A dynamic video surveillance resource management system

Abstract

The present invention relates to the technical field of video surveillance resource management, and specifically relates to a dynamic video surveillance resource management system. The system includes a task urgency calculation module, a video storage resource measurement module, a video storage allocation and optimization module, a monitoring target residence analysis module, and a video storage level adjustment module. In the present invention, the task urgency is calculated through event trigger information, task trigger sources, event categories, and coverage radii, achieving precise task priority division, dynamically adjusting the storage resource allocation ratio, enabling high-priority tasks to obtain better storage guarantees, calculating the storage requirements of high-urgency tasks, and optimizing the storage structure in combination with the data fragmentation rate to improve the utilization rate of storage space. The storage retention duration is adjusted according to the target entry frequency, and low-priority task storage compression is performed in combination with the load status of the storage server to achieve efficient release of storage space, reduce storage redundancy, and improve storage stability and task scheduling efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of video surveillance resource management, and particularly to a dynamic video surveillance resource management system. Background Art

[0002] The technical field of video surveillance resource management includes core contents such as the acquisition, storage, scheduling, retrieval, and management of surveillance video data, and involves the organization method, index structure, access mechanism, and storage optimization strategy of massive video data to improve the data utilization efficiency and management ability of the surveillance system. It also covers content such as data access permission control, optimized configuration of storage media, distributed storage architecture, data flow scheduling strategy, and data consistency management to ensure the stable storage, efficient transmission, and fast invocation of surveillance data. With the growth of video surveillance requirements, it gradually develops towards automated and intelligent management, making the utilization of surveillance resources more efficient and accurate.

[0003] Among them, a dynamic video surveillance resource management system refers to a system that can dynamically allocate, schedule, and optimize the management of video surveillance resources. In view of the dynamic change characteristics of surveillance data, a storage management strategy based on real-time video data analysis is adopted, and the video data is hierarchically archived through data tagging to optimize the storage structure. For the access requirements of video resources, a time series feature analysis method is used for the retrieval and scheduling of video data, and the resource location accuracy is improved through the regional video content indexing method. For data storage optimization, a hierarchical storage management mechanism is adopted to automatically classify and store video data according to factors such as time, frequency, and importance. For the data consistency problem, a data synchronization control mechanism is used to coordinate resources in a distributed storage environment to ensure data integrity and consistency during multi-node access.

[0004] In the existing video surveillance resource management process, there are problems of fixed storage strategies in the management of surveillance video data. The method of dividing storage priorities is relatively single and cannot be adjusted in real time according to the task urgency, resulting in a lack of pertinence in storage resource allocation and affecting the execution efficiency of surveillance tasks. During the storage management process, the storage structure cannot be dynamically optimized in combination with the data fragmentation rate, and storage fragments accumulate after long-term operation, affecting the data retrieval efficiency and the overall performance of the storage system. The target storage management lacks in-depth analysis of the activity characteristics of surveillance objects, and only relies on static rules for data storage, failing to make full use of parameters such as target residence time, entry frequency, and departure interval to optimize the storage level, resulting in low-frequency target data occupying storage resources, while high-frequency target data may be difficult to be preferentially stored due to rigid storage strategies. The adjustment of the storage level depends on a fixed storage duration and lacks an adaptation mechanism for the storage load state, making it difficult to reasonably release storage resources under high load conditions and affecting the long-term operation stability and data management efficiency of the system. Summary of the Invention

[0005] The object of the present invention is to solve the drawbacks existing in the prior art, and a dynamic video surveillance resource management system is proposed.

[0006] To achieve the above object, the present invention adopts the following technical solutions: A dynamic video surveillance resource management system includes:

[0007] The task urgency calculation module obtains the event trigger information of the video surveillance task, calculates the monitoring task coverage radius, analyzes the monitoring task urgency, and obtains the video surveillance task urgency analysis result;

[0008] The video storage resource measurement module reads the storage space utilization rate of the storage server based on the video surveillance task urgency analysis result, calculates the storage ratio that can be allocated for each video task, and obtains the video task storage ratio allocation result;

[0009] The video storage allocation and optimization module calculates the video storage requirements of high-urgency tasks based on the video task storage ratio allocation result, performs video data block merging, and obtains the optimized video task storage allocation status;

[0010] The monitoring target residence analysis module calculates the video recording duration of the target in the monitoring area, the target entry frequency, and the target departure time interval based on the optimized video task storage allocation status, judges the video storage level requirements, and obtains the video surveillance target storage level requirement analysis result.

[0011] As a further solution of the present invention, the video surveillance task urgency analysis result includes the task trigger source category, the event severity, the monitoring task coverage radius, and the monitoring task priority. The video task storage ratio allocation result includes the storage ratio of high-priority tasks, the storage ratio of medium-priority tasks, the storage ratio of low-priority tasks, and the storage server storage load ratio. The video task storage allocation status includes the storage allocation situation of high-urgency tasks, the storage server data fragmentation rate, and the video data block optimization status. The video surveillance target storage level requirement analysis result includes the target video recording duration, the target entry frequency, the target departure time interval, and the storage level requirement level.

[0012] As a further solution of the present invention, the task urgency calculation module includes:

[0013] The event trigger information acquisition sub-module obtains the event trigger information of the video surveillance task, reads the task trigger source and the event category, analyzes the time characteristics and space characteristics of the event category, extracts the correlation information between the event occurrence location, the event occurrence time, the event type, and the task source, and obtains the event correlation information parameter set;

[0014] The monitoring task coverage radius calculation sub-module analyzes and determines the required coverage range of the monitoring task based on the event correlation information parameter set, using the formula:

[0015] ;

[0016] Calculate the monitoring task coverage radius value , where represents the area of the event impact region, represents the event propagation speed, represents the response required time;

[0017] The monitoring task urgency analysis sub-module calculates the urgency index of the current monitoring task according to the monitoring task coverage radius value, in combination with the event type risk coefficient, the event occurrence density, and the historical urgency distribution, and obtains the video monitoring task urgency analysis result.

