Quantum security enhanced simulcast method and system for intelligent community

Abstract

The invention provides a quantum security enhanced simulcasting method and system for an intelligent community. The quantum security enhanced simulcast method comprises the steps that a quantum key release network is established for a community, and the quantum key release network covers simulcast terminal equipment corresponding to each building; taking simulcast terminal equipment corresponding to each building as node equipment, and dynamically screening main node equipment through a video management platform; the video management platform sends video data to the main node equipment in a quantum encryption mode; the method comprises the following steps: performing fragmentation processing on a simulcast video through a master node device, and publishing a video data packet after fragmentation processing to a slave node device through a quantum encryption mode; the system comprises modules corresponding to the steps of the method.

Description

Technical Field

[0001] The present invention proposes a quantum security-enhanced simulcast method and system for smart communities, belonging to the technical field of community video simulcast control. Background Art

[0002] With the rapid development of the digital economy, the pace of smart city construction is accelerating. As a key component of smart cities, smart communities are increasingly becoming more information-based. Smart communities contain a large amount of sensitive information about residents, such as personal identity information, home addresses, and spending records. Furthermore, various business systems within the community, such as security monitoring, access control, and property management, also generate a large amount of critical business data. The security and confidentiality of this data are crucial. Once leaked or tampered with, it not only violates residents' privacy but can also seriously impact the safety and stability of the community. However, data security in smart communities currently faces numerous challenges.

[0003] Traditional encryption technologies rely primarily on the complexity of mathematical calculations to ensure security. Furthermore, traditional video simulcast systems face numerous challenges when applied to the large-scale and complex scenarios of smart communities. For example, as communities expand and the number of simulcast terminals increases, achieving efficient and stable video data transmission 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 issues that smart community video simulcast systems must address. Summary of the Invention

[0004] The present invention provides a quantum-safe enhanced simulcast method and system for smart communities to address the problems in the prior art. The technical solutions adopted are as follows: A quantum-secure enhanced simulcast method for smart communities, comprising: Establishing a quantum key distribution network for the community, and the quantum key distribution network covers the corresponding simulcast terminal devices in each building; The simulcast terminal devices corresponding to each building are used as node devices, and the main node devices are dynamically selected through the video management platform; The video management platform sends the video data to the master node device through quantum encryption; The simulcast video is segmented by the master node device, and the segmented video data packets are published to the slave node devices through quantum encryption.

[0005] Furthermore, a quantum key distribution network is established for the community, and the quantum key distribution network covers the corresponding broadcast terminal devices of each building, including: Count the number of simulcast terminal devices in the community and set the key update frequency; Deploy quantum security modules to the video management platform and the corresponding simulcast terminal devices in each building; A quantum encryption communication network is established between each simulcast terminal device, and a quantum encryption communication network is established between the simulcast terminal device corresponding to each building and the video management platform.

[0006] Furthermore, the video management platform dynamically screens the master node devices, including: Extracting the area range corresponding to the community, and dividing the area range corresponding to the community into grids according to the number and distribution density of simulcast terminal devices contained in the community, to obtain multiple grid areas; Determine the device distribution radius of the area corresponding to the community based on the number of simulcast 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; Extracting the straight-line distance between the target building and the video management platform; Determine the unit length of optical fiber according to the distribution radius and straight-line distance of the equipment, and obtain the optical fiber screening coefficient corresponding to the unit length of optical fiber; An initial master node device is determined according to the optical fiber screening coefficient.

[0007] Furthermore, the area corresponding to the community is divided into grids according to the number and distribution density of the simulcast terminal devices contained in the community, and multiple grid areas are obtained, including: Obtain the corresponding position coordinates of each broadcast terminal device through GPS or Beidou; Obtaining the density of simulcast terminal devices corresponding to the simulcast terminal devices according to the position coordinates corresponding to each simulcast terminal device; Extracting the effective communication radius corresponding to each simulcast terminal device; wherein the effective communication radius is the distance corresponding to the measured signal strength attenuation to -90dBm; Obtaining an average effective communication radius according to the effective communication radius corresponding to each simulcast terminal device; Obtaining a grid radius using the average value of the effective communication radius; The extreme value point of the density of the simulcast terminal device is selected as the network center to start grid growth, forming a plurality of grid areas.

[0008] Furthermore, the device distribution radius is determined based on the number of simulcast terminal devices and the distance between buildings in each grid area, including: Obtain the average building distance based on the number of simulcast terminal devices and the building distance in each grid area; Obtain the sub-device distribution radius parameter corresponding to each grid area based on the average distance between buildings in each grid area; Perform weighted averaging on the sub-device distribution radius parameters corresponding to all grid areas to obtain the device distribution radius of the area corresponding to the community.

[0009] Furthermore, determining an initial master node device according to the optical fiber screening coefficient includes: Extracting the simulcast terminal device on the optical fiber line corresponding to the maximum optical fiber screening coefficient as the candidate node device; For each candidate node device, a verifiable statement is made and the authenticity of the statement is proved using the zk-SNARKs method; 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 of the candidate node device removed from the candidate list is triggered; The non-candidate node devices contained in each grid area vote for the candidate nodes based on their own computing power weights, and obtain the voting weight corresponding to each candidate node device; The candidate node device corresponding to the maximum voting weight is used as the initial master node device.

[0010] Furthermore, dynamically screening the master node device through the video management platform also includes: Real-time monitoring of the network bandwidth usage of the master node device; When the network bandwidth occupancy rate of the master node device exceeds a preset occupancy rate threshold, the dynamic screening period 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 the CPU occupancy rate and the fiber screening coefficient corresponding to the optical fiber where the simulcast terminal device is located; Monitor the operating parameters of the master node device and the operating parameters of the slave node device in real time during each master node device dynamic screening cycle; Obtaining a node safety factor according to the operating parameters of the master node device and the operating parameters of the slave node device; The simulcast terminal device corresponding to the maximum value of the node safety factor in each master node device dynamic screening cycle is used as the master node device of the next master node device dynamic screening cycle.

[0011] Furthermore, the video management platform sends the video data to the master node device through quantum encryption, including: The video management platform and the master node device negotiate a shared key through the BB84 protocol; The amount of video data released to trigger key rotation is set according to the optical fiber screening coefficient and the optical fiber laying length between the video management platform and the master node device; When the amount of video data sent by the video management platform to the master node device reaches the amount of video data released, key rotation is triggered.

[0012] Furthermore, the master node device performs segmentation processing on the simulcast video, and publishes the segmented video data packets to the slave node devices through quantum encryption, including: The master node device segments the simulcast video according to a preset segmentation rule to obtain metadata of multiple segments; The master node device sets the number of shard metadata pieces for a single transmission according to the node computing power of the slave node device; The master node device shares the shard metadata to the slave node device using quantum encryption according to the number of shard metadata pieces corresponding to each slave node device; The slave node device performs SPHINCS+ signature verification once each time it receives a group of fragment metadata, and performs streaming decryption and playback on the received fragment metadata.

[0013] A quantum-secure enhanced simulcast system for smart communities, comprising: A network establishment module is used to establish a quantum key distribution network for the community, and the quantum key distribution network covers the simulcast terminal devices corresponding to each building; The master node dynamic screening module is used to use the simulcast terminal devices corresponding to each building as node devices and dynamically screen the master node devices through the video management platform; A first quantum encryption transmission module is used for 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 fragment the simulcast video through the master node device, and publish the fragmented video data packets to the slave node device through quantum encryption.

[0014] Beneficial effects of the present invention: The quantum-secure, enhanced simulcast method and system for smart communities proposed in this paper uses a master node device to segment the simulcast video and then transmit the video data packets to slave nodes in a distributed manner, avoiding the network bandwidth bottlenecks caused by centralized transmission. Slave nodes in different regions and with different performance levels can flexibly receive data based on their own requirements, reducing the possibility of network congestion and enabling faster and more stable transmission of video data to each simulcast terminal device. Utilizing quantum key encryption, data transmission from the video management platform to the master node device and from the master node to the slave nodes leverages the unconditional security of quantum cryptography. Even under powerful quantum computing attacks, attackers cannot decrypt 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 simply needs to be connected to the quantum key distribution network and configured by the video management platform to enable communication and data transmission with the master node device, eliminating the need for major system modifications. This master-slave node architecture provides the system with a certain degree of fault tolerance. If a slave node device fails, the master node device automatically adjusts its data transmission strategy and assigns tasks to other functioning devices, ensuring uninterrupted video streaming service. The video management platform centrally manages and monitors master and slave node devices, providing real-time visibility into device operating status and video streaming performance. When system issues arise, the fault point can be quickly located and remedial measures implemented. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 A flow chart of the method of the present invention; Figure 2 This is a system block diagram of the system of the present invention. DETAILED DESCRIPTION

[0016] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0017] The embodiment of the present invention proposes a quantum security enhanced simulcast method for smart communities, such as Figure 1 As shown, the quantum security enhanced simulcast method includes: S1. Establish a quantum key distribution network for the community, and the quantum key distribution network covers the corresponding simulcast terminal devices in each building; S2. Use the simulcast terminal devices corresponding to each building as node devices and dynamically select the master node devices through the video management platform; S3. The video management platform sends the video data to the master node device through quantum encryption; S4. The master node device segments the simulcast video and publishes the segmented video data packets to the slave node devices using quantum encryption.

[0018] The technical solution works by building a community-wide quantum key distribution network infrastructure. Using quantum key distribution technologies (such as those based on quantum entanglement and quantum teleportation), it establishes a secure quantum communication link between the community center and the corresponding simulcast terminal devices in each building. This ensures that each building's simulcast terminal device 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 master node from a pool of simulcast terminals based on a series of pre-set rules and algorithms, including device performance indicators (including processing power, storage capacity, and network bandwidth), device location within the community (to optimize data transmission routing), and device real-time operating status (such as whether it is online or under high load). This ensures that the master node device has the ability to efficiently process and distribute video data, while also allowing for flexible adjustments based on community conditions, ensuring the stable operation of the entire simulcast system.

