Software development application data processing method and system based on cloud computing

Abstract

The invention relates to the technical field of cloud computing data processing, and discloses a software development application data processing method and system based on cloud computing, and the method comprises the steps: obtaining application document data generated in a software development process, and carrying out the classification processing; merging a plurality of application document data of the same category into an archived file; and solving by utilizing the archived file storage optimization model to obtain a scheduling storage strategy of the archived file, and carrying out storage processing on the archived file according to a solving result. According to the method, the application document data is divided into application document data categories with higher semantic similarity and higher phrase approximation degree, the storage unbalance degree, the communication pressure and the load pressure are converted into a multi-target storage optimization function, optimization solution of a scheduling storage strategy is carried out with the minimum server unbalance degree and pressure as the target, and the optimization solution efficiency is improved. And the archived file is stored, so that the storage life of the server cluster is prolonged, and the load balance of data storage in the cloud computing process is realized.

Description

Technical Field

[0001] The present invention relates to the field of cloud computing data processing, and in particular to a method and system for processing software development application data based on cloud computing. Background Art

[0002] With the rapid development of Internet technology, especially the widespread application of cloud computing technology, various types of document data in the software development process have also shown an explosive growth trend. These document data include requirement documents, design documents, test documents, user manuals, API documents, etc., which run through the entire software development life cycle. Document management and storage have become an important link that cannot be ignored in the software development process. Cloud computing provides new ideas and means for the storage and management of these document data, and can process massive document data at a lower cost and higher efficiency. However, how to achieve efficient storage and optimization of massive software development document data in a cloud computing environment is still a problem that needs to be solved urgently. Summary of the invention

[0003] In view of this, the present invention proposes a software development application data processing method based on cloud computing, which processes massive software development document data through efficient distributed storage and computing capabilities to achieve document storage optimization.

[0004] To achieve the above object, the present invention provides a method for processing software development application data based on cloud computing, comprising the following steps:

[0005] S1: Acquire application document data generated during the software development process and classify the application document data;

[0006] S2: Using cloud computing technology to archive application document data of different categories, and merge multiple application document data of the same category into an archive file;

[0007] S3: constructing an archive file storage optimization model, wherein the archive file storage optimization model takes the archive file as the target object and uses multi-objective optimization to obtain a scheduling storage strategy for the archive file;

[0008] S4: Using the archive file storage optimization model to solve the archive file scheduling storage strategy, and perform storage processing on the archive file according to the solution result.

[0009] As a further improvement method of the present invention:

[0010] Optionally, obtaining application document data generated during the software development process in step S1 includes:

[0011] Obtain application document data generated during software development, where the application document data type includes software development requirements data , Development project management data , Development code data , Software Test Sample Data And development documentation data , the collected application document data is:

[0012] { X n |n∈[1,N]} ;

[0013] in:

[0014] including an nth group of application document data, where N represents the total number of application document data in the software development process;

[0015] The acquired application document data is classified and processed to obtain the type of each group of application document data. In an embodiment of the present invention, the software development requirement data includes user requirements, market research, project goals and key performance indicators, the development project management data includes project planning, resource management and budget cost management, the development code data includes architecture design, UI design, code base and development environment, the software test sample data includes test plan and test case, and the development document data includes system design documents, API documents, operation guides and user manuals.

[0016] Optionally, the classifying the application document data includes:

[0017] The classification processing flow of the application document data is as follows:

[0018] S11: Obtain application document data to be classified and encode the application document data. The encoding result is , i ∈ [1, num n ] , Represents application document data Middle The word vectors of the phrases, Represents application document data The total number of phrases in ;

[0019] S12: Based on the frequency of the word group in different application document data types, the word vectors in the application document data encoding result are weighted to obtain the weighted word vectors of the word vectors in different application document data types, where the word vector In the application document data type The weighted word vector of is:

[0020] Y n i (j)= w ̅ n i (j) Y n i ,j ∈ [1,5] ;

[0021] ;

[0022] w n i (j)= count n i (j) Count j ( X n i ) [- ∑ j=1 5 count n i (j) Count j ( X n i ) log( count n i (j) Count j ( X n i ) ) ] ;

[0023] in:

[0024] Representing word vectors In the application document data type The weighted word vector of ;

[0025] Representing word vectors In the application document data type The weight of Representation weight The normalized result of

[0026] Expressing phrases In application document data The frequency of occurrence in Expressing phrases In the application document data type The average frequency of all application document data;

[0027] S13: Constructing a document vector of the application document data, wherein the application document data The document vector is:

[0028] ;

[0029] ;

[0030] in:

[0031] Represents application document data The document vector of Represents application document data Prefer application document data types The biased document vector of

[0032] S14: construct standard document data of different application document data types, and obtain the number of phrases of the standard document data and the biased document vector of the corresponding application document data type, wherein the application document data type The number of phrases in the standard document data is , the biased document vector is ;

[0033] S15: Calculate the bias of document vectors in different application document data types, where document vector In the application document data type The bias is:

[0034] ;

[0035] ;

[0036] ;

[0037] ;

[0038] in:

[0039] Represents document vector In the application document data type bias;

[0040] represents an exponential function with a natural constant as base;

[0041] Represents biased document vector and The similarity between

[0042] Represents bias towards document vector and The number of similar phrases between

[0043] , Represents biased document vector Any weighted word vector in ;

[0044] Represents similar phrase count parameters, This indicates a bias towards document vectors Existence and Similar weighted word vectors;

[0045] Indicates the preset word vector similarity threshold;

[0046] S16: Select the application document data type with the highest bias as the classification result of the application document data corresponding to the document vector.

