Smart security monitoring and management system based on the Internet of Things

Abstract

The present invention relates to the technical field of security service management, specifically to an intelligent security monitoring and management system based on the Internet of Things. The system includes: a data flow anti-interference module, which establishes an external disturbance and inherent uncertainty extended state model based on the characteristics of the monitoring data flow, calculates the data flow rate, data packet size and noise interference parameters, and dynamically adjusts the data flow path and strategy. In the present invention, an external disturbance and inherent uncertainty extended state model is used to perform real-time analysis of the data flow rate, data packet size and noise interference parameters. By dynamically adjusting the data flow path and strategy, abnormal fluctuations in the data flow are effectively suppressed, and the stability of the monitoring system in a complex network environment is improved. In addition, the present invention accurately identifies the weak links and key nodes in the network through network topology mapping and multi-dimensional structure analysis, and further optimizes the connectivity and stability of the network.

Description

Technical Field

[0001] The present invention relates to the technical field of security service management, and in particular to an intelligent security monitoring and management system based on the Internet of Things. Background Art

[0002] Security service management is a broad technical field encompassing various technologies, systems, and management strategies used to protect people and property. This area involves protecting people, assets, and information, preventing potential threats, and implementing effective emergency response and management when incidents occur. Security service management technologies include monitoring systems, intrusion detection systems, access control systems, emergency response and recovery management, identity authentication, and rights management. With the advancement of information technology, modern security service management increasingly relies on automated and intelligent technologies, such as artificial intelligence, the Internet of Things, and big data analytics, to improve the efficiency and accuracy of security management.

[0003] Smart security monitoring and management systems utilize IoT sensors, smart cameras, and network communication technologies to enable real-time monitoring, data collection, and remote management of monitored areas. Their primary purpose is to improve the security system's response speed and accuracy, thereby enhancing the efficiency and effectiveness of overall security management. These systems are widely used in residential, commercial, and public spaces, among other areas requiring high-level security.

[0004] However, because existing monitoring systems mainly rely on fixed monitoring parameters and simple network structure management, they are slow to respond to emergencies in complex network environments, making it difficult to achieve dynamic adjustment and precise control. When faced with sudden data flow fluctuations or network node failures, existing technologies are often unable to identify and process them in a timely manner, resulting in the loss or delay of monitoring data, thereby reducing the security and reliability of the system. In addition, network management in existing technologies usually relies on static topology structures and cannot effectively identify and process weak links and key nodes in the network, resulting in the network being prone to connectivity degradation or network paralysis when facing high loads or node failures. Therefore, improvements are needed. Summary of the Invention

[0005] The purpose of the present invention is to solve the shortcomings of the existing technology and propose an intelligent security monitoring and management system based on the Internet of Things.

[0006] In order to achieve the above objectives, the present invention adopts the following technical solutions: The intelligent security monitoring and management system based on the Internet of Things includes:

[0007] The data flow anti-interference module builds an extended state model of external disturbances and inherent uncertainties based on the characteristics of the monitored data flow, calculates the data flow rate, packet size, and noise interference parameters, dynamically adjusts the data flow path and strategy, obtains and processes changes and fluctuations in real-time data flow, and generates disturbance compensation data;

[0008] The network topology analysis module builds a network topology map based on the monitoring of network nodes and connection relationships, analyzes node connections, handles redundant paths and isolated nodes, detects weak links and key points through point-by-point mapping and multi-dimensional structure, and generates a key node structure;

[0009] A reliability enhancement module, based on the key node structure, identifies areas with weak connectivity, reallocates paths and node roles, constructs multi-level redundant paths and optimizes network connections, analyzes multi-level node connections and path stability, and generates enhanced connectivity paths;

[0010] A dynamic control module, based on the disturbance compensation data, aggregates data flow rate and buffer parameters, calculates and adjusts control parameters and buffer settings, handles flow fluctuations and buffer load, performs state adjustment, and generates adjustment feedback parameters;

[0011] The network stabilization module analyzes the global and local topological structures based on the enhanced connectivity path and the adjustment feedback parameters, coordinates node connection relationships, adjusts cross-level connections, performs global and local node optimization and load balancing, establishes and optimizes cross-level connections of the network, and generates an overall stable structure.

[0012] As a further solution of the present invention, the disturbance compensation data includes rate adjustment parameters, noise filtering coefficients and packet loss recovery information, the key node structure includes connection strength values, node redundancy marks and isolated node identification identifiers, the enhanced connectivity path includes priority connection paths, redundant backup paths and node configurations, the adjustment feedback parameters include gain adjustment values, cache configuration parameters and traffic distribution ratios, and the overall stable structure includes a global connection graph and load balancing configuration.

[0013] As a further solution of the present invention, the data stream anti-interference module includes:

[0014] The disturbance modeling submodule establishes an extended state model that includes external disturbances and inherent uncertainties based on the characteristics of the monitored data stream. By calculating the data stream rate, packet size, and noise interference parameters, it uses an extended state observer to extract and analyze disturbance features and generate disturbance model parameters.

[0015] A dynamic adjustment submodule adjusts the data flow path and strategy based on the disturbance model parameters, dynamically adjusts the data flow rate and noise suppression level by comparing the disturbance characteristics with the preset threshold, and generates a dynamic adjustment strategy;

[0016] The data processing submodule processes the real-time data stream based on the dynamic adjustment strategy, balances the data stream fluctuation and the disturbance effect through segmented processing and gradual suppression of disturbance characteristics, and generates disturbance compensation data.