[0019] Before sending video data to the master node, the video management platform encrypts the video data using a quantum encryption algorithm (such as the quantum one-time pad algorithm) using a pre-negotiated quantum key. The encrypted video data is then transmitted to the master node via conventional communication networks (such as Ethernet and fiber optic networks). Due to the unconditional security of quantum encryption technology, it effectively prevents video data from being eavesdropped or tampered with during transmission, ensuring the integrity and confidentiality of the video content. After receiving the encrypted video data, the master node segments it into smaller packets based on factors such as the number of slave nodes, network bandwidth, and real-time video playback requirements. The master node then re-encrypts the segmented video packets using the quantum key corresponding to each slave node and distributes the encrypted packets to each slave node via the community's communication network. This segmentation and encrypted distribution improves the transmission efficiency and security of video data in complex network environments, ensuring that each slave node receives complete and secure video data, enabling community-wide simulcasting.

[0020] The above technical solution achieves this goal: After the master node device segments the simulcast video, it transmits the video data packets in a distributed manner to the slave node devices, avoiding the network bandwidth bottlenecks caused by centralized transmission. Slave node devices in different regions and with different performance characteristics can flexibly receive data based on their own conditions, reducing the possibility of network congestion and enabling faster and more stable transmission of video data to each simulcast terminal device. Quantum key encryption ensures the unconditional security of quantum encryption technology, both for data transmission from the video management platform to the master node device and from the master node to the slave node devices. Even under powerful quantum computing attacks, attackers cannot decrypt 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 community adds a new simulcast terminal device (slave node), it simply connects to the quantum key distribution network and performs simple configuration on the video management platform to enable communication and data transmission with the master node device, eliminating the need for major system modifications. This master-slave node architecture provides the system with a certain degree of fault tolerance. If a slave node device fails, the master node device automatically adjusts its data transmission strategy and assigns tasks to other functioning devices, ensuring uninterrupted video streaming service. The video management platform centrally manages and monitors master and slave node devices, providing real-time visibility into device operating status and video streaming performance. When system issues arise, the fault point can be quickly located and remedial measures implemented.

[0021] In one embodiment of the present invention, a quantum key distribution network is established for a community, and the quantum key distribution network covers the simulcast terminal devices corresponding to each building, including: S101. Count the number of simulcast terminal devices in the community and set the key update frequency; S102. Deploy the quantum security module to the video management platform and the corresponding simulcast terminal devices in each building; S103. Establish a quantum encryption communication network between each simulcast terminal device, and establish a quantum encryption communication network between the simulcast terminal device corresponding to each building and the video management platform.

[0022] The working principle of the above technical solution is to conduct a comprehensive inspection and registration of all simulcast terminal devices within the community to accurately determine the specific number, location, and model of devices. This information forms the foundation for the subsequent construction of a quantum key distribution network and facilitates the rational planning of network architecture and resource allocation. An appropriate key update frequency is determined based on the community's security needs, device performance, and the characteristics of quantum key distribution technology. A higher key update frequency improves data transmission security, but also increases 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 simulcast terminal devices in each building. These modules provide functions such as quantum key generation, storage, distribution, and management, and are the core components for quantum cryptographic communication. The quantum security modules use a quantum random number generator to generate true random numbers as the key basis. They then use quantum key distribution protocols (such as the BB84 protocol) to negotiate and distribute keys with other devices in the quantum key distribution network. The modules also store and manage keys, ensuring key security and availability. A quantum cryptographic communication network covering all simulcast terminal devices within the community is constructed. Through optical fiber, wireless, and other communication media, the broadcast terminals in each building are connected to form an organic whole. A communication network with the video management platform is established. Similarly, a quantum encrypted communication network is established between the broadcast terminals in each building and the video management platform, ensuring that video data can be transmitted securely and efficiently from the video management platform to each broadcast terminal.

[0023] The above technical solution leverages the unclonability and unconditional security of quantum keys to provide absolute security for video data transmission. Even an attacker with unlimited computing power cannot decrypt video data encrypted with quantum keys. With the advancement of quantum computing technology, traditional encryption algorithms face the risk of being cracked. Quantum key distribution technology, however, effectively protects against quantum computing attacks, providing long-term security for community video simulcast systems. The deployment of quantum security modules and the establishment of a quantum encryption communication network take into account the compatibility of different types of simulcast terminal devices, enabling the system to adapt to diverse and complex device environments within a community. Redundancy is incorporated into the construction of the quantum encryption communication network to ensure that if any network device or communication link fails, the system automatically switches to a backup link, ensuring smooth video data transmission. Thanks to the quantum key distribution network and master-slave node architecture, new simulcast terminals in a community can be seamlessly integrated with existing systems by simply connecting them to the quantum key distribution network and performing simple configuration on the video management platform. This technical solution is not only applicable to existing video simulcast services but also provides a sound foundation for future expansion into other secure communication services that communities may develop, 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, simulcast terminals, and quantum security modules. Administrators can access real-time information such as device operating status, key updates, and network traffic, enabling timely identification and resolution of issues. The system also features automated operations and maintenance capabilities, automatically completing tasks such as key updates, device configuration, and fault diagnosis, reducing manual intervention and improving system operation and maintenance efficiency.

[0024] One embodiment of the present invention dynamically screens master node devices through a video management platform, including: S201, extracting the area range corresponding to the community, and dividing the area range corresponding to the community into grids according to the number and distribution density of simulcast terminal devices contained in the community, to obtain multiple grid areas; S202: Determine the device distribution radius of the area corresponding to the community based on the number of simulcast terminal devices and the distance between buildings in each grid area; S203: select the building with the shortest straight-line distance to the video management platform as the target building; S204: extracting the straight-line distance between the target building and the video management platform; S205. Determine a unit length of optical fiber based on the device distribution radius and the straight-line distance (wherein the unit length of optical fiber is an average value of 1 / 10 of the device distribution radius and the straight-line distance), and obtain a fiber screening coefficient corresponding to the unit length of optical fiber; The optical fiber screening coefficient corresponding to the unit length of the optical fiber is obtained by the following formula: Where S represents the optical fiber screening coefficient; R f Indicates the device distribution radius of the area corresponding to the community; L g Indicates the unit length of optical fiber; L z represents the straight-line distance between the target building and the video management platform; α represents the optical fiber attenuation coefficient; r represents the distance-coverage ratio correction factor, which ranges from 0.1 to 0.3; specifically, Taking the minimum value of the two is intended to consider the relationship between fiber length and device distribution range. When the fiber length exceeds the device distribution range, the excess part has little significance for the current fiber screening based on device distribution. Taking a smaller value can highlight factors related to the effective range of action. middle It is a quantitative representation of the degree of attenuation. Logarithmic operation can reasonably scale the range of variation of the attenuation coefficient, making it easier to calculate in the formula. z It is the straight-line distance between the target building and the video management platform. The longer the distance, the greater the signal transmission loss, so its impact on the screening coefficient is reflected in the denominator. middle The ratio of the distance between the target building and the video management platform to the device distribution radius reflects the relative relationship between the distance and the device distribution range. The square of the ratio is multiplied by the correction factor r and then exponentially calculated to consider the impact of the relative relationship between the distance and the device distribution range on signal coverage and transmission performance. The exponential function can highlight the nonlinear changes in this impact. This formula comprehensively considers the device distribution range (through R f Reflection), fiber length (L g ), the distance between the target building and the platform (L z ), fiber attenuation characteristics (α), and the relative relationship between distance and coverage (via r and Through the combined calculation of these factors, a screening coefficient is quantified that reflects the suitability of optical fibers for connecting to master node devices under the conditions of device distribution and spatial distance in a specific community. Calculating the optical fiber screening coefficient based on multiple factors can more accurately screen out optical fibers suitable for connecting to master node devices from a large number of optical fibers, avoid unreasonable selections caused by considering a single factor, and improve the accuracy of determining master node devices. Taking into account multiple variable factors such as device distribution, distance, and attenuation, the screening method can adapt to diverse scenarios such as different community layouts, device densities, and building distances, thereby enhancing the system's adaptability in different environments. Reasonable screening of optical fibers helps optimize the connection resources between the video management platform and master node devices, improves signal transmission efficiency and stability, and avoids resource waste or signal transmission problems caused by improper optical fiber selection.

[0025] S206. Determine an initial master node device according to the optical fiber screening coefficient.

[0026] The working principle of the above technical solution is as follows: To more accurately assess the distribution of simulcast terminal devices within a community and provide basic data for subsequent master node device selection, the community's corresponding area is first determined. Then, based on the number and density of simulcast terminal devices within the community, the area is divided into multiple grid areas. This allows for the subdivision of complex community areas, facilitating the analysis of device distribution characteristics within each small area. The density of device distribution within the community is quantified to provide a basis for the subsequent calculation of parameters such as fiber unit length. The device distribution radius within the community area is determined based on the number of simulcast terminal devices in each grid area and the distance between buildings. This radius reflects the distribution range and density of devices within the community space. The larger the number of devices and the more concentrated the distribution, the smaller the radius may be. To identify buildings with a relatively unique location relationship to the video management platform (the shortest straight-line distance) as a reference point for subsequent fiber unit length calculation, the building with the shortest straight-line distance to the video management platform is selected as the target building among all buildings in the community, and the straight-line distance is extracted. This operation takes into account the impact of spatial location on data transmission; buildings with shorter straight-line distances may have a relative advantage in data transmission. By comprehensively considering the device distribution radius and the straight-line distance between the target building and the video management platform, a unit length is derived to measure fiber transmission characteristics. The fiber unit length is calculated based on the device distribution radius and straight-line distance using the average value of the fiber unit length equal to 1 / 10 of the device distribution radius and straight-line distance. This calculation method comprehensively considers the spatial extent of device 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 transmission characteristics within the community. To further quantify fiber transmission characteristics, the fiber attenuation coefficient and the distance-to-coverage ratio correction factor are combined to provide more comprehensive parameters for evaluating initial master node devices. The fiber screening coefficient is calculated using a given formula that integrates multiple factors, including the device distribution radius, fiber unit length, the straight-line distance between the target building and the video management platform, the fiber attenuation coefficient, and the distance-to-coverage ratio correction factor. The fiber screening coefficient provides a more comprehensive reflection of the fiber transmission performance and coverage capabilities within the community. Based on the previously calculated fiber screening coefficient, the most likely candidate master node device is selected from the numerous simulcast terminal devices. The initial master node device is determined based on the fiber screening coefficient. Since the fiber screening coefficient comprehensively considers multiple factors such as device distribution and fiber transmission characteristics, the initial master node device screened by this coefficient has relative advantages in data transmission performance, coverage, etc., and can better assume the responsibilities of the master node device.

