A software development application data processing method and system based on cloud computing

Abstract

The present invention relates to the technical field of cloud computing data processing, and discloses a method and system for processing software development application data based on cloud computing, the method comprising: obtaining application document data generated during the software development process and classifying and processing them; merging multiple application document data of the same category into archive files; solving a scheduling storage strategy for the archive files using an archive file storage optimization model, and performing storage processing on the archive files according to the solution results. The present invention divides application document data into application document data categories with stronger semantic similarity and higher similarity of adopted phrases, converts storage imbalance, communication pressure, and load pressure into a multi-objective storage optimization function, optimizes and solves the scheduling storage strategy with the goal of minimizing server imbalance and pressure, and then performs storage processing on the archive files, thereby improving the storage life of the server cluster and achieving load balancing of data storage in the cloud computing process.

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 and application data based on cloud computing. Background Art

[0002] With the rapid development of internet technology, especially the widespread adoption of cloud computing, the volume of document data in software development is exploding. This data, including requirements documents, design documents, test documents, user manuals, and API documentation, spans the entire software development lifecycle. Document management and storage have become crucial components of software development. Cloud computing offers new approaches and methods for storing and managing this data, enabling the processing of massive amounts of document data at lower costs and higher efficiency. However, achieving efficient storage and optimization of this massive volume of software development document data in a cloud computing environment remains a pressing challenge. 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 objectives, the present invention provides a cloud computing-based software development application data processing method, comprising the following steps:

[0005] S1: Obtain 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 archive files;

[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 a multi-objective optimization solution to obtain a scheduling storage strategy for the archive file;

[0008] S4: Utilize 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 requirement data , development project management data , development code data , software test sample data And development document data , the collected application document data is:

[0012] { X n |n∈[1,N]} ;

[0013] in:

[0014] Including the nth group of application document data, 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, software development requirement data includes user requirements, market research, project goals, and key performance indicators; development project management data includes project planning, resource management, and budget cost management; development code data includes architecture design, UI design, code library, and development environment; software test sample data includes test plans and test cases; and development document data includes system design documents, API documentation, 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 the 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 word frequency of the phrase in different application document data types, the word vectors in the application document data encoding results are weighted to obtain the weighted word vectors of the word vectors in different application document data types, where the word vector In application document data types 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 application document data types The weighted word vector of ;

[0025] Representing word vectors In application document data types The weight of Represents 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 occurrence in 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 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, where the application document data type The number of phrases in the standard document data is , the bias document vector is ;

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

[0034] ;

[0035] ;

[0036] ;

[0037] ;

[0038] in:

[0039] Represents document vector In application document data types bias;

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

[0041] Represents biased document vector and similarity between

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

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

[0044] Represents similar phrase counting parameters, This indicates a bias towards the document vector There exists 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, wherein the text format includes plain, bold, underline and italic, and number the text format of the phrase, wherein the phrase The text format is , In turn, they represent plain, bold, underline, and italic;

[0053] S113: Perform one-hot encoding on each phrase in the phrase sequence, where The one-hot encoding representation of ;

[0054] S114: Combine the position of the phrase in the document and encode the phrase into a word vector. 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 word vectors;

[0058] Expressing phrases The total number of phrases in the text line, phrase 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 encoding 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;

[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 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 method.

[0072] Optionally, building 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, where the data parameters include the data volume and storage encoding 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 server storage status includes the current storage data volume, maximum storage data volume, CPU utilization, and network bandwidth utilization;

[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 j-th 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 Indicates 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 servers are numbered from 1 to K. A larger server number indicates a greater product of the server's hard disk utilization, CPU utilization, and network bandwidth utilization, where the hard disk utilization is the current storage data volume / maximum storage data volume.

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

[0086] ;

[0087] in:

[0088] Indicates the u-th group of scheduling storage strategies generated by initialization. Indicates the scheduling storage policy 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 t-th iteration result of the u-th group scheduling storage policy is ;

[0090] S45: The U group scheduling storage strategies obtained in the t-th iteration are sequentially used as inputs 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. Indicates 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 iterative control parameter;

[0100] S47: Let t=t+1, 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 formal representation of the scheduling storage process of the archive file in step S41 includes:

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

[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.

