CN115982646A - Multi-source test data management method and system based on cloud platform - Google Patents
Multi-source test data management method and system based on cloud platform Download PDFInfo
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Abstract
The invention discloses a multi-source test data management method and system based on a cloud platform, which comprises the following steps: collecting test data of a tested piece, matching the test tag, sending the test data to a cloud platform for storage and data cleaning, and matching the test tag with a judgment standard of a test index in the test data set to obtain index interpretation data; guiding the index interpretation data and the multi-source test data sequence into a low-dimensional vector space, and performing representation learning through a non-directional heterogeneous graph; and constructing a test data comprehensive analysis model, carrying out data fusion on the multi-source test data to obtain a comprehensive analysis result, and generating an analysis report of the tested piece according to a preset test item of the tested piece. According to the invention, the multi-source test data of the piece to be tested in different test systems are reorganized and fused in a multi-dimensional manner, and comprehensive intelligent analysis of the data is directly performed on the multi-source test data and the comprehensive macroscopic data in real time, so that the high-performance requirement of the intelligent analysis of the data is met while the test data is acquired.
Description
Technical Field
The invention relates to the technical field of data management, in particular to a cloud platform-based multi-source test data management method and system.
Background
With the rapid development of computer technology, computer automatic test systems have been widely used in various industries, and as core function data management in computer automatic test systems, how to manage test data more concisely and effectively is a problem and puzzlement to be solved urgently by each system test research and development staff, how to manage multi-source test projects in a unified manner, and more attention of users and development breakthroughs of various technologies are received. On the enterprise level, how to effectively manage the original data, index data and the like of each testing stage becomes the key for shortening the research and development period of new products and guaranteeing the dominant position of the new products in the market of enterprises.
At present, a large number of automatic test systems generate massive test data, which covers original data, interpretation result data, test environment data and index interpretation data of tested products at each test stage, and the data are dispersedly stored in each test system. In the management of test data, how to perform comprehensive intelligent analysis according to multi-source test data is one of the problems that need to be solved currently.
Disclosure of Invention
In order to solve the technical problem, the invention provides a multi-source test data management method and system based on a cloud platform.
The invention provides a multi-source test data management method based on a cloud platform, which comprises the following steps:
collecting test data of a tested piece, matching the test label and sending the test label to a cloud platform for storage, and carrying out data cleaning on the multi-source test data by the cloud platform to remove obvious abnormal test data in each multi-source test data sequence;
classifying the multi-source test data after data cleaning according to test labels to obtain test data sets under the test labels, and matching judgment standards of test indexes in the test data sets by using the test labels to obtain index interpretation data;
importing the index interpretation data and the multi-source test data sequence into a low-dimensional vector space, and representing and learning the multi-source test data and the index interpretation data corresponding to the test indexes under each label in the low-dimensional vector space through a non-directional heterogeneous graph;
and constructing a test data comprehensive analysis model, performing data fusion on the multi-source test data, acquiring a comprehensive analysis result, matching various report forms according to a preset test item of the piece to be tested, and generating an analysis report of the piece to be tested based on the test report.
In the scheme, multi-source test data after data cleaning is classified according to test labels, test data sets under the test labels are obtained, the test labels are used for matching judgment standards of test indexes in the test data sets, and index interpretation data are obtained, specifically:
acquiring a multi-source test data sequence after data cleaning, clustering multi-source test data under the same test label, forming a test data set under each test label according to an acquisition timestamp, and obtaining a test index according to the test data set;
establishing a retrieval task by using the test labels of each test data set, and calculating and acquiring a test index with similarity meeting a preset standard and a corresponding judgment standard in a cloud platform data search space by using the similarity;
carrying out data matching on the retrieved test indexes and the test indexes in each test data set, and after all the test indexes in each test data set are matched, distributing corresponding judgment standards to the test indexes to serve as the judgment standards of the current test indexes in each test data set;
and judging the multi-source test data of the tested piece according to the judgment standard of the current test index to obtain index interpretation data.
In the scheme, the index interpretation data and the multi-source test data sequence are imported into a low-dimensional vector space, and the multi-source test data and the index interpretation data corresponding to the test indexes under each label are represented and learned through a non-directional heterogeneous graph in the low-dimensional vector space, specifically:
common test contents and test items are obtained through data statistics of a cloud platform, data retrieval is carried out according to the common test contents and the test items to obtain a corresponding analysis report, and historical test indexes and historical test index comprehensive analysis results in the analysis report are read;
importing current multi-source test data and index interpretation data of a piece to be tested into a low-dimensional vector space, and judging whether a historical interactive relation exists in the test indexes under the current test item according to a historical test index comprehensive analysis result;
the method comprises the steps of constructing a nondirectional heteromorphic graph of test data by utilizing multisource test data and index interpretation data of a piece to be tested in a low-dimensional vector space, taking the test data and the index interpretation data of each test index as nodes of the nondirectional heteromorphic graph, marking the nodes as test index nodes, and taking historical interaction relations among the test indexes as edge structures among the nodes.
