CN115203364A - Software fault feedback processing method, device, equipment and readable storage medium - Google Patents
Software fault feedback processing method, device, equipment and readable storage medium Download PDFInfo
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- G06—COMPUTING; CALCULATING OR COUNTING
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- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F16/33—Querying
- G06F16/3331—Query processing
- G06F16/334—Query execution
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F40/00—Handling natural language data
- G06F40/20—Natural language analysis
- G06F40/279—Recognition of textual entities
- G06F40/289—Phrasal analysis, e.g. finite state techniques or chunking
- G06F40/295—Named entity recognition
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V30/00—Character recognition; Recognising digital ink; Document-oriented image-based pattern recognition
- G06V30/10—Character recognition
- G06V30/14—Image acquisition
- G06V30/148—Segmentation of character regions
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- G—PHYSICS
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- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
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- G06V30/191—Design or setup of recognition systems or techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
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Abstract
The invention relates to a natural language processing technology, and discloses a software fault feedback processing method, which comprises the following steps: performing entity extraction on the fault feedback text to obtain a first fault entity; performing text recognition on the fault feedback image to obtain an image text; performing entity extraction on the image text to obtain a second fault entity; identifying a fault type corresponding to the fault feedback text; screening all processing method knowledge bases by utilizing fault categories to obtain a target processing method knowledge base; using the first fault entity and the second fault entity as processing method texts in a screening target processing method knowledge base to obtain a target processing method text; and sending the target processing method text to preset terminal equipment. The invention also relates to a block chain technology, and the processing method knowledge base can be deployed in the block chain. The invention also provides a software fault feedback processing device, electronic equipment and a readable storage medium. The invention can improve the accuracy of software fault feedback processing.
Description
Technical Field
The invention relates to the field of natural language processing, in particular to a software fault feedback processing method and device, electronic equipment and a readable storage medium.
Background
With the development of internet technology, application programs are more and more indispensable in the life of people, but when a user feedback software has a problem, how to process the feedback problem and enable the user to obtain a processing method for solving the problem and how to enable a developer to locate a corresponding problem category become more and more concerned problems for people.
The existing feedback problem processing method can only carry out matching in a single processing method knowledge base according to user fault feedback text information, and the matching dimensionality is single, so that the accuracy of software fault feedback processing is low.
Disclosure of Invention
The invention provides a software fault feedback processing method and device, electronic equipment and a computer readable storage medium, and mainly aims to improve the accuracy of software fault feedback processing.
In order to achieve the above object, the present invention provides a software failure feedback processing method, which includes:
acquiring a fault feedback text and a fault feedback image corresponding to the same fault feedback of the software;
performing fault category identification on the fault feedback text to obtain a target fault category;
performing entity extraction on the fault feedback text to obtain a first fault entity;
segmenting an error reporting area in the fault feedback image, and identifying a text in the segmented image to obtain an image text;
performing entity extraction on the image text to obtain a second fault entity;
acquiring one or more processing method knowledge bases, wherein each processing method knowledge base is provided with a corresponding knowledge base label, and screening all the processing method knowledge bases by using the target fault category and the similarity degree of the knowledge base labels to obtain a target processing method knowledge base;
screening the processing method texts in the target processing method knowledge base by taking the first fault entity and the second fault entity as screening conditions to obtain target processing method texts;
and sending the target processing method text to preset terminal equipment.
Optionally, the performing fault category identification on the fault feedback text to obtain a target fault category includes:
performing feature extraction on the fault feedback text by using the trained BERT model to obtain a text feature vector;
calculating the text feature vector by using a softmax function to obtain the recognition probabilities of different preset fault categories;
confirming the fault category corresponding to the maximum recognition probability as the target fault category;
optionally, the segmenting the error reporting region in the fault feedback image, and identifying a text in the segmented image to obtain an image text, includes:
cutting an error reporting area in the fault feedback image to obtain a cut image;
performing graying processing on the cut image to obtain a grayed image;
filtering the grayed image to obtain a standard image;
and performing text recognition processing on the standard image by using a preset text extraction algorithm to obtain the image text.
