CN112632249A - Method and device for displaying different versions of information of product, computer equipment and medium - Google Patents
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Abstract
The application relates to an artificial intelligence technology, and discloses a method, a device, computer equipment and a medium for displaying information of different versions of a product, which comprises the steps of obtaining data of each version of the product; extracting documents from data of each version of a product, and identifying keywords of the documents; expanding all keywords of the same version to child nodes under nodes of the same version of a preset thinking guide graph according to the attributes of the keywords, wherein the child nodes correspond to various father nodes under the nodes of the same version; matching every two child nodes belonging to nodes of different versions but belonging to similar father nodes by using a pre-trained association model, and calculating a matching value, wherein the association model is obtained by training historical keyword data; and associating and displaying the two sub-nodes corresponding to the matching values meeting the preset conditions in the preset mind map. The application also relates to blockchain techniques, where versions and resulting mind map data are stored. The method and the device can rapidly and comprehensively display the relationship among all versions of the product.
Description
Technical Field
The application relates to the technical field of artificial intelligence, in particular to a method and a device for displaying different versions of information of a product, computer equipment and a medium.
Background
In real life, many products exist, whether hardware products or software products, iterative update is performed on the products at regular intervals, with the continuous change of product versions, business personnel or other personnel continuously provide requirements on function establishment or optimization, and developers develop and implement the functions according to the requirements. In the prior art, only the text descriptions of each version stage are displayed, and the product requirements, background propositions and the like of products of different versions are not comprehensively displayed according to the versions, so that the prior art cannot quickly know the relevant information of each version and the association among the versions. Therefore, how to quickly and comprehensively display the relationship among the versions of the product becomes a problem to be solved urgently.
Disclosure of Invention
The application provides a method and a device for displaying information of different versions of a product, computer equipment and a storage medium, and aims to solve the problems that the relationship among the versions of the product cannot be displayed quickly and the displayed relationship is not comprehensive enough in the prior art.
In order to solve the above problem, the present application provides a method for displaying information of different versions of a product, including:
acquiring data of each version of a product;
extracting documents from the data of each version of the product, and identifying keywords of the documents;
expanding all keywords of the same version to child nodes under nodes of the same version of a preset thinking guide graph according to the attributes of the keywords, wherein the child nodes correspond to various father nodes under the nodes of the same version;
matching every two child nodes belonging to nodes of different versions but belonging to similar father nodes by using a pre-trained association model, and calculating a matching value, wherein the association model is obtained by training historical keyword data;
and associating and displaying the two sub-nodes corresponding to the matching values meeting the preset conditions in the preset mind map.
Further, before the obtaining data of each version of the product, the method further includes:
sending a calling request to a database, wherein the calling request carries a signature checking token;
receiving a signature checking result returned by the database, and calling data of each version of the product in the database when the signature checking result is passed;
the signature verification mode is an RSA asymmetric encryption mode.
Further, the identifying the keywords of the document includes:
and identifying keywords in the document by using a keyword extraction model, wherein the keyword extraction model is obtained by training by using a historical word stock.
Further, before extending all the keywords of the same version to the child nodes under the node of the same version of the preset mind map, the method further includes:
extracting each version number of the product as the version node;
setting four nodes of a demand target, a demand background, an optional scheme and a data flow as father nodes under the version nodes;
and constructing the preset mind map according to the version nodes and the father nodes.
Further, the expanding all the keywords of the same version to the child nodes under the node of the same version of the preset mind map according to the attribute of the keyword includes:
classifying the keywords according to the attributes of the keywords to obtain requirement target keywords, requirement background keywords, optional keywords and data flow keywords;
expanding the requirement target class key words of the same version to child nodes under the requirement target father node; expanding the requirement background class key words of the same version to child nodes under the requirement background father nodes; expanding the optional scheme type key words of the same version to child nodes under the optional scheme parent node; and expanding the data flow class key words of the same version to child nodes under the data flow father node.
Further, the performing pairwise matching and calculating matching values on the child nodes belonging to different version nodes but belonging to the same kind of father nodes by using the pre-trained association model includes:
and calculating entropy values of the keywords through the association model, and determining matching values among the keywords according to the entropy values, wherein the association model is obtained based on RNN model training.
Further, the associating and displaying the two child nodes corresponding to the matching values meeting the preset condition in the preset mind map includes:
comparing the match value to a first threshold;
connecting the two keywords with a solid line for presentation when the matching value is greater than a first threshold;
comparing the match value to a second threshold when the match value is less than a first threshold;
connecting the two keywords with a dotted line for presentation when the matching value is less than a first threshold and greater than the second threshold; wherein the first threshold is greater than the second threshold.
In order to solve the above problem, the present application further provides a device for displaying different versions of information of a product, the device including:
the acquisition module is used for acquiring data of each version of the product;
the extraction and identification module is used for extracting documents from the data of each version of the product and identifying keywords of the documents;
the filling module is used for expanding all keywords of the same version to child nodes under nodes of the same version of a preset thinking guide graph according to the attributes of the keywords, and the child nodes correspond to various father nodes under the nodes of the same version;
the matching module is used for pairwise matching each child node belonging to different version nodes but belonging to the same kind of father nodes by utilizing a pre-trained correlation model, and calculating a matching value, wherein the correlation model is obtained by training historical keyword data;
and the association display module is used for associating and displaying the two sub-nodes corresponding to the matching values meeting the preset conditions in the preset mind map.