[0027] The above technical solution achieves the following: By comprehensively considering multiple factors, including the distribution of simulcast terminal devices within a community (regional grid division, device distribution radius) and fiber transmission characteristics (fiber unit length, fiber screening coefficient), it enables a more comprehensive and accurate assessment of each simulcast terminal's potential as a master node, thereby improving the accuracy of master node selection. Because initial master node devices are selected based on multiple parameters related to fiber transmission, these devices possess relative advantages in data transmission. They are better equipped to handle video data transmission, reduce data transmission latency and packet loss, and improve the data transmission performance of the entire community video simulcast system. This technical solution, which considers the distribution characteristics of devices within the community and fiber transmission characteristics, ensures that the selected initial master node devices are more adaptable to the community's specific circumstances. As the community expands or the device distribution changes, this solution can be easily adjusted and optimized, ensuring system scalability and adaptability. By calculating parameters such as the fiber screening coefficient, not only can initial master node devices be selected, but it also provides important reference for subsequent system optimization. For example, the fiber screening coefficient can be used to optimize fiber networks in different areas, improving overall system performance and stability. Furthermore, a grid is created based on the number and density of simulcast terminal devices within the community, ensuring a relatively balanced distribution of devices within each grid area. This allows for refined resource allocation tailored to the device needs of each grid area, avoiding over-concentration or uneven resource allocation, and improving resource utilization efficiency within the video management platform. A series of steps are used to determine the fiber screening coefficient and, in turn, the initial master node device. This takes into account factors such as the device distribution radius and the linear distance between the target building and the platform. This allows for the selection of master node devices that best meet actual needs, ensuring that the master node devices bear a reasonable load within the network and optimizing network resource allocation. Determining the target building and linear distance, and then determining the fiber unit length and screening coefficient based on the device distribution radius, optimizes the fiber connection layout. Shorter fiber lengths and appropriate connection methods reduce signal attenuation and interference during transmission, improving signal transmission stability and quality. By determining relevant parameters based on device distribution, master node devices can better cover and serve surrounding simulcast terminal devices, reducing signal interruptions and lags caused by insufficient coverage or transmission bottlenecks, and improving overall signal transmission stability. By dynamically screening master node devices through the video management platform, master node devices can be adjusted promptly based on the actual distribution and changes of devices within the community. When the number or distribution of devices changes, the appropriate master node device can be quickly reassessed and determined, enabling the system to adapt more quickly and reducing response delays caused by untimely device adjustments. Reasonable grid division and master node device screening optimize the network topology, making data transmission paths within the network shorter and more efficient, thereby accelerating the system's response to device requests and operations and improving the user experience.The grid division method can be adjusted based on the number of devices and distribution density in different communities. For newly added communities or those with changes in device layout, rapid re-division and resource allocation can be implemented, making the system highly scalable and easily adaptable to communities of varying sizes and layouts. Master node devices are selected based on a comprehensive consideration of factors such as device distribution and distance, allowing the system to adapt to different geographic environments (such as varying distances between buildings) and device deployment scenarios. This improves the system's adaptability in diverse scenarios and reduces the risk of performance degradation due to environmental changes.

[0028] In one embodiment of the present invention, a region corresponding to a community is divided into grids according to the number and distribution density of simulcast terminal devices contained in the community, and multiple grid regions are obtained, including: S2011. Obtain the position coordinates corresponding to each simulcast terminal device through GPS or Beidou; S2012, obtaining a simulcast terminal device density corresponding to each simulcast terminal device according to the position coordinates corresponding to each simulcast terminal device; The structure of the simulcast terminal device density is as follows: Where D(x, y) represents the density of simulcast terminal devices at position (x, y) in the community plane; n represents the number of simulcast terminal devices; h represents the bandwidth of kernel density estimation (positively correlated with the average distance between devices); x i and y i They represent the position coordinates of the corresponding broadcast terminal devices respectively; x represents the horizontal coordinate in the community plan map; y represents the vertical coordinate in the community plan map; specifically, This is the normalization constant in kernel density estimation. In kernel density estimation on a two-dimensional plane, from the perspective of the probability density function, such a constant is required to ensure that the integral of the density calculated for all devices on the entire plane is 1, that is, to ensure the normalization of the probability. This is the form of the Gaussian kernel function. The calculation is the difference between the position (x, y) where the density is to be calculated and the position of the i-th simulcast terminal device (x i ,y i ) divided by 2h 2 The exponential is taken to describe the contribution of the device to the density of the surrounding locations using the characteristics of the Gaussian function. The smaller the value, the closer the exponential term is to 1, and the greater the device's contribution to the density at that location. The farther the distance, the closer the exponential term is to 0, and the smaller the contribution. h controls how quickly this contribution decays with distance. A larger h indicates slower decay and a larger impact range for a single device. A smaller h indicates faster decay and a smaller impact range for a single device.

[0029] S2013. Extracting the effective communication radius corresponding to each simulcast terminal device; wherein the effective communication radius is the distance corresponding to the measured signal strength attenuation to -90 dBm; S2014. Obtain an average effective communication radius according to the effective communication radius corresponding to each simulcast terminal device; S2015. Obtaining a grid radius using the average value of the effective communication radius; The grid radius is obtained by the following formula: Where L represents the grid radius; R represents the average effective communication radius; ρ represents the average number of devices within a preset unit area, and the value range of the preset unit area is 300m 2 ——1000m 2 a represents the network load factor, with a value range of 0.1-0.5; b represents the terrain complexity hyperparameter of the community's location, with a value range of 1.0-3.0; n represents the number of simulcast terminal devices; C represents the adjustment coefficient, which is used to adjust the sensitivity of the radius setting, with a value range of 1.2-1.7; specifically, The denominator is comprehensively considered, impacting the number of devices and terrain factors, which in turn influence the grid radius. Taking the cube root is a nonlinear adjustment for the combined impact of the denominator, ensuring that the impact of each factor on the grid radius conforms to a specific scaling pattern. Factors such as device communication capabilities (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 a combined calculation of these factors, an appropriate grid radius is quantified that reflects the current device communication status and environmental conditions. Accurately calculating the grid radius based on multiple factors allows for reasonable network area division, aligning the grid division with device communication capabilities and the actual environment, optimizing the network topology, and improving network resource utilization efficiency. Considering factors such as device distribution, load, and terrain can avoid signal interference and coverage issues caused by inappropriate grid division, ensuring communication quality between simulcast terminals and reducing signal loss and transmission delay. This method is adaptable to the device scale, distribution density, network load, and terrain conditions of different communities, making it versatile and capable of determining an appropriate grid radius in various scenarios, enhancing system adaptability.

[0030] S2016: Select the extreme value point of the density of the simulcast terminal device as the network center to start grid growth, forming multiple grid areas.

[0031] The working principle of the above technical solution is to provide basic data for subsequent device density calculation, effective communication radius analysis, and grid division. The geographic coordinates of each simulcast terminal device are obtained using GPS or the Beidou system. These coordinates accurately determine the device's position on the community plane. To quantify the density of device distribution at different locations within the community and facilitate more scientific grid division, a kernel density estimation method is used based on the obtained device geographic coordinates to calculate the simulcast terminal device density at each location within the community plane. This method considers factors such as the number of devices and the kernel density estimation bandwidth (which is positively correlated with the average device spacing), providing a relatively accurate reflection of the spatial distribution of devices. To understand the communication coverage of devices and provide key parameters for grid radius calculation, the effective communication radius of each simulcast terminal device is obtained by measuring the distance at which the signal strength decays to -90dBm. The average effective communication radius of all devices is then calculated, representing the overall level of communication coverage of devices within the community. An appropriate grid size is determined based on the device communication capabilities and the actual conditions of the community for grid division. The grid radius is calculated using a given formula using the average effective communication radius, combined with parameters such as the average number of devices per unit area, the network load factor, the community's terrain complexity hyperparameter, and the number of simulcast terminals. These parameters comprehensively consider factors such as device distribution, network load, and terrain conditions, ensuring that the calculated grid radius is more accurately adapted to the community's actual conditions. The community is divided into multiple, rationally defined grid areas to facilitate subsequent device management and analysis. The network center is selected as the network center to initiate grid growth. These density extremes are typically areas with dense device distribution. Grid growth based on these extremes ensures more consistent coverage of the community. This approach creates multiple grid areas, each with similarities in device distribution and communication conditions.