In the scheme, a test data comprehensive analysis model is constructed, data fusion is carried out on multi-source test data, and a comprehensive analysis result is obtained, specifically:
building a test data comprehensive analysis model based on deep learning, performing representation learning on an undirected heterogeneous graph of test data through a graph convolution neural network, acquiring a preset test item of a to-be-tested piece, and calculating the information contribution rate of each test index according to the preset test item;
selecting a test index with the maximum information contribution rate, marking a corresponding test index node, acquiring a node which has a connection relation with a marked node in a non-directional heteromorphic graph as an associated node, and generating a characteristic test index node set by the marked node and the associated node;
acquiring the Mahalanobis distance between each characteristic test index node in the characteristic test index node set and other test index nodes, presetting a Mahalanobis distance threshold range, judging whether the Mahalanobis distance is in the preset Mahalanobis distance threshold range, and if so, taking the test index node as an adjacent node of the characteristic test index node closest to the characteristic test index node;
heterogeneous fusion is carried out on the adjacent nodes and the characteristic test index nodes through a message transmission mechanism and a neighbor aggregation mechanism of the graph convolutional neural network, and an embedded expression of the characteristic test index nodes is generated;
acquiring related test data and prediction data according to a test project of a to-be-tested piece, generating a training data set, training a gated cyclic neural network through the training data, and inputting the embedded representation of the characteristic test index node into the gated cyclic neural network for prediction analysis;
and matching the predictive analysis with a preset report form, and outputting a predictive analysis result.
In the scheme, an attention mechanism is introduced into a neighbor aggregation mechanism of a graph convolution neural network, and the attention mechanism specifically comprises the following steps:
the information contribution rate of other test indexes is obtained, and the information contribution rate is used as attention weight to carry out weight distribution on the characteristic test index nodes;
and performing feature aggregation by combining with other test index nodes according to the attention weight, updating the self representation of the feature index nodes, and generating the embedded vector representation with other test index data features.
In this scheme, still include, according to the self-defined test data analysis template of test data integrated analysis model, specifically do:
acquiring the position of basic data corresponding to the fusion test data in a low-dimensional vector space according to the test data comprehensive analysis model, training the comprehensive analysis model according to the basic data, selecting a report form style after the comprehensive analysis model meets a verification standard, and matching a test item label to generate a data analysis template;
structuring the data analysis templates of different test items, storing the data analysis templates into a cloud platform database, and calling the data analysis templates according to the test requirements of the to-be-tested piece;
acquiring historical analysis reports of different tested pieces within preset time of the same enterprise user, extracting test label information, judgment indexes, judgment standard information and report information of the enterprise user in the historical analysis reports, and acquiring preference characteristics of the enterprise user according to the extracted information;
customizing the enterprise user portrait through the preference characteristics, configuring a test system and a multi-source test data management analysis environment of the current piece to be tested in advance according to the enterprise user portrait, and updating the user portrait according to the change of the test requirements of the enterprise user.
The second aspect of the present invention also provides a cloud platform-based multi-source test data management system, which includes: the management method program of the multi-source test data based on the cloud platform realizes the following steps when being executed by the processor:
collecting test data of a tested piece, matching the test label and sending the test label to a cloud platform for storage, and carrying out data cleaning on the multi-source test data by the cloud platform to remove obvious abnormal test data in each multi-source test data sequence;
classifying the multi-source test data after data cleaning according to test labels to obtain test data sets under the test labels, and matching judgment standards of test indexes in the test data sets by using the test labels to obtain index interpretation data;
importing the index interpretation data and the multi-source test data sequence into a low-dimensional vector space, and representing and learning the multi-source test data and the index interpretation data corresponding to the test indexes under each label in the low-dimensional vector space through a non-directional heterogeneous graph;
and constructing a test data comprehensive analysis model, performing data fusion on the multi-source test data, acquiring a comprehensive analysis result, matching various report forms according to a preset test item of the piece to be tested, and generating an analysis report of the piece to be tested based on the test report.
The invention discloses a multi-source test data management method and system based on a cloud platform, which comprises the following steps: the method comprises the steps of collecting test data of a tested piece, matching a test tag, sending the test data to a cloud platform for storage and data cleaning, matching a judgment standard of a test index in a test data set by using the test tag, and obtaining index judgment data; guiding the index interpretation data and the multi-source test data sequence into a low-dimensional vector space, and performing representation learning through a non-directional heterogeneous graph; and constructing a test data comprehensive analysis model, carrying out data fusion on the multi-source test data to obtain a comprehensive analysis result, and generating an analysis report of the tested piece according to a preset test item of the tested piece. According to the invention, the multi-source test data of the piece to be tested in different test systems are reorganized and fused in a multi-dimensional manner, and comprehensive intelligent analysis of the data is directly performed on the multi-source test data and the comprehensive macroscopic data in real time, so that the high-performance requirement of the intelligent analysis of the data is met while the test data is acquired.
