One-click deployment system on cloud platform

Abstract

The present invention provides a cloud platform one-key deployment system, including: a collection module, configured to collect the data to be deployed in the virtual machine to be deployed and the virtual machine type of the virtual machine to be deployed; a transmission module, configured to transmit the data to be deployed to a blank VPC network to obtain a deployed VPC network; a deployment module, configured to copy a virtual machine of the same type as the virtual machine type to a virtual subnet of the deployed VPC network to obtain a deployment system; and a verification module, configured to perform a normal operation verification on the deployment system.

Description

Technical Field

[0001] The present invention relates to the technical field of cloud computing, and particularly to a one-key deployment system for a cloud platform. Background Art

[0002] With the development of cloud computing technology and the continuous progress of cloud computing services, cloud computing will play an increasingly important role in the IT field. There are a large number of host machines (i.e., physical machines, or hardware physical servers) or virtual machines running on host machines in the cloud computing environment. Before working, both host machines and virtual machines need to be gradually installed with operating systems and application software according to prompts and complete deployment operations such as system configuration. Each host machine or virtual machine usually installs the operating system through a system installation disc, then installs each application software through a software installation disc or network download, and configures system parameters manually. These operations consume a large amount of manpower, time, and installation resources. Therefore, the most basic point in cloud computing technology is to provide rapid and unified installation and deployment of the operating systems and applications of host machines or virtual machines in the cloud computing environment, and to be able to quickly and uniformly configure host machines or virtual machines. Currently, the automatic deployment cloud systems on the market do not perform running verification on the deployed systems after deployment, which easily leads to problems in the deployed systems not being discovered in time and reduces the deployment quality of the cloud platform system. Therefore, it is necessary to propose a one-key deployment system for a cloud platform to perform running verification on the deployed system after system deployment and improve the deployment quality of the cloud platform system. Summary of the Invention

[0003] The present invention provides a one-key deployment system for a cloud platform, which is used to solve the problem of performing running verification on the deployed system after system deployment and improving the deployment quality of the cloud platform system.

[0004] A one-key deployment system for a cloud platform includes:

[0005] An acquisition module, configured to acquire the data to be deployed in the virtual machine to be deployed and the virtual machine type of the virtual machine to be deployed;

[0006] A transmission module, configured to transmit the data to be deployed to a blank VPC network to obtain a deployed VPC network;

[0007] A deployment module, configured to copy a virtual machine of the same type as the virtual machine to the virtual subnet of the deployed VPC network to obtain a deployed system;

[0008] A verification module, configured to perform normal running verification on the deployed system.

[0009] As an embodiment of the present invention, the acquisition module includes:

[0010] An initial VPC network establishment sub-module, configured to establish an initial VPC network;

[0011] A data transfer sub-module, which is used to transfer the virtual machine to be deployed to the initial VPC network;

[0012] A data collection sub-module, which is used to collect the data to be deployed in the initial VPC network;

[0013] A type collection sub-module, which is used to collect the virtual machine type of the virtual machine to be deployed.

[0014] As an embodiment of the present invention, a cloud platform one-key deployment system further includes:

[0015] A blank VPC network establishment module, which is used to establish a blank VPC network.

[0016] As an embodiment of the present invention, the deployment module includes:

[0017] A virtual machine selection sub-module, which is used to receive the virtual machine type of the virtual machine to be deployed and select a virtual machine with the same virtual machine type to obtain a deployed virtual machine;

[0018] A replication path determination sub-module, which is used to select a virtual subnet in the deployment VPC network as the virtual machine replication path;

[0019] A paste sub-module, which is used to copy the deployed virtual machine to the virtual subnet of the deployment VPC network according to the virtual machine replication path to obtain a deployment system.

[0020] As an embodiment of the present invention, the verification module includes:

[0021] An application detection sub-module, which is used to detect whether the application to which the deployment system belongs can be opened and used normally;

[0022] A database detection sub-module, which is used to detect whether the database to which the deployment system belongs can be opened and used normally;

[0023] A browser detection sub-module, which is used to detect whether the browser to which the deployment system belongs can be logged in and accessed normally;

[0024] A client detection sub-module, which is used to detect whether the client to which the deployment system belongs can be logged in and accessed normally;

[0025] A result output sub-module, which is used to display a deployment success signal on the display page of the deployment system when the application, database, browser and client to which the deployment system belongs are all normal.

[0026] As an embodiment of the present invention, a cloud platform one-key deployment system further includes: an alarm module, which is used to send an alarm signal when the deployment system cannot run normally.

[0027] As an embodiment of the present invention, the alarm module includes:

[0028] A fault location sub-module is used to locate faults in the non - normally operating sections of the deployment system to obtain the faulty sections; wherein, the sections include: application programs, databases, browsers, and clients.

