A method and system for handling security risks of remote video transmission based on the Internet

Abstract

The present invention discloses a method and system for handling security risks in remote video transmission based on the Internet, relating to the field of video transmission processing. The method comprises: setting initial transmission control indicators for remote video, deploying transmission control nodes for remote video on the Internet using multi-order node control technology, and constructing a remote video transmission control model based on the deployment results; regulating the transmission process of remote video according to the remote video transmission control model, and utilizing trajectory virtual analysis technology to identify security situations during the transmission process and detect intrusions into the Internet network; constructing an intuitive fuzzy set of security situations based on the intrusion detection results, identifying security risks during remote video transmission using the fuzzy set, and matching security risks with treatment measures to obtain risk treatment results. The present invention improves the overall security and efficiency of remote video transmission by comprehensively utilizing network optimization, dynamic adjustment, security analysis, and automated processing technologies.

Description

Technical Field

[0001] The present invention relates to the field of video transmission processing, and in particular to a method and system for processing security risks of remote video transmission based on the Internet. Background Art

[0002] Internet-based remote video transmission refers to the process of transmitting video signals from one location to another over a network, typically relying on the internet as the transmission medium. This technology utilizes video compression, encoding, decoding, transmission protocols, and network bandwidth management to enable real-time or recorded video communication between people or devices in different locations.

[0003] However, if security risks in remote video transmission cannot be effectively addressed, video data will be at risk of attack or leakage during transmission. Unencrypted or non-dynamically adjustable network paths may expose data to interception, tampering, or theft. Furthermore, the lack of intelligent identification and security monitoring mechanisms prevents timely detection of potential malicious intrusions, network attacks, and identity impersonation, leading to the leakage, tampering, or interruption of video conference content, seriously compromising the reliability and security of video transmission. Without the ability to dynamically adjust transmission paths and nodes, network fluctuations, excessive latency, or packet loss cannot be promptly optimized, ultimately resulting in reduced transmission quality and a negative user experience.

[0004] Currently, no effective solutions have been proposed for the problems in related technologies. Summary of the Invention

[0005] In order to solve the above problems, the present invention proposes a method and system for handling security risks of remote video transmission based on the Internet, which can achieve the purpose of intelligently identifying abnormal behaviors and security risks during the transmission process.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions:

[0007] In a first aspect, the present invention provides a method for handling security risks of remote video transmission based on the Internet, the method comprising:

[0008] Set the initial transmission control indicators for remote video, use multi-stage node control technology to deploy remote video transmission control nodes on the Internet, and build a remote video transmission control model based on the deployment results;

[0009] The remote video transmission process is regulated based on the remote video transmission control model, and trajectory virtual analysis technology is used to identify security situations during the transmission process and detect intrusions into the Internet network;

[0010] Based on the intrusion detection results, an intuitive fuzzy set of security situation is constructed. The security risks in the remote video transmission process are identified according to the fuzzy set. The security risks are matched with the treatment measures to obtain the risk treatment results.

[0011] Preferably, setting the remote video initial transmission control index, deploying the remote video transmission control node on the Internet using multi-stage node control technology, and constructing the remote video transmission control model based on the deployment result include:

[0012] Define transmission parameters based on the data status of the remote video, use the transmission parameters to set initial transmission control indicators, and generate a node system including load thresholds and packet loss rate upper limits based on the initial transmission control indicators;

[0013] Analyze the minimum violation sequence corresponding to the node system, suppress the nodes in the minimum violation sequence according to the suppression priority score, update the minimum violation sequence, and use the minimum violation sequence to optimize the node system;

[0014] Deploy transmission control nodes on the Internet based on the optimized node system and multi-stage node control technology, and analyze transmission priorities based on the location of the transmission control nodes and parameter information of the remote video;

[0015] The remote video is encrypted in batches, and a remote video transmission control model is built based on the encryption results and transmission priority. The transmission node of the remote video is output and the shortest video transmission path is determined.

[0016] Preferably, analyzing the minimum violation sequence corresponding to the node system, suppressing the nodes in the minimum violation sequence according to the suppression priority score to update the minimum violation sequence, and optimizing the node system using the minimum violation sequence includes:

[0017] Find the nodes in the node system whose load value is greater than the load threshold and whose packet loss rate is greater than the packet loss rate upper limit as the minimum violation sequence, and use the preset frequent sequence to find the frequent sequence set within the minimum violation sequence;

[0018] Use the frequent sequence set to construct a minimum infeasible sequence tree, select any node from the minimum infeasible sequence tree, and determine the loss caused by discarding the node. Calculate the suppression score of any node based on the loss result.

[0019] Obtain the suppression score of each node in the minimum infeasible sequence tree, determine to discard the nodes that are greater than the loss requirement based on the suppression score, and update the minimum violation sequence based on the set result, and merge the updated minimum violation sequence into the node system;

[0020] A transmission tree is established based on the node system. The Laplace mechanism is used to add noise to each node according to the privacy budget of the transmission tree, thereby increasing the privacy function of the node during transmission and determining the final node system.

[0021] Preferably, a transmission tree is established based on the node system, and noise is added to each node using the Laplace mechanism according to the privacy budget of the transmission tree to increase the privacy function of the node during transmission. The final node system is determined to include:

[0022] Based on the merged node system, several node distribution data sets are randomly generated. The terminal connection paths between the nodes in the node distribution data sets are obtained. The terminal connection paths corresponding to the minimum number of fluctuations between any two nodes in each path are selected as the first group.

