Multi-level security and protection monitoring data transmission method under optical fiber network

Abstract

The invention relates to the technical field of security and protection monitoring, and discloses a multi-level security and protection monitoring data transmission method under an optical fiber network, which comprises the following steps: firstly, constructing an optical fiber network comprising an edge layer, a convergence layer and a core layer to support effective acquisition and processing of data, and after the construction of the optical fiber network is completed, transmitting the data to the core layer; according to the method, a three-dimensional tensor model for dynamic bandwidth allocation is generated and used for representing bandwidth requirements of each terminal device in different time periods and levels, and then a Tucker decomposition method is utilized to decompose the three-dimensional tensor model so as to extract the bandwidth requirements of different terminal devices in different levels and time periods; meanwhile, performing non-negative matrix factorization on monitoring video stream data to be transmitted; and after data decomposition optimization is completed, the NTRU algorithm is adopted to encrypt the data, and finally, the redundancy of error correction coding is dynamically adjusted according to the real-time utilization rate of a network link. According to the method, efficient data processing and analysis models are integrated, and the technical effect of improving the data processing capacity is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of security monitoring, in particular to a multi-level security monitoring data transmission method in an optical fiber network. Background Art

[0002] In the modern security surveillance sector, traditional monitoring systems typically rely on copper cables or low-speed networks for data transmission. The physical limitations of this approach result in slow data transmission speeds. Especially in high-traffic surveillance scenarios, transmission delays often make it difficult to meet the demands of real-time monitoring. As security demands continue to grow, existing technologies are facing the challenge of being unable to efficiently process large amounts of data.

[0003] Furthermore, traditional monitoring systems lack sufficient data processing capabilities. Existing technologies often employ a linear process, resulting in a lengthy process from data collection to analysis and a slow overall response. This approach not only hinders the identification of critical data but can also lead to missed opportunities for optimal response in crisis situations.

[0004] Furthermore, existing technologies lack dynamic performance evaluation. Once a monitoring system is set up, it's difficult to adjust it based on real-time conditions. Resource allocations are often fixed, which can lead to performance bottlenecks. When system performance is poor, users struggle to immediately identify the problem, limiting the system's flexibility and effectiveness.

[0005] Furthermore, existing technologies often present data in a limited format, often using only conventional text and simple graphics. This lacks intuitiveness, making it difficult for users to quickly understand monitoring status and potential security risks. The lack of diverse visualization tools also results in users lacking comprehensive information support when making decisions, making them prone to misjudgment. Summary of the Invention

[0006] In response to the shortcomings of the existing technology, the present invention provides a multi-level security monitoring data transmission method under a fiber optic network, which solves the shortcomings of traditional security monitoring systems in data transmission speed, processing power, dynamic performance evaluation and user interactive experience, and improves the real-time and intelligent level of the system.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: a multi-level security monitoring data transmission method under an optical fiber network, comprising the following steps:

[0008] S1: First, build a fiber optic network consisting of edge, aggregation, and core layers to support efficient data collection and processing;

[0009] S2: After the fiber network is built, a three-dimensional tensor model of dynamic bandwidth allocation is generated to represent the bandwidth requirements of each terminal device at different time periods and levels;

[0010] S3: The three-dimensional tensor model is then decomposed using the Tucker decomposition method to extract the bandwidth requirements of different terminal devices at each layer and time period;

[0011] S4: performing non-negative matrix factorization on the surveillance video stream data to be transmitted;

[0012] S5: After completing the data decomposition optimization, the NTRU algorithm is used to encrypt the data;

[0013] S6: Finally, the redundancy of the error correction coding is dynamically adjusted according to the real-time utilization of the network link.

[0014] Preferably, the physical topology of the optical fiber network is formed by node sets and edge sets, wherein the node sets represent devices at each level, and the edge sets represent optical fiber links between devices, thereby ensuring smooth data transmission.

[0015] Preferably, the three-dimensional tensor model of dynamic bandwidth allocation includes the number of nodes, the number of time periods and the number of levels, so as to represent the bandwidth requirements of different devices at different times and levels, thereby ensuring the reasonable allocation of bandwidth resources.

[0016] Preferably, the quantum key distribution module generates a random key K(t) via a quantum channel and sets the key update interval to approximately every 30 frames to ensure the security and randomness of the key, where K(t) represents the random security key generated at time t. This key is dynamic, meaning that different keys may be used during each transmission process to enhance security.