[0029] A fault alarm sub-module is used to send a section alarm signal corresponding to the faulty section according to the faulty section; wherein, the section alarm signals include application program alarm signals, database alarm signals, browser alarm signals, and client alarm signals.

[0030] As an embodiment of the present invention, the data transmission sub-module further includes:

[0031] An anti - retransmission unit is used to perform data anti - retransmission verification on the to - be - deployed data during the transmission of the to - be - deployed data to the blank VPC network.

[0032] As an embodiment of the present invention, the anti - retransmission unit performs the following operations:

[0033] Classify the to - be - deployed data to obtain several different types of target to - be - deployed data information;

[0034] Extract type feature and storage capacity feature from the target to - be - deployed data information to obtain the first type modal information and the first storage modal information;

[0035] Obtain the deployed data information in the blank VPC network, extract type feature and information storage feature from the deployed data information to obtain the second type modal information and the second storage modal information;

[0036] Obtain a recurrent network model for circularly identifying the deployment data information; wherein, the recurrent network model includes a type recurrent sequence model associated with the first type modal information and the second type modal information and a storage recurrent sequence model associated with the first storage modal information and the second storage modal information. The type recurrent sequence model includes a type recurrent sequence representation learning layer and a type recurrent similarity measurement layer, and the storage recurrent sequence model includes a storage recurrent sequence representation learning layer and a storage recurrent similarity measurement layer;

[0037] Input the first type modal information and the second type modal information into the type recurrent sequence model, perform sequence feature learning on the first type modal information and the second type modal information through the type recurrent sequence representation learning layer to obtain the first type learning feature and the second type learning feature, and input the first type learning feature and the second type learning feature into the type recurrent similarity measurement layer to obtain the type modal information similarity result;

[0038] Input the first storage mode information and the second storage mode information into the storage recurrent sequence model. Through the storage recurrent sequence representation learning layer, perform sequence feature learning on the first storage mode information and the second storage mode information to obtain the first storage learning feature and the second storage learning feature. Input the first storage learning feature and the second storage learning feature into the storage recurrent similarity measurement layer to obtain the storage mode information similarity result;

[0039] If the type mode information similarity result is that the first type mode information and the second type mode information are similar, and the storage mode information similarity result is that the first storage mode information and the second storage mode information are similar, then the target data information to be deployed is retransmission data information, and reject transmitting the target data information to be deployed into the blank VPC network;

[0040] If the type mode information similarity result is that the first type mode information and the second type mode information are not similar, and / or the storage mode information similarity result is that the first storage mode information and the second storage mode information are not similar, then the target data information to be deployed is not retransmission data information, and agree to transmit the target data information to be deployed into the blank VPC network.

[0041] As an embodiment of the present invention, classify the data to be deployed to obtain several different types of target data information to be deployed, including:

[0042] Based on the data field types in the preset database to be deployed and the point mutual information between the labels in the preset label set, construct a data label index library; wherein, the data label index library includes the corresponding relationship between the data field types and the labels in the preset label set; the data field types include application program data field types, database data field types, browser data field types, client data field types; the labels in the preset label set include application program labels, database labels, browser labels, client labels;

[0043] Classify the data fields of the data to be deployed to obtain the data field set of the data to be deployed;

[0044] Obtain the first label set related to each data field in the data field set from a group of label information corresponding to each data field type in the data label index library; wherein, the first label set includes the labels of each data field in the data field set;

[0045] Through a pre-trained vector model, obtain the data field vector representation of each data field in the data field set and the label vector representation of each label in the first label set;

[0046] Concatenate the data field vector representation with the label vector representation of each label respectively to obtain the label prediction feature vector;

[0047] Predict and score the label prediction feature vectors through a preset depth prediction sorting model to obtain a prediction label set of the data to be deployed;

[0048] Classify the data to be deployed according to the prediction label set of the data to be deployed to obtain several different types of target data information to be deployed.

[0049] Other features and advantages of the present invention will be described in the following specification, and in part will be obvious from the specification, or will be understood by implementing the present invention. The objectives and other advantages of the present invention can be achieved and obtained by the structures specifically pointed out in the written specification and the drawings.