[0017] As a further solution of the present invention, the network topology analysis module includes:

[0018] The topology mapping submodule builds a network topology map based on the monitoring network nodes and connection relationships. By analyzing the node connection strength point by point, it maps and organizes the network connection relationships and generates a topology connection diagram.

[0019] The node analysis submodule identifies node redundancy and isolation based on the topological connection diagram, extracts key network nodes by analyzing connection strength and node importance, and generates node structure analysis results;

[0020] The structure detection submodule detects weak links and key points based on the node structure analysis results, screens weak links and key nodes in the network through multi-dimensional structure analysis and comparison, and generates a key node structure.

[0021] As a further solution of the present invention, the reliability enhancement module includes:

[0022] A connectivity identification submodule, based on the key node structure, identifies areas with weak connectivity in the network, extracts connectivity weaknesses through connection relationships and connectivity analysis between nodes, and generates connectivity identification results;

[0023] A path allocation submodule, which reallocates paths and node roles based on the connectivity identification results, optimizes path allocation by analyzing path importance and node connectivity, and generates a path allocation plan;

[0024] The redundancy construction submodule constructs multi-level redundant paths in the network based on the path allocation scheme, establishes a redundant connection structure by enhancing the connectivity and path stability between nodes, and generates enhanced connectivity paths.

[0025] As a further solution of the present invention, the dynamic control module includes:

[0026] The flow monitoring submodule is based on the disturbance compensation data, the data flow rate and the buffer area parameters, and records the real-time flow changes by monitoring the fluctuation of the data flow and the buffer area occupancy to generate the flow monitoring results;

[0027] A parameter adjustment submodule adjusts control parameters and buffer settings based on the flow monitoring results, optimizes control parameters and buffer configurations by analyzing flow changes and loads, and generates parameter adjustment configurations;

[0028] The state adjustment submodule adjusts the state based on the parameter adjustment configuration, balances the load and flow fluctuations by adjusting the control parameters and load balancing, and generates adjustment feedback parameters.

[0029] As a further solution of the present invention, the network stabilization module includes:

[0030] A global analysis submodule analyzes the global topology structure and node connection relationship based on the enhanced connectivity path and the adjustment feedback parameter, extracts the global network structure by analyzing the global node distribution and connection relationship, and generates a global analysis graph;

[0031] A connection coordination submodule coordinates the connection relationship between local and global nodes based on the global analysis graph, optimizes the connectivity between nodes by adjusting the node connection order and cross-level connections, and generates a connection coordination plan;

[0032] The load balancing submodule balances the load of global and local nodes based on the connection coordination scheme, optimizes cross-level load balancing by adjusting load distribution and node connections, and generates an overall stable structure.

[0033] As a further solution of the present invention, the extended state observer is according to the formula:

[0034] ;

[0035] Calculate the perturbation model parameter values, where is the estimated value of the extended state, is the system input gain coefficient, is the input signal, To adjust the parameters, is the disturbance change rate weight coefficient, is the time rate of change of the disturbance estimate, is the index correction coefficient, is the suppression intensity coefficient, is the interference buffer constant.

[0036] Compared with the prior art, the advantages and positive effects of the present invention are:

[0037] In the present invention, an extended state model of external disturbances and inherent uncertainties is used to perform real-time analysis of data flow rate, packet size and noise interference parameters. By dynamically adjusting the data flow path and strategy, abnormal fluctuations in the data flow are effectively suppressed, and the stability of the monitoring system in a complex network environment is improved. In addition, the present invention accurately identifies weak links and key nodes in the network through network topology mapping and multi-dimensional structural analysis, further optimizing the connectivity and stability of the network. By reallocating paths and node roles and establishing multi-level redundant paths, the fault tolerance of the network is enhanced, ensuring the continuous and stable operation of the system in the event of node failure or network fluctuations. The present invention also combines traffic monitoring and control parameter adjustment to dynamically optimize cache area settings and traffic processing strategies, further enhancing the system's response speed and data processing capabilities under high load conditions. In summary, the present invention improves the security, stability and efficiency of the monitoring system, and can respond to complex and changing monitoring environments more quickly and accurately. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 Schematic diagram of the system framework of the present invention. DETAILED DESCRIPTION

[0039] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0040] In the description of the present invention, it should be understood that the terms "length," "width," "up," "down," "front," "back," "left," "right," "vertical," "horizontal," "top," "bottom," "inside," "outside," and the like, indicating positions or relationships, are based on the positions or relationships shown in the accompanying drawings and are intended only to facilitate the description of the present invention and simplify the description. They do not indicate or imply that the devices or elements referred to must have a specific orientation, be constructed, or operate in a specific orientation. Therefore, they should not be construed as limiting the present invention. Furthermore, in the description of the present invention, "plurality" means two or more, unless otherwise expressly and specifically defined.