Drawings
FIG. 1 is a flow chart of a method for managing multi-source test data based on a cloud platform according to the present invention;
FIG. 2 illustrates a flow chart of a method for representing learning multi-source test data and index interpretation data through a non-directional heterogeneous graph according to the present invention;
FIG. 3 is a flow chart illustrating a method for constructing a comprehensive analysis model of test data to obtain comprehensive analysis results according to the present invention;
FIG. 4 shows a block diagram of a cloud platform-based multi-source test data management system according to the present invention.
Detailed Description
In order that the above objects, features and advantages of the present invention can be more clearly understood, a more particular description of the invention, taken in conjunction with the accompanying drawings and detailed description, is set forth below. It should be noted that the embodiments and features of the embodiments of the present application may be combined with each other without conflict.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, however, the present invention may be practiced in other ways than those specifically described herein, and therefore the scope of the present invention is not limited by the specific embodiments disclosed below.
Fig. 1 shows a flowchart of a multi-source test data management method based on a cloud platform according to the present invention.
As shown in fig. 1, a first aspect of the present invention provides a cloud platform-based multi-source test data management method, including:
s102, collecting test data of a tested piece, matching the test label, sending the test label to a cloud platform for storage, and cleaning the multi-source test data by the cloud platform to remove obvious abnormal test data in each multi-source test data sequence;
s104, classifying the multi-source test data after data cleaning according to test labels, obtaining test data sets under the test labels, matching judgment standards of test indexes in the test data sets by using the test labels, and obtaining index judgment data;
s106, importing the index interpretation data and the multi-source test data sequence into a low-dimensional vector space, and representing and learning the multi-source test data and the index interpretation data corresponding to the test indexes under each label in the low-dimensional vector space through a non-directional heterogeneous graph;
and S108, constructing a test data comprehensive analysis model, performing data fusion on multi-source test data, acquiring a comprehensive analysis result, matching various report forms according to preset test items of the piece to be tested, and generating an analysis report of the piece to be tested based on the test report.
It should be noted that the multi-source test data of the to-be-tested object is obtained through the automatic test system, the automatic test system calls the communication DLL program to perform message intercommunication with the data management system in the cloud platform, and the data management system in the cloud platform monitors data files, analyzes and checks data and stores data through the file monitoring program.
It is to be noted that, a multi-source test data sequence after data cleaning is obtained, multi-source test data under the same test label are clustered, test data sets under each test label are formed according to an acquisition timestamp, and a test index is obtained according to the test data sets, wherein the test label comprises information such as test content, test environment, test time and the like; establishing a retrieval task by using the test labels of each test data set, and calculating and acquiring a test index with similarity meeting a preset standard and a corresponding judgment standard in a cloud platform data search space by using the similarity; carrying out data matching on the retrieved test indexes and the test indexes in each test data set, and after all the test indexes in each test data set are matched, distributing corresponding judgment standards to the test indexes to serve as the judgment standards of the current test indexes in each test data set; and judging the multi-source test data of the tested piece according to the judgment standard of the current test index to obtain index interpretation data.
FIG. 2 shows a flow chart of a method for representing learning multi-source test data and index interpretation data through a non-directional heterogeneous graph according to the invention.
According to the embodiment of the invention, the index interpretation data and the multi-source test data sequence are imported into a low-dimensional vector space, and the multi-source test data and the index interpretation data corresponding to the test indexes under each label are represented and learned through a non-directional heterogeneous graph in the low-dimensional vector space, specifically:
s202, common test contents and test items are obtained through cloud platform data statistics, data retrieval is carried out according to the common test contents and the test items to obtain a corresponding analysis report, and historical test indexes and historical test index comprehensive analysis results in the analysis report are read;
s204, importing current multi-source test data and index interpretation data of the piece to be tested into a low-dimensional vector space, and judging whether the test indexes under the current test item have historical interaction relation according to a historical test index comprehensive analysis result;
s206, constructing a non-directional abnormal graph of the test data by using the multi-source test data and the index interpretation data of the piece to be tested in the low-dimensional vector space, taking the test data and the index interpretation data of each test index as nodes of the non-directional abnormal graph, recording the nodes as test index nodes, and taking the historical interaction relationship between the test indexes as an edge structure between the nodes.
It should be noted that the undirected heterogeneous graph
,/>
Representing a node set of test indicators including multi-source test data set indicator interpretation data in combination with test indicators>
And representing a set of interactive relations between the test index nodes, and if the historical test comprehensive analysis result simultaneously contains the original basic data of the two test indexes, proving that the two test indexes jointly participate in the comprehensive analysis and that the two test indexes have the interactive relations.
FIG. 3 is a flow chart of a method for constructing a comprehensive analysis model of test data to obtain comprehensive analysis results according to the present invention.