[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 k-th server, represents the communication pressure of the k-th server, Indicates 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 and application data processing system based on cloud computing, characterized in that the system includes:

[0121] The 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 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, comprising:

[0125] a memory storing at least one instruction;

[0126] Communication interfaces to enable 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, which stores at least one instruction, and 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 existing technology, the present invention proposes a cloud computing-based software development application data processing method, 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 different application document data types in turn to form biased document vectors of application document data in different application document data types. Based on the similarity of the biased document vectors and the phrase similarity ratio, the application document data is classified and divided into application document data categories with stronger semantic similarity and higher degree of phrase similarity. Cloud computing technology is used to generate storage codes for application document data, and the storage codes are used as keys to select servers for distributed storage of 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, formally represents 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 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 and 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 and application data processing system provided by one embodiment of the present invention;

[0134] Figure 2 In: 100 software development and 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 one embodiment of the present invention.

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

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

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

[0139] The embodiments of the present application provide a cloud computing-based software development application data processing method. The execution entity of the cloud computing-based software development application data processing method includes, but is not limited to, at least one of electronic devices such as a server or a terminal that can be configured to execute the method provided by the embodiments of the present application. In other words, the cloud computing-based software development application data processing method can be executed by software or hardware installed on a terminal device or a server device, where 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.

[0140] Example 1:

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

[0142] The step S1 of obtaining 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 requirement data , development project management data , development code data , software test sample data And development document data , the collected application document data is:

[0144] { X n |n∈[1,N]} ;

[0145] in:

[0146] Including the nth group of application document data, 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, software development requirement data includes user requirements, market research, project goals, and key performance indicators; development project management data includes project planning, resource management, and budget cost management; development code data includes architecture design, UI design, code library, and development environment; software test sample data includes test plans and test cases; and development document data includes system design documents, API documentation, 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 the 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 word frequency of the phrase in different application document data types, the word vectors in the application document data encoding results are weighted to obtain the weighted word vectors of the word vectors in different application document data types, where the word vector In application document data types 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 application document data types The weighted word vector of ;

[0157] Representing word vectors In application document data types The weight of Represents 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 occurrence in 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 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, where the application document data type The number of phrases in the standard document data is , the bias document vector is ;

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

[0166] ;

[0167] ;

[0168] ;

[0169] ;

[0170] in:

[0171] Represents document vector In application document data types bias;

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

[0173] Represents biased document vector and similarity between

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

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

[0176] Represents similar phrase counting parameters, This indicates a bias towards the document vector There exists 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, wherein the text format includes plain, bold, underline and italic, and number the text format of the phrase, wherein the phrase The text format is , In turn, they represent plain, bold, underline, and italic;

[0185] S113: Perform one-hot encoding on each phrase in the phrase sequence, where The one-hot encoding representation of ;

[0186] S114: Combine the position of the phrase in the document and encode the phrase into a word vector. 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 word vectors;

[0190] Expressing phrases The total number of phrases in the text line, phrase 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: Use cloud computing technology to archive application document data of different categories, and merge multiple application document data of the same category into archive files.

[0194] In 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 encoding 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;

[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 is ;

[0204] 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 method.

[0205] 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.

[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, where the data parameters include the data volume and storage encoding 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 server storage status includes the current storage data volume, maximum storage data volume, CPU utilization, and network bandwidth utilization;

[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: Utilize 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 step S4, the scheduling storage strategy for archive files is obtained by using the archive 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 j-th 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 Indicates 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 servers are numbered from 1 to K. A larger server number indicates a greater product of the server's hard disk utilization, CPU utilization, and network bandwidth utilization, where the hard disk utilization is the current storage data volume / maximum storage data volume.

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

[0221] ;

[0222] in:

[0223] Indicates the u-th group of scheduling storage strategies generated by initialization. Indicates the scheduling storage policy 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 t-th iteration result of the u-th group scheduling storage policy is ;

[0225] S45: The U group scheduling storage strategies obtained in the t-th iteration are sequentially used as inputs 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. Indicates 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 iterative control parameter;

[0235] S47: Let t=t+1, 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 files 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] In turn, it represents the data volume and storage code of the mth application document data in the archive file;

[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.

