Method for accelerating video image scheduling based on airport multi-level video networking architecture

Abstract

The present invention discloses a method for accelerating the scheduling of video images based on a multi-level video networking architecture of an airport, comprising: dividing a video monitoring system into several levels based on the actual layout and security monitoring requirements of the airport to construct a multi-level video networking architecture; analyzing and classifying video streams based on the multi-level video networking architecture, classifying the video streams into different categories of video streams according to characteristics and importance, and then optimizing the video streams of different categories; dynamically adjusting the transmission order and rate of the optimized video streams based on a real-time scheduling algorithm according to the priority and time slice allocation of the video streams; setting a distributed cache mechanism in each level to ensure that data can be quickly obtained from the cache when needed by temporarily storing high-frequency access or important video data; and implementing a load balancing strategy for dynamically allocating video processing resources based on the distributed cache mechanism, and dynamically adjusting and allocating video processing resources according to the actual load conditions of each level.

Description

Technical Field

[0001] The present invention relates to the field of video monitoring technology, and in particular to a method for accelerating the scheduling of video images based on a multi-level video networking architecture of an airport. Background Art

[0002] With the rapid development of the global aviation industry, airports, as transportation hubs, have increasing security and management requirements. Video surveillance systems have become an important means of airport security control and are widely used in key areas such as terminals, boarding gates, baggage claim areas, and aprons. These monitoring systems collect video data in real time through a large number of cameras to ensure that potential security threats can be discovered and responded to in a timely manner. However, with the expansion of airport scale and the increase in security requirements, traditional video surveillance systems face many technical challenges and are difficult to meet the efficient and safe management requirements of modern airports.

[0003] Traditional airport video surveillance systems usually adopt a centralized architecture. As the number of cameras increases, the management of the system becomes more and more complicated. When faced with a large number of cameras, traditional scheduling methods are prone to low management efficiency and insufficient scalability, making it difficult to adapt to the growing monitoring needs. Different areas and time periods of the airport have different monitoring requirements. For example, boarding gates require higher monitoring frequencies during peak hours, while some remote areas have lower requirements for real-time performance. Existing systems are difficult to flexibly schedule according to these differentiated requirements, resulting in resource waste and monitoring blind spots. When faced with multi-level video stream processing, traditional methods usually find it difficult to ensure the real-time and priority processing of video data in key areas, which results in the inability to transmit and process important monitoring data in an emergency, affecting the airport's safety prevention and control capabilities. Since the monitoring system needs to transmit a large amount of video data in real time, traditional methods are prone to network transmission delays when faced with complex network environments, affecting the access speed of video data. This poses a huge challenge to the requirements of real-time monitoring, especially when it involves multi-level monitoring across regions, where network delays seriously affect the monitoring effect. As the scale of monitoring expands, the amount of tasks handled by the system increases significantly. Traditional video scheduling methods often cannot maintain the overall processing efficiency and stability of the system, and are prone to system bottlenecks or crashes, affecting the reliability and continuity of the monitoring system.

[0004] In summary, the existing airport video surveillance scheduling methods have the problems of low scheduling efficiency and insufficient real-time performance when facing such large-scale, multi-level video stream processing, and cannot meet the needs of efficient and accurate security monitoring in modern airports. Therefore, there is an urgent need for an accelerated scheduling method based on a multi-level video networking architecture. Summary of the invention

[0005] The present invention provides a technical method for accelerating the scheduling of video images based on a multi-level video networking architecture of an airport to solve the above-mentioned problems existing in the prior art.

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

[0007] The method for accelerating the scheduling of video images based on the airport multi-level video networking architecture includes:

[0008] S101: Based on the actual layout and security monitoring requirements of the airport, the video surveillance system is divided into several levels to build a multi-level video networking architecture;

[0009] S102: Analyze and classify video streams based on a multi-level video networking architecture, classify the video streams into different categories of video streams according to characteristics and importance, and then optimize the different categories of video streams;

[0010] S103: Based on the real-time scheduling algorithm, dynamically adjust the transmission order and rate of the optimized video stream according to the priority of the video stream and the time slice allocation;

[0011] S104: Setting a distributed cache mechanism at each level to temporarily store frequently accessed or important video data to ensure that data can be quickly obtained from the cache when needed;

[0012] S105: Based on the distributed cache mechanism, a load balancing strategy for dynamically allocating video processing resources is implemented, and video processing resources are dynamically adjusted and allocated according to the actual load conditions of each level.

[0013] Wherein, step S101 includes:

[0014] S1011: Acquire actual layout data and security monitoring demand data of the airport, and identify and obtain video monitoring system hierarchical data from the layout data and the demand data, wherein the video monitoring system hierarchical data includes monitoring hierarchical parameters and hierarchical configuration parameters;

[0015] S1012: Performing hierarchical processing on hierarchical data of the video surveillance system according to monitoring hierarchical parameters and hierarchical configuration parameters to obtain a multi-level video networking architecture.

[0016] Wherein, step S102 includes:

[0017] S1021: receiving video streams from multiple video sources through a multi-level video networking architecture;

[0018] S1022: Analyze the video stream and extract characteristic parameters of the video stream, where the characteristic parameters include resolution, frame rate, color information, and motion detection result;

[0019] S1023: based on the characteristic parameters and the importance of the video streams, the video streams are divided into different categories of video streams, where the different categories of video streams include a real-time monitoring stream, a playback stream, and a low priority stream;

[0020] S1024: Execute corresponding optimization processing operations for different categories of video streams, wherein video encoding parameters are adjusted for real-time monitoring streams, compression algorithms are used to compress playback streams, and video quality or frame rate is reduced for low-priority streams.

[0021] The transmission order and rate of the optimized video stream are dynamically adjusted, including:

[0022] Obtaining priority information of each video stream, where the priority information is determined based on the importance and real-time requirements of the video stream;

[0023] Determine the currently available time slices and allocate initial time slices to each video stream. The time slice allocation is calculated based on the priority of the video stream and the current status of network resources.

[0024] Based on the allocated time slices and the priority of the video streams, the initial transmission order and rate of each video stream are planned, and the transmission order and rate are optimized and adjusted according to the real-time scheduling algorithm;

[0025] Dynamically monitor the usage of network resources and obtain the real-time transmission status of each video stream;

[0026] Based on the results of real-time monitoring, evaluate the transmission efficiency of the current video stream and determine whether the transmission order and rate need to be adjusted;

[0027] If adjustment is needed, return to the optimization steps of execution priority, time slice allocation and transmission order, and re-plan the transmission order and rate of each video stream;

[0028] Repeat the monitoring, evaluation and adjustment steps until all video streams reach the optimal transmission state under the current network resources;

[0029] After the transmission order and rate of all video streams are determined, the unallocated time slices are reallocated according to the real-time scheduling algorithm to ensure balanced utilization of network resources;

[0030] During the video stream transmission process, if a new video stream is added or the priority of an existing video stream changes, the time slice allocation and transmission sequence planning steps are re-executed to dynamically adjust the transmission strategy of the overall video stream.

[0031] Among them, a distributed cache mechanism is set up at each level, including:

[0032] Obtain access frequency and importance information of each video data, and determine the data that needs to be cached based on the information;

[0033] Initialize a distributed cache mechanism at each level of video data transmission. The distributed cache mechanism includes multiple cache nodes distributed on network nodes at different levels.

