Cloud storage node-based intelligent lock cloud storage resource scheduling systemTechnical Field
The invention relates to the technical field of data replication and migration, in particular to an intelligent lock cloud storage resource scheduling system based on cloud storage nodes.
Background
With the rapid development of cloud computing, more and more organizations and individuals store data on a cloud platform, a large-scale cloud storage environment needs an efficient resource scheduling and management system to meet the demands of users on storage space, access speed and the like, in the cloud storage environment, data backup and disaster recovery are very important demands, an intelligent lock cloud storage resource scheduling system can conduct redundancy backup on a plurality of nodes through a data replication and migration technology, so that high availability and reliability are provided, the intelligent lock cloud storage resource scheduling system has the replication and migration capability of the data to achieve high availability and load balancing of the data, the data replication can conduct redundancy backup on the data among a plurality of storage nodes, and the data migration can migrate the data from one node to another node to achieve load balancing and resource optimization.
The current intelligent lock cloud storage resource scheduling system based on cloud storage nodes obviously has the following problems according to the replication and migration capabilities of data: 1. under the condition that the network transmission speed is low, a large amount of data is backed up to the cloud storage node for a long time, when the cloud storage node is selected, the network bandwidth and the transmission speed are not considered, so that the backup process can not be completed within a reasonable time range, and when the system is operated, faults or other abnormal conditions can be possibly encountered, but the process of prediction and repair is troublesome, and the historical fault information cannot be predicted and analyzed in advance.
2. In the process of data copying and migration, due to network delay and node faults, data inconsistency can be caused, the data copying and migration involve a large amount of data transmission, the requirement on network bandwidth is large, in a large-scale cloud storage environment, the network bandwidth is insufficient in the process of copying or migration, the copying or migration process can be slow, and the performance of a system is affected.
Disclosure of Invention
The invention aims to provide an intelligent lock cloud storage resource scheduling system based on cloud storage nodes, which solves the problems in the background technology.
In order to solve the technical problems, the invention adopts the following technical scheme: the invention provides an intelligent lock cloud storage resource scheduling system based on cloud storage nodes, which comprises the following steps: the data acquisition module is used for acquiring use information and data corresponding to each user of the intelligent lock in the main storage server, wherein the use information comprises the use frequency of the intelligent lock, the biological identification unlocking times and the unlocking failure times;
The data analysis module is used for analyzing and obtaining the backup grade corresponding to the data of each user according to the use information and the data corresponding to each user;
And a transmission detection module: before data backup, detecting the transmission process of the main storage server and each cloud storage node, so as to obtain transmission information between the main storage server and each cloud storage node;
The transmission analysis module is used for analyzing the network fluctuation value corresponding to the main storage server and each cloud storage node according to the transmission information between the main storage server and each cloud storage node;
the node analysis module is used for acquiring performance information and historical fault information corresponding to each cloud storage node, further analyzing the backup grade corresponding to each cloud storage node, and detecting and predicting faults of the cloud storage nodes by analyzing the historical data;
the data backup module is used for comparing the backup grade corresponding to each user data with the backup grade corresponding to each cloud storage node and confirming the cloud storage node to be backed up corresponding to each user data;
and the display terminal is used for carrying out backup completion display prompt after the data backup of each user is completed.
Preferably, the analysis obtains the backup level corresponding to each user data, and the specific calculation process is as follows: according to the formulaObtaining a backup priority coefficient eta q corresponding to each user data, wherein Xq, yq and Zq respectively represent the intelligent lock use frequency, the biological recognition unlocking times and the unlocking failure times of the q-th user, X, Y, Z are respectively the intelligent lock use frequency, the biological recognition unlocking times and the unlocking failure times in a set safety range, q is the number of each user, q=1 and 2.
And comparing the backup priority coefficient corresponding to each user data with each set backup priority coefficient interval, wherein each backup priority coefficient interval corresponds to each backup grade, and when the backup priority coefficient corresponding to certain user data belongs to the set backup priority coefficient interval, the backup grade corresponding to the user data is the backup grade corresponding to the backup priority coefficient interval, so that the backup grade corresponding to each user data is analyzed.
Preferably, the transmission process of the main storage server and each cloud storage node is detected, and the specific detection process is as follows: the method comprises the steps that data to be backed up in a main storage server are distributed at each time point according to a preset time interval, detection devices corresponding to all storage nodes are operated, network speed, packet loss rate and network throughput corresponding to all cloud storage nodes at each time point are collected, and the network speed, the packet loss rate and the network throughput corresponding to all cloud storage nodes at each time point are used as transmission information between the main storage server and all cloud storage nodes.