Optionally, the screening all the processing method knowledge bases by using the target fault category and the similarity degree of the knowledge base tags to obtain a target processing method knowledge base includes:
converting the target fault category into a vector to obtain a category vector;
converting the knowledge base tags into vectors to obtain tag vectors;
calculating the similarity between the category vector and the label vector to obtain a similarity value;
and screening all the processing method knowledge bases according to the similarity values to obtain a target processing method knowledge base.
Optionally, the converting the knowledge base tag into a vector to obtain a tag vector includes:
converting each character in the knowledge base label into a vector to obtain a character vector;
combining all the character vectors according to the sequence of the corresponding characters in the knowledge base labels to obtain a knowledge base label matrix;
carrying out convolution on the knowledge base tag matrix by using a preset convolution core to obtain a convolution matrix;
and reducing the dimension of the convolution matrix to obtain the label vector.
Optionally, the reducing the dimension of the convolution matrix to obtain the tag vector includes:
calculating the average value of each row of elements in the convolution matrix to obtain a row characteristic value;
and replacing each column in the convolution matrix with the corresponding column eigenvalue to obtain the label vector.
In order to solve the above problem, the present invention further provides a software failure feedback processing apparatus, including:
the text processing module is used for acquiring a fault feedback text and a fault feedback image corresponding to the same fault feedback of the software; identifying the fault type of the fault feedback text to obtain a target fault type; performing entity extraction on the fault feedback text to obtain a first fault entity;
the image processing module is used for segmenting the error reporting area in the fault feedback image and identifying a text in the segmented image to obtain an image text; performing entity extraction on the image text to obtain a second fault entity;
the processing method screening module is used for acquiring one or more processing method knowledge bases, wherein each processing method knowledge base is provided with a corresponding knowledge base label, and all the processing method knowledge bases are screened by using the target fault category and the similarity degree of the knowledge base labels to obtain a target processing method knowledge base; screening the processing method texts in the target processing method knowledge base by taking the first fault entity and the second fault entity as screening conditions to obtain target processing method texts; and sending the target processing method text to preset terminal equipment.
Optionally, the performing dimension reduction on the convolution matrix to obtain the tag vector includes:
calculating the average value of each row of elements in the convolution matrix to obtain a row characteristic value;
and replacing each column in the convolution matrix with the corresponding column characteristic value to obtain the label vector.
In order to solve the above problem, the present invention also provides an electronic device, including:
a memory storing at least one computer program; and
and the processor executes the computer program stored in the memory to realize the software fault feedback processing method.
In order to solve the above problem, the present invention also provides a computer-readable storage medium, in which at least one computer program is stored, the at least one computer program being executed by a processor in an electronic device to implement the software failure feedback processing method described above.
In the embodiment of the invention, all the processing method knowledge bases are screened by utilizing the target fault category and the similarity degree of the knowledge base labels to obtain a target processing method knowledge base, and the screening range of the processing method knowledge base is narrowed through the target fault category and the similarity degree of the knowledge base labels; further taking the first fault entity and the second fault entity as screening conditions to screen processing method texts in the target processing method knowledge base; the processing method corresponding to the fault entity matching is obtained by utilizing the two dimensions of the text and the image, and the matched dimensions are more diversified, so that the screening of the processing method is more accurate, and the accuracy of software fault feedback processing is improved. Therefore, the software fault feedback processing method, the software fault feedback processing device, the electronic equipment and the computer readable storage medium provided by the embodiment of the invention improve the accuracy of software fault feedback processing.
Drawings
Fig. 1 is a schematic flow chart of a software failure feedback processing method according to an embodiment of the present invention;
fig. 2 is a block diagram of a software failure feedback processing apparatus according to an embodiment of the present invention;
fig. 3 is a schematic diagram of an internal structure of an electronic device implementing a software failure feedback processing method according to an embodiment of the present invention;
the implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The embodiment of the invention provides a software fault feedback processing method. The execution subject of the software failure feedback processing method includes, but is not limited to, at least one of electronic devices such as a server and a terminal that can be configured to execute the method provided by the embodiments of the present application. In other words, the software fault feedback processing method may be executed by software or hardware installed in the terminal device or the server device, and the software may be a block chain platform. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, and the like.