[0034] Temporarily store frequently accessed video data and high-importance video data in each cache node, and dynamically adjust the cache strategy based on the access frequency and importance of the video data;

[0035] When a video data request is issued, the cache node at the corresponding level is queried first to obtain the temporary data in the cache node;

[0036] If the requested video data is not found in the cache node of the current level, the cache nodes of higher levels are queried step by step until the requested video data is found;

[0037] Return the queried video data to the requester, and update or add the requested video data in the lower-level cache nodes to optimize the efficiency of subsequent access;

[0038] Dynamically monitor the storage status of each cache node, and adjust the cache content based on the real-time access frequency and importance of video data, removing no longer needed low-frequency access or low-importance video data;

[0039] Trigger the global optimization process of the cache mechanism regularly or according to preset conditions, reallocate cache resources at each level, and ensure the optimal cache strategy for video data;

[0040] When the storage space of the cache node reaches the preset threshold, based on the access frequency and importance of the video data, high-frequency access and high-importance video data are retained first, and low-frequency access and low-importance video data are cleared or moved down to lower-level cache nodes.

[0041] The video processing resources are dynamically adjusted and allocated, including:

[0042] Obtain the current load status information of each layer and determine the actual processing requirements of each layer based on the load status;

[0043] Initialize a distributed cache mechanism in a video processing system, the cache mechanism including a plurality of distributed nodes;

[0044] Dynamically adjust the video processing resources of each distributed node based on the actual load of each layer, including increasing or decreasing the allocation of processing resources;

[0045] When the load of a certain level reaches the preset threshold, the video processing resources are redistributed among the distributed nodes of this level first to ensure load balancing;

[0046] If load balancing cannot be achieved through resource redistribution at the current level, video processing resources are requested or released from the upper or lower levels to dynamically adjust resource allocation across levels;

[0047] Continuously monitor the load status of each distributed node and update the allocation strategy of video processing resources based on real-time monitoring data;

[0048] When the overall system load changes, the global load balancing strategy is triggered to reallocate video processing resources at each level to optimize global processing efficiency.

[0049] Based on the allocation results of video processing resources, the video data in the distributed cache is updated to match the new resource configuration to ensure the effectiveness of caches at all levels.

[0050] Among them, a multi-level video networking architecture is obtained, including:

[0051] Get the level classification type from the monitoring level parameters, and get the level device parameter type from the level configuration parameters;

[0052] Obtain the level equipment parameter type associated with each level classification type as a monitoring level data group;

[0053] After sorting each monitoring level data group according to the level classification type, the data corresponding to each level classification type and level device parameter type is stored in the corresponding monitoring level data group to obtain a multi-level video networking architecture.

[0054] The video streams are divided into different categories, including:

[0055] Based on the characteristic parameters, the application scenarios of the video stream are preliminarily identified and classified into different importance levels;

[0056] Combining the characteristic parameters of video streams with the importance of application scenarios, video streams are subdivided into categories such as real-time monitoring streams, playback streams, and low-priority streams.

[0057] The initial transmission order and rate of each video stream are planned, including:

[0058] Based on the allocated time slice and the priority of the video stream, identifying and obtaining priority data and time slice allocation data of the current video stream, wherein the priority data includes a priority parameter and a time slice parameter of the video stream;

[0059] According to the priority parameter of the video stream and the time slice parameter, the priority data and the time slice allocation data of the current video stream are processed in layers to obtain the data to be planned for transmission;

[0060] Obtain real-time scheduling algorithm data, and obtain scheduling standard data from the real-time scheduling algorithm data;

[0061] After optimizing the scheduling standard data, the data scheduling vector is constructed, and the scheduling related data is obtained from the real-time scheduling algorithm data;

[0062] After clustering the data scheduling vectors according to the scheduling related data, a transmission data group to be optimized is obtained, and after scheduling training of the initial model with the transmission data group to be optimized, a real-time scheduling optimization model is obtained;

[0063] Input the transmission data to be planned into the preset real-time scheduling optimization model one by one to obtain the video stream transmission benchmark, which includes:

[0064] Inputting the first transmission data to be planned into the real-time scheduling optimization model, performing priority matching on the first transmission data to be planned in the real-time scheduling optimization model, and outputting a first transmission order and rate according to the matching result;

[0065] Starting from the second transmission data to be planned, the previous transmission order and rate result are input into the real-time scheduling optimization model to obtain the corresponding optimized transmission result, and the first transmission order and rate result and all optimized transmission results are integrated to obtain the video stream transmission benchmark;

[0066] The transmission control data is generated according to the video stream transmission benchmark and the priority data of the current video stream, and the transmission control data is input into the real-time scheduling optimization model to obtain the final video stream transmission order and rate.

[0067] The data to be planned for transmission is obtained, including:

[0068] Get the time slice type from the time slice parameter, and get the priority type from the priority parameter;

[0069] Obtain the priority type associated with each time slice type as the data group to be planned;

[0070] After sorting each data group to be planned according to the time slice type, the data corresponding to each time slice type and priority type is stored in the corresponding data group to be planned, and the transmission data to be planned is obtained.

[0071] Compared with the prior art, the present invention has the following advantages:

[0072] The method for accelerating the scheduling of video images based on the airport's multi-level video networking architecture includes: dividing the video monitoring system into several levels based on the actual layout of the airport and security monitoring requirements to build a multi-level video networking architecture; analyzing and classifying video streams based on the multi-level video networking architecture, classifying the video streams into different categories of video streams according to characteristics and importance, and then optimizing the video streams of different categories; dynamically adjusting the transmission order and rate of the optimized video streams based on the real-time scheduling algorithm according to the priority and time slice allocation of the video streams; setting a distributed cache mechanism in each level to ensure that data can be quickly obtained from the cache when needed by temporarily storing high-frequency access or important video data; and implementing a load balancing strategy for dynamically allocating video processing resources based on the distributed cache mechanism to dynamically adjust and allocate video processing resources based on the actual load conditions of each level. The design of the multi-level video networking architecture realizes the efficient management and scalability of the video surveillance system. The video stream classification and optimization method can provide differentiated services according to different needs. The real-time scheduling algorithm ensures the real-time and priority of important video surveillance data. The distributed caching mechanism effectively reduces network transmission delay and improves data access speed. The load balancing strategy improves the overall processing efficiency and stability of the system.

[0073] Other features and advantages of the present invention will be set forth in the description which follows, and in part will be apparent from the description, or may be learned by practice of the present invention.

[0074] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0075] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0076] Figure 1 It is a flow chart of a method for accelerating video image scheduling based on a multi-level video networking architecture of an airport in an embodiment of the present invention;

[0077] Figure 2 A flowchart of constructing a multi-level video networking architecture in an embodiment of the present invention;

[0078] Figure 3 The figure is a flow chart of optimizing the processing of different types of video streams in an embodiment of the present invention. DETAILED DESCRIPTION

[0079] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0080] The embodiment of the present invention provides a method for accelerating the scheduling of video images based on a multi-level video networking architecture of an airport, comprising:

[0081] S101: Based on the actual layout and security monitoring requirements of the airport, the video surveillance system is divided into several levels to build a multi-level video networking architecture;

[0082] S102: Analyze and classify video streams based on a multi-level video networking architecture, classify the video streams into different categories of video streams according to characteristics and importance, and then optimize the different categories of video streams;

[0083] S103: Based on the real-time scheduling algorithm, dynamically adjust the transmission order and rate of the optimized video stream according to the priority of the video stream and the time slice allocation;

[0084] S104: Setting a distributed cache mechanism at each level to temporarily store frequently accessed or important video data to ensure that data can be quickly obtained from the cache when needed;

[0085] S105: Based on the distributed cache mechanism, a load balancing strategy for dynamically allocating video processing resources is implemented, and video processing resources are dynamically adjusted and allocated according to the actual load conditions of each level.