[0018] As a further solution of the present invention, the video storage resource measurement module includes

[0019] The storage resource acquisition sub-module acquires the available storage capacity of all current video storage servers, reads the storage space utilization rate of the storage servers, counts the total number of tasks in the current video task queue, extracts the storage resource status data of each server, and obtains the storage resource status parameter set;

[0020] The task priority calculation sub-module divides high, medium, and low priority tasks based on the storage resource status parameter set, in combination with the video monitoring task urgency analysis result, according to the task urgency, counts the number of high, medium, and low priority tasks, and uses the formula

[0021] ;

[0022] Calculate the proportion of the th type of task , and integrate to obtain the proportion data of each priority task, where represents the number of the th type of task, represents the total amount of high, medium, and low priority tasks;

[0023] The storage ratio allocation sub-module adjusts the video storage resource allocation ratio based on the proportion data of each priority task, calculates the storage ratio that can be allocated to each video task, and obtains the video task storage ratio allocation result.

[0024] As a further solution of the present invention, the video storage allocation and optimization module includes

[0025] The high-urgency task storage requirement calculation sub-module combines the above video task storage proportion allocation result, extracts the storage allocation data corresponding to high-urgency tasks, calculates the video storage requirements of high-urgency tasks, and obtains high-urgency task storage requirement data;

[0026] Based on the high-urgency task storage requirement data, the video data fragmentation rate calculation sub-module obtains the existing video data storage status in the storage server, extracts the video data occupancy of each storage unit, and uses the formula:

[0027] ;

[0028] Calculate the video data fragmentation rate , and obtain the storage server data fragmentation rate data, where represents the amount of video data already stored in the th storage unit, represents the free storage capacity of the th storage unit, represents the total number of storage units with stored data, represents the total number of storage units of the current server;

[0029] Based on the storage server data fragmentation rate data, the video data block merging sub-module performs video data block merging, adjusts the arrangement of fragmented data blocks and the video storage structure within the storage unit, and obtains an optimized video task storage allocation status.

[0030] As a further solution of the present invention, the monitoring target residence analysis module includes:

[0031] According to the optimized video task storage allocation status, the monitoring target residence duration calculation sub-module obtains the entry timestamp of the monitoring target, reads the target departure timestamp, calculates the video recording duration of the target in the monitoring area, screens the corresponding relationship between consecutive entry and departure records, calculates the residence duration of different targets in different time periods, and summarizes the residence time distribution of targets in the monitoring area to obtain monitoring target residence duration data;

[0032] Based on the monitoring target residence duration data, the target entry frequency statistics sub-module counts the number of target entries, analyzes the activity of different targets in the monitoring area, calculates the entry frequency of the target in combination with the observation period, and analyzes the time distribution characteristics of the target entry area to obtain target entry frequency data;

[0033] The storage level requirement judgment sub-module calculates the departure time interval of the target according to the target entry frequency data, counts the residence periods of different targets, combines the video stay duration, entry frequency, and departure time interval of the target in the monitoring area, compares the storage priorities of different targets, and adjusts the video storage level according to the storage requirements classification to obtain the analysis result of the video monitoring target storage level requirements.

[0034] As a further solution of the present invention, the system further includes a video storage level adjustment module;

[0035] The video storage level adjustment module adjusts the video storage retention duration based on the analysis result of the video monitoring target storage level requirements, combines the load status of the storage server, performs storage compression on low-priority video tasks, releases storage space, and obtains the video storage level adjustment result;

[0036] The video storage level adjustment result includes the adjustment situation of the video storage retention duration, the load balancing status of the storage server, the storage compression ratio of low-priority tasks, and the storage space release amount.

[0037] As a further solution of the present invention, the video storage level adjustment module includes:

[0038] The target entry frequency analysis sub-module combines the analysis result of the video monitoring target storage level requirements, obtains the entry records of the monitoring targets, counts the number of entries of the targets within a specified time period, calculates the entry frequency values of different targets, screens the targets with an entry frequency higher than the set threshold, and calculates the entry frequency distribution interval to obtain the target entry frequency distribution data;

[0039] The storage retention duration adjustment sub-module sets a storage retention duration adjustment factor for the targets in different frequency intervals based on the target entry frequency distribution data, calculates the storage duration adjustment values corresponding to each target, and adjusts the storage retention duration to obtain the storage retention duration adjustment data;

[0040] The storage server load monitoring sub-module monitors the current load of the storage server, collects the storage occupancy rate, access rate, and storage pressure value, calculates the storage occupancy rate, and determines whether the load status exceeds the load threshold to obtain the storage server load status data;

[0041] The low-priority video storage compression sub-module filters the low-priority video data based on the storage retention duration adjustment data and the storage server load status data, and uses the formula:

[0042] ;

[0043] Calculate the storage compression ratio of low-priority video data , the video storage level adjustment result is obtained; among them, represents the storage size of the th video file, represents the remaining storage duration of the video, represents the target entry frequency of the video, represents the load ratio of the server where the video is located, represents the bandwidth occupancy of the video storage server, represents the average data traffic during the video storage period, represents the current bandwidth consumption of the video storage, represents the access frequency of the video, represents the number of compression histories of the video,

[0044] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0045] In the present invention, by obtaining the event trigger information of the video monitoring task and combining the task trigger source, event category, and monitoring task coverage radius, a dynamic priority evaluation mechanism is formed, making the classification of monitoring tasks more accurate and ensuring that the resource allocation meets the actual needs. Combining the available storage capacity, storage space utilization rate, and total task queue volume of the storage server, based on the dynamic calculation method of task urgency, the task storage priority is divided, and the resource allocation ratio is adjusted, optimizing the storage ratio of high-priority tasks and improving the storage guarantee of key video data. Calculate the storage requirements of high-urgency tasks, and combine the data fragmentation rate of the storage server to analyze the storage optimization direction, and perform intelligent merging of video data blocks, reducing the storage fragmentation rate and improving the data access efficiency. Based on the entry timestamp and departure timestamp of the monitoring target, calculate the target residence time, entry frequency, and departure interval, making the division of storage level requirements more accurate and ensuring that the video data of important targets can be stored at the appropriate level. Dynamically adjust the storage retention duration according to the target entry frequency, and perform low-priority task storage compression in combination with the load status of the storage server, making the release of storage resources more reasonable, optimizing the storage space utilization rate while reducing the system load and enhancing the storage stability and scheduling flexibility of the system. Brief Description of the Drawings

[0046] Figure 1 is the system flow chart of the present invention;

[0047] Figure 2 is the flow chart of the task urgency calculation module of the present invention;

[0048] Figure 3 is the flow chart of the video storage resource measurement module of the present invention;

[0049] Figure 4Flow chart of the video storage allocation and optimization module of the present invention;

[0050] Figure 5 Flow chart of the monitoring target residence analysis module of the present invention;

[0051] Figure 6 Flow chart of the video storage level adjustment module of the present invention. Specific implementation mode

[0052] In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0053] In the description of the present invention, it should be understood that the orientation or positional relationships indicated by the terms "length", "width", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. are based on the orientation or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as limiting the present invention. In addition, in the description of the present invention, "a plurality of" means two or more unless otherwise specifically defined.