[0050] The technical solution of the present invention will be further described in detail below through the drawings and embodiments. Description of the Drawings

[0051] The drawings are used to provide a further understanding of the present invention, and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation to the present invention. In the drawings:

[0052] Figure 1 It is a flowchart of a one-click deployment system for a cloud platform in an embodiment of the present invention;

[0053] Figure 2 It is a flowchart of an acquisition module of a one-click deployment system for a cloud platform in an embodiment of the present invention;

[0054] Figure 3 It is a flowchart of a one-click deployment system for a cloud platform in an embodiment of the present invention Figure 2 ;

[0055] Figure 4 It is a flowchart of a deployment module of a one-click deployment system for a cloud platform in an embodiment of the present invention;

[0056] Figure 5 It is a flowchart of a verification module of a one-click deployment system for a cloud platform in an embodiment of the present invention;

[0057] Figure 6 It is a flowchart of an alarm module of a one-click deployment system for a cloud platform in an embodiment of the present invention. Detailed Embodiments

[0058] The following describes the preferred embodiments of the present invention with reference to the drawings. It should be understood that the preferred embodiments described herein are only for the purpose of illustrating and explaining the present invention, and are not used to limit the present invention.

[0059] Please refer to Figure 1 , an embodiment of the present invention provides a one-click deployment system for a cloud platform, including:

[0060] The acquisition module is used to acquire the data to be deployed in the virtual machine to be deployed and the virtual machine type of the virtual machine to be deployed;

[0061] The transmission module is used to transmit the data to be deployed to the blank VPC network to obtain the deployed VPC network;

[0062] The deployment module is used to copy the virtual machine of the same type as the virtual machine type to the virtual subnet of the deployed VPC network to obtain the deployed system;

[0063] The verification module is used to verify the normal operation of the deployed system;

[0064] The working principle of the above technical solution is: A one-key deployment system for a cloud platform includes an acquisition module, a transmission module, a deployment module, and a verification module. The acquisition module is used to acquire the data to be deployed in the virtual machine to be deployed and the virtual machine type of the virtual machine to be deployed. Among them, the virtual machine to be deployed is preferably the virtual machine where the existing system is located, and the data to be deployed includes, but is not limited to, VPC router data, virtual subnet data, load balancing data, etc.; the transmission module is used to transmit the data to be deployed to the blank VPC network to obtain the deployed VPC network; the deployment module is used to copy the virtual machine of the same type as the virtual machine type of the virtual machine to be deployed to the virtual subnet of the deployed VPC network to obtain the deployed system; the verification module is used to verify the normal operation of the deployed system and determine whether the deployed system can run normally;

[0065] The beneficial effects of the above technical solution are: Through the acquisition module, the transmission module, and the deployment module, it is possible to perform one-key deployment of the system required by the virtual machine on the cloud platform, reduce manual operations, and improve the deployment efficiency. Through the verification module, it is possible to prevent problems in the system after deployment from affecting the user's perception of the virtual machine on the cloud platform, and timely discover problems and have them processed by the operator, which is beneficial to improving the deployment quality of the cloud platform system.

[0066] Please refer to Figure 2 , in one embodiment, the acquisition module includes:

[0067] The initial VPC network establishment sub-module is used to establish the initial VPC network;

[0068] The data transfer sub-module is used to transfer the virtual machine to be deployed to the initial VPC network;

[0069] The data acquisition sub-module is used to acquire the data to be deployed in the initial VPC network;

[0070] The type acquisition sub-module is used to acquire the virtual machine type of the virtual machine to be deployed;

[0071] The working principle of the above technical solution is as follows: The acquisition module includes an initial VPC network establishment sub-module, a data transfer sub-module, a data acquisition sub-module, and a type acquisition sub-module. The initial VPC network establishment sub-module is used to establish an initial VPC network. Initially, the initial VPC network preferably does not include VPC router data, virtual subnet data, and load balancing data. The data transfer sub-module is used to transfer the virtual machine to be deployed into the initial VPC network, that is, to migrate data such as VPC router data, virtual subnet data, and load balancing data in the virtual machine to be deployed into the initial VPC network. At this time, the initial VPC network contains the data to be deployed in the deployed virtual machine. The data acquisition sub-module is used to acquire the data to be deployed in the initial VPC network. Specifically, it includes: first, finding the default storage location of the data to be deployed in the initial VPC network, entering the default storage location to search for, acquire, and copy the data to be deployed in the initial VPC network. The type acquisition sub-module is used to acquire the virtual machine type of the virtual machine to be deployed. The virtual machine type preferably includes types such as Microsoft virtual machine, Mac virtual machine, BM virtual machine, HP virtual machine, SWsoft virtual machine, SUN virtual machine, Intel virtual machine, AMD virtual machine, Java virtual machine, BB virtual machine, and Linux virtual machine. Further, the type acquisition sub-module is also used to acquire data such as virtual memory used by the virtual machine to be deployed.

[0072] Please refer to Figure 3 , in one embodiment, a cloud platform one-click deployment system further includes:

[0073] A blank VPC network establishment module, used to establish a blank VPC network.

