CN116521078A - Architecture optimization method for image acquisition and storage - Google Patents
Architecture optimization method for image acquisition and storage Download PDFInfo
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- CN116521078A CN116521078A CN202310490804.3A CN202310490804A CN116521078A CN 116521078 A CN116521078 A CN 116521078A CN 202310490804 A CN202310490804 A CN 202310490804A CN 116521078 A CN116521078 A CN 116521078A
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- G—PHYSICS
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- G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
- G06F3/06—Digital input from, or digital output to, record carriers, e.g. RAID, emulated record carriers or networked record carriers
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- G06F3/0604—Improving or facilitating administration, e.g. storage management
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- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
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- G06F3/06—Digital input from, or digital output to, record carriers, e.g. RAID, emulated record carriers or networked record carriers
- G06F3/0601—Interfaces specially adapted for storage systems
- G06F3/0602—Interfaces specially adapted for storage systems specifically adapted to achieve a particular effect
- G06F3/062—Securing storage systems
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- G06F3/06—Digital input from, or digital output to, record carriers, e.g. RAID, emulated record carriers or networked record carriers
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- G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
- G06F3/06—Digital input from, or digital output to, record carriers, e.g. RAID, emulated record carriers or networked record carriers
- G06F3/0601—Interfaces specially adapted for storage systems
- G06F3/0668—Interfaces specially adapted for storage systems adopting a particular infrastructure
- G06F3/067—Distributed or networked storage systems, e.g. storage area networks [SAN], network attached storage [NAS]
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Abstract
The invention relates to the technical field of image storage, in particular to a framework optimization method for image acquisition and storage, which comprises a data layer, an access layer, a convergence layer, an application layer and a user layer.
Description
Technical Field
The invention relates to the technical field of image storage, in particular to a framework optimization method for image acquisition and storage.
Background
At present, the security industry develops rapidly, more video data and image data need to be processed, and the unstructured data have the characteristics of occupying more space, being difficult to retrieve and having higher security requirements. The main ways of improving the storage space in the market are mainly two, one way is to increase the capacity of the equipment, and the capacity-expansion-based mode is simple and direct, but is the most resource-consuming; the other is to compress the data source from the data source itself, so as to reduce the space occupation rate of the data; this mode is mainly stored by using a database, and many relational databases support binary type columns, so that pictures can be converted into binary modes for storage.
For the existing common architecture of image acquisition and storage, there are some problems, including that the existing architecture excessively depends on nfs, and when the nfs server of the picture server has problems, the front-end web server may be affected. The problem with NFS is mainly that the lock is very prone to deadlock and can only be resolved by a hardware restart. Especially when the pictures reach a certain level, nfs has serious performance problems, and in addition, only one picture server for externally providing downloading is provided, so that single-point faults are easy to occur; the dependence between the picture servers is too much and the lateral expansion margin is not enough. The nfs mode can be used for modifying the content in nfs at will for people with the passwords of the web server, and the security level is not high. Therefore, the system and the method are used for generating, storing, protecting, optimizing and utilizing the data until the data becomes an asset and meet the requirements of the data on storage, and the like.
Disclosure of Invention
The invention aims to provide an architecture optimization method for image acquisition and storage, which has higher storage efficiency and higher query speed.
The invention relates to a framework optimization method for image acquisition and storage, which comprises a data layer, an access layer, a convergence layer, an application layer and a user layer, wherein the data layer is used as a data source for image acquisition and storage and mainly comprises acquisition equipment, a lower platform and an acquisition system, wherein the acquisition equipment is mainly an intelligent camera, image information is acquired through snapshot, the lower platform is mainly a lower-level view library, the acquisition system is mainly a third-party image acquisition platform and comprises face, human body, motor vehicles and non-motor vehicle image data, and various image data are mainly divided into large image data and small image data; the access layer is mainly deployed with a service cluster and mainly comprises an access interface service conforming to GA/T1400-2017 protocol, the access service is a key link of a platform and image data, the image information data of the acquisition end is accessed through a standard interface issued by the access service, the data of the acquisition end is transmitted to the access service cluster through http protocol speaking data, then file metadata information is converged in a message producer cluster, meanwhile, picture base64 data is documented, abnormal processing data is thrown into the message cluster, and access service pressure generated by a large amount of data inrush is reduced; the convergence layer is mainly deployed in a service cluster and comprises an object storage interface service based on MinIO, the object storage interface service mainly stores image BASE64 file metadata, the message cluster interface service based on Kafka mainly comprises a producer cluster and a consumer cluster, a push normal message queue and an abnormal message queue are stored, the link carries out data transmission in a message subscription and push mode, the data is transmitted to a distributed dispatching center in a regular synchronous mode, the distributed dispatching service is responsible for pushing image information to an upper platform through a subscription interface, the text information storage interface service based on ElaticSearch mainly stores image text path information, the image acquisition platform is mainly deployed in an application layer and realizes the service cluster through the interface, the image acquisition platform gathers image information acquired by acquisition equipment, a lower platform and an acquisition system in the acquisition end into the platform in a mode of using dynamic storage nodes in the object storage service cluster, and the user layer corresponds to an operation user.