[0032] The above technical solution achieves this by comprehensively considering device geographic coordinates, device density, effective communication radius, and community specific conditions (such as the average number of devices, network load, and terrain complexity). A scientific approach is used to calculate the grid radius and perform grid division. This division ensures that the grid areas more closely align with the distribution and communication characteristics of devices within the community, improving the scientific and rational nature of the grid division. Dividing the community into multiple grid areas allows for independent management and analysis of the simulcast terminal devices within each grid area. For example, optimization and adjustments can be made based on the device distribution and communication quality of each grid area, improving the targeted and efficient management of devices. Reasonable grid division helps optimize the network performance of the community video simulcast system. Distributing devices across different grid areas reduces interference between devices and improves signal transmission quality. Furthermore, network resources can be rationally allocated based on device density and communication requirements within the grid area, enhancing network load balancing capabilities. This technical solution considers the terrain complexity hyperparameter of the community's geographic location, making the grid division adaptable to communities with varying terrain conditions. Whether in a flat or complex terrain community, reasonable grid division can be achieved by adjusting the terrain complexity hyperparameter, enhancing the adaptability and versatility of the technical solution. The multiple grid areas generated after gridding provide a foundation for subsequent device analysis, network optimization, and troubleshooting. Detailed data statistics and analysis can be performed for each grid area to understand the device operating status and network performance in different areas, providing a basis for further system optimization and improvement. Furthermore, the geographic coordinates of each simulcast terminal device, obtained through GPS or Beidou, accurately determine the device's actual location. This provides an accurate foundation for subsequent location-based calculations and operations. This ensures that calculations of device density and gridding are based on precise location data, avoiding errors in subsequent analysis and operations caused by location errors, thereby improving the accuracy of the entire system's location-related performance indicators. Calculating simulcast terminal device density based on device geographic coordinates accurately reflects the distribution density of devices within a community. This density calculation based on accurate location information allows for a more accurate assessment of the concentration of devices in different areas, providing a scientific basis for subsequent gridding. This ensures that gridding more closely matches the actual device distribution, avoiding inappropriate gridding (e.g., grids that are too large in densely populated areas and too small in sparsely populated areas), resulting in improved performance on density-related performance indicators. The grid radius is calculated by taking the average value of the effective communication radius and taking into account the actual communication capabilities of the device. This creates a grid area that better adapts to the device's communication coverage range, avoiding issues such as insufficient signal coverage due to the grid area exceeding the device's communication capabilities, or wasting resources due to the grid area being too small. This optimizes communication coverage performance indicators and improves the stability and effectiveness of signal transmission.Grid growth is initiated by selecting the extreme point of the simulcast terminal device density as the network center to form the grid area, leveraging the clustering characteristics of device distribution. This approach allows for rapid and rational grid division, placing the grid center at a key location for device distribution. This ensures relative balance of devices within each grid while accommodating the diverse distribution of devices across different communities. It excels in performance metrics such as grid division efficiency and adaptability to diverse scenarios, helping to improve the operational efficiency and stability of the entire system.

[0033] In one embodiment of the present invention, determining the device distribution radius based on the number of simulcast terminal devices and the distance between buildings in each grid area includes: S2021. Obtain an average building distance based on the number of simulcast terminal devices and the building distance in each grid area; S2022. Obtain a sub-device distribution radius parameter corresponding to each grid area based on the average distance between buildings in each grid area; The sub-device distribution radius parameter is obtained by the following formula: Among them, R sc Indicates the sub-device distribution radius parameter corresponding to each grid area; d avg represents the average distance between buildings in each grid area; N represents the number of simulcast terminal devices in each grid area; δ represents the environmental adaptation coefficient, which ranges from 0.8 to 1.4; λ represents the distance dispersion coefficient, which ranges from 0.05 to 0.2; L d represents the building spacing variance corresponding to each grid area; specifically, Medium avg is the average building spacing. The ln(1+N) logarithmic function can appropriately scale the range of device number variations. As the number of devices increases, its impact on the results does not increase linearly. Instead, the characteristics of the logarithmic function show a more realistic growth trend, reflecting the comprehensive impact of the increasing number of devices on the degree of regional distribution. The impact of two key factors, the number of devices and the degree of discreteness of building spacing, on the distribution of devices within the grid area are comprehensively considered. dThe corresponding units of calculation remain consistent. By performing specific mathematical transformations on these two factors and then multiplying them, the calculation of the sub-device distribution radius parameter balances the combined effects of device quantity and building spacing, more accurately reflecting the comprehensive characteristics of device distribution within the grid area and providing a reasonable quantitative basis for the subsequent determination of the device distribution radius. This method comprehensively and accurately quantifies the device distribution characteristics within each grid area, comprehensively considering multiple factors to avoid the bias of a single factor, providing accurate and reliable basic data for the subsequent determination of the overall device distribution radius of the community. By considering the environmental adaptation coefficient and the building spacing, the parameter calculation is adaptable to different geographical environments, building layouts, and other specific conditions. Whether the buildings are arranged in a regular or a chaotic manner, a reasonable sub-device distribution radius parameter can be calculated that meets the actual requirements. Accurate sub-device distribution radius parameters facilitate more rational planning and allocation of network resources, such as determining appropriate signal coverage and master node device location, improving resource utilization efficiency and ensuring network performance.

[0034] S2023. Perform weighted averaging on the sub-device distribution radius parameters corresponding to all grid areas to obtain the device distribution radius of the area corresponding to the community.

[0035] The working principle of the above technical solution is as follows: for each grid area, the average building distance is calculated through a specific algorithm or statistical method based on the number of simulcast terminal devices in the area and the distance between buildings. The average building distance reflects the relative density between buildings in the grid area and is one of the basic parameters for the subsequent calculation of the device distribution radius. Based on the average building distance in each grid area, the sub-device distribution radius parameter is calculated using a given formula. This formula takes into account multiple factors: the sub-device distribution radius parameters corresponding to all grid areas are weighted averaged to obtain the device distribution radius of the area corresponding to the community. The weighted average processing can assign different weights according to factors such as the importance and area size of each grid area, thereby more accurately reflecting the device distribution of the entire community.

[0036] The above technical solution comprehensively considers two key factors: the number of simulcast terminal devices within a grid area and the distance between buildings. The number of devices reflects the concentration of devices within the area. A larger number increases the complexity of potential signal interaction and resource competition. The distance between buildings reflects the physical characteristics of the space, influencing signal propagation paths, attenuation, and other factors. The average building spacing is first calculated to provide basic data for measuring spatial characteristics. A specific formula is used to calculate the sub-device distribution radius parameter based on the average building spacing, the number of devices, the environmental adaptation coefficient, and the distance dispersion coefficient. This quantifies the combined impact of factors such as the number of devices and the distance between buildings on regional device distribution, resulting in characteristic parameters for each grid area. Different grid areas may have varying importance and influence within a community. A weighted average of the sub-device distribution radius parameters for all grid areas is taken, integrating information from each grid area to determine a radius that represents the device distribution of the entire community. This provides a key parameter for subsequent decisions based on the overall device distribution in the community. By comprehensively considering multiple factors, such as the number of devices and building spacing, and performing a detailed analysis of each grid area and ultimately taking a weighted average, this approach more accurately characterizes the device distribution within a community. Compared to considering a single factor, the resulting device distribution radius is more realistic, providing a more accurate basis for subsequent resource allocation and network planning. An accurate device distribution radius helps optimize resource allocation. For example, when deploying communication base stations and allocating network bandwidth, device distribution can be used to rationally arrange and allocate resources, avoiding resource waste or uneven allocation, improving resource utilization efficiency, and enhancing overall system performance. This solution adapts to the device distribution and building layout of different communities. Whether in communities with dense or sparse device density or with regular or irregular building spacing, the optimal device distribution radius can be calculated, ensuring stable and efficient system operation in various scenarios and enhancing the system's adaptability and versatility. When planning networks (such as determining signal coverage and master node device locations), accurate device distribution radiuses enable more efficient planning, reducing signal coverage blind spots, minimizing interference, and improving network communication quality and stability, enhancing relevant performance indicators.

[0037] Furthermore, by comprehensively considering building spacing, the number of devices, and multiple adjustment coefficients, the device distribution radius within 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 results more realistic. The introduction of the environmental adaptation coefficient and the distance dispersion coefficient allows this technical solution to adapt to diverse community environments. Whether in communities with complex terrain and irregular building layouts or those with relatively uniform building spacing, these two coefficients can be adjusted to accurately determine the device distribution radius. An accurate device distribution radius provides a crucial basis for community network planning. Network planners can use this parameter to optimally deploy simulcast terminal devices, ensuring that the coverage and signal strength 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, devices can be increased 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, resources such as network bandwidth and power supply can be rationally allocated to avoid resource waste; in areas with sparse device distribution, targeted resource investment can be increased to improve service quality. This technical solution offers a degree of flexibility, allowing for dynamic adjustments based on community development and actual needs. For example, when new buildings are added or demolished within a community, the distance between buildings and the number of devices will change. The device distribution radius can then be recalculated to adapt to the new community environment. Through rational device distribution and network planning, the signal quality and stability of simulcast terminal devices within the community can be improved, thereby enhancing the user viewing experience. Users can receive simulcast video signals more stably, with less signal interruptions and freezes.

[0038] In one embodiment of the present invention, determining an initial master node device according to the optical fiber screening coefficient includes: S2061. Extract the simulcast terminal device on the optical fiber line corresponding to the maximum optical fiber screening coefficient as a candidate node device; S2062. For each candidate node device, a verifiable statement (e.g., “This node’s packet loss rate is less than 0.5% in the past 24 hours”) is made, and the authenticity of the statement is verified using the zk-SNARKs method. S2063. When the authenticity verification of the candidate node device fails, the candidate node device is immediately removed from the candidate list, and a security audit of the candidate node device removed from the candidate list is triggered; S2064. The non-candidate node devices contained in each grid area vote for the candidate node based on their own computing power weights, and obtain the voting weight corresponding to each candidate node device; The voting weight corresponding to the candidate node device is obtained by the following formula: Where W represents the voting weight corresponding to the candidate node device; X represents the confidence level corresponding to each candidate node device; Z h Indicates the video playback delay rate corresponding to the candidate node device; Z w Indicates the video playback delay rate and value of all simulcast terminal devices; 1-Z h Relative performance index after removing the effect of delay. The lower the delay rate, the better. 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 delay on node performance from the perspective of reverse quantization. exp(Z w ) represents the use of exponential functions to nonlinearly amplify the overall latency level; ln(1+X) transforms the confidence level using a logarithmic function, resulting in a nonlinear relationship between the confidence level and the voting weight. This formula comprehensively considers three key factors: the candidate node device's own video playback latency, the overall system video playback latency, and the candidate node device's confidence level. By performing specific mathematical operations on these three factors, a voting weight is quantified that reflects the candidate node device's overall strength in the network. Low device latency, low overall system latency, and high confidence result in a higher voting weight, while lower weights are associated with lower confidence levels. This multi-dimensional approach accurately selects candidate node devices with superior video playback performance and reliability as the initial master node, improving the quality of master nodes and ensuring smooth and reliable video playback and other services. By considering overall system latency, the voting weight calculation is adaptable to diverse network environments. The voting weight of candidate node devices can be appropriately determined regardless of high or low network latency, enhancing the system's adaptability to varying network conditions. Calculating voting weights based on device confidence can encourage more trusted devices in the network to become master nodes, improving the security and credibility of the system to a certain extent and reducing the potential risks brought about by untrusted devices becoming master nodes.

