CN115665252A - Cloud storage scheduling system applied to video monitoring - Google Patents
Cloud storage scheduling system applied to video monitoring Download PDFInfo
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- H—ELECTRICITY
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Abstract
The application discloses cloud storage scheduling system for in video monitoring includes: the system comprises a data collection module, a data analysis module and an equipment scheduling module; the data collection module is used for acquiring video data in the video monitoring equipment, collecting state data of each resource pool, and carrying out primary aggregation processing on the video data and the state data to obtain structured data, wherein the state data comprises bandwidth, concurrency and storage; the data analysis module is used for carrying out resource pool load analysis and load state comprehensive analysis according to the structured data to respectively obtain a single load rating and a comprehensive load rating; and the equipment scheduling module is used for calculating the concurrency required by scheduling according to the single load rating and the comprehensive load rating, and selecting the equipment required to be scheduled in the same region or the adjacent region according to the concurrency required by scheduling. The method and the device can solve the technical problem that the existing cloud storage technology cannot meet flexible scheduling requirements and cause the storage process to lack high efficiency.
Description
Technical Field
The application relates to the technical field of cloud storage, in particular to a cloud storage scheduling system applied to video monitoring.
Background
With the continuous development and improvement of the video monitoring industry and the enhancement of the security awareness of residents, the market of the security industry in China is continuously expanded, and the cloud of monitoring data becomes a main growth point in the field of video monitoring. The storage mode of camera monitoring videos in the industry is generally 2 types: local file storage and cloud storage. Based on a cloud storage scene, how to solve cloud storage scheduling of large-scale video monitoring equipment, how to guarantee a cloud storage success rate, how to guarantee load balance of each resource pool in each area becomes a problem to be solved by a cloud storage scheduling system.
Currently, cloud storage services generally only provide cloud storage capacity, and cannot meet the requirements of flexible, real-time and efficient scheduling in a cloud storage scene with a large amount of equipment and a plurality of distributed areas.
Disclosure of Invention
The application provides a cloud storage scheduling system applied to video monitoring, which is used for solving the technical problem that the existing cloud storage technology cannot meet flexible scheduling requirements and the storage process lacks high efficiency.
In view of this, a first aspect of the present application provides a cloud storage scheduling system applied in video monitoring, including: the system comprises a data collection module, a data analysis module and an equipment scheduling module;
the data collection module is used for acquiring video data in video monitoring equipment, collecting state data of each resource pool, and performing primary aggregation processing on the video data and the state data to obtain structured data, wherein the state data comprises bandwidth, concurrency and storage;
the data analysis module is used for carrying out resource pool load analysis and load state comprehensive analysis according to the structured data to respectively obtain a single load rating and a comprehensive load rating;
and the equipment scheduling module is used for calculating the concurrency required by scheduling according to the single load rating and the comprehensive load rating, and selecting the required scheduling equipment in the same region or an adjacent region according to the concurrency required by scheduling.
Further, still include: a data storage module;
the data storage module is configured to store the structured data, the individual load ratings, and the composite load ratings.
Further, the method also comprises the following steps: a load monitoring module;
and the load monitoring module is used for monitoring the load according to the single load rating and the comprehensive load rating and triggering a preset alarm mechanism when the load rating exceeds a threshold value.
Further, still include: displaying an interface;
the display interface is used for visually displaying the structured data, the single load rating and the comprehensive load rating and performing graded rendering display on a resource pool according to the single load rating and the comprehensive load rating.
Further, the device scheduling module is specifically configured to:
calculating a scheduling proportion according to the single load rating and the comprehensive load rating;
calculating the concurrency required by scheduling based on the scheduling proportion;
and judging whether the available concurrency of the resource pools in the same region is sufficient or not according to the scheduling required concurrency, if so, selecting required scheduling equipment from the resource pools in the same region according to the scheduling required concurrency, and if not, selecting the required scheduling equipment from the resource pools in adjacent regions according to a preset adjacent region configuration table.
