcast method for smart communities, comprising:

[0006] A quantum key distribution network is established for the community, and the quantum key distribution network covers the broadcast terminal equipment corresponding to each building;

[0007] The broadcast terminal equipment corresponding to each building is used as node equipment, and the main node equipment is dynamically selected through the video management platform;

[0008] The video management platform sends video data to the master node device using quantum encryption.

[0009] The master node device segments the broadcast video and then publishes the segmented video data packets to the slave node devices using quantum encryption.

[0010] Furthermore, a quantum key distribution network is established for the community, and the quantum key distribution network covers the broadcast terminal equipment corresponding to each building, including:

[0011] Count the number of broadcast terminal devices in the community and set the key update frequency;

[0012] The quantum security module will be deployed in the video management platform and the corresponding broadcast terminal equipment for each building;

[0013] A quantum-encrypted communication network will be established between each broadcast terminal device and between the broadcast terminal device and the video management platform for each building.

[0014] Furthermore, the video management platform dynamically filters master node devices, including:

[0015] Extract the area corresponding to the community, and divide the area corresponding to the community into grids according to the number and distribution density of the broadcast terminal devices contained in the community to obtain multiple grid areas;

[0016] The radius of equipment distribution in the corresponding area of ​​the community is determined based on the number of broadcast terminal devices and the distance between buildings in each grid area;

[0017] The building with the shortest straight-line distance to the video management platform will be selected as the target building.

[0018] Extract the straight-line distance between the target building and the video management platform;

[0019] The unit length of the optical fiber is determined based on the distribution radius and straight-line distance of the equipment, and the optical fiber screening coefficient corresponding to the unit length of the optical fiber is obtained.

[0020] The initial master node device is determined based on the fiber selection coefficient.

[0021] Furthermore, based on the number and distribution density of the network terminal devices within the community, the corresponding area of ​​the community is divided into grids, resulting in multiple grid areas, including:

[0022] The location coordinates of each broadcast terminal device are obtained through GPS or BeiDou.

[0023] The density of the broadcast terminal devices is obtained based on the location coordinates of each broadcast terminal device.

[0024] Extract the effective communication radius corresponding to each broadcast terminal device; wherein, the effective communication radius is the distance at which the measured signal strength attenuates to -90dBm;

[0025] The average effective communication radius is obtained based on the effective communication radius corresponding to each broadcast terminal device.

[0026] The grid radius is obtained using the average effective communication radius.

[0027] The density extreme point of the broadcast terminal device density is selected as the network center to start grid growth, forming multiple grid regions.

[0028] Furthermore, the radius of equipment distribution is determined based on the number of LAN terminal devices in each grid area and the distance between buildings, including:

[0029] The average building spacing is obtained based on the number of LAN terminal devices and the distance between buildings in each grid area;

[0030] The distribution radius parameter of sub-devices in each grid area is obtained based on the average building spacing within each grid area.

[0031] The distribution radius of sub-devices in all grid areas is weighted and averaged to obtain the distribution radius of devices in the area corresponding to the community.

[0032] Further, determining the initial master node device based on the fiber selection coefficient includes:

[0033] Extract the broadcast terminal equipment on the fiber optic line corresponding to the maximum value of the fiber screening coefficient as candidate node equipment;

[0034] For each candidate node device, a verifiable claim is made, and the zk-SNARKs method is used to prove the authenticity of the claim.

[0035] If the authenticity verification of a candidate node device fails, the candidate node device will be immediately removed from the candidate list, and a security audit will be triggered for the candidate node device that has been removed from the candidate list.

[0036] Within each grid area, non-candidate node devices vote for candidate nodes based on their own computing power weight, and obtain the voting weight corresponding to each candidate node device;

[0037] The candidate node device corresponding to the maximum voting weight is selected as the initial master node device.

[0038] Furthermore, dynamically selecting master node devices through the video management platform also includes:

[0039] Real-time monitoring of network bandwidth utilization of master node devices;

[0040] When the network bandwidth utilization rate of the master node device exceeds the preset utilization rate threshold, the dynamic filtering cycle of the master node device is set using the operating parameters of the master node device and the operating parameters of the slave node device; wherein, the operating parameters include CPU utilization rate and fiber filtering coefficient corresponding to the fiber where the broadcast terminal device is located;

[0041] The operating parameters of the master node device and the slave node device are monitored in real time during each dynamic screening cycle of the master node device.

[0042] The node security factor is obtained based on the operating parameters of the master node device and the slave node device.

[0043] The broadcast terminal device corresponding to the maximum node security coefficient within each master node device's dynamic screening cycle will be used as the master node device for the next master node device's dynamic screening cycle.

[0044] Furthermore, the video management platform sends video data to the master node device using quantum encryption, including:

[0045] The video management platform and the master node device negotiate and share a key via the BB84 protocol;

[0046] The amount of video data to be released that triggers key rotation is set based on the fiber optic screening coefficient and fiber optic deployment length between the video management platform and the master node device.

[0047] When the cumulative amount of video data sent by the video management platform to the master node device reaches the amount of video data to be published, key rotation is triggered.

[0048] Furthermore, the master node device segments the cascaded video, and the segmented video data packets are then distributed to the slave node devices using quantum encryption, including:

[0049] The master node device performs segmentation processing on the simulcast video according to the preset segmentation rules and obtains multiple segmentation metadata.

[0050] The master node device sets the number of fragmented metadata fragments for a single transmission based on the node computing power of the slave node device;

[0051] The master node device shares the metadata of each shard to the slave node device using quantum encryption, according to the number of shards of metadata corresponding to each slave node device.

[0052] The slave node device performs SPHINCS+ signature verification once for each set of fragment metadata it receives, and then streams and decrypts the received fragment metadata for playback.

[0053] A quantum-safe enhanced broadcast system for smart communities, comprising:

[0054] A network establishment module is used to establish a quantum key distribution network for the community, and the quantum key distribution network covers the broadcast terminal equipment corresponding to each building;

[0055] The main node dynamic filtering module is used to use the broadcast terminal equipment corresponding to each building as node equipment and dynamically filter the main node equipment through the video management platform.

[0056] The first quantum encryption transmission module is used by the video management platform to send video data to the master node device using quantum encryption.

[0057] The second quantum encryption transmission module is used to segment the simulcast video through the master node device and publish the segmented video data packets to the slave node device through quantum encryption.

[0058] Beneficial effects of this invention:

[0059] The quantum-secure enhanced simulcast method and system proposed in this invention for smart communities segment the simulcast video using a master node device and then distribute the video data packets to slave node devices, avoiding the network bandwidth bottleneck caused by centralized transmission. Slave node devices in different regions and with different performance levels can flexibly receive data according to their own conditions, reducing the possibility of network congestion and enabling video data to be transmitted to various simulcast terminal devices more quickly and stably. Using quantum key encryption, data transmission from the video management platform to the master node device, and from the master node device to the slave node devices, relies on the unconditional security of quantum encryption technology. Even in the face of powerful quantum computing attacks, attackers cannot crack the encrypted video data, effectively preventing video data leakage and malicious tampering. Due to the master-slave node architecture and quantum key distribution network, when a new simulcast terminal device (slave node) is added to the community, it only needs to be connected to the quantum key distribution network and simply configured by the video management platform to achieve communication and data transmission with the master node device, without requiring large-scale modifications to the entire system. The master-slave node architecture also gives the system a certain degree of fault tolerance. When a slave node device fails, the master node device can automatically adjust its data transmission strategy, distributing tasks to other functioning devices to ensure uninterrupted video broadcasting service. The video management platform provides unified management and monitoring of both master and slave nodes, enabling real-time monitoring of device operating status and video broadcasting activity. When system problems occur, the platform can quickly locate the fault and take appropriate measures. Attached Figure Description

[0060] Figure 1 This is a flowchart of the method described in this invention;

[0061] Figure 2 This is a system block diagram of the system described in this invention. Detailed Implementation

[0062] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0063] This invention proposes a quantum-safe enhanced syndicated broadcast method for smart communities, such as... Figure 1 As shown, the quantum-safe enhanced broadcast method includes:

[0064] S1. Establish a quantum key distribution network for the community, and the quantum key distribution network covers the broadcast terminal equipment corresponding to each building;

[0065] S2. Use the network terminal equipment corresponding to each building as node equipment, and dynamically select the main node equipment through the video management platform;

[0066] S3. The video management platform sends video data to the master node device using quantum encryption.

[0067] S4. The master node device segments the broadcast video and publishes the segmented video data packets to the slave node device using quantum encryption.

[0068] The working principle of the above technical solution is as follows: A quantum key distribution network infrastructure is built within the community. Using quantum key distribution technology (such as schemes based on quantum entanglement and quantum teleportation), a secure quantum communication link is established between the community center and the corresponding broadcast terminal equipment in each building. This ensures that each building's broadcast terminal equipment can obtain a unique and absolutely secure quantum key through this network, laying the foundation for subsequent encrypted data transmission. The video management platform dynamically selects the most suitable device as the master node from numerous broadcast terminal devices based on a series of preset rules and algorithms, such as device performance indicators (including processing power, storage capacity, network bandwidth, etc.), device location distribution within the community (to achieve optimal data transmission path planning), and real-time device operating status (such as whether it is online, whether it is under high load, etc.). This ensures that the master node device has the ability to efficiently process and distribute video data, while also being able to flexibly adjust according to the actual situation in the community, ensuring the stable operation of the entire broadcast system.

[0069] Before sending video data to the master node device, the video management platform encrypts the video data using a quantum encryption algorithm (such as quantum one-time pad) with a pre-negotiated quantum key. The encrypted video data is then transmitted to the master node device via a conventional communication network (such as Ethernet or fiber optic networks). Because quantum encryption technology offers unconditional security, it effectively prevents video data from being stolen or tampered with during transmission, ensuring the integrity and confidentiality of the video content. Upon receiving the encrypted video data, the master node device segments the video data into multiple smaller data packets based on factors such as the number of slave nodes, network bandwidth, and the real-time requirements of video playback. Then, the master node device uses the quantum key corresponding to the slave node device to re-encrypt the segmented video data packets and distributes the encrypted data packets to each slave node device through the community's communication network. Through segmentation and encrypted distribution, the transmission efficiency and security of video data in complex network environments are improved, ensuring that each slave node device receives complete and secure video data, enabling community-wide cascading.