[0017] Preferably, the core tensor obtained by Tucker decomposition is used to characterize the bandwidth requirements of different terminal devices at different levels, and the formula is as follows:

[0018]

[0019] in:

[0020] B ntk represents the bandwidth requirement of device n at time slice t and level k;

[0021] represents the core tensor, which represents the main features after decomposition;

[0022] A (N) ,A (T) ,A (K) Factor matrices representing the device dimension, time dimension, and level dimension respectively.

[0023] Preferably, the optimization goal of the mixed integer programming is to minimize the total delay τ of the link in the transmission path. total , its expression is:

[0024] Σ eij∈E τ ij x ij ;

[0025] in:

[0026] τ ij Indicates link e ij transmission delay;

[0027] x ij Indicates the allocation to link e ij bandwidth;

[0028] E represents the set of all links.

[0029] Preferably, the non-negative matrix decomposition is performed by decomposing the data matrix M into a plurality of non-negative factors

[0030] Matrices U and V are specifically expressed as:

[0031] M≈U·V T ;

[0032] in:

[0033] M represents the data matrix to be decomposed;

[0034] U represents a non-negative factor matrix, which represents the potential characteristics of the data;

[0035] V represents a non-negative factor matrix, which represents the combination form of data;

[0036] V T Represents the transpose of matrix V, used for matrix multiplication.

[0037] Preferably, the data is encrypted using the NTRU algorithm during transmission. The specific encryption process can be expressed as follows:

[0038] c(x)=m(x)·h(x)+e(x)mod q;

[0039] in:

[0040] c(x) represents the encrypted ciphertext;

[0041] m(x) represents the plaintext polynomial to be encrypted;

[0042] h(x) represents the public key;

[0043] e(x) represents a random noise term, which improves the security of encryption;

[0044] q represents the modulus, which prevents overflow of the hierarchical security monitoring data transmission method.

[0045] Preferably, the selection of the modulation format is determined based on the size of the currently transmitted data, ensuring that a suitable modulation method is selected under different data sizes to improve the efficiency and accuracy of data transmission.

[0046] The redundancy r of the error correction code FEC Dynamic adjustment is made based on the real-time utilization ρ of the network link. The specific relationship is:

[0047]

[0048] in:

[0049] r FEC Indicates the redundancy of error correction coding;

[0050] ρ represents the real-time utilization of the network link and the current bandwidth occupancy ratio.

[0051] The present invention provides a multi-level security monitoring data transmission method in an optical fiber network. It has the following beneficial effects:

[0052] 1. This invention utilizes a multi-layered security monitoring data transmission solution based on a fiber-optic network, achieving the technical effect of increasing system transmission speed. Compared to traditional network methods in the existing technology, data transmission latency is significantly reduced, enabling real-time monitoring and rapid response, and enhancing the effectiveness of security monitoring.

[0053] 2. This invention integrates efficient data processing and analysis models, achieving the technical effect of improving data processing capabilities. Compared with the more cumbersome processing procedures in the existing technology, this invention can quickly process and analyze large amounts of monitoring data, greatly improving system efficiency and ensuring that critical events are identified and responded to in a timely manner.

[0054] 3. This invention introduces an intelligent performance evaluation and feedback mechanism, achieving the technical effect of dynamically optimizing system performance. This approach resolves the performance bottleneck caused by fixed configurations in existing technologies, enabling the system to automatically adjust resource allocation based on real-time data to maintain maximum operational efficiency.

[0055] 4. This invention achieves the technical effect of improving the user interaction experience through data visualization technology. Unlike the relatively simple monitoring display methods used in current technologies, this invention provides a diversified graphical display to help users intuitively understand system status and potential risks, thereby improving the effectiveness and accuracy of decision-making. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 Schematic diagram of the method of the present invention. DETAILED DESCRIPTION

[0057] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the present specification. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0058] Please see the attached Figure 1 , an embodiment of the present invention provides a multi-level security monitoring data transmission method under an optical fiber network, including.

[0059] S1: First, build a fiber optic network consisting of edge, aggregation, and core layers to support efficient data collection and processing;

[0060] Specifically, the physical topology of the fiber optic network is constructed, consisting of an edge layer, a convergence layer, and a core layer to enable efficient data collection and processing. The edge layer contains multiple terminal devices, which are core components of the security monitoring system, such as high-definition cameras, motion detection sensors, and environmental monitoring devices.