[0086] The working principle of the above technical scheme is as follows: according to the actual layout and security monitoring requirements of the airport, the video surveillance system is divided into multiple levels, each level contains several cameras and video processing units, forming a distributed video surveillance network. This multi-level architecture can better manage and expand the video surveillance system, and is also conducive to the subsequent video stream scheduling and processing. A video stream classification and optimization method is proposed for the massive video streams generated in the airport video surveillance system. First, according to the characteristics and importance of the video streams, they are classified into real-time monitoring streams, playback streams, etc. Then, different categories of video streams are optimized, such as by adjusting video encoding parameters, compression algorithms, etc., to reduce the amount of data transmitted over the network and improve the transmission efficiency of the video streams. The real-time scheduling algorithm based on priority and time slice dynamically adjusts the transmission order and rate of the video stream according to the priority and time slice allocation of the video stream. Specifically, the real-time monitoring stream is given a higher priority due to its high real-time requirements, ensuring that it can be transmitted and processed first when network resources are limited. At the same time, through a reasonable time slice allocation strategy, a certain video stream is prevented from occupying network resources for a long time, thereby achieving balanced transmission of the video stream. In order to reduce network transmission delay and improve the access speed of video data, a distributed cache mechanism is set up at each level. This mechanism temporarily stores high-frequency access or important video data, so that data can be quickly obtained from the cache when needed, without downloading from the remote server through the network. This can not only reduce network transmission delay, but also reduce the load pressure on the server. In order to achieve load balancing between levels and improve the processing capacity and stability of the overall system, a load balancing strategy for dynamically allocating video processing resources is adopted. This strategy dynamically allocates and adjusts video processing resources according to the actual load of each level to ensure that the processing capacity of each level matches its load. This can avoid performance bottlenecks or failures at certain levels due to excessive load, thereby improving the stability and reliability of the entire video surveillance system.

[0087] The beneficial effects of the above technical solution are: the design of the multi-level video networking architecture realizes the efficient management and scalability of the video surveillance system; the video stream classification and optimization method can provide differentiated services according to different needs; the real-time scheduling algorithm ensures the real-time and priority of important video surveillance data; the distributed caching mechanism effectively reduces network transmission delay and improves data access speed; the load balancing strategy improves the overall processing efficiency and stability of the system.

[0088] In another embodiment, step S101 includes:

[0089] S1011: Acquire actual layout data and security monitoring demand data of the airport, and identify and obtain video monitoring system hierarchical data from the layout data and the demand data, wherein the video monitoring system hierarchical data includes monitoring hierarchical parameters and hierarchical configuration parameters;

[0090] S1012: Performing hierarchical processing on hierarchical data of the video surveillance system according to monitoring hierarchical parameters and hierarchical configuration parameters to obtain a multi-level video networking architecture.

[0091] The working principle of the above technical solution is as follows: First, relevant data is obtained from the actual layout of the airport, including the physical location of each area, personnel flow, location of key facilities, etc. Then, demand data is obtained from the security monitoring demand analysis, which usually includes the security level requirements of each area, monitoring coverage, camera distribution density, etc. By analyzing the layout data and demand data, the hierarchical data of the video surveillance system is identified and generated. The hierarchical data includes:

[0092] Monitoring layer parameters: These parameters determine the different layers of the monitoring system, such as core layer, regional layer, peripheral layer, etc. Layer configuration parameters: These parameters define the specific configuration in each layer, such as the number of cameras, transmission bandwidth, storage requirements, etc.

[0093] According to the identified monitoring level parameters, the entire video surveillance system is divided into multiple levels, each level corresponds to a specific area or function in the airport. For example, the core layer may cover the central control room and key facilities, the regional layer covers various waiting areas, and the peripheral layer covers the airport entrance and parking lot. According to the level configuration parameters, each level is configured in detail, including determining the specific layout of cameras in each level, the routing design of video transmission, the allocation of storage strategies, etc. After completing the level division and configuration, a multi-level video networking architecture is finally formed, which can effectively coordinate the flow of video data between levels, ensure that monitoring information can be smoothly transmitted from the bottom to the top, and be uniformly scheduled and managed when necessary.

[0094] The beneficial effects of the above technical solution are: through precise hierarchical division and configuration, it is ensured that each area of ​​the airport is properly monitored, especially the key areas can get higher monitoring priority to ensure safety without blind spots; the multi-level architecture enables monitoring resources to be allocated in a targeted manner according to the needs of different areas, avoiding waste of resources. For example, high-traffic areas may require more cameras and bandwidth, while low-traffic areas can be configured with fewer. The multi-level architecture has strong flexibility and can adapt to changes in airport layout or security needs. If there are new areas or changes in needs in the future, expansion can be achieved by adjusting the configuration parameters of the relevant levels. Through hierarchical management, airport security personnel can monitor and handle the situation in each area more efficiently, especially in the event of an emergency, and can quickly locate and respond to problems in key areas. Video data transmission in the hierarchical architecture can be optimized through hierarchical routing, reducing network congestion, ensuring that key monitoring data can be transmitted first, and improving the real-time performance of the system.

[0095] In another embodiment, step S102 includes:

[0096] S1021: receiving video streams from multiple video sources through a multi-level video networking architecture;

[0097] S1022: Analyze the video stream and extract characteristic parameters of the video stream, where the characteristic parameters include resolution, frame rate, color information, and motion detection result;

[0098] S1023: based on the characteristic parameters and the importance of the video streams, the video streams are divided into different categories of video streams, where the different categories of video streams include a real-time monitoring stream, a playback stream, and a low priority stream;

[0099] S1024: Execute corresponding optimization processing operations for different categories of video streams, wherein video encoding parameters are adjusted for real-time monitoring streams, compression algorithms are used to compress playback streams, and video quality or frame rate is reduced for low-priority streams.

[0100] The working principle of the above technical solution is as follows: the airport's multi-level video networking architecture can receive video streams from various monitoring points, including waiting areas, boarding gates, security checkpoints, parking lots, etc. The camera at each monitoring point transmits the video stream to the central monitoring system through the network. The video stream is processed by the multi-level architecture to ensure that it can be effectively integrated and transmitted to the subsequent analysis module. The system analyzes the received video stream and extracts key information, mainly including:

[0101] Resolution: refers to the clarity of the video image, usually expressed in pixels (e.g. 1920x1080).

[0102] Frame Rate: refers to the number of frames displayed per second, usually measured in FPS (Frames Per Second).

[0103] Color information: including color space, brightness, contrast, etc., which helps to identify anomalies or important events.

[0104] Motion detection results: By analyzing the motion features in the video, dynamic objects in the video, such as people and vehicles, are identified.

[0105] Based on the extracted characteristic parameters, the system classifies the video streams. The classification criteria mainly consider the importance and purpose of the video streams:

[0106] Real-time monitoring streams: These video streams usually come from critical areas or areas with higher security requirements during a specific time period. They need to be monitored in real time and have a higher priority.

[0107] Playback streams: These video streams are usually used for post-analysis and therefore do not require real-time processing. They have a relatively low priority and are usually used for video archiving.

[0108] Low priority streams: These video streams may come from secondary areas within the surveillance coverage area or areas that are inactive during a specific time period and can be processed with a lower priority.