[0070] The above technical solution achieves the following results: After the master node device segments the syndicated video, it distributes the video data packets to the slave node devices, avoiding the network bandwidth bottleneck caused by centralized transmission. Slave node devices in different regions and with different performance levels can flexibly receive data according to their own conditions, reducing the possibility of network congestion and enabling video data to be transmitted to various syndicated terminal devices more quickly and stably. Using quantum key distribution for encryption ensures unconditional security for data transmission between the video management platform and the master node device, as well as between the master node device and the slave node devices. Even under powerful quantum computing attacks, attackers cannot crack the encrypted video data, effectively preventing video data leakage and malicious tampering. Due to the master-slave node architecture and quantum key distribution network, when a new syndicated terminal device (slave node) is added to the community, it only needs to be connected to the quantum key distribution network and simply configured by the video management platform to achieve communication and data transmission with the master node device, without requiring large-scale modifications to the entire system. The master-slave node architecture also provides the system with a certain degree of fault tolerance. When a slave node device fails, the master node device can automatically adjust its data transmission strategy, distributing tasks to other functioning devices to ensure uninterrupted video broadcasting service. The video management platform provides unified management and monitoring of both master and slave nodes, enabling real-time monitoring of device operating status and video broadcasting activity. When system problems occur, the platform can quickly locate the fault and take appropriate measures.

[0071] One embodiment of the present invention establishes a quantum key distribution network for a community, wherein the quantum key distribution network covers the broadcast terminal equipment corresponding to each building, including:

[0072] S101. Count the number of broadcast terminal devices in the community and set the key update frequency;

[0073] S102. Deploy the quantum security module into the video management platform and the corresponding broadcast terminal equipment for each building;

[0074] S103. Establish a quantum-encrypted communication network between each broadcast terminal device and between the broadcast terminal device and the video management platform for each building.

[0075] The working principle of the above technical solution is as follows: A comprehensive survey and registration of all broadcast terminal devices within the community is conducted to accurately determine the specific number, distribution location, and model of the devices. This information forms the basis for subsequently constructing a quantum key distribution network, helping to rationally plan the network architecture and resource allocation. Based on the community's security needs, device performance, and the characteristics of quantum key distribution technology, a suitable key update frequency is determined. A higher key update frequency can improve data transmission security but will also increase the system's computational and communication overhead; a lower key update frequency may reduce security. Dedicated quantum security modules are integrated into the video management platform and the broadcast terminal devices in each building. These modules possess functions such as quantum key generation, storage, distribution, and management, and are the core components for realizing quantum encrypted communication. The quantum security module uses a quantum random number generator to generate truly random numbers as the key basis, and combines this with a quantum key distribution protocol (such as the BB84 protocol) to negotiate and distribute keys with other devices in the quantum key distribution network. Simultaneously, the module is also responsible for key storage and management, ensuring key security and availability. A quantum encrypted communication network covering all broadcast terminal devices is then constructed within the community. The broadcast terminal equipment in each building is connected via fiber optic and wireless communication media to form an integrated system. A communication network is established with the video management platform. Similarly, a quantum-encrypted communication network is established between the broadcast terminal equipment in each building and the video management platform. This ensures that video data can be securely and efficiently transmitted from the video management platform to each broadcast terminal equipment.

[0076] The above technical solution provides absolute security for video data transmission by leveraging the non-cloning and unconditional security of quantum keys. Even if an attacker possesses unlimited computing power, they cannot crack video data encrypted with quantum keys. With the development of quantum computing technology, traditional encryption algorithms face the risk of being cracked. Quantum key distribution technology effectively resists quantum computing attacks, providing long-term security for the community video broadcasting system. The deployment of the quantum security module and the establishment of the quantum encrypted communication network consider the compatibility of different types of broadcasting terminal devices, enabling the system to adapt to various complex device environments within the community. A redundancy design strategy was adopted when constructing the quantum encrypted communication network, ensuring that the system can automatically switch to a backup link to guarantee normal video data transmission when some network devices or communication links fail. Due to the adoption of a quantum key distribution network and master-slave node architecture, when new broadcasting terminal devices are added to the community, they only need to be connected to the quantum key distribution network and simply configured by the video management platform to achieve seamless integration with the existing system. This technical solution is not only applicable to existing video broadcasting services but also provides a good foundation for future expansion of other secure communication services that the community may undertake (such as smart home security control and community security monitoring). The video management platform enables centralized management and monitoring of the quantum key distribution network, cascading terminal devices, and quantum security modules. Administrators can use the platform to monitor device operating status, key update information, and network traffic in real time, allowing for timely problem identification and resolution. The system features automated operation and maintenance capabilities, automatically performing tasks such as key updates, device configuration, and fault diagnosis, reducing manual intervention and improving system operational efficiency.

[0077] One embodiment of the present invention involves dynamically filtering master node devices through a video management platform, including:

[0078] S201. Extract the area corresponding to the community, and divide the area corresponding to the community into grids according to the number and distribution density of the broadcast terminal devices contained in the community, and obtain multiple grid areas.

[0079] S202. Determine the radius of equipment distribution in the corresponding area of ​​the community based on the number of broadcast terminal devices and the distance between buildings in each grid area;

[0080] S203. The building with the shortest straight-line distance to the video management platform is selected as the target building;

[0081] S204. Extract the straight-line distance between the target building and the video management platform;

[0082] S205. Determine the fiber unit length based on the device distribution radius and straight-line distance (wherein, the fiber unit length is 1 / 10 times the average value corresponding to the device distribution radius and straight-line distance), and obtain the fiber screening coefficient corresponding to the fiber unit length;

[0083] The fiber screening coefficient per unit length of the optical fiber is obtained by the following formula:

[0084]

[0085] Where S represents the fiber selection coefficient; R f L represents the radius of equipment distribution within the corresponding area of ​​the community. g L represents the unit length of the optical fiber. z The distance between the target building and the video management platform is represented by α; the fiber optic attenuation coefficient is represented by r; and the distance-coverage ratio correction factor is represented by r, which ranges from 0.1 to 0.3. Specifically, Taking the minimum of the two values ​​aims to consider the relationship between fiber length and equipment distribution range. When the fiber length exceeds the equipment distribution range, the excess part is not very meaningful for the current fiber selection based on equipment distribution. Taking the smaller value can highlight the relevant factors of the effective range. middle It is a quantitative representation of the degree of attenuation. Logarithmic operations here can appropriately scale the range of variation of the attenuation coefficient, facilitating comprehensive calculations in the formula. The L in the denominator... z It is the straight-line distance between the target building and the video management platform. The greater the distance, the greater the signal transmission loss, so its impact on the screening coefficient is reflected in the denominator. middle This represents the ratio of the distance between the target building and the video management platform to the radius of the equipment distribution range. This ratio reflects the relative relationship between distance and equipment distribution range. Squaring this ratio, multiplying it by a correction factor r, and then taking the exponent, takes into account the impact of the relative relationship between distance and equipment distribution range on signal coverage and transmission performance. The exponential function's characteristics highlight the non-linear variation of this impact. This formula comprehensively considers the equipment distribution range (via R...) f (reflection), fiber length (L) g ), Distance between target building and platform (L) z ), fiber attenuation characteristics (α), and the relationship between distance and coverage (via r and (This is reflected in the calculation of these factors). By combining and calculating these factors, a selection coefficient is quantified to determine which optical fiber is suitable for use as a main node device connection under specific community equipment distribution and spatial distance conditions. Calculating the fiber selection coefficient by comprehensively considering multiple factors allows for more accurate selection of suitable optical fibers from a large pool, avoiding unreasonable selections due to a single factor and improving the accuracy of main node device determination. Considering various variable factors such as equipment distribution, distance, and attenuation, the selection method can adapt to diverse scenarios such as different community layouts, equipment densities, and building distances, enhancing the system's adaptability in different environments. Reasonable fiber selection helps optimize connection resources between the video management platform and the main node device, improving signal transmission efficiency and stability, and avoiding resource waste or signal transmission problems caused by improper fiber selection.

[0086] S206. Determine the initial master node device based on the fiber screening coefficient.

[0087] The working principle of the above technical solution is as follows: To more accurately assess the distribution of broadcast terminal equipment within the community and provide basic data for the subsequent selection of master node equipment, the area corresponding to the community is first determined. Then, based on the number and distribution density of broadcast terminal equipment within the community, this area is divided into multiple grid areas. This method allows for the subdivision of complex community areas, facilitating the analysis of equipment distribution characteristics within each small area. The density of equipment distribution within the community is quantified, providing a basis for subsequent calculations of parameters such as fiber optic unit length. The radius of equipment distribution within the community area is comprehensively determined based on the number of broadcast terminal equipment and the distance between buildings in each grid area. This radius reflects the distribution range and density of equipment in the community space; the more numerous and concentrated the equipment, the smaller the radius value may be. To identify buildings with a relatively special location relationship (shortest straight-line distance) to the video management platform, serving as a reference point for subsequent calculations of fiber optic unit length, the building with the shortest straight-line distance to the video management platform is selected from all buildings in the community as the target building, and this straight-line distance is extracted. This operation considers the impact of spatial location factors on data transmission; buildings with shorter straight-line distances may have a relative advantage in data transmission. Taking into account both the equipment distribution radius and the straight-line distance between the target building and the video management platform, a unit length for measuring fiber optic transmission characteristics is obtained. The fiber unit length is calculated based on the equipment distribution radius and straight-line distance, using a formula where the fiber unit length is 1 / 10 of the average of the equipment distribution radius and straight-line distance. This calculation method comprehensively considers the spatial range of equipment distribution and the positional relationship between the video management platform and the target building, aiming to obtain a characteristic length that reflects the overall fiber optic transmission situation within the community. To further quantify fiber optic transmission characteristics, fiber attenuation coefficient and distance-to-coverage ratio correction factor are combined to provide more comprehensive parameters for evaluating the initial master node equipment. A fiber selection coefficient is calculated using a given formula that integrates multiple factors such as equipment distribution radius, fiber unit length, straight-line distance between the target building and the video management platform, fiber attenuation coefficient, and distance-to-coverage ratio correction factor. The fiber selection coefficient more comprehensively reflects the transmission performance and coverage capability of fiber optics within the community. Based on the previously calculated fiber selection coefficient, the most likely candidate equipment to serve as the master node equipment is selected from numerous LAN terminal devices. The initial master node equipment is determined based on the magnitude of the fiber selection coefficient. Since the fiber selection factor takes into account multiple factors such as equipment distribution and fiber transmission characteristics, the initial master node equipment selected by this factor has a relative advantage in terms of data transmission performance and coverage, and can better assume the responsibilities of the master node equipment.