[0054] Please refer to Figure 1 , a dynamic video surveillance resource management system includes:

[0055] The task urgency calculation module obtains the event trigger information of the video surveillance task, reads the task trigger source and event category, calculates the monitoring task coverage radius, analyzes the monitoring task urgency, and obtains the video surveillance task urgency analysis result;

[0056] The video storage resource measurement module, based on the video surveillance task urgency analysis result, obtains the available storage capacity of all current video storage servers, reads the storage space utilization rate of the storage servers, counts the total number of tasks in the current video task queue, divides the tasks into high, medium, and low priority tasks according to the task urgency, calculates the proportions of high, medium, and low priority tasks, adjusts the video storage resource allocation ratio, and calculates the storage ratio that can be allocated to each video task, so as to obtain the video task storage ratio allocation result;

[0057] The video storage allocation and optimization module, based on the video task storage ratio allocation result, calculates the video storage requirements of high-urgency tasks, calculates the video data fragmentation rate of the storage servers, and performs video data block merging to obtain the optimized video task storage allocation status;

[0058] Based on the optimized video task storage allocation status, the monitoring target residence analysis module obtains the entry timestamp of the monitoring target, reads the target departure timestamp, calculates the video recording duration of the target in the monitoring area, counts the entry frequency of the target, calculates the target departure time interval, and determines the video storage level requirement based on the video residence duration, entry frequency, and departure time interval of the target in the monitoring area, obtaining the analysis result of the video monitoring target storage level requirement;

[0059] Based on the analysis result of the video monitoring target storage level requirement, the video storage level adjustment module adjusts the video storage retention duration according to the entry frequency of the target, combines the storage server load status, performs storage compression on low-priority video tasks, and releases storage space, obtaining the video storage level adjustment result.

[0060] The analysis result of the video monitoring task urgency includes the task trigger source category, event severity, monitoring task coverage radius, and monitoring task priority. The video task storage proportion allocation result includes the high-priority task storage proportion, medium-priority task storage proportion, low-priority task storage proportion, and storage server storage load proportion. The video task storage allocation status includes the high-urgency task storage allocation situation, storage server data fragmentation rate, and video data block optimization status. The analysis result of the video monitoring target storage level requirement includes the target video recording duration, target entry frequency, target departure time interval, and storage level requirement level. The video storage level adjustment result includes the adjustment situation of the video storage retention duration, storage server load balancing status, low-priority task storage compression ratio, and storage space release amount.

[0061] Please refer to Figure 2 , the task urgency calculation module includes:

[0062] The event trigger information acquisition sub-module acquires the event trigger information of the video monitoring task, reads the task trigger source and event category, analyzes the time characteristics and space characteristics of the event category, and extracts the correlation information between the event occurrence location, event occurrence time, event type, and task source, obtaining the event correlation information parameter set;

[0063] The event trigger information for video surveillance tasks comes from multiple data sources, including traffic surveillance cameras, public security cameras in public places, intelligent sensing devices, etc. First, it is necessary to collect surveillance data from these devices, parse the data format, and extract key information such as the target recognition results in image frames, timestamp data in video streams, and the geographical location information of sensors. Suppose a surveillance task involves urban road traffic events. The system will extract image data containing the event occurrence area, time, and event category from traffic surveillance cameras, and calculate the event occurrence time based on the timestamp information. After parsing the data, the system matches the corresponding preset categories according to the event type (such as vehicle collision, pedestrian running a red light, traffic jam, etc.), and at the same time analyzes the task trigger source, for example, whether the event is triggered by an automatic monitoring system, or by manual alarm or data input provided by other external systems. Further analyze the time characteristics of the event category. For example, some events are persistent, such as traffic congestion, and its duration can be calculated from historical data. For sudden events such as vehicle collisions, their characteristics may be limited to sudden changes within a short period of time. In terms of spatial characteristics analysis, the system will estimate the event impact area by combining the GPS coordinates provided by sensing devices, the camera angle, and the event occurrence range. Suppose an accident occurs at a road intersection, then the system needs to calculate the number of lanes affected by the accident and the congestion spread range according to the camera perspective and traffic flow data. In order to ensure the relevance of data, when the system extracts the correlation information between the event occurrence location, event occurrence time, event type, and task source, it compares multi-source data. For example, the accident report provided by the traffic police system can be time-matched with the video surveillance record. If the time error between the two records of the accident occurrence time is less than 1 second, it can be determined as the same event. As shown in Table 1, the time error ranges of different types of events are listed, and finally the event correlation information parameter set is obtained.

[0064] Table 1 Table of Event Types and Time Error Ranges

[0065]

[0066] As shown in Table 1, the time error ranges of different event types are different, and these error values affect the matching degree of event data and ensure the accuracy of event trigger information.

[0067] The monitoring task coverage radius calculation sub-module analyzes and determines the required coverage range of the monitoring task based on the event correlation information parameter set, using the formula:

[0068] ;

[0069] Calculate the monitoring task coverage radius value , where represents the event impact area, represents the event propagation speed, Represents the required response time;

[0070] Based on the event correlation information parameter set, it is necessary to calculate the coverage required for the monitoring task. The calculation involves multiple key parameters, including the area of the event impact region, the event propagation speed, and the required response time. First, the system determines the area of the event impact region , if the event is a traffic congestion, the system can calculate its impact region through historical traffic flow data. For example, a one-way congestion on a main road may affect the traffic flow within a range of 1 kilometer. Then can be approximated as 1000 meters × 20 meters (road width) = 20,000 square meters. The event propagation speed can be determined according to different event types. For example, the impact propagation speed at the accident scene can be estimated by monitoring the moving speed of vehicles. Suppose an accident causes the vehicle speed to drop to 10 km / h, while the normal vehicle speed is 60 km / h. Then the impact propagation speed can be calculated to be approximately 50 km / h (i.e., 13.89 m / s). The required response time can be calculated from historical response data. For example, the average response time of traffic police to handle traffic accidents is 5 minutes (300 seconds). Substituting the above data into the formula:

[0071] ;

[0072] Perform the calculation:

[0073] ;

[0074] Finally, the coverage radius value of the monitoring task is obtained. The result shows that the minimum coverage radius of the monitoring task should be set to approximately 88 meters to ensure comprehensive monitoring of the accident impact region.