Referring to fig. 1, which is a schematic flow diagram of a software failure feedback processing method according to an embodiment of the present invention, in an embodiment of the present invention, the software failure feedback processing method includes:
s1, acquiring a fault feedback text and a fault feedback image corresponding to the same fault feedback of software;
in the embodiment of the invention, the fault feedback text is a text description of the software problem fed back by the user, and the fault feedback image is a problem screenshot corresponding to the software problem fed back by the user; further, in the embodiment of the present invention, the fault feedback text and the corresponding fault feedback image may be acquired through a fault feedback text and a fault feedback image input module of software or a web page.
S2, performing fault type identification on the fault feedback text to obtain a target fault type;
in the embodiment of the present invention, the fault feedback text is a question input by a user and intended to be consulted or fed back, for example: the fault feedback text can be acquired through a text input module of the webpage.
Further, in order to better match a corresponding processing method for the fault corresponding to the fault feedback in the embodiment of the present invention, the fault type identification is performed on the fault feedback text, so as to obtain the target fault type.
For example: the fault category of the target is "network connection fault", "play card pause fault", and the like, and the fault category is not limited in the embodiment of the present invention.
In detail, in the embodiment of the present invention, performing fault category identification on the fault feedback text to obtain a target fault category, includes:
performing feature extraction on the fault feedback text by using the trained BERT model to obtain a text feature vector;
calculating the text feature vector by using a softmax function to obtain the recognition probabilities of different preset fault categories;
confirming the fault category corresponding to the maximum recognition probability as the target fault category;
further, before performing the fault category identification on the fault feedback text in the embodiment of the present invention, the method further includes:
step A: acquiring a historical fault text set, and carrying out fault category marking on the historical fault text set to obtain a training set;
in this embodiment of the present invention, the historical fault text set is a set of different historical fault texts, and the historical fault text may be text data that has different content from the fault text but belongs to the same type. Further, the embodiment of the invention marks the fault category of each historical fault text in the historical fault text set to obtain the training set.
And B: and performing iterative training on the pre-constructed BERT model by using the training set until the BERT model converges to obtain the trained BERT model.
And the screening range of a subsequent processing method is narrowed by identifying the fault type of the fault text, so that the screening efficiency is improved.
S3, performing entity extraction on the fault feedback text to obtain a first fault entity;
in the embodiment of the invention, in order to locate the fault problem in the fault feedback text, entity extraction is carried out on the fault feedback text to obtain a first fault entity.
Optionally, the entity extraction processing in the embodiment of the present invention includes: and identifying the fault feedback text after the word processing by using a Named Entity identification technology (NER for short) to obtain a first fault Entity. Wherein, the named entity recognition technology can identify entities (such as personal names, place names, organization names, proper nouns, etc.) with specific meanings in the fault feedback text to represent corresponding faults. For example: the fault feedback text is ' network connection is interrupted ', the fault feedback text after word segmentation processing cannot be connected ' is ' network connection is interrupted ', ' cannot ' or ' connected ', and the first fault entity can be obtained by identifying the fault feedback text by using the named entity identification technology and is ' network connection is interrupted '.
S4, segmenting an error reporting area in the fault feedback image, and identifying a text in the segmented image to obtain an image text;
in the embodiment of the invention, the fault feedback image is a screenshot of a software problem fed back by a user, and information in the fault feedback image can help to identify the problem fed back by the user, so that the fault feedback image is preprocessed and text-identified to obtain the image text.
In detail, in the embodiment of the present invention, in order to remove an influence of an irrelevant area in the failure feedback image, an error reporting area in the failure feedback image is clipped to obtain a clipped image, and preferably, in the embodiment of the present invention, the error reporting area is an error reporting text display area of the failure feedback image.