[0074] Please refer to Figure 4 , in one embodiment, the deployment module includes:

[0075] A virtual machine selection sub-module, used to receive the virtual machine type of the virtual machine to be deployed and select a virtual machine of the same type as the virtual machine type to obtain a deployed virtual machine;

[0076] A copy path determination sub-module, used to select a virtual subnet in the deployed VPC network as the virtual machine copy path;

[0077] A paste sub-module, used to copy the deployed virtual machine to the virtual subnet of the deployed VPC network according to the virtual machine copy path to obtain a deployment system;

[0078] The working principle of the above technical solution is as follows: The deployment module includes a virtual machine selection sub-module, a replication path determination sub-module, and a paste sub-module. Among them, the virtual machine selection sub-module is used to receive the virtual machine type of the virtual machine to be deployed and select a virtual machine with the same virtual machine type to obtain a deployed virtual machine. The preferred selection method is to copy the virtual machine to be deployed to obtain the deployed virtual machine. The replication path determination sub-module is used to select a virtual subnet in the deployed VPC network as the virtual machine replication path. The paste sub-module is used to copy the deployed virtual machine to the virtual subnet of the deployed VPC network according to the virtual machine replication path, thereby obtaining a deployed system.

[0079] Please refer to Figure 5 , in one embodiment, the verification module includes:

[0080] An application detection sub-module, which is used to detect whether the application to which the deployed system belongs can be opened and used normally;

[0081] A database detection sub-module, which is used to detect whether the database to which the deployed system belongs can be opened and used normally;

[0082] A browser detection sub-module, which is used to detect whether the browser to which the deployed system belongs can be logged in and accessed normally;

[0083] A client detection sub-module, which is used to detect whether the client to which the deployed system belongs can be logged in and accessed normally;

[0084] A result output sub-module, which is used to display a deployment success signal on the display page of the deployed system when the application, database, browser, and client to which the deployed system belongs are all normal;

[0085] The working principle of the above technical solution is as follows: The verification module includes an application detection sub-module, a database detection sub-module, a browser detection sub-module, a client detection sub-module, and a result output sub-module. Among them, the application detection sub-module is used to detect whether the application to which the deployment system belongs can be opened and used normally. The detection method of the application detection sub-module preferably includes: obtaining the global parameters of the application to be detected, configuring a detection unit for detecting the current application through the application detection sub-module, and using this detection unit to detect the global parameters of the application. The detection includes, when using this detection unit to detect the global parameters of the application, instructing the application to send a call request to other applications, and instructing the application to receive a called request from other applications, for determining whether the application can be opened and used normally. If any one of the instructions to instruct the application to send a call request to other applications or to instruct the application to receive a called request from other applications fails, it indicates that the application cannot be opened and used normally; The database detection sub-module is used to detect whether the database to which the deployment system belongs can be opened and used normally. The database detection sub-module preferably adopts the database heartbeat detection technology. The detection method of the database detection sub-module preferably includes: determining the database to which the system belongs and connecting to this database, instructing the database to start running normally, using the database heartbeat detection technology to detect the running state of this database. When the running state is the available state, stop the detection and send the detection result that this database can be opened and used normally. If the running state is the unavailable state, send the detection result that this database cannot be opened and used normally; The browser detection sub-module is used to detect whether the browser to which the deployment system belongs can be logged in and accessed normally. The detection method of the browser detection sub-module preferably includes: obtaining the URLs of several common web pages, and inputting the URLs one by one in the browser to be detected to obtain experimental web pages, performing a static compatibility scan on this experimental web page to obtain a set of functional modules to be detected. For each functional module in this set of functional modules, generate a state machine for the functional module to run under the browser to be detected, obtain the state transition function of the functional module corresponding to the browser to be detected respectively, obtain the normal state transition function of the functional module of this experimental web page working under a normal browser, compare this state transition function with the normal state transition function to determine the functional differences of the functional modules of this experimental web page on the normal browser and the browser to be detected, and judge whether this difference meets the expected standard. If it meets, it is determined that the browser to be detected can be logged in and accessed normally, otherwise it is determined that the browser to be detected cannot be logged in and accessed normally;The client detection sub-module is used to detect whether the client to which the deployment system belongs can log in and access normally. The detection method of the client detection sub-module preferably includes: after the system deployment is completed, start the client debug mode to obtain debug information, which includes voltage information, temperature information, operating parameter information, and log data information during client operation in the debug mode. Determine whether there is a fault based on the preset normal value range for the debug information. If all values in the debug information meet the preset normal value range, it indicates that the client has no fault and can log in and access normally; otherwise, it is determined that the client has a fault and cannot log in and access normally; when the above detection sub-module detects that the application program, database, browser, and client to which the deployment system belongs are all normal, the result output sub-module displays a deployment success signal on the display page of the deployment system; otherwise, it outputs a deployment failure signal to prompt the user.