[0088] The above technical solution achieves the following results: By comprehensively considering multiple factors such as the distribution of broadcast terminal equipment within the community (regional grid division, equipment distribution radius) and fiber optic transmission characteristics (fiber unit length, fiber selection coefficient), it can more comprehensively and accurately assess the potential of each broadcast terminal equipment as a master node, thereby improving the accuracy of master node equipment selection. Since the initial master node equipment is selected based on multiple parameters related to fiber optic transmission, these devices have a relative advantage in data transmission. They can better handle video data transmission tasks, reduce data transmission latency and packet loss rate, and improve the data transmission performance of the entire community video broadcast system. This technical solution considers the distribution characteristics of equipment within the community and the characteristics of fiber optic transmission, enabling the selected initial master node equipment to better adapt to the actual situation of the community. When the community scale expands or the equipment distribution changes, this solution can be adjusted and optimized relatively easily, ensuring the scalability and adaptability of the system. By calculating parameters such as the fiber selection coefficient, not only can the initial master node equipment be selected, but it can also provide important reference for subsequent system optimization. For example, the fiber optic network in different areas can be optimized based on the magnitude of the fiber selection coefficient, improving the performance and stability of the entire system. Simultaneously, grid division is performed based on the number and distribution density of broadcast terminal devices within the community, ensuring a relatively balanced distribution of devices within each grid area. This allows for refined management of device needs in different grid areas during resource allocation, avoiding excessive concentration or uneven distribution of resources and improving the resource utilization efficiency of the video management platform. A series of steps determine the fiber optic screening coefficients and subsequently the initial master node devices. By comprehensively considering factors such as the radius of device distribution and the straight-line distance between the target building and the platform, master node devices that better meet actual needs can be selected, ensuring that the master node devices bear a reasonable load in the network and optimizing network resource allocation. Determining the target building and its straight-line distance, combined with the radius of device distribution, to determine the fiber optic unit length and screening coefficients, enables a more rational fiber optic connection layout. Shorter fiber lengths and appropriate connection methods can reduce signal attenuation and interference during transmission, improving signal transmission stability and quality. Determining relevant parameters based on device distribution allows the master node devices to better cover and serve surrounding broadcast terminal devices, reducing signal interruptions and stuttering caused by insufficient signal coverage or transmission bottlenecks, and improving the overall stability of signal transmission. By dynamically selecting master node devices through the video management platform, the distribution and changes of devices within the community can be adjusted promptly. When the number or distribution of devices changes, suitable master node devices can be quickly reassessed and determined, enabling the system to adapt to changes more quickly and reducing response delays caused by untimely device adjustments. Reasonable grid partitioning and master node device selection optimize the network topology, resulting in shorter and more efficient data transmission paths within the network. This accelerates the system's response speed to device requests and operations, improving user experience.The grid partitioning method can be adjusted according to the number and distribution density of devices in different communities. For newly added communities or communities with changes in device layout, it can quickly repartition and allocate resources, giving the system good scalability and making it easy to adapt to communities of different sizes and layouts. The selection of master node devices comprehensively considers factors such as device distribution and distance, enabling the system to adapt to different geographical environments (such as different building spacing) and device deployment situations. This improves the system's adaptability in diverse scenarios and reduces the risk of system performance degradation due to environmental changes.

[0089] In one embodiment of the present invention, the area corresponding to the community is divided into grids based on the number and distribution density of the broadcast terminal devices contained within the community, thereby obtaining multiple grid areas, including:

[0090] S2011. Obtain the location coordinates of each broadcast terminal device via GPS or BeiDou.

[0091] S2012. Obtain the density of the broadcast terminal devices corresponding to each broadcast terminal device based on the location coordinates of each broadcast terminal device.

[0092] The structure of the density of the broadcast terminal equipment is as follows:

[0093]

[0094] Where D(x, y) represents the density of LAN terminal devices at location (x, y) within the community plane; n represents the number of LAN terminal devices; h represents the bandwidth of the kernel density estimate (positively correlated with the average spacing between devices); x i and y i These represent the location coordinates of the broadcast terminal equipment; x represents the horizontal coordinate on the community map; y represents the vertical coordinate on the community map; specifically, This part is the normalization constant in kernel density estimation. In kernel density estimation in a two-dimensional plane, from the perspective of the probability density function, such a constant is needed to ensure that the density integral calculated for all devices across the entire plane is 1, that is, to ensure the normalization of the probability. This is the form of the Gaussian kernel function. Wherein, The calculation involves the location (x, y) within the community plane where the density needs to be calculated and the location (x, y) of the i-th broadcast terminal device. i ,y i The square of the distance between them. Divide by 2h 2 The exponentiation method utilizes the properties of the Gaussian function to describe the contribution of a device to the density of its surrounding locations. The closer the device is to its location (i.e., the higher the density), the greater the contribution. The smaller the value, the closer the exponent term is to 1, and the greater the contribution of the device to the density at that location; the farther the distance, the closer the exponent term is to 0, and the smaller the contribution. Here, h controls the rate at which this contribution decays with distance; the larger h is, the slower the decay and the larger the influence range of a single device; the smaller h is, the faster the decay and the smaller the influence range of a single device.

[0095] S2013. Extract the effective communication radius corresponding to each broadcast terminal device; wherein, the effective communication radius is the distance at which the measured signal strength attenuates to -90dBm;

[0096] S2014. Obtain the average effective communication radius based on the effective communication radius corresponding to each broadcast terminal device;

[0097] S2015. Obtain the grid radius using the average effective communication radius;

[0098] The grid radius is obtained using the following formula:

[0099]

[0100] Where L represents the grid radius; R represents the average effective communication radius; ρ represents the average number of devices per preset unit area, and the preset unit area ranges from 300m. 2 ——1000m 2 ; a represents the network load factor, ranging from 0.1 to 0.5; b represents the terrain complexity hyperparameter of the community's location, ranging from 1.0 to 3.0; n represents the number of broadcast terminal devices; C represents the adjustment coefficient, used to adjust the sensitivity of the radius setting, ranging from 1.2 to 1.7; specifically, The grid radius is influenced by comprehensively considering the impact of device quantity and terrain factors on the denominator. Taking the cube root is a non-linear adjustment method to account for the combined influence of the denominator, ensuring that the impact of each factor on the grid radius conforms to a specific scale variation law. Factors such as device communication capability (reflected by the average effective communication radius R), device spatial distribution (ρ and n), network load (a), and terrain conditions (b) are also considered. Through combined calculations of these factors, a suitable grid radius value reflecting the current device communication status and environmental conditions is quantified. Accurate calculation of the grid radius based on multiple factors allows for reasonable division of network areas, ensuring that grid division matches device communication capabilities and the actual environment, optimizing network topology, and improving network resource utilization efficiency. Considering factors such as device distribution, load, and terrain avoids signal interference and insufficient coverage caused by unreasonable grid division, ensuring communication quality between broadcast terminal devices and reducing signal loss and transmission delays. The method is adaptable to different community device scales, distribution densities, network loads, and terrain conditions, making it universal and capable of determining suitable grid radii in various scenarios, thus enhancing system adaptability.

[0101] S2016. Select the density long extreme point of the network terminal device density as the network center to start grid growth and form multiple grid regions.

[0102] The working principle of the above technical solution is as follows: It provides foundational data for subsequent device density calculation, effective communication radius analysis, and grid division. The geographical coordinates of each broadcast terminal device are obtained through GPS or BeiDou systems, precisely determining the device's location within the community. To quantify the density of device distribution at different locations within the community for more scientific grid division, a kernel density estimation method is used to calculate the density of broadcast terminal devices at each location within the community, based on the obtained device geographical coordinates. This method considers factors such as the number of devices and the kernel density estimation bandwidth (positively correlated with the average spacing between devices), accurately reflecting the spatial distribution of devices. To understand the communication coverage of the devices and provide key parameters for calculating the grid radius, the effective communication radius corresponding to each broadcast terminal device is obtained by measuring the distance at which the signal strength attenuates to -90dBm. Then, the average effective communication radius of all devices is calculated, representing an overall level of device communication coverage within the community. Based on the device communication capabilities and the actual situation of the community, a reasonable grid size is determined for grid division. The grid radius is calculated using a given formula, taking into account factors such as the average number of devices per unit area, network load factor, community terrain complexity hyperparameter, and the number of broadcast terminal devices. These parameters comprehensively consider factors like device distribution, network load, and terrain conditions, ensuring the calculated grid radius better adapts to the community's actual situation. The community is divided into multiple reasonable grid areas to facilitate subsequent device management and analysis. The density extreme point of the broadcast terminal device density is selected as the network center to initiate grid growth. Density extreme points are typically areas with dense device distribution; using these as centers for grid growth allows for more reasonable coverage of the community. In this way, multiple grid areas are formed, each with similar device distribution and communication patterns.

[0103] The above technical solution achieves the following results: By comprehensively considering the geographical coordinates of the devices, device density, effective communication radius, and the actual conditions of the community (such as the average number of devices, network load, and terrain complexity), a scientific method is used to calculate the grid radius and divide the grid. This division method makes the grid area more consistent with the distribution and communication characteristics of devices within the community, improving the scientificity and rationality of the grid division. After dividing the community into multiple grid areas, the broadcast terminal devices in each grid area can be managed and analyzed independently. For example, optimization and adjustments can be made based on the device distribution and communication quality of different grid areas, improving the targeting and efficiency of device management. A reasonable grid division helps optimize the network performance of the community video broadcast system. By distributing devices in different grid areas, interference between devices can be reduced, improving signal transmission quality. Simultaneously, network resources can be rationally allocated based on the device density and communication needs within the grid area, improving the network's load balancing capability. This technical solution considers the terrain complexity hyperparameter of the community's geographical location, enabling the grid division to adapt to communities with different terrain conditions. Whether the community is flat or has complex terrain, a reasonable grid division result can be obtained by adjusting the terrain complexity hyperparameter, improving the adaptability and versatility of the technical solution. The resulting grid regions provide a foundation for subsequent equipment analysis, network optimization, and fault diagnosis. Detailed data statistics and analysis can be performed on each grid region to understand the operational status of equipment and network performance in different areas, providing a basis for further system optimization and improvement. Simultaneously, obtaining the geographical coordinates of each broadcast terminal device via GPS or BeiDou allows for precise determination of the device's actual location. This provides an accurate foundation for subsequent calculations and operations based on location information, ensuring that calculations of device density and grid division are based on accurate location data. This avoids deviations in subsequent analysis and operations caused by location errors, thereby improving the accuracy of the entire system's location-related performance indicators. Calculating the density of broadcast terminal devices based on their geographical coordinates accurately reflects the distribution of devices within the community. This density calculation based on accurate location information more reasonably measures the degree of device aggregation in different areas, providing a scientific basis for subsequent grid division. This ensures that the grid division more closely matches the actual device distribution, avoiding unreasonable grid division (such as excessively large grids in densely populated areas and excessively small grids in sparsely populated areas), thus resulting in better performance in density-related indicators. The grid radius is calculated by averaging the effective communication radius and taking into account the actual communication capabilities of the device. This grid division better matches the device's communication coverage area, avoiding issues such as insufficient signal coverage due to grid areas exceeding the device's communication capabilities, or resource waste due to grid areas being too small. This optimizes communication coverage performance indicators and improves the stability and effectiveness of signal transmission.By selecting the extreme point of the network terminal device density as the network center to initiate grid growth and form the grid region, the clustering characteristics of device distribution are utilized. This method enables rapid and reasonable grid division, placing the grid center in a critical position of device distribution. It ensures the relative balance of devices within each grid while adapting to the diversity of device distribution in different communities. It performs excellently in terms of grid division efficiency and adaptability to different scenarios, contributing to improved overall system efficiency and stability.