In detail, since the key area may have different colors, in order to reduce the data amount, the storage space, and the image processing time, the key area is grayed; further, since image noise exists in the key region, in order to reduce the influence of the image noise on subsequent processing, the embodiment of the present invention performs filtering processing on the key region, and preferably, the embodiment of the present invention performs filtering processing on the clipped image by using a median filtering algorithm.
Further, in order to obtain text information in the cut image, the embodiment of the present invention performs text extraction processing on the cut image by using a text extraction algorithm, so as to extract characters in the cut image. In one embodiment of the present invention, the text extraction algorithm may be a known OCR (Optical Character Recognition) algorithm.
Therefore, to sum up, in the embodiment of the present invention, the preprocessing and text recognition of the fault feedback image includes: cutting an error reporting area in the fault feedback image to obtain a cut image; performing graying processing on the cut image to obtain a grayed image; filtering the grayed image to obtain the standard image; and performing text recognition processing on the standard image by using a preset text extraction algorithm to obtain the image text.
S5, performing entity extraction on the image text to obtain a second fault entity;
in the embodiment of the invention, in order to locate the fault problem in the image text, the entity extraction is carried out on the image text to obtain a second fault entity.
Further, the entity extraction manner in this step in the embodiment of the present invention is the same as S3, and is not described herein again.
S6, acquiring one or more processing method knowledge bases, wherein each processing method knowledge base is provided with a corresponding knowledge base label, and screening all the processing method knowledge bases by using the target fault category and the similarity degree of the knowledge base labels to obtain a target processing method knowledge base;
in the embodiment of the invention, the processing method knowledge base is a database formed by software knowledge with limited function categories which is pre-constructed. For example: the software covers a number of functional categories including: the embodiment of the invention collects and stores the knowledge related to video playing into a database to obtain a video playing function processing method knowledge base, collects and stores the knowledge related to network connection into the database to obtain a network connection function processing method knowledge base, and collects and stores the knowledge related to video downloading function into the database to obtain a video downloading function processing method knowledge base, wherein the labels of the knowledge bases are short texts for identifying the knowledge types in the knowledge base.
Furthermore, in the embodiment of the present invention, there are one or more processing method knowledge bases, and in order to accurately locate the database where the fault processing method corresponding to the fault feedback is located, all the processing method knowledge bases are screened by using the target fault category and the similarity degree of the knowledge base tags, so as to obtain a target processing method knowledge base.
Specifically, in the embodiment of the present invention, the screening of all the processing method knowledge bases by using the target fault category and the similarity degree of the knowledge base tags to obtain a target processing method knowledge base includes:
converting the target fault category into a vector to obtain a category vector;
converting the knowledge base tags into vectors to obtain tag vectors;
calculating the similarity between the category vector and the label vector to obtain a similarity value;
specifically, in the embodiment of the present invention, the similarity between the category vector and the tag vector may be calculated by using an algorithm such as an euclidean distance, a manhattan distance, and an angle cosine.
And screening all the processing method knowledge bases according to the similarity values to obtain a target processing method knowledge base.
Specifically, in the embodiment of the present invention, the tag vector corresponding to the maximum similarity value is determined as a target tag vector; determining a knowledge base tag corresponding to the target tag vector as a target knowledge base tag; and determining the processing method knowledge base corresponding to the target knowledge base label as a target processing method knowledge base.
Further, in the embodiment of the present invention, converting the knowledge base tag into a vector to obtain a tag vector, including:
converting each character in the knowledge base label into a vector to obtain a character vector;
combining all the character vectors according to the sequence of the corresponding characters in the knowledge base labels to obtain a knowledge base label matrix;
carrying out convolution on the knowledge base tag matrix by using a preset convolution core to obtain a convolution matrix;
and reducing the dimension of the convolution matrix to obtain the label vector.
In detail, in the embodiment of the present invention, performing dimension reduction on the convolution matrix to obtain the tag vector includes:
calculating the average value of each row of elements in the convolution matrix to obtain a row characteristic value;
and replacing each column in the convolution matrix with the corresponding column eigenvalue to obtain the label vector.
In another embodiment of the invention, the processing method knowledge base is composed of limited domain knowledge and can be deployed on a block chain.