[0041] Example

[0042] See also Figure 1 The present invention provides a technical solution: an intelligent security monitoring and management system based on the Internet of Things includes:

[0043] The data flow anti-interference module builds an extended state model of external disturbances and inherent uncertainties based on the characteristics of the monitored data flow, calculates the data flow rate, packet size, and noise interference parameters, dynamically adjusts the data flow path and strategy, obtains and processes changes and fluctuations in real-time data flow, and generates disturbance compensation data;

[0044] The network topology analysis module builds a network topology map based on the monitoring of network nodes and connection relationships, analyzes node connections, handles redundant paths and isolated nodes, detects weak links and key points through point-by-point mapping and multi-dimensional structure, and generates a key node structure;

[0045] The reliability enhancement module identifies areas of weak connectivity based on key node structures, reallocates paths and node roles, builds multi-level redundant paths and optimizes network connections, analyzes multi-layer node connections and path stability, and generates enhanced connectivity paths.

[0046] The dynamic control module calculates and adjusts control parameters and buffer settings based on disturbance compensation data, data flow rate, and buffer parameters, handles flow fluctuations and buffer load, performs state adjustments, and generates adjustment feedback parameters.

[0047] The network stabilization module analyzes the global and local topological structures based on enhanced connectivity paths and adjusted feedback parameters, coordinates node connection relationships, adjusts cross-level connections, performs global and local node optimization and load balancing, establishes and optimizes cross-level connections of the network, and generates an overall stable structure.

[0048] The disturbance compensation data includes rate adjustment parameters, noise filtering coefficients and packet loss recovery information; the key node structure includes connection strength values, node redundancy marks and isolated node identification marks; the enhanced connectivity path includes priority connection paths, redundant backup paths and node configurations; the adjustment feedback parameters include gain adjustment values, cache configuration parameters and traffic distribution ratios; and the overall stable structure includes a global connection graph and load balancing configuration.

[0049] In the data stream anti-disturbance module, an extended state model is established based on the characteristics of the monitored data stream, encompassing both external disturbances and inherent uncertainties. The implementation process first calculates statistical characteristics and their ranges based on the collected data stream rate, packet size, and noise interference parameters. An extended state observer is then used to extract and analyze disturbance signatures from these characteristic parameters. The extended state observer, through real-time monitoring of the input-output relationship of the data stream, establishes a dynamic state estimation model. Disturbance signatures are extracted by comparing the deviation between the actual state of the current data stream and the ideal state. The observer feedback mechanism dynamically adjusts the state estimate, and through continuous monitoring and analysis of deviations, disturbance model parameters are generated. These disturbance model parameters include rate adjustment parameters, noise filtering coefficients, and packet loss recovery information. Through real-time calculation and updating of these parameters, the system can promptly adjust the data stream routing and processing strategy when anomalies occur, generating disturbance compensation data. This compensation data plays a key role in subsequent modules, ensuring the stability and reliability of data processing.

[0050] In the network topology analysis module, a topological map of the network is constructed by monitoring an initial dataset of network nodes and connection relationships. The module first analyzes the connection strength of each node point by point to determine the relative position and connection quality of each node. Then, by gradually constructing a topological map, the connections between nodes are labeled and categorized. Node connection strength and connectivity are repeatedly calculated and analyzed as key parameters, ultimately constructing a topological connection map that reflects the overall structure of the network. Next, each node is analyzed in detail, identifying key nodes in the network by examining their redundancy and isolation. Parameters used in this process include node degree, connectivity, and their importance within the network. Combined with the analysis results, the node structure is categorized to generate a node structure analysis result. Finally, the analysis results are used to further detect weak links and key points in the network. Multidimensional structural analysis is then performed to identify weak links and key nodes within the network, generating a key node structure. This structure is then used in subsequent modules to optimize network stability and connectivity.

[0051] In the reliability enhancement module, based on the key node structure, areas of weak network connectivity are first identified. This process involves a detailed analysis of the connectivity of nodes within the key node structure and the quality of their connections with surrounding nodes. The connectivity index and path importance weight for each node are calculated, identifying nodes and paths that play a core role in the network but have weak connectivity. Next, based on the identified results, path and node roles are reallocated. A comprehensive analysis of path usage frequency, node load capacity, and connectivity is conducted to optimize network path allocation and ensure the stability and redundancy of high-priority paths. Finally, a multi-level redundant path construction is performed, combining path importance and node load capacity to create multiple redundant paths, strengthen connections between nodes, establish a network redundancy structure, and generate enhanced connectivity paths. These paths are then used in subsequent modules to improve the overall stability and resilience of the network.

[0052] In the dynamic control module, based on disturbance compensation data, the data flow rate and buffer parameters are combined. By real-time monitoring and recording of changes in the data flow rate and buffer occupancy, the dynamic characteristics of the data flow are determined. In the specific implementation, the real-time monitored data flow rate and buffer occupancy are first combined to calculate the instantaneous data flow rate and the buffer load ratio. Then, by analyzing the relationship between the data, the current system load state and regulation requirements are determined. Next, the control parameters and buffer settings are refined. Based on the dynamic characteristics of the data flow and the current buffer occupancy, the control parameter weights are dynamically adjusted. The buffer size and allocation strategy are optimized to ensure that the system maintains stable traffic handling capacity even when the data flow fluctuates significantly. Finally, the parameter adjustment configuration is generated. Finally, these adjustment configurations are used to adjust the system's internal state, address buffer load and flow fluctuations, and generate regulation feedback parameters. These parameters are then used in subsequent modules to further optimize the network's global and local states.