[0047] Optionally, the step S11 of obtaining the application document data to be classified and encoding the application document data includes:

[0048] S111: Perform word segmentation processing on the application document data to obtain a phrase sequence of the application document data, wherein the application document data The phrase sequence is:

[0049] ;

[0050] in:

[0051] Represents application document data No. phrases;

[0052] S112: Obtain the text format of each phrase in the phrase sequence, where the text format includes plain, bold, underline, and italic, and number the text formats of the phrases. The text format is , They represent plain, bold, underline, and italic respectively;

[0053] S113: Perform a unique hot encoding on each phrase in the phrase sequence, where The one-hot encoding result is ;

[0054] S114: Encode the word vector for the word according to the position of the word in the document. The word vector encoding result is:

[0055] Y n i =[cos( Loc( Y n i ) Len( Y n i ) π), [ C n i , x n i ] 1+exp(- W 1 *[ C n i , x n i ]) ] ;

[0056] in:

[0057] Expressing phrases The word vector of

[0058] Expressing phrases The total number of phrases in the text line. The first phrases;

[0059] represents the encoding weight matrix, * represents the convolution operator;

[0060] Represents an exponential function with a natural constant as its base.

[0061] Optionally, in step S2, using cloud computing technology to archive different types of application document data includes:

[0062] Cloud computing technology is used to archive different types of application document data. The archiving process of application document data is as follows:

[0063] S21: Divide all current servers into 5 server clusters, where the servers in the jth server cluster are used to store application document data types. Application document data;

[0064] S22: Select five different word vectors with the largest weights in the corresponding application document data type, and convert the selected word vectors into storage codes of the application document data, where the application document data The storage code is:

[0065] code n =[Label( X n ),[ X n (1), X n (2), X n (3), X n (4), X n (5)] mod 2 β rsh(β)]] ;

[0066] in:

[0067] Represents application document data Storage code of

[0068] Represents application document data Application document data types, Label( X n ) ∈[1,5] , j represents the application document data The application document data type is ;

[0069] Indicates storage encoding parameters;

[0070] represents the remainder operator, represents the right shift operator, This means the right shift step length is ;

[0071] S23: Based on the application document data type, the application document data is divided into corresponding server clusters. The server cluster uses the storage code as the key and the application document data as the value, and merges the received multiple application document data into an archive file and stores them in a key-value storage manner.

[0072] Optionally, constructing an archive file storage optimization model in the S3 step includes:

[0073] Constructing an archive file storage optimization model, wherein the archive file storage optimization model takes the archive file as the target object and adopts a multi-objective optimization solution to obtain a scheduling storage strategy for the archive file, wherein the archive file storage optimization model includes an input layer, an environmental information collection layer, a multi-objective storage optimization function construction layer, and a scheduling storage strategy solution layer;

[0074] The input layer is used to obtain data parameters of different application document data in the archive file, wherein the data parameters include the data volume and storage coding of the application document data;

[0075] The environment information collection layer is used to collect the storage status of all servers in the server cluster, where the storage status of the server includes the current storage data volume, the maximum storage data volume, the CPU utilization rate, and the network bandwidth utilization rate;

[0076] The multi-objective optimization function construction layer is used to construct multi-objective storage optimization functions for different server clusters;

[0077] The scheduling storage strategy solving layer is used to optimize and solve the multi-objective storage optimization function to form the scheduling storage strategy for archive files in different server clusters.

[0078] Optionally, in step S4, obtaining a scheduling storage strategy for archive files by using an archive file storage optimization model includes:

[0079] The scheduling storage strategy of archive files is solved by using the archive file storage optimization model. The scheduling storage strategy solution process of archive files in the jth server cluster is as follows:

[0080] S41: The input layer obtains data parameters of different application document data in the archive file, and the environment information collection layer collects the storage status of all servers in the j-th server cluster, and formalizes the scheduling storage process of the archive file;

[0081] S42: Based on the formal representation results of the scheduling storage process, a multi-objective storage optimization function is constructed: ,in Represents the scheduling storage strategy for different application document data in the archive file:

[0082] ;

[0083] in:

[0084] Indicates the scheduling storage location of the M application document data in the archive file in the jth server cluster, that is, the server number in the server cluster, represents the scheduled storage location of the mth application document data in the archive file in the jth server cluster, and M represents the total number of application document data in the archive file; in the embodiment of the present invention, the server is numbered from 1 to K, and the larger the number, the larger the product of the hard disk utilization, CPU utilization and network bandwidth utilization of the server, wherein the hard disk utilization is the current storage data volume / maximum storage data volume;

[0085] S43: Initialize U group scheduling storage strategies, wherein the initialized u-th group scheduling storage strategy is:

[0086] ;

[0087] in:

[0088] Indicates the u-th group of scheduling storage strategies generated by initialization. Indicates the scheduling storage strategy The scheduling storage location of the mth application document data;