[0023] Pick out the sequence with the minimum cost from the node distribution data set containing any two nodes mentioned above, and take the most frequent nodes in the node distribution data set where the sequence is located as the second group, and repeat the selection until all nodes are iteratively selected into the transmission tree;

[0024] Analyze the total length of the terminal connection paths of nodes in the transmission tree, match the privacy budget of the transmission tree based on the total length, and use the Laplace mechanism to equally divide the privacy budget among each node;

[0025] According to the privacy budget division results, noise is added to the nodes to increase the privacy function of the nodes during transmission, and the final node system is determined based on the addition results and the node position in the transmission tree.

[0026] Preferably, deploying transmission control nodes in the Internet based on the optimized node system and multi-stage node control technology, and analyzing transmission priorities according to the location of the transmission control nodes and parameter information of the remote video includes:

[0027] Boolean sensing technology is used to analyze the transmission coverage of nodes in the optimized node system, adjust the node position to obtain the coverage of the Internet transmission area, and simulate and analyze the data transmission energy consumption to determine the energy consumption of communication between nodes;

[0028] The virtual force that the node bears during the transmission process is analyzed based on the communication energy consumption, and the real value of the virtual force is estimated to obtain the virtual force matrix, generate the deployment location of the node, and deploy the transmission control node in the Internet;

[0029] A dynamic priority model is built based on the transmission latency and transmission memory of remote videos, and a combined transmission avoidance strategy is customized based on the priority collision avoidance mechanism and the location of the transmission control node.

[0030] The Internet transmission channel is divided into congestion detection areas, and the transmission congestion risk of remote videos is measured in combination with a combined transmission avoidance strategy. The transmission priority of remote videos is adjusted based on the congestion risk.

[0031] Preferably, using Boolean sensing technology to analyze the transmission coverage of nodes in the optimized node system, adjusting the node positions to obtain the coverage of the Internet transmission area, and simulating and analyzing the data transmission energy consumption to determine the energy consumption of communication between nodes includes:

[0032] Generate transmission control nodes based on the optimized node system, determine the Euclidean distance from each node to the transmission control node, and determine the node coverage based on Euclidean distance and Boolean perception technology;

[0033] Adjust the node position based on the node coverage situation and determine the probability of the node covering the Internet transmission area. After the coverage probability meets the set value, the node position is determined and the node position adjustment operation is stopped;

[0034] The simulation determines the energy consumption from any node to the transmission control node and the energy consumption required for any node to transmit to another node during the adjusted communication transmission process, and combines the energy consumption results as the total energy consumption of inter-node communication.

[0035] Preferably, a dynamic priority model is constructed based on the transmission waiting time and transmission memory of the remote video, and a customized combined transmission avoidance strategy is developed under the conditions of the priority collision avoidance mechanism and the location of the transmission control node, including:

[0036] Based on the requirements of remote video transmission, the scheduling delay within the Internet and the memory space occupied by the buffer during the transmission process are defined as the transmission waiting time and transmission memory. A regression equation is generated based on the comprehensive score of the node system quality as a dynamic priority model.

[0037] Based on the dynamic priority model, the transmission order of remote videos is initially generated, and the resource occupancy on the same transmission path is identified. The resource occupancy is combined with the control information of the transmission control node to determine the inclusion of key nodes in the transmission path;

[0038] According to the judgment results, a node distribution matrix is ​​constructed as the priority of remote data transmission data, and the local path transfer strategy is called to customize the combined transmission avoidance strategy to determine the transmission path scheduling result.

[0039] Preferably, regulating the transmission process of the remote video according to the remote video transmission control model, and using the trajectory virtual analysis technology to identify the security situation during the transmission process, and detecting the intrusion of the Internet network include:

[0040] Based on the shortest video transmission path output by the video transmission control model, the node connection relationship of the remote video is regulated, the remote data transmission work is performed, and the transmission process data during the transmission process is obtained;

[0041] Based on the transmission process data and the multi-tuple concept, a multi-tuple trajectory extraction model is constructed to divide the trajectory point distribution vector set of the transmission process data, and the trajectory is used to virtually reconstruct the action trajectory extraction value;

[0042] The standard security situation analyzer is trained and transmitted using a multi-granularity filter to set the standard trajectory overlap value, transform the action trajectory extraction value into a dot multiplication operation, and calculate the boundary minimum rectangular trajectory overlap rate value;

[0043] The matrix transformation between the standard trajectory overlap value and the boundary minimum rectangular trajectory overlap rate value is judged by Euclidean distance, the degree of fluctuation of the transmission trajectory of remote data is clarified, the security situation status during the transmission process is evaluated, and the intrusion of the Internet network is detected based on the evaluation results.

[0044] In a second aspect, the present invention further provides an Internet-based remote video transmission security risk processing system, the system comprising:

[0045] The transmission control model construction module is used to set the initial transmission control indicators of remote video, deploy the transmission control nodes of remote video on the Internet using multi-stage node control technology, and build the remote video transmission control model based on the deployment results;

[0046] The network intrusion detection module is used to control the transmission process of remote video according to the remote video transmission control model, and use trajectory virtual analysis technology to identify the security situation during the transmission process and detect intrusions on the Internet network;

[0047] The security hazard analysis and processing module is used to construct an intuitive fuzzy set of security situation based on the intrusion detection results, identify security hazards in the remote video transmission process according to the fuzzy set, and match the security hazards with treatment measures to obtain the hazard treatment results.