[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] Example 2:

[0255] like Figure 2 , which is a functional module diagram of a cloud computing-based software development application data processing system provided by an 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 and application data processing system 100 described in the present invention can be installed in an electronic device. Depending on the functionality implemented, the cloud computing-based software development and application data processing system can include a data classification module 101, an archiving module 102, and a storage optimization device 103. A module, also referred to as a unit, is a series of computer program segments that can be executed by an electronic device processor and perform a fixed function, and is stored in the electronic device's memory.

[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, the modules in the cloud computing-based software development application data processing system 100 in the embodiment of the present invention are used in the same manner as above. Figure 1 The cloud computing-based software development application data processing method described in the same technical means and can produce the same technical effects, which will not be repeated here.

[0261] Example 3:

[0262] like Figure 3 , which is a structural diagram of an electronic device for implementing a cloud computing-based software development application data processing method provided by one 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 further 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, including flash memory, a removable hard disk, a multimedia card, a card-type memory (e.g., SD or DX memory), a magnetic memory, a magnetic 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 removable 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 removable hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a flash memory card, etc. equipped on the electronic device 1. Furthermore, the memory 11 may include both an internal storage unit of the electronic device 1 and an external storage device. The memory 11 can be used not only to store application software installed in the electronic device 1 and various types of data, such as the code of the program 12, but also to temporarily store data that has been output or is about to be output.

[0265] In some embodiments, the processor 10 may be comprised of an integrated circuit, such as a single packaged integrated circuit or a plurality of packaged integrated circuits with the same or different functions, including a combination of one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and various control chips. The processor 10 is the control core (control unit) of the electronic device, connecting the various components of the electronic device using various interfaces and circuits. It executes or runs programs or modules stored in the memory 11 (such as the program 12 for implementing cloud computing-based software development and application data processing) and accesses data stored in the memory 11 to perform various functions and process data of the electronic device 1.

[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 devices.

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

[0268] Figure 3 Only the electronic device with components is shown, and it can be understood by those skilled in the art 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 further include a power supply (e.g., a battery) to power various components. Preferably, the power supply may be logically connected to the at least one processor 10 via a power management device, thereby enabling functions such as charge management, discharge management, and power consumption management via the power management device. The power supply may further include any components such as one or more DC or AC power supplies, a recharging device, a power failure detection circuit, a power converter or inverter, and a power status indicator. The electronic device 1 may also include various sensors, Bluetooth modules, Wi-Fi modules, etc., which are not further described here.

[0270] Optionally, the electronic device 1 may further include a user interface, which may be a display or an input unit (such as a keyboard). Optionally, the user interface may also be 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, or an OLED (Organic Light-Emitting Diode) touchscreen. The display may also be appropriately referred to as a display screen or a display unit, and is used to display information processed by 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-mentioned embodiments of the present invention are for descriptive purposes only and do not represent the advantages or disadvantages of the embodiments. In addition, the terms "including", "comprising" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, device, article or method comprising 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 comprising the element.

[0273] Through the description of the above embodiments, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus the necessary general hardware platform, and of course can also be implemented by hardware, but in many cases the former is a better embodiment. Based on this 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 enabling a terminal device (which can be a mobile phone, computer, server, or 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 description 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: Obtain 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 archive files; 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 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 storing the archive file according to the solution result; 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 j-th 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: f(θ), where θ represents the scheduling storage strategy for different application document data in the archive file: θ=(θ1,θ2,...,θ m ,...,θ M ); in: θ1,θ2,...,θ m ,...,θ M represents 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, θ m 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 the U-th group scheduling storage policy, wherein the initialized u-th group scheduling storage policy is: in: θ u (0) represents the u-th group scheduling storage strategy generated by initialization, Represents the scheduling storage strategy θ u (0) The scheduling storage location of the mth application document data; S44: Set the current iteration number of the scheduling storage strategy to t and the maximum iteration number to Max, then the t-th iteration result of the u-th group scheduling storage strategy is θ u (t); 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 makes the multi-objective storage optimization function reach the minimum is used as the optimal scheduling storage strategy θ obtained in the t-th iteration best (t); S46: Iterate the scheduling storage policy, where the iteration formula is: i u (t+1)=θ u (t)δ(t)+rand(0,1)(θ best (t)-rand(1,2)·θ u (t)); in: rand(0,1) represents a random number between 0 and 1, and rand(1,2) represents a random number between 1 and 2; ε represents the shape parameter, and ε is set to 5; E represents the control parameter, set E to 100; δ(t) represents the iterative control parameter; S47: Let t=t+1, 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.