[0089] S44: Set the current iteration number of the scheduling storage policy to t and the maximum iteration number to Max, then the tth iteration result of the uth group scheduling storage policy is ;

[0090] S45: The U group scheduling storage strategies obtained in the t-th iteration are sequentially used as the input of the multi-objective storage optimization function, and the scheduling storage strategy that minimizes the multi-objective storage optimization function is used as the optimal scheduling storage strategy obtained in the t-th iteration. ;

[0091] S46: Iterate the scheduling storage policy, where the iteration formula is:

[0092] ;

[0093] δ(t)= 1 a t exp[- 1 2 ( a t ) 2 ]cos( 4 a t ) ;

[0094] ;

[0095] in:

[0096] Represents a random number between 0 and 1. Represents a random number between 1 and 2;

[0097] represents the shape parameter, Set to 5;

[0098] Indicates the control parameter, set E to 100;

[0099] represents the iteration control parameter;

[0100] S47: Let t=t+1, and return to step S45 until the maximum number of iterations is reached, and use the optimal scheduling storage strategy at this time as the scheduling storage strategy for the archive file, and store the archive file according to the solved scheduling storage strategy.

[0101] Optionally, the scheduling storage process of the archived file in step S41 is formally represented, including:

[0102] The data parameter set of different application document data of archived files in the j-th server cluster is:

[0103] { e m |m∈[1,M]} ;

[0104] ;

[0105] in:

[0106] The data parameter representing the mth application document data in the archive file, where M represents the total number of application document data in the archive file;

[0107] represents the data volume and storage code of the mth application document data in the archive file in turn;

[0108] The storage status set of all servers in the j-th server cluster is:

[0109] { H k =( H k 1 , H k 2 , H k 3 , H k 4 )|k∈[1,K]} ;

[0110] in:

[0111] represents the storage status of the kth server in the jth server cluster, and K represents the total number of servers in the jth server cluster;

[0112] It represents the current storage data volume, maximum storage data volume, CPU utilization and network bandwidth utilization of the kth server in the jth server cluster respectively.

[0113] Optionally, the multi-objective storage optimization function in step S42 for:

[0114] ;

[0115] in:

[0116] Indicates that the storage policy is based on the schedule , the formal representation result of whether the mth application document data is stored in the kth server, This means that the mth application document data will be stored on the kth server. This means that the mth application document data is not stored on the kth server;

[0117] Indicates that the storage policy is based on the schedule After scheduling storage, the hard disk utilization of the kth server is Indicates that the storage policy is based on the schedule The average hard disk utilization of K servers after scheduling storage;

[0118] Indicates that in other server clusters The number of stored codes whose cosine similarity reaches a preset threshold;

[0119] In an embodiment of the present invention, Indicates the imbalance degree of usage of the kth server in the server cluster, Indicates the CPU load pressure of the kth server, represents the communication pressure of the kth server, Characterizes the communication call frequency of the mth application document data.

[0120] In order to solve the above problems, the present invention provides a software development application data processing system based on cloud computing, characterized in that the system comprises:

[0121] A data classification module is used to obtain application document data generated during the software development process and classify the application document data;

[0122] The archiving module is used to archive application document data of different categories by using cloud computing technology, and merge multiple application document data of the same category into an archive file;

[0123] The storage optimization device is used to use the archive file storage optimization model to solve the scheduling storage strategy of the archive file, and perform storage processing on the archive file according to the solution result.

[0124] In order to solve the above problem, the present invention further provides an electronic device, the electronic device comprising:

[0125] A memory storing at least one instruction;

[0126] Communication interface, enabling electronic equipment to communicate; and

[0127] The processor executes the instructions stored in the memory to implement the above-mentioned cloud computing-based software development application data processing method.

[0128] In order to solve the above problems, the present invention also provides a computer-readable storage medium, in which at least one instruction is stored. The at least one instruction is executed by a processor in an electronic device to implement the above-mentioned cloud computing-based software development application data processing method.

[0129] Compared with the prior art, the present invention proposes a method for processing software development application data based on cloud computing, which has the following advantages:

[0130] First, this solution obtains multiple groups of application document data generated during the software development process, and performs word segmentation, word encoding, and word frequency bias calculation on the application document data in turn, to form biased document vectors of the application document data in different application document data types, and then classifies the application document data according to the similarity of the biased document vectors and the phrase similarity ratio, and divides the application document data into application document data categories with stronger semantic similarity and higher degree of phrase approximation, and uses cloud computing technology to generate storage codes for the application document data, and uses the storage codes as keys to select servers for distributed storage of the application document data.

[0131] At the same time, this solution constructs an archive file storage optimization model based on the storage imbalance, communication pressure and load pressure of the server after storing the archive files, formalizes the archive files to be stored and the server storage status, converts the storage imbalance, communication pressure and load pressure into a multi-objective storage optimization function, and optimizes and solves the scheduling storage strategy with the goal of minimizing server imbalance and pressure. The archive files are stored and processed according to the scheduling storage strategy obtained, thereby improving the storage life of the server cluster. BRIEF DESCRIPTION OF THE DRAWINGS

[0132] Figure 1 A flowchart of a method for processing software development application data based on cloud computing provided by one embodiment of the present invention;

[0133] Figure 2 A functional module diagram of a cloud computing-based software development application data processing system provided by an embodiment of the present invention;

[0134] Figure 2 In: 100 software development application data processing system based on cloud computing, 101 data classification module, 102 archiving module, 103 storage optimization device;

[0135] Figure 3 A schematic diagram of the structure of an electronic device for implementing a cloud computing-based software development application data processing method provided by an embodiment of the present invention.