[0075] The monitoring task urgency analysis sub-module calculates the urgency index of the current monitoring task according to the coverage radius value of the monitoring task, combined with the event type risk coefficient, the event occurrence density, and the historical urgency distribution, and obtains the monitoring task urgency analysis result;

[0076] Call the coverage radius value of the monitoring task, combine it with the event type risk coefficient, the event occurrence density, and the historical urgency distribution to calculate the urgency index of the current monitoring task. The event type risk coefficient is assigned a value by analyzing historical data. For example, the risk coefficient of a traffic accident may be set to 1.5, while that of ordinary traffic congestion is set to 1.0. The value is determined according to the degree of impact of the accident type on road safety. For example, traffic accidents usually involve casualties and road blockages, and the risk is significantly higher than that of ordinary congestion events. Therefore, the assigned value is higher than 1.0. If the accident involves multi-vehicle chain collisions or full closure of the main road, the risk coefficient may be further increased to 2.0 and above. The event occurrence density It can be calculated by the number of events per unit time. Suppose there are 3 traffic accidents per hour in a certain area, then its density is 3 events / hour. The value is derived from the actual monitoring data of the area within the past 24 hours. If the frequency of accidents increases within a short period of time, the value will also increase accordingly. For example, if there are already 3 accidents within 30 minutes, the density may be adjusted to 6 events / hour. Historical urgency distribution It is provided by historical monitoring data. For example, within the past week, the average urgency index of the area is 2.3. The value is obtained by calculating the arithmetic mean of the urgency indices in different time periods, and the urgency indices in different time periods are calculated separately according to the daytime peak periods (such as 7:00 - 9:00, 17:00 - 19:00) and the nighttime low - traffic periods (such as 0:00 - 5:00) to evaluate the changing trend of the urgency level. Based on these parameters, the urgency index of the current task is calculated , using the formula:

[0077] ;

[0078] Substitute specific values:

[0079] ;

[0080] ;

[0081] The setting basis of the judgment criteria is as follows:

[0082] (1) Low urgency (0.0 - 1.9): The monitoring tasks corresponding to this interval usually involve minor congestion or single violation events. For example, there is occasional illegal parking on a certain road section, resulting in a short - term vehicle stagnation. If the event density is less than 1 event / hour, and the accident risk coefficient is less than 1.2, then the urgency index is usually less than 1.9. In this case, the response priority of the monitoring task is relatively low, and only routine video inspections are required, without the need for emergency resource allocation;

[0083] (2) Medium urgency (2.0 - 3.9): This interval usually involves general traffic accidents or medium - scale traffic congestion. For example, a small fender - bender occurs at an intersection, affecting the traffic of 1 - 2 lanes. If the accident risk coefficient of the area is between 1.2 - 1.5, and the accident density per unit time is in the range of 1 - 2 events / hour, then the calculated urgency index usually falls within the interval of 2.0 - 3.9. In this case, the priority of the monitoring task needs to be enhanced, key area inspections are arranged, and the traffic management department is notified to intervene and handle when necessary;

[0084] (3) High urgency level (4.0 - 5.9): This range covers serious traffic accidents or large-scale traffic congestion. For example, a series of rear-end collisions occur on an urban expressway, resulting in the obstruction of 3 or more lanes. The risk coefficient of such accidents is usually greater than 1.5. If the accident occurrence density exceeds 3 incidents / hour, or multiple accidents occur in a short period of time, then the urgency index may exceed 4.0. In this case, the monitoring task must be given priority, monitor resources should be scheduled in real-time, and the traffic police or emergency rescue team should be notified to intervene and handle the situation;

[0085] (4) Extreme urgency (6.0 and above): This range represents a serious abnormality in the traffic system. For example, a large-scale traffic accident occurs on a highway, resulting in a full closure, or multiple serious accidents occur on the main urban road, affecting thousands of vehicles. The risk coefficient of such events can reach 2.0 and above. The accident occurrence density may exceed 5 incidents / hour, causing the urgency index to exceed 6.0. In this case, the monitoring task enters the highest response level, all relevant areas need to be monitored in real-time, and the traffic management department and rescue agencies should be coordinated to intervene simultaneously and take emergency control measures.

[0086] Table 2 Judgment Criteria for Task Urgency Index

[0087]

[0088] As shown in Table 2, the results show that the current urgency index of the area is 4.526. According to the judgment criteria for the urgency index (see Table 2), the value falls within the "high urgency" range (4.0 and above), which means that the area monitoring task should be set as a high priority, and it is necessary to immediately respond and allocate monitoring resources for real-time tracking and analysis. At the same time, the value is greatly affected by the accident occurrence density changes. If the number of new accidents reaches 5 or more within the next 1 hour, the urgency index may exceed 6.0 and enter the extreme urgency state, and the emergency response level needs to be upgraded.

[0089] Please refer to Figure 3 , the video storage resource calculation module includes

[0090] The storage resource acquisition sub-module obtains the available storage capacity of all current video storage servers, reads the storage space utilization rate of the storage servers, counts the total number of tasks in the current video task queue, extracts the storage resource status data of each server, and obtains the storage resource status parameter set;

[0091] Obtain the available storage capacity of all current video storage servers. First, make data calls for each storage server to count the storage capacity of the server and the current available storage space. For example, a data center is configured with 5 storage servers, and the total storage capacity of each server is 100TB, 120TB, 90TB, 110TB, and 130TB respectively, and the current available storage spaces are 30TB, 40TB, 20TB, 35TB, and 50TB respectively. Then the total available storage capacity is 175TB. Obtain the storage space utilization rate of the storage server, and calculate the storage space utilization rate as (total storage capacity - available storage capacity) / total storage capacity. For example, for a certain server with a total storage capacity of 100TB and an available storage space of 30TB, the storage space utilization rate is calculated as (100TB - 30TB) / 100TB = 70%. The average value of the storage space utilization rates of all servers is calculated as follows:

[0092] ;

[0093] Count the total number of tasks in the current video task queue, extract the number of tasks to be stored currently from the task database. For example, the current task queue contains 240 video storage tasks, arranged in time priority. Extract the storage resource status data of each server, including the available storage space of the server, the storage utilization rate, and the task volume in the task queue, to obtain a set of storage resource status parameters.

[0094] Table 3 Storage Server Status Table

[0095]

[0096] As shown in Table 3, the storage status parameters of the current storage server have been obtained and can be used for subsequent calculation of task priorities and storage ratios.