[0053] In the network stabilization module, based on enhanced connectivity paths and adjusted feedback parameters, a detailed analysis of the global and local topologies is first performed. By analyzing the global node distribution and connectivity relationships, the structural characteristics of the global network are extracted. During implementation, the enhanced connectivity paths and adjusted feedback parameters are applied to the global and local topologies to calculate the connection strength and load distribution between nodes and assess the overall stability of the network. Next, the connectivity relationships between local and global nodes are coordinated. By adjusting the node connection order and cross-level connections, the connectivity and load balancing strategy of nodes in the network are optimized to ensure that the entire network remains efficient after the node connection order is adjusted, and a connection coordination solution is generated. Finally, this connection coordination solution is used to balance the load of global and local nodes, adjust the load distribution strategy, redistribute and optimize the load based on the node load capacity and current load situation, establish a cross-level connection structure, and generate an overall stable structure.

[0054] In this embodiment, the data stream anti-interference module includes:

[0055] The disturbance modeling submodule establishes an extended state model that includes external disturbances and inherent uncertainties based on the characteristics of the monitored data stream. By calculating the data stream rate, packet size, and noise interference parameters, it uses an extended state observer to extract and analyze disturbance features and generate disturbance model parameters.

[0056] The dynamic adjustment submodule adjusts the data flow path and strategy based on the disturbance model parameters. By comparing the disturbance characteristics with the preset threshold, it dynamically adjusts the data flow rate and noise suppression level and generates a dynamic adjustment strategy.

[0057] The data processing submodule processes the real-time data stream based on a dynamic adjustment strategy. By segmented processing and gradual suppression of disturbance characteristics, it balances the data stream fluctuations and disturbance effects and generates disturbance compensation data.

[0058] In the disturbance modeling submodule, an extended state model is established by monitoring the characteristics of the data flow, encompassing both external disturbances and inherent uncertainties. This model continuously monitors the data flow rate, packet size, and noise interference parameters, and employs an extended state observer to extract and analyze disturbance signatures. Specifically, the data flow rate is formatted as a time series, recording the changes in the transmission rate of packets within a certain time interval. The packet size is represented as a discrete numerical sequence, reflecting the actual transmission size of each packet. The noise interference parameter is represented by a floating range of noise intensity values. The extended state observer first analyzes the data flow rate in the time domain to identify abnormal fluctuations. This is then correlated with the discrete sequence of packet sizes to detect any transmission fluctuations caused by abnormal packets. The observer further performs a Fourier transform on the noise interference parameters, converting the time domain noise interference data into frequency domain features. This allows the identification of the dominant frequency components of the interference signal and the assessment of its impact on the data flow. Through comprehensive data processing and analysis, key indicators representing the disturbance signature are extracted. These indicators are then fed into the extended state model, which then adjusts and optimizes the model parameters, ultimately generating disturbance model parameters that reflect the overall state of the data flow. The disturbance model parameters provide basic data for the dynamic adjustment of subsequent modules, ensuring that the system can accurately identify and respond to anomalies in the data stream.

[0059] In the dynamic adjustment submodule, perturbation model parameters are used to adjust data flow paths and policies. Specifically, the system first compares the perturbation model parameters with preset safety thresholds to identify anomalies in the current data flow, such as sudden increases or decreases in data flow rate, unusual fluctuations in packet size, and unusual noise interference intensity. The system then dynamically adjusts the data flow's transmission path and processing strategy based on pre-set response strategies. Data flow rate adjustments are achieved by changing packet priority and allocation. The system dynamically assesses the urgency of each packet and adjusts its transmission path within the network accordingly. To address noise interference, the system introduces additional noise suppression mechanisms within the transmission path, such as adding filters or adjusting the frequency range of the transmission channel, to reduce the impact of noise interference on data transmission. Through this dynamic adjustment, the system optimizes the data flow's transmission path and processing strategy in real time, generating a dynamic adjustment strategy. This generated strategy not only ensures stable data flow transmission but also provides an optimized solution for subsequent data processing, ensuring that the data flow maintains efficient and stable operation under varying network conditions.

[0060] In the data processing submodule, a dynamic adjustment strategy is used to process real-time data streams, ensuring their stability and reliability. First, the system segments the data stream according to the dynamic adjustment strategy, applying different processing priorities and strategies to different segments to address their specific disturbance characteristics. For example, high-priority packets are processed faster and given priority in network resources, while low-priority packets are processed in a delayed or batched manner. During processing, the system gradually suppresses disturbances in the data stream, balancing data flow volatility by adjusting packet transmission rates, sizes, and transmission paths in real time. Simultaneously, the system monitors the overall data flow processing and promptly adjusts intervention measures to address potential new disturbances. Through this gradual intervention and adjustment, the system effectively suppresses abnormal fluctuations in the data stream, maintains data flow stability, and ultimately generates disturbance compensation data. This compensation data plays a crucial role in subsequent system operations, ensuring stable operation in various complex network environments and providing a reliable foundation for further network stability optimization.

[0061] In this embodiment, the network topology analysis module includes:

[0062] The topology mapping submodule builds a network topology map based on the monitoring network nodes and connection relationships. By analyzing the node connection strength point by point, it maps and organizes the network connection relationships and generates a topology connection diagram.

[0063] The node analysis submodule identifies node redundancy and isolation based on the topological connection diagram, extracts key network nodes by analyzing connection strength and node importance, and generates node structure analysis results.

[0064] The structure detection submodule detects weak links and key points based on the results of node structure analysis. Through multi-dimensional structural analysis and comparison, it screens weak links and key nodes in the network and generates key node structures.