[0136] Figure 3 In: 1 electronic device, 10 processor, 11 memory, 12 program, 13 communication interface;

[0137] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0138] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0139] The embodiment of the present application provides a method for processing software development application data based on cloud computing. The execution subject of the software development application data processing method based on cloud computing includes but is not limited to at least one of the electronic devices such as a server, a terminal, etc. that can be configured to execute the method provided by the embodiment of the present application. In other words, the software development application data processing method based on cloud computing can be executed by software or hardware installed on a terminal device or a server device, and the software can be a blockchain platform. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, etc.

[0140] Embodiment 1:

[0141] S1: Acquire application document data generated during the software development process and classify the application document data.

[0142] The step S1 of acquiring application document data generated during the software development process includes:

[0143] Obtain application document data generated during software development, where the application document data type includes software development requirements data , Development project management data , Development code data , Software Test Sample Data And development documentation data , the collected application document data is:

[0144] { X n |n∈[1,N]} ;

[0145] in:

[0146] including an nth group of application document data, where N represents the total number of application document data in the software development process;

[0147] The acquired application document data is classified and processed to obtain the type of each group of application document data. In an embodiment of the present invention, the software development requirement data includes user requirements, market research, project goals and key performance indicators, the development project management data includes project planning, resource management and budget cost management, the development code data includes architecture design, UI design, code base and development environment, the software test sample data includes test plan and test case, and the development document data includes system design documents, API documents, operation guides and user manuals.

[0148] The classifying and processing the application document data includes:

[0149] The classification processing flow of the application document data is as follows:

[0150] S11: Obtain application document data to be classified and encode the application document data. The encoding result is , i ∈ [1, num n ] , Represents application document data Middle The word vectors of the phrases, Represents application document data The total number of phrases in ;

[0151] S12: Based on the frequency of the word group in different application document data types, the word vectors in the application document data encoding result are weighted to obtain the weighted word vectors of the word vectors in different application document data types, where the word vector In the application document data type The weighted word vector of is:

[0152] Y n i (j)= w ̅ n i (j) Y n i ,j ∈ [1,5] ;

[0153] ;

[0154] w n i (j)= count n i (j) Count j ( X n i ) [- ∑ j=1 5 count n i (j) Count j ( X n i ) log( count n i (j) Count j ( X n i ) ) ] ;

[0155] in:

[0156] Representing word vectors In the application document data type The weighted word vector of ;

[0157] Representing word vectors In the application document data type The weight of Representation weight The normalized result of

[0158] Expressing phrases In application document data The frequency of occurrence in Expressing phrases In the application document data type The average frequency of all application document data;

[0159] S13: Constructing a document vector of the application document data, wherein the application document data The document vector is:

[0160] ;

[0161] ;

[0162] in:

[0163] Represents application document data The document vector of Represents application document data Prefer application document data types The biased document vector of

[0164] S14: construct standard document data of different application document data types, and obtain the number of phrases of the standard document data and the biased document vector of the corresponding application document data type, wherein the application document data type The number of phrases in the standard document data is , the biased document vector is ;

[0165] S15: Calculate the bias of document vectors in different application document data types, where document vector In the application document data type The bias is:

[0166] ;

[0167] ;

[0168] ;

[0169] ;

[0170] in:

[0171] Represents document vector In the application document data type bias;

[0172] represents an exponential function with a natural constant as base;

[0173] Represents bias towards document vector and The similarity between

[0174] Represents bias towards document vector and The number of similar phrases between

[0175] , Represents bias towards document vector Any weighted word vector in ;

[0176] Represents similar phrase count parameters, This indicates a bias towards document vectors Existence and Similar weighted word vectors;

[0177] Indicates the preset word vector similarity threshold;

[0178] S16: Select the application document data type with the highest bias as the classification result of the application document data corresponding to the document vector.

[0179] The step S11 of obtaining the application document data to be classified and encoding the application document data includes:

[0180] S111: Perform word segmentation processing on the application document data to obtain a phrase sequence of the application document data, wherein the application document data The phrase sequence is:

[0181] ;

[0182] in:

[0183] Represents application document data No. phrases;

[0184] S112: Obtain the text format of each phrase in the phrase sequence, where the text format includes plain, bold, underline, and italic, and number the text formats of the phrases. The text format is , They represent plain, bold, underline, and italic respectively;

[0185] S113: Perform a unique hot encoding on each phrase in the phrase sequence, where The one-hot encoding result is ;

[0186] S114: Encode the word vector for the word according to the position of the word in the document. The word vector encoding result is:

[0187] Y n i =[cos( Loc( Y n i ) Len( Y n i ) π), [ C n i , x n i ] 1+exp(- W 1 *[ C n i , x n i ]) ] ;

[0188] in:

[0189] Expressing phrases The word vector of

[0190] Expressing phrases The total number of phrases in the text line. The first phrases;

[0191] represents the encoding weight matrix, * represents the convolution operator;

[0192] Represents an exponential function with a natural constant as its base.