[0086] In one embodiment, a cloud platform one-key deployment system further includes: an alarm module for sending an alarm signal when the deployment system cannot run normally;

[0087] The beneficial effect of the above technical solution is that through the alarm module, the user can be timely reminded when the deployment system cannot run normally, which is beneficial to improving the deployment efficiency and preventing the user from being unaware when the deployment fails.

[0088] Please refer to Figure 6 , in one embodiment, the alarm module includes:

[0089] A fault location sub-module for locating the fault of the non-normally running section in the deployment system and determining the location of the section with the fault; wherein, the section includes: application program, database, browser, client;

[0090] A fault alarm sub-module for sending a corresponding section alarm signal according to the location of the section with the fault; wherein, the section alarm signal includes an application program alarm signal, a database alarm signal, a browser alarm signal, and a client alarm signal;

[0091] The working principle of the above technical solution is as follows: The alarm module is connected to the verification module. The alarm module includes a fault location sub-module and a fault alarm sub-module. Among them, the fault location sub-module is used to locate the faults of the sectors in the deployment system that cannot operate normally and determine the positions of the sectors with faults. The sectors that cannot operate normally include: application program sectors, database sectors, browser sectors, and client sectors; the fault alarm sub-module is used to send corresponding sector alarm signals according to the positions of the sectors with faults. The sector alarm signals include application program alarm signals, database alarm signals, browser alarm signals, and client alarm signals. For example, when the fault location sub-module receives the detection information sent by the verification module that the application program cannot be opened and used normally (this step is to determine the position of the sector with faults), it sends a signal indicating that there is a fault in the application program to the fault alarm sub-module, and the fault alarm sub-module then sends an application program alarm signal to prompt the user;

[0092] The beneficial effects of the above technical solution are as follows: Through the fault location sub-module and the fault alarm sub-module, it is possible to accurately find out the reasons why the deployment system cannot operate normally and timely remind the user, reducing the time for the user to check running errors and improving the deployment efficiency of the system deployment.

[0093] In one embodiment, the data transmission sub-module further includes:

[0094] A duplicate transmission prevention unit, which is used to perform data duplicate transmission prevention verification on the to-be-deployed data when transmitting the to-be-deployed data to the blank VPC network;

[0095] The beneficial effects of the above technical solution are as follows: Through the duplicate transmission prevention unit, it is beneficial to prevent the to-be-deployed data from being repeatedly transmitted to the blank VPC network multiple times, resulting in data duplication in the blank VPC network and causing data redundancy in the blank VPC network; and through the duplicate transmission prevention unit, preventing data redundancy in the blank VPC network is beneficial to improving the probability that the subsequent application programs, databases, browsers, and clients belonging to the deployment system all operate normally.

[0096] In one embodiment, the duplicate transmission prevention unit performs the following operations:

[0097] Classify the to-be-deployed data to obtain several different types of target to-be-deployed data information;

[0098] Extract type characteristics and storage capacity characteristics from the target to-be-deployed data information to obtain the first type modal information and the first storage modal information;

[0099] Obtain the deployed data information in the blank VPC network, extract type characteristics and information storage characteristics from the deployed data information to obtain the second type modal information and the second storage modal information;

[0100] Obtain a recurrent neural network model for circularly identifying deployment data information; wherein, the recurrent neural network model includes a type recurrent sequence model associated with first type modal information and second type modal information and a storage recurrent sequence model associated with first storage modal information and second storage modal information, the type recurrent sequence model includes a type recurrent sequence representation learning layer and a type recurrent similarity measurement layer, and the storage recurrent sequence model includes a storage recurrent sequence representation learning layer and a storage recurrent similarity measurement layer;

[0101] Input the first type modal information and the second type modal information into the type recurrent sequence model, perform sequence feature learning on the first type modal information and the second type modal information through the type recurrent sequence representation learning layer to obtain a first type learning feature and a second type learning feature, and input the first type learning feature and the second type learning feature into the type recurrent similarity measurement layer to obtain a type modal information similarity result;

[0102] Input the first storage modal information and the second storage modal information into the storage recurrent sequence model, perform sequence feature learning on the first storage modal information and the second storage modal information through the storage recurrent sequence representation learning layer to obtain a first storage learning feature and a second storage learning feature, and input the first storage learning feature and the second storage learning feature into the storage recurrent similarity measurement layer to obtain a storage modal information similarity result;

[0103] If the type modal information similarity result is that the first type modal information and the second type modal information are similar, and the storage modal information similarity result is that the first storage modal information and the second storage modal information are similar, then the target data information to be deployed is retransmission data information, and reject transmitting the target data information to be deployed to the blank VPC network;

[0104] If the type modal information similarity result is that the first type modal information and the second type modal information are not similar, and / or the storage modal information similarity result is that the first storage modal information and the second storage modal information are not similar, then the target data information to be deployed is not retransmission data information, and agree to transmit the target data information to be deployed to the blank VPC network;