In the embodiment of the present invention, the target fault category and the knowledge base tag are both texts, and further, a method for converting the texts into vectors is not limited in the embodiment of the present invention.
S7, screening the processing method texts in the target processing method knowledge base by taking the first fault entity and the second fault entity as screening conditions to obtain target processing method texts;
specifically, in the embodiment of the present invention, the querying of the processing method in the target processing method knowledge base is performed by using the first faulty entity and the second faulty entity as query conditions, so as to obtain the target processing method, where the method includes:
converting the first fault entity into a vector to obtain a first entity vector;
converting the second fault entity into a vector to obtain a second entity vector;
converting each processing method text in the target processing method knowledge base into a vector to obtain a processing method vector;
calculating the correlation degree of the first entity vector and the processing method vector to obtain a first correlation degree;
optionally, in the embodiment of the present invention, an algorithm such as an euclidean distance, a manhattan distance, and an angle cosine may be used to calculate a correlation between the first entity vector and the processing method vector.
Calculating the correlation degree of the second entity vector and the processing method vector to obtain a second correlation degree;
based on a preset weighting coefficient, performing weighting calculation according to the first correlation and the second correlation corresponding to each processing method vector to obtain a matching coefficient corresponding to the processing method vector;
and screening the processing method texts in the target processing method knowledge base by using the matching coefficient to obtain a target processing method text.
In detail, in the embodiment of the present invention, based on a preset weighting coefficient, performing weighting calculation according to a first correlation and a second correlation corresponding to each processing method vector to obtain a matching coefficient corresponding to the processing method vector, where the method includes:
extracting a first similarity weighting coefficient from the weighting coefficients, and calculating the product of the first similarity coefficient and the first similarity to obtain a first similarity weighted value;
extracting a second similarity weighting coefficient from the weighting coefficients, and calculating the product of the second similarity coefficient and the second similarity to obtain a second similarity weighting value;
and adding the first similarity weighted value and the second similarity weighted value corresponding to each processing method vector to obtain a matching coefficient corresponding to the processing method vector.
Specifically, in the embodiment of the present invention, the screening of the processing method text in the target processing method knowledge base by using the matching coefficient and a preset matching coefficient threshold to obtain the target processing method text includes:
determining the processing method vector corresponding to the maximum matching coefficient as a target processing method vector;
and determining the processing method text corresponding to the target processing method vector as the target processing method text.
And S8, sending the target processing method text to preset terminal equipment.
In the embodiment of the present invention, the sending of the target processing method text to a preset terminal device, where the terminal device may be a terminal device that sends the failure feedback, includes: intelligent terminals such as mobile phones, computers and tablets.
Further, the embodiment of the invention pushes the target processing method text to the user, simultaneously obtains feedback information whether the user approves the target processing method text, and correspondingly updates data in the processing method knowledge base based on the feedback information, thereby realizing the error correction processing of the processing method knowledge base.
Fig. 2 is a functional block diagram of the software failure feedback processing apparatus according to the present invention.
The software failure feedback processing apparatus 100 according to the present invention may be installed in an electronic device. According to the realized functions, the software failure feedback processing device may include a text processing module 101, an image processing module 102, and a processing method screening module 103, which may also be referred to as a unit, and refers to a series of computer program segments that can be executed by a processor of an electronic device and can perform fixed functions, and are stored in a memory of the electronic device.
In the present embodiment, the functions regarding the respective modules/units are as follows:
the text processing module 101 is configured to obtain a fault feedback text and a fault feedback image corresponding to the same fault feedback of the software; identifying the fault type of the fault feedback text to obtain a target fault type; performing entity extraction on the fault feedback text to obtain a first fault entity;
the image processing module 102 is configured to segment an error reporting region in the fault feedback image, and identify a text in the segmented image to obtain an image text; performing entity extraction on the image text to obtain a second fault entity;
the processing method screening module 103 is configured to obtain one or more processing method knowledge bases, where each processing method knowledge base has a corresponding knowledge base tag, and screen all the processing method knowledge bases by using the target fault category and the similarity degree of the knowledge base tags to obtain a target processing method knowledge base; screening the processing method texts in the target processing method knowledge base by taking the first fault entity and the second fault entity as screening conditions to obtain a target processing method text; and sending the target processing method text to preset terminal equipment.