[0039] S2065: Use the candidate node device corresponding to the maximum voting weight as the initial master node device.

[0040] The above technical solution works as follows: Based on the fiber screening coefficient, the simulcast terminal devices on the fiber line corresponding to the maximum value are selected as candidate node devices. This is based on a comprehensive consideration of factors such as device distribution and fiber transmission characteristics. These devices are considered to have potential advantages in data transmission performance and are suitable candidates for master node devices. A verifiable claim (such as "This node's packet loss rate was <0.5% over the past 24 hours") is generated for each candidate node device, and the authenticity of the claim is verified using zk-SNARKs. zk-SNARKs is a zero-knowledge proof technology that can prove the validity of a claim without revealing specific data information, ensuring that the candidate node device's relevant performance indicators meet the requirements. If the authenticity verification of a candidate node device fails, it is immediately removed from the candidate list, and a security audit of the removed candidate node device is triggered. This process is designed to ensure the reliability and compliance of the candidate node devices and prevent non-compliant devices from becoming master node devices. Non-candidate node devices within each grid area vote on the candidate node based on their computing power weight. The computing power weight reflects the computing power and influence of the non-candidate node device in the system. The computing power weight voting can comprehensively consider the opinions of different devices. The voting weight corresponding to each candidate node device is calculated using the above formula. This formula comprehensively considers factors such as the candidate node device's confidence level, video playback delay rate, and the video playback delay rates and values of all simulcast 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 most qualified candidate node device is selected as the initial master node device, ensuring that the master node device has good performance and stability and is capable of assuming the responsibilities of the master node device.

[0041] The above technical solution achieves the following: candidate node devices are screened using fiber screening coefficients and their authenticity verified using zk-SNARKs, ensuring that the candidate node devices meet the requirements for data transmission performance and reliability. Furthermore, the initial master node device is selected through voting and weighted calculation, further improving the reliability of master node selection. Security audits are conducted on candidate node devices that fail authenticity verification to promptly identify and address potential security issues, enhancing system security. Furthermore, selecting initial master node devices with high performance and stability helps improve the stability of the entire community video simulcast system, reducing system outages and data loss caused by master node device failures. Using computing power-weighted voting allows non-candidate node devices in each grid area to participate in the master node selection process, ensuring fairness in node selection. Furthermore, by comprehensively considering multiple factors in calculating voting weights, the selection results are more reasonable and fully reflect the comprehensive performance of the candidate node devices. By selecting high-performing initial master node devices, better coordination and management of simulcast terminal devices within the community can be achieved, optimizing data transmission paths and resource allocation, and improving overall system performance. For example, this can reduce data transmission latency and enhance video playback quality. This technical solution comprehensively considers multiple factors, including fiber transmission characteristics, device performance, and network load, and is adaptable to complex and ever-changing community environments. Whether in densely populated areas or complex terrain, this solution enables the selection of appropriate initial master node devices to ensure system operation.

[0042] Candidate node devices are first selected using the fiber screening coefficient. Voting weights are then calculated based on metrics such as video playback latency (both for the candidate node itself and the system as a whole) and confidence. This multi-dimensional evaluation of devices allows for more precise selection of suitable initial master nodes than single-metric screening, improving the alignment of master node devices with system requirements. zk-SNARKs are used to verify the authenticity of claims, promptly removing candidate node devices that fail verification and effectively eliminating devices with potential performance issues or untrustworthiness. This further ensures the quality of selected master node devices and improves overall system stability. Voting weights prioritize video playback latency, prioritizing candidate node devices with low latency as initial master node devices. This effectively reduces video playback delay, minimizes lag and buffering, improves the smoothness and user experience of video viewing, and optimizes relevant video playback performance metrics. Selected high-quality master node devices can better manage and allocate network resources, ensuring efficient video data transmission and quality of video playback services, and mitigating issues associated with poor performance of master node devices. The zk-SNARKs method is used to verify the authenticity of candidate node device claims, preventing devices from providing false information. This effectively mitigates the risk of malicious devices masquerading as high-quality nodes to gain masternode status, enhancing system security and protecting the system from potential attacks. Triggering security audits for devices removed from the candidate list helps promptly identify and troubleshoot system security risks, further improving the system's security protection system and enhancing the system's ability to respond to security threats. Voting weights are calculated and dynamically selected based on various factors, allowing the system to flexibly adjust to varying network environments and device status, adapting to changing network conditions and improving the system's adaptability in various scenarios. The selected initial masternode device offers superior performance and better balances network load. As the number of devices in the system increases or business volume fluctuates, proper masternode configuration ensures system operation and enhances scalability.

[0043] One embodiment of the present invention dynamically screens master node devices through a video management platform, further comprising: Step 1: Monitor the network bandwidth usage of the master node device in real time; Step 2: When the network bandwidth occupancy rate of the master node device exceeds a preset occupancy rate threshold, a dynamic screening period of the master node device is set using operating parameters of the master node device and operating parameters of the slave node device; wherein the operating parameters include CPU occupancy rate and a fiber screening coefficient corresponding to the optical fiber where the simulcast terminal device is located; The dynamic screening period of the master node device is obtained by the following formula: Wherein, T represents the dynamic screening period of the master node device; T0 represents the preset initial dynamic screening period of the master node device; P z Indicates the network bandwidth usage of the master node device; P fp Indicates the average network bandwidth usage of the slave node device; S z Indicates the optical fiber screening coefficient corresponding to the optical fiber where the master node device is located; S fp Indicates the average value of the optical fiber screening coefficient corresponding to the optical fiber where the slave node device is located; specifically, is a nonlinear function based on the fiber screening coefficient, S z The larger the value (i.e., the better the fiber transmission performance), the closer the function value approaches 1. Multiplying the two and adding 1 adjusts the dynamic screening period based on a comprehensive consideration of the master node's network load and the transmission performance of its connected fiber. When the master node has high bandwidth utilization and good fiber performance, the screening period is adjusted more significantly. This parameter represents the inverse adjustment effect of the slave node's network load and fiber performance on the master node's dynamic selection cycle. High bandwidth utilization and good fiber performance on the slave node shorten the master node's selection cycle. By comprehensively considering factors such as the master node's own network bandwidth utilization and its fiber selection coefficient, as well as the average network bandwidth utilization and average fiber selection coefficient of the slave nodes, and utilizing a specific mathematical combination, a dynamic selection cycle for the master node is quantified to reflect the current device operating status and network environment. When the master node's network load is high and its fiber performance is good, or when the slave node's network load is high and its fiber performance is good, the selection cycle is shortened to allow for timely replacement of the master node and optimize network performance. Otherwise, the selection cycle is extended. The dynamic selection cycle for the master node can be adaptively adjusted in real time based on the actual operating parameters of the master and slave nodes, enabling the system to quickly respond to changes in network load and device performance, avoiding network resource misallocation or master node performance bottlenecks caused by fixed selection cycles. Properly adjusting the screening cycle helps promptly replace underperforming master node devices, balance network loads, improve network bandwidth utilization, and ensure efficient transmission of video data, thereby enhancing the network performance and service quality of the entire video management platform. By taking into account factors such as the fiber screening coefficient and adjusting the screening cycle based on the transmission performance of the fiber connecting the device, network resource allocation can be further optimized, ensuring that both master and slave node devices can more effectively utilize network resources under varying network conditions, improving resource utilization efficiency.

[0044] Step 3: Monitor the operating parameters of the master node device and the operating parameters of the slave node device in real time during each master node device dynamic screening cycle; Step 4: Obtaining a node safety factor based on the operating parameters of the master node device and the operating parameters of the slave node device; The node safety factor is obtained by the following formula: Where E represents the node safety factor; P zc and P c Respectively represents the CPU occupancy rate of the master node device and the CPU occupancy rate of the slave node device; S z and S c They respectively represent the optical fiber screening coefficient corresponding to the optical fiber where the master node device is located and the optical fiber screening coefficient corresponding to the optical fiber where the slave node device is located; specifically, Medium P zc -P c The difference between the two reflects the difference in CPU load between the master and slave nodes. A positive and larger difference indicates that the master node has a higher CPU load than the slave node and may face greater operational pressure. Medium S z and S cThe fiber screening coefficients corresponding to the optical fibers used by the master and slave node devices, respectively, are used. The absolute value of their difference reflects the difference in optical fiber transmission performance between the master and slave nodes. The absolute values are then processed using a logarithmic function. The logarithmic function's characteristics mitigate the impact of small differences on the results and increase the impact of large differences, effectively quantifying the impact of fiber performance differences on the overall calculation. The addition of 1 to the denominator avoids special cases such as logarithmic terms being zero, ensuring the denominator's significance. The overall denominator scales the CPU usage difference in the numerator to comprehensively account for the impact of fiber performance differences on CPU load differences. Here, the previously calculated values are used as inputs to a sine function. Leveraging the nonlinear characteristics of the sine function, the CPU usage difference and fiber screening coefficient difference between the master and slave nodes are mapped into a coefficient that represents the node's safety level. This formula comprehensively considers the CPU usage difference and fiber screening coefficient differences between the master and slave nodes, using a specific combination of mathematical operations to quantify these factors into a node safety factor. CPU utilization reflects the pressure on a device's computing resources, while the fiber screening coefficient reflects the transmission performance of the fiber connecting the device. Together, these factors influence the node's operational safety status. A formula is used to convert these factors into a single numerical value to represent the node's safety level. This comprehensive assessment of the operational safety status of both master and slave nodes goes beyond single factors like CPU load or fiber performance, providing system administrators with more comprehensive and accurate node safety information. The quantified node safety factor allows for timely identification of potential security risks in node devices. A low safety factor indicates potential system operational issues due to excessive device load or significant fiber performance variations, allowing proactive optimization or adjustment measures to ensure stable system operation. This provides a basis for system resource scheduling. Based on the node safety factor, tasks and resources can be allocated appropriately, such as shifting some load from master nodes with low safety factors to slave nodes or performing targeted optimization on nodes with poor fiber performance, improving overall system resource utilization efficiency and operational safety.