[0109] According to different types of video streams, the system performs corresponding optimization processing operations:

[0110] Processing of real-time monitoring streams: Due to the need for real-time monitoring, the system will adjust the encoding parameters of these video streams to ensure that the video quality and frame rate are high enough to support real-time analysis and response, for example, increasing the video bitrate or using a more efficient video encoding format (such as H.265) to ensure smoothness and clarity. Processing of playback streams: The playback stream can be compressed using a compression algorithm to save storage space. For example, a video compression algorithm such as H.264 or MPEG-4 is used to compress the video while maintaining sufficient image quality for subsequent viewing. Processing of low-priority streams: For these video streams, the system can reduce their video quality or frame rate. For example, reduce the resolution or reduce the frame rate to reduce the use of network bandwidth and storage resources.

[0111] The beneficial effects of the above technical solution are: by optimizing the real-time monitoring stream, it is ensured that the video of the key area can be transmitted to the monitoring center in a timely and clear manner, helping security personnel to quickly respond to potential threats or emergencies. The compression and degradation of the playback stream and low-priority stream effectively reduces the storage space occupied and the pressure of network transmission, so that system resources can be used more efficiently. The system classifies and processes the video stream according to its importance, and can reasonably allocate computing and storage resources without sacrificing key monitoring needs to avoid resource waste. The system can dynamically adjust the classification and processing of video streams according to the needs of different scenes or time periods, and adapt to changes in the airport environment and fluctuations in security needs. Through the effective compression processing of the playback stream, not only the image quality requirements of post-analysis are guaranteed, but also the efficiency of video storage is improved, which provides a guarantee for subsequent investigation and evidence collection.

[0112] In another embodiment, dynamically adjusting the transmission order and rate of the optimized video stream includes:

[0113] Obtaining priority information of each video stream, where the priority information is determined based on the importance and real-time requirements of the video stream;

[0114] Determine the currently available time slices and allocate initial time slices to each video stream. The time slice allocation is calculated based on the priority of the video stream and the current status of network resources.

[0115] Based on the allocated time slices and the priority of the video streams, the initial transmission order and rate of each video stream are planned, and the transmission order and rate are optimized and adjusted according to the real-time scheduling algorithm;

[0116] Dynamically monitor the usage of network resources and obtain the real-time transmission status of each video stream;

[0117] Based on the results of real-time monitoring, evaluate the transmission efficiency of the current video stream and determine whether the transmission order and rate need to be adjusted;

[0118] If adjustment is needed, return to the optimization steps of execution priority, time slice allocation and transmission order, and re-plan the transmission order and rate of each video stream;

[0119] Repeat the monitoring, evaluation and adjustment steps until all video streams reach the optimal transmission state under the current network resources;

[0120] After the transmission order and rate of all video streams are determined, the unallocated time slices are reallocated according to the real-time scheduling algorithm to ensure balanced utilization of network resources;

[0121] During the video stream transmission process, if a new video stream is added or the priority of an existing video stream changes, the time slice allocation and transmission sequence planning steps are re-executed to dynamically adjust the transmission strategy of the overall video stream.

[0122] The working principle of the above technical solution is as follows: First, the system will prioritize all video streams. The priority is determined based on the importance and real-time requirements of the video stream. For example, the video stream of the camera monitoring the terminal entrance has a higher priority because of its high real-time and security requirements. Priority is usually expressed as a weight value [ i express,[ i The higher it is, the higher the priority of video stream i.

[0123] The system evaluates the current availability of network resources and determines the time slices available for video streaming. A time slice refers to the available bandwidth of network resources in a specific period of time.

[0124] According to the priority P of the video stream i Based on the current status of network resources, the system uses a weighted allocation algorithm to allocate an initial time slice T for each video stream. i , where T i With P i Directly proportional.

[0125] After determining the time slice allocation, the system plans the transmission order and rate of each video stream. The transmission order is usually sorted from high to low according to the priority of the video stream, and the transmission rate is determined based on the allocated time slice.

[0126] Rate R i Expressed as Wherein, Δt is the transmission period.

[0127] The system continuously monitors the real-time usage of network resources, such as the current bandwidth utilization, latency, etc. At the same time, it obtains the real-time transmission status of each video stream, such as key indicators such as packet loss rate and latency. Based on the monitoring results, the system evaluates the transmission efficiency of the current video stream. If the transmission effect of some video streams does not meet expectations, such as high latency or increased packet loss rate, the system will determine whether the transmission order and rate need to be adjusted. If adjustment is required, the system will re-evaluate the priority of the video stream and the current network status, allocate time slices again, and plan the transmission order and rate. This process involves repeating the steps of priority calculation, time slice allocation, and transmission order planning until all video streams can achieve the optimal transmission status under the current network resources.

[0128] After determining the transmission order and rate of all video streams, the system will reallocate the unallocated time slices to ensure balanced use of network resources. During the video stream transmission process, if a new video stream is added or the priority of an existing video stream changes, the system will re-execute the time slice allocation and transmission order planning steps, dynamically adjust the overall transmission strategy, and ensure the optimal transmission effect under the new network status.

[0129] The beneficial effects of the above technical solution are as follows: through the priority allocation mechanism, the system can ensure the priority transmission of critical video streams (such as security monitoring streams), thereby ensuring the monitoring quality and real-time performance of key locations in the airport. The system can dynamically adjust the transmission strategy of the video stream according to the real-time network resource usage, maximize the use of available bandwidth, and respond quickly when the network conditions change. Through reasonable time slice allocation and transmission sequence planning, the system can optimize the transmission rate of the video stream, reduce packet loss and delay, and improve the overall transmission efficiency. The system has powerful expansion capabilities and can quickly adjust the transmission strategy when new video streams are added or the priority of existing video streams changes, ensuring the transmission quality of new streams or changed streams. In the process of allocating time slices, the system can ensure the balanced use of network resources and avoid resource waste or the occurrence of certain traffic congestion problems.

[0130] In another embodiment, a distributed cache mechanism is set up in each level, including:

[0131] Obtain access frequency and importance information of each video data, and determine the data that needs to be cached based on the information;

[0132] Initialize a distributed cache mechanism at each level of video data transmission. The distributed cache mechanism includes multiple cache nodes distributed on network nodes at different levels.

[0133] Temporarily store frequently accessed video data and high-importance video data in each cache node, and dynamically adjust the cache strategy based on the access frequency and importance of the video data;

[0134] When a video data request is issued, the cache node at the corresponding level is queried first to obtain the temporary data in the cache node;

[0135] If the requested video data is not found in the cache node of the current level, the cache nodes of higher levels are queried step by step until the requested video data is found;

[0136] Return the queried video data to the requester, and update or add the requested video data in the lower-level cache nodes to optimize the efficiency of subsequent access;

[0137] Dynamically monitor the storage status of each cache node, and adjust the cache content based on the real-time access frequency and importance of video data, removing no longer needed low-frequency access or low-importance video data;

[0138] Trigger the global optimization process of the cache mechanism regularly or according to preset conditions, reallocate cache resources at each level, and ensure the optimal cache strategy for video data;

[0139] When the storage space of the cache node reaches the preset threshold, based on the access frequency and importance of the video data, high-frequency access and high-importance video data are retained first, and low-frequency access and low-importance video data are cleared or moved down to lower-level cache nodes.

[0140] The working principle of the above technical solution is as follows: Analyze data access frequency: Use the monitoring system to count the number of times each video is requested (access frequency) in real time and record these data. For example, if video A has been accessed 1,000 times in the past hour, while video B has only been accessed 50 times, then the access frequency of video A is significantly higher than that of video B. Importance evaluation: Evaluate the importance of the video based on the characteristics of the video content, user preferences, timeliness and other factors. Suppose the access frequency of video i is F i , the importance is I i , then a comprehensive score S i Indicates the cache priority of the video:

[0141] S i =α×F i +β×I i

[0142] Among them, α and β are weight coefficients, which are used to balance the impact of access frequency and importance on the score.