[0061] In terms of physical structure, the terminal devices in the edge layer are directly connected to the data processing nodes in the aggregation layer through optical fiber interfaces.

[0062] The number of devices is represented by the variable N, and the device D i The data flow of (i=1,2,…,N) is

[0063] D i =F i ×R i ;

[0064] in:

[0065] D i Indicates the data traffic generated by the i-th device

[0066] F i represents the sampling frequency of the i-th device;

[0067] R i Indicates the size of multiple frames of data.

[0068] because

[0069] Different devices have different acquisition frequencies and resolutions. The total data flow T generated can be expressed as:

[0070]

[0071] in:

[0072] T represents the total data traffic generated by all edge devices per unit time;

[0073] N is the total number of monitoring devices in the edge layer;

[0074] The left part Indicates that the data traffic of all devices is directly summed;

[0075] The right part By summing the specific composition of the device data flow, the results of the two expressions are equal, ensuring the logical consistency of the formula.

[0076] The aggregation layer consists of multiple data processing nodes, and the number of nodes is set to M. Processing node P j (j=1,2,…,M) has processing capacity C j , defined as:

[0077] C j =B j ×F j ;

[0078] in:

[0079] C j is the processing capacity of the jth data processing node;

[0080] B j is the bandwidth of the jth data processing node, which indicates the amount of data that the node can receive or process per second;

[0081] F j It is the number of parallel data streams that the node can process simultaneously, which can be considered as the concurrent processing capability of the node.

[0082] To avoid data congestion, the following conditions need to be met:

[0083]

[0084] in:

[0085] This formula shows that the connection to node P j The sum of the data traffic of all edge devices Cannot exceed the processing capacity C of the node j .

[0086] In terms of physical connections, the data processing nodes in the aggregation layer are connected to the storage and analysis centers in the core layer via optical fiber links, enabling high-bandwidth, low-latency data transmission. The core layer consists of high-performance servers and large-capacity storage devices that can receive and process data from the aggregation layer in real time. The core layer's receiving capacity, R, is defined as:

[0087]

[0088] in:

[0089] R represents the total receiving capacity of the core layer, usually measured in bytes per second (Bps). This value reflects the core layer's ability to process and store data received from the aggregation layer and is an important parameter to consider when designing the network architecture.

[0090] M is the total number of data processing nodes in the aggregation layer. The processing capacity of each node may be different, so the processing capacity of all nodes needs to be summed up to calculate the total receiving capacity of the core layer;

[0091] In this formula, the expression It represents the sum of the processing capabilities of all the data processing nodes in the aggregation layer, thereby obtaining the total receiving capacity R of the core layer. Each C j The summation of the values ensures that the processing capabilities of all nodes are taken into account, resulting in a comprehensive view of the system's reception capacity.

[0092] Ensure that the total data flow T output by the aggregation layer does not exceed the receiving capacity R of the core layer to ensure smooth data processing.

[0093] The implementation process begins by establishing the physical topology of the fiber optic network, specifically the connection between the edge layer and the aggregation layer. Next, each edge device begins collecting data in real time and transmits it to the aggregation layer nodes via fiber optic links. Finally, the aggregation layer processing nodes perform preliminary analysis and processing on the collected data before transmitting it to the core layer for storage and further analysis.

[0094] Throughout the implementation process, the control of data flow and the rational allocation of data processing capabilities are key links to ensure the high efficiency and stability of the system. This structure effectively reduces data transmission latency and enhances the concurrent processing capabilities of data, thereby achieving efficient, secure, and reliable data transmission for multi-level security monitoring.

[0095] S2: After the fiber network is built, a three-dimensional tensor model of dynamic bandwidth allocation is generated to represent the bandwidth requirements of each terminal device at different time periods and levels;

[0096] Specifically, first, a bandwidth demand model is established. This model can dynamically calculate the bandwidth demand of each monitoring terminal in different time periods by analyzing and predicting the historical data traffic of the monitoring equipment. Define variable B to represent the total bandwidth demand, and D i Indicates the data flow collected by the i-th device within a certain period of time, specifically:

[0097]

[0098] in:

[0099] B is the total bandwidth requirement;

[0100] N is the total number of monitoring devices;

[0101] D i is the flow of the i-th device, and its calculation formula is:

[0102] D i =F i ×R i ;

[0103] in:

[0104] F i is the sampling frequency of the i-th device, which indicates the number of times data is collected per second;

[0105] R i The size of each frame of data is determined by the video resolution and encoding method;

[0106] The flow rate of each device D i Indicates the total amount of data generated by the device in one second.