In detail, when the modules in the software failure feedback processing apparatus 100 according to the embodiment of the present invention are used, the same technical means as the software failure feedback processing method described in fig. 1 above are adopted, and the same technical effects can be produced, which is not described herein again.
Fig. 3 is a schematic structural diagram of an electronic device implementing the software failure feedback processing method according to the present invention.
The electronic device 1 may comprise a processor 10, a memory 11 and a bus, and may further comprise a computer program, such as a software failure feedback handling program 12, stored in the memory 11 and executable on the processor 10.
The memory 11 includes at least one type of readable storage medium, which includes flash memory, removable hard disk, multimedia card, card-type memory (e.g., SD or DX memory, etc.), magnetic memory, magnetic disk, optical disk, etc. The memory 11 may in some embodiments be an internal storage unit of the electronic device 1, such as a removable hard disk of the electronic device 1. The memory 11 may also be an external storage device of the electronic device 1 in other embodiments, such as a plug-in mobile hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, which are provided on the electronic device 1. Further, the memory 11 may also include both an internal storage unit and an external storage device of the electronic device 1. The memory 11 may be used not only to store application software installed in the electronic device 1 and various types of data, such as codes of software failure feedback processing programs, etc., but also to temporarily store data that has been output or is to be output.
The processor 10 may be composed of an integrated circuit in some embodiments, for example, a single packaged integrated circuit, or may be composed of a plurality of integrated circuits packaged with the same or different functions, including one or more Central Processing Units (CPUs), microprocessors, digital Processing chips, graphics processors, and combinations of various control chips. The processor 10 is a Control Unit (Control Unit) of the electronic device, connects various components of the whole electronic device by using various interfaces and lines, and executes various functions and processes data of the electronic device 1 by running or executing programs or modules (e.g., software failure feedback processing programs, etc.) stored in the memory 11 and calling data stored in the memory 11.
The bus may be a PerIPheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. The bus is arranged to enable connection communication between the memory 11 and at least one processor 10 or the like.
Fig. 3 shows only an electronic device with components, and it will be understood by those skilled in the art that the structure shown in fig. 3 does not constitute a limitation of the electronic device 1, and may comprise fewer or more components than those shown, or some components may be combined, or a different arrangement of components.
For example, although not shown, the electronic device 1 may further include a power supply (such as a battery) for supplying power to each component, and preferably, the power supply may be logically connected to the at least one processor 10 through a power management device, so as to implement functions of charge management, discharge management, power consumption management, and the like through the power management device. The power supply may also include any component of one or more dc or ac power sources, recharging devices, power failure detection circuitry, power converters or inverters, power status indicators, and the like. The electronic device 1 may further include various sensors, a bluetooth module, a Wi-Fi module, and the like, which are not described herein again.
Further, the electronic device 1 may further include a network interface, and optionally, the network interface may include a wired interface and/or a wireless interface (such as a WI-FI interface, a bluetooth interface, etc.), which are generally used to establish a communication connection between the electronic device 1 and another electronic device.
Optionally, the electronic device 1 may further comprise a user interface, which may be a Display (Display), an input unit (such as a Keyboard), and optionally a standard wired interface, a wireless interface. Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch device, or the like. The display, which may also be referred to as a display screen or display unit, is suitable for displaying information processed in the electronic device 1 and for displaying a visualized user interface, among other things.
It is to be understood that the described embodiments are for purposes of illustration only and that the scope of the appended claims is not limited to such structures.