[0104] One embodiment of the present invention determines the radius of equipment distribution based on the number of LAN terminal devices and the distance between buildings within each grid area, including:

[0105] S2021. Obtain the average building spacing based on the number of LAN terminal devices and the building spacing distance within each grid area;

[0106] S2022. Obtain the sub-device distribution radius parameter for each grid area based on the average building spacing within each grid area;

[0107] The distribution radius parameter of the sub-devices is obtained by the following formula:

[0108]

[0109] Among them, R sc The radius parameter representing the distribution degree of sub-devices corresponding to each grid region; d avg δ represents the average building spacing within each grid area; N represents the number of LAN terminal devices within each grid area; δ represents the environmental adaptability coefficient, ranging from 0.8 to 1.4; λ represents the distance dispersion coefficient, ranging from 0.05 to 0.2; L d This represents the variance of building spacing for each grid region; specifically, d avg The average building spacing is represented by the logarithmic function ln(1+N), which can reasonably scale the range of changes in the number of devices. As the number of devices increases, its impact on the result does not increase linearly, but rather, through the characteristics of the logarithmic function, it presents a more realistic growth trend, reflecting the comprehensive impact of the increase in the number of devices on the degree of regional distribution. The impact of two key factors—the number of devices and the dispersion of building spacing—on the device distribution within the grid area was comprehensively considered. In engineering calculations, a default value of 1 and subsequent values ​​of L were used. dThe units used in the calculations remain consistent. By performing specific mathematical transformations on these two factors separately and then multiplying them, the calculation of the sub-device distribution radius parameter can balance the combined effects of the number of devices and the dispersion of building spacing, thus more accurately reflecting the comprehensive characteristics of device distribution within the grid area and providing a reasonable quantitative basis for subsequently determining the device distribution radius. It can comprehensively and accurately quantify the degree characteristics of device distribution within each grid area, comprehensively considering multiple factors and avoiding the one-sided influence of a single factor, providing accurate and reliable basic data for subsequently determining the overall device distribution radius of the community. Through environmental adaptation coefficients and consideration of the dispersion of building spacing, the parameter calculation can adapt to different geographical environments, building layouts, and other actual situations. Whether the buildings are arranged regularly or randomly, it can reasonably calculate the sub-device distribution radius parameter that conforms to reality. Accurate sub-device distribution radius parameters help to more rationally plan and configure network resources, such as determining appropriate signal coverage areas and master node device locations, improving resource utilization efficiency and ensuring network performance.

[0110] S2023. Perform a weighted average of the sub-device distribution radius parameters corresponding to all grid areas to obtain the device distribution radius of the area corresponding to the community.

[0111] The working principle of the above technical solution is as follows: For each grid area, based on the number of network terminal devices and the distance between buildings, the average building spacing is calculated using a specific algorithm or statistical method. The average building spacing reflects the relative density between buildings within the grid area and is one of the fundamental parameters for subsequently calculating the device distribution radius. Based on the average building spacing of each grid area, the sub-device distribution radius parameter is calculated using a given formula. This formula comprehensively considers multiple factors: a weighted average is applied to the sub-device distribution radius parameters corresponding to all grid areas to obtain the device distribution radius for the corresponding area of ​​the community. The weighted average processing can assign different weights based on factors such as the importance and size of each grid area, thereby more accurately reflecting the device distribution of the entire community.

[0112] The above technical solution achieves the following results: It comprehensively considers two key factors within the grid area: the number of broadcast terminal devices and the distance between buildings. The number of devices reflects the degree of device aggregation within the area; the more devices there are, the more complex the potential signal interaction and resource competition become. The distance between buildings reflects physical spatial characteristics, affecting signal propagation paths, attenuation, etc. First, the average building spacing is calculated to provide basic data for measuring spatial characteristics. Then, using a specific formula, the sub-device distribution radius parameter is calculated based on the average building spacing, number of devices, environmental adaptability coefficient, and distance dispersion coefficient. This quantifies the comprehensive impact of factors such as the number of devices and building spacing on the regional device distribution, obtaining characteristic parameters for each grid area. Different grid areas may have different importance and influence within the community. A weighted average of the sub-device distribution radius parameters for all grid areas is used to synthesize information from each grid area, thereby determining a radius that represents the overall device distribution in the community. This provides key parameters for subsequent decisions based on the overall device distribution in the community. By comprehensively considering factors such as the number of devices and building spacing, and by conducting detailed analysis and weighted averaging for each grid area, the distribution of devices in a community can be more accurately characterized. Compared to considering a single factor, the resulting device distribution radius is more consistent with reality, providing a more accurate basis for subsequent resource allocation and network planning. An accurate device distribution radius helps optimize resource allocation. For example, in deploying communication base stations and allocating network bandwidth, the distribution of devices can be rationally planned and allocated, avoiding resource waste or uneven distribution, improving resource utilization efficiency, and enhancing overall system performance. This solution can adapt to different community device distributions and building layouts. Whether the community has dense or sparse devices, or regular or irregular building spacing, a reasonable device distribution radius can be calculated, enabling the system to operate stably and efficiently in different scenarios, enhancing the system's adaptability and versatility. When conducting network planning (such as determining signal coverage areas and the location of master nodes), based on the accurate device distribution radius, more reasonable planning schemes can be formulated, reducing signal coverage blind spots, minimizing interference, improving network communication quality and stability, and enhancing relevant performance indicators.

[0113] Furthermore, by comprehensively considering building spacing, the number of devices, and multiple adjustment coefficients, the device distribution in each grid area can be accurately assessed. This assessment method not only considers the number of devices but also incorporates the physical distribution characteristics of buildings and environmental factors, making the assessment results more consistent with reality. The introduction of environmental adaptability coefficients and distance dispersion coefficients allows this technical solution to adapt to different community environments. Whether it's a community with complex terrain and irregular building layouts or a community with relatively uniform building spacing, the accurate device distribution radius can be obtained by adjusting these two coefficients. The accurate device distribution radius provides an important basis for community network planning. Network planners can use this parameter to rationally deploy network terminal equipment, ensuring that the coverage and signal strength of the equipment meet the needs of community residents. For example, in areas with dense device distribution, the number of devices can be appropriately reduced, while in areas with sparse device distribution, more devices need to be added to improve network coverage and stability. Understanding the device distribution radius helps optimize the allocation of community resources. For example, in areas with concentrated device distribution, network bandwidth, power supply, and other resources can be rationally allocated to avoid resource waste; in areas with sparse device distribution, resource investment can be targeted to improve service quality. This technical solution offers a degree of flexibility, allowing for dynamic adjustments based on community development and actual needs. For instance, when new buildings are added or demolished within the community, the spacing between buildings and the number of devices will change. In such cases, the radius of device distribution can be recalculated to adapt to the new community environment. Through rational device distribution and network planning, the signal quality and stability of the broadcast terminals within the community can be improved, thereby enhancing the user's viewing experience. Users can receive broadcast video signals more stably, reducing signal interruptions and buffering.

[0114] One embodiment of the present invention, determining the initial master node device based on the fiber selection coefficient, includes:

[0115] S2061. Extract the broadcast terminal equipment on the optical fiber line corresponding to the maximum value of the optical fiber screening coefficient, and use it as a candidate node equipment.

[0116] S2062. For each candidate node device to make a verifiable claim (such as "the packet loss rate of this node in the past 24 hours is <0.5%), the zk-SNARKs method is used to prove the authenticity of the claim;

[0117] S2063. When the authenticity verification of a candidate node device fails, the candidate node device is immediately removed from the candidate list, and a security audit is triggered for the candidate node device that is removed from the candidate list.

[0118] S2064. Non-candidate node devices within each grid area vote for candidate nodes based on their own computing power weight, and obtain the voting weight corresponding to each candidate node device;

[0119] The voting weight of the candidate node device is obtained by the following formula:

[0120]

[0121] Where W represents the voting weight of the candidate node device; X represents the confidence level of each candidate node device; Z h Z represents the video playback latency rate corresponding to the candidate node device; w This represents the video playback latency rate and value of all broadcast terminal devices; 1-Z h This represents the relative performance metric after removing the effects of latency. A lower latency rate indicates a higher relative performance (1-Z). h The closer the value is to 1, the better the candidate node device performs in terms of video playback smoothness, highlighting the impact of latency on node performance from the perspective of inverse quantization. exp(Z w The formula uses the exponential function to non-linearly amplify the overall latency level; ln(1+X) transforms the confidence level through a logarithmic function, making the impact of confidence level changes on voting weights non-linear. This formula comprehensively considers three key factors: the video playback latency of the candidate node device itself, the overall video playback latency of the system, and the confidence level of the candidate node device. By performing specific mathematical operations on these three factors, a voting weight value that reflects the comprehensive advantages of the candidate node device in the network is quantified. A higher voting weight is awarded when the device's latency is low, the overall system latency is low, and the confidence level is high, and vice versa. This approach can accurately select candidate node devices with better video playback performance and credibility as initial master node devices by comprehensively considering multiple factors, improving the quality of master node devices and ensuring the smoothness and reliability of video playback and other services. It considers the overall system latency, making the voting weight calculation adaptable to different network environments. It can reasonably determine the voting weight of candidate node devices under both high and low network latency conditions, enhancing the system's adaptability to different network conditions. By combining device confidence levels with voting weight calculations, more trustworthy devices in the network can be promoted to become master nodes, which can improve the security and trustworthiness of the system to a certain extent and reduce the potential risks brought about by untrustworthy devices becoming master nodes.

[0122] S2065. Select the candidate node device corresponding to the maximum voting weight as the initial master node device.