[0107] After establishing the bandwidth demand model, we further consider the dynamic changes in traffic. To obtain accurate traffic change trends, we can perform linear regression analysis on historical data. Assuming the historical data traffic is D(t), we can establish a linear prediction model:

[0108] D(t)=a·t+b;

[0109] in:

[0110] D(t) represents the current data flow of the monitoring device at time t;

[0111] t represents a time variable, usually in seconds (s), which is used to represent the flow trend within a specific time period. By changing the t value, the flow of the device in the future time period can be predicted;

[0112] a is the slope of the linear model, which represents the rate of change of data traffic per time unit. The positive or negative value of the slope a indicates the direction and magnitude of traffic change. If a is a positive value, it means that traffic increases with time; if it is a negative value, it means that traffic decreases with time.

[0113] b is the intercept of the linear model, which represents the initial data flow at time t=0. This value provides a starting point for the prediction model, reflecting the data flow level when the system just starts running.

[0114] Then, the key distribution mechanism uses quantum key distribution (QKD) technology to ensure the security of data transmission. The specific process is to assign a unique key K to each monitoring terminal. i The key is generated by the QKD module of each edge device, using the principle of quantum state transmission. Set each key K in the system i for:

[0115] K i =QKD(Device ID i );

[0116] in:

[0117] Device ID i is the unique identifier of the i-th device;

[0118] QKD stands for Quantum Key Distribution, a protocol used to generate and distribute secure keys based on the principles of quantum mechanics.

[0119] In terms of the logical connection mechanism, the bandwidth demand model is updated in real time through the data transmission network. Each edge device can receive the latest bandwidth requirements and obtain the required encryption key through the QKD module. This process ensures the uniqueness and security of the key during data transmission.

[0120] The specific implementation process is as follows: First, a bandwidth demand model is established to calculate the total bandwidth requirements of all monitoring devices. Then, a real-time monitoring algorithm is applied to dynamically adjust the model to reflect changes in traffic volume. Next, key generation and distribution are performed to ensure that each device obtains a unique security key through the QKD module, thereby achieving efficient and secure data transmission.

[0121] This implementation utilizes quantum key distribution technology to ensure that keys cannot be stolen during transmission, providing extremely high security and further enhancing the security of the entire security monitoring system. Dynamic monitoring and real-time adjustment of bandwidth demand models effectively avoids network congestion, ensuring efficient and real-time data transmission.

[0122] S3: The three-dimensional tensor model is then decomposed using the Tucker decomposition method to extract the bandwidth requirements of different terminal devices at each layer and time period;

[0123] Specifically, first, a dynamic bandwidth demand model is established. The core tensor B generated by the Tucker decomposition method is ntk The definition is as follows:

[0124] B ntk =G×1A (N) ×2A (T) ×3A (K) ;

[0125] in:

[0126] B ntk represents the bandwidth requirement of device n at time slice t and level k;

[0127] G represents the core tensor, which represents the main features obtained by decomposition;

[0128] A (N) The factor matrix representing the device dimension, representing the eigenvector of each device;

[0129] A (T) The factor matrix representing the time dimension, which represents the weight of each time slice;

[0130] A (K) The factor matrix representing the hierarchical dimension represents the influence weights of different levels.

[0131] After collecting relevant data, the bandwidth requirements of edge devices are modeled in the form of a three-dimensional tensor. This model can reflect the dynamic changes in bandwidth requirements in real time.

[0132] Next, the quantum key distribution module generates a random key K(t) through the quantum channel. As shown in the figure, the specific process is as follows:

[0133] 1. Before the edge device sends data, the quantum key distribution module generates a key through the quantum state transmission mechanism.

[0134] 2. Apply quantum state noise resistance strategies to ensure the security and randomness of keys.

[0135] 3. The time period for dynamic key update is set to once every 30 frames to ensure the frequency and variability of key usage.

