A cloud storage scheduling system applied to video surveillance

Abstract

The present application discloses a cloud storage scheduling system applied to video surveillance, including: a data collection module, a data analysis module, and a device scheduling module; the data collection module is used to obtain video data in a video surveillance device, collect status data of each resource pool, perform preliminary aggregation processing on the video data and the status data to obtain structured data, and the status data includes bandwidth, concurrency, and storage; the data analysis module is used to perform resource pool load analysis and comprehensive load status analysis according to the structured data to respectively obtain a single load rating and a comprehensive load rating; the device scheduling module is used to calculate the required concurrency for scheduling according to the single load rating and the comprehensive load rating, and select the required scheduling devices in the same area or adjacent areas according to the required concurrency for scheduling. The present application can solve the technical problem that the existing cloud storage technology cannot meet the flexible scheduling requirements, resulting in the lack of efficiency in the storage process.

Description

Technical Field

[0001] This application relates to the field of cloud storage technology, and particularly to a cloud storage scheduling system applied to video surveillance. Background Art

[0002] With the continuous development and improvement of the video surveillance industry and the enhancement of residents' awareness of security prevention, the security industry market in China has been continuously expanding, and the cloud storage of surveillance data has become a major growth point in the field of video surveillance. Generally, there are two types of storage methods for camera surveillance videos in the industry: local file storage and cloud storage. Based on the scenario of cloud storage, how to solve the cloud storage scheduling of large-scale video surveillance devices, how to ensure the success rate of cloud storage, and how to ensure the load balance of each region and each resource pool have become problems that need to be solved by the cloud storage scheduling system.

[0003] Currently, cloud storage services generally only provide cloud storage capabilities, and in the cloud storage scenario with a large number of devices distributed in multiple regions, they cannot meet the requirements of flexible, real-time, and efficient scheduling. Summary of the Invention

[0004] This application provides a cloud storage scheduling system applied to video surveillance, which is used to solve the technical problem that the existing cloud storage technology cannot meet the flexible scheduling requirements, resulting in a lack of efficiency in the storage process.

[0005] In view of this, the first aspect of this application provides a cloud storage scheduling system applied to video surveillance, including: a data collection module, a data analysis module, and a device scheduling module;

[0006] The data collection module is used to obtain video data in video surveillance devices, collect status data of each resource pool, and perform preliminary aggregation processing on the video data and the status data to obtain structured data. The status data includes bandwidth, concurrency, and storage;

[0007] The data analysis module is used to perform resource pool load analysis and comprehensive load status analysis based on the structured data to obtain single-item load ratings and comprehensive load ratings respectively;

[0008] The device scheduling module is used to calculate the required concurrency for scheduling based on the single-item load rating and the comprehensive load rating, and select the required scheduling devices in the same region or adjacent regions according to the required concurrency for scheduling.

[0009] Further, it further includes: a data storage module;

[0010] The data storage module is used to store the structured data, the single-item load rating, and the comprehensive load rating.

[0011] Further, it further includes: a load monitoring module;

[0012] The load monitoring module is used to perform load monitoring based on the individual load rating and the comprehensive load rating, and trigger a preset alarm mechanism when the load level exceeds the threshold.

[0013] Furthermore, it further includes: a display interface;

[0014] The display interface is used to visually display the structured data, the individual load rating, and the comprehensive load rating, and perform hierarchical rendering and display on the resource pool according to the individual load rating and the comprehensive load rating.

[0015] Furthermore, the device scheduling module is specifically used for:

[0016] Calculate the scheduling ratio according to the individual load rating and the comprehensive load rating;

[0017] Calculate the required concurrency for scheduling based on the scheduling ratio;

[0018] Judge whether the available concurrency of the resource pool in the same region is sufficient according to the required concurrency for scheduling. If so, select the required scheduling devices from the resource pools in the same region according to the required concurrency for scheduling. If not, select the required scheduling devices from the resource pools in the adjacent region according to the preset adjacent region configuration table.

[0019] It can be seen from the above technical solutions that the embodiments of the present application have the following advantages:

[0020] In the present application, a cloud storage scheduling system applied to video monitoring is provided, including: a data collection module, a data analysis module, and a device scheduling module; the data collection module is used to obtain video data in video monitoring devices, collect the status data of each resource pool, and perform preliminary aggregation processing on the video data and the status data to obtain structured data, and the status data includes bandwidth, concurrency, and storage; the data analysis module is used to perform resource pool load analysis and comprehensive load status analysis according to the structured data, and respectively obtain an individual load rating and a comprehensive load rating; the device scheduling module is used to calculate the required concurrency for scheduling according to the individual load rating and the comprehensive load rating, and select the required scheduling devices in the same region or adjacent region according to the required concurrency for scheduling.