[0143] Distribution of cache nodes: Multiple cache nodes are deployed at different levels of the network (such as user devices, local servers, regional servers, and central data centers). Each cache node is responsible for storing part of the video data at a specific level to reduce the data transmission delay of user requests. Initialization of the cache mechanism: Based on the above comprehensive score S i To decide which videos should be cached and at which level, videos that are frequently accessed and of high importance are preferentially cached in nodes closer to the user.

[0144] Data caching strategy: When a video data request is issued, the system first queries whether the required video exists in the cache node closest to the user. If so, it is returned directly to the user; otherwise, the system queries higher-level cache nodes step by step until the required video data is found. Cache content update: After finding the video data, the system will not only return the data to the user, but also update the cache in the lower-level cache nodes to optimize subsequent access efficiency. Dynamic adjustment of cache strategy: The system will monitor the access frequency and importance of video data in real time, and dynamically adjust the cache content based on the latest statistical results. Video data with low frequency of access or low importance will be removed or moved down to lower-level cache nodes to free up cache space.

[0145] Periodic optimization: The system will trigger the global optimization process periodically or under certain conditions, re-evaluate the video data in all cache nodes, and reallocate cache resources based on the latest access frequency and importance to ensure that the cache strategy is always in the optimal state. Storage space management: When the storage space of a cache node reaches the preset threshold, the system will give priority to retaining high-frequency access and high-importance video data, and clean up or move low-frequency or low-importance video data to lower-level cache nodes.

[0146] The beneficial effects of the above technical solution are as follows: by caching high-frequency access and high-importance video data at various levels, users can obtain the required content faster, reduce data transmission delays, and improve user experience. Through the distributed caching mechanism, the frequency of data centers sending video data to users is reduced, thereby saving network bandwidth, especially in the case of high concurrent access. The system dynamically adjusts the caching strategy to ensure that the storage space of the cache node is used most effectively, and high-frequency access and high-importance video data can always be retained in the cache nodes close to the user. Through distributed caching, the system can flexibly add or remove cache nodes according to actual needs, thereby responding to different load requirements and improving the scalability of the system. Since the video data is stored in a distributed cache, even if a cache node fails, other cache nodes can still provide data services, enhancing the fault tolerance of the system.

[0147] In another embodiment, dynamically adjusting and allocating video processing resources includes:

[0148] Obtain the current load status information of each layer and determine the actual processing requirements of each layer based on the load status;

[0149] Initialize a distributed cache mechanism in a video processing system, the cache mechanism including a plurality of distributed nodes;

[0150] Dynamically adjust the video processing resources of each distributed node based on the actual load of each layer, including increasing or decreasing the allocation of processing resources;

[0151] When the load of a certain level reaches the preset threshold, the video processing resources are redistributed among the distributed nodes of this level first to ensure load balancing;

[0152] If load balancing cannot be achieved through resource redistribution at the current level, video processing resources are requested or released from the upper or lower levels to dynamically adjust resource allocation across levels;

[0153] Continuously monitor the load status of each distributed node and update the allocation strategy of video processing resources based on real-time monitoring data;

[0154] When the overall system load changes, the global load balancing strategy is triggered to reallocate video processing resources at each level to optimize global processing efficiency.

[0155] Based on the allocation results of video processing resources, the video data in the distributed cache is updated to match the new resource configuration to ensure the effectiveness of caches at all levels.

[0156] The working principle of the above technical solution is as follows: In airport video surveillance, the video processing system needs to deal with a large number of video streams from different cameras and process these video data in real time, such as face recognition, baggage tracking, abnormal behavior detection, etc. This system needs to efficiently manage computing resources to ensure stable operation under high load conditions. To this end, a multi-level dynamic resource management strategy based on a distributed cache mechanism is adopted.

[0157] Obtaining load status: The system first obtains the current load status information through the monitoring modules at each level. The load status is usually expressed in indicators such as CPU usage, memory usage, and network bandwidth utilization. Each distributed node regularly reports this data to the central controller. Initializing the distributed cache mechanism: When the video processing system starts, the distributed cache mechanism is initialized. The mechanism consists of multiple distributed nodes, each of which has a certain amount of video data cache space to accelerate subsequent video processing. The cache mechanism uses a consistent hashing algorithm to ensure that data is evenly distributed among the nodes. Dynamically adjust resource allocation: Based on real-time load status information, the central controller dynamically adjusts the video processing resources of each distributed node. If the load of a node exceeds its preset threshold, the controller can respond to the increased load by increasing the computing resources of the node (such as allocating more CPU cores or memory). Resource redistribution between distributed nodes: When the load of a certain level reaches the preset threshold, the system prioritizes resource redistribution between distributed nodes within the level. For example, if the load of node A is too high, while the load of node B at the same level is low, the system can transfer part of the video processing tasks from node A to node B. Cross-level resource adjustment: If load balancing cannot be achieved within the current level, the system will request or release video processing resources from the upper or lower level. For example, if the resources of the entire processing level are insufficient, the system will request more resources from the upper level; on the contrary, if there is an excess of resources, the system can release some resources for use by other levels. Continuous monitoring and policy updates: The system continuously monitors the load status of each distributed node and continuously updates the allocation strategy of video processing resources based on the latest data, which ensures that the system can adapt to load changes at any time. Global load balancing: When the overall system load changes significantly (for example, during peak flight hours), the global load balancing strategy will be triggered. At this time, the system will re-evaluate the video processing resources of all levels and globally reallocate them to optimize the overall processing efficiency. Update cache data: After resource reallocation, the system will update the video data in the distributed cache to match the new resource configuration. This ensures that the cache is still valid under the new processing resource distribution.

[0158] The beneficial effects of the above technical solution are: dynamically adjust the resource allocation strategy to ensure that the computing resources of each distributed node are fully utilized and reduce idle resources; when the load reaches a peak, the system can avoid overload through internal and cross-level resource allocation to improve overall stability; through resource redistribution between distributed nodes, the system can effectively balance the load and prevent a single node from becoming a bottleneck. Triggering global load balancing when the overall load changes can further optimize the system processing efficiency and ensure efficient operation under various load conditions. By updating cache data in real time, it is ensured that the distributed cache always matches the resource allocation strategy to avoid performance problems caused by outdated cache data. The system has good scalability and can respond quickly when the load changes to maintain stable and efficient system performance.

[0159] In another embodiment, a multi-level video networking architecture is obtained, including:

[0160] Get the level classification type from the monitoring level parameters, and get the level device parameter type from the level configuration parameters;

[0161] Obtain the level equipment parameter type associated with each level classification type as a monitoring level data group;

[0162] After sorting each monitoring level data group according to the level classification type, the data corresponding to each level classification type and level device parameter type is stored in the corresponding monitoring level data group to obtain a multi-level video networking architecture.