[0048] The beneficial effects of the present invention are:

[0049] 1. By setting initial transmission control indicators and deploying multi-stage node control technology, the present invention can dynamically adjust the transmission path and node distribution according to the actual situation of the network environment, thereby optimizing bandwidth, reducing latency and improving the reliability of video transmission. Based on the construction and regulation of the remote video transmission control model, it ensures real-time monitoring and adaptive adjustment of the network status during the transmission process. At the same time, the introduction of trajectory virtual analysis technology can intelligently identify abnormal behavior and security risks during the transmission process, reducing the risk of attack or data leakage. Therefore, through the comprehensive application of network optimization, dynamic adjustment, security analysis and automated processing technologies, the overall security and efficiency of remote video transmission are improved.

[0050] 2. The present invention ensures the optimal configuration of video streams in terms of bandwidth, delay and reliability by defining precise transmission parameters and initial transmission control indicators, and adopts minimum violation sequence analysis technology to dynamically optimize the node system, intelligently adjust the network topology, and effectively reduce the packet loss rate and network congestion risk. At the same time, the batch encryption mechanism maintains transmission efficiency while ensuring data security, balancing security and real-time requirements, so that the coordination of various dimensions of parameterized control, intelligent node deployment and dynamic path optimization can be achieved, thereby achieving a comprehensive improvement in transmission quality, security protection and resource utilization.

[0051] 3. The present invention uses a multi-tuple trajectory extraction model and trajectory virtual reconstruction technology to comprehensively analyze the behavioral patterns of remote video transmission, divide the precise trajectory point distribution and action trajectory extraction values, and provide detailed dynamic data support for security situation analysis. It also uses a multi-granularity filter to train a standard security situation analyzer and calculates the overlap between the changes in the action trajectory and the standard trajectory, which can more accurately detect abnormal behavior. At the same time, by calculating the trajectory overlap rate and the matrix transformation of the Euclidean distance, it can quantify the degree of fluctuation during the transmission process, judge potential security threats in the network in real time, quickly identify intrusion attacks, and reduce the risk of false alarms or missed alarms, thereby improving the accuracy of security detection during remote video transmission and enhancing the anti-interference ability and recoverability of remote video transmission in complex network environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] The accompanying drawings, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0053] Figure 1 is a flow chart of a method for handling security risks of remote video transmission based on the Internet according to an embodiment of the present invention;

[0054] Figure 2 The present invention is a block diagram of a system for handling security risks in remote video transmission based on the Internet.

[0055] In the picture:

[0056] 1. Transmission control model construction module; 2. Network intrusion detection module; 3. Security risk analysis and processing module. DETAILED DESCRIPTION

[0057] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0058] It should be noted that the following detailed descriptions are exemplary and intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used in the present invention have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.

[0059] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0060] In the absence of conflict, the embodiments of the present invention and the features thereof may be combined with each other.

[0061] See also Figure 1 The present invention provides a method for processing security risks of remote video transmission based on the Internet, the processing method comprising:

[0062] Set the initial transmission control indicators of remote video;

[0063] S1. Use multi-order node control technology to deploy remote video transmission control nodes on the Internet, and build a remote video transmission control model based on the deployment results.

[0064] In one embodiment, initial remote video transmission control indicators are set, multi-stage node control technology is used to deploy remote video transmission control nodes on the Internet, and a remote video transmission control model is constructed based on the deployment results, including:

[0065] Define transmission parameters based on the data status of the remote video, use the transmission parameters to set initial transmission control indicators, and generate a node system including load thresholds and packet loss rate upper limits based on the initial transmission control indicators;

[0066] Analyze the minimum violation sequence corresponding to the node system, suppress the nodes in the minimum violation sequence according to the suppression priority score, update the minimum violation sequence, and use the minimum violation sequence to optimize the node system;

[0067] Deploy transmission control nodes on the Internet based on the optimized node system and multi-stage node control technology, and analyze transmission priorities based on the location of the transmission control nodes and parameter information of the remote video;

[0068] The remote video is encrypted in batches, and a remote video transmission control model is built based on the encryption results and transmission priority. The transmission node of the remote video is output and the shortest video transmission path is determined.

[0069] Among them, when analyzing the minimum violation sequence corresponding to the node system, the nodes in the minimum violation sequence are suppressed and updated according to the suppression priority score. In the process of optimizing the node system using the minimum violation sequence, the nodes in the node system with a load value greater than the load threshold and a packet loss rate greater than the packet loss rate upper limit can be found as the minimum violation sequence, and the preset frequent sequence is used to find the frequent sequence set in the minimum violation sequence; the frequent sequence set is used to construct a minimum infeasible sequence tree, and any node is selected from the minimum infeasible sequence tree, and the loss caused by discarding the arbitrary node is judged, and the suppression score of any node is calculated according to the loss result; the suppression score of each node in the minimum infeasible sequence tree is obtained, and the node with a loss greater than the loss requirement is discarded according to the suppression score, and the minimum violation sequence is updated based on the set result, and the updated minimum violation sequence is merged into the node system; a transmission tree is established according to the node system, and the Laplace mechanism is used to add noise to each node according to the privacy budget of the transmission tree, to increase the privacy function of the node during transmission and use, and to determine the final node system.