In order to solve the above problem, the present application also provides a computer device, including:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein,
the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the method for displaying the different versions of the product.
In order to solve the above problem, the present application further provides a non-volatile computer readable storage medium, on which computer readable instructions are stored, and when the computer readable instructions are executed by a processor, the method for displaying different versions of information of a product is implemented as described above.
According to the method, the device, the computer equipment and the medium for displaying the information of different versions of the product, compared with the prior art, the method and the device have the following beneficial effects that: extracting a document from the data of each version of a product by acquiring the data of each version of the product, and identifying a keyword in the document; refining the document data in this manner; expanding all keywords of the same version to child nodes under nodes of the same version of a preset thinking map according to the attributes of the keywords, wherein the child nodes correspond to various father nodes under the nodes of the same version; and performing pairwise matching on keywords which belong to nodes of different versions but belong to similar father nodes by using an association model, calculating a matching value, realizing association matching between different versions, associating and displaying the matching value which meets two child nodes corresponding to preset conditions in the preset thinking guide graph, and associating and visualizing the two keywords which meet the requirements, thereby realizing rapid and comprehensive display of the relationship between the versions of the product.
Drawings
In order to more clearly illustrate the solution of the present application, a brief description will be given below of the drawings required for describing the embodiments of the present application, and it is obvious that the drawings in the following description are some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings without inventive effort.
Fig. 1 is a schematic flowchart of a method for displaying different versions of information of a product according to an embodiment of the present application;
FIG. 2 is a diagram illustrating a default thinking diagram according to an embodiment of the present application;
FIG. 3 is a diagram illustrating the effect of the thinking diagram provided by an embodiment of the present application;
FIG. 4 is a block diagram of an apparatus for displaying different versions of information of a product according to an embodiment of the present disclosure;
fig. 5 is a schematic structural diagram of a computer device according to an embodiment of the present application.
Detailed Description
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs; the terminology used in the description of the application herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application; the terms "including" and "having," and any variations thereof, in the description and claims of this application and the description of the above figures are intended to cover non-exclusive inclusions. The terms "first," "second," and the like in the description and claims of this application or in the above-described drawings are used for distinguishing between different objects and not for describing a particular order.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. One skilled in the art will explicitly or implicitly appreciate that the embodiments described herein can be combined with other embodiments.
The application provides a method for displaying different versions of information of a product. Referring to fig. 1, a schematic flow chart of a method for displaying different versions of information of a product according to an embodiment of the present application is shown.
In this embodiment, the method for displaying different versions of information of a product includes:
s1, acquiring data of each version of the product;
specifically, the data of each version of the product is acquired, including acquiring a requirement document, a development document and the like of each version, wherein the requirement document records the functional requirements and optimization requirements of business personnel on the product, namely requirement targets, requirement backgrounds and the like, and the development document records alternative explanation, data flow and the like.
Further, before the obtaining data of each version of the product, the method further includes:
sending a calling request to a database, wherein the calling request carries a signature checking token;
receiving a signature checking result returned by the database, and calling data of each version of the product in the database when the signature checking result is passed;
the signature verification mode is an RSA asymmetric encryption mode.
Specifically, since each version data belongs to confidential data, a signature verification step is required when each version data is called to the database. To verify whether the user invoking the data is the correct user or an internal user.
The data security of each version of the product is ensured by the way of checking the label.
S2, extracting documents from the data of each version of the product, and identifying keywords of the documents;
specifically, by extracting a requirement document and a development document in each version of the product, and extracting keywords in the requirement document and the development document;
further, the identifying the keywords of the document includes:
and identifying keywords in the document by using a keyword extraction model, wherein the keyword extraction model is obtained by training by using a historical word stock.
For example: in the requirement 1, the process is to make a call at a fixed time task, identify a blacklist and cancel the call;
extracting by a keyword extraction model to obtain: timing tasks, phone dialing, blacklisting, and dialing-off keywords.
In requirement 2, the process is to make a call by a timed task;
extracting by a keyword extraction model to obtain: timing tasks and dialing phone keywords.
Specifically, the keyword extraction model firstly obtains N words according to a word segmentation algorithm, compares the N words with historical keywords respectively, and proposes words close to the historical keywords in height as keywords.
The keyword extraction model is obtained based on a TF-IDF model or an LDA model and the like.
The accurate extraction of the keywords is realized by extracting the keywords by using a keyword extraction model.
S3, expanding all keywords of the same version to child nodes under nodes of the same version of a preset thinking map according to the attributes of the keywords, wherein the child nodes correspond to various father nodes under the nodes of the same version;
specifically, the keywords of the unified version are filled into the child nodes under the nodes of the unified version, so that unified management of the keywords is realized.