[0193] S2: Using cloud computing technology to archive application document data of different categories, and merging multiple application document data of the same category into an archive file.

[0194] In the step S2, cloud computing technology is used to archive different types of application document data, including:

[0195] Cloud computing technology is used to archive different types of application document data. The archiving process of application document data is as follows:

[0196] S21: Divide all current servers into 5 server clusters, where the servers in the jth server cluster are used to store application document data types. Application document data;

[0197] S22: Select five different word vectors with the largest weights in the corresponding application document data type, and convert the selected word vectors into storage codes of the application document data, where the application document data The storage code is:

[0198] code n =[Label( X n ),[ X n (1), X n (2), X n (3), X n (4), X n (5)] mod 2 β rsh(β)]] ;

[0199] in:

[0200] Represents application document data Storage code of

[0201] Represents application document data Application document data types, Label( X n ) ∈[1,5] , j represents the application document data The application document data type is ;

[0202] Indicates storage encoding parameters;

[0203] represents the remainder operator, represents the right shift operator, This means the right shift step length is ;

[0204] S23: Divide the application document data into corresponding server clusters based on the application document data type. The server cluster uses the storage code as the key and the application document data as the value, and merges the received multiple application document data into an archive file, and stores them in a key-value storage manner.

[0205] S3: Construct an archive file storage optimization model, wherein the archive file storage optimization model takes the archive file as the target object and adopts a multi-objective optimization solution to obtain a scheduling storage strategy for the archive file.

[0206] The S3 step constructs an archive file storage optimization model, including:

[0207] Constructing an archive file storage optimization model, wherein the archive file storage optimization model takes the archive file as the target object and adopts a multi-objective optimization solution to obtain a scheduling storage strategy for the archive file, wherein the archive file storage optimization model includes an input layer, an environmental information collection layer, a multi-objective storage optimization function construction layer, and a scheduling storage strategy solution layer;

[0208] The input layer is used to obtain data parameters of different application document data in the archive file, wherein the data parameters include the data volume and storage coding of the application document data;

[0209] The environment information collection layer is used to collect the storage status of all servers in the server cluster, where the storage status of the server includes the current storage data volume, the maximum storage data volume, the CPU utilization rate, and the network bandwidth utilization rate;

[0210] The multi-objective optimization function construction layer is used to construct multi-objective storage optimization functions for different server clusters;

[0211] The scheduling storage strategy solving layer is used to optimize and solve the multi-objective storage optimization function to form the scheduling storage strategy for archive files in different server clusters.

[0212] S4: Using the archive file storage optimization model to solve the archive file scheduling storage strategy, and perform storage processing on the archive file according to the solution result.

[0213] In the step S4, the scheduling storage strategy of the archived files is obtained by using the archived file storage optimization model, including:

[0214] The scheduling storage strategy of archive files is solved by using the archive file storage optimization model. The scheduling storage strategy solution process of archive files in the jth server cluster is as follows:

[0215] S41: The input layer obtains data parameters of different application document data in the archive file, and the environment information collection layer collects the storage status of all servers in the j-th server cluster, and formalizes the scheduling storage process of the archive file;

[0216] S42: Based on the formal representation results of the scheduling storage process, a multi-objective storage optimization function is constructed: ,in Represents the scheduling storage strategy for different application document data in the archive file:

[0217] ;

[0218] in:

[0219] Indicates the scheduling storage location of the M application document data in the archive file in the jth server cluster, that is, the server number in the server cluster, represents the scheduled storage location of the mth application document data in the archive file in the jth server cluster, and M represents the total number of application document data in the archive file; in the embodiment of the present invention, the server is numbered from 1 to K, and the larger the number, the larger the product of the hard disk utilization, CPU utilization and network bandwidth utilization of the server, wherein the hard disk utilization is the current storage data volume / maximum storage data volume;

[0220] S43: Initialize U group scheduling storage strategies, wherein the initialized u-th group scheduling storage strategy is:

[0221] ;

[0222] in:

[0223] Indicates the u-th group of scheduling storage strategies generated by initialization. Indicates the scheduling storage strategy The scheduling storage location of the mth application document data;

[0224] S44: Set the current iteration number of the scheduling storage policy to t and the maximum iteration number to Max, then the tth iteration result of the uth group scheduling storage policy is ;

[0225] S45: The U group scheduling storage strategies obtained in the t-th iteration are sequentially used as the input of the multi-objective storage optimization function, and the scheduling storage strategy that minimizes the multi-objective storage optimization function is used as the optimal scheduling storage strategy obtained in the t-th iteration. ;

[0226] S46: Iterate the scheduling storage policy, where the iteration formula is:

[0227] ;

[0228] δ(t)= 1 a t exp[- 1 2 ( a t ) 2 ]cos( 4 a t ) ;

[0229] ;

[0230] in:

[0231] Represents a random number between 0 and 1. Represents a random number between 1 and 2;

[0232] represents the shape parameter, Set to 5;

[0233] Indicates the control parameter, set E to 100;

[0234] represents the iteration control parameter;

[0235] S47: Let t=t+1, and return to step S45 until the maximum number of iterations is reached, and use the optimal scheduling storage strategy at this time as the scheduling storage strategy for the archive file, and store the archive file according to the solved scheduling storage strategy.