According to the embodiment of the invention, a test data comprehensive analysis model is constructed, data fusion is carried out on multi-source test data, and a comprehensive analysis result is obtained, wherein the comprehensive analysis model specifically comprises the following steps:
s302, a comprehensive analysis model of the test data is built based on deep learning, an undirected heterogeneous graph of the test data is represented and learned through a graph convolution neural network, a preset test item of the piece to be tested is obtained, and the information contribution rate of each test index is calculated according to the preset test item;
s304, selecting a test index with the largest information contribution rate, marking a corresponding test index node, acquiring a node which has a connection relation with a marked node in a non-directional heteromorphic graph as an associated node, and generating a characteristic test index node set by the marked node and the associated node;
s306, acquiring the Mahalanobis distance between each characteristic test index node in the characteristic test index node set and other test index nodes, presetting a Mahalanobis distance threshold range, judging whether the Mahalanobis distance is in the preset Mahalanobis distance threshold range, and if so, taking the test index node as an adjacent node of the characteristic test index node closest to the characteristic test index node;
s308, carrying out heterogeneous fusion on the adjacent nodes and the characteristic test index nodes through a message transmission mechanism and a neighbor aggregation mechanism of the graph convolution neural network to generate embedded representation of the characteristic test index nodes;
s310, acquiring related test data and prediction data according to a test item of a to-be-tested piece, generating a training data set, training a gated cyclic neural network through the training data, and inputting the embedded expression of the characteristic test index node into the gated cyclic neural network for prediction analysis;
and S312, matching the predictive analysis with a preset report form, and outputting a predictive analysis result.
It should be noted that, by obtaining the information contribution rates of other test indexes, the information contribution rates are used as attention weights to perform weight distribution on the feature test index nodes; performing feature aggregation by combining with other test index nodes according to the attention weight, updating the self representation of the feature index nodes, and generating an embedded vector representation with other test index data features;
calculating the contribution degree of the test index information, specifically, marking m test indexes as
The test criterion is standardized and a correlation coefficient matrix is calculated on the basis of the standardized test criterion>
Is greater than or equal to>
，
，/>
Representing an m-order identity matrix; obtaining test factors of the test indexes, supposing that the test items comprise information of all test indexes, judging the capability of the test factors for explaining the test items through variance contribution of the test factors, carrying out comparison judgment according to a preset variance contribution threshold, reserving important test factors of all the test indexes, adding the proportion of the information of each important test factor in the test indexes to the information of the test items to obtain an index ^ or>
The information contribution rate and the test index of
Can be expressed as a number of test factors->
And factor load>
Adding the products, the load factor matrix being ^ or ^>
M represents the total number of the test indexes, i represents the number of test index items, n represents the total number of factor loads in the test indexes i, and j represents the number of factor load items in the test indexes i;
the information contribution rate
The calculation formula of (2) is as follows: />
(ii) a The information contribution rate is used as the attention weight, the attention weight is combined with other test index nodes to carry out feature aggregation, the self expression of the feature index nodes is updated, the embedded expression of the feature test index nodes is obtained according to a neighbor aggregation mechanism, and the formula for aggregation of adjacent nodes is as follows: />
Wherein is present>
Representing an embedded representation of a feature test pointer node, <' >>
Represents an activation function, <' > is selected>
Represents an initial representation of a characteristic test index node, and->
Representing adjacent nodes>
Attention weight, <' > based on the status of the blood pressure sensor>
Representing a feature transformation parameter matrix.
Generally, comprehensive analysis of a to-be-tested piece comprises life prediction, degradation prediction, performance prediction and the like, a gated cyclic neural network is selected as a second half data analysis part of a test data comprehensive analysis model, the part can carry out adaptability setting of deep learning networks such as CNN (convolutional neural network) and LSTM (linear neural network) according to test items, embedded expression of characteristic test index nodes is led into the gated cyclic neural network, and the gated cyclic neural network comprises two gated structures, namely a reset gate and an update gate. The final output state of the gated recurrent neural network is the preamble state
And a candidate status->
Are added by weight, the weight of the two is obtained by an update gate
Control, the candidate state is based on the reset door>
Control to acquire a final evaluation status->
，
。
It should be noted that, the self-defining of the test data analysis template according to the test data comprehensive analysis model specifically includes: acquiring the position of basic data corresponding to the fusion test data in a low-dimensional vector space according to the test data comprehensive analysis model, training the comprehensive analysis model according to the basic data, selecting a report form style after the comprehensive analysis model meets a verification standard, and matching a test item label to generate a data analysis template; carrying out structuralization processing on data analysis templates of different test items, storing the data analysis templates into a cloud platform database, and calling the data analysis templates according to the test requirements of the piece to be tested; acquiring historical analysis reports of different tested pieces within preset time of the same enterprise user, extracting test label information, judgment indexes, judgment standard information and report information of the enterprise user in the historical analysis reports, and acquiring preference characteristics of the enterprise user according to the extracted information; customizing the enterprise user portrait through the preference characteristics, configuring a test system and a multi-source test data management analysis environment of the current piece to be tested in advance according to the enterprise user portrait, and updating the user portrait according to the change of the test requirements of the enterprise user.
If the current test item is matched by the test data analysis template, the existing test data analysis template does not exist, calculating the information contribution rate of the test index of the current test item, and screening the test index meeting the preset requirement according to the information contribution rate; sequencing the test indexes obtained by screening according to the information contribution rate, and sequentially calculating the Pearson correlation coefficient with the test indexes corresponding to the test data analysis templates in the cloud platform database according to the sequencing result; acquiring a test data analysis template corresponding to test data with the Pearson correlation coefficient meeting the requirement, and carrying out priority setting on the acquired test data analysis template according to the sorting result of the screened test indexes; and selecting a test data analysis template with the highest priority for data migration training, and performing comprehensive analysis on the current test data by using the test data analysis template after the migration training.