[0163] The working principle of the above technical solution is as follows: First, the classification type of each monitoring level is obtained from the monitoring level parameters. The classification type is usually based on function or geographical location, such as terminal entrance, boarding gate, baggage claim area, etc. These classification types help define the structure and priority of the monitoring system. Obtaining the level device parameter type: Next, the device parameter type of each level is obtained from the level configuration parameters. The device parameter type includes the camera's resolution, viewing angle, sensor type (such as infrared, high-definition visible light), processing power, etc. These parameters determine how the monitoring equipment at each level is configured to meet specific monitoring needs. Associating level classification with device parameters: By associating each level classification type with its corresponding level device parameter type, a monitoring level data group is formed. For example, the monitoring level at the terminal entrance requires a wide-angle high-definition camera, while the baggage claim area requires a camera with automatic tracking function. Each monitoring level data group contains these matching level classifications and device parameters. Sorting monitoring hierarchy data groups: Sort each monitoring hierarchy data group according to the hierarchy classification type. The basis for sorting is the importance of the hierarchy, the priority of monitoring coverage, or other predefined rules. For example, the most critical security areas (such as boarding gates) need to be processed first, and then the secondary areas. Storing classification and parameter data: After the sorting is completed, the data corresponding to each hierarchy classification type and hierarchy device parameter type is stored in the corresponding monitoring hierarchy data group. This step ensures the orderliness and consistency of the data, allowing the system to quickly access and use the data. Build a multi-level video networking architecture: Through the monitoring hierarchy data groups processed by the above steps, the system can build a multi-level video networking architecture. This architecture supports the coordination between monitoring equipment at different levels and ensures the effective operation of the security monitoring network throughout the airport.

[0164] The beneficial effects of the above technical solution are as follows: by associating the hierarchical classification type and the equipment parameter type, the system can accurately match the monitoring requirements of different areas, for example, deploy more powerful monitoring equipment in high-risk areas, thereby improving the overall safety of the system. Sorting the monitoring hierarchical data groups enables the system to allocate resources according to importance and priority, ensuring that the most important areas are always monitored at the highest level. By storing the classification and parameter data in an orderly manner, the system can quickly access the required information when performing monitoring operations, thereby improving the response speed and processing efficiency. The method allows the flexible addition of new monitoring levels or equipment parameters, facilitating system expansion to cope with new monitoring needs that may be added to the airport in the future. The multi-level video networking architecture ensures coordination between the various monitoring levels, reduces the possibility of monitoring blind spots and information omissions, and improves the coverage and reliability of the overall monitoring. The system can dynamically adjust the order and content of the monitoring hierarchical data groups according to actual conditions to ensure that efficient monitoring can be maintained when the operating conditions of the airport change.

[0165] In another embodiment, the video stream is divided into different categories of video streams, including:

[0166] Based on the characteristic parameters, the application scenarios of the video stream are preliminarily identified and classified into different importance levels;

[0167] Combining the characteristic parameters of video streams with the importance of application scenarios, video streams are subdivided into categories such as real-time monitoring streams, playback streams, and low-priority streams.

[0168] The working principle of the above technical solution is as follows: Collection and preliminary identification of characteristic parameters: First, the system collects the characteristic parameters of each video stream. These parameters include frame rate, resolution, bit rate, color depth, motion intensity, detected activity type, etc. By analyzing these characteristic parameters, the system preliminarily identifies the application scenario of the video stream. For example, high-frame-rate, high-resolution video streams come from key areas such as security checkpoints and boarding gates, while low-resolution video streams come from secondary areas such as corridors or employee rest areas. Classification of importance levels: Based on the characteristic parameters of the video stream, the system can preliminarily determine its importance level. For example, the importance of the monitored area can be determined based on its potential security threats or traffic flow. Key areas such as boarding gates and baggage claim areas are classified as high importance levels, while corridors or employee rest areas are classified as low importance levels. Video stream segmentation: After identifying the application scenario and importance level of the video stream, the system segments it based on the characteristic parameters of the video stream. The segmentation process is divided into the following categories according to the real-time requirements of the video stream and the actual purpose of monitoring:

[0169] Real-time surveillance streams: These are high-importance video streams that need to be monitored in real time. For example, surveillance videos at security checkpoints and boarding gates. Real-time surveillance streams usually have high frame rates and high resolutions to detect potential security threats in a timely manner.

[0170] Replay streams: These video streams are used for post-analysis and review and are less important than live surveillance streams. For example, surveillance video from a baggage claim area may be used primarily to find lost luggage. Replay streams can have a lower frame rate to reduce storage space requirements while still maintaining adequate image clarity.

[0171] Low priority streams: These video streams are from areas with low importance levels, such as corridors or employee lounges. Low priority streams have lower frame rates and resolutions and are only raised to a higher monitoring level in specific circumstances (such as when unusual activity is detected).

[0172] Dynamic adjustment and management: The system monitors the characteristic parameters and application scenarios of each video stream and dynamically adjusts the classification of video streams according to demand. This dynamic management method allows the system to increase the priority of specific video streams in an emergency or lower the priority when the situation returns to normal. Storage and transmission optimization: After classifying the video streams, the system will optimize the storage and transmission strategies according to different categories of video streams. Real-time monitoring streams are transmitted to the monitoring center first and use fast storage media. Playback streams and low-priority streams can choose lower-speed storage solutions to save bandwidth and storage resources.

[0173] Based on the characteristic parameters, preliminarily identify the application scenarios of the video stream and classify them into different importance levels, including: preliminarily analyzing the video stream according to the characteristic parameter data and identifying the application scenario types of the video stream, the application scenario types include different areas of the security check area, boarding gate, and baggage claim area;

[0174] Acquiring environmental parameters for each application scenario type, the environmental parameters including information on crowd density, lighting conditions, and camera position;

[0175] Based on the application scenario type and the environment parameters, characteristic parameters associated with each application scenario type are obtained to form a characteristic parameter cluster;

[0176] After sorting the characteristic parameters of each application scenario type according to the characteristic parameter cluster, the application scenario type is matched with the corresponding characteristic parameter cluster to obtain a preliminary recognition result.

[0177] Based on the preliminary identification results, the application scenario types are classified into importance levels, including high importance, medium importance and low importance;

[0178] Obtain reference standard data of the airport monitoring system, calibrate the importance level according to the reference standard data, and obtain a calibrated importance level;

[0179] According to the corrected importance levels, the application scenario types are reordered to generate scenario level division data;

[0180] Acquire multi-level video surveillance data of the airport, further analyze the scene classification data, and form the final importance classification result.

[0181] The beneficial effects of the above technical solution are as follows: by identifying and classifying video streams, the system can ensure that high-importance areas are always monitored in real time, effectively reducing the risk of key security events being ignored; by dividing video streams into different categories, bandwidth and storage resources are allocated according to actual needs. This resource optimization reduces the burden of system operation and ensures that video streams in important areas will not be affected by insufficient resources; when the system detects abnormal activities, the priority of related video streams can be dynamically increased to achieve rapid response. This flexible adjustment capability improves the system's response speed to emergencies; by properly processing low-priority video streams, the system can effectively reduce unnecessary data transmission and storage, which not only saves storage space but also reduces the operating cost of the system; the setting of the playback stream ensures that detailed post-event analysis and investigation can be carried out when an incident occurs. Even if the problem is not discovered in real time, the ability to review it afterwards also enhances the overall security of the system; by segmenting and classifying video streams, the system can more accurately identify and process activities in different scenarios, thereby reducing the false alarm rate and improving the accuracy of monitoring.