The software failure feedback processing program 12 stored in the memory 11 of the electronic device 1 is a combination of a plurality of computer programs, and when running in the processor 10, can realize:
acquiring a fault feedback text and a fault feedback image corresponding to the same fault feedback of the software;
identifying the fault type of the fault feedback text to obtain a target fault type;
performing entity extraction on the fault feedback text to obtain a first fault entity;
segmenting an error reporting area in the fault feedback image, and identifying a text in the segmented image to obtain an image text;
performing entity extraction on the image text to obtain a second fault entity;
acquiring one or more processing method knowledge bases, wherein each processing method knowledge base is provided with a corresponding knowledge base label, and screening all the processing method knowledge bases by using the target fault category and the similarity degree of the knowledge base labels to obtain a target processing method knowledge base;
screening the processing method texts in the target processing method knowledge base by taking the first fault entity and the second fault entity as screening conditions to obtain target processing method texts;
and sending the target processing method text to preset terminal equipment.
Specifically, the processor 10 may refer to the description of the relevant steps in the embodiment corresponding to fig. 1 for a specific implementation method of the computer program, which is not described herein again.
Further, the integrated modules/units of the electronic device 1, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. The computer readable medium may be non-volatile or volatile. The computer-readable medium may include: any entity or device capable of carrying said computer program code, recording medium, U-disk, removable hard disk, magnetic disk, optical disk, computer Memory, read-Only Memory (ROM).
Embodiments of the present invention may also provide a computer-readable storage medium, where the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor of an electronic device, the computer program may implement:
acquiring a fault feedback text and a fault feedback image corresponding to the same fault feedback of the software;
identifying the fault type of the fault feedback text to obtain a target fault type;
performing entity extraction on the fault feedback text to obtain a first fault entity;
segmenting the error reporting area in the fault feedback image, and identifying a text in the segmented image to obtain an image text;
performing entity extraction on the image text to obtain a second fault entity;
acquiring one or more processing method knowledge bases, wherein each processing method knowledge base is provided with a corresponding knowledge base label, and screening all the processing method knowledge bases by using the target fault category and the similarity degree of the knowledge base labels to obtain a target processing method knowledge base;
screening the processing method texts in the target processing method knowledge base by taking the first fault entity and the second fault entity as screening conditions to obtain target processing method texts;
and sending the target processing method text to preset terminal equipment.
Further, the computer usable storage medium may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function, and the like; the storage data area may store data created according to the use of the blockchain node, and the like.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus, device and method can be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is only one logical functional division, and other divisions may be realized in practice.
The modules described as separate parts may or may not be physically separate, and parts displayed as modules may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, functional modules in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are 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 module.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof.
The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference signs in the claims shall not be construed as limiting the claim concerned.
The block chain is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, a consensus mechanism, an encryption algorithm and the like. A block chain (Blockchain), which is essentially a decentralized database, is a series of data blocks associated by using a cryptographic method, and each data block contains information of a batch of network transactions, so as to verify the validity (anti-counterfeiting) of the information and generate a next block. The blockchain may include a blockchain underlying platform, a platform product service layer, an application service layer, and the like.
Furthermore, it will be obvious that the term "comprising" does not exclude other elements or steps, and the singular does not exclude the plural. A plurality of units or means recited in the system claims may also be implemented by one unit or means in software or hardware. The terms second, etc. are used to denote names, but not any particular order.
Finally, it should be noted that the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting, and although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.
Claims (10)
1. A software failure feedback processing method, characterized in that the method comprises:
acquiring a fault feedback text and a fault feedback image corresponding to the same fault feedback of the software;
identifying the fault type of the fault feedback text to obtain a target fault type;
performing entity extraction on the fault feedback text to obtain a first fault entity;
segmenting an error reporting area in the fault feedback image, and identifying a text in the segmented image to obtain an image text;
performing entity extraction on the image text to obtain a second fault entity;
acquiring one or more processing method knowledge bases, wherein each processing method knowledge base is provided with a corresponding knowledge base label, and screening all the processing method knowledge bases by using the target fault category and the similarity degree of the knowledge base labels to obtain a target processing method knowledge base;
screening the processing method texts in the target processing method knowledge base by taking the first fault entity and the second fault entity as screening conditions to obtain target processing method texts;
and sending the target processing method text to preset terminal equipment.