FIG. 4 shows a block diagram of a cloud platform-based multi-source test data management system according to the present invention.
The second aspect of the present invention also provides a cloud platform-based multi-source test data management system 4, which includes: a memory 41 and a processor 42, where the memory includes a management method program for multi-source test data based on a cloud platform, and when executed by the processor, the management method program for multi-source test data based on a cloud platform implements the following steps:
collecting test data of a tested piece, matching the test label and sending the test label to a cloud platform for storage, and carrying out data cleaning on the multi-source test data by the cloud platform to remove obvious abnormal test data in each multi-source test data sequence;
classifying the multi-source test data after data cleaning according to test labels to obtain test data sets under the test labels, and matching judgment standards of test indexes in the test data sets by using the test labels to obtain index interpretation data;
importing the index interpretation data and the multi-source test data sequence into a low-dimensional vector space, and representing and learning the multi-source test data and the index interpretation data corresponding to the test indexes under each label in the low-dimensional vector space through a non-directional heterogeneous graph;
and constructing a test data comprehensive analysis model, performing data fusion on the multi-source test data, acquiring a comprehensive analysis result, matching various report forms according to a preset test item of the piece to be tested, and generating an analysis report of the piece to be tested based on the test report.
It should be noted that the multi-source test data of the to-be-tested object is obtained through the automatic test system, the automatic test system calls the communication DLL program to perform message intercommunication with the data management system in the cloud platform, and the data management system in the cloud platform monitors data files, analyzes and checks data and stores data through the file monitoring program.
It should be noted that, a multi-source test data sequence after data cleaning is obtained, the multi-source test data under the same test label are clustered, a test data set under each test label is formed according to an acquisition timestamp, and a test index is obtained according to the test data set, wherein the test label comprises information such as test content, test environment and test time; establishing a retrieval task by using the test labels of each test data set, and calculating and acquiring a test index with similarity meeting a preset standard and a corresponding judgment standard in a cloud platform data search space by using the similarity; carrying out data matching on the retrieved test indexes and the test indexes in each test data set, and after all the test indexes in each test data set are matched, distributing corresponding judgment standards to the test indexes to serve as the judgment standards of the current test indexes in each test data set; and judging the multi-source test data of the tested piece according to the judgment standard of the current test index to obtain index interpretation data.
According to the embodiment of the invention, the index interpretation data and the multi-source test data sequence are imported into a low-dimensional vector space, and the multi-source test data and the index interpretation data corresponding to the test indexes under each label are represented and learned through a non-directional heterogeneous graph in the low-dimensional vector space, specifically:
common test contents and test items are obtained through data statistics of a cloud platform, data retrieval is carried out according to the common test contents and the test items to obtain a corresponding analysis report, and historical test indexes and historical test index comprehensive analysis results in the analysis report are read;
importing current multi-source test data and index interpretation data of a piece to be tested into a low-dimensional vector space, and judging whether a historical interactive relation exists in the test indexes under the current test item according to a historical test index comprehensive analysis result;
the method comprises the steps of constructing a non-directional abnormal graph of test data by utilizing multi-source test data and index interpretation data of a piece to be tested in a low-dimensional vector space, taking the test data and the index interpretation data of each test index as nodes of the non-directional abnormal graph, marking as test index nodes, and taking historical interaction relations among the test indexes as edge structures among the nodes.
It should be noted that the undirected heterogeneous graph
,/>
Representing a node set of test indicators, the nodes including multi-source test data set indicator interpretation data under the test indicators, and/or>
Set representing interaction between test index nodesIf the historical test comprehensive analysis result contains the original basic data of the two test indexes at the same time, the two test indexes are proved to participate in the comprehensive analysis together, and the two test indexes have an interaction relation.
According to the embodiment of the invention, a test data comprehensive analysis model is constructed, data fusion is carried out on multi-source test data, and a comprehensive analysis result is obtained, wherein the comprehensive analysis model specifically comprises the following steps:
building a test data comprehensive analysis model based on deep learning, performing representation learning on an undirected heterogeneous graph of test data through a graph convolution neural network, acquiring a preset test item of a to-be-tested piece, and calculating the information contribution rate of each test index according to the preset test item;
selecting a test index with the maximum information contribution rate, marking a corresponding test index node, acquiring a node which has a connection relation with a marked node in a non-directional heteromorphic graph as an associated node, and generating a characteristic test index node set by the marked node and the associated node;
acquiring the Mahalanobis distance between each characteristic test index node in the characteristic test index node set and other test index nodes, presetting a Mahalanobis distance threshold range, judging whether the Mahalanobis distance is in the preset Mahalanobis distance threshold range, and if so, taking the test index node as an adjacent node of the characteristic test index node closest to the characteristic test index node;
heterogeneous fusion is carried out on the adjacent nodes and the characteristic test index nodes through a message transmission mechanism and a neighbor aggregation mechanism of the graph convolutional neural network, and an embedded expression of the characteristic test index nodes is generated;
acquiring related test data and prediction data according to a test project of a to-be-tested piece, generating a training data set, training a gated cyclic neural network through the training data, and inputting the embedded representation of the characteristic test index node into the gated cyclic neural network for prediction analysis;
and matching the predictive analysis with a preset report form, and outputting a predictive analysis result.