[0065] In the topology mapping submodule, the network topology map is constructed by monitoring the initial data set of network nodes and their connection relationships. First, the system collects the connection data of each node, including the distance between nodes, signal strength, data transmission delay, etc., and stores it in a structured data format. Then, the connection strength of the nodes is analyzed point by point, and the quality of the connection is quantified by calculating the changes in signal strength and data transmission rate between nodes. The system weights the connections between nodes according to the quality parameters, generates a weighted adjacency matrix, and converts it into a visual topology graph. In this process, the system maps and organizes the connection relationships, uses matrix operations to optimize the layout of network connections, and adjusts the position of nodes in the topology graph through optimization algorithms to ensure the connectivity and simplicity of the graph. The final topology connection graph not only shows the overall structure of the network, but also clearly marks the connection strength and path weight of each node, providing basic data for subsequent node analysis and structure detection.

[0066] In the node analysis submodule, the topological connectivity graph is used to identify node redundancy and isolation within the network. First, the system takes the adjacency matrix from the topological connectivity graph as input and analyzes parameters such as the number of connections, connection strength, and path length for each node. For each node, the system calculates its degree centrality, closeness centrality, and betweenness centrality. Degree centrality measures the number of connections, closeness centrality reflects the node's position in the network, and betweenness centrality indicates the node's importance within the path. Next, the system identifies redundant and isolated nodes using centrality metrics. Redundant nodes have high degree centrality but low closeness centrality, while isolated nodes exhibit low degree centrality and low closeness centrality. After identifying redundant nodes, the system further analyzes their necessity in the network, determining whether their connections should be retained or reallocated. Finally, the system extracts the network's key nodes and generates a node structure analysis result. This result not only includes a list of key nodes but also provides a detailed description of each node's redundancy and importance, laying the foundation for further network optimization and structural testing.

[0067] In the structural detection submodule, the weak links and key points in the network are detected through the results of node structure analysis. First, the system imports the key node information from the node structure analysis results to identify potential weak connections in the network. By calculating parameters such as connectivity, transmission bandwidth, and data throughput between nodes, the system identifies connection paths that may become bottlenecks in the network, and screens out weak connections based on the importance of the nodes. During the detection process, the system performs a multi-dimensional structural analysis of the network, including path redundancy, load balancing, and fault recovery capabilities, compares the performance of different network structures, and identifies areas with the most potential risks. In addition, the system also detects the stability of key nodes and evaluates their performance under high load conditions by analyzing the node load and failure rate. Finally, the system generates a key node structure, indicating the location and status of weak connections and key nodes in the network, providing a detailed reference for subsequent network optimization and fault prevention.

[0068] In this embodiment, the reliability enhancement module includes:

[0069] The connectivity identification submodule identifies areas with weak connectivity in the network based on the key node structure. By analyzing the connection relationship and connectivity between nodes, it extracts connectivity weaknesses and generates connectivity identification results.

[0070] The path allocation submodule redistributes paths and node roles based on the connectivity identification results, optimizes path allocation by analyzing path importance and node connectivity, and generates a path allocation plan;

[0071] The redundancy construction submodule builds multi-level redundant paths in the network based on the path allocation scheme, establishes a redundant connection structure by enhancing the connectivity and path stability between nodes, and generates enhanced connectivity paths.

[0072] In the connectivity identification submodule, a dataset of key node structures is used to identify areas of weak connectivity within the network. Specifically, the system first extracts node connectivity and connection strength data from the key node structure. This data is stored in the form of an adjacency matrix and a weight matrix. The adjacency matrix describes the connectivity relationships between nodes, while the weight matrix quantifies the connection strength between nodes. The system combines these two matrices to calculate a connectivity index for each node, assessing its level of connectivity within the entire network. The connectivity index is derived by taking a weighted average of each node's degree centrality, closeness centrality, and betweenness centrality. Degree centrality measures the number of direct connections a node has, closeness centrality reflects the relative distance between nodes in the network, and betweenness centrality indicates the importance of a node in the shortest path. The system further compares the index with a set connectivity threshold to identify nodes below the threshold. The areas containing these nodes are then designated as areas of weak connectivity. Based on this, the system identifies connectivity weaknesses and generates a connectivity identification result. This result identifies weak nodes in the network and their corresponding connection strengths, providing an accurate basis for subsequent path allocation and optimization.

[0073] In the path allocation submodule, network paths and node roles are reallocated using a dataset of connectivity identification results. Specifically, the system first imports data on weak nodes and their adjacent nodes from the connectivity identification results. By analyzing the nodes' connection strength, path length, and importance weights, the system assesses their suitability for their roles within the network. The system employs a comprehensive scoring mechanism to assess the connectivity, importance, and load balance of each path and node. The connectivity score is determined by the node's degree centrality and the total connection strength of its path. The importance score is based on the node's betweenness centrality within the global network. The load balance score considers the match between the node's current load and its processing capacity. The system then takes a weighted average of the scores to determine the role and importance of each node and path in the new allocation scheme and reallocates paths and node roles based on the scores. Path optimization involves reselecting high-priority nodes as core nodes and adjusting their corresponding path priorities to ensure sufficient redundancy and connectivity on the network's critical paths. The path allocation solution ultimately generated by the system provides an optimized topology for the network, giving the network higher fault tolerance and recovery capabilities when facing node failures or path interruptions.