[0070] It should be explained that when setting the initial transmission control indicators of remote video and building the node system, it is necessary to set parameters based on the status of remote video data (such as frame rate, resolution, real-time requirements, etc.), including maximum load, maximum packet loss rate, etc., and convert the parameters into network layer indicators. Build a node system containing "load threshold + packet loss rate upper limit", find nodes that do not meet the indicators (load value is too high, packet loss is serious), and at the same time, identify the most frequently erroneous paths through frequent item set mining to build a minimum non-feasible sequence tree (MFST), and then calculate the suppression score of each node (measure the impact cost of eliminating the node), and at the same time eliminate nodes with scores higher than the threshold, and merge the optimization results back into the node system, thereby improving the health and feasibility of the node system, reducing the impact of bottleneck nodes on the overall network performance, and ensuring the robustness of the transmission path.

[0071] Specifically, a transmission tree is established based on the node system, and noise is added to each node using the Laplace mechanism according to the privacy budget of the transmission tree to increase the privacy function of the node during transmission. When determining the final node system, several node distribution data sets can be randomly generated based on the merged node system, and the terminal connection paths connecting the nodes in the node distribution data set are obtained. The terminal connection paths corresponding to the minimum number of fluctuations between any two nodes in each path are selected as the first group; the sequence with the minimum cost is selected from the node distribution data set containing the above-mentioned any two nodes, and the most frequent nodes in the node distribution data set where the sequence is located are selected as the second group. The selection is repeated until all nodes are iteratively selected into the transmission tree; the total length of the terminal connection paths of the nodes in the transmission tree is analyzed, and the privacy budget of the transmission tree is matched based on the total length. The privacy budget is equally divided among the nodes using the Laplace mechanism; noise is added to the nodes according to the privacy budget division result to increase the privacy function of the nodes during transmission, and the final node system is determined based on the addition result and the node position in the transmission tree.

[0072] It needs to be explained that a connection topology is established based on the updated node system, the node distribution and communication path are randomly simulated, the path with minimum fluctuation (first group) + frequent nodes (second group) are extracted, and a transmission tree is generated. At the same time, the total privacy budget is matched according to the transmission tree, and the Laplace mechanism is used to distribute the equal value to each node. Noise is added to the node to achieve differential privacy protection, ensure the security of user sensitive information during data transmission, and update the final node system. This can enhance data privacy, meet the differential privacy standards, reduce the risk of eavesdropping by malicious nodes or traffic reconstruction, and improve credibility.

[0073] Specifically, in the process of deploying transmission control nodes in the Internet based on the optimized node system and multi-order node control technology, and analyzing the transmission priority according to the location of the transmission control node and the parameter information of the remote video, the Boolean perception technology can be used to analyze the transmission coverage of the nodes in the optimized node system, adjust the node position to obtain the coverage of the Internet transmission area, and simulate and analyze the data transmission energy consumption to determine the communication energy consumption between nodes; according to the communication energy consumption, the virtual force that the node bears during the transmission process is analyzed, and the real value of the virtual force is estimated to obtain the virtual force matrix, generate the deployment position of the node, and deploy the transmission control node in the Internet; construct a dynamic priority model based on the transmission waiting time and transmission memory of the remote video, and customize the combined transmission avoidance strategy under the conditions of the priority avoidance mechanism and the location of the transmission control node; divide the Internet transmission channel into congestion detection areas, and combine the combined transmission avoidance strategy to measure the transmission congestion risk of the remote video, and adjust the transmission priority of the remote video based on the congestion risk.

[0074] Among them, when using Boolean sensing technology to analyze the transmission coverage of nodes in the optimized node system, adjusting the node position to obtain the coverage rate of the Internet transmission area, and simulating and analyzing the data transmission energy consumption to determine the energy consumption of communication between nodes, a transmission control node can be generated according to the optimized node system, and the Euclidean distance of each node to the transmission control node can be determined, and the node coverage situation can be clarified based on the Euclidean distance and Boolean sensing technology; the node position is adjusted based on the node coverage situation, and the probability of the Internet transmission area covered by the node is determined. After the coverage probability meets the set value, the node position is determined and the node position adjustment operation is stopped; the energy consumption from any node to the transmission control node and the energy consumption required for any node to transmit to another node in the communication transmission process after adjustment are simulated and determined, and the energy consumption results are combined as the total energy consumption of communication between nodes.

[0075] It should be explained that Boolean sensing technology is used to evaluate the transmission coverage of nodes, and based on Euclidean distance and coverage probability, the node position is automatically adjusted to lock the node, and the energy consumption of any node during communication is simulated (including the path with the control node and the transmission between adjacent nodes). The total communication energy consumption is summarized as a quantitative indicator of the deployment quality, which can improve the coverage integrity of the network area, reduce the retransmission of data packets caused by "dead zones", and reduce energy consumption.

[0076] Among them, in the process of building a dynamic priority model based on the transmission waiting time and transmission memory of the remote video, and customizing the combined transmission avoidance strategy under the conditions of the priority collision avoidance mechanism and the position of the transmission control node, the scheduling delay in the Internet during the transmission process and the memory space occupied in the buffer zone can be defined as the transmission waiting time and transmission memory according to the remote video transmission requirements, and a regression equation is generated in combination with the comprehensive score of the node system quality as a dynamic priority model; based on the dynamic priority model, the transmission order of the remote video is preliminarily generated, and the resource occupancy on the same transmission path is identified, and the resource occupancy is combined with the control information of the transmission control node to determine the inclusion of key nodes in the transmission path; according to the judgment result, a node distribution matrix is ​​constructed as the priority item of the remote data transmission data, and the local path transfer strategy is called to customize the combined transmission avoidance strategy to determine the transmission path scheduling result.