Preferably, the analyzing the network fluctuation value corresponding to each cloud storage node includes the following specific analysis process: substituting the network speed, the packet loss rate and the network throughput corresponding to each cloud storage node at each time point into a calculation formulaObtaining a network fluctuation value beta i corresponding to the network fluctuation value corresponding to each cloud storage node, whereinRespectively representing the network speed, the packet loss rate and the network throughput corresponding to the ith cloud storage node at the t-th time point, t being the number corresponding to each time point, t=1, 2,..,The values of network speed, packet loss rate and network throughput of the ith cloud storage node at the t-1 time are respectively set, deltaV, deltaB and DeltaC are respectively set allowable network speed difference, allowable packet loss rate and allowable network throughput difference, and K1、K2、K3 is respectively set weight factors of network speed, packet loss rate and network throughput.
Preferably, the performance information comprises storage capacity and response speed, and the historical fault information comprises fault times and fault maintenance time periods.
Preferably, the analyzing the backup level corresponding to each cloud storage node includes the following specific analysis process: according to the historical fault information corresponding to each cloud storage node, analyzing the safety influence factor corresponding to each cloud storage node, and marking as alphai;
According to the storage capacity, response speed and network fluctuation value corresponding to each cloud storage node, calculating to obtain storage evaluation coefficients corresponding to each cloud storage node, further calculating to obtain cloud storage coefficients corresponding to each cloud storage node according to the storage evaluation coefficients corresponding to each cloud storage node and the security influence factors, further comparing the cloud storage coefficients corresponding to each cloud storage node with backup grades corresponding to set cloud storage coefficient intervals, judging that each cloud storage node belongs to the backup grade corresponding to the cloud storage coefficient interval if the cloud storage coefficient corresponding to each cloud storage node is in the set cloud storage coefficient interval, and judging that each cloud storage node does not belong to the backup grade corresponding to the cloud storage coefficient interval if the cloud storage coefficient corresponding to each cloud storage node is not in the set cloud storage coefficient interval.
Preferably, the analyzing the security impact factor corresponding to each cloud storage node includes the following specific analysis process: substituting the historical fault information corresponding to each cloud storage node into a calculation formulaThe method comprises the steps of obtaining a safety influence factor alphai corresponding to each cloud storage node, wherein Ai、Bi respectively represents the number of faults and the maintenance duration of each fault corresponding to the ith cloud storage node, A 'and B' respectively represent the preset standard number of faults and the maintenance duration of each standard fault, delta A and delta B respectively represent the preset allowable number of faults and the allowable maintenance duration of each fault, and lambda1、λ2 respectively represents the preset number of faults and the weight factor corresponding to the maintenance duration of each fault.
Preferably, the calculating the storage evaluation coefficient corresponding to each cloud storage node includes the following specific calculating process: substituting the storage capacity, response speed and network fluctuation value corresponding to each cloud storage node into a calculation formulaThe storage evaluation coefficient psii corresponding to each cloud storage node is obtained, wherein Xi、Yi、βi respectively represents the storage capacity, the response speed and the network fluctuation value corresponding to the ith cloud storage node, X ', Y ', betai ' respectively represent the set standard storage capacity, standard response speed and standard network fluctuation value, deltaX, deltaY and DeltaZ respectively represent the set allowable storage capacity difference, allowable response speed difference and allowable network fluctuation value difference, and mu1、μ2、μ3 respectively represent the set storage capacity, response speed and the weight factors corresponding to the network fluctuation value.
Preferably, the calculating the storage coefficients corresponding to each storage node includes the following specific calculating process: according to the storage evaluation coefficient and the safety influence factor corresponding to each cloud storage node and the formulaAnd obtaining storage coefficients xii corresponding to each storage node, wherein alphai、ψi is a security influence factor and a storage evaluation coefficient corresponding to each cloud storage node respectively.
Preferably, the confirming the cloud storage node to be backed up corresponding to each user data specifically includes the following steps: and comparing the backup grade corresponding to each user data with the backup grade corresponding to each cloud storage node, and if the backup grade corresponding to certain user data is the same as the backup grade corresponding to certain cloud storage node, taking the cloud storage node as the cloud storage node to be backed up corresponding to the user data, so as to confirm the cloud storage node to be backed up corresponding to each user data.
The invention has the beneficial effects that: 1. according to the cloud storage node-based intelligent lock cloud storage resource scheduling system, the mode and trend of high frequency or potential problems in the system can be identified through analysis of historical fault information, problem prediction is facilitated, measures are taken in advance to prevent faults, and accordingly reliability and stability of the system are improved.