According to the technical scheme, the embodiment of the application has the following advantages:
in this application, a cloud storage scheduling system for video monitoring is provided, including: the system comprises a data collection module, a data analysis module and an equipment scheduling module; the data collection module is used for acquiring video data in the video monitoring equipment, collecting state data of each resource pool, and carrying out primary aggregation processing on the video data and the state data to obtain structured data, wherein the state data comprises bandwidth, concurrency and storage; the data analysis module is used for carrying out resource pool load analysis and load state comprehensive analysis according to the structured data to respectively obtain a single load rating and a comprehensive load rating; and the equipment scheduling module is used for calculating the concurrency required by scheduling according to the single load rating and the comprehensive load rating, and selecting the equipment required to be scheduled in the same region or the adjacent region according to the concurrency required by scheduling.
According to the cloud storage scheduling system applied to video monitoring, the load state in the storage equipment can be determined in real time by analyzing the stored video data and the state data of the cloud storage resource pool, and then single and comprehensive load ratings can be obtained; the equipment in the same area and different areas can be dispatched and distributed according to the load rating, so that the storage efficiency is prevented from being influenced by ultrahigh load; the storage equipment is integrally scheduled in a load pressure dispersing mode, and the flexibility and the storage efficiency of the storage system are improved. Therefore, the technical problem that the existing cloud storage technology cannot meet flexible scheduling requirements and the storage process is lack of high efficiency can be solved.
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Fig. 1 is a schematic structural diagram of a cloud storage scheduling system applied to video monitoring according to an embodiment of the present disclosure;
fig. 2 is a schematic diagram of a hardware structure of a cloud storage scheduling system applied to video monitoring according to an application example of the present application.
Detailed Description
In order to make those skilled in the art better understand the technical solutions of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
For convenience of understanding, please refer to fig. 1, an embodiment of a cloud storage scheduling system applied in video monitoring provided in the present application includes: a data collection module 101, a data analysis module 102 and a device scheduling module 103.
The data collection module 101 is configured to obtain video data in the video monitoring device, collect status data of each resource pool, and perform preliminary aggregation processing on the video data and the status data to obtain structured data, where the status data includes bandwidth, concurrency, and storage.
The video data is acquired by a camera of the video monitoring equipment, and is generally divided into regions according to the geographical position, namely, different video monitoring systems formed by different video monitoring equipment are arranged in different regions, and the acquired video data collection value is stored in a cloud storage platform.
The state data is state information of a resource pool in the cloud storage platform, and comprises real-time bandwidth, concurrency, storage space and the like, and can also comprise operation record information; the resource pool is a cloud storage resource pool and is used for receiving and storing monitoring time data uploaded by the equipment, recording logs in real time, providing current state data and the like.
The preliminary aggregation processing can be used for structuring and correlating the data, and the operation is mainly used for collating the data into a format convenient for analysis and use and correlating and establishing the related data; and the structured data is also convenient to store, call and the like.
And the data analysis module 102 is configured to perform resource pool load analysis and load state comprehensive analysis according to the structured data, and obtain a single load rating and a comprehensive load rating respectively.
According to the structured data, rated values of each resource pool, namely, rated equipment concurrency, rated storage space, rated bandwidth and the like can be determined, and according to the rated information, a load state interval can be determined for subsequent rating.
Firstly, the device access concurrency utilization rate, the storage utilization rate and the bandwidth utilization rate between a certain time period T0-T1 can be calculated according to the video data and the state data:
the equipment concurrency utilization rate is as follows: maximum adopted value/rated value x 100% for the period of R1= T0 to T1;
storage utilization rate: r2= used memory space value/nominal value x 100% of T1 time;
bandwidth utilization: the maximum sampling value/rated value x 100% of the R2= T0 to T1 period.