Further, the storage node information is filled in a device configuration page, the storage node is a string of codes customized by an image acquisition platform, after the device configures an ip address of an access server, image data captured by a camera is sent to the access service through a GAT1400-2017 protocol, after the access service acquires the storage node information, the device id is associated with the storage node id, the image information of the device is distributed and stored in an object storage service according to the configured storage node information through the interface judgment of the access service, the image file metadata information is submitted to the object storage service for processing, and when the image file metadata information is queried after being stored, the current device storage node information and the region information are queried through the association table of the device id and the storage node id, and the image data acquired by the device is queried from the corresponding server node.
Further, the acquisition end acquires the graphic information and the information of the equipment to verify whether the node of the area exists or not, if so, the node is stored on the dynamic storage node, and if the storage is successful, the storage is completed; if the storage is not successful, capturing the abnormal data, re-executing the storage operation, re-judging whether the node of the area exists or not, and then circulating the flow.
Furthermore, the dynamic storage nodes are used for image acquisition and storage in the access service layer, and the images of each area are correspondingly stored in the storage nodes in the storage clusters, and then the storage clusters perform unified scheduling for load balancing.
The beneficial effects of the invention are as follows:
1. the method adopts the strategy of the regional dynamic storage node, reduces the influence on the IO performance of the storage node of the server caused by a large amount of picture information rushing into the server.
2. After the image information is put in storage, the inquiry time is reduced, and the image inquiry efficiency is improved when the image inquiry is carried out on the area of the user.
3. The method is beneficial to realizing the demand realization of the data in the area of the local market, and can be realized more quickly when a user puts forward the demand of the local market data which is not discharged from the local market.
4. The invention distinguishes the picture data through the dynamic storage nodes, is convenient for the regional management of an administrator, reduces the low inquiry performance caused by data dispersion, and is also convenient for a certain city to push and share the regional information.
5. The picture data is stored in the base64 encryption mode, so that the safety of the data is ensured, meanwhile, the load balancing can be automatically carried out, and the situation that the whole storage service cannot normally operate due to the problem of one node is avoided.
Drawings
FIG. 1 is a diagram of an image acquisition platform architecture of the present invention;
FIG. 2 is a schematic diagram of the structure of the present invention;
FIG. 3 is a flow chart of a dynamic storage node of the present invention;
FIG. 4 is a graph of dynamic storage node usage contrast according to the present invention.
Detailed Description
As shown in fig. 1-4, the architecture optimization method for image acquisition and storage of the invention comprises a data layer, an access layer, a convergence layer, an application layer and a user layer, wherein the data layer is used as a data source for image acquisition and storage, and mainly comprises acquisition equipment, a lower-level platform and an acquisition system, wherein the acquisition equipment is mainly an intelligent camera, the image information is acquired through snapshot, the lower-level platform is mainly a lower-level view library, the acquisition system is mainly a third-party image acquisition platform, the image acquisition platform comprises face, human body, motor vehicles and non-motor vehicle image data, and various image data are mainly divided into large image data and small image data; the access layer is mainly deployed with a service cluster and mainly comprises an access interface service conforming to GA/T1400-2017 protocol, the access service is a key link of a platform and image data, the image information data of the acquisition end is accessed through a standard interface issued by the access service, the data of the acquisition end is transmitted to the access service cluster through http protocol speaking data, then file metadata information is converged in a message producer cluster, meanwhile, picture base64 data is documented, abnormal processing data is thrown into the message cluster, and access service pressure generated by a large amount of data inrush is reduced; the convergence layer is mainly deployed in a service cluster and comprises an object storage interface service based on MinIO, the object storage interface service mainly stores image BASE64 file metadata, the message cluster interface service based on Kafka mainly comprises a producer cluster and a consumer cluster, a push normal message queue and an abnormal message queue are stored, the link carries out data transmission in a message subscription and push mode, the data is transmitted to a distributed dispatching center in a regular synchronous mode, the distributed dispatching service is responsible for pushing image information to an upper platform through a subscription interface, the text information storage interface service based on ElaticSearch mainly stores image text path information, the image acquisition platform is mainly deployed in an application layer and realizes the service cluster through the interface, the image acquisition platform gathers image information acquired by acquisition equipment, a lower platform and an acquisition system in the acquisition end into the platform in a mode of using dynamic storage nodes in the object storage service cluster, and the user layer corresponds to an operation user.