[0182] In another embodiment, planning the initial transmission order and rate of each video stream includes:

[0183] Based on the allocated time slice and the priority of the video stream, identifying and obtaining priority data and time slice allocation data of the current video stream, wherein the priority data includes a priority parameter and a time slice parameter of the video stream;

[0184] According to the priority parameter of the video stream and the time slice parameter, the priority data and the time slice allocation data of the current video stream are processed in layers to obtain the data to be planned for transmission;

[0185] Obtain real-time scheduling algorithm data, and obtain scheduling standard data from the real-time scheduling algorithm data;

[0186] After optimizing the scheduling standard data, the data scheduling vector is constructed, and the scheduling related data is obtained from the real-time scheduling algorithm data;

[0187] After clustering the data scheduling vectors according to the scheduling related data, a transmission data group to be optimized is obtained, and after scheduling training of the initial model with the transmission data group to be optimized, a real-time scheduling optimization model is obtained;

[0188] Input the transmission data to be planned into the preset real-time scheduling optimization model one by one to obtain the video stream transmission benchmark, which includes:

[0189] Inputting the first transmission data to be planned into the real-time scheduling optimization model, performing priority matching on the first transmission data to be planned in the real-time scheduling optimization model, and outputting a first transmission order and rate according to the matching result;

[0190] Starting from the second transmission data to be planned, the previous transmission order and rate result are input into the real-time scheduling optimization model to obtain the corresponding optimized transmission result, and the first transmission order and rate result and all optimized transmission results are integrated to obtain the video stream transmission benchmark;

[0191] The transmission control data is generated according to the video stream transmission benchmark and the priority data of the current video stream, and the transmission control data is input into the real-time scheduling optimization model to obtain the final video stream transmission order and rate.

[0192] The working principle of the above technical solution is as follows: Identification of priority data and time slice allocation data: First, according to the priority parameters of the video stream (for example: real-time, importance, etc.) and time slice parameters (time slice refers to the time unit allocated by the system to each video stream for transmission), identify and obtain the priority data and time slice allocation data of the current video stream. Layered processing: According to the above identified data, the priority and time slice allocation of the video stream are layered. The goal of this step is to determine the priority level of each video stream, thereby determining its order and rate in transmission. Assume that the priority of video stream i is P i , the time slice parameter is T i ,The result of hierarchical processing can be represented as a transmission data set to be planned.

[0193] Acquisition and optimization of real-time scheduling algorithm: Acquire standard data of real-time scheduling algorithm, for example, how to define the urgency of transmission, bandwidth requirements, etc. Then, optimize these scheduling standard data to construct data scheduling vector, which can be represented as a vector group containing transmission parameters of each video stream, denoted as V.

[0194] Clustering of data scheduling vectors and generation of optimized transmission data groups: Clustering of data scheduling vectors is performed based on scheduling association data (i.e., correlation data between video streams), and similar video streams are grouped together to form transmission data groups to be optimized. Subsequently, these transmission data groups to be optimized are input into the initial model for scheduling training to generate a real-time scheduling optimization model. Generation of video stream transmission benchmarks: Input the first transmission data to be planned into the real-time scheduling optimization model. The model will perform priority matching based on the priority of the video stream and output the transmission order and rate of the first video stream. Then, starting from the second transmission data to be planned, the transmission order and rate results of the previous video stream are input into the model together with the current video stream to obtain the corresponding optimized transmission results. By integrating these results, the final video stream transmission benchmark is generated. Generation and application of transmission control data: Based on the generated video stream transmission benchmark and the priority data of the current video stream, the transmission control data is generated and input into the real-time scheduling optimization model. Finally, the transmission order and rate of all video streams are obtained. These data will be used to control the actual transmission of video streams in the airport video surveillance system.

[0195] The beneficial effects of the above technical solution are: through the hierarchical processing of priority and time slice allocation, it can ensure that important and urgent video streams are transmitted first, thereby ensuring the real-time performance of key airport monitoring tasks; the real-time scheduling algorithm combines the scheduling-related data to cluster the video streams, which can effectively reduce redundancy and conflict during transmission and make full use of available bandwidth resources; through dynamic scheduling and real-time optimization, the method can adapt to changes in demand in different scenarios, such as sudden emergencies, and can adjust the transmission order and rate of video streams in a timely manner. Through the generation of transmission benchmarks and the optimization of scheduling models, the system can maintain a high transmission stability in a complex network environment and avoid video frame loss or delays caused by network congestion.

[0196] In another embodiment, obtaining the data to be planned for transmission includes:

[0197] Get the time slice type from the time slice parameter, and get the priority type from the priority parameter;

[0198] Obtain the priority type associated with each time slice type as the data group to be planned;

[0199] After sorting each data group to be planned according to the time slice type, the data corresponding to each time slice type and priority type is stored in the corresponding data group to be planned, and the transmission data to be planned is obtained.

[0200] The working principle of the above technical solution is as follows: Time slice parameter: In video stream transmission, the time slice parameter refers to the transmission time unit allocated by the system to each video stream. First, we extract the time slice type from the time slice parameter, for example, short time slice (applicable to emergency video streams) and long time slice (applicable to ordinary monitoring video streams).

[0201] Priority parameter: The priority parameter is used to indicate the importance and urgency of the video stream. We extract the priority type from the priority parameter, for example, high priority (such as security alarm video) and low priority (such as regular surveillance video).

[0202] Each time slot type corresponds to one or more priority types. By analyzing these associations, we establish a mapping between time slot types and priority types. For example, short time slots correspond to high-priority video streams, while long time slots correspond to low-priority video streams.

[0203] Generation of data groups to be planned: Based on the above association relationships, multiple data groups to be planned are generated, each of which consists of a specific time slice type and its corresponding priority type. For example, a data group to be planned contains all video stream data of short time slices and high priority.

[0204] Sorting processing: Sort the video stream data within each data group to be planned based on the priority of time slice type and priority type. Video streams with high priority will be processed and transmitted first to ensure that they can be processed faster.

[0205] Generation of transmission data to be planned: The video stream data corresponding to each time slice type and priority type is sorted and stored in the corresponding data group to be planned. Finally, the transmission data to be planned contains the transmission order and rate information of each video stream, which will be used for further transmission scheduling.

[0206] The beneficial effects of the above technical solution are as follows: by associating time slices and priority parameters, and organizing and sorting data according to their types, the rapid transmission of high-priority video streams in the system is ensured, and delayed transmission of important information is effectively avoided; different time slice types correspond to different priority video streams, so that the system can flexibly allocate bandwidth and time resources according to actual needs, maximize the use of system resources, and avoid resource waste; by prioritizing video streams, high-priority video streams can be transmitted in a timely manner, which improves the real-time performance of the system, especially in emergency situations, it can respond quickly to ensure airport safety; the method can dynamically adjust the transmission order and resource allocation according to the specific time slice type and priority type, adapt to the ever-changing scene requirements, and provide a good foundation for future expansion and upgrading.