[0074] In the redundancy construction submodule, the path allocation plan dataset is used to construct multi-level redundant paths within the network. The system first imports all path data from the path allocation plan and performs a secondary analysis of path connectivity, importance, and load distribution to ensure that each path is appropriately configured within the new plan. The system then calculates the connectivity redundancy of each path to determine whether it requires additional redundant paths. Connectivity redundancy is calculated by weighting the betweenness centrality and path length of each node along the path. Paths with high connectivity redundancy indicate greater reliability within the network. For areas where additional redundant paths are needed, the system gradually adds secondary paths and backup nodes in descending order of redundancy based on the distribution of existing nodes and paths, building a multi-level redundant structure. The system also optimizes load balancing across redundant paths, ensuring that each redundant path effectively shares the burden of the primary path under high load conditions. Ultimately, the resulting enhanced connectivity paths not only improve overall network stability but also enable rapid failover to backup paths in the event of localized failures, ensuring continued stable network operation.

[0075] In this embodiment, the dynamic control module includes:

[0076] The flow monitoring submodule, based on disturbance compensation data, integrates data flow rate and buffer area parameters, monitors data flow fluctuations and buffer area occupancy, records real-time flow changes, and generates flow monitoring results;

[0077] The parameter adjustment submodule adjusts the control parameters and buffer settings based on the flow monitoring results. By analyzing the flow changes and load, the control parameters and buffer configuration are optimized and the parameter adjustment configuration is generated.

[0078] The state adjustment submodule adjusts the state based on the parameter adjustment configuration, balances the load and traffic fluctuations by adjusting the control parameters and load balancing, and generates adjustment feedback parameters.

[0079] In the traffic monitoring submodule, disturbance compensation data is used to integrate and analyze data flow rates and buffer parameters. The system first converts the data flow rate and buffer occupancy into a structured data format suitable for analysis by time series processing. The data flow rate, represented as a continuous time series, reflects the packet transmission rate at each point in time. Buffer parameters include buffer size, current occupancy, and historical occupancy. The system uses sliding window analysis on the time series data to monitor data flow fluctuations in real time and, based on the changing trends in buffer occupancy, assess the instantaneous data flow. By monitoring the dynamic relationship between data flow and buffer, the system identifies potential traffic fluctuations and buffer load pressures. The monitoring results are then compared with historical data to identify the time points of abnormal fluctuations and the corresponding buffer occupancy status. Finally, the system records the real-time traffic change data obtained during the monitoring process to generate traffic monitoring results. These results reflect the system's data flow and buffer usage over various time periods, providing critical data support for subsequent parameter adjustments.

[0080] In the parameter adjustment submodule, traffic monitoring results are used to dynamically adjust control parameters and buffer settings. The system first imports data from traffic monitoring results and analyzes the fluctuation range of data flow rates and buffer occupancy to identify control parameters and buffer settings that require adjustment. Specifically, the system calculates the standard deviation of the data flow rate and the gradient of the buffer occupancy to quantify the severity of traffic fluctuations and the speed of change in buffer load. Based on these calculations, the system makes detailed adjustments to control parameters, including the upper limit of data flow transmission rates, priority allocation strategies, and dynamic buffer allocation schemes. Through the interaction between control parameters and buffer settings, the system adjusts the buffer size and data flow allocation ratio to optimize system performance. During this process, the system monitors the effects of adjustments in real time and incorporates feedback into the parameter adjustment process to further refine the adjustment plan. The resulting parameter adjustment configuration file not only records the adjusted parameter values ​​but also describes the performance of the parameters under different traffic conditions. This provides a flexible configuration solution to ensure stable operation even with large traffic fluctuations.

[0081] In the state adjustment submodule, the system state is comprehensively adjusted through parameter adjustment configuration. The system first imports the control parameters and buffer settings in the parameter adjustment configuration, and compares them with the current system state to identify the parts that need to be adjusted. In specific operations, the system determines the priority of adjustment by calculating the matching degree between the current load and traffic fluctuations. The matching degree is obtained by analyzing the difference between the current load capacity of the system and the demand of the data flow. Then, the system makes fine adjustments to the control parameters, including adjusting the priority of the data flow, modifying the allocation strategy of the cache area, and dynamically adjusting the flow limit threshold. The system also monitors the state changes after adjustment, and further optimizes the adjustment strategy by analyzing the balance between the load balancing effect and traffic fluctuations, and finally generates the adjustment feedback parameters. This parameter describes in detail the optimal configuration of the system under different load and traffic conditions, provides basic data for the continuous optimization of the system, and ensures the stability and efficiency of the system under various complex conditions.

[0082] In this embodiment, the network stabilization module includes:

[0083] The global analysis submodule analyzes the global topology and node connection relationships based on enhanced connectivity paths and adjusted feedback parameters. By analyzing the global node distribution and connection relationships, it extracts the global network structure and generates a global analysis graph.

[0084] The connection coordination submodule coordinates the connection relationship between local and global nodes based on the global analysis graph, optimizes the connectivity between nodes by adjusting the node connection order and cross-level connections, and generates a connection coordination plan;

[0085] The load balancing submodule balances the load of global and local nodes based on the connection coordination scheme. By adjusting the load distribution and node connections, it optimizes the cross-level load balancing and generates an overall stable structure.