[0105] The working principle of the above technical solution is as follows: Classify the data to be deployed to obtain several pieces of target data information to be deployed of different types, and then extract type feature and storage capacity feature of the target data information to be deployed to obtain the first type of modal information and the first storage modal information; Obtain the deployed data information in the blank VPC network, that is, the target data information to be deployed that has been transmitted to the blank VPC network. Initially, the deployed data information in the blank VPC network is empty. Extract type feature and information storage feature of the deployed data information to obtain the second type of modal information and the second storage modal information, and obtain a recurrent network model for circularly identifying the deployed data information; The recurrent network model is preferably a recurrent identification model of deployed data based on multi-modal information; Among them, the recurrent network model includes a type recurrent sequence model associated with the first type of modal information and the second type of modal information and a storage recurrent sequence model associated with the first storage modal information and the second storage modal information. The type recurrent sequence model includes a type recurrent sequence representation learning layer and a type recurrent similarity measurement layer, and the storage recurrent sequence model includes a storage recurrent sequence representation learning layer and a storage recurrent similarity measurement layer; Input the first type of modal information and the second type of modal information into the type recurrent sequence model, and perform sequence feature learning on the first type of modal information and the second type of modal information through the type recurrent sequence representation learning layer to obtain the first type of learning feature and the second type of learning feature. Input the first type of learning feature and the second type of learning feature into the type recurrent similarity measurement layer to obtain a type modal information similarity result, and the type modal information similarity result includes two results: the first type of modal information and the second type of modal information are similar and the first type of modal information and the second type of modal information are not similar; Input the first storage modal information and the second storage modal information into the storage recurrent sequence model, and perform sequence feature learning on the first storage modal information and the second storage modal information through the storage recurrent sequence representation learning layer to obtain the first storage learning feature and the second storage learning feature. Input the first storage learning feature and the second storage learning feature into the storage recurrent similarity measurement layer to obtain a storage modal information similarity result, and the storage modal information similarity result includes two results: the first storage modal information and the second storage modal information are similar and the first storage modal information and the second storage modal information are not similar; Among them, if the type modal information similarity result is that the first type of modal information and the second type of modal information are similar, and the storage modal information similarity result is that the first storage modal information and the second storage modal information are similar, then the target data information to be deployed is retransmission data information, and the target data information to be deployed is refused to be transmitted into the blank VPC network;If the similarity result of the type modal information is that the first type modal information and the second type modal information are not similar, and / or the similarity result of the stored modal information is that the first stored modal information and the second stored modal information are not similar, then the target data information to be deployed is not retransmission data information, and it is agreed to transmit the target data information to be deployed into the blank VPC network;

[0106] Furthermore, before transmission, the total amount of data to be deployed is obtained in advance, which includes the total type distribution of the data, the storage situation of the data, etc. During the transmission process, the data to be deployed in the blank VPC network is continuously verified according to the total amount of data to be deployed, and it is judged whether the transmission is completed here, that is, it is judged whether all the target data information to be deployed in the data to be deployed is already included in the blank VPC network. When the verification is successful, the transmission is stopped;

[0107] The beneficial effects of the above technical solution are as follows: The information contained in the data is very rich. By comparing the first stored modal information and the first type modal information of the target data information to be transmitted with the second stored modal information and the second type modal information of the deployed data information that has been transmitted in the blank VPC network, it is beneficial to accurately judge whether the target data information to be transmitted has been transmitted to the blank VPC network, thereby improving the anti-retransmission efficiency of the anti-retransmission sub-module.

[0108] In one embodiment, the data to be deployed is classified to obtain several different types of target data information to be deployed, including:

[0109] Based on the data field types in the preset database to be deployed and the point mutual information between the labels in the preset label set, a data label index library is constructed; wherein, the data label index library includes the corresponding relationship between the data field types and the labels in the preset label set; the data field types include application program data field types, database data field types, browser data field types, client data field types; the labels in the preset label set include application program labels, database labels, browser labels, client labels;

[0110] The data to be deployed is classified by data fields to obtain the data field set of the data to be deployed;

[0111] A first label set related to each data field in the data field set is obtained from a group of label information corresponding to each data field type in the data label index library; wherein, the first label set includes the labels of each data field in the data field set;

[0112] The data field vector representation of each data field in the data field set and the label vector representation of each label in the first label set are obtained through a pre-trained vector model;

[0113] Concatenate the data field vector representation and the label vector representation of each label respectively to obtain a label prediction feature vector;

[0114] Perform prediction scoring on the label prediction feature vector through a preset depth prediction ranking model to obtain a predicted label set of the data to be deployed;

[0115] Classify the data to be deployed according to the predicted label set of the data to be deployed to obtain several pieces of target data information to be deployed of different types;