[0021] The cloud storage scheduling system applied to video surveillance provided by this application can, by analyzing the stored video data and the status data of the cloud storage resource pool, clearly identify the load status in the storage device in real time, and then obtain single-item and comprehensive load ratings. Based on the load ratings, devices in the same area and different areas can be scheduled and allocated to avoid excessive load affecting the storage efficiency. That is, by dispersing the load pressure, the storage devices are coordinated and scheduled to improve the flexibility and storage efficiency of the storage system. Therefore, this application can solve the technical problem that the existing cloud storage technology cannot meet the flexible scheduling requirements, resulting in the lack of efficiency in the storage process. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 It is a schematic structural diagram of a cloud storage scheduling system applied to video surveillance provided by an embodiment of this application;

[0023] Figure 2 It is a schematic hardware structure diagram of a cloud storage scheduling system applied to video surveillance provided by an application example of this application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0024] In order to enable those skilled in the art to better understand the solution of this application, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of this application. Obviously, the described embodiments are only a part of the embodiments of this application, rather than all the embodiments. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of this application.

[0025] For ease of understanding, please refer to Figure 1 , an embodiment of a cloud storage scheduling system applied to video surveillance provided by this application, includes: a data collection module 101, a data analysis module 102, and a device scheduling module 103.

[0026] The data collection module 101 is used to obtain the video data in the video surveillance device, collect the status data of each resource pool, and perform preliminary aggregation processing on the video data and the status data to obtain structured data. The status data includes bandwidth, concurrency, and storage.

[0027] The video data is obtained by the cameras of the video surveillance devices. Generally, it is divided into regions according to the geographical location, that is, different regions have video surveillance systems formed by different video surveillance devices, and the obtained video data is collected and stored in the cloud storage platform.

[0028] The status data is the status information of the resource pool in the cloud storage platform, including real-time bandwidth, concurrency, storage space, etc., and may also include operation record information; the resource pool refers to the cloud storage resource pool, which is used to receive and store the monitored time data uploaded by the device, can also record logs in real time, and provide the current status data, etc.

[0029] The preliminary aggregation process can structure and correlate the data. This operation is mainly to organize the data into a format convenient for analysis and use, and to establish associations between relevant data; moreover, structured data is also convenient for storage, retrieval, etc.

[0030] The data analysis module 102 is used to perform resource pool load analysis and comprehensive load status analysis based on the structured data, and obtain single-item load ratings and comprehensive load ratings respectively.

[0031] Based on the structured data, the rated values of each resource pool can be determined, that is, the rated device concurrency, rated storage space, rated bandwidth, etc. Based on these rated information, the load status interval can be determined for subsequent rating.

[0032] First, the device access concurrency utilization rate, storage utilization rate, and bandwidth utilization rate between a certain time period T0 - T1 can be calculated based on the video data and status data:

[0033] Device concurrency utilization rate: R1 = maximum adopted value in the time period T0 - T1 / rated value × 100%;

[0034] Storage utilization rate: R2 = used storage space value at time T1 / rated value × 100%;

[0035] Bandwidth utilization rate: R2 = maximum adopted value in the time period T0 - T1 / rated value × 100%.

[0036] The single-item load rating is a process of dividing grades from low to high according to the utilization rate. The obtained load grades include low load, normal load, high load, and ultra-high load. Among them, the utilization rate of low load can be set at 40% and below, the utilization rate of normal load is set between 40% and 75%, the utilization rate of high load is set between 75% and 90%, and ultra-high load is set above 90%. This is just an example, and the allocation range can also be set according to the actual situation.

[0037] The device concurrent load D1, storage load D2, and bandwidth load D3 can be calculated according to a preset data range. Then, based on the obtained different loads, a comprehensive load rating analysis can be performed to obtain the comprehensive load levels: low load, normal load, high load, ultra-high load, etc. Specifically, low load means that D1, D2, and D3 are all at low load; normal load means that one or more of D1, D2, and D3 are at normal load, without high load and ultra-high load; high load means that one or more of D1, D2, and D3 are at high load, without ultra-high load; ultra-high load: one or more of D1, D2, and D3 are at ultra-high load. It can be understood that the load division is achieved according to the rated parameters or preset reference values, that is, if it exceeds the preset value, it is described according to a certain level.

[0038] The device scheduling module 103 is used to calculate the required concurrency for scheduling according to the single-load rating and the comprehensive load rating, and select the required scheduling devices in the same area or adjacent areas according to the required concurrency for scheduling.