[0097] The task priority calculation sub-module, based on the set of storage resource status parameters, combines the analysis results of the urgency of video monitoring tasks, divides high, medium, and low priority tasks according to the task urgency, counts the number of tasks of high, medium, and low priority tasks, and uses the formula

[0098] ;

[0099] Calculate the proportion of the type of tasks , and integrate to obtain the proportion data of tasks of each priority. Among them, represents the number of tasks of the type, represents the total amount of high, medium, and low priority tasks;

[0100] Based on the set of storage resource status parameters, call the analysis results of the urgency of video surveillance tasks, divide the priorities of video tasks according to the urgency, and divide them into high-priority, medium-priority, and low-priority according to the task urgency, which respectively represent that the task urgency is in the range of 80 - 100, 50 - 79, and 0 - 49. Count the number of tasks with different priorities. For example, among the 240 tasks in the current task queue, there are 80 high-priority tasks, 100 medium-priority tasks, and 60 low-priority tasks. Calculate the proportion of tasks with different priorities, and substitute the actual values for calculation:

[0101] ;

[0102] ;

[0103] ;

[0104] Obtain the proportion of tasks with each priority.

[0105] Table 4 Task Priority Proportion Table

[0106]

[0107] As shown in Table 4, the task priority distribution has been determined and can be used for the subsequent calculation of storage proportion allocation.

[0108] The storage proportion allocation sub-module adjusts the video storage resource allocation proportion based on the proportion data of tasks with each priority, calculates the storage proportion that can be allocated for each video task, and obtains the video task storage proportion allocation result;

[0109] According to the proportion data of tasks with each priority, adjust the storage resource allocation proportion. Based on the proportions of high, medium, and low-priority tasks, set the allocation weights of storage resources. The setting of the storage resource allocation weights is based on the urgency level of the tasks, the storage demand of the tasks, and the timeliness requirements for task completion. In the actual monitoring scenario, high-priority tasks usually involve emergency tasks such as emergency event monitoring and abnormal behavior detection. Their storage demand is generally large and requires higher storage bandwidth support. Therefore, the allocated storage weight is relatively high; medium-priority tasks involve daily monitoring or general security patrol tasks, and the storage demand is relatively balanced, with a medium weight; low-priority tasks are mainly used for non-emergency tasks such as historical data archiving and daily background data storage, and the storage demand is relatively small. Therefore, the weight is the lowest. The specific weights are set as 0.5 for high-priority tasks, 0.3 for medium-priority tasks, and 0.2 for low-priority tasks. Set according to the proportion of task urgency and the statistical data of storage demand, calculate the storage proportion that can be allocated for each task, and use the formula:

[0110] ;

[0111] Among them, Represents the average storage allocation for type tasks, represents the storage weight of task priority, represents the total available storage capacity of 175TB, represents the number of high-priority tasks.

[0112] The setting basis of the task storage weight is as follows:

[0113] High-priority tasks (weight 0.5): Emergency tasks usually need to be stored for a long time for subsequent analysis, such as abnormal behavior recognition or crime tracking tasks. The general storage duration can reach 30-60 days, and the video data volume of a single task is large (usually 50GB-200GB / hour), so the highest weight is given.

[0114] Medium-priority tasks (weight 0.3): Daily monitoring tasks are mainly used for real-time viewing and short-term archiving. The storage period is generally 15-30 days, and the data volume is small (usually 20GB-100GB / hour), and the storage requirements are relatively medium.

[0115] Low-priority tasks (weight 0.2): Low-priority tasks are mainly used for data archiving or long-term storage. The storage period is usually more than 60 days, but the data access frequency is low, so they occupy less storage resources and have the smallest storage requirements.

[0116] Substitute data for calculation:

[0117] ;

[0118] ;

[0119] ;

[0120] After the calculation, the storage proportion allocation result of the video task is obtained. The result shows that a single high-priority task can be allocated 0.365TB, a single medium-priority task can be allocated 0.219TB, and a single low-priority task can be allocated 0.146TB. This data can be used for storage task scheduling and allocation.

[0121] Please refer to Figure 4 , the video storage allocation and optimization module includes

[0122] The high-urgency task storage requirement calculation sub-module combines the storage proportion allocation result of the video task, extracts the storage allocation data corresponding to the high-urgency task, calculates the video storage requirement of the high-urgency task, and obtains the high-urgency task storage requirement data;

[0123] According to the storage proportion allocation result of video tasks, determine the storage allocation data corresponding to high-urgency tasks. The calculation of storage demand first requires identifying the storage duration, video encoding format, resolution, and frame rate of each high-urgency task. Based on these parameters, calculate the storage demand for a single task. Assume that the average storage duration of high-urgency tasks is 2 hours, the video encoding format is H.265, the resolution is 1080P, and the frame rate is 30 frames per second. If each frame occupies approximately 0.5MB, the storage demand for a single task is calculated as follows:

[0124] ;

[0125] ;

[0126] Assume there are 100 high-urgency tasks in the current system, then the total storage demand is calculated as follows

[0127] ;

[0128] ;

[0129] Finally, the storage demand value for high-urgency tasks is obtained as 10.8TB.

[0130] The video data fragmentation rate calculation sub-module, based on the storage demand data of high-urgency tasks, obtains the existing video data storage status in the storage server, extracts the video data occupancy of each storage unit, and uses the formula:

[0131]

[0132] Calculate the video data fragmentation rate , and obtain the data fragmentation rate data of the storage server. Among them, represents the amount of video data already stored in the th storage unit, represents the free storage capacity of the th storage unit, represents the total number of storage units with stored data, represents the total number of storage units in the current server;

[0133] Obtain the existing video data storage status in the storage server, extract the video data occupancy of each storage unit. The distribution status of internal storage blocks in the storage server affects the data fragmentation rate. Assume that a certain storage server contains 500 storage units, among which 250 storage units have already stored video data and 250 storage units are free. Assume the amount of video data already stored in each storage unit is as follows (partial examples)

[0134] Table 5 Video Data Storage Unit Usage Table

[0135]

[0136] As shown in Table 5, the storage capacities of the storage units are different. Assuming that the total stored data volume is 20000 GB and the total free capacity is 10000 GB, the data fragmentation rate is calculated as follows

[0137] ;

[0138] ;

[0139] The calculated data fragmentation rate value of the storage server is 0.187, that is, the fragmentation rate is 18.7%.