[0123] The working principle of the above technical solution is as follows: Based on the fiber optic screening coefficient, the broadcast terminal equipment on the fiber optic line corresponding to the maximum value is extracted as candidate node equipment. This is based on the fiber optic screening coefficient, which comprehensively considers factors such as equipment distribution and fiber optic transmission characteristics, and believes that these devices have potential advantages in data transmission performance and are suitable as candidates for master node equipment. A verifiable claim (such as "This node's packet loss rate in the past 24 hours < 0.5%) is generated for each candidate node equipment, and the authenticity of the claim is proved using the zk-SNARKs method. zk-SNARKs is a zero-knowledge proof technology that can prove the validity of a claim without disclosing specific data information, ensuring that the relevant performance indicators of the candidate node equipment meet the requirements. When the authenticity verification of a candidate node equipment fails, it is immediately removed from the candidate list, and a security audit of the removed candidate node equipment is triggered. This process aims to ensure the reliability and compliance of candidate node equipment and prevent unqualified equipment from becoming master node equipment. Non-candidate node equipment within each grid area votes for candidate nodes based on its own computing power weight. The computing power weight reflects the computing power and influence of non-candidate node equipment in the system, and voting based on computing power weight can comprehensively consider the opinions of different devices. The voting weight for each candidate node device is calculated using the formula described above. This formula comprehensively considers factors such as the confidence level of the candidate node device, the video playback latency rate, and the sum of the video playback latency rates of all broadcast terminal devices, aiming to comprehensively evaluate the performance and stability of the candidate node devices. The candidate node device with the highest voting weight is selected as the initial master node device. By comprehensively considering multiple factors in voting and weight calculation, the candidate node device that best meets the requirements is selected as the initial master node device, ensuring that the master node device has good performance and stability and can fulfill the responsibilities of a master node device.

[0124] The above technical solution achieves the following results: Candidate node devices are screened using fiber optic screening coefficients, and the authenticity of these devices is verified using the zk-SNARKs method, ensuring that they meet requirements in terms of data transmission performance and reliability. Based on this, the initial master node device is selected through voting and weighted calculation, further improving the reliability of the master node device selection. Security audits are conducted on candidate node devices that fail authenticity verification, allowing for the timely detection and handling of potential security issues, thus enhancing system security. Simultaneously, selecting a high-performance, highly stable initial master node device helps improve the stability of the entire community video broadcast system, reducing system interruptions and data loss caused by master node device failures. Using a computing power weighted voting method allows non-candidate node devices within each grid area to participate in the master node device selection process, achieving fairness in node selection. Furthermore, comprehensively considering multiple factors to calculate voting weights makes the selection results more reasonable and fully reflects the comprehensive performance of the candidate node devices. By selecting a high-performance initial master node device, the broadcast terminal devices within the community can be better coordinated and managed, optimizing data transmission paths and resource allocation, and improving the overall system performance. For example, this reduces data transmission latency and improves video playback quality. This technical solution comprehensively considers multiple factors such as fiber optic transmission characteristics, equipment performance, and network load, enabling it to adapt to complex and ever-changing community environments. Whether in densely populated or geographically complex communities, this solution can select suitable initial master node equipment to ensure the normal operation of the system.

[0125] Candidate node devices are first selected using fiber optic screening coefficients. Then, voting weights are calculated by comprehensively considering indicators such as video playback latency (including both the candidate node itself and the system as a whole) and confidence levels. This multi-dimensional evaluation of devices, compared to single-indicator screening, more accurately selects suitable devices as initial master nodes, improving the fit between master node devices and system requirements. The zk-SNARKs method is used to verify the authenticity of claims, promptly removing candidate node devices that fail verification. This effectively eliminates devices with potential performance issues or that are untrusted, further ensuring the quality of the selected master node devices and improving overall system stability. Video playback latency is a primary consideration when calculating voting weights, prioritizing candidate node devices with low latency as initial master nodes. This effectively reduces video playback delays, minimizes stuttering and buffering, improves the smoothness and user experience of watching videos, and optimizes video playback-related performance indicators. The selected high-quality master node devices can better manage and allocate network resources, ensuring efficient transmission of video data, guaranteeing video playback service quality, and reducing video playback quality degradation caused by poor master node device performance. The zk-SNARKs method is used to verify the authenticity of candidate node device claims, preventing devices from providing false information and effectively resisting the risk of malicious devices impersonating high-quality nodes to gain master node status. This enhances system security and protects the system from potential attacks. Triggering security audits on devices removed from the candidate list helps to promptly identify and investigate system security vulnerabilities, further improving the system's security protection system and enhancing its ability to respond to security threats. A comprehensive consideration of multiple factors is used to calculate voting weights and dynamically select master node devices, enabling the system to flexibly adjust according to different network environments and device states, adapting to changes in network conditions and improving the system's adaptability in different scenarios. The selected initial master node devices have better performance and can better balance network load. When the number of devices in the system increases or the traffic changes, reasonable master node device configuration can ensure the normal operation of the system, enhancing system scalability.

[0126] One embodiment of the present invention, which dynamically filters master node devices through a video management platform, further includes:

[0127] Step 1: Monitor the network bandwidth utilization of the master node device in real time;

[0128] Step 2: When the network bandwidth utilization rate of the master node device exceeds the preset utilization rate threshold, the dynamic filtering cycle of the master node device is set using the operating parameters of the master node device and the operating parameters of the slave node device; wherein, the operating parameters include CPU utilization rate and fiber filtering coefficient corresponding to the fiber where the broadcast terminal device is located.

[0129] The dynamic screening period of the master node device is obtained by the following formula:

[0130]

[0131] Where T represents the dynamic selection period for master node devices; T0 represents the preset initial dynamic selection period for master node devices; P z This indicates the network bandwidth utilization rate of the master node device; P fp S represents the average network bandwidth utilization of the slave node devices; z S represents the fiber selection factor corresponding to the fiber where the master node device is located; fp This represents the average fiber selection factor corresponding to the fiber where the node device is located; specifically, It is a nonlinear function based on the fiber screening coefficient, S z The larger the value (i.e., the better the fiber optic transmission performance), the closer the function value is to 1. Multiplying the two values ​​and adding 1 adjusts the dynamic filtering period by comprehensively considering the network load of the master node device and the transmission performance of its connecting fiber. When the master node device has high bandwidth utilization and good fiber performance, the adjustment range of the filtering period will be larger. This indicates the inverse adjustment effect of network load and fiber performance of slave nodes on the dynamic screening cycle of master nodes. When slave nodes have high bandwidth utilization and good fiber performance, the screening cycle of master nodes will be shortened. By comprehensively considering factors such as the master node's own network bandwidth utilization, its fiber screening coefficient, and the average network bandwidth utilization and average fiber screening coefficient of slave nodes, a specific mathematical combination is used to quantify a dynamic screening cycle of master nodes that reflects the current operating status of the devices and the network environment. When the master node has high network load and good fiber performance, or when a slave node has high network load and good fiber performance, the screening cycle will be shortened accordingly to facilitate timely replacement of the master node and optimize network performance; conversely, the screening cycle will be extended. The system can adaptively adjust the dynamic screening cycle of master nodes in real time based on the actual operating parameters of master and slave nodes, enabling the system to quickly respond to changes in network load and device performance, and avoiding unreasonable network resource allocation or master node performance bottlenecks caused by a fixed screening cycle. A well-adjusted screening cycle helps to promptly replace underperforming master node devices, balance network load, improve network bandwidth utilization, ensure efficient transmission of video data, and enhance the overall network performance and service quality of the video management platform. Considering factors such as fiber optic screening coefficients, the screening cycle can be adjusted based on the transmission performance of the fiber optic cables connecting the devices, further optimizing network resource allocation and ensuring that both master and slave node devices can utilize network resources more effectively under different network conditions, thereby improving resource utilization efficiency.

[0132] Step 3: Monitor the operating parameters of the master node device and the slave node device in real time during each master node device dynamic screening cycle;

[0133] Step 4: Obtain the node security factor based on the operating parameters of the master node device and the slave node device;

[0134] The node security factor is obtained using the following formula:

[0135]

[0136] Where E represents the node safety factor; P zc and P c These represent the CPU utilization rates of the master node and slave node devices, respectively; S z and S c These represent the fiber selection factor corresponding to the fiber where the master node device is located and the fiber selection factor corresponding to the fiber where the slave node device is located, respectively; specifically, China P zc -P c The difference between the two values ​​reflects the difference in CPU load between the master node and the slave node. A positive and larger difference indicates that the master node has a higher CPU load relative to the slave node, and may face greater operational pressure. S z and S cThese are the fiber selection coefficients corresponding to the optical fibers of the master and slave nodes, respectively. The absolute value of their difference reflects the difference in fiber transmission performance between the master and slave nodes. After taking the absolute value, a logarithmic function is applied. The logarithmic function's characteristics mean that the impact on the result is relatively mild when the difference is small, and the impact increases when the difference is large, which can reasonably quantify the effect of fiber performance differences on the overall calculation. Adding 1 to the denominator is to avoid special cases such as the logarithmic term being 0, ensuring that the denominator is meaningful. The overall denominator is scaled and adjusted for the difference in CPU utilization in the numerator, comprehensively considering the impact of fiber performance differences on CPU load differences. Here, the value after the previous calculation is used as the input of a sine function. Utilizing the nonlinear variation characteristics of the sine function, the difference in CPU utilization and the difference in fiber selection coefficients between the master and slave nodes are comprehensively mapped into a coefficient that can represent the node security level. This formula, by comprehensively considering the differences in CPU utilization and fiber selection coefficients of the master and slave nodes, uses a specific combination of mathematical operations to quantify these factors into a node security coefficient. CPU utilization reflects the pressure on the device's computing resources, while the fiber optic screening coefficient reflects the transmission performance of the fiber optic connection. Both factors jointly affect the node's operational security status. A formula is used to calculate and convert these factors into a single numerical value to characterize the node's security level. This system comprehensively assesses the operational security status of both master and slave nodes, moving beyond simple CPU load or fiber optic performance to provide system administrators with more comprehensive and accurate node security information. The quantified node security coefficient allows for the timely detection of potential security risks in node devices. A low security coefficient indicates potential operational problems due to excessive device load or significant differences in fiber optic performance, facilitating proactive optimization and adjustments to ensure stable system operation. It also provides a basis for system resource scheduling. Based on the node security coefficient, tasks and resources can be allocated rationally, such as shifting some load from master nodes with low security coefficients to slave nodes, or performing targeted optimization on nodes with poor fiber optic performance, thereby improving overall system resource utilization efficiency and operational security.

[0137] Step 5: Select the broadcast terminal device corresponding to the maximum node security coefficient within the dynamic screening cycle of each master node device as the master node device for the next dynamic screening cycle.