[0039] Furthermore, the device scheduling module 103 is specifically used for:

[0040] Calculate the scheduling ratio according to the single-load rating and the comprehensive load rating;

[0041] Calculate the required concurrency for scheduling based on the scheduling ratio;

[0042] Judge whether the available concurrency in the resource pool in the same area is sufficient according to the required concurrency for scheduling. If so, select the required scheduling devices in the resource pool in the same area according to the required concurrency for scheduling. If not, select the required scheduling devices in the resource pool in the adjacent area according to the preset adjacent area configuration table.

[0043] The single-load rating and the comprehensive load rating include the load levels of the devices, so they can reflect the utilization of the devices. According to the load rating, the resource pool can be initially screened, sorted according to the rating from high to low, and after selecting the optimal ones, the scheduling ratio can be calculated; the scheduling ratio also includes the scheduling ratio related to bandwidth, the scheduling ratio related to concurrency, and the scheduling ratio related to storage, which can be calculated respectively as follows:

[0044] Bandwidth: Scheduling ratio A = (Current bandwidth utilization rate R2 - Intermediate value of normal load) / R2

[0045] Required concurrent number to be scheduled B = A × Rated concurrency

[0046] Required bandwidth number to be scheduled C = A × Bandwidth concurrency

[0047] Concurrency: Scheduling ratio A = (Current concurrency utilization rate R1 - Intermediate value of normal load) / R1

[0048] Required concurrent number to be scheduled B = A × Rated concurrency

[0049] The required scheduling bandwidth quantity C = A × bandwidth concurrency

[0050] Storage: The scheduling ratio A = the scheduling ratio configured by the system at different levels

[0051] The required scheduling concurrency quantity B = A × rated concurrency

[0052] The required scheduling bandwidth quantity C = A × bandwidth concurrency

[0053] Among them, the scheduling concurrency quantity is the required concurrency quantity for scheduling. The change trends of the corresponding scheduling concurrency quantity and scheduling bandwidth quantity calculated according to the scheduling ratio are the same. Therefore, only the required concurrency quantity for scheduling needs to be discussed during the scheduling process. Based on the required concurrency quantity for scheduling, the number of scheduling devices to be selected can be determined. Devices are preferentially selected from the resource pools in the same area. If the available concurrency quantity in the resource pool in the same area is sufficient, scheduling can be directly carried out in the same area, which is fast and efficient. If the same area cannot meet the scheduling requirements, scheduling devices need to be selected from the resource pools in adjacent areas, that is, cross-region scheduling. The preset adjacent area configuration table can clarify the specific adjacent areas, such as Adjacent Area 1, Adjacent Area 2, Adjacent Area 3, and Adjacent Area 4, etc., and configure specific adjacent priorities, assuming they are in the order of the serial numbers. One or more adjacent areas can be selected as the target scheduling areas according to the adjacent area priorities in the table. The target resource pool available for scheduling in the target scheduling area, that is, the shared pool, can also be determined. Correspondingly, non-shared pools are also included in this area. The shared pool is configured with a maximum shared concurrency, and the maximum shared concurrency cannot be exceeded during cross-region scheduling.

[0054] It should be noted that the system in this embodiment supports mass device cloud storage partition scheduling. The areas, resource pools, and devices available for scheduling can all calculate the number of devices that can receive incoming devices in advance according to their bandwidth and concurrency resource conditions. In addition, the platform can send new cloud storage policies to the devices that need to be scheduled, and the devices will store the new monitored video data in the new resource pools.

[0055] Furthermore, it further includes: a data storage module 104;

[0056] The data storage module is used to store structured data, single-item load ratings, and comprehensive load ratings.

[0057] The configured relational database is used to store structured data, single-item load ratings, and comprehensive load ratings. In addition, other data information can also be stored according to the system needs, which is not limited here.

[0058] Furthermore, it further includes: a load monitoring module 105;

[0059] A load monitoring module is used to perform load monitoring based on single - item load ratings and comprehensive load ratings, and trigger a preset alarm mechanism when the load level exceeds a threshold.

[0060] Load monitoring can achieve global monitoring, regional monitoring, and resource - pool monitoring. When the load level exceeds the threshold, a preset alarm mechanism is triggered for alarm. The threshold is the preset load alarm level, which can be set or adjusted according to the actual situation and is not limited here. Global monitoring is to view the global load situation and the load situation of each region, providing a basis for resource - pool expansion / construction; regional monitoring uses the data of each resource pool to obtain the load of each region, providing the load situation of each pool in the same region; resource - pool monitoring can be accurate to specific load indicators, providing a basis for early warning and specific scheduling. The preset alarm mechanism varies according to storage, concurrency, and bandwidth, and it can be known that the corresponding device scheduling is also different, and it can be specifically set according to the actual situation.