[0140] Based on the data fragmentation rate data of the storage server, the video data block merging sub-module performs video data block merging, adjusts the arrangement of fragmented data blocks and the video storage structure within the storage unit, and obtains the optimized video task storage allocation state;

[0141] According to the data fragmentation rate value of the storage server, video data block merging is performed. The fragmented data blocks of the storage server affect the storage efficiency. Assume that there are multiple data blocks in a server, and each block has a different size. Now, a data block merging strategy is adopted to rearrange the storage blocks and fill the free storage units to optimize the storage structure. Assume that the available storage space of the server is 10000 GB and the current storage requirement for high-urgency tasks is 10800 GB. Then the adjustment plan is as follows

[0142] Identify the amount of video data already stored in the storage unit, as shown in Table 5;

[0143] Calculate the filling ratio of each storage unit to ensure that high-urgency tasks can be stored preferentially;

[0144] Sort according to the task urgency and reallocate the storage blocks. For example, merge the scattered data blocks into continuous storage blocks.

[0145] After adjustment, use the above formula to recalculate the fragmentation rate after storage optimization. Assume that the total fragmentation rate after merging is reduced to 10.5%, indicating that the fragmentation rate has dropped from 18.7% to 10.5%, demonstrating a significant optimization effect, and finally obtaining the optimized video task storage allocation state.

[0146] Please refer to Figure 5 , the monitoring target residence analysis module includes:

[0147] The monitoring target residence duration calculation sub-module obtains the entry timestamp of the monitoring target according to the optimized video task storage allocation status, reads the target departure timestamp, calculates the video recording duration of the target in the monitoring area, screens the corresponding relationship between consecutive entry and departure records, calculates the residence durations of different targets in different time periods, summarizes the residence time distribution of the targets in the monitoring area, and obtains the monitoring target residence duration data;

[0148] Call the optimized video task storage allocation status, obtain the entry timestamp of the monitoring target, read the target departure timestamp, perform matching after ensuring data integrity. If there are multiple entry and departure records for the target, screen the corresponding entry and departure times in chronological order, calculate the video recording duration of the monitoring target in the monitoring area. First, extract the historical timestamp data for each target, screen out abnormal data, such as duplicate, missing, or invalid timestamps, correct the data and establish a target time series. Subsequently, calculate the residence duration based on the timestamp difference. For example, if a target enters the monitoring area at 08:30:00 and leaves at 09:15:00, the residence duration is 45 minutes. If the target enters and exits multiple times in a day, merge the time periods to calculate its total residence time, count the residence durations of multiple monitoring targets in different time periods, summarize the residence situation according to the time range. For example, there are 15 targets resident in the 08:00 - 10:00 interval, and the total residence duration is 8 hours in total. Record the daily residence time distribution, and finally obtain the monitoring target residence duration data.

[0149] The target entry frequency statistics sub-module, based on the monitoring target residence duration data, counts the number of entries of the target, analyzes the activity levels of different targets in the monitoring area, calculates the entry frequency of the target in combination with the observation period, and analyzes the time distribution characteristics of the target entering the area to obtain the target entry frequency data;

[0150] Based on the monitoring target residence duration data, count the number of entries of the target, obtain the activity records of the target in the monitoring area, extract the entry records for each day or each hour, remove duplicate records and organize them into an entry time series. For example, if a target enters the monitoring area at 10:05, 12:30, and 16:20, its single-day entry count is 3 times. Screen out the targets with high entry frequencies, set the observation period, such as 7 days or 30 days, count the total number of entries within the statistical period. If a target enters the monitoring area 28 times in 7 days, calculate its average entry frequency. Divide the time intervals by hour, day, and week, and summarize the time distribution characteristics of the target entering the area. For example, the entry count proportion in the 08:00 - 10:00 time period is 40%, form a statistical trend of entry behavior, and finally obtain the target entry frequency data.

[0151] Based on the target entry frequency data, the storage level requirement judgment sub-module calculates the target's departure time interval, counts the residence periods of different targets, combines the video stay duration, entry frequency, and departure time interval of the target in the monitoring area, compares the storage priorities of different targets, and adjusts the video storage level according to the storage requirements classification to obtain the analysis result of the video surveillance target storage level requirements;

[0152] Combined with the target entry frequency data, calculate the target's departure time interval, analyze the stay patterns of targets in the monitoring area, and classify all monitored targets according to the entry frequency, residence duration, and departure interval. The specific classification basis is as follows:

[0153] High-frequency short-stay targets: This type of target enters the monitoring area with a high frequency, but stays for a short time each time. For example, a certain target enters the monitoring area 10 times a day, and each stay does not exceed 5 minutes. This type of target usually appears in crowded areas such as shopping mall entrances and transportation hubs. The storage requirements mainly rely on short-term storage, such as caching or temporary storage;

[0154] Low-frequency long-stay targets: This type of target enters the monitoring area with a low frequency, but stays for a long time each time. For example, a certain target enters the monitoring area an average of 1 time a day, but each residence time exceeds 3 hours. This type of target may be staff, resident personnel, etc. The storage requirements tend to long-term storage for subsequent data traceability;

[0155] Random entry targets: The entry time and residence time of this type of target do not have obvious patterns. For example, a certain target may enter the monitoring area multiple times in a day, but the entry situations on different days vary greatly. The storage requirements of this type of target need to be dynamically adjusted according to the entry records, and the storage priority is at a medium level;

[0156] High-frequency long-stay targets: This type of target has both a high entry frequency and a long residence time. For example, a certain target enters the monitoring area 8 times a day, and each stay is more than 2 hours. This type of target usually has a high storage priority, and its complete video record needs to be ensured, and high-level storage is adopted;

[0157] Low-frequency short-stay targets: This type of target has both a low entry frequency and a short stay time. For example, a certain target enters the monitoring area 2 times a week on average, and each residence time is only 3 minutes. The storage requirements of this type of target are relatively low, and the lowest-level storage or interval storage strategy can be arranged.

[0158] During the classification process, first calculate the average daily entry times of each target, count the residence time of each target, classify the targets based on the set thresholds. For example, the high-frequency standard is set to more than 5 entries per day, the long-stay standard is set to a residence time exceeding 1 hour, and the short-stay standard is set to a residence time less than 10 minutes. Finally, match the storage level according to the classification results, and adjust the storage strategy to obtain the analysis result of the video surveillance target storage level requirements.