[0045] Step 5: The simulcast terminal device corresponding to the maximum node safety factor in each master node device dynamic screening cycle is used as the master node device in the next master node device dynamic screening cycle.

[0046] The working principle of the above technical solution is to continuously monitor the network bandwidth utilization of the master node device in real time. This is a basic indicator for determining 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 promptly understood. 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, necessitating dynamic adjustment of the master node device's screening cycle. The dynamic screening cycle for the master node device is set using the operating parameters of the master node and slave node devices (including CPU utilization and fiber screening coefficient). These parameters comprehensively consider the device's computing power and fiber transmission characteristics, more accurately reflecting the device's performance and importance in the system. Using a specific formula to calculate the dynamic screening cycle makes cycle adjustment more scientific and reasonable, adapting to varying network load conditions. During each dynamic screening cycle, the operating parameters of the master node and slave node devices are monitored in real time, specifically key indicators such as CPU utilization, network bandwidth utilization, and fiber screening coefficient. Continuous monitoring of these parameters allows for timely understanding of the device's operating status and performance changes, providing data support for subsequent node safety factor calculations and master node device selection. The node safety factor is calculated based on the monitored operating parameters of the master and slave node devices. This factor is a comprehensive indicator that reflects the stability and reliability of the device within the system. While the specific calculation method is not clearly defined, it is speculated that it likely takes into account factors such as the device's operating status, performance indicators, and network environment, using a specific algorithm or model. After each dynamic screening cycle, the simulcast terminal device with the highest safety factor calculated within that cycle is selected as the master node for the next dynamic screening cycle. This selection method ensures that the best-performing and most stable device is selected as the master node, thereby improving the reliability and stability of the entire system.

[0047] The above technical solution provides the following benefits: By monitoring network bandwidth utilization in real time, the load status of master node devices can be promptly identified. When the load is excessive, the selection cycle is dynamically adjusted to accelerate the replacement frequency of master node devices, thereby improving the system's responsiveness to network changes and ensuring that the system can quickly adapt to varying network load conditions. By comprehensively considering the operating parameters of both master and slave node devices to set the dynamic selection cycle, system resources can be more efficiently allocated. Dynamically adjusting the selection of master node devices based on the actual performance and load of the devices ensures more efficient resource utilization and improves overall system performance. Selecting the device with the maximum node safety factor as the master node ensures excellent stability and reliability. This helps reduce system outages and data loss caused by master node device failures, thereby improving the stability of the entire community video simulcast system. This technical solution dynamically adjusts the selection cycle and selection criteria for master node devices based on varying network load conditions and device performance. This solution ensures the selection of appropriate master node devices during periods of peak network traffic and fluctuating device performance, ensuring the normal operation of the system in complex network environments. Dynamic selection of master node devices allows for the timely detection and resolution of device performance issues. When a device's node safety factor is low, the system can promptly adjust the master node device to avoid the risks that might arise from the device continuing to serve as the master node. This dynamic adjustment mechanism also facilitates system maintenance and management, enabling more flexible response to device failures and performance changes.

[0048] Furthermore, the network bandwidth utilization of the master node device is monitored in real time. When the threshold is exceeded, the screening cycle is dynamically set based on operating parameters such as the CPU utilization and fiber screening coefficient of the master and slave nodes. This allows for flexible adjustment of the screening frequency based on actual network load. During periods of high network load, the screening cycle can be shortened to allow for timely replacement of underperforming master node devices, thereby avoiding excessive concentration of network resources on high-load devices and improving network bandwidth utilization. During periods of low network load, the screening 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 during each screening cycle and selecting the master node for the next cycle based on the node safety factor, the load on the master and slave nodes can be effectively balanced. Prioritizing devices with high safety factors (indicating healthy operation) as master nodes prevents performance degradation of individual devices due to prolonged high load operation, ensuring relatively balanced load across the network and improving overall network resource utilization. Real-time monitoring of device operating parameters and calculation of node safety factors can promptly identify potential operational risks. When a device's CPU utilization is excessive or fiber performance is poor, resulting in a reduced safety factor, the system can proactively detect this and take measures before a device failure occurs, such as adjusting task allocation and optimizing resource allocation. This prevents system service interruptions caused by device failures and enhances system stability. The master node device is selected based on the maximum node safety factor, ensuring that the best performing device is selected to assume master node duties during each screening cycle. Even if performance fluctuations occur on some devices, a more stable device can be quickly switched to, ensuring service continuity for the video management platform and reducing service interruptions or lags experienced by users due to master node device issues. Using node safety factors to quantify the security status of master and slave node devices allows for more accurate identification of security risks within the network. The system categorizes and manages devices based on their safety factors, strengthening monitoring and maintenance of devices with low safety factors, promptly identifying potential security risks, and mitigating network security risks such as data leaks and malicious attacks caused by device security issues. In abnormal situations (such as sudden network traffic spikes or attacks on certain devices), the system can quickly respond and adjust master node devices by dynamically screening them and focusing on the safety factors of key nodes, restoring the network to normal operation as quickly 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 perceive dynamic changes in parameters such as network bandwidth utilization and device CPU utilization in real time, and flexibly adjust master node device screening strategies based on these changes. Whether it's daily fluctuations in network traffic or sudden business peaks, it can quickly adapt, ensuring stable system operation in diverse network environments and improving the system's adaptability to network changes. As the video management platform expands, the number of devices and business traffic are likely to continue to increase.Through dynamic screening of master node devices and a management mechanism based on node safety factors, the system can maintain good performance and stability even when the device scale and business complexity increase, providing strong support for the platform's business expansion and enhancing the system's scalability.

[0049] In one embodiment of the present invention, the video management platform sends video data to a master node device using quantum encryption, including: S301, the video management platform and the master node device negotiate a shared key through the BB84 protocol; S302: Setting the amount of video data released to trigger key rotation based on the optical fiber screening coefficient and the optical fiber layout length between the video management platform and the master node device; The amount of video data released to trigger key rotation is obtained by the following formula: Among them, G represents the amount of video data released to trigger key rotation; G0 represents the preset basic data amount; S xp Indicates the optical fiber screening coefficient between the video management platform and the main node device; g indicates the optical fiber attenuation coefficient, which is generally 0.023 / km; L indicates the optical fiber length between the video management platform and the main node device; A indicates the preset security enhancement factor, which ranges from 1 to 5 and is set according to the application scenario; P v Indicates the bit error rate in the quantum channel transmission between the video management platform and the master node device; specifically, Medium S xp Indicates the optical fiber screening coefficient between the video management platform and the main node device, reflecting the characteristics of the optical fiber in terms of transmission performance, etc. This function represents 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 an exponential function effectively reflects the trend of signal attenuation over distance. The product of these two factors provides a quantitative measure of transmission quality, taking into account both the fiber's inherent transmission performance and the attenuation due to length. The overall representation scales the numerator calculation result based on comprehensive considerations of security requirements and transmission error rates. A larger denominator results in a smaller amount of video data released to trigger key rotation, reflecting the 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 (reflected by the fiber screening coefficient), signal attenuation due to fiber length, pre-defined security enhancement requirements, and the bit error rate (BER) in quantum channel transmission. By combining these factors, an appropriate amount of video data released to trigger key rotation is quantified, reflecting the current transmission environment and security requirements. This amount is relatively large when transmission performance is good, the fiber is short, security requirements are low, and the BER is low; otherwise, it is smaller. By determining the amount of video data required for key rotation based on various factors that affect transmission security and quality, key rotation can be performed promptly despite changes in optical fiber transmission characteristics or fluctuations in the BER. This prevents data from being hacked due to extended key usage or changes in the transmission environment, ensuring the security of video data during transmission. This avoids the resource waste and security risks associated with fixed key rotation strategies. Rather than simply rotating keys based on fixed data volumes or time intervals, this method dynamically adjusts according to actual transmission conditions. This ensures security while rationally utilizing computing and time resources, improving system efficiency. By considering multiple variables, this method adapts to varying fiber lengths, transmission performance, and application scenarios (via security enhancement factor adjustments). This method accurately determines the amount of video data required to trigger key rotation in diverse network environments and application requirements, enhancing the system's adaptability and versatility.

[0050] S303: When the amount of video data sent by the video management platform to the master node device reaches the amount of video data released, key rotation is triggered.