The single load rating is a process of rating according to the utilization rate from low to high, and the obtained load rating comprises low load, normal load, high load and ultrahigh load. Here, the utilization rate of the low load may be set to 40% or less, the utilization rate of the normal load may be set to 40% to 75%, the utilization rate of the high load may be set to 75% to 90%, and the ultra-high load may be set to 90% or more, which is just an example, and the allocation range may also be set according to actual circumstances.
The device concurrent load D1, the storage load D2, and the bandwidth load D3 may be calculated according to a preset data interval, and then a comprehensive load rating analysis may be performed according to the obtained different loads to obtain a comprehensive load rating: low load, normal load, high load, ultra high load, etc. Specifically, low load means that D1, D2, and D3 are all at low load; the normal load refers to that one or more of D1, D2 and D3 are in normal load, and no high load or ultrahigh load exists; the high load means that one or more of D1, D2 and D3 are in high load and no ultrahigh load exists; ultrahigh load: one or more of D1, D2, D3 are at ultra-high load. It is understood that the load division is performed according to the rated parameters or preset reference values, i.e. the load division is described according to a level when the preset value is exceeded.
And the device scheduling module 103 is configured to calculate a concurrency amount required for scheduling according to the single load rating and the comprehensive load rating, and select a device to be scheduled in the same region or an adjacent region according to the concurrency amount required for scheduling.
Further, the device scheduling module 103 is specifically configured to:
calculating a scheduling proportion according to the single load rating and the comprehensive load rating;
calculating the concurrency required by scheduling based on the scheduling proportion;
and judging whether the available concurrency of the resource pools in the same region is sufficient or not according to the concurrency required by the scheduling, if so, selecting the required scheduling equipment from the resource pools in the same region according to the concurrency required by the scheduling, and if not, selecting the required scheduling equipment from the resource pools in adjacent regions according to a preset adjacent region configuration table.
The single load rating and the comprehensive load rating comprise the load grade of the equipment, so that the utilization condition of the equipment can be reflected, the resource pool can be preliminarily screened according to the load rating, sorted according to the rating, and subjected to scheduling proportion calculation after preferential selection; the scheduling ratio also includes a bandwidth-related scheduling ratio, a concurrency-related scheduling ratio, and a storage-related scheduling ratio, which may be respectively calculated as follows:
bandwidth: scheduling ratio a = (current bandwidth utilization R2-normal load intermediate value)/R2
Number of required scheduling concurrencies B = a × nominal concurrency
Number of required scheduling bandwidths C = a × bandwidth concurrency
And (3) concurrence: scheduling ratio a = (current concurrent utilization R1 — normal load median)/R1
Number of required scheduling concurrencies B = a × nominal concurrency
Number of required scheduling bandwidths C = a × bandwidth concurrence
And (3) storing: scheduling ratio a = scheduling ratio configured in system level
Number of required scheduling concurrencies B = a × nominal concurrency
Number of required scheduling bandwidths C = a × bandwidth concurrence
The scheduling concurrency number is the concurrency amount required by scheduling, and the change trend of the corresponding scheduling concurrency number and the scheduling bandwidth number calculated according to the scheduling proportion is the same, so that the scheduling process only needs to discuss the concurrency amount required by scheduling. The number of scheduling equipment to be selected can be determined according to the concurrency quantity required by scheduling, equipment selection is preferentially carried out in the resource pools in the same area, if the available concurrency quantity of the resource pools in the same area is sufficient, the scheduling is directly carried out in the same area, and the method is fast and efficient; if the same region can not meet the scheduling requirement, scheduling equipment needs to be selected from resource pools of adjacent regions, namely cross-region scheduling; the preset adjacent region configuration table can clarify specific adjacent regions, such as an adjacent region 1, an adjacent region 2, an adjacent region 3, an adjacent region 4 and the like, and configure specific adjacent priorities, assuming that the sequence is the sequence of the sequence numbers; one or more adjacent regions can be selected as target scheduling regions according to the priority of the adjacent regions in the table; a target resource pool, namely a shared pool, available for scheduling in a target scheduling region can also be determined, and the region also comprises a non-shared pool, wherein the shared pool corresponds to the target resource pool; the sharing pool is configured with the maximum sharing concurrency, and the maximum sharing concurrency can not be exceeded during cross-region scheduling.