[0236] The scheduling storage process of the archived file in step S41 is formally represented, including:

[0237] The data parameter set of different application document data of archived files in the j-th server cluster is:

[0238] { e m |m∈[1,M]} ;

[0239] ;

[0240] in:

[0241] The data parameter representing the mth application document data in the archive file, where M represents the total number of application document data in the archive file;

[0242] represents the data volume and storage code of the mth application document data in the archive file in turn;

[0243] The storage status set of all servers in the j-th server cluster is:

[0244] { H k =( H k 1 , H k 2 , H k 3 , H k 4 )|k∈[1,K]} ;

[0245] in:

[0246] represents the storage status of the kth server in the jth server cluster, and K represents the total number of servers in the jth server cluster;

[0247] It represents the current storage data volume, maximum storage data volume, CPU utilization and network bandwidth utilization of the kth server in the jth server cluster respectively.

[0248] The multi-objective storage optimization function in step S42 for:

[0249] ;

[0250] in:

[0251] Indicates that the storage policy is based on the schedule , the formal representation result of whether the mth application document data is stored in the kth server, This means that the mth application document data will be stored on the kth server. This means that the mth application document data is not stored on the kth server;

[0252] Indicates that the storage policy is based on the schedule After scheduling storage, the hard disk utilization of the kth server is Indicates that the storage policy is based on the schedule The average hard disk utilization of K servers after scheduling storage;

[0253] Indicates that in other server clusters The number of stored codes whose cosine similarity reaches a preset threshold.

[0254] Embodiment 2:

[0255] like Figure 2 , which is a functional module diagram of a cloud computing-based software development application data processing system provided in one embodiment of the present invention, which can implement the cloud computing-based software development application data processing method in Example 1.

[0256] The cloud computing-based software development application data processing system 100 of the present invention can be installed in an electronic device. According to the functions to be implemented, the cloud computing-based software development application data processing system can include a data classification module 101, an archiving module 102 and a storage optimization device 103. The module of the present invention can also be called a unit, which refers to a series of computer program segments that can be executed by an electronic device processor and can complete fixed functions, which are stored in the memory of the electronic device.

[0257] The data classification module 101 is used to obtain application document data generated during the software development process and classify the application document data;

[0258] The archiving module 102 is used to archive application document data of different categories using cloud computing technology, and merge multiple application document data of the same category into an archive file;

[0259] The storage optimization device 103 is used to use the archive file storage optimization model to solve the scheduling storage strategy of the archive file, and perform storage processing on the archive file according to the solution result.

[0260] In detail, each module in the cloud computing-based software development application data processing system 100 in the embodiment of the present invention is used in the same manner as described above. Figure 1 The same technical means are used as the cloud computing-based software development application data processing method described in, and can produce the same technical effects, so they will not be repeated here.

[0261] Embodiment 3:

[0262] like Figure 3 , which is a schematic diagram of the structure of an electronic device for implementing a cloud computing-based software development application data processing method provided by an embodiment of the present invention.

[0263] The electronic device 1 may include a processor 10 , a memory 11 , a communication interface 13 and a bus, and may also include a computer program stored in the memory 11 and executable on the processor 10 , such as a program 12 .

[0264] The memory 11 includes at least one type of readable storage medium, and the readable storage medium includes a flash memory, a mobile hard disk, a multimedia card, a card-type memory (e.g., SD or DX memory, etc.), a magnetic memory, a disk, an optical disk, etc. In some embodiments, the memory 11 may be an internal storage unit of the electronic device 1, such as a mobile hard disk of the electronic device 1. In other embodiments, the memory 11 may also be an external storage device of the electronic device 1, such as a plug-in mobile hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (SecureDigital, SD) card, a flash card (Flash Card), etc. equipped on the electronic device 1. Further, the memory 11 may also include both an internal storage unit of the electronic device 1 and an external storage device. The memory 11 may not only be used to store application software and various types of data installed in the electronic device 1, such as the code of the program 12, etc., but also be used to temporarily store data that has been output or is to be output.

[0265] The processor 10 may be composed of an integrated circuit in some embodiments, for example, a single packaged integrated circuit, or a plurality of packaged integrated circuits with the same or different functions, including one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and combinations of various control chips, etc. The processor 10 is the control core (Control Unit) of the electronic device, and uses various interfaces and lines to connect various components of the entire electronic device, and executes or executes programs or modules stored in the memory 11 (such as programs 12 for realizing software development application data processing based on cloud computing), and calls data stored in the memory 11 to execute various functions of the electronic device 1 and process data.

[0266] The communication interface 13 may include a wired interface and / or a wireless interface (such as a WI-FI interface, a Bluetooth interface, etc.), which is generally used to establish a communication connection between the electronic device 1 and other electronic devices, and to achieve connection and communication between internal components of the electronic device.

[0267] The bus may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. The bus is configured to realize connection and communication between the memory 11 and at least one processor 10, etc.

[0268] Figure 3 Only an electronic device with components is shown, and those skilled in the art will understand that Figure 3 The structure shown does not constitute a limitation on the electronic device 1, and may include fewer or more components than shown in the figure, or combine certain components, or arrange the components differently.