Further, the storage node information is filled in a device configuration page, the storage node is a string of codes customized by an image acquisition platform, after the device configures an ip address of an access server, image data captured by a camera is sent to the access service through a GAT1400-2017 protocol, after the access service acquires the storage node information, the device id is associated with the storage node id, the image information of the device is distributed and stored in an object storage service according to the configured storage node information through the interface judgment of the access service, the image file metadata information is submitted to the object storage service for processing, and when the image file metadata information is queried after being stored, the current device storage node information and the region information are queried through the association table of the device id and the storage node id, and the image data acquired by the device is queried from the corresponding server node.
Further, the acquisition end acquires the graphic information and the information of the equipment to verify whether the node of the area exists or not, if so, the node is stored on the dynamic storage node, and if the storage is successful, the storage is completed; if the storage is not successful, capturing the abnormal data, re-executing the storage operation, re-judging whether the node of the area exists or not, and then circulating the flow.
Furthermore, the dynamic storage nodes are used for image acquisition and storage in the access service layer, and the images of each area are correspondingly stored in the storage nodes in the storage clusters, and then the storage clusters perform unified scheduling for load balancing.
The method mainly provides a storage optimization method for users when designing an image information acquisition platform framework, solves the problems of performance degradation caused by overlarge pressure of a single storage node during image information storage of different regions during image acquisition and overlong response time of the platform during image information inquiry, and achieves high-availability standard from image access to the platform to storage and forwarding, thereby eliminating and avoiding unnecessary bottleneck problems during later use.
In order to solve the technical problem, the invention firstly fills in storage node information in a device configuration page, wherein the storage node is a self-defined code string of an image acquisition platform, for example: and (3) configuring the belonging area code, the belonging area name, the public network address, the intranet address, the port, the accessKey, the secretKey and the device upper limit, and whether to store the parameter contents by default. After the equipment configures an ip address of an access server, sending image data captured by a camera to an access service through a GAT1400-2017 protocol, after the access service acquires storage node information, associating the equipment id with the storage node id, judging through an interface of the access service, and distributing and storing the image information of the equipment into an object storage service according to the configured storage node information in a nearby principle. The image file metadata information is passed to the object storage service for processing. After the storage is carried out by the method, when the inquiry is carried out, the storage node information and the belonging area information of the current equipment are inquired through the association table of the equipment id and the storage node id, and the picture data acquired by the equipment are inquired from the corresponding server node. The purposes of quick inquiry and high efficiency are achieved.
As shown in fig. 3, the collection end collects the graphic information and the information of the equipment to verify whether the node of the area exists, if so, the node is stored on the dynamic storage node, and if the storage is successful, the storage is completed; if the storage is not successful, capturing the abnormal data, re-executing the storage operation, re-judging whether the node of the area exists or not, and then circulating the flow.
As shown in fig. 4, in the access service layer, dynamic storage nodes are used for image acquisition and storage, and also, pictures of each region are correspondingly stored in the storage nodes in the storage clusters, and then the storage clusters perform unified scheduling for load balancing. The image acquisition and storage are carried out in a mode of not using the dynamic storage node in daily market, compared with the dynamic storage node, the mode of a single storage node is easy to cause a fault, so that the whole storage cannot normally operate, but the dynamic storage can carry out load balancing, and the normal and stable operation of the storage of the whole platform is ensured.
The embodiment sets up 5000 production environment camera devices to collect picture data, wherein the picture data are collected for 1 day, the face data are about 130 ten thousand, the human body data are about 80 ten thousand, the motor vehicles are about 40 ten thousand, and the non-motor vehicles are about 30 ten thousand; 8 storage server clusters are provided, 32 cores and 64G memory and 1T memory are configured. On the premise of ensuring complete receiving of image data, 8 same-configuration server clusters are needed to meet the requirements of gathering, storing and pushing image data information of 5000 pieces of equipment to be captured without using a dynamic storage node method. By using the method for dynamically storing the nodes, the access capability is dynamically expanded, the storage efficiency is improved, and the storage requirement for receiving the data can be met only by 4 server clusters. Without the dynamic storage node method, the single-node storage service receives the data, and about 50% of image data information is lost because the interface is blocked and overtime and the storage cannot be processed in time. The method for dynamically storing the nodes has the dynamic expansion capability, and can receive and collect image data information in 100% in time. And storing the picture information on the corresponding storage node. The dynamic storage node method is not used, since the snap-shot picture data can age and float, when the collection peak is high and the image data is large, the concurrency capability of the interface is limited, and the access scale can not be expanded. Only an increased number of servers can be loaded to solve this problem. The method for dynamically storing the nodes firstly has good expansibility, improves the processing concurrency capability, and can dynamically expand the number of the nodes according to the equipment access scale so as to meet the requirements.