It should be noted that the system of this embodiment supports scheduling of cloud storage partitions of mass devices. The regions, resource pools and devices available for scheduling can calculate the number of devices available for receiving and calling in advance according to the bandwidth and the concurrent resource condition. In addition, the platform can issue a new cloud storage policy to the device needing scheduling, and the device can store new monitoring video data into a new resource pool.
Further, still include: a data storage module 104;
and the data storage module is used for storing the structured data, the single load rating and the comprehensive load rating.
The relational database is configured to store the structured data, the individual load ratings and the comprehensive load ratings, and may store other data information according to the system requirements, which is not limited herein.
Further, the method also comprises the following steps: a load monitoring module 105;
and the load monitoring module is used for monitoring the load according to the single load rating and the comprehensive load rating and triggering a preset alarm mechanism when the load rating exceeds a threshold value.
The load monitoring can realize global monitoring, regional monitoring and resource pool monitoring, and a preset alarm mechanism is triggered to alarm when the load grade exceeds a threshold value. The threshold, i.e. the preset load alarm level, may be set or adjusted according to the actual situation, and is not limited herein. The global monitoring is carried out to check the global load condition and the load condition of each region and provide a basis for the expansion/construction of the resource pool; the region monitoring utilizes the data of each resource pool to obtain the load of each region and provides the load condition of each pool in the same region; the resource pool monitoring can be accurate to specific load indexes, and early warning and specific scheduling bases are provided. The preset alarm mechanism is different according to storage, concurrency and bandwidth, and it can be known that the corresponding equipment scheduling is different, and the preset alarm mechanism can be specifically set according to actual conditions.
Further, still include: a display interface 106;
and the display interface is used for visually displaying the structured data, the single load rating and the comprehensive load rating and performing graded rendering display on the resource pool according to the single load rating and the comprehensive load rating. The display interface is used for facilitating the operator to check the monitoring data and the load condition during early warning, so that the storage state of the cloud storage system can be checked more visually, and the monitoring visualization effect is improved. In addition, the display interface can be used for displaying the resource pool after different colors are rendered according to different ratings, and the visualization effect is improved.
For convenience of understanding, the application example of the cloud storage scheduling system applied to video monitoring is provided, a hardware structure of the cloud storage scheduling system is shown in fig. 2, resource pools are arranged in different areas, and each resource pool is provided with a specific storage device, a data synchronization module can perform structured storage on data in the resource pools, and then perform operations such as data analysis and rating through a data analysis module; the monitoring module can monitor the load, and the scheduling module sends an equipment scheduling instruction to complete a scheduling task of cloud storage.
According to the cloud storage scheduling system applied to video monitoring, the load state in the storage equipment can be determined in real time by analyzing the stored video data and the state data of the cloud storage resource pool, and further single and comprehensive load ratings can be obtained; equipment in the same area and equipment in different areas can be dispatched and distributed according to the load grades, so that the storage efficiency is prevented from being influenced by ultrahigh load; the storage equipment is integrally scheduled in a load pressure dispersing mode, and the flexibility and the storage efficiency of the storage system are improved. Therefore, the technical problem that the existing cloud storage technology cannot meet flexible scheduling requirements and accordingly the storage process is lack of high efficiency can be solved.
In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be substantially implemented or contributed to by the prior art, or all or part of the technical solution may be embodied in a software product, which is stored in a storage medium and includes instructions for executing all or part of the steps of the method described in the embodiments of the present application through a computer device (which may be a personal computer, a server, or a network device). And the aforementioned storage medium includes: a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
The above embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions in the embodiments of the present application.