[0077] It should be explained that the input features of the dynamic priority model are transmission waiting time + memory usage. The regression model is trained in combination with the quality score of the node system (such as bandwidth and processing power), priority is predicted, a preliminary transmission order is generated, path resource occupancy is identified (such as conflicts between multiple high-priority flows), a node distribution matrix is ​​constructed, and key congested nodes are extracted. At the same time, local path transfer strategies (such as hops, detours, and micro-path reconstruction) are used to generate combined avoidance strategies and output scheduling results to ensure that priority flows do not conflict, thereby achieving on-demand transmission and prioritizing important flows, reducing the collision and retry rate of video flows in the network.

[0078] S2. Use trajectory virtual analysis technology to identify security trends during transmission and detect intrusions into Internet networks.

[0079] Before using trajectory virtual analysis technology to identify the security situation during the transmission process and detect intrusions into the Internet network, it also includes regulating the transmission process of the remote video according to the remote video transmission control model.

[0080] In one embodiment, the remote video transmission process is regulated according to the remote video transmission control model, and the trajectory virtual analysis technology is used to identify the security situation during the transmission process. The detection of intrusion in the Internet network includes:

[0081] Based on the shortest video transmission path output by the video transmission control model, the node connection relationship of the remote video is regulated, the remote data transmission work is performed, and the transmission process data during the transmission process is obtained;

[0082] According to the transmission process data and the multi-tuple concept, a multi-tuple trajectory extraction model is constructed, the trajectory point distribution vector set of the transmission process data is divided, and the trajectory is used to virtually reconstruct the action trajectory extraction value;

[0083] The standard security situation analyzer is trained and transmitted using a multi-granularity filter to set the standard trajectory overlap value, transform the action trajectory extraction value into a dot multiplication operation, and calculate the boundary minimum rectangular trajectory overlap rate value;

[0084] The matrix transformation between the standard trajectory overlap value and the boundary minimum rectangular trajectory overlap rate value is judged by Euclidean distance, the degree of fluctuation of the transmission trajectory of remote data is clarified, the security situation status during the transmission process is evaluated, and the intrusion of the Internet network is detected based on the evaluation results.

[0085] Specifically, in the process of constructing a multi-tuple trajectory extraction model based on the transmission process data and multi-tuple concepts, dividing the trajectory point distribution vector set of the transmission process data, and using the trajectory to virtually reconstruct the motion trajectory extraction value, multi-tuple parameters can be extracted based on the transmission process data, and multi-tuple expressions can be defined according to the multi-tuple parameters. The critical value of the support vector machine for motion trajectory segmentation is determined, and the support vector machine is trained as the multi-tuple trajectory extraction model; the transmission passing points in the transmission process data are extracted using the multi-tuple trajectory extraction model as the trajectory point distribution vector set, and association rule mining is performed on the transmission passing points, and associated actions of different attributes are projected into the associated distribution area; within the associated distribution area, data fusion technology and trajectory virtual fusion strategy are used to analyze the transmission action characteristics, and vectorization quantization technology is used to perform block detection on the feature analysis results to obtain a subset of trajectory points as the motion trajectory extraction value.

[0086] It should be explained that during the detection of intrusions on the Internet network, the shortest path can be output based on the transmission requirements of the remote video and the network topology, based on the transmission control model. This shortest path ensures that the transmission delay of data from the source node to the target node is minimized, ensuring the real-time and smoothness of the video data. Based on the output of the shortest path, the node connection relationship in the remote video transmission path is adjusted to ensure that the optimal node is always selected during the data transmission process and congested nodes are avoided. The video data transmission work is performed according to the optimized node system, and detailed data of each transmission stage, such as delay, packet loss rate, bandwidth utilization, etc., is collected as input for subsequent security analysis. The selection of the shortest path reduces the transmission delay of the video data and ensures the smooth transmission of videos with high real-time requirements. At the same time, the dynamic adjustment of the connection between nodes avoids packet loss or delay caused by node overload or congestion.

[0087] When using the multi-tuple concept to construct a trajectory extraction model, multi-tuple parameters (such as timestamp, transmission rate, node information, etc.) can be extracted based on the data obtained during the transmission process. These parameters help to build a mathematical model of the transmission process. Based on the extracted parameters, a multi-tuple expression is constructed to represent the status of each transmission data point (such as transmission bandwidth, packet loss rate, transmission delay, etc.). The multi-tuple parameters are used to train the support vector machine (SVM) to determine the critical value for motion trajectory segmentation. The SVM model can effectively classify different events in the transmission process and help extract valid transmission trajectories.

[0088] Support vector machines are used to extract key nodes in the transmission process and generate a set of trajectory point distribution vectors. Vectors are important data that describe the network nodes and their status that the data flow passes through. Association rule mining is implemented on the trajectory points, and associated actions of different attributes (such as different packet loss patterns and delay change patterns) are projected into associated distribution areas, which can help identify potential abnormal patterns in the video stream. At the same time, through multi-tuple parameters and SVM training, the transmission mode and network status of the video stream can be dynamically identified to ensure the stability of the video data. Through trajectory point and association rule mining, abnormal behaviors and risk points in the transmission process can be extracted, providing an important basis for subsequent security situation analysis. The SVM model has a high classification ability for complex data sets and can help quickly identify key change features in the data.