[0136] The key generation relationship is expressed as:

[0137] K(t)=f(Q(t),S(t));

[0138] in:

[0139] K(t) represents the random key generated at time t;

[0140] Q(t) represents the current quantum state, reflecting the state of the quantum channel;

[0141] S(t) represents other channel noise and environmental factors that affect key generation;

[0142] f represents the key generation function, which combines the quantum state and channel state to output a random key.

[0143] The generated key is then used in the encryption process of data transmission. The encryption process uses the NTRU algorithm for data protection, which is expressed as follows:

[0144] c(x)=m(x)·h(x)+e(x)mod q;

[0145] in:

[0146] c(x) represents the encrypted ciphertext;

[0147] m(x) represents the plaintext data to be encrypted;

[0148] h(x) represents the public key, which is used in the encryption process;

[0149] e(x) represents a random noise term, which increases the security of encryption;

[0150] q represents the modulus, which ensures the correctness of the encryption process and avoids data overflow.

[0151] Finally, to optimize the data transmission path in the optical fiber network, a mixed integer programming model is used. The goal of this model is to minimize the total delay of the link, which is expressed as:

[0152]

[0153] in:

[0154] τ ij Indicates link e ij The transmission delay indicates the delay time of data on the link;

[0155] x ij Indicates the allocation to link e ij Bandwidth refers to the amount of data transmitted on the link;

[0156] E represents the set of all links, including all fiber links connected in the network.

[0157] In summary, this implementation achieves both secure and efficient data transmission by combining quantum key distribution technology, Tucker decomposition, and mixed integer programming. This comprehensive approach effectively meets the stringent data transmission requirements of modern security monitoring systems.

[0158] S4: performing non-negative matrix factorization on the surveillance video stream data to be transmitted;

[0159] Specifically, first, a matrix representation of the surveillance video stream data to be transmitted is established. Assume that the frame data of the surveillance video can be represented as a matrix M with a size of m×n, where m represents the number of video frames in the time dimension and n represents the number of image pixels.

[0160] Before performing non-negative matrix factorization (NMF), the original video frame data M needs to be preprocessed to remove noise and redundant information. This process includes normalizing the data to ensure that all values are in the same numerical range, enhancing the effectiveness of NMF.

[0161] Next, perform non-negative matrix decomposition, and decompose the matrix M into the product of two non-negative matrices U and V, specifically expressed as M≈U·V T ;

[0162] in:

[0163] M represents the surveillance video frame data matrix to be decomposed, with a scale of m×n, representing all frame information in the video stream;

[0164] U represents a non-negative factor matrix with a size of m×r, representing the potential features of the video frame, where r represents the number of selected features;

[0165] V represents a non-negative factor matrix with a size of n × r, representing an image and indicating the combined influence of image contents;

[0166] V T Represents the transpose of matrix V, used for matrix multiplication.

[0167] When performing NMF, the following loss function needs to be optimized so that the above approximate relationship holds. The loss function is:

[0168]

[0169] in:

[0170] ||·|| F represents the Frobenius norm, which computes the difference between the original matrix M and its approximation.

[0171] During the solution process, alternating least squares (ALS) or multiplicative update rule (MU) is used to iteratively optimize U and V. This processing step can effectively obtain a low-dimensional representation of video frame data while retaining key features.

[0172] After non-negative matrix factorization, the outputs U and V can be used for subsequent data compression and transmission. The secondary optimization step is to restore the product of U and V combined with the original monitoring data to ensure the efficiency of system performance during transmission.

[0173] Finally, in the data transmission stage, combining the dynamic bandwidth model generated in the first three steps with the quantum key distribution scheme can ensure that video data is efficiently and securely transmitted to the aggregation layer for processing and storage.

[0174] Through the above implementation, utilizing non-negative matrix factorization, the transmission efficiency of surveillance video streams over optical fiber networks is improved. This process not only reduces data volume but also ensures real-time performance and accuracy, making the system more suitable for the needs of modern security monitoring applications. Thus, the implementation of this invention will effectively enhance the data processing capabilities and operational efficiency of the entire security monitoring system.