Claims (5)
1. A cloud storage scheduling system applied to video monitoring is characterized by comprising: the system comprises a data collection module, a data analysis module and an equipment scheduling module;
the data collection module is used for acquiring video data in video monitoring equipment, collecting state data of each resource pool, and performing primary aggregation processing on the video data and the state data to obtain structured data, wherein the state data comprises bandwidth, concurrency and storage;
the data analysis module is used for carrying out resource pool load analysis and load state comprehensive analysis according to the structured data to respectively obtain a single load rating and a comprehensive load rating;
and the equipment scheduling module is used for calculating the concurrency required by scheduling according to the single load rating and the comprehensive load rating, and selecting the equipment required to be scheduled in the same region or an adjacent region according to the concurrency required by scheduling.
2. The cloud storage scheduling system applied to video monitoring of claim 1, further comprising: a data storage module;
the data storage module is configured to store the structured data, the individual load ratings, and the composite load ratings.
3. The cloud storage scheduling system applied to video monitoring of claim 1, further comprising: a load monitoring module;
and the load monitoring module is used for monitoring the load according to the single load rating and the comprehensive load rating and triggering a preset alarm mechanism when the load level exceeds a threshold value.
4. The cloud storage scheduling system applied to video monitoring of claim 1, further comprising: displaying an interface;
the display interface is used for visually displaying the structured data, the single load rating and the comprehensive load rating and performing graded rendering display on a resource pool according to the single load rating and the comprehensive load rating.
5. The cloud storage scheduling system applied to video monitoring of claim 1, wherein the device scheduling module is specifically configured to:
calculating a scheduling proportion according to the single load rating and the comprehensive load rating;
calculating the concurrency required by scheduling based on the scheduling proportion;
and judging whether the available concurrency of the resource pools in the same region is sufficient or not according to the scheduling required concurrency, if so, selecting required scheduling equipment from the resource pools in the same region according to the scheduling required concurrency, and if not, selecting the required scheduling equipment from the resource pools in adjacent regions according to a preset adjacent region configuration table.
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| CN202211289421.1A CN115665252A (en) | 2022-10-20 | 2022-10-20 | Cloud storage scheduling system applied to video monitoring |
| PCT/CN2023/117438 WO2024082861A1 (en) | 2022-10-20 | 2023-09-07 | Cloud storage scheduling system applied to video monitoring |
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| CN107943559A (en) * | 2017-11-21 | 2018-04-20 | 广东奥飞数据科技股份有限公司 | A kind of big data resource scheduling system and its method |
| CN108363713A (en) * | 2017-12-20 | 2018-08-03 | 武汉烽火众智数字技术有限责任公司 | Video image information resolver, system and method |
| US10831720B2 (en) * | 2018-05-31 | 2020-11-10 | Microsoft Technology Licensing, Llc | Cloud storage distributed file system |
| CN110149395A (en) * | 2019-05-20 | 2019-08-20 | 华南理工大学 | One kind is based on dynamic load balancing method in the case of mass small documents high concurrent |
| US11018991B1 (en) * | 2020-02-07 | 2021-05-25 | EMC IP Holding Company LLC | System and method for autonomous and dynamic resource allocation in storage systems |
| CN111897636A (en) * | 2020-07-14 | 2020-11-06 | 广东科徕尼智能科技有限公司 | Scheduling method, device and storage medium based on data calculation and analysis |
| CN112749008A (en) * | 2020-12-29 | 2021-05-04 | 中国人寿保险股份有限公司上海数据中心 | Cloud resource distribution system based on OpenStack and construction method thereof |
| CN113626282B (en) * | 2021-07-16 | 2023-12-22 | 济南浪潮数据技术有限公司 | Cloud computing physical node load monitoring method, device, terminal and storage medium |
| CN115665252A (en) * | 2022-10-20 | 2023-01-31 | 天翼数字生活科技有限公司 | Cloud storage scheduling system applied to video monitoring |
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| WO2024082861A1 (en) * | 2022-10-20 | 2024-04-25 | 天翼数字生活科技有限公司 | Cloud storage scheduling system applied to video monitoring |
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