[0207] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A method for accelerating video image scheduling based on a multi-level video networking architecture of an airport, characterized in that: include: S101: Based on the actual layout and security monitoring requirements of the airport, the video surveillance system is divided into several levels to build a multi-level video networking architecture; S102: Analyze and classify video streams based on a multi-level video networking architecture, classify the video streams into different categories of video streams according to characteristics and importance, and then optimize the different categories of video streams; Step S102 includes: S1021: receiving video streams from multiple video sources through a multi-level video networking architecture; S1022: Analyze the video stream and extract characteristic parameters of the video stream, where the characteristic parameters include resolution, frame rate, color information, and motion detection result; S1023: based on the characteristic parameters and the importance of the video streams, the video streams are divided into different categories of video streams, where the different categories of video streams include a real-time monitoring stream, a playback stream, and a low priority stream; S1024: performing corresponding optimization processing operations for different types of video streams, wherein video encoding parameters are adjusted for real-time monitoring streams, compression algorithms are used to compress playback streams, and video quality or frame rate is reduced for low-priority streams; S103: Based on the real-time scheduling algorithm, dynamically adjust the transmission order and rate of the optimized video stream according to the priority of the video stream and the time slice allocation; Dynamically adjust the transmission order and rate of optimized video streams, including: Obtaining priority information of each video stream, where the priority information is determined based on the importance and real-time requirements of the video stream; Determine the currently available time slices and allocate initial time slices to each video stream. The time slice allocation is calculated based on the priority of the video stream and the current status of network resources. Based on the allocated time slices and the priority of the video streams, the initial transmission order and rate of each video stream are planned, and the transmission order and rate are optimized and adjusted according to the real-time scheduling algorithm; Dynamically monitor the usage of network resources and obtain the real-time transmission status of each video stream; Based on the results of real-time monitoring, evaluate the transmission efficiency of the current video stream and determine whether the transmission order and rate need to be adjusted; If adjustment is needed, return to the optimization steps of execution priority, time slice allocation and transmission order, and re-plan the transmission order and rate of each video stream; Repeat the monitoring, evaluation and adjustment steps until all video streams reach the optimal transmission state under the current network resources; After the transmission order and rate of all video streams are determined, the unallocated time slices are reallocated according to the real-time scheduling algorithm to ensure balanced utilization of network resources; During the video stream transmission process, if a new video stream is added or the priority of an existing video stream changes, the time slice allocation and transmission sequence planning steps are re-executed to dynamically adjust the transmission strategy of the overall video stream; S104: Setting a distributed cache mechanism at each level to temporarily store frequently accessed or important video data to ensure that data can be quickly obtained from the cache when needed; S105: Based on the distributed cache mechanism, a load balancing strategy for dynamically allocating video processing resources is implemented, and video processing resources are dynamically adjusted and allocated according to the actual load conditions of each layer; Dynamically adjust and allocate video processing resources, including: Obtain the current load status information of each layer and determine the actual processing requirements of each layer based on the load status; Initialize a distributed cache mechanism in a video processing system, the cache mechanism including a plurality of distributed nodes; Dynamically adjust the video processing resources of each distributed node based on the actual load of each layer, including increasing or decreasing the allocation of processing resources; When the load of a certain level reaches the preset threshold, the video processing resources are redistributed among the distributed nodes of this level first to ensure load balancing; If load balancing cannot be achieved through resource redistribution at the current level, video processing resources are requested or released from the upper or lower levels to dynamically adjust resource allocation across levels; Continuously monitor the load status of each distributed node and update the allocation strategy of video processing resources based on real-time monitoring data; When the overall system load changes, the global load balancing strategy is triggered to reallocate video processing resources at each level to optimize global processing efficiency. Based on the allocation results of video processing resources, the video data in the distributed cache is updated to match the new resource configuration to ensure the effectiveness of caches at all levels.

2. The method for accelerating video image scheduling based on the airport multi-level video networking architecture according to claim 1 is characterized in that: Step S101 includes: S1011: Acquire actual layout data and security monitoring demand data of the airport, and identify and obtain video monitoring system hierarchical data from the layout data and the demand data, wherein the video monitoring system hierarchical data includes monitoring hierarchical parameters and hierarchical configuration parameters; S1012: Performing hierarchical processing on hierarchical data of the video surveillance system according to monitoring hierarchical parameters and hierarchical configuration parameters to obtain a multi-level video networking architecture.

3. The method for accelerating video image scheduling based on the airport multi-level video networking architecture according to claim 1 is characterized in that: Set up distributed caching mechanisms at various levels, including: Obtain access frequency and importance information of each video data, and determine the data that needs to be cached based on the information; Initialize a distributed cache mechanism at each level of video data transmission. The distributed cache mechanism includes multiple cache nodes distributed on network nodes at different levels. Temporarily store frequently accessed video data and high-importance video data in each cache node, and dynamically adjust the cache strategy based on the access frequency and importance of the video data; When a video data request is issued, the cache node at the corresponding level is queried first to obtain the temporary data in the cache node; If the requested video data is not found in the cache node of the current level, the cache nodes of higher levels are queried step by step until the requested video data is found; Return the queried video data to the requester, and update or add the requested video data in the lower-level cache nodes to optimize the efficiency of subsequent access; Dynamically monitor the storage status of each cache node, and adjust the cache content based on the real-time access frequency and importance of video data, removing no longer needed low-frequency access or low-importance video data; Trigger the global optimization process of the cache mechanism periodically or according to preset conditions, reallocate cache resources at each level, and ensure the optimal cache strategy for video data; When the storage space of the cache node reaches the preset threshold, based on the access frequency and importance of the video data, high-frequency access and high-importance video data are retained first, and low-frequency access and low-importance video data are cleared or moved down to lower-level cache nodes.

4. The method for accelerating video image scheduling based on the airport multi-level video networking architecture according to claim 2 is characterized in that: Get a multi-level video networking architecture, including: Get the level classification type from the monitoring level parameters, and get the level device parameter type from the level configuration parameters; Obtain the level equipment parameter type associated with each level classification type as a monitoring level data group; After sorting each monitoring level data group according to the level classification type, the data corresponding to each level classification type and level device parameter type is stored in the corresponding monitoring level data group to obtain a multi-level video networking architecture.

5. The method for accelerating video image scheduling based on the airport multi-level video networking architecture according to claim 1 is characterized in that: The video streams are divided into different categories, including: Based on the characteristic parameters, the application scenarios of the video stream are preliminarily identified and classified into different importance levels; Combining the characteristic parameters of video streams with the importance of application scenarios, video streams are subdivided into categories such as real-time monitoring streams, playback streams, and low-priority streams.

6. The method for accelerating video image scheduling based on the airport multi-level video networking architecture according to claim 1 is characterized in that: Plan the initial transmission order and rate of each video stream, including: Based on the allocated time slice and the priority of the video stream, identifying and obtaining priority data and time slice allocation data of the current video stream, wherein the priority data includes a priority parameter and a time slice parameter of the video stream; According to the priority parameter of the video stream and the time slice parameter, the priority data and the time slice allocation data of the current video stream are processed in layers to obtain the data to be planned for transmission; Obtain real-time scheduling algorithm data, and obtain scheduling standard data from the real-time scheduling algorithm data; After optimizing the scheduling standard data, the data scheduling vector is constructed, and the scheduling related data is obtained from the real-time scheduling algorithm data; After clustering the data scheduling vectors according to the scheduling related data, a transmission data group to be optimized is obtained, and after scheduling training of the initial model with the transmission data group to be optimized, a real-time scheduling optimization model is obtained; Input the transmission data to be planned into the preset real-time scheduling optimization model one by one to obtain the video stream transmission benchmark, which includes: Inputting the first transmission data to be planned into the real-time scheduling optimization model, performing priority matching on the first transmission data to be planned in the real-time scheduling optimization model, and outputting a first transmission order and rate according to the matching result; Starting from the second transmission data to be planned, the previous transmission order and rate result are input into the real-time scheduling optimization model to obtain the corresponding optimized transmission result, and the first transmission order and rate result and all optimized transmission results are integrated to obtain the video stream transmission benchmark; The transmission control data is generated according to the video stream transmission benchmark and the priority data of the current video stream, and the transmission control data is input into the real-time scheduling optimization model to obtain the final video stream transmission order and rate.

7. The method for accelerating video image scheduling based on the airport multi-level video networking architecture according to claim 6 is characterized in that: Get the transmission data to be planned, including: Get the time slice type from the time slice parameter, and get the priority type from the priority parameter; Obtain the priority type associated with each time slice type as the data group to be planned; After sorting each data group to be planned according to the time slice type, the data corresponding to each time slice type and priority type is stored in the corresponding data group to be planned, and the transmission data to be planned is obtained.

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