[0051] The working principle of the above technical solution is as follows: The video management platform and the master node device conduct key negotiation using the BB84 protocol. The BB84 protocol is a quantum key distribution protocol that leverages the non-cloning and measurement interference properties of quantum states to ensure key distribution security. During the negotiation process, both parties transmit quantum bits via a quantum channel and perform operations such as measurement basis comparisons using a classical channel. Ultimately, a shared key is generated, which is used for subsequent video data encryption and decryption. The video data release volume that triggers key rotation is set by comprehensively considering the fiber screening coefficient and fiber length between the video management platform and the master node device. The fiber screening coefficient reflects the quality of fiber transmission performance, while the fiber length affects signal attenuation and transmission delay within the fiber. Based on these factors, an appropriate data volume threshold is determined. When the cumulative video data volume sent by the video management platform to the master node device reaches this threshold, a key rotation operation is triggered. The video management platform monitors the video data volume sent to the master node device in real time. When the cumulative data volume reaches the preset video data release volume, the video management platform initiates the key rotation process. This means that both parties will re-negotiate and generate a new shared key through the BB84 protocol to ensure the security of subsequent video data transmission.

[0052] The above technical solution achieves the following: Quantum encryption, specifically shared key negotiation based on the BB84 protocol, leverages the properties of quantum mechanics to fundamentally ensure the security of key distribution. During quantum key distribution, any eavesdropping interferes with the quantum state, making it detectable to both parties communicating legitimately. This effectively prevents key theft or tampering, providing highly secure encryption for video data. Key rotation trigger conditions are set based on the fiber screening coefficient and fiber run length, allowing the key rotation mechanism to adapt to varying transmission environments. In environments with good fiber transmission performance and short run lengths, the data volume threshold can be appropriately increased to reduce the frequency of key rotation. In environments with poor transmission conditions and long run lengths, the data volume threshold can be lowered to allow for timely key rotation, ensuring the security of video data transmission in diverse environments. Regularly 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 periodic key update limits the attacker's window of opportunity to decrypt video data, effectively protecting the confidentiality of the video data. This data-based key rotation mechanism offers flexibility and scalability. Parameters such as the fiber screening coefficient, fiber layout length, and data volume threshold can be adjusted based on actual needs to accommodate varying 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.

[0053] Furthermore, the amount of video data released to trigger key rotation is dynamically set based on factors such as the fiber screening coefficient, fiber length, and bit error rate. When the transmission environment changes (such as increased fiber attenuation or bit error rate), key rotation is triggered promptly, reducing the risk of key cracking, ensuring the confidentiality of video data during transmission, and enhancing the security of quantum cryptographic communications. By presetting the security enhancement factor A, security policies can be flexibly adjusted to suit different application scenarios. For scenarios with high security requirements, the amount of data required to trigger key rotation is reduced, allowing for more frequent key changes. For scenarios with lower security requirements, the amount of data required to trigger key rotation is appropriately increased, balancing resource consumption while meeting security requirements, comprehensively enhancing system security. The data volume is calculated by combining factors such as the fiber screening coefficient and fiber length. The fiber screening coefficient reflects fiber transmission performance and, combined with fiber length, more accurately assesses signal attenuation and quality changes during transmission. When fiber transmission performance deteriorates or fiber length is excessive, resulting in severe signal attenuation, timely key rotation is triggered to avoid data transmission errors caused by degraded signal quality and ensure stable video data transmission. Incorporating the bit error rate of quantum channel transmission into the calculation enables adaptive adjustment based on the probability of transmission errors. An increase in the bit error rate means that transmission stability is affected. In this case, reducing the amount of data that triggers key rotation can timely update the key, reduce the risk of data transmission failure due to accumulated bit errors, and improve the stability of video data transmission. Compared with a fixed key rotation strategy, dynamically calculating the triggering data volume can avoid unnecessary key rotation. A unified fixed threshold does not lead to frequent key changes when the transmission environment is good, reducing the computing resources and time overhead associated with operations such as key generation and negotiation, improving system efficiency, and optimizing resource utilization. Determining the timing of key rotation based on actual transmission conditions allows system resources to be more rationally allocated to data transmission and security. While ensuring data security and transmission stability, it reduces resource waste and ensures that the video management platform can operate efficiently under different transmission conditions.

[0054] In one embodiment of the present invention, a master node device is used to segment a simulcast video, and the segmented video data packets are released to a slave node device using quantum encryption, including: S401: The master node device segments the simulcast video according to a preset segmentation rule to obtain metadata of multiple segments; S402: The master node device sets the number of shard metadata pieces for a single transmission according to the node computing power of the slave node device; The number of fragment metadata pieces transmitted in a single transmission is obtained by the following formula: Where U represents the number of shard metadata pieces transmitted in a single transmission; J represents the average size of shard metadata (MB / piece); H represents the slave node computing power index, which is obtained by normalizing the CPU / GPU performance and has a value range of [1,100]; B represents the current available bandwidth (unit: data volume / time); t c Indicates the shard processing time window; Y r Represents the safety redundancy coefficient, and its value range is R∈[1.2, 3.0]. Specifically, It indicates the total amount of data that can theoretically be processed and transmitted within a given time window, based on the computing power of the slave nodes and the network transmission capacity. This represents a constraint and adjustment on the theoretically total amount of data that can be transmitted, taking into account data shard size and security redundancy. Dividing the numerator by the denominator yields the number of shard metadata pieces per transmission. This factor comprehensively considers factors such as the computing power of the slave node, network bandwidth, data shard size, processing time, and security redundancy to determine an appropriate number of shards per transmission, aligning data transmission with the processing power of the slave node. This method can rationally determine the number of shard metadata pieces per transmission based on the actual computing power and network bandwidth of the slave node. This avoids data backlogs caused by the amount of data being transmitted exceeding the slave node's processing capacity, or resource waste caused by insufficient data transmission. This balances data transmission with device processing power, improving overall system efficiency. By accounting for variables such as the security redundancy factor and the shard processing time window, the system can adapt to network fluctuations and varying service processing requirements. Even in unstable networks or with limited service processing time, the system can determine the appropriate number of shards to transmit, ensuring system stability and reliability. The number of shards is calculated based on the slave node's computing power index and available bandwidth, fully utilizing the slave node's computing and network bandwidth resources. Dynamically adjust the transmission volume according to device performance, improve resource utilization, and reduce system performance bottlenecks caused by unreasonable resource allocation.

[0055] S403: The master node device shares the shard metadata with the slave node devices using quantum encryption according to the number of shard metadata pieces corresponding to each slave node device. S404: The slave node device performs SPHINCS+signature verification once each time it receives a group of fragment metadata, and performs streaming decryption and playback on the received fragment metadata.

[0056] The working principle of the above technical solution is as follows: The master node device divides the simulcast video into multiple small video segments based on preset segmentation rules and generates corresponding segment metadata. This segment metadata contains key information about the video segments, such as segment sequence number, duration, and size, for subsequent video transmission and processing. The master node device dynamically sets the number of segments of segment metadata to be transmitted to each slave node based on the node computing power of the slave nodes. Slave nodes with higher computing power receive more segment metadata, while those with lower computing power receive less data, ensuring efficient data transmission and processing. The master node device uses quantum encryption to share the segment metadata with the slave nodes according to the number of segments assigned to each slave node. Quantum encryption leverages 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 performs SPHINCS+ signature verification. SPHINCS+ is a stateless hash signature scheme that ensures the integrity and authenticity of received segment metadata by verifying the signature. Once verified, the slave node decrypts and plays the received segment metadata in a streaming manner, decrypting and playing the encrypted video data in real time.

[0057] The above technical solution achieves the following benefits: Slicing videos can reduce the size of individual data packets, reduce transmission latency, and improve the real-time nature of video transmission. Slicing also facilitates error recovery and retransmission. If a segment fails to transmit, only that segment needs to be retransmitted, eliminating the need to retransmit the entire video. Setting the number of slice metadata pieces in a single transmission based on the computing power of the slave node device maximizes the processing power of the slave node device, preventing it from being overwhelmed by excessive data volume, thereby improving the efficiency of video transmission and processing. Transmitting slice metadata using quantum encryption leverages the properties of quantum mechanics to ensure absolute data security. 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. Slave nodes verify the received slice metadata using SPHINCS+ signatures, further ensuring data integrity and authenticity. Signature verification prevents data tampering or forgery during transmission, enhancing the security of video transmission. Slave nodes decrypt and stream the received slice metadata, enabling real-time video playback. Users can start watching the video without having to wait for the entire video to download, which improves the user experience. At the same time, streaming decryption playback also reduces the storage pressure on the device, because only the currently playing video data needs to be cached. This technical solution can be dynamically adjusted according to different network environments and device performance. In cases where the network bandwidth is small or the computing power of the slave node device is weak, the number of sharded metadata pieces can be reduced to reduce transmission and processing pressure; in cases where the network bandwidth is large or the computing power of the slave node device is strong, the number of sharded metadata pieces can be increased to improve transmission and processing efficiency. Therefore, this technical solution has strong adaptability and flexibility and can adapt to different application scenarios.

[0058] The master node sets the number of metadata segments to transmit in a single transmission based on the computing power of the slave nodes. Slave nodes with higher computing power receive more segments, fully utilizing their processing capacity; devices with lower computing power receive fewer segments to avoid data overload and processing delays. This on-demand allocation method aligns data transmission with the processing power of the slave nodes, reducing latency and backlogs and improving overall transmission efficiency. Simulcast videos are fragmented, splitting large videos into multiple smaller metadata segments. Smaller segments consume less bandwidth and transmit faster. Different segments can be transmitted in parallel, further reducing transmission time and improving the efficiency of video data transmission from the master node to the slave nodes. Segment metadata is shared with the slave nodes using quantum encryption. Based on the principles of quantum mechanics, quantum encryption offers extremely high security, effectively preventing data theft and tampering during transmission, and ensuring the confidentiality and integrity of video data transmitted between the master and slave nodes. Slave nodes perform SPHINCS+ signature verification on each received segment metadata. SPHINCS+ is a quantum-resistant hash signature scheme that ensures the authenticity and source of received data, preventing the infiltration of maliciously forged data and further enhancing system security. Slave nodes decrypt and play back received shard metadata in a streaming manner. This approach allows simultaneous reception, decryption, and playback of video data without waiting for all data to be received and decrypted. This reduces latency before playback, effectively minimizing lag and providing users with a smoother video playback experience and improving playback-related performance. Sharding metadata is allocated based on node computing power, allowing slave nodes to process data in an orderly manner within their own processing capabilities, avoiding processing delays caused by a concentrated influx of data and ensuring continuous and smooth video playback. The pre-set sharding rules can be flexibly adjusted to meet actual needs, allowing adjustments to the sharding scheme to adapt to changes in video content, the number of node devices, or computing power. This flexibility ensures efficient and stable system operation despite changes such as business expansion and equipment upgrades, enhancing system scalability. Allocating transmission tasks based on slave node computing power facilitates the integration of new slave nodes. When a new device is added, you only need to evaluate its computing power and allocate the corresponding number of shard metadata slices accordingly, so that it can be quickly integrated into the system, making the system easy to expand and adapt to growing business needs and device scale.