[0159] Please refer to Figure 6 , the video storage level adjustment module includes:

[0160] The target entry frequency analysis sub-module combines the analysis results of the storage level requirements of the video surveillance target, obtains the entry records of the surveillance target, counts the number of entries of the target within a specified time period, calculates the entry frequency values of different targets, filters out the targets with an entry frequency higher than the set threshold, and calculates the entry frequency distribution interval to obtain the target entry frequency distribution data;

[0161] First, the monitoring system collects data on the entry records of the target. Taking 24 hours as the time period, the entry situation of the target is recorded every hour to form a complete data set. The number of entries of each target within the period is counted, the entry frequency of the target is calculated, and the interval range of the entry frequency is set, such as low frequency (0 - 2 times), medium frequency (3 - 6 times), high frequency (7 times and above). Classification is carried out based on this. In an actual application scenario, taking the monitoring of the mall entrance as an example, the number of times different customers enter the mall can be recorded. If a certain customer enters the mall 5 times in a day, then his entry frequency belongs to the medium frequency interval. All high-frequency targets are screened, and the mean and standard deviation of their entry frequencies are calculated to obtain the target entry frequency distribution data.

[0162] The storage retention duration adjustment sub-module, based on the target entry frequency distribution data, sets storage retention duration adjustment factors for targets in different frequency intervals, calculates the storage duration adjustment values corresponding to each target, and adjusts the storage retention duration to obtain the storage retention duration adjustment data;

[0163] Based on the target entry frequency distribution data, set the storage retention duration adjustment factors according to the entry frequency interval. For example, the video of high-frequency targets is retained for 72 hours, medium-frequency targets for 48 hours, and low-frequency targets for 24 hours. Calculate the storage retention time of each target, determine the adjustment factor according to the entry frequency value, and use the formula:

[0164] ;

[0165] where is the default storage time, is the entry frequency of the target, and are the lowest and highest entry frequencies respectively. If the default storage time is 24 hours, the target entry frequency is 5, the highest entry frequency is 10, and the lowest is 1, then calculate:

[0166] ;

[0167] Finally, the video storage duration of the target is adjusted to 34.56 hours to obtain the storage retention duration adjustment data.

[0168] The storage server load monitoring sub-module monitors the current load of the storage server, collects the storage occupancy rate, access rate, and storage pressure value, calculates the storage occupancy rate, determines whether the load status exceeds the load threshold, and obtains the storage server load status data;

[0169] Monitor the storage occupancy rate, access rate, and storage pressure value of the storage server, record the storage usage every 10 minutes. The threshold setting of the storage occupancy rate is based on the capacity upper limit of the storage device and the data writing rate. In the current storage architecture, if the actual available capacity of the storage device is lower than 15% of the total storage capacity, the storage occupancy rate exceeds 85%. This value is calculated based on the storage space allocation strategy. Considering the redundant storage overhead of the RAID mechanism, when the storage space reaches 85%, the writing rate usually decreases, affecting the stability of data storage. Therefore, 85% is set as the high load threshold. The setting of the access rate is based on the data throughput capacity. In the current storage architecture, the peak value of the data writing rate can reach 120MB / s. When the storage pressure is high, the I / O processing capacity of the storage controller approaches the upper limit. Therefore, when the access rate exceeds 100MB / s, the storage load tends to be saturated, resulting in a decrease in data access and storage efficiency. Therefore, 100MB / s is set as the high load threshold for the access rate. The setting of the storage pressure value is based on the analysis of IOPS (Input / Output Operations Per Second) and the queue length. When the IOPS exceeds 5000 and the queue depth is greater than 10, the response time of the storage system will be significantly delayed. At this time, the calculated storage pressure value is usually higher than 0.8. Therefore, 0.8 is set as the high load threshold for the storage pressure. When in high load, the storage occupancy rate exceeds 85%, the access rate is higher than 100MB / s, and the storage pressure value is greater than 0.8. When in normal load, the storage occupancy rate is between 60% - 85%, the access rate is 50MB / s - 100MB / s, and the storage pressure value is between 0.5 - 0.8. If the storage occupancy rate of a certain server at a certain moment is 88%, the access rate is 110MB / s, and the storage pressure value is 0.85, then it is determined that it is in a high load state, and the storage server load status data is obtained.

[0170] The low-priority video storage compression sub-module adjusts the data and the storage server load status data based on the storage retention duration, filters out the low-priority video data, and uses the formula:

[0171] ;

[0172] Calculate the storage compression ratio of the low-priority video data , and obtain the video storage level adjustment result; where, represents the storage size of the th video file, represents the remaining storage duration of the video, Represents the target entry frequency of the video, Represents the load ratio of the server where the video is located, Represents the bandwidth occupancy of the video storage server, Represents the average data traffic during the video storage period, Represents the current bandwidth consumption of the video storage, Represents the access frequency of the video, Represents the number of times the video has been compressed, Is the total number of low-priority videos;

[0173] Based on the storage retention duration, adjust the data and the storage server load status data, filter the video data with a storage duration less than 48 hours and the video data when the server is in a high-load state, calculate the storage compression ratio for low-priority videos. Assume the stored video data is as follows:

[0174] Table 6 Low-Priority Video Storage Parameter Table

[0175]

[0176] Substitute the data into the formula for calculation:

[0177] ;

[0178] ;

[0179] ;

[0180] The storage compression ratio adjustment value is 17.9, indicating that the low-priority video data can be compressed 17.9 times in storage, and then the video storage level adjustment result can be obtained.

[0181] The above is only the preferred embodiment of the present invention, and does not limit the present invention in other forms. Any person skilled in the art may use the disclosed technical content to make changes or modifications into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution content of the present invention still fall within the protection scope of the technical solution of the present invention.