[0089] In the process of using trajectory virtual reconstruction and action trajectory extraction value analysis, the trajectory point data obtained from the multi-tuple trajectory extraction model can be used to reconstruct the action trajectory of video data transmission through trajectory virtual reconstruction technology, and then the transmission action of each node can be reconstructed. According to the reconstructed trajectory, the action trajectory extraction value reflects the behavior pattern of the data stream during the transmission process. Through virtual reconstruction, the transmission status of video data in the network can be evaluated, and whether there is abnormal node behavior (such as a node suddenly stops responding, the data transmission rate fluctuates violently, etc.) can be identified. Then, through trajectory virtual reconstruction technology, the changing rules of data during transmission in the network can be captured, providing more fine-grained transmission process data for subsequent security situation analysis, which can better determine whether a network intrusion has occurred.

[0090] Using a multi-granularity filter to train a standard security situation analyzer involves processing extracted motion trajectory data using a multi-granularity filter. This analyzer can handle data at different levels and precision levels to identify potential intrusions. By calculating the overlap of extracted motion trajectory values, an overlap threshold for the standard trajectory is set. This threshold is used to assess whether the current transmission path exhibits abnormal fluctuations or non-compliant behavior. The trajectory overlap ratio is calculated using a dot product and the minimum bounding rectangle algorithm. The overlap ratio is used to assess the security situation during transmission. Furthermore, through multi-granularity filtering, security situation analysis can be performed on data at different precision levels, improving the accuracy of intrusion detection. The overlap value and trajectory overlap ratio of the standard trajectories can identify fluctuations and potential security risks during data transmission, enabling timely detection of intrusions. Transmission trajectory fluctuations and security situation are assessed using Euclidean distance. Trajectories with large fluctuations may indicate abnormal network behavior (such as intrusion attacks).

[0091] Furthermore, through technical means such as multi-group concepts, support vector machine (SVM) training, and trajectory virtual reconstruction, the transmission process of video data can be accurately monitored and analyzed, and safety hazards can be identified in a timely manner during the transmission process.

[0092] S3. Construct an intuitive fuzzy set of security situation based on the intrusion detection results, identify the security risks in the remote video transmission process according to the fuzzy set, and match the security risks with the treatment measures to obtain the risk treatment results.

[0093] In one embodiment, the process of identifying security risks is based on the previous stage of trajectory virtual analysis and situation assessment to obtain intrusion alarm data of each node in the network, including abnormal fluctuation frequency of nodes, abnormal energy consumption in the communication path, Euclidean distance deviation between trajectory points and standard trajectory, and the overlap rate of action trajectories below the threshold, etc., and the detection features are quantified into fuzzy factors, such as trajectory overlap rate. x 1 (low → fuzzy risk); communication energy consumption x 2 (High → Potential Attack); Node Response Delay x 3 (abnormal → attack or collapse); construct an intuitionistic fuzzy set definition for each factor: membership degree μ ( x ) indicates the degree to which a feature is considered “safe” or “abnormal”; non-membership α ( x ) indicates the degree to which something does not belong to the set; intuitive degree β ( x )=1- μ ( x )- α ( x ), is the expression of uncertainty, and the membership and non-membership of each node / path on each feature are combined into a vector, specifically IFS( x i )=( μ ( x i ), α ( x i ), β ( x i ))), the IFS vectors of all nodes are spliced ​​into the system's security situation intuitive fuzzy set, forming a multi-dimensional security situation map. The use of IFS structured features helps to graph and modularize the overall security status of the network, which is convenient for dynamic monitoring.

[0094] In the process of identifying security risks during remote video transmission, a set of rules can be constructed to determine which IFS combination constitutes a "security risk". Specifically: μ ( x1) <0.3 and α ( x 2) > 0.6 → The communication link is determined to be unstable; if β ( x 3) >0.4 → The node exhibits highly uncertain behavior and requires intervention. Fuzzy reasoning mechanisms (such as the Mamdani model) are used to match and reason with fuzzy vectors according to preset rules to identify potential hazards (for example, link congestion, data hijacking, and node crashes). Path segments or nodes identified as having hazards are labeled, and their hazard type, hazard level, and impact range are recorded. Fuzzy rule combinations can flexibly respond to diverse transmission anomalies. Rule-based judgments allow each "hazard" to be traced back to its inference source, facilitating subsequent auditing and optimization.

[0095] In the process of matching treatment measures to obtain hidden danger treatment results, a hidden danger-treatment measure mapping table needs to be established. Each hidden danger type has a corresponding treatment measure preset, for example:

[0096] Network congestion → triggers the path replanning algorithm;

[0097] Abnormal node fluctuations → Enhanced encryption and isolated communications;

[0098] Multi-path data duplication anomaly → Enable behavior auditing and verify source.