[0138] The working principle of the above technical solution is as follows: Continuous real-time monitoring of the network bandwidth utilization of the master node device is a fundamental indicator for judging the current load status of the master node device. By acquiring this data in real time, the network transmission pressure on the master node device can be understood promptly. When the network bandwidth utilization of the master node device exceeds a preset threshold, it indicates that the master node device may be facing significant network transmission pressure, at which point the selection cycle of the master node device needs to be dynamically adjusted. The dynamic selection cycle of the master node device is set using the operating parameters of the master and slave node devices (including CPU utilization and fiber optic selection coefficient). These parameters comprehensively consider the computing power of the device and the characteristics of fiber optic transmission, and can more accurately reflect the performance and importance of the device in the system. The dynamic selection cycle is calculated using a specific formula, making the cycle adjustment more scientific and reasonable, and adaptable to different network load conditions. Within each dynamic selection cycle, the operating parameters of the master and slave node devices are monitored in real time, specifically including key indicators such as CPU utilization, network bandwidth utilization, and fiber optic selection coefficient. By continuously monitoring these parameters, the operating status and performance changes of the devices can be grasped in a timely manner, providing data support for subsequent node security coefficient calculation and master node device selection. Based on the monitored operating parameters of the master and slave nodes, a node security factor is calculated. The node security factor is a comprehensive indicator reflecting the stability and reliability of the equipment within the system. Although the specific calculation method is not explicitly given, it can be inferred that it likely considers factors such as the equipment's operating status, performance indicators, and network environment, and is calculated using a specific algorithm or model. After each dynamic filtering cycle, based on the node security factors calculated within that cycle, the broadcast terminal device with the highest security factor is selected as the master node device for the next dynamic filtering cycle. This selection method ensures that the most optimal and stable device is chosen as the master node device, thereby improving the overall reliability and stability of the system.

[0139] The above technical solution achieves the following effects: By monitoring network bandwidth utilization in real time, the load status of master node devices can be detected promptly. When the load is too high, the selection cycle is dynamically adjusted to accelerate the replacement frequency of master node devices, thereby improving the system's response speed to network changes and ensuring that the system can quickly adapt to different network load conditions. Setting the dynamic selection cycle by comprehensively considering the operating parameters of both master and slave node devices allows for more rational allocation of system resources. Dynamically adjusting the selection of master node devices based on actual device performance and load conditions ensures more efficient resource utilization and improves overall system performance. Selecting the device with the highest node safety coefficient as the master node device ensures its stability and reliability. This helps reduce system interruptions and data loss caused by master node device failures, improving the stability of the entire community video broadcast system. This technical solution can dynamically adjust the selection cycle and selection criteria of master node devices according to different network load conditions and device performance. Whether during peak network traffic periods or periods of fluctuating device performance, this solution can select suitable master node devices, ensuring the normal operation of the system in complex network environments. Dynamically selecting master node devices allows for the timely detection and handling of device performance issues. When the node security factor of a certain device is low, the system can promptly adjust the master node device to avoid the risks that might arise from continuing to use that device as the master node. At the same time, this dynamic adjustment mechanism also facilitates system maintenance and management, enabling more flexible responses to device failures and performance changes.

[0140] On the other hand, real-time monitoring of the network bandwidth utilization of the master node device allows for dynamic adjustment of the filtering cycle based on operating parameters such as CPU utilization and fiber optic filtering coefficients of the master and slave node devices when the threshold is exceeded. This enables flexible adjustment of the filtering frequency according to the actual network load. During peak network conditions, the filtering cycle can be shortened to promptly replace underperforming master node devices, preventing excessive concentration of network resources on high-load devices and improving network bandwidth utilization. Conversely, during periods of low network load, the filtering cycle can be extended to reduce unnecessary device switching, lower system overhead, and achieve rational allocation and efficient utilization of network resources. By monitoring device operating parameters within each filtering cycle and selecting the master node device for the next cycle based on the node safety coefficient, the load on master and slave node devices can be effectively balanced. Prioritizing devices with high safety coefficients (indicating good operating status) as master nodes prevents individual devices from experiencing performance degradation due to prolonged high-load operation, ensuring a relatively balanced load across all devices in the network and improving overall network resource utilization efficiency. Real-time monitoring of device operating parameters and calculation of node safety coefficients allows for the timely detection of potential operational risks. When high CPU utilization or poor fiber optic performance leads to a decrease in security, the system can detect this in advance and take measures before equipment failure occurs, such as adjusting task allocation and optimizing resource configuration, to avoid system service interruptions caused by equipment failure and enhance system stability. The system selects the master node device based on the maximum node security coefficient, ensuring that the device with the best operating status is selected to undertake the master node task in each screening cycle. Even if some devices experience performance fluctuations, the system can quickly switch to more stable devices, ensuring the continuity of video management platform services and reducing service interruptions or lag experienced by users due to master node device problems. Quantifying the security status of master and slave node devices using node security coefficients allows for more accurate identification of network security risks. The system can classify and manage devices according to their security coefficients, strengthening monitoring and maintenance of devices with low security coefficients, promptly identifying security vulnerabilities, and reducing network security risks caused by equipment security issues, such as data leaks and malicious attacks. In abnormal situations (such as sudden network traffic spikes or attacks on some devices), by dynamically filtering master node devices and monitoring the security coefficients of nodes, the system can quickly respond and adjust master node devices, enabling the network to return to normal operation as soon as possible. This enhances the system's resilience to abnormal situations and ensures the information security of the video management platform. This technical solution can sense the dynamic changes in parameters such as network bandwidth utilization and device CPU utilization in real time, and flexibly adjust the master node device filtering strategy based on these changes. It can quickly adapt to both daily fluctuations in network traffic and sudden business peaks, ensuring stable operation in different network environments and improving the system's adaptability to network changes. As the video management platform's business expands, the number of devices and business traffic may continue to increase.By dynamically selecting master node devices and using a management mechanism based on node security coefficients, the system can maintain good performance and stability even as the scale of devices and the complexity of business increase, providing strong support for the platform's business expansion and enhancing the system's scalability.

[0141] In one embodiment of the present invention, the video management platform sends video data to the master node device using quantum encryption, including:

[0142] S301, The video management platform and the master node device negotiate and share a key through the BB84 protocol;

[0143] S302. Set the amount of video data to be published to trigger key rotation based on the fiber optic screening coefficient and fiber optic deployment length between the video management platform and the master node device.

[0144] The amount of video data released to trigger the key rotation is obtained using the following formula:

[0145]

[0146] Where G represents the amount of video data released to trigger key rotation; G0 represents the preset base data amount; S xp This represents the fiber optic screening coefficient between the video management platform and the master node device; g represents the fiber optic attenuation coefficient, typically 0.023 g / km; L represents the fiber optic cable length between the video management platform and the master node device; A represents the preset security enhancement coefficient, ranging from 1 to 5, set according to the application scenario; P v This represents the bit error rate in the quantum channel transmission between the video management platform and the master node device; specifically, S xp This represents the fiber optic screening factor between the video management platform and the master node device, reflecting the characteristics of the fiber optic cable in terms of transmission performance, etc. This indicates the degree to which the signal weakens due to attenuation as the fiber length increases. This is based on the physical laws of fiber signal attenuation, and the exponential function effectively reflects the trend of signal attenuation with distance. Multiplying the two together provides a quantitative representation of transmission quality that comprehensively considers both the fiber's inherent transmission performance and the attenuation caused by length. The overall calculation method considers both security requirements and transmission error rates, scaling the numerator calculation results. A larger denominator results in a smaller final calculated video data release volume that triggers key rotation, reflecting a trade-off between security and transmission quality. This formula comprehensively considers factors such as the transmission performance of the optical fiber between the video management platform and the master node device (reflected by the fiber selection coefficient), signal attenuation due to fiber length, preset security enhancement requirements, and the bit error rate in quantum channel transmission. Through the combined calculation of these factors, a suitable video data release volume for triggering key rotation under the current transmission environment and security requirements is quantified. This volume will be relatively large when transmission performance is good, fiber length is short, security requirements are low, and the bit error rate is low; conversely, it will be smaller. Determining the video data volume for key rotation based on various factors affecting transmission security and quality allows for timely key rotation even under changes in optical fiber transmission characteristics and bit error rate fluctuations. This prevents data from being compromised due to prolonged key use or changes in the transmission environment, ensuring the security of video data during transmission. It avoids the resource waste or security risks that may arise from a fixed key rotation strategy. Instead of simply rotating keys according to a fixed amount of data or time, this method dynamically adjusts based on actual transmission conditions. While ensuring security, it rationally utilizes computational and time resources to improve system efficiency. It considers various variable factors, enabling it to adapt to different fiber optic deployment lengths, transmission performance, and application scenarios (adjusted through security enhancement coefficients). Under different network environments and application requirements, it can reasonably determine the amount of video data triggering key rotation, enhancing the system's adaptability and versatility.

[0147] S303. When the cumulative amount of video data sent by the video management platform to the master node device reaches the amount of video data to be published, key rotation is triggered.

[0148] The working principle of the above technical solution is as follows: The video management platform and the master node device negotiate keys using the BB84 protocol. The BB84 protocol is a quantum key distribution protocol that utilizes the non-cloning and measurement interference properties of quantum states to ensure the security of key distribution. During the negotiation process, both parties transmit qubits through a quantum channel and perform operations such as comparing measurement bases using a classical channel, ultimately generating a shared key for subsequent encryption and decryption of video data. Taking into account both the fiber optic screening factor and the fiber optic deployment length between the video management platform and the master node device, a threshold for the amount of video data to trigger key rotation is set. The fiber optic screening factor reflects the quality of fiber optic transmission, while the fiber optic deployment length affects signal attenuation and transmission delay in the fiber. Based on these factors, a suitable data volume threshold is determined. When the cumulative amount of video data sent by the video management platform to the master node device reaches this threshold, the key rotation operation is triggered. The video management platform monitors the amount of video data sent to the master node device in real time. When the cumulative data volume reaches the preset video data deployment volume, the video management platform initiates the key rotation process. This means that both parties will renegotiate and generate a new shared key using the BB84 protocol to ensure the security of subsequent video data transmission.

[0149] The above technical solution achieves the following results: Employing quantum encryption, particularly shared key negotiation based on the BB84 protocol, leverages the properties of quantum mechanics to fundamentally guarantee the security of key distribution. During quantum key distribution, any eavesdropping will interfere with the quantum state, thus being detected by legitimate communicating parties, effectively preventing key theft or tampering and providing high-security encryption for video data. Key rotation triggering conditions are set considering fiber optic screening coefficients and fiber optic deployment length, allowing the key rotation mechanism to adapt to different transmission environments. When fiber optic transmission performance is good and the deployment length is short, the data volume threshold can be appropriately increased to reduce the frequency of key rotation; conversely, when the transmission environment is poor and the deployment length is long, the data volume threshold can be lowered to ensure timely key rotation and guarantee the security of video data transmission under different environments. Periodically triggering key rotation reduces the risk of key leakage due to prolonged use. Even if a potential attacker obtains the key at some point, the time window for the attacker to decrypt video data using the key is very limited because the key is updated periodically, effectively protecting the confidentiality of the video data. This data volume-based key rotation mechanism offers a degree of flexibility and scalability. Parameters such as fiber optic screening coefficients, fiber optic deployment lengths, and data volume thresholds can be adjusted according to actual needs to adapt to different system scales and business requirements. Furthermore, this mechanism can be combined with other security technologies to further enhance the security of the entire video management platform.