2. By calculating the corresponding backup grade when each user uses the intelligent lock and the corresponding backup grade of each cloud storage node and comparing the two, the backup strategy can be ensured to be matched with the data value and the importance, the reasonable configuration of the backup resources is facilitated, the waste of the resources is avoided, the use condition and the potential deficiency of the backup resources can be identified, the allocation and the adjustment of the backup resources are facilitated, and the resource utilization rate is improved.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic diagram of the system structure of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, an enterprise scientific and technological achievement evaluation management system based on digital information includes: the system comprises a data acquisition module, a data analysis module, a transmission detection module, a transmission analysis module, a node analysis module, a data backup module and a display terminal.
The data acquisition module is connected with the data analysis module, the data analysis module is connected with the data backup module, the transmission detection module is connected with the transmission analysis module, the transmission analysis module is connected with the node analysis module, the data backup module is connected with the node analysis module, and the data backup module is connected with the display terminal.
The data acquisition module is used for acquiring use information and data corresponding to each user of the intelligent lock in the main storage server, wherein the use information comprises the use frequency of the intelligent lock, the number of times of biological identification unlocking and the number of times of unlocking failure;
The data analysis module is used for analyzing and obtaining the backup grade corresponding to the data of each user according to the use information corresponding to each user;
the usage information corresponding to each user is obtained from the main storage server.
In a specific embodiment, the analysis obtains the backup level corresponding to each user data, and the specific calculation process is as follows: according to the formula
Obtaining a backup priority coefficient eta q corresponding to each user data, wherein Xq, yq and Zq respectively represent the intelligent lock use frequency, the biological recognition unlocking times and the unlocking failure times of the q-th user, X, Y, Z are respectively the intelligent lock use frequency, the biological recognition unlocking times and the unlocking failure times in a set safety range, q is the number of each user, q=1 and 2.
And comparing the backup priority coefficient corresponding to each user data with each set backup priority coefficient interval, wherein each backup priority coefficient interval corresponds to each backup grade, and when the backup priority coefficient corresponding to certain user data belongs to the set backup priority coefficient interval, the backup grade corresponding to the user data is the backup grade corresponding to the backup priority coefficient interval, so that the backup grade corresponding to each user data is analyzed.
The transmission detection module: before data backup, detecting the transmission process of the main storage server and each cloud storage node, so as to obtain transmission information between the main storage server and each cloud storage node;
In a specific embodiment, the transmission process between the main storage server and each cloud storage node is detected, and the specific detection process is as follows: the method comprises the steps that data to be backed up in a main storage server are distributed at each time point according to a preset time interval, detection devices corresponding to all storage nodes are operated, network speed, packet loss rate and network throughput corresponding to all cloud storage nodes at each time point are collected, and the network speed, the packet loss rate and the network throughput corresponding to all cloud storage nodes at each time point are used as transmission information between the main storage server and all cloud storage nodes.
The transmission analysis module is used for analyzing the network fluctuation value corresponding to the main storage server and each cloud storage node according to the transmission information between the main storage server and each cloud storage node;
In a specific embodiment, the analyzing the network fluctuation value corresponding to each cloud storage node includes the following specific analysis process: substituting the network speed, the packet loss rate and the network throughput corresponding to each cloud storage node at each time point into a calculation formulaObtaining a network fluctuation value beta i corresponding to the network fluctuation value corresponding to each cloud storage node, whereinRespectively representing the network speed, the packet loss rate and the network throughput corresponding to the ith cloud storage node at the t-th time point, t being the number corresponding to each time point, t=1, 2,..,The values of network speed, packet loss rate and network throughput of the ith cloud storage node at the t-1 time are respectively set, deltaV, deltaB and DeltaC are respectively set allowable network speed difference, allowable packet loss rate and allowable network throughput difference, and K1、K2、K3 is respectively set weight factors of network speed, packet loss rate and network throughput.
It should be noted that, preset time intervals are set in the main storage server, data backup is performed at these time points, each storage node is equipped with a detection device for monitoring performance parameters of network transmission, and at each preset time point, the detection device is operated to collect network speed, packet loss rate and network throughput of each cloud storage node, and the performance data at each time point is used as transmission information between the main storage server and each cloud storage node.