Claims

1. A dynamic video surveillance resource management system, characterized in that, The system includes: The task urgency calculation module obtains the event trigger information of the video monitoring task, calculates the coverage radius of the monitoring task, analyzes the urgency of the monitoring task, and obtains the analysis result of the video monitoring task urgency; The video storage resource measurement module reads the storage space utilization rate of the storage server based on the analysis result of the video monitoring task urgency, calculates the storage ratio that can be allocated for each video task, and obtains the video task storage ratio allocation result; The video storage allocation and optimization module calculates the video storage requirements of high-urgency tasks based on the video task storage ratio allocation result, performs video data block merging, and obtains the optimized video task storage allocation status; The monitoring target residence analysis module calculates the video recording duration of the target in the monitoring area, the target entry frequency, and the target departure time interval based on the optimized video task storage allocation status, judges the video storage level requirements, and obtains the analysis result of the video monitoring target storage level requirements; The video storage allocation and optimization module includes: The high-urgency task storage requirement calculation sub-module combines the video task storage ratio allocation result, extracts the storage allocation data corresponding to the high-urgency task, calculates the video storage requirement of the high-urgency task, and obtains the high-urgency task storage requirement data; The video data fragmentation rate calculation sub-module obtains the existing video data storage status in the storage server based on the high-urgency task storage requirement data, extracts the video data occupancy of each storage unit, and uses the formula: ； Calculate the video data fragmentation rate , and obtain the data fragmentation rate data of the storage server, where represents the amount of video data stored in the th storage unit, represents the free storage capacity of the th storage unit, represents the total number of storage units storing data, represents the total number of storage units of the current server; The video data block merging sub-module performs video data block merging based on the storage server data fragmentation rate data, adjusts the arrangement of fragmented data blocks and the video storage structure in the storage unit, and obtains the optimized video task storage allocation status.

2. The dynamic video surveillance resource management system according to claim 1, wherein The analysis result of the video monitoring task urgency includes the task trigger source category, event severity, monitoring task coverage radius, and monitoring task priority. The video task storage ratio allocation result includes the storage ratio of high-priority tasks, medium-priority tasks, low-priority tasks, and the storage server storage load ratio. The video task storage allocation status includes the storage allocation situation of high-urgency tasks, the storage server data fragmentation rate, and the video data block optimization status. The analysis result of the video monitoring target storage level requirements includes the target video recording duration, target entry frequency, target departure time interval, and storage level requirement level.

3. The dynamic video surveillance resource management system according to claim 1, wherein The task urgency calculation module includes: The event trigger information acquisition sub-module obtains the event trigger information of the video monitoring task, reads the task trigger source and event category, analyzes the time characteristics and space characteristics of the event category, and extracts the association information between the event occurrence location, event occurrence time, event type, and task source to obtain the event association information parameter set; The monitoring task coverage radius calculation sub-module analyzes and determines the required coverage range of the monitoring task based on the event association information parameter set, and uses the formula: ； Calculate the coverage radius value of the monitoring task , where represents the area of the event impact region represents the event propagation speed represents the response time required The monitoring task urgency analysis sub-module calculates the urgency index of the current monitoring task based on the monitoring task coverage radius value, in combination with the risk coefficient of the event type, the event occurrence density, and the historical urgency distribution, and obtains the video monitoring task urgency analysis result.

4. The dynamic video surveillance resource management system according to claim 1, wherein The video storage resource measurement module includes: The storage resource acquisition sub-module obtains the available storage capacity of all current video storage servers, reads the storage space utilization rate of the storage servers, counts the total number of tasks in the current video task queue, extracts the storage resource status data of each server, and obtains the storage resource status parameter set; The task priority calculation sub-module, based on the storage resource status parameter set, in combination with the video monitoring task urgency analysis result, divides high, medium, and low priority tasks according to the task urgency, counts the number of tasks of high, medium, and low priorities, and uses the formula: ； Calculate the proportion of type of tasks , and integrate to obtain the proportion data of tasks with each priority. Among them, represents the number of type of tasks, represents the total amount of high, medium, and low priority tasks; The storage ratio allocation sub-module adjusts the video storage resource allocation ratio based on the proportion data of each priority task, calculates the storage ratio that can be allocated to each video task, and obtains the video task storage ratio allocation result.

5. The dynamic video surveillance resource management system according to claim 1, wherein The monitored target residence analysis module includes: The monitored target residence duration calculation sub-module, according to the optimized video task storage allocation status, obtains the entry timestamp of the monitored target, reads the target departure timestamp, calculates the video recording duration of the target in the monitoring area, screens the corresponding relationship between consecutive entry and departure records, calculates the residence duration of different targets in different time periods, and summarizes the residence time distribution of targets in the monitoring area to obtain the monitored target residence duration data; The target entry frequency statistics sub-module, based on the monitored target residence duration data, counts the number of target entries, analyzes the activity of different targets in the monitoring area, calculates the target entry frequency in combination with the observation period, and analyzes the time distribution characteristics of the target entry area to obtain the target entry frequency data; The storage level requirement judgment sub-module calculates the target departure time interval based on the target entry frequency data, counts the retention periods of different targets, combines the video residence duration, entry frequency, and departure time interval of the target in the monitoring area, compares the storage priorities of different targets, and adjusts the video storage level according to the storage requirements classification to obtain the video monitoring target storage level requirement analysis result.

6. The dynamic video surveillance resource management system according to claim 1, characterized in that The system further includes a video storage level adjustment module; The video storage level adjustment module adjusts the video storage retention duration based on the video monitoring target storage level requirement analysis result, in combination with the storage server load status, performs storage compression on low-priority video tasks to release storage space, and obtains the video storage level adjustment result; The video storage level adjustment result includes the adjustment of the video storage retention duration, the storage server load balancing status, the storage compression ratio of low-priority tasks, and the storage space release amount.

7. The dynamic video surveillance resource management system according to claim 6, wherein The video storage level adjustment module includes: The target entry frequency analysis sub-module combines the results of the video surveillance target storage hierarchy requirement analysis, obtains the entry records of the surveillance targets, counts the number of entries of the targets within a specified time period, calculates the entry frequency values of different targets, filters the targets with entry frequencies higher than the set threshold, and calculates the entry frequency distribution interval to obtain the target entry frequency distribution data; The storage retention duration adjustment sub-module sets storage retention duration adjustment factors for targets in different frequency intervals based on the target entry frequency distribution data, calculates the storage duration adjustment values corresponding to each target, and adjusts the storage retention duration to obtain the storage retention duration adjustment data; The storage server load monitoring sub-module monitors the current load of the storage server, collects the storage occupancy rate, access rate, and storage pressure values, calculates the storage occupancy rate, and determines whether the load status exceeds the load threshold to obtain the storage server load status data; The low-priority video storage compression sub-module filters the low-priority video data based on the storage retention duration adjustment data and the storage server load status data, using the formula: ； Calculate the storage compression ratio of low-priority video data , and obtain the video storage level adjustment result; where represents the storage size of the th video file, represents the remaining storage duration of the video, represents the target entry frequency of the video, represents the load ratio of the server where the video is located, represents the bandwidth occupancy of the video storage server, represents the average data traffic during the video storage period, represents the current bandwidth consumption of the video storage, represents the access frequency of the video, represents the number of compression histories of the video, is the total number of low-priority videos.

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