It should be noted that, by obtaining the information contribution rates of other test indexes, the information contribution rates are used as attention weights to perform weight distribution on the feature test index nodes; performing feature aggregation by combining with other test index nodes according to the attention weight, updating the self representation of the feature index nodes, and generating an embedded vector representation with other test index data features;
calculating the contribution degree of the test index information, specifically, marking m test indexes as
Standardizing the test index, and calculating a correlation coefficient matrix based on the standardized test index>
Is greater than or equal to>
，
，/>
Representing an m-order identity matrix; obtaining test factors of the test indexes, supposing that the test items comprise information of all test indexes, judging the capability of the test factors for explaining the test items through variance contribution of the test factors, carrying out comparison judgment according to a preset variance contribution threshold, reserving important test factors of all the test indexes, adding the proportion of the information of each important test factor in the test indexes to the information of the test items to obtain an index ^ or>
Information contribution rate of (2), test index
Can be expressed as a number of test factors>
And factor load>
Addition of the products with a load factor matrix of->
M represents the total number of the test indexes, i represents the number of test index items, n represents the total number of factor loads in the test index i, and j represents the number of factor load items in the test index i;
the information contribution rate
The calculation formula of (c) is: />
(ii) a And utilizing the information contribution rate as an attention weight, combining the attention weight with other test index nodes to perform feature aggregation, updating the self representation of the feature index nodes, and acquiring the embedded representation of the feature test index nodes according to a neighbor aggregation mechanism.
Generally, comprehensive analysis of a to-be-tested piece comprises life prediction, degradation prediction, performance prediction and the like, a gated cyclic neural network is selected as a second half data analysis part of a test data comprehensive analysis model, the part can be used for carrying out adaptive setting of deep learning networks such as CNN (cyclic neural network) and LSTM (local learning model) according to test items, embedded expression of characteristic test index nodes is led into the gated cyclic neural network, and the gated cyclic neural network comprises two gated structures, namely a reset gate and an update gate. The final output state of the gated recurrent neural network is the preamble state
And candidate status>
Are added by weight, the weight of the two is obtained by an update gate
Control, the candidate state is based on the reset door>
Control to acquire a final evaluation state>
，
。
It should be noted that, the self-defining of the test data analysis template according to the test data comprehensive analysis model specifically includes: acquiring the position of basic data corresponding to the fusion test data in a low-dimensional vector space according to the test data comprehensive analysis model, training the comprehensive analysis model according to the basic data, selecting a report form style after the comprehensive analysis model meets a verification standard, and matching a test item label to generate a data analysis template; carrying out structuralization processing on data analysis templates of different test items, storing the data analysis templates into a cloud platform database, and calling the data analysis templates according to the test requirements of the piece to be tested; acquiring historical analysis reports of different tested pieces within preset time of the same enterprise user, extracting test label information, judgment indexes, judgment standard information and report information of the enterprise user in the historical analysis reports, and acquiring preference characteristics of the enterprise user according to the extracted information; customizing the enterprise user portrait through the preference characteristics, configuring a test system and a multi-source test data management analysis environment of the current piece to be tested in advance according to the enterprise user portrait, and updating the user portrait according to the change of the test requirements of the enterprise user.
The third aspect of the present invention further provides a computer-readable storage medium, where the computer-readable storage medium includes a cloud platform-based multi-source test data management method program, and when the cloud platform-based multi-source test data management method program is executed by a processor, the method implements any one of the above steps.
In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other manners. The above-described device embodiments are merely illustrative, for example, the division of the unit is only a logical functional division, and there may be other division ways in actual implementation, such as: multiple units or components may be combined, or may be integrated into another system, or some features may be omitted, or not implemented. In addition, the coupling, direct coupling or communication connection between the components shown or discussed may be through some interfaces, and the indirect coupling or communication connection between the devices or units may be electrical, mechanical or in other forms.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units; can be located in one place or distributed on a plurality of network units; some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, all the functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately regarded as one unit, or two or more units may be integrated into one unit; the integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional unit.
Those of ordinary skill in the art will understand that: all or part of the steps of implementing the method embodiments may be implemented by hardware related to program instructions, and the program may be stored in a computer-readable storage medium, and when executed, executes the steps including the method embodiments; and the aforementioned storage medium includes: a mobile storage device, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
Alternatively, the integrated unit of the present invention may be stored in a computer-readable storage medium if it is implemented in the form of a software functional module and sold or used as a separate product. Based on such understanding, the technical solutions of the embodiments of the present invention may be essentially implemented or a part contributing to the prior art may be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the methods described in the embodiments of the present invention. And the aforementioned storage medium includes: a removable storage device, a ROM, a RAM, a magnetic or optical disk, or various other media that can store program code.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and all the changes or substitutions should be covered within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.