[0150] On the other hand, the amount of video data to be released that triggers key rotation is dynamically set based on factors such as fiber screening coefficient, fiber length, and bit error rate. When the transmission environment changes (e.g., increased fiber attenuation, increased bit error rate), key rotation can be triggered in a timely manner, reducing the risk of key cracking, ensuring the confidentiality of video data during transmission, and improving the security of quantum encrypted communication. By preset security enhancement coefficient A, the security strategy can be flexibly adjusted according to different application scenarios. For scenarios with high security requirements, the amount of data that triggers key rotation is reduced, and the key is changed more frequently; for scenarios with relatively low security requirements, the amount of data is appropriately increased, balancing resource consumption while meeting security needs, and comprehensively enhancing the system's security performance. The amount of data is calculated by comprehensively considering factors such as fiber screening coefficient and fiber deployment length. The fiber screening coefficient reflects the fiber transmission performance, and combined with the length, it can more accurately assess the signal attenuation and quality changes during transmission. When the fiber transmission performance deteriorates or the length is too long, causing severe signal attenuation, key rotation is triggered in a timely manner, which can avoid data transmission errors caused by signal quality degradation and ensure stable transmission of video data. Incorporating the quantum channel transmission bit error rate into the calculation allows for adaptive adjustment based on the probability of errors occurring during transmission. An increased bit error rate (BER) means compromised transmission stability. Reducing the amount of data triggering key rotation allows for timely key updates, mitigating the risk of data transmission failures due to accumulated errors and improving the stability of video data transmission. Compared to a fixed key rotation strategy, dynamically calculating the trigger data amount avoids unnecessary key rotations. It avoids frequent key changes under favorable transmission conditions due to a uniform, fixed threshold, reducing computational resources and time overhead associated with key generation and negotiation, improving system efficiency, and optimizing resource utilization. Determining the timing of key rotation based on actual transmission conditions allows for a more rational allocation of system resources to data transmission and security. This ensures data security and transmission stability while minimizing resource waste, guaranteeing efficient operation of the video management platform under various transmission conditions.

[0151] In one embodiment of the present invention, a master node device performs segmentation processing on a cascaded video, and then publishes the segmented video data packets to a slave node device using quantum encryption, comprising:

[0152] S401. The master node device performs segmentation processing on the broadcast video according to the preset segmentation rules and obtains multiple segmentation metadata.

[0153] S402, The master node device sets the number of fragmented metadata fragments for a single transmission based on the node computing power of the slave node device;

[0154] The number of fragmented metadata fragments in a single transmission is obtained using the following formula:

[0155]

[0156] Where U represents the number of fragmented metadata fragments in a single transmission; J represents the average size of fragmented metadata (MB / fragment); H represents the slave node computing power index, obtained by normalizing the combined CPU / GPU performance, with a value range of [1, 100]; B represents the currently available bandwidth (unit: data volume / time); t c Indicates the time window for fragmented processing; Y r This represents the safety redundancy coefficient, with a value range of R∈[1.2, 3.0]; specifically, This represents the total amount of data that can theoretically be processed and transmitted within a given time window, based on the computing power and network transmission capabilities of the slave node. This represents a constraint and adjustment on the theoretically processable total amount of data to be transmitted, considering data fragment size and security redundancy. The number of fragmented data fragments in a single transmission is obtained by dividing the numerator by the denominator. This comprehensively considers factors such as the computing power of the slave node device, network bandwidth, data fragment size, processing time, and security redundancy to determine a suitable number of fragments for a single transmission, ensuring that data transmission matches the processing capacity of the slave node device. It can rationally determine the number of fragmented data fragments for a single transmission based on the actual computing power and network bandwidth of the slave node device, avoiding data backlog caused by exceeding the processing capacity of the slave node device, or resource waste caused by insufficient transmission, thus achieving a balance between data transmission and device processing capacity and improving the overall system operating efficiency. It considers variable factors such as security redundancy coefficients and fragment processing time windows, enabling the system to adapt to network environment fluctuations and different business processing needs. Even under unstable network conditions or limited business processing time, it can determine a suitable number of transmission fragments, ensuring system stability and reliability. Combining the slave node computing power index and available bandwidth to calculate the number of fragments allows for full utilization of the slave node device's computing and network bandwidth resources. The transmission volume is dynamically adjusted based on equipment performance to improve resource utilization and reduce system performance bottlenecks caused by unreasonable resource allocation.

[0157] S403. The master node device shares the fragmented metadata to the slave node device according to the number of fragmented metadata fragments corresponding to each slave node device through quantum encryption.

[0158] S404. The slave node device performs SPHINCS+ signature verification once for each set of fragment metadata it receives, and streams and decrypts the received fragment metadata.

[0159] The working principle of the above technical solution is as follows: The master node device divides the cascading video into multiple small video segments according to preset segmentation rules and generates corresponding segment metadata. This segment metadata contains key information about the video segments, such as the segment number, duration, and size, for subsequent video transmission and processing. The master node device dynamically sets the number of segment metadata fragments transmitted to each slave node device in a single transmission based on the node computing power of the slave node devices. Slave node devices with stronger computing power can receive more segment metadata, while devices with weaker computing power receive less data, ensuring efficient data transmission and processing. The master node device shares the segment metadata with the slave node devices using quantum encryption according to the number of segment metadata fragments corresponding to each slave node device. Quantum encryption utilizes the properties of quantum mechanics, such as the non-cloning of quantum states and measurement interference, to ensure data security during transmission and prevent data theft or tampering. After receiving each set of segment metadata, the slave node device performs a SPHINCS+ signature verification. SPHINCS+ is a stateless hash signature scheme that ensures the integrity and authenticity of received fragment metadata by verifying the signature. After successful verification, the slave node device streams and decrypts the received fragment metadata, that is, it decrypts and plays the encrypted video data in real time.

[0160] The above technical solution achieves the following effects: video segmentation reduces the size of individual data packets, lowers transmission latency, and improves the real-time performance of video transmission. Segmentation also facilitates error recovery and retransmission; if a segment fails to transmit, only that segment needs to be retransmitted, rather than the entire video. Setting the number of metadata fragments transmitted per transmission based on the node computing power of the slave devices fully utilizes their processing capabilities, preventing them from being overwhelmed by excessive data transmission and thus improving the efficiency of video transmission and processing. Employing quantum encryption for transmitting metadata ensures absolute security through the properties of quantum mechanics. The unbreakable nature of quantum encryption prevents attackers from stealing or tampering with transmitted video data, effectively protecting the confidentiality and integrity of the video content. SPHINCS+ signature verification of received metadata by the slave devices further ensures data integrity and authenticity. Signature verification prevents data from being tampered with or forged during transmission, improving video transmission security. Streaming decryption and playback of received metadata by the slave devices enables real-time video playback. Users can start watching without waiting for the entire video to download, improving the user experience. Simultaneously, streaming decryption reduces device storage pressure, as only the currently playing video data needs to be cached. This technical solution can dynamically adjust according to different network environments and device performance. In situations with low network bandwidth or weak slave node computing power, the number of fragmented metadata fragments can be reduced, lowering transmission and processing pressure; conversely, in situations with high network bandwidth or strong slave node computing power, the number of fragmented metadata fragments can be increased, improving transmission and processing efficiency. Therefore, this technical solution possesses strong adaptability and flexibility, capable of adapting to different application scenarios.

[0161] On the other hand, the master node device sets the number of fragmented metadata fragments transmitted in a single transmission based on the computing power of the slave node devices. Slave node devices with strong computing power can receive more fragments, fully utilizing their processing capabilities; devices with weaker computing power receive fewer fragments to avoid excessive data transmission and subsequent processing delays. This on-demand allocation method matches data transmission with the processing capabilities of the slave node devices, reducing waiting and backlog, and improving overall transmission efficiency. For cascading video, fragmentation processing is performed, splitting large videos into multiple smaller fragmented metadata. Smaller fragments consume relatively less bandwidth during transmission, resulting in faster transmission speeds, and different fragments can be transmitted in parallel, further shortening transmission time and improving the efficiency of video data transmission from the master node to the slave node. Fragmented metadata is shared with slave node devices using quantum encryption. Quantum encryption, based on quantum mechanics principles, offers extremely high security, effectively preventing data theft and tampering during transmission, ensuring the confidentiality and integrity of video data transmitted between master and slave nodes. Each time a slave node device receives a set of fragmented metadata, it performs a SPHINCS+ signature verification. SPHINCS+ is a quantum-resistant hash signature scheme that ensures the authenticity and reliability of received data, prevents malicious forged data from being mixed in, and further enhances system security. Slave nodes perform streaming decryption and playback of received fragmented metadata. This method allows video data to be received, decrypted, and played simultaneously, without waiting for all data to be received and decrypted, reducing pre-playback waiting time, effectively reducing stuttering, providing users with a smoother video playback experience, and improving playback-related performance metrics. The number of fragmented metadata fragments is allocated based on node computing power, enabling slave nodes to process data in an orderly manner within their processing capabilities, avoiding processing delays caused by concentrated data influx, and ensuring the continuity and smoothness of video playback. Preset fragmentation rules can be flexibly adjusted according to actual needs. Whether it's changes in video content or changes in the number of node devices or computing power, the fragmentation method can be adjusted to adapt to new situations. This flexibility allows the system to maintain efficient and stable operation when facing changes such as business expansion and equipment updates, enhancing system scalability. Transmission tasks are allocated based on the computing power of slave nodes, making it easy to incorporate new slave nodes. When new devices are added, their computing power can be assessed and the corresponding number of data shards allocated accordingly, allowing them to be quickly integrated into the system. This makes the system easy to expand and adapt to the ever-growing business needs and device scale.

[0162] This invention proposes a quantum-safe enhanced broadcast system for smart communities, such as... Figure 2 As shown, the quantum-safe enhanced broadcast system includes:

[0163] A network establishment module is used to establish a quantum key distribution network for the community, and the quantum key distribution network covers the broadcast terminal equipment corresponding to each building;

[0164] The main node dynamic filtering module is used to use the broadcast terminal equipment corresponding to each building as node equipment and dynamically filter the main node equipment through the video management platform.

[0165] The first quantum encryption transmission module is used by the video management platform to send video data to the master node device using quantum encryption.

[0166] The second quantum encryption transmission module is used to segment the simulcast video through the master node device and publish the segmented video data packets to the slave node device through quantum encryption.