The method has the advantages of wide application range, low development difficulty and high storage efficiency, and can provide convenient dynamic storage service according to the regional nodes for the image resource storage on each acquisition end.
The invention adopts a unified access standard, and uses a method of dynamic storage nodes to realize image convergence on the image information from different acquisition ends; the data of the acquisition end is transmitted to the access service cluster through the http protocol, then file metadata information is converged in the message producer cluster and then transmitted to the object storage, the image file is subjected to persistent storage by the data producer cluster, the base64 is also adopted for encryption, the safety of pictures and data in the transmission process is ensured, the consumer cluster screens abnormal data, the processed data file information is stored through the data warehouse service, and finally, query index information is formed and transmitted to the upper platform for use by the distributed scheduling center. The architecture optimization method for image acquisition and storage is realized in such a way; therefore, the purposes of higher storage efficiency, safer and faster query speed are achieved.
Claims (4)
1. An architecture optimization method for image acquisition and storage is characterized by comprising the following steps: the system comprises a data layer, an access layer, a convergence layer, an application layer and a user layer, wherein the data layer is used as a data source for image acquisition and storage and mainly comprises acquisition equipment, a lower platform and an acquisition system, wherein the acquisition equipment is mainly an intelligent camera, the image information is acquired through snapshot, the lower platform is mainly a lower view library, the acquisition system is mainly a third party image acquisition platform and comprises human face, human body, motor vehicles and non-motor vehicle image data, and various image data are mainly divided into large image data and small image data; the access layer is mainly deployed with a service cluster and mainly comprises an access interface service conforming to GA/T1400-2017 protocol, the access service is a key link of a platform and image data, the image information data of the acquisition end is accessed through a standard interface issued by the access service, the data of the acquisition end is transmitted to the access service cluster through http protocol speaking data, then file metadata information is converged in a message producer cluster, meanwhile, picture base64 data is documented, abnormal processing data is thrown into the message cluster, and access service pressure generated by a large amount of data inrush is reduced; the convergence layer is mainly deployed in a service cluster and comprises an object storage interface service based on MinIO, the object storage interface service mainly stores image BASE64 file metadata, the message cluster interface service based on Kafka mainly comprises a producer cluster and a consumer cluster, a push normal message queue and an abnormal message queue are stored, the link carries out data transmission in a message subscription and push mode, the data is transmitted to a distributed dispatching center in a regular synchronous mode, the distributed dispatching service is responsible for pushing image information to an upper platform through a subscription interface, the text information storage interface service based on ElaticSearch mainly stores image text path information, the image acquisition platform is mainly deployed in an application layer and realizes the service cluster through the interface, the image acquisition platform gathers image information acquired by acquisition equipment, a lower platform and an acquisition system in the acquisition end into the platform in a mode of using dynamic storage nodes in the object storage service cluster, and the user layer corresponds to an operation user.
2. The architecture optimization method for image acquisition and storage according to claim 1, wherein: filling storage node information in a device configuration page, wherein the storage node is a string of codes customized by an image acquisition platform, after the device configures an ip address of an access server, sending image data captured by a camera to the access service through a GAT1400-2017 protocol, after the access service acquires the storage node information, associating the device id with the storage node id, judging through an interface of the access service, distributing and storing the image information of the device into an object storage service according to the configured storage node information in a nearby principle, delivering the image file metadata information to the object storage service for processing, and inquiring the current storage node information and the affiliated area information of the device through an association table of the device id and the storage node id when inquiring after storing, and inquiring the image data acquired by the device from a corresponding server node.
3. The architecture optimization method for image acquisition and storage according to claim 2, wherein: the acquisition end acquires the graphic information and the information of the equipment to verify whether the node of the area exists or not, if so, the graphic information and the information of the equipment are stored in the dynamic storage node, and if the storage is successful, the storage is completed; if the storage is not successful, capturing the abnormal data, re-executing the storage operation, re-judging whether the node of the area exists or not, and then circulating the flow.
4. A method for optimizing architecture for image acquisition and storage according to claim 3, wherein: and the dynamic storage nodes are used for image acquisition and storage in the access service layer, and the images of each region are correspondingly stored in the storage nodes in the storage clusters, and then the storage clusters perform unified scheduling for load balancing.
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