Claims (10)
1. A multi-source test data management method based on a cloud platform is characterized by comprising the following steps:
collecting test data of a tested piece, matching the test label and sending the test label to a cloud platform for storage, and carrying out data cleaning on the multi-source test data by the cloud platform to remove obvious abnormal test data in each multi-source test data sequence;
classifying the multi-source test data after data cleaning according to test labels to obtain test data sets under the test labels, and matching judgment standards of test indexes in the test data sets by using the test labels to obtain index interpretation data;
importing the index interpretation data and the multi-source test data sequence into a low-dimensional vector space, and representing and learning the multi-source test data and the index interpretation data corresponding to the test indexes under each label in the low-dimensional vector space through a non-directional heterogeneous graph;
and constructing a test data comprehensive analysis model, performing data fusion on the multi-source test data, acquiring a comprehensive analysis result, matching various report forms according to a preset test item of the piece to be tested, and generating an analysis report of the piece to be tested based on the test report.
2. The cloud platform-based multi-source test data management method according to claim 1, wherein the multi-source test data after data cleaning is classified according to test tags, a test data set under each test tag is obtained, the test tags are used to match judgment criteria of test indexes in the test data set, and index interpretation data are obtained, specifically:
acquiring a multi-source test data sequence after data cleaning, clustering multi-source test data under the same test label, forming a test data set under each test label according to an acquisition timestamp, and obtaining a test index according to the test data set;
establishing a retrieval task by using the test labels of each test data set, and calculating and acquiring a test index with similarity meeting a preset standard and a corresponding judgment standard in a cloud platform data search space by using the similarity;
carrying out data matching on the retrieved test indexes and the test indexes in each test data set, and after all the test indexes in each test data set are matched, distributing corresponding judgment standards to the test indexes to serve as the judgment standards of the current test indexes in each test data set;
and judging the multi-source test data of the tested piece according to the judgment standard of the current test index to obtain index interpretation data.
3. The cloud platform-based multi-source test data management method according to claim 1, wherein the index interpretation data and the multi-source test data sequence are imported into a low-dimensional vector space, and the multi-source test data and the index interpretation data corresponding to the test indexes under each label are represented and learned through a nondirectional heterogeneous graph in the low-dimensional vector space, specifically:
common test contents and test items are obtained through data statistics of a cloud platform, data retrieval is carried out according to the common test contents and the test items to obtain a corresponding analysis report, and historical test indexes and historical test index comprehensive analysis results in the analysis report are read;
importing current multi-source test data and index interpretation data of a piece to be tested into a low-dimensional vector space, and judging whether a historical interactive relation exists in the test indexes under the current test item according to a historical test index comprehensive analysis result;
the method comprises the steps of constructing a non-directional abnormal graph of test data by utilizing multi-source test data and index interpretation data of a piece to be tested in a low-dimensional vector space, taking the test data and the index interpretation data of each test index as nodes of the non-directional abnormal graph, marking as test index nodes, and taking historical interaction relations among the test indexes as edge structures among the nodes.
4. The cloud platform-based multi-source test data management method according to claim 1, wherein a test data comprehensive analysis model is constructed, data fusion is performed on multi-source test data, and a comprehensive analysis result is obtained, and the method specifically comprises the following steps:
building a test data comprehensive analysis model based on deep learning, performing representation learning on an undirected heterogeneous graph of test data through a graph convolution neural network, acquiring a preset test item of a to-be-tested piece, and calculating the information contribution rate of each test index according to the preset test item;
selecting a test index with the maximum information contribution rate, marking a corresponding test index node, acquiring a node which has a connection relation with a marked node in a non-directional heteromorphic graph as an associated node, and generating a characteristic test index node set by the marked node and the associated node;
acquiring the Mahalanobis distance between each characteristic test index node in the characteristic test index node set and other test index nodes, presetting a Mahalanobis distance threshold range, judging whether the Mahalanobis distance is in the preset Mahalanobis distance threshold range, and if so, taking the test index node as an adjacent node of the characteristic test index node closest to the characteristic test index node;
heterogeneous fusion is carried out on the adjacent nodes and the characteristic test index nodes through a message transmission mechanism and a neighbor aggregation mechanism of the graph convolutional neural network, and an embedded expression of the characteristic test index nodes is generated;
acquiring related test data and prediction data according to a test item of a to-be-tested piece to generate a training data set, training a gated cyclic neural network through the training data, and inputting the embedded representation of a characteristic test index node into the gated cyclic neural network for prediction analysis;
and matching the predictive analysis with a preset report form, and outputting a predictive analysis result.
5. The cloud platform-based multi-source test data management method according to claim 4, wherein the generating of the embedded representation of the feature test index node specifically comprises:
the information contribution rate of other test indexes is obtained and is used as attention weight to carry out weight distribution on the characteristic test index nodes;
and performing feature aggregation by combining with other test index nodes according to the attention weight, updating the self representation of the feature index nodes, and generating the embedded vector representation with other test index data features.