[0269] For example, although not shown, the electronic device 1 may also include a power source (such as a battery) for supplying power to various components. Preferably, the power source may be logically connected to the at least one processor 10 through a power management device, so that the power management device can realize functions such as charging management, discharging management, and power consumption management. The power source may also include one or more DC or AC power sources, recharging devices, power failure detection circuits, power converters or inverters, power status indicators, and other arbitrary components. The electronic device 1 may also include a variety of sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be repeated here.

[0270] Optionally, the electronic device 1 may further include a user interface, which may be a display, an input unit (such as a keyboard), or a standard wired interface or a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, and an OLED (Organic Light-Emitting Diode) touch device. The display may also be appropriately referred to as a display screen or a display unit, which is used to display information processed in the electronic device 1 and to display a visual user interface.

[0271] It should be understood that the embodiment is for illustration only and the scope of the patent application is not limited to this structure.

[0272] It should be noted that the serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages and disadvantages of the embodiments. And the terms "including", "comprising" or any other variants thereof in this article are intended to cover non-exclusive inclusion, so that a process, device, article or method including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, device, article or method. In the absence of further restrictions, an element defined by the sentence "including a ..." does not exclude the presence of other identical elements in the process, device, article or method including the element.

[0273] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes a number of instructions for a terminal device (which can be a mobile phone, a computer, a server, or a network device, etc.) to execute the methods described in each embodiment of the present invention.

[0274] The above are only preferred embodiments of the present invention, and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A method for processing software development application data based on cloud computing, characterized in that: The method comprises: S1: Acquire application document data generated during the software development process and classify the application document data; S2: Using cloud computing technology to archive application document data of different categories, and merge multiple application document data of the same category into an archive file; S3: constructing an archive file storage optimization model, wherein the archive file storage optimization model takes the archive file as the target object and adopts a multi-objective optimization solution to obtain a scheduling storage strategy for the archive file; S4: Using the archive file storage optimization model to solve the archive file scheduling storage strategy, and perform storage processing on the archive file according to the solution result.

2. A method for processing software development and application data based on cloud computing as claimed in claim 1, characterized in that: The step S1 of acquiring application document data generated during the software development process includes: Obtain application document data generated during software development, where the application document data type includes software development requirements data , Development project management data , Development code data , Software Test Sample Data And development documentation data , the collected application document data is: ； in: The nth group of application document data is included, where N represents the total number of application document data in the software development process; The acquired application document data is classified to obtain the type of each group of application document data.

3. A method for processing software development and application data based on cloud computing as claimed in claim 2, characterized in that: The classifying and processing the application document data includes: The classification processing flow of the application document data is as follows: S11: Obtain application document data to be classified and encode the application document data. The encoding result is , , Represents application document data Middle The word vectors of the phrases, Represents application document data The total number of phrases in ; S12: Based on the frequency of the word group in different application document data types, the word vectors in the application document data encoding result are weighted to obtain the weighted word vectors of the word vectors in different application document data types, where the word vector In the application document data type The weighted word vector of ; S13: Constructing a document vector of the application document data, wherein the application document data The document vector is: ； ； in: Represents application document data The document vector of Represents application document data Prefer application document data types The biased document vector of S14: construct standard document data of different application document data types, and obtain the number of phrases of the standard document data and the biased document vector of the corresponding application document data type, wherein the application document data type The number of phrases in the standard document data is , the biased document vector is ; S15: Calculate the bias of document vectors in different application document data types, where document vector In the application document data type The bias is: ； ； ； ； in: Represents document vector In the application document data type bias; represents an exponential function with a natural constant as base; Represents bias towards document vector and The similarity between Represents bias towards document vector and The number of similar phrases between , Represents bias towards document vector Any weighted word vector in ; Represents similar phrase count parameters, This indicates a bias towards document vectors Existence and Similar weighted word vectors; Indicates the preset word vector similarity threshold; S16: Select the application document data type with the highest bias as the classification result of the application document data corresponding to the document vector.

4. A method for processing software development and application data based on cloud computing as claimed in claim 2, characterized in that: The step S11 of obtaining the application document data to be classified and encoding the application document data includes: S111: Perform word segmentation processing on the application document data to obtain a phrase sequence of the application document data, wherein the application document data The phrase sequence is: ； in: Represents application document data No. phrases; S112: Obtain the text format of each phrase in the phrase sequence, where the text format includes plain, bold, underline, and italic, and number the text formats of the phrases. The text format is , They represent plain, bold, underline, and italic respectively; S113: Perform a unique hot encoding on each phrase in the phrase sequence, where The one-hot encoding result is ; S114: Encode the word vector for the word according to the position of the word in the document. The word vector encoding result is: ； in: Expressing phrases The word vector of Expressing phrases The total number of phrases in the text line. The first phrases; represents the encoding weight matrix, * represents the convolution operator; Represents an exponential function with a natural constant as its base.

5. A method for processing software development and application data based on cloud computing as claimed in claim 3, characterized in that: In the step S2, cloud computing technology is used to archive different types of application document data, including: Cloud computing technology is used to archive different types of application document data. The archiving process of application document data is as follows: S21: Divide all current servers into 5 server clusters, where the servers in the jth server cluster are used to store application document data types. Application document data; S22: Select five different word vectors with the largest weights in the corresponding application document data type, and convert the selected word vectors into storage codes of the application document data, where the application document data The storage code is: ； in: Represents application document data Storage code of Represents application document data Application document data types, , j represents the application document data The application document data type is ; Indicates storage encoding parameters; represents the remainder operator, represents the right shift operator, This means the right shift step length is ; S23: Based on the application document data type, the application document data is divided into corresponding server clusters. The server cluster uses the storage code as the key and the application document data as the value, and merges the received multiple application document data into an archive file and stores them in a key-value storage manner.