[0061] Furthermore, it further includes: a display interface 106;

[0062] The display interface is used to visually display structured data, single - item load ratings, and comprehensive load ratings, and perform hierarchical rendering and display of the resource pool according to the single - item load rating and the comprehensive load rating. The display interface is to facilitate the operator to view the monitoring data and the load situation during early warning, more intuitively view the storage status of the cloud storage system, and improve the monitoring visualization effect. In addition, the display interface can perform different - color rendering on the resource pool according to different ratings and then display it to improve the visualization effect.

[0063] For ease of understanding, the present application provides an application example of a cloud storage scheduling system applied to video monitoring. Its hardware structure is as Figure 2 shown. There are resource pools in different regions, and there are specific storage devices in the resource pools. The data synchronization module can structurally store the data in the resource pool, and then perform data analysis and rating operations through the data analysis module; the monitoring module can perform load monitoring, and the scheduling module issues device scheduling instructions to complete the cloud storage scheduling task.

[0064] The cloud storage scheduling system provided by the embodiments of the present application can, by analyzing the stored video data and the status data of the cloud storage resource pool, clearly determine the load status in the storage device in real time, and then obtain single - item and comprehensive load ratings; and according to the load ratings, the devices in the same region and different regions can be scheduled and allocated to avoid ultra - high load affecting the storage efficiency; that is, by dispersing the load pressure, the storage devices are overall scheduled to improve the flexibility and storage efficiency of the storage system. Therefore, the embodiments of the present application can solve the technical problem that the existing cloud storage technology cannot meet the flexible scheduling requirements, resulting in the lack of high efficiency in the storage process.

[0065] In several embodiments provided by the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections between each other can be through some interfaces. The indirect couplings or communication connections of the devices or units can be in electrical, mechanical or other forms.

[0066] The units described as separate components may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they can be located in one place, or they can be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0067] In addition, in each embodiment of the present application, the functional units can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above-mentioned integrated units can be implemented in the form of hardware or in the form of software functional units.

[0068] If the above-mentioned integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, or all or part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in each embodiment of the present application. And the aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (English full name: Read-Only Memory, English abbreviation: ROM), random access memories (English full name: Random Access Memory, English abbreviation: RAM), magnetic disks or optical discs that can store program codes.

[0069] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the various embodiments of the present application.

Claims

1. A cloud storage scheduling system applied to video surveillance, characterized in that, Including: A data collection module, a data analysis module, and a device scheduling module; The data collection module is used to obtain video data in video surveillance devices, collect status data of each resource pool, and perform preliminary aggregation processing on the video data and the status data to obtain structured data. The status data includes bandwidth, concurrency, and storage; The data analysis module is used to perform resource pool load analysis and comprehensive load status analysis based on the structured data, and respectively obtain a single load rating and a comprehensive load rating; The device scheduling module is used to calculate the required concurrency for scheduling according to the single load rating and the comprehensive load rating, and select the required scheduling devices in the same area or adjacent areas according to the required concurrency for scheduling; Specifically, the device scheduling module is used for: Calculating a scheduling ratio according to the single load rating and the comprehensive load rating; Calculating the required concurrency for scheduling based on the scheduling ratio; Judging whether the available concurrency of the resource pool in the same area is sufficient according to the required concurrency for scheduling. If so, selecting the required scheduling devices from the resource pools in the same area according to the required concurrency for scheduling. If not, selecting the required scheduling devices from the resource pools in adjacent areas according to a preset adjacent area configuration table; Wherein, the scheduling ratio includes a scheduling ratio related to bandwidth, a scheduling ratio related to concurrency, and a scheduling ratio related to storage; The scheduling ratio related to bandwidth = (current bandwidth utilization rate R2 - intermediate value of normal load) / R2; The scheduling ratio related to concurrency = (current concurrency utilization rate R1 - intermediate value of normal load) / R1; The scheduling ratio related to storage = the scheduling ratio configured at the system level by level.

2. The cloud storage scheduling system applied to video surveillance according to claim 1, wherein It further includes: A data storage module; The data storage module is used to store the structured data, the single load rating, and the comprehensive load rating.

3. The cloud storage scheduling system applied to video surveillance according to claim 1, wherein, It further includes: a load monitoring module; The load monitoring module is used to perform load monitoring according to the single load rating and the comprehensive load rating, and trigger a preset alarm mechanism when the load level exceeds the threshold.

4. The cloud storage scheduling system applied to video surveillance according to claim 1, characterized in that, It further includes: A display interface; The display interface is used to visually display the structured data, the single load rating, and the comprehensive load rating, and perform hierarchical rendering display on the resource pool according to the single load rating and the comprehensive load rating.

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