6. The cloud platform-based multi-source test data management method according to claim 1, further comprising:
acquiring the position of basic data corresponding to the fusion test data in a low-dimensional vector space according to the test data comprehensive analysis model, training the comprehensive analysis model according to the basic data, selecting a report form style after the comprehensive analysis model meets a verification standard, and matching a test item label to generate a data analysis template;
carrying out structuralization processing on data analysis templates of different test items, storing the data analysis templates into a cloud platform database, and calling the data analysis templates according to the test requirements of the piece to be tested;
acquiring historical analysis reports of different tested pieces within preset time of the same enterprise user, extracting test label information, judgment indexes, judgment standard information and report information of the enterprise user in the historical analysis reports, and acquiring preference characteristics of the enterprise user according to the extracted information;
customizing the enterprise user portrait through the preference characteristics, configuring a test system and a multi-source test data management analysis environment of the current piece to be tested in advance according to the enterprise user portrait, and updating the user portrait according to the change of the test requirements of the enterprise user.
7. A multi-source test data management system based on a cloud platform is characterized by comprising: the management method program of the multi-source test data based on the cloud platform realizes the following steps when being executed by the processor:
collecting test data of a tested piece, matching the test label and sending the test label to a cloud platform for storage, and carrying out data cleaning on the multi-source test data by the cloud platform to remove obvious abnormal test data in each multi-source test data sequence;
classifying the multi-source test data after data cleaning according to test labels to obtain test data sets under the test labels, and matching judgment standards of test indexes in the test data sets by using the test labels to obtain index interpretation data;
importing the index interpretation data and the multi-source test data sequence into a low-dimensional vector space, and representing and learning the multi-source test data and the index interpretation data corresponding to the test indexes under each label in the low-dimensional vector space through a non-directional heterogeneous graph;
and constructing a test data comprehensive analysis model, performing data fusion on the multi-source test data, acquiring a comprehensive analysis result, matching various report forms according to a preset test item of the piece to be tested, and generating an analysis report of the piece to be tested based on the test report.
8. The cloud platform-based multi-source test data management system according to claim 7, wherein the index interpretation data and the multi-source test data sequence are imported into a low-dimensional vector space, and the multi-source test data and the index interpretation data corresponding to the test indexes under each label are represented and learned through an undirected heterogeneous graph in the low-dimensional vector space, specifically:
common test contents and test items are obtained through data statistics of a cloud platform, data retrieval is carried out according to the common test contents and the test items to obtain a corresponding analysis report, and historical test indexes and historical test index comprehensive analysis results in the analysis report are read;
importing current multi-source test data and index interpretation data of a piece to be tested into a low-dimensional vector space, and judging whether a historical interactive relation exists in the test indexes under the current test item according to a historical test index comprehensive analysis result;
the method comprises the steps of constructing a non-directional abnormal graph of test data by utilizing multi-source test data and index interpretation data of a piece to be tested in a low-dimensional vector space, taking the test data and the index interpretation data of each test index as nodes of the non-directional abnormal graph, marking as test index nodes, and taking historical interaction relations among the test indexes as edge structures among the nodes.
9. The cloud platform-based multi-source test data management system according to claim 7, wherein a test data comprehensive analysis model is constructed, data fusion is performed on multi-source test data, and a comprehensive analysis result is obtained, and specifically:
constructing a test data comprehensive analysis model based on deep learning, performing representation learning on an undirected heterogeneous graph of test data through a graph convolution neural network, acquiring a preset test item of a piece to be tested, and calculating the information contribution rate of each test index according to the preset test item;
selecting a test index with the maximum information contribution rate, marking a corresponding test index node, acquiring a node which has a connection relation with a marked node in a non-directional heteromorphic graph as an associated node, and generating a characteristic test index node set by the marked node and the associated node;
acquiring the Mahalanobis distance between each characteristic test index node in the characteristic test index node set and other test index nodes, presetting a Mahalanobis distance threshold range, judging whether the Mahalanobis distance is in the preset Mahalanobis distance threshold range, and if so, taking the test index node as an adjacent node of the characteristic test index node closest to the characteristic test index node;
heterogeneous fusion is carried out on the adjacent nodes and the characteristic test index nodes through a message transmission mechanism and a neighbor aggregation mechanism of the graph convolutional neural network, and an embedded expression of the characteristic test index nodes is generated;
acquiring related test data and prediction data according to a test item of a to-be-tested piece to generate a training data set, training a gated cyclic neural network through the training data, and inputting the embedded representation of a characteristic test index node into the gated cyclic neural network for prediction analysis;
and matching the predictive analysis with a preset report form, and outputting a predictive analysis result.
10. The cloud platform-based multi-source test data management system according to claim 9, wherein the embedded representation of the feature test index node is generated specifically as follows:
the information contribution rate of other test indexes is obtained and is used as attention weight to carry out weight distribution on the characteristic test index nodes;
and performing feature aggregation by combining with other test index nodes according to the attention weight, updating the self representation of the feature index nodes, and generating the embedded vector representation with other test index data features.
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