[0175] S5: After completing the data decomposition optimization, the NTRU algorithm is used to encrypt the data;

[0176] Specifically, first, the NTRU algorithm is used to encrypt data to ensure that the monitoring data transmitted in the Internet environment is not accessible to unauthorized access. The encryption process can be expressed as the following formula:

[0177] c(x)=m(x)·h(x)+e(x)mod q;

[0178] in:

[0179] c(x) represents the encrypted ciphertext;

[0180] m(x) represents the plaintext polynomial to be encrypted;

[0181] h(x) represents the public key;

[0182] e(x) represents a random noise term, which improves the security of encryption;

[0183] q represents the modulus, which prevents overflow of the hierarchical security monitoring data transmission method.

[0184] Before performing the encryption step, the monitoring data m(x) to be transmitted is first obtained from the edge device. After obtaining the data, the encryption module built into the edge device encrypts the monitoring data using the generated key K(t). This process ensures the confidentiality and integrity of the data during network transmission.

[0185] Next, dynamic modulation format selection is implemented. This process involves evaluating the size of the data to be transmitted to select the most appropriate modulation format. For example, when the data size is less than or equal to 500 bytes, quadrature phase shift keying (QPSK) can be selected; when the data size exceeds 500 bytes, 16-QAM modulation is selected to improve data transmission accuracy and efficiency.

[0186] The decision process for dynamic modulation format selection can be expressed as:

[0187]

[0188] in:

[0189] size(m(x)): The size of the plaintext data m(x) to be transmitted, in bytes.

[0190] Format: The selected data modulation format.

[0191] In the data transmission link, the edge device first encrypts the plaintext monitoring data m(x) to obtain the ciphertext c(x). The encrypted data is transmitted in the optical fiber network using the selected modulation format.

[0192] To achieve this dynamic selection process, the edge device's control module needs to be able to monitor the size of the data to be transmitted in real time. Upon detecting changes in the data, the modulation method should be updated promptly to ensure the effectiveness and accuracy of data transmission under different circumstances.

[0193] Finally, by combining encryption with modulation format selection, the security and efficiency of surveillance data transmission can be effectively improved. This process not only meets the stringent data transmission security requirements of modern urban security systems, but also enhances the system's flexibility and adaptability in practical applications.

[0194] In summary, this implementation combines encryption technology with a dynamic modulation selection algorithm to form an efficient and secure monitoring data transmission system. This implementation can effectively improve the overall performance and efficiency of security monitoring systems in multiple application scenarios.

[0195] S6: Finally, the redundancy of the error correction code is dynamically adjusted according to the real-time utilization of the network link;

[0196] Specifically, first, a delay evaluation model for the transmission link is established. The data transmission delay of the selected link can be expressed as:

[0197] τ ij =f(x ij ,L ij ,R ij );

[0198] in:

[0199] τ ij Indicates link e ij The transmission delay indicates the delay time of data on the link;

[0200] x ij Indicates the allocation to link e ij Bandwidth refers to the amount of data transmitted on the link;

[0201] L ij Indicates link e ij The length of , which indicates the physical distance of data transmission;

[0202] Rij Indicates link e ij The transmission rate in bits per second.

[0203] After evaluating the delay, the system dynamically adjusts the redundancy r of the error correction code according to the real-time link utilization ρ FEC Error correction coding improves the reliability of data transmission by adding redundant information to the data. The dynamic adjustment process can be expressed as:

[0204]

[0205] in:

[0206] r FEC Indicates the currently set redundancy, used for error correction coding;

[0207] r0 represents the basic redundancy, ensuring the minimum error correction capability;

[0208] r1 represents the incremental redundancy, which is dynamically adjusted as the link utilization increases;

[0209] Δρ represents the segment range of link utilization and is used for hierarchical adjustment.

[0210] Based on the real-time monitored link utilization, ρ, the system updates the required redundancy in real time to maintain data transmission stability when the network load is high. The utilization of the transmission link needs to be obtained by the link monitoring module and transmitted to the control center for analysis.

[0211] At the transmitting end of the data stream, an error-correction coding algorithm then applies error correction coding to the data, combining link latency and the selected redundancy level. This coding process employs methods such as Hamming or convolutional coding to enhance the integrity and reliability of data transmission.

[0212] Finally, the optimized data stream is sent to the aggregation layer in the form of data packets with error correction coding, which contains the necessary redundant information to ensure that the data can be effectively received and correctly reconstructed even in the event of packet loss or errors in the network.