[0086] In the global analysis submodule, the system comprehensively analyzes the global topology and node connectivity by enhancing connectivity paths and adjusting feedback parameters. First, the system extracts all nodes and their connections from the enhanced connectivity paths, storing the connection weights between nodes in a matrix format. This data includes node connection strength, path length, and relative position in the network. Next, the system combines the load distribution and traffic information from the adjusted feedback parameters to conduct a detailed analysis of the node distribution in the global network. By calculating the degree centrality, closeness centrality, and betweenness centrality of each node, the system assesses the importance of each node in the global topology. Using cluster analysis, the system groups nodes based on connectivity strength and centrality metrics, identifying core nodes, edge nodes, and critical connection paths. The system then conducts an in-depth analysis of the connectivity between each node group, calculating the connectivity and redundancy between node groups. This analysis identifies potential bottlenecks and redundant connections in the global network structure, and visualizes the results as a global analysis graph. This global analysis graph illustrates the distribution of each node and path in the network and their global importance, providing a scientific basis for subsequent connection coordination and load balancing.

[0087] In the connection coordination submodule, the connection relationship between local and global nodes is coordinated and optimized through the global analysis graph. The system first extracts the connection order and cross-level connection relationship of each node and path from the global analysis graph, and presents the connection order of key nodes and their connection between different levels in the form of a list. By analyzing the connection order, the system identifies node connections that may have bottlenecks or imbalances, and optimizes and adjusts the connection order. Specifically, the system increases the connection priority of nodes with poor connectivity and adjusts their connection order with other nodes to ensure that the connection path of key nodes is shorter and more direct. In addition, the system improves the communication efficiency between nodes at different levels and reduces the delay and load of cross-level communication by optimizing cross-level connections. This solution clarifies the optimized node connection relationship, lays the foundation for the efficient operation and stability of the network, and provides clear guidance for the execution of the load balancing submodule.

[0088] In the load balancing submodule, the load of global and local nodes is balanced through the connection coordination scheme. The system first extracts the optimized node connection relationship and path allocation information from the connection coordination scheme, and combines it with the real-time monitored traffic and load data to conduct a detailed analysis of the current load situation of each node. The system evaluates the load distribution of each node in the network by calculating the real-time load rate and traffic processing capacity of the node, and compares and analyzes nodes with excessive loads with nodes with lighter loads. Then, the system adjusts the load distribution strategy to transfer overloaded communication tasks to nodes with lighter loads, or redistribute traffic in cross-level connections to balance the load pressure of global and local nodes. The overall stable structure records in detail the load distribution of each node in the network and its optimal configuration under different states, providing a strong guarantee for the continuous stability and efficiency of the system.

[0089] In this embodiment, the extended state observer is based on the formula:

[0090] ;

[0091] Calculate the perturbation model parameter values, where is the disturbance eigenvalue output by the extended state observer, is the estimated value of the extended state, is the system input gain coefficient, is the input signal, To adjust the parameters, is the disturbance change rate weight coefficient, is the time rate of change of the disturbance estimate, is the index correction coefficient, is the suppression intensity coefficient, is the interference buffer constant;

[0092] Calculation process: First, calculate the basic perturbation eigenvalue, through the formula Calculate the initial estimate of the disturbance, then, based on the rate of change of the disturbance estimate Calculate the effect of the change on the eigenvalue and compare it with the perturbation change rate weight coefficient Multiplied and added to the initial estimate, the calculation is then based on The exponential correction term of the ratio of the square to the input signal is used. Amplify and multiply by the suppression strength coefficient Adjust while using the disturbance buffer constant To avoid the situation where the denominator is zero, the correction term is summed with the previous value to obtain the perturbation model parameters.

[0093] It should be understood that, although the various steps in the flow chart of each embodiment of the present invention are shown in sequence according to the indication of the arrows, these steps are not necessarily performed in sequence according to the order indicated by the arrows. Unless otherwise specified herein, the execution of these steps is not strictly limited in order, and these steps can be performed in other orders. Moreover, at least a portion of the steps in each embodiment may include a plurality of sub-steps or a plurality of stages, and these sub-steps or stages are not necessarily performed at the same time, but can be performed at different times, and the execution order of these sub-steps or stages is not necessarily performed in sequence, but can be performed in turn or alternately with at least a portion of other steps or sub-steps or stages of other steps.

[0094] Those skilled in the art will appreciate that all or part of the processes in the above-described method embodiments can be implemented by instructing the relevant hardware through a computer program. The program can be stored in a non-volatile computer-readable storage medium. When executed, the program can include the processes of the above-described method embodiments. Any reference to memory, storage, database, or other media used in the various embodiments provided herein may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct RAMbus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM).

[0095] The above are merely preferred embodiments of the present invention and do not limit the present invention in any other form. Any technician familiar with the profession may use the technical content disclosed above to change or modify it into an equivalent embodiment with equivalent changes and apply it to other fields. However, any simple modification, equivalent change and modification made to the above embodiment based on the technical essence of the present invention without departing from the content of the technical solution of the present invention shall still fall within the scope of protection of the technical solution of the present invention.