[0116] The working principle of the above technical solution is: classify the data to be deployed to obtain several pieces of target data information to be deployed of different types, including: constructing a data label index library based on the data field types in the preset database to be deployed and the point mutual information between the labels in the preset label set; specifically, obtaining the data field types corresponding to each label in the preset label set according to the first point mutual information between the data field types in the preset database to be deployed and the labels in the preset label set, and constructing a data label index library; among them, the data field types in the preset database to be deployed are preferably the most commonly used data field types in the cloud platform deployment system. Further, the data field types in the preset database to be deployed can also be added, deleted, queried, and modified according to the wishes of the user; the data label index library includes the corresponding relationship between the data field types and the labels in the preset label set; the data field types include but are not limited to application program data field types, database data field types, browser data field types, and client data field types; the labels in the preset label set include application program labels, database labels, browser labels, and client labels. Further, there are several application program sub-labels, database sub-labels, browser sub-labels, and client sub-labels in the application program labels, database labels, browser labels, and client labels. Preferably, the application program labels, database labels, browser labels, and client labels are divided into several application program sub-labels, database sub-labels, browser sub-labels, and client sub-labels according to the differences in application programs, databases, browsers, and clients; further, the point mutual information is generally used to measure the correlation between two events. In this embodiment, the preset database to be deployed can be understood as an event set, or understood as a large data field set. There is a label on each data field. By classifying the data fields of the data to be deployed, a data field is obtained, and then the PMI value between this data field and the label can be calculated. The calculation method is preferably: where n is any data field type in the preset database to be deployed, m is the label corresponding to n, P(n,m) is the PMI value between the data field and the label, X n is the number of times n appears in the preset database to be deployed, X mTo preset the number of occurrences of m in the database to be deployed, X nm To preset the number of occurrences of n and m together in the database to be deployed, Q is a preset constant. By calculating the PMI value between the data field and the label, the association relationship between the data field and the label can be initially established, and then the label index library based on PMI can be constructed; this algorithm can increase the discrimination ability of label relevance more than other PMI algorithms and improve the accuracy of labels; classify the data fields of the data to be deployed, integrate the classified data fields, and obtain the data field set of the data to be deployed; obtain the first label set related to each data field in the data field set from a group of label information corresponding to each data field type in the data label index library; wherein, the first label set includes the labels of each data field in the data field set; obtain the data field vector representation of each data field in the data field set and the label vector representation of each label in the first label set through a pre-trained vector model; splice the data field vector representation and the label vector representation of each label respectively to obtain the label prediction feature vector; perform prediction scoring on the label prediction feature vector through a preset deep prediction ranking model to obtain the prediction label set of the data to be deployed; wherein, the preset deep prediction ranking model is preferably a prediction ranking model based on deep learning; classify the data to be deployed according to the prediction label set of the data to be deployed to obtain several different types of target data information to be deployed;

[0117] The beneficial effects of the above technical solution are: by establishing a data label index library, the accuracy of data field labels is improved. By classifying the data to be deployed according to the prediction label set of the data to be deployed, several different types of target data information to be deployed are obtained, which is beneficial to improving the accuracy and processing efficiency of data classification.

[0118] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention also intends to include these changes and modifications.

Claims

1. A one - key deployment system for a cloud platform, characterized in that, Including: A collection module, configured to collect the data to be deployed in the virtual machine to be deployed and the virtual machine type of the virtual machine to be deployed; A transmission module, configured to transmit the data to be deployed to a blank VPC network to obtain a deployed VPC network; A deployment module, configured to copy a virtual machine of the same type as the virtual machine type to a virtual subnet of the deployed VPC network to obtain a deployed system; A verification module, configured to verify the normal operation of the deployed system; The data transmission sub-module further includes: A duplicate transmission prevention unit, configured to perform data duplicate transmission prevention verification on the data to be deployed during transmission when transmitting the data to be deployed to the blank VPC network; The duplicate transmission prevention unit performs the following operations: Classify the data to be deployed to obtain several different types of target data information to be deployed; Extract type feature and storage capacity feature from the target data information to be deployed to obtain a first type modal information and a first storage modal information; Obtain the deployed data information in the blank VPC network, extract type feature and information storage feature from the deployed data information to obtain a second type modal information and a second storage modal information; Obtain a recurrent network model for circularly identifying the deployed data information; wherein, the recurrent network model includes a type recurrent sequence model associated with the first type modal information and the second type modal information and a storage recurrent sequence model associated with the first storage modal information and the second storage modal information, the type recurrent sequence model includes a type recurrent sequence representation learning layer and a type recurrent similarity measurement layer, and the storage recurrent sequence model includes a storage recurrent sequence representation learning layer and a storage recurrent similarity measurement layer; Input the first type modal information and the second type modal information into the type recurrent sequence model, perform sequence feature learning on the first type modal information and the second type modal information through the type recurrent sequence representation learning layer to obtain a first type learning feature and a second type learning feature, and input the first type learning feature and the second type learning feature into the type recurrent similarity measurement layer to obtain a type modal information similarity result; Input the first storage modal information and the second storage modal information into the storage recurrent sequence model, perform sequence feature learning on the first storage modal information and the second storage modal information through the storage recurrent sequence representation learning layer to obtain a first storage learning feature and a second storage learning feature, and input the first storage learning feature and the second storage learning feature into the storage recurrent similarity measurement layer to obtain a storage modal information similarity result; If the type modal information similarity result is that the first type modal information and the second type modal information are similar, and the storage modal information similarity result is that the first storage modal information and the second storage modal information are similar, then the target data information to be deployed is duplicate transmitted data information, and reject transmitting the target data information to be deployed to the blank VPC network; If the similarity result of the type modal information is that the first type modal information and the second type modal information are not similar, and / or the similarity result of the stored modal information is that the first stored modal information and the second stored modal information are not similar, then the target data information to be deployed is not retransmitted data information, and it is agreed to transmit the target data information to be deployed into the blank VPC network.