[0167] The working principle of the above technical solution is as follows: A quantum key distribution network infrastructure is built within the community. Using quantum key distribution technology (such as schemes based on quantum entanglement and quantum teleportation), a secure quantum communication link is established between the community center and the corresponding broadcast terminal equipment in each building. This ensures that each building's broadcast terminal equipment can obtain a unique and absolutely secure quantum key through this network, laying the foundation for subsequent encrypted data transmission. The video management platform dynamically selects the most suitable device as the master node from numerous broadcast terminal devices based on a series of preset rules and algorithms, such as device performance indicators (including processing power, storage capacity, network bandwidth, etc.), device location distribution within the community (to achieve optimal data transmission path planning), and real-time device operating status (such as whether it is online, whether it is under high load, etc.). This ensures that the master node device has the ability to efficiently process and distribute video data, while also being able to flexibly adjust according to the actual situation in the community, ensuring the stable operation of the entire broadcast system.

[0168] Before sending video data to the master node device, the video management platform encrypts the video data using a quantum encryption algorithm (such as quantum one-time pad) with a pre-negotiated quantum key. The encrypted video data is then transmitted to the master node device via a conventional communication network (such as Ethernet or fiber optic networks). Because quantum encryption technology offers unconditional security, it effectively prevents video data from being stolen or tampered with during transmission, ensuring the integrity and confidentiality of the video content. Upon receiving the encrypted video data, the master node device segments the video data into multiple smaller data packets based on factors such as the number of slave nodes, network bandwidth, and the real-time requirements of video playback. Then, the master node device uses the quantum key corresponding to the slave node device to re-encrypt the segmented video data packets and distributes the encrypted data packets to each slave node device through the community's communication network. Through segmentation and encrypted distribution, the transmission efficiency and security of video data in complex network environments are improved, ensuring that each slave node device receives complete and secure video data, enabling community-wide cascading.

[0169] The above technical solution achieves the following results: After the master node device segments the syndicated video, it distributes the video data packets to the slave node devices, avoiding the network bandwidth bottleneck caused by centralized transmission. Slave node devices in different regions and with different performance levels can flexibly receive data according to their own conditions, reducing the possibility of network congestion and enabling video data to be transmitted to various syndicated terminal devices more quickly and stably. Using quantum key distribution for encryption ensures unconditional security for data transmission between the video management platform and the master node device, as well as between the master node device and the slave node devices. Even under powerful quantum computing attacks, attackers cannot crack the encrypted video data, effectively preventing video data leakage and malicious tampering. Due to the master-slave node architecture and quantum key distribution network, when a new syndicated terminal device (slave node) is added to the community, it only needs to be connected to the quantum key distribution network and simply configured by the video management platform to achieve communication and data transmission with the master node device, without requiring large-scale modifications to the entire system. The master-slave node architecture also provides the system with a certain degree of fault tolerance. When a slave node device fails, the master node device can automatically adjust its data transmission strategy, distributing tasks to other functioning devices to ensure uninterrupted video broadcasting service. The video management platform provides unified management and monitoring of both master and slave nodes, enabling real-time monitoring of device operating status and video broadcasting activity. When system problems occur, the platform can quickly locate the fault and take appropriate measures.

[0170] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A quantum-safe enhanced broadcasting method for smart communities, characterized in that, The quantum-safe enhanced broadcast method includes: A quantum key distribution network is established for the community, and the quantum key distribution network covers the broadcast terminal equipment corresponding to each building; The broadcast terminal equipment corresponding to each building is used as node equipment, and the main node equipment is dynamically selected through the video management platform; The video management platform sends video data to the master node device using quantum encryption. The master node device segments the broadcast video into pieces, and then publishes the segmented video data packets to the slave node devices using quantum encryption. The video management platform dynamically filters master node devices, including: Extract the area corresponding to the community, and divide the area corresponding to the community into grids according to the number and distribution density of the broadcast terminal devices contained in the community to obtain multiple grid areas; The radius of equipment distribution in the corresponding area of ​​the community is determined based on the number of broadcast terminal devices and the distance between buildings in each grid area; the building with the shortest straight-line distance to the video management platform is selected as the target building; Extract the straight-line distance between the target building and the video management platform; determine the fiber optic unit length based on the device distribution radius and the straight-line distance, and obtain the fiber optic screening coefficient corresponding to the fiber optic unit length; wherein, the fiber optic screening coefficient corresponding to the fiber optic unit length is obtained by the following formula: Where S represents the fiber selection coefficient; R f L represents the radius of equipment distribution within the corresponding area of ​​the community. g L represents the unit length of the optical fiber. z The distance between the target building and the video management platform is represented by α; the fiber optic attenuation coefficient is represented by r; and the distance-coverage ratio correction factor is represented by r, which ranges from 0.1 to 0.

3. The initial master node device is determined based on the fiber selection coefficient.

2. The quantum-safe enhanced broadcast method for smart communities according to claim 1, characterized in that, A quantum key distribution network is established for the community, and the quantum key distribution network covers the broadcast terminal equipment corresponding to each building, including: Count the number of broadcast terminal devices in the community and set the key update frequency; The quantum security module will be deployed in the video management platform and the corresponding broadcast terminal equipment for each building; A quantum-encrypted communication network will be established between each broadcast terminal device and between the broadcast terminal device and the video management platform for each building.

3. The quantum-safe enhanced broadcasting method for smart communities according to claim 1, characterized in that, The community's area is divided into grids based on the number and distribution density of the network terminal devices within the community, resulting in multiple grid areas, including: The location coordinates of each broadcast terminal device are obtained through GPS or BeiDou. The density of the broadcast terminal devices is obtained based on the location coordinates of each broadcast terminal device. Extract the effective communication radius corresponding to each broadcast terminal device; wherein, the effective communication radius is the distance at which the measured signal strength attenuates to -90dBm; The average effective communication radius is obtained based on the effective communication radius corresponding to each broadcast terminal device. The grid radius is obtained using the average effective communication radius. The density extreme point of the broadcast terminal device density is selected as the network center to start grid growth, forming multiple grid regions.

4. The quantum-safe enhanced broadcasting method for smart communities according to claim 1, characterized in that, The radius of equipment distribution is determined based on the number of LAN terminal devices and the distance between buildings within each grid area, including: The average building spacing is obtained based on the number of LAN terminal devices and the distance between buildings in each grid area; The distribution radius parameter of sub-devices in each grid area is obtained based on the average building spacing within each grid area. The distribution radius of sub-devices in all grid areas is weighted and averaged to obtain the distribution radius of devices in the area corresponding to the community.

5. The quantum-safe enhanced broadcast method for smart communities according to claim 1, characterized in that, The initial master node device is determined based on the fiber selection coefficient, including: Extract the broadcast terminal equipment on the fiber optic line corresponding to the maximum value of the fiber screening coefficient as candidate node equipment; For each candidate node device, a verifiable claim is made, and the zk-SNARKs method is used to prove the authenticity of the claim. If the authenticity verification of a candidate node device fails, the candidate node device will be immediately removed from the candidate list, and a security audit will be triggered for the candidate node device that has been removed from the candidate list. Within each grid area, non-candidate node devices vote for candidate nodes based on their own computing power weight, and obtain the voting weight corresponding to each candidate node device; The candidate node device corresponding to the maximum voting weight is selected as the initial master node device.

6. The quantum-safe enhanced broadcasting method for smart communities according to claim 1, characterized in that, Dynamically selecting master node devices through the video management platform also includes: Real-time monitoring of network bandwidth utilization of master node devices; When the network bandwidth utilization rate of the master node device exceeds the preset utilization rate threshold, the dynamic filtering cycle of the master node device is set using the operating parameters of the master node device and the operating parameters of the slave node device; wherein, the operating parameters include CPU utilization rate and fiber filtering coefficient corresponding to the fiber where the broadcast terminal device is located; The operating parameters of the master node device and the slave node device are monitored in real time during each dynamic screening cycle of the master node device. The node security factor is obtained based on the operating parameters of the master node device and the slave node device. The broadcast terminal device corresponding to the maximum node security coefficient within each master node device's dynamic screening cycle will be used as the master node device for the next master node device's dynamic screening cycle.

7. The quantum-safe enhanced broadcasting method for smart communities according to claim 1, characterized in that, The video management platform sends video data to the master node device using quantum encryption, including: The video management platform and the master node device negotiate and share a key via the BB84 protocol; The amount of video data to be released that triggers key rotation is set based on the fiber optic screening coefficient and fiber optic deployment length between the video management platform and the master node device. When the cumulative amount of video data sent by the video management platform to the master node device reaches the amount of video data to be published, key rotation is triggered.

8. The quantum-safe enhanced broadcast method for smart communities according to claim 1, characterized in that, The master node device segments the cascading video, and then distributes the segmented video data packets to the slave node devices using quantum encryption, including: The master node device performs segmentation processing on the simulcast video according to the preset segmentation rules and obtains multiple segmentation metadata. The master node device sets the number of fragmented metadata fragments for a single transmission based on the node computing power of the slave node device; The master node device shares the metadata of each shard to the slave node device using quantum encryption, according to the number of shards of metadata corresponding to each slave node device. The slave node device performs SPHINCS+ signature verification once for each set of fragment metadata it receives, and then streams and decrypts the received fragment metadata for playback.

9. A quantum-safe enhanced broadcast system for smart communities, characterized in that: The quantum-safe enhanced broadcast system includes: A network establishment module is used to establish a quantum key distribution network for the community, and the quantum key distribution network covers the broadcast terminal equipment corresponding to each building; The main node dynamic filtering module is used to use the broadcast terminal equipment corresponding to each building as node equipment and dynamically filter the main node equipment through the video management platform. The first quantum encryption transmission module is used by the video management platform to send video data to the master node device using quantum encryption. The second quantum encryption transmission module is used to segment the simulcast video through the master node device and publish the segmented video data packets to the slave node device through quantum encryption. The video management platform dynamically filters master node devices, including: Extract the area corresponding to the community, and divide the area corresponding to the community into grids according to the number and distribution density of the broadcast terminal devices contained in the community to obtain multiple grid areas; The radius of equipment distribution in the corresponding area of ​​the community is determined based on the number of broadcast terminal devices and the distance between buildings in each grid area; the building with the shortest straight-line distance to the video management platform is selected as the target building; Extract the straight-line distance between the target building and the video management platform; determine the fiber optic unit length based on the device distribution radius and the straight-line distance, and obtain the fiber optic screening coefficient corresponding to the fiber optic unit length; wherein, the fiber optic screening coefficient corresponding to the fiber optic unit length is obtained by the following formula: Where S represents the fiber selection coefficient; R f L represents the radius of equipment distribution within the corresponding area of ​​the community. g L represents the unit length of the optical fiber. z The distance between the target building and the video management platform is represented by α; the fiber optic attenuation coefficient is represented by r; and the distance-coverage ratio correction factor is represented by r, which ranges from 0.1 to 0.

3. The initial master node device is determined based on the fiber selection coefficient.

Images