[0099] Taking into account the severity of hidden dangers (light / medium / severe) and specific scenarios (edge ​​nodes, central nodes, etc.), the optimal processing method is selected, and a weight-based fuzzy matching strategy is used to obtain a set of processing measures, prioritize them, execute processing, and generate processing reports, such as modifying the transmission path, adding redundant encryption, temporary node interruption, load transfer, etc. Through the precise mapping of hidden danger characteristics and processing strategies, a "point-to-point" customized response is achieved to avoid the waste of resources caused by generalized intervention. Then, through intuitive fuzzy theory, the "fuzzy but potentially serious" network risks are quantitatively analyzed, and the specific hidden danger types are quickly identified. Combined with rule and strategy mapping, the processing measures are accurately output to realize the intelligent, secure, and adaptive response mechanism of the remote video transmission system.

[0100] See also Figure 2 The present invention also provides an Internet-based remote video transmission security risk processing system, the system comprising:

[0101] Transmission control model construction module 1 is used to set the initial transmission control indicators of remote video, deploy the transmission control nodes of remote video in the Internet using multi-stage node control technology, and build the remote video transmission control model based on the deployment results;

[0102] Network intrusion detection module 2 is used to control the transmission process of remote video according to the remote video transmission control model, and use trajectory virtual analysis technology to identify the security situation during the transmission process and detect intrusions on the Internet network;

[0103] The security risk analysis and processing module 3 is used to construct an intuitive fuzzy set of security situation based on the intrusion detection results, identify security risks in the remote video transmission process according to the fuzzy set, and match the security risks with treatment measures to obtain the risk treatment results.

[0104] Those skilled in the art will appreciate that the units and algorithm steps of the various examples described in conjunction with this embodiment can be implemented in electronic hardware or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0105] Although the above describes the specific embodiments of the present invention in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art on the basis of the technical solution of the present invention without any creative work are still within the scope of protection of the present invention.

Claims

1. A method for handling security risks of remote video transmission based on the Internet, characterized in that: The treatment method includes: Set the initial transmission control indicators for remote video, use multi-stage node control technology to deploy remote video transmission control nodes on the Internet, and build a remote video transmission control model based on the deployment results; According to the remote video transmission control model, the transmission process of remote video is controlled, and the trajectory virtual analysis technology is used to identify the security situation during the transmission process and detect the intrusion of the Internet network; specifically: based on the transmission process data and the multi-tuple concept, a multi-tuple trajectory extraction model is constructed, and the transmission points in the transmission process data are extracted as the trajectory point distribution vector set, and the transmission points are mined according to the association rules, and the associated actions of different attributes are projected into the associated distribution area; within the associated distribution area, the data fusion technology and the trajectory virtual fusion strategy are used to analyze the transmission action characteristics, and the vectorization quantization technology is used to perform block detection on the feature analysis results to obtain the trajectory point subset as the action trajectory extraction value; the transmission standard security situation analyzer is trained using a multi-granularity filter to set the standard trajectory overlap value, the action trajectory extraction value is changed into a point multiplication operation, and the boundary minimum rectangular trajectory overlap rate value is calculated; the matrix transformation between the standard trajectory overlap value and the boundary minimum rectangular trajectory overlap rate value is judged by the Euclidean distance, the degree of fluctuation of the transmission trajectory of the remote data is clarified, the security situation status during the transmission process is evaluated, and the intrusion of the Internet network is detected based on the evaluation results; Based on the intrusion detection results, an intuitive fuzzy set of security situation is constructed. The security risks in the remote video transmission process are identified according to the fuzzy set. The security risks are matched with the treatment measures to obtain the treatment results. The multi-order node control technology is used to deploy transmission control nodes for remote videos on the Internet. Specifically, the following steps are performed: transmission parameters are defined according to the data status of the remote video, initial transmission control indicators are set using the transmission parameters, and a node system including a load threshold and a packet loss rate upper limit is generated based on the initial transmission control indicators; the minimum violation sequence corresponding to the node system is analyzed, the nodes in the minimum violation sequence are suppressed and updated according to the suppression priority score, and the node system is optimized using the minimum violation sequence; the transmission coverage of the nodes in the optimized node system is analyzed using Boolean perception technology, the node positions are adjusted to obtain the coverage of the Internet transmission area, and the data transmission energy consumption is simulated and analyzed to determine the communication energy consumption between nodes. The virtual force borne by the node during the transmission process is analyzed based on the communication energy consumption, and the real value of the virtual force is estimated to obtain a virtual force matrix, the deployment position of the node is generated, and the transmission control node is deployed on the Internet.

2. The method for handling security risks of remote video transmission based on the Internet according to claim 1, characterized in that: The setting of the remote video initial transmission control index, the deployment of the remote video transmission control node on the Internet using a multi-stage node control technology, and the construction of the remote video transmission control model based on the deployment result further include: Deploy transmission control nodes on the Internet based on the optimized node system and multi-stage node control technology, and analyze transmission priorities based on the location of the transmission control nodes and parameter information of the remote video; The remote video is encrypted in batches, and a remote video transmission control model is built based on the encryption results and transmission priority. The transmission node of the remote video is output and the shortest video transmission path is determined.

3. The method for handling security risks of remote video transmission based on the Internet according to claim 2, characterized in that: The analyzing the minimum violation sequence corresponding to the node system, suppressing the nodes in the minimum violation sequence according to the suppression priority score, and updating the minimum violation sequence, and optimizing the node system using the minimum violation sequence include: Find the nodes in the node system whose load value is greater than the load threshold and whose packet loss rate is greater than the packet loss rate upper limit as the minimum violation sequence, and use the preset frequent sequence to find the frequent sequence set within the minimum violation sequence; Use the frequent sequence set to construct a minimum infeasible sequence tree, select any node from the minimum infeasible sequence tree, and determine the loss caused by discarding the node. Calculate the suppression score of any node based on the loss result. Obtain the suppression score of each node in the minimum infeasible sequence tree, determine to discard the nodes that are greater than the loss requirement based on the suppression score, and update the minimum violation sequence based on the set result, and merge the updated minimum violation sequence into the node system; A transmission tree is established based on the node system. The Laplace mechanism is used to add noise to each node according to the privacy budget of the transmission tree, thereby increasing the privacy function of the node during transmission and determining the final node system.