Claims

1. The intelligent security monitoring and management system based on the Internet of Things is characterized by: The system comprises: The data flow anti-interference module establishes an extended state model of external disturbances and internal uncertainties based on the characteristics of the monitored data flow, calculates the data flow rate, packet size and noise interference parameters, dynamically adjusts the data flow path and strategy, obtains and processes the changes and fluctuations of the real-time data flow, and generates disturbance compensation data; The network topology analysis module builds network topology mapping based on monitoring network nodes and connection relationships, analyzes node connections, handles redundant paths and isolated nodes, detects weak links and key points through point-by-point mapping and multi-dimensional structure, and generates key node structures; A reliability enhancement module, based on the key node structure, identifies areas with weak connectivity, reallocates paths and node roles, performs multi-level redundant path construction and network connection optimization, analyzes multi-level node connections and path stability, and generates enhanced connectivity paths; A dynamic control module, based on the disturbance compensation data, combines the data flow rate and the buffer area parameters, calculates and adjusts the control parameters and buffer area settings, handles the flow fluctuation and the buffer area load, performs state adjustment, and generates adjustment feedback parameters; A network stabilization module, based on the enhanced connectivity path and the adjustment feedback parameters, analyzes global and local topological structures, coordinates node connection relationships, adjusts cross-level connections, performs global and local node optimization and load balancing, establishes and optimizes cross-level connections of the network, and generates an overall stable structure; The data stream anti-interference module comprises: The disturbance modeling submodule establishes an extended state model including external disturbances and inherent uncertainties based on the characteristics of the monitored data stream. By calculating the data stream rate, packet size and noise interference parameters, an extended state observer is used to extract and analyze disturbance features to generate disturbance model parameters. A dynamic adjustment submodule adjusts the data flow path and strategy based on the disturbance model parameters, dynamically adjusts the data flow rate and noise suppression level by comparing the disturbance characteristics with the preset threshold, and generates a dynamic adjustment strategy; The data processing submodule processes the real-time data stream based on the dynamic adjustment strategy, balances the data stream fluctuation and the disturbance influence through segmented processing and gradual suppression of disturbance characteristics, and generates disturbance compensation data; The extended state observer is based on the formula: ； Calculate the perturbation model parameter values, where is the estimated value of the extended state, Enter the gain factor for the system, is the input signal, To adjust the parameters, is the disturbance change rate weight coefficient, is the time rate of change of the disturbance estimate, is the index correction factor, is the suppression intensity coefficient, is the interference buffer constant.

2. The smart security monitoring and management system based on the Internet of Things according to claim 1 is characterized by: The disturbance compensation data includes rate adjustment parameters, noise filtering coefficients and packet loss recovery information; the key node structure includes connection strength values, node redundancy marks and isolated node identification marks; the enhanced connectivity path includes priority connection paths, redundant backup paths and node configurations; the adjustment feedback parameters include gain adjustment values, cache configuration parameters and traffic distribution ratios; and the overall stable structure includes a global connection graph and load balancing configurations.

3. The smart security monitoring and management system based on the Internet of Things according to claim 1 is characterized in that: The network topology analysis module includes: The topology mapping submodule builds a network topology map based on monitoring network nodes and connection relationships. It maps and organizes network connection relationships by analyzing node connection strength point by point to generate a topology connection diagram. The node analysis submodule identifies node redundancy and isolation based on the topological connection diagram, extracts key network nodes by analyzing connection strength and node importance, and generates node structure analysis results; The structure detection submodule detects weak links and key points based on the node structure analysis results, screens weak links and key nodes in the network through multi-dimensional structure analysis and comparison, and generates key node structures.

4. The smart security monitoring and management system based on the Internet of Things according to claim 1 is characterized in that: The reliability enhancement module comprises: A connectivity identification submodule, based on the key node structure, identifies areas with weak connectivity in the network, extracts connectivity weaknesses through connection relationships and connectivity analysis between nodes, and generates connectivity identification results; A path allocation submodule, based on the connectivity identification result, reallocates the paths and node roles, optimizes the path allocation by analyzing the path importance and node connectivity, and generates a path allocation plan; The redundancy construction submodule constructs a multi-level redundant path in the network based on the path allocation scheme, establishes a redundant connection structure by enhancing the connectivity and path stability between nodes, and generates an enhanced connectivity path.

5. The smart security monitoring and management system based on the Internet of Things according to claim 1 is characterized in that: The dynamic control module includes: The flow monitoring submodule, based on the disturbance compensation data, combines the data flow rate and the buffer area parameters, monitors the fluctuation of the data flow and the buffer area occupancy, records the real-time flow changes, and generates the flow monitoring results; A parameter adjustment submodule adjusts the control parameters and buffer settings based on the flow monitoring results, optimizes the control parameters and buffer configuration by analyzing flow changes and loads, and generates parameter adjustment configurations; The state adjustment submodule adjusts the state based on the parameter adjustment configuration, balances the load and flow fluctuations by adjusting the control parameters and load balancing, and generates adjustment feedback parameters.

6. The smart security monitoring and management system based on the Internet of Things according to claim 1 is characterized in that: The network stabilization module includes: A global analysis submodule analyzes the global topological structure and node connection relationship based on the enhanced connectivity path and the adjustment feedback parameter, extracts the global network structure by analyzing the global node distribution and connection relationship, and generates a global analysis graph; A connection coordination submodule coordinates the connection relationship between local and global nodes based on the global analysis graph, optimizes the connectivity between nodes by adjusting the node connection sequence and cross-level connections, and generates a connection coordination plan; The load balancing submodule balances the load of global and local nodes based on the connection coordination scheme, optimizes cross-level load balancing by adjusting load distribution and node connections, and generates an overall stable structure.

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