The node analysis module is used for acquiring performance information and historical fault information corresponding to each cloud storage node, further analyzing backup grades corresponding to each cloud storage node, and detecting and predicting faults of the cloud storage nodes by analyzing historical data;
In a specific embodiment, the performance information includes a storage capacity and a response speed, the historical fault information includes a fault number and a maintenance duration of each fault, and the analyzing the backup level corresponding to each cloud storage node includes the following specific analysis process: according to the historical fault information corresponding to each cloud storage node, analyzing the safety influence factor corresponding to each cloud storage node, and marking as alphai;
In a specific embodiment, according to the storage capacity, the response speed and the network fluctuation value corresponding to each cloud storage node, a storage evaluation coefficient corresponding to each cloud storage node is calculated, further according to the storage evaluation coefficient corresponding to each cloud storage node and the security influence factor, the cloud storage coefficient corresponding to each cloud storage node is calculated, further, the cloud storage coefficient corresponding to each cloud storage node is compared with the backup level corresponding to each set cloud storage coefficient interval, if the cloud storage coefficient corresponding to each cloud storage node is in each set cloud storage coefficient interval, the cloud storage node is judged to belong to the backup level corresponding to the cloud storage coefficient interval, and if the cloud storage coefficient corresponding to each cloud storage node is not in each set cloud storage coefficient interval, the cloud storage node is judged not to belong to the backup level corresponding to the cloud storage coefficient interval.
In a specific embodiment, the analyzing the security impact factor corresponding to each cloud storage node includes the following specific analysis process: substituting the historical fault information corresponding to each cloud storage node into a calculation formulaThe method comprises the steps of obtaining a safety influence factor alphai corresponding to each cloud storage node, wherein Ai、Bi respectively represents the number of faults and the maintenance duration of each fault corresponding to the ith cloud storage node, A 'and B' respectively represent the preset standard number of faults and the maintenance duration of each standard fault, delta A and delta B respectively represent the preset allowable number of faults and the allowable maintenance duration of each fault, and lambda1、λ2 respectively represents the preset number of faults and the weight factor corresponding to the maintenance duration of each fault.
The safety influence factors corresponding to the cloud storage nodes are obtained by analyzing the fault times and the fault maintenance time of each cloud storage node, and serve as important basis for calculating the storage coefficients corresponding to each storage node.
In a specific embodiment, the calculating the storage evaluation coefficient corresponding to each cloud storage node specifically includes the following steps: substituting the storage capacity, response speed and network fluctuation value corresponding to each cloud storage node into a calculation formulaThe storage evaluation coefficient psii corresponding to each cloud storage node is obtained, wherein Xi、Yi、βi respectively represents the storage capacity, the response speed and the network fluctuation value corresponding to the ith cloud storage node, X ', Y ', betai ' respectively represent the set standard storage capacity, standard response speed and standard network fluctuation value, deltaX, deltaY and DeltaZ respectively represent the set allowable storage capacity difference, allowable response speed difference and allowable network fluctuation value difference, and mu1、μ2、μ3 respectively represent the set storage capacity, response speed and the weight factors corresponding to the network fluctuation value.
In a specific embodiment, the calculating the storage coefficients corresponding to each storage node includes the following specific calculation process: according to the storage evaluation coefficient and the safety influence factor corresponding to each cloud storage node and the formulaAnd obtaining storage coefficients xii corresponding to each storage node, wherein alphai、ψi is a security influence factor and a storage evaluation coefficient corresponding to each cloud storage node respectively.
The data backup module is used for comparing the backup grade corresponding to each user data with the backup grade corresponding to each cloud storage node and confirming the cloud storage node to be backed up corresponding to each user data;
In a specific embodiment, the specific confirmation process of confirming the cloud storage node to be backed up corresponding to each user data is as follows: and comparing the backup grade corresponding to each user data with the backup grade corresponding to each cloud storage node, and if the backup grade corresponding to certain user data is the same as the backup grade corresponding to certain cloud storage node, taking the cloud storage node as the cloud storage node to be backed up corresponding to the user data, so as to confirm the cloud storage node to be backed up corresponding to each user data.
By calculating the corresponding backup grade when each user uses the intelligent lock and the corresponding backup grade of each cloud storage node and comparing the two, the backup strategy can be ensured to be matched with the data value and the importance, the reasonable configuration of the backup resources is facilitated, the waste of the resources is avoided, the use condition and the potential deficiency of the backup resources can be identified, the allocation and the adjustment of the backup resources are facilitated, and the resource utilization rate is improved.
According to the cloud storage node-based intelligent lock cloud storage resource scheduling system, the mode and trend of high frequency or potential problems in the system can be identified through analysis of historical fault information, problem prediction is facilitated, measures are taken in advance to prevent faults, and accordingly reliability and stability of the system are improved.
The foregoing is merely illustrative and explanatory of the principles of the invention, as various modifications and additions may be made to the specific embodiments described, or similar thereto, by those skilled in the art, without departing from the principles of the invention or beyond the scope of the appended claims.