[0213] The above implementation steps, combined with delay assessment and dynamic error correction coding, effectively improve the stability and reliability of monitoring data transmission. This significantly enhances the efficiency and security of data transmission in multi-layered security monitoring systems over fiber optic networks, effectively meeting the demands of modern urban security systems for rapid response and accurate monitoring.

[0214] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A multi-level security monitoring data transmission method under an optical fiber network, characterized in that: The following steps are involved: S1: First, build a fiber optic network consisting of edge, aggregation, and core layers to support efficient data collection and processing; S2: After the fiber network is built, a three-dimensional tensor model of dynamic bandwidth allocation is generated to represent the bandwidth requirements of each terminal device at different time periods and levels; S3: The three-dimensional tensor model is then decomposed using the Tucker decomposition method to extract the bandwidth requirements of different terminal devices at each layer and time period; S4: performing non-negative matrix factorization on the surveillance video stream data to be transmitted; S5: After completing the data decomposition optimization, the NTRU algorithm is used to encrypt the data; S6: Finally, the redundancy of the error correction coding is dynamically adjusted according to the real-time utilization of the network link.

2. The multi-level security monitoring data transmission method under the optical fiber network according to claim 1 is characterized in that: The physical topology of the optical fiber network is formed by node sets and edge sets. The node sets represent devices at each level, and the edge sets represent optical fiber links between devices to ensure smooth data transmission.

3. The multi-level security monitoring data transmission method under the optical fiber network according to claim 1 is characterized in that: The three-dimensional tensor model of dynamic bandwidth allocation includes the number of nodes, the number of time periods and the number of levels, which is used to represent the bandwidth requirements of different devices at different times and levels, ensuring the reasonable allocation of bandwidth resources.

4. The multi-level security monitoring data transmission method under the optical fiber network according to claim 1 is characterized in that: The quantum key distribution module generates a random key, K(t), over a quantum channel and updates it every 30 frames to ensure key security and randomness. K(t) represents the random, secure key generated at time t. This key is dynamic, meaning it can be used differently during each transmission, enhancing security.

5. The multi-level security monitoring data transmission method under the optical fiber network according to claim 1 is characterized in that: The core tensor obtained by Tucker decomposition is used to characterize the bandwidth requirements of different terminal devices at different levels. The formula is as follows: in: B ntk represents the bandwidth requirement of device n at time slice t and level k; represents the core tensor, which represents the main features after decomposition; A (N) ,A (T) ,A (K) Factor matrices representing the device dimension, time dimension, and level dimension respectively.

6. The multi-level security monitoring data transmission method in an optical fiber network according to claim 1, characterized in that: The optimization goal of the mixed integer programming is to minimize the link in the transmission path. Total delay τ total , its expression is: in: τ ij Indicates link e ij transmission delay; x ij Indicates the allocation to link e ij bandwidth; E represents the set of all links.

7. The multi-level security monitoring data transmission method in an optical fiber network according to claim 1, characterized in that: The non-negative matrix factorization decomposes the data matrix M into multiple non-negative factors Matrices U and V are specifically expressed as: M≈U·V T ; in: M represents the data matrix to be decomposed; U represents a non-negative factor matrix, which represents the potential characteristics of the data; V represents a non-negative factor matrix, which represents the combination form of data; V T Represents the transpose of matrix V, used for matrix multiplication.

8. The optical fiber network display monitoring data transmission method according to claim 1, characterized in that: The data is encrypted using the NTRU algorithm during transmission. The specific encryption process can be expressed as follows: c(x)=m(x)·h(x)+e(x)mod q; in: c(x) represents the encrypted ciphertext; m(x) represents the plaintext polynomial to be encrypted; h(x) represents the public key; e(x) represents a random noise term, which improves the security of encryption; q represents the modulus, which prevents overflow of the hierarchical security monitoring data transmission method.

9. The multi-level security monitoring data transmission method in an optical fiber network according to claim 1, characterized in that: The selection of the modulation format is determined based on the size of the currently transmitted data, ensuring that a suitable modulation method is selected under different data sizes to improve the efficiency and accuracy of data transmission.

10. The optical fiber network representation monitoring data transmission method according to claim 1, wherein the redundancy of the error correction code is r FEC Dynamic adjustment is made based on the real-time utilization ρ of the network link. The specific relationship is: in: r FEC Indicates the redundancy of error correction coding; ρ represents the real-time utilization of the network link, which indicates the current bandwidth usage ratio.

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