2. The software failure feedback processing method according to claim 1, wherein the performing failure category identification on the failure feedback text to obtain a target failure category comprises:
performing feature extraction on the fault feedback text by using the trained BERT model to obtain a text feature vector;
calculating the text feature vector by using a softmax function to obtain the recognition probabilities of different preset fault categories;
and confirming the fault category corresponding to the maximum recognition probability as the target fault category.
3. The software failure feedback processing method of claim 1, wherein the segmenting the error reporting region in the failure feedback image and identifying the text in the segmented image to obtain the image text comprises:
cutting an error reporting area in the fault feedback image to obtain a cut image;
performing graying processing on the cut image to obtain a grayed image;
filtering the grayed image to obtain a standard image;
and performing text recognition processing on the standard image by using a preset text extraction algorithm to obtain the image text.
4. The software failure feedback processing method according to any one of claims 1 to 3, wherein the step of screening all the processing method knowledge bases by using the target failure category and the similarity degree of the knowledge base tag to obtain a target processing method knowledge base comprises the steps of:
converting the target fault category into a vector to obtain a category vector;
converting the knowledge base tags into vectors to obtain tag vectors;
calculating the similarity between the category vector and the label vector to obtain a similarity value;
and screening all the processing method knowledge bases according to the similarity values to obtain a target processing method knowledge base.
5. The software fault feedback processing method of claim 4, wherein converting the knowledge base tags into vectors to obtain tag vectors comprises:
converting each character in the knowledge base label into a vector to obtain a character vector;
combining all the character vectors according to the sequence of the corresponding characters in the knowledge base labels to obtain a knowledge base label matrix;
carrying out convolution on the knowledge base tag matrix by using a preset convolution core to obtain a convolution matrix;
and reducing the dimension of the convolution matrix to obtain the label vector.
6. The software fault feedback processing method of claim 5, wherein said reducing dimensions of said convolution matrix to obtain said tag vector comprises:
calculating the average value of each row of elements in the convolution matrix to obtain a row characteristic value;
and replacing each column in the convolution matrix with the corresponding column characteristic value to obtain the label vector.
7. A software failure feedback processing apparatus, comprising:
the text processing module is used for acquiring a fault feedback text and a fault feedback image corresponding to the same fault feedback of the software; identifying the fault type of the fault feedback text to obtain a target fault type; performing entity extraction on the fault feedback text to obtain a first fault entity;
the image processing module is used for segmenting the error reporting area in the fault feedback image and identifying a text in the segmented image to obtain an image text; performing entity extraction on the image text to obtain a second fault entity;
the processing method screening module is used for acquiring one or more processing method knowledge bases, wherein each processing method knowledge base is provided with a corresponding knowledge base label, and all the processing method knowledge bases are screened by using the target fault category and the similarity degree of the knowledge base labels to obtain a target processing method knowledge base; screening the processing method texts in the target processing method knowledge base by taking the first fault entity and the second fault entity as screening conditions to obtain target processing method texts; and sending the target processing method text to preset terminal equipment.
8. The software fault feedback processing apparatus of claim 7, wherein said reducing dimensions of said convolution matrix to obtain said tag vector comprises:
calculating the average value of each row of elements in the convolution matrix to obtain a row characteristic value;
and replacing each column in the convolution matrix with the corresponding column eigenvalue to obtain the label vector.
9. An electronic device, characterized in that the electronic device comprises:
at least one processor; and (c) a second step of,
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores computer program instructions executable by the at least one processor to cause the at least one processor to perform the software fault feedback processing method of any one of claims 1 to 6.
10. A computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the software failure feedback processing method according to any one of claims 1 to 6.
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| CN117873909A (en) * | 2024-03-13 | 2024-04-12 | 上海爱可生信息技术股份有限公司 | Fault diagnosis execution method, fault diagnosis execution system, electronic device, and storage medium |
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| CN117873909A (en) * | 2024-03-13 | 2024-04-12 | 上海爱可生信息技术股份有限公司 | Fault diagnosis execution method, fault diagnosis execution system, electronic device, and storage medium |
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