4. The method for handling security risks of remote video transmission based on the Internet according to claim 3, characterized in that: The transmission tree is established based on the node system, and noise is added to each node using the Laplace mechanism according to the privacy budget of the transmission tree to increase the privacy function of the node during transmission. The final node system is determined as follows: Based on the merged node system, several node distribution data sets are randomly generated. The terminal connection paths between the nodes in the node distribution data sets are obtained. The terminal connection paths corresponding to the minimum number of fluctuations between any two nodes in each path are selected as the first group. Pick out the sequence with the minimum cost from the node distribution data set containing any two nodes mentioned above, and take the most frequent nodes in the node distribution data set where the sequence is located as the second group, and repeat the selection until all nodes are iteratively selected into the transmission tree; Analyze the total length of the terminal connection paths of nodes in the transmission tree, match the privacy budget of the transmission tree based on the total length, and use the Laplace mechanism to equally divide the privacy budget among each node; According to the privacy budget division results, noise is added to the nodes to increase the privacy function of the nodes during transmission, and the final node system is determined based on the addition results and the node position in the transmission tree.

5. The method for handling security risks of remote video transmission based on the Internet according to claim 4, characterized in that: The method further includes deploying a transmission control node on the Internet based on the optimized node system and multi-stage node control technology, and analyzing the transmission priority according to the location of the transmission control node and parameter information of the remote video. A dynamic priority model is built based on the transmission latency and transmission memory of remote videos, and a combined transmission avoidance strategy is customized based on the priority collision avoidance mechanism and the location of the transmission control node. The Internet transmission channel is divided into congestion detection areas, and the transmission congestion risk of remote videos is measured in combination with a combined transmission avoidance strategy. The transmission priority of remote videos is adjusted based on the congestion risk.

6. The method for handling security risks of remote video transmission based on the Internet according to claim 5, characterized in that: The method of using Boolean sensing technology to analyze the transmission coverage of nodes in the optimized node system, adjusting the node positions to obtain the coverage of the Internet transmission area, and simulating and analyzing the data transmission energy consumption to determine the energy consumption of communication between nodes includes: Generate transmission control nodes based on the optimized node system, determine the Euclidean distance from each node to the transmission control node, and determine the node coverage based on Euclidean distance and Boolean perception technology; Adjust the node position based on the node coverage situation and determine the probability of the node covering the Internet transmission area. After the coverage probability meets the set value, the node position is determined and the node position adjustment operation is stopped; The simulation determines the energy consumption from any node to the transmission control node and the energy consumption required for any node to transmit to another node during the adjusted communication transmission process, and combines the energy consumption results as the total energy consumption of inter-node communication.

7. The method for handling security risks of remote video transmission based on the Internet according to claim 6, characterized in that: The dynamic priority model is constructed based on the transmission waiting time and transmission memory of the remote video, and the customized combined transmission avoidance strategy is constructed under the priority collision avoidance mechanism and the position conditions of the transmission control node. The strategy includes: Based on the requirements of remote video transmission, the scheduling delay within the Internet and the memory space occupied by the buffer during the transmission process are defined as the transmission waiting time and transmission memory. A regression equation is generated based on the comprehensive score of the node system quality as a dynamic priority model. Based on the dynamic priority model, the transmission order of remote videos is initially generated, and the resource occupancy on the same transmission path is identified. The resource occupancy is combined with the control information of the transmission control node to determine the inclusion of key nodes in the transmission path; According to the judgment results, a node distribution matrix is ​​constructed as the priority of remote data transmission data, and the local path transfer strategy is called to customize the combined transmission avoidance strategy to determine the transmission path scheduling result.

8. The method for handling security risks of remote video transmission based on the Internet according to claim 7, characterized in that: The method of constructing a multi-tuple trajectory extraction model based on the transmission process data and the multi-tuple concept and extracting the transmission passing points in the transmission process data as the trajectory point distribution vector set includes: The shortest video transmission path output by the video transmission control model is used to regulate the node connection relationship of the remote video, perform remote data transmission, and obtain the transmission process data during the transmission process.

9. A system for handling security risks of remote video transmission based on the Internet, used to implement the method for handling security risks of remote video transmission based on the Internet according to any one of claims 1 to 8, characterized in that: The system includes: The transmission control model construction module is used to set the initial transmission control indicators of remote video, deploy the transmission control nodes of remote video on the Internet using multi-stage node control technology, and build the remote video transmission control model based on the deployment results; The network intrusion detection module is used to control the transmission process of remote video according to the remote video transmission control model, and use trajectory virtual analysis technology to identify the security situation during the transmission process and detect intrusions on the Internet network; The security hazard analysis and processing module is used to construct an intuitive fuzzy set of security situation based on the intrusion detection results, identify security hazards in the remote video transmission process according to the fuzzy set, and match the security hazards with treatment measures to obtain the hazard treatment results.

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