[0059] The embodiment of the present invention proposes a quantum security enhanced simulcast system for smart communities, such as Figure 2 As shown, the quantum security enhanced simulcast 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 simulcast terminal devices corresponding to each building; The master node dynamic screening module is used to use the simulcast terminal devices corresponding to each building as node devices and dynamically screen the master node devices through the video management platform; A first quantum encryption transmission module is used for 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 fragment the simulcast video through the master node device, and publish the fragmented video data packets to the slave node device through quantum encryption.

[0060] The technical solution works by building a community-wide quantum key distribution network infrastructure. Using quantum key distribution technologies (such as those based on quantum entanglement and quantum teleportation), it establishes a secure quantum communication link between the community center and the corresponding simulcast terminal devices in each building. This ensures that each building's simulcast terminal device 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 master node from a pool of simulcast terminals based on a series of pre-set rules and algorithms, including device performance indicators (including processing power, storage capacity, and network bandwidth), device location within the community (to optimize data transmission routing), and device real-time operating status (such as whether it is online or under high load). This ensures that the master node device has the ability to efficiently process and distribute video data, while also allowing for flexible adjustments based on community conditions, ensuring the stable operation of the entire simulcast system.

[0061] Before sending video data to the master node, the video management platform encrypts the video data using a quantum encryption algorithm (such as the quantum one-time pad algorithm) using a pre-negotiated quantum key. The encrypted video data is then transmitted to the master node via conventional communication networks (such as Ethernet and fiber optic networks). Due to the unconditional security of quantum encryption technology, it effectively prevents video data from being eavesdropped or tampered with during transmission, ensuring the integrity and confidentiality of the video content. After receiving the encrypted video data, the master node segments it into smaller packets based on factors such as the number of slave nodes, network bandwidth, and real-time video playback requirements. The master node then re-encrypts the segmented video packets using the quantum key corresponding to each slave node and distributes the encrypted packets to each slave node via the community's communication network. This segmentation and encrypted distribution improves the transmission efficiency and security of video data in complex network environments, ensuring that each slave node receives complete and secure video data, enabling community-wide simulcasting.

[0062] The above technical solution achieves this goal: After the master node device segments the simulcast video, it transmits the video data packets in a distributed manner to the slave node devices, avoiding the network bandwidth bottlenecks caused by centralized transmission. Slave node devices in different regions and with different performance characteristics can flexibly receive data based on their own conditions, reducing the possibility of network congestion and enabling faster and more stable transmission of video data to each simulcast terminal device. Quantum key encryption ensures the unconditional security of quantum encryption technology, both for data transmission from the video management platform to the master node device and from the master node to the slave node devices. Even under powerful quantum computing attacks, attackers cannot decrypt 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 community adds a new simulcast terminal device (slave node), it simply connects to the quantum key distribution network and performs simple configuration on the video management platform to enable communication and data transmission with the master node device, eliminating the need for major system modifications. This master-slave node architecture provides the system with a certain degree of fault tolerance. If a slave node device fails, the master node device automatically adjusts its data transmission strategy and assigns tasks to other functioning devices, ensuring uninterrupted video streaming service. The video management platform centrally manages and monitors master and slave node devices, providing real-time visibility into device operating status and video streaming performance. When system issues arise, the fault point can be quickly located and remedial measures implemented.

[0063] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A quantum-safe enhanced simulcast method for smart communities, characterized by: The quantum security enhanced simulcast method includes: Establishing a quantum key distribution network for the community, and the quantum key distribution network covers the corresponding simulcast terminal devices in each building; The simulcast terminal devices corresponding to each building are used as node devices, and the main node devices are dynamically selected through the video management platform; The video management platform sends the video data to the master node device through quantum encryption; The simulcast video is segmented by the master node device, and the segmented video data packets are published to the slave node devices through quantum encryption.

2. The quantum security enhanced simulcast 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 corresponding broadcast terminal devices of each building, including: Count the number of simulcast terminal devices in the community and set the key update frequency; Deploy quantum security modules to the video management platform and the corresponding simulcast terminal devices in each building; A quantum encryption communication network is established between each simulcast terminal device, and a quantum encryption communication network is established between the simulcast terminal device corresponding to each building and the video management platform.

3. The quantum security enhanced simulcast method for smart communities according to claim 1, characterized in that: Dynamically screen master node devices through the video management platform, including: Extracting the area range corresponding to the community, and dividing the area range corresponding to the community into grids according to the number and distribution density of simulcast terminal devices contained in the community, to obtain multiple grid areas; Determine the device distribution radius of the area corresponding to the community based on the number of simulcast 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; Extracting the straight-line distance between the target building and the video management platform; Determine the unit length of optical fiber according to the distribution radius and straight-line distance of the equipment, and obtain the optical fiber screening coefficient corresponding to the unit length of optical fiber; An initial master node device is determined according to the optical fiber screening coefficient.

4. The quantum security enhanced simulcast method for smart communities according to claim 3, characterized in that: The area corresponding to the community is divided into grids according to the number and distribution density of the simulcast terminal devices contained in the community, and multiple grid areas are obtained, including: Obtain the corresponding position coordinates of each broadcast terminal device through GPS or Beidou; Obtaining the density of simulcast terminal devices corresponding to the simulcast terminal devices according to the position coordinates corresponding to each simulcast terminal device; Extracting the effective communication radius corresponding to each simulcast terminal device; wherein the effective communication radius is the distance corresponding to the measured signal strength attenuation to -90dBm; Obtaining an average effective communication radius according to the effective communication radius corresponding to each simulcast terminal device; Obtaining a grid radius using the average value of the effective communication radius; The extreme value point of the density of the simulcast terminal device is selected as the network center to start grid growth, forming a plurality of grid areas.

5. The quantum security enhanced simulcast method for smart communities according to claim 3, characterized in that: The device distribution radius is determined based on the number of simulcast terminal devices in each grid area and the distance between buildings, including: Obtain the average building distance based on the number of simulcast terminal devices and the building distance in each grid area; Obtain the sub-device distribution radius parameter corresponding to each grid area based on the average distance between buildings in each grid area; Perform weighted averaging on the sub-device distribution radius parameters corresponding to all grid areas to obtain the device distribution radius of the area corresponding to the community.

6. The quantum security enhanced simulcast method for smart communities according to claim 3, characterized in that: Determining an initial master node device according to the optical fiber screening coefficient includes: Extracting the simulcast terminal device on the optical fiber line corresponding to the maximum optical fiber screening coefficient as the candidate node device; For each candidate node device, a verifiable statement is made and the authenticity of the statement is proved using the zk-SNARKs method; 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 of the candidate node device removed from the candidate list is triggered; The non-candidate node devices contained in each grid area vote for the candidate nodes based on their own computing power weights, and obtain the voting weight corresponding to each candidate node device; The candidate node device corresponding to the maximum voting weight is used as the initial master node device.

7. The quantum security enhanced simulcast method for smart communities according to claim 1, characterized in that: Dynamically screen master node devices through the video management platform, including: Real-time monitoring of the network bandwidth usage of the master node device; When the network bandwidth occupancy rate of the master node device exceeds a preset occupancy rate threshold, the dynamic screening period 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 the CPU occupancy rate and the fiber screening coefficient corresponding to the optical fiber where the simulcast terminal device is located; Monitor the operating parameters of the master node device and the operating parameters of the slave node device in real time during each master node device dynamic screening cycle; Obtaining a node safety factor according to the operating parameters of the master node device and the operating parameters of the slave node device; The simulcast terminal device corresponding to the maximum value of the node safety factor in each master node device dynamic screening cycle is used as the master node device of the next master node device dynamic screening cycle.

8. The quantum security enhanced simulcast method for smart communities according to claim 1, characterized in that: The video management platform sends video data to the master node device through quantum encryption, including: The video management platform and the master node device negotiate a shared key through the BB84 protocol; The amount of video data released to trigger key rotation is set according to the optical fiber screening coefficient and the optical fiber laying length between the video management platform and the master node device; When the amount of video data sent by the video management platform to the master node device reaches the amount of video data released, key rotation is triggered.

9. The quantum security enhanced simulcast method for smart communities according to claim 1, characterized in that: The master node device segments the simulcast video and publishes the segmented video data packets to the slave node devices using quantum encryption, including: The master node device segments the simulcast video according to a preset segmentation rule to obtain metadata of multiple segments; The master node device sets the number of shard metadata pieces for a single transmission according to the node computing power of the slave node device; The master node device shares the shard metadata to the slave node device using quantum encryption according to the number of shard metadata pieces corresponding to each slave node device; The slave node device performs SPHINCS+ signature verification once each time it receives a group of fragment metadata, and performs streaming decryption and playback on the received fragment metadata.

10. Quantum security enhanced simulcast system for smart communities, characterized by: The quantum-secure enhanced simulcast 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 simulcast terminal devices corresponding to each building; The master node dynamic screening module is used to use the simulcast terminal devices corresponding to each building as node devices and dynamically screen the master node devices through the video management platform; A first quantum encryption transmission module is used for 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 fragment the simulcast video through the master node device, and publish the fragmented video data packets to the slave node device through quantum encryption.

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