2. The method for processing software development and application data based on cloud computing according to claim 1, wherein: The step S1 of obtaining application document data generated during the software development process includes: Acquire application document data generated during the software development process. The application document data types include software development requirement data Label 1, development project management data Label 2, development code data Label 3, software test sample data Label 4, and development document data Label 5. The collected application document data are: {X n |n∈[1,N]}; in: X n Including the nth group of application document data, 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. The method for processing software development and application data based on cloud computing according to claim 2, wherein: The classifying and processing the application document data includes: The classification processing flow of the application document data is as follows: S11: Obtain the application document data to be classified and encode the application document data, wherein the application document data X n The encoding result is Represents application document data X n The word vector of the i-th phrase in , num n Represents application document data X n The total number of phrases in ; S12: Based on the word frequency of the phrase in different application document data types, the word vectors in the application document data encoding results 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 Label j The weighted word vector of S13: Constructing a document vector for the application document data, where the application document data X n The document vector is: in: F n Represents application document data X n The document vector of Represents application document data X n Prefer application document data type Label j Biased document vector of ; S14: Construct standard document data of different application document data types, and obtain the number of phrases in the standard document data and the biased document vector of the corresponding application document data type, where the application document data type Label j The number of phrases in the standard document data is NUM j , the biased document vector is F(j); S15: Calculate the bias of the document vector in different application document data types, where the document vector F n In the application document data type Label j The bias is: in: Represents the document vector F n In the application document data type Label j bias; exp(·) represents an exponential function with a natural constant as the base; Represents biased document vector The similarity between F(j) and F(j); Represents biased document vector The number of similar phrases with F(j); r∈F(j), where r represents any weighted word vector in the biased document vector F(j); Represents similar phrase counting parameters, It means that there is a bias in the document vector F(j) 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 according to claim 3, 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 on the application document data to obtain a phrase sequence of the application document data, where the application document data X n The phrase sequence is: in: Represents application document data X n The i-th phrase of ; S112: Obtain the text format of each phrase in the phrase sequence, wherein the text format includes plain, bold, underline and italic, and number the text format of the phrase, wherein the phrase The text format is In turn, they represent plain, bold, underline, and italic; S113: Perform one-hot encoding on each phrase in the phrase sequence, where The one-hot encoding representation of S114: Combine the position of the phrase in the document and encode the phrase into a word vector. The word vector encoding result is: in: Expressing phrases word vectors; Expressing phrases The total number of phrases in the text line, phrase The first phrases; W1 represents the encoding weight matrix, * represents the convolution operator; exp(·) represents an exponential function with a natural constant as its base.

5. The method for processing software development and application data based on cloud computing according to claim 3, wherein: In 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 documents with data type Label. j 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 for the application document data, where the application document data X n The storage encoding is: 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)]]; in: code n Represents application document data X n Storage code; Label(X n ) represents application document data X n Application document data type, Label(X n )∈[1,5],Label(X n ) is j in the application document data X n The application document data type is Label j ; β represents the storage encoding parameter; mod represents the modulus operator, rsh(·) represents the right shift operator, and rsh(β) represents the right shift step size of β; 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 method.

6. The method for processing software development and application data based on cloud computing according to claim 1, wherein: 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, where the data parameters include the data volume and storage encoding 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 server storage status includes the current storage data volume, maximum storage data volume, CPU utilization, and network bandwidth utilization; 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 according to claim 6, characterized in that: The scheduling storage process of the archived files 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: {have been m |m∈[1,M]}; in: e m 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; In turn, it represents the data volume and storage code of the mth application document data in the archive file; The storage status set of all servers in the j-th server cluster is: in: H k 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.

8. The method for processing software development and application data based on cloud computing according to claim 7, wherein: The multi-objective storage optimization function f(θ) is: in: It represents the formal representation result of whether the m-th application document data is stored in the k-th server according to the scheduling storage strategy θ. 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; represents the hard disk utilization of the kth server after scheduling storage according to the scheduling storage strategy θ, and μ(θ) represents the mean hard disk utilization of K servers after scheduling storage according to the scheduling storage strategy θ; Indicates that in other server clusters The number of stored codes whose cosine similarity reaches a preset threshold.

9. A software development application data processing system based on cloud computing, characterized in that: The system comprises: The 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 1-8.

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