6. A method for processing software development application data based on cloud computing as claimed in claim 1, characterized in that: The S3 step constructs an archive file storage optimization model, including: Constructing an archive file storage optimization model, wherein the archive file storage optimization model takes the archive file as the target object and adopts a multi-objective optimization solution to obtain a scheduling storage strategy for the archive file, wherein the archive file storage optimization model includes an input layer, an environmental information collection layer, a multi-objective storage optimization function construction layer, and a scheduling storage strategy solution layer; The input layer is used to obtain data parameters of different application document data in the archive file, wherein the data parameters include the data volume and storage coding of the application document data; The environment information collection layer is used to collect the storage status of all servers in the server cluster, where the storage status of the server includes the current storage data volume, the maximum storage data volume, the CPU utilization rate, and the network bandwidth utilization rate; The multi-objective optimization function construction layer is used to construct multi-objective storage optimization functions for different server clusters; The scheduling storage strategy solving layer is used to optimize and solve the multi-objective storage optimization function to form the scheduling storage strategy for archive files in different server clusters.

7. A method for processing software development and application data based on cloud computing as claimed in claim 6, characterized in that: In the step S4, the scheduling storage strategy of the archived files is obtained by using the archived file storage optimization model, including: The scheduling storage strategy of archive files is solved by using the archive file storage optimization model. The scheduling storage strategy solution process of archive files in the jth server cluster is as follows: S41: The input layer obtains data parameters of different application document data in the archive file, and the environment information collection layer collects the storage status of all servers in the j-th server cluster, and formalizes the scheduling storage process of the archive file; S42: Based on the formal representation results of the scheduling storage process, a multi-objective storage optimization function is constructed: ,in Represents the scheduling storage strategy for different application document data in the archive file: ； in: Indicates the scheduling storage location of the M application document data in the archive file in the jth server cluster, that is, the server number in the server cluster, represents the scheduled storage location of the mth application document data in the archive file in the jth server cluster, and M represents the total number of application document data in the archive file; S43: Initialize U group scheduling storage strategies, wherein the initialized u-th group scheduling storage strategy is: ； in: Indicates the u-th group of scheduling storage strategies generated by initialization. Indicates the scheduling storage strategy The scheduling storage location of the mth application document data; S44: Set the current iteration number of the scheduling storage policy to t and the maximum iteration number to Max, then the tth iteration result of the uth group scheduling storage policy is ; S45: The U group scheduling storage strategies obtained in the t-th iteration are sequentially used as the input of the multi-objective storage optimization function, and the scheduling storage strategy that minimizes the multi-objective storage optimization function is used as the optimal scheduling storage strategy obtained in the t-th iteration. ; S46: Iterate the scheduling storage policy, where the iteration formula is: ； ； ； in: Represents a random number between 0 and 1. Represents a random number between 1 and 2; represents the shape parameter, Set to 5; Indicates the control parameter, set E to 100; represents the iteration control parameter; S47: Let t=t+1, and return to step S45 until the maximum number of iterations is reached, and use the optimal scheduling storage strategy at this time as the scheduling storage strategy for the archive file, and store the archive file according to the solved scheduling storage strategy.

8. A method for processing software development and application data based on cloud computing as claimed in claim 7, characterized in that: The scheduling storage process of the archived file in step S41 is formally represented, including: The data parameter set of different application document data of archived files in the j-th server cluster is: ； ； in: The data parameter representing the mth application document data in the archive file, where M represents the total number of application document data in the archive file; represents the data volume and storage code of the mth application document data in the archive file in turn; The storage status set of all servers in the j-th server cluster is: ； in: represents the storage status of the kth server in the jth server cluster, and K represents the total number of servers in the jth server cluster; It represents the current storage data volume, maximum storage data volume, CPU utilization and network bandwidth utilization of the kth server in the jth server cluster respectively.

9. A method for processing software development and application data based on cloud computing as claimed in claim 7, characterized in that: The multi-objective storage optimization function in step S42 for: ； in: Indicates that the storage policy is based on the schedule , the formal representation result of whether the mth application document data is stored in the kth server, This means that the mth application document data will be stored on the kth server. This means that the mth application document data is not stored on the kth server; Indicates that the storage policy is based on the schedule After scheduling storage, the hard disk utilization of the kth server is Indicates that the storage policy is based on the schedule The average hard disk utilization of K servers after scheduling storage; Indicates that in other server clusters The number of stored codes whose cosine similarity reaches a preset threshold.

10. A cloud computing-based software development application data processing system, characterized in that: The system comprises: A data classification module is used to obtain application document data generated during the software development process and classify the application document data; The archiving module is used to archive application document data of different categories using cloud computing technology, and merge multiple application document data of the same category into an archive file; A storage optimization device is used to use an archive file storage optimization model to solve a scheduling storage strategy for archive files, and perform storage processing on the archive files according to the solution results, so as to implement a cloud computing-based software development application data processing method as described in any one of claims 1-9.

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