2. The one - key deployment system for a cloud platform according to claim 1, characterized in that, The acquisition module includes: An initial VPC network establishment sub-module, used to establish an initial VPC network; A data transfer sub-module, used to transfer the virtual machine to be deployed into the initial VPC network; A data acquisition sub-module, used to acquire the data to be deployed in the initial VPC network; A type acquisition sub-module, used to acquire the virtual machine type of the virtual machine to be deployed.

3. The one - key deployment system for a cloud platform according to claim 1, characterized in that, It also includes: A blank VPC network establishment module, used to establish a blank VPC network.

4. The one - key deployment system for a cloud platform according to claim 1, characterized in that, The deployment module includes: A virtual machine selection sub-module, used to receive the virtual machine type of the virtual machine to be deployed and select a virtual machine with the same virtual machine type as the deployment virtual machine; A replication path determination sub-module, used to select a virtual subnet in the deployment VPC network as the virtual machine replication path; A paste sub-module, used to copy the deployment virtual machine to the virtual subnet of the deployment VPC network according to the virtual machine replication path to obtain a deployment system.

5. The one - key deployment system for a cloud platform according to claim 1, characterized in that, The verification module includes: An application detection sub-module, used to detect whether the application to which the deployment system belongs can be normally opened and used; A database detection sub-module, used to detect whether the database to which the deployment system belongs can be normally opened and used; A browser detection sub-module, used to detect whether the browser to which the deployment system belongs can be normally logged in and accessed; A client detection sub-module, used to detect whether the client to which the deployment system belongs can be normally logged in and accessed; A result output sub-module, used to display a deployment success signal on the display page of the deployment system when the application, database, browser, and client to which the deployment system belongs are all normal.

6. The one-key deployment system for a cloud platform according to claim 5, characterized in that, It also includes: An alarm module, used to emit an alarm signal when the deployment system cannot run normally.

7. The one-key deployment system for a cloud platform according to claim 6, characterized in that, The alarm module includes: A fault location sub-module, used to locate the fault of the non-normally running section in the deployment system to obtain the fault section; where the section includes: application, database, browser, client; A fault alarm sub-module, used to emit a section alarm signal corresponding to the fault section according to the fault section; where the section alarm signal includes an application alarm signal, a database alarm signal, a browser alarm signal, and a client alarm signal.

8. The one-key deployment system for a cloud platform according to claim 1, characterized in that, Classifying the data to be deployed to obtain several different types of target data information to be deployed, including: Construct a data label index library based on the point mutual information between the data field types in the preset database to be deployed and the labels in the preset label set; wherein, the data label index library includes the corresponding relationship between the data field types and the labels in the preset label set; the data field types include application data field types, database data field types, browser data field types, and client data field types; the labels in the preset label set include application labels, database labels, browser labels, and client labels; Classify the data fields of the data to be deployed to obtain the data field set of the data to be deployed; Obtain the first label set related to each data field in the data field set from a group of label information corresponding to each data field type in the data label index library; wherein, the first label set includes the labels of each data field in the data field set; Obtain the data field vector representation of each data field in the data field set and the label vector representation of each label in the first label set through a pre-trained vector model; Concatenate the data field vector representation and the label vector representation of each label respectively to obtain a label prediction feature vector; Perform a prediction score on the label prediction feature vector through a preset deep prediction ranking model to obtain the predicted label set of the data to be deployed; Classify the data to be deployed according to the predicted label set of the data to be deployed to obtain several different types of target data information to be deployed.

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