Further, before extending all the keywords of the same version to the child nodes under the node of the same version of the preset mind map, the method further includes:
extracting each version number of the product as the version node;
setting four nodes of a demand target, a demand background, an optional scheme and a data flow as father nodes under the version nodes;
and constructing the preset mind map according to the version nodes and the father nodes.
Specifically, the version number of each version product is extracted from each version data as the version node, and in this embodiment, as shown in fig. 2, a preset mind map sets mind maps of four father nodes of a demand target, a demand background, an alternative scheme, and a data flow;
the four father nodes of the requirement target, the requirement background, the optional scheme and the data flow correspond to a version node, namely a version 1 node or a version 2 node in the graph; in a complete preset thinking graph, a plurality of version nodes are correspondingly arranged, and each version node is provided with four father nodes of a demand target, a demand background, an optional scheme and a data flow.
The method is convenient for directly filling the keywords into the corresponding child nodes subsequently by presetting the thinking guide graph, and improves the processing efficiency.
Still further, the expanding all the keywords of the same version to the child nodes under the node of the same version of the preset mind map according to the attribute of the keyword includes:
classifying the keywords according to the attributes of the keywords to obtain requirement target keywords, requirement background keywords, optional keywords and data flow keywords;
expanding the requirement target class key words of the same version to child nodes under the requirement target father node; expanding the requirement background class key words of the same version to child nodes under the requirement background father nodes; expanding the optional scheme type key words of the same version to child nodes under the optional scheme parent node; and expanding the data flow class key words of the same version to child nodes under the data flow father node.
Specifically, the keywords are classified by using a preset database, the words stored in each database have different attributes, for example, after two keywords are identified, the keywords are matched with words in a demand target word database, a demand background word database, an alternative word database and a data flow word database one by one, and if the keywords are successfully matched with the words in the demand target word database, the keywords belong to the demand target category keywords; if the matching with the words in the requirement background word database is successful, the keywords belong to requirement background keywords; if the matching with the words in the alternative scheme word database is successful, the keywords belong to the alternative scheme keywords; similarly, if the matching with the words in the data flow word database is successful, the keywords belong to the data flow keywords.
Correspondingly, different types of keywords are filled in the child nodes under the corresponding father nodes, for example, the data flow type keywords are expanded to the child nodes under the data flow father nodes, and other types of keywords are the same.
And classifying the keywords according to the attributes of the keywords and filling the keywords into child nodes under corresponding father nodes, so as to realize comprehensive display of each version of data.
S4, matching every two child nodes belonging to different versions of nodes but belonging to similar father nodes by using a pre-trained association model, and calculating a matching value, wherein the association model is obtained by training historical keyword data;
further, the performing pairwise matching and calculating matching values on the child nodes belonging to different version nodes but belonging to the same kind of father nodes by using the pre-trained association model includes:
and calculating entropy values of the keywords through the association model, and determining matching values among the keywords according to the entropy values, wherein the association model is obtained based on RNN model training.
Specifically, the association model calculates an entropy value of each keyword, and obtains a similarity, i.e., a matching value, by calculating a distance between entropy values corresponding to any two keywords; by entering a keyword into the association model, a matching value for any two of the keywords can be obtained. The matching value of the two keywords can be adjusted by adjusting the distance.
And traversing the whole keyword, and calculating the similarity between every two keywords which belong to the same kind of father nodes but have different versions, namely the matching value.
By using the association model to calculate a matching value between two keywords, preparation is made for a subsequent association step.
And S5, associating and displaying the two child nodes corresponding to the matching values meeting the preset conditions in the preset thinking map.
Specifically, the similarity of every two keywords is obtained, namely the matching value is obtained, and the two keywords with the matching values larger than a first threshold are associated. And according to the matching value between the two keywords, the two characteristic words are connected by using the solid line and the dotted line, and finally, when a network is formed, the matching value is distinguished through the difference between the solid line and the dotted line, so that the relation between the keywords can be more clearly embodied, and the relation between products of various versions is further embodied.
Further, the associating and displaying the two child nodes corresponding to the matching values meeting the preset condition in the preset mind map includes:
comparing the match value to a first threshold;
connecting the two keywords with a solid line for presentation when the matching value is greater than a first threshold;
comparing the match value to a second threshold when the match value is less than a first threshold;
connecting the two keywords with a dotted line for presentation when the matching value is less than a first threshold and greater than the second threshold; wherein the first threshold is greater than the second threshold.
Specifically, the two keywords meeting the requirements are associated in the above manner, and the difference between the association sizes is made by using the implementation and the dotted line. And when the matching value is smaller than the first threshold value and larger than the second threshold value, connecting the two keywords by using a dotted line, and when the matching value is larger than the first threshold value, connecting the two keywords by using a solid line.
The association step of the keywords can also receive user instructions to perform manual association.
For example: the two ends connected by the red arrow indicate that the two are related.
In other embodiments of the present application, two keywords meeting a preset condition may also be associated in any other distinguishable manner, for example, as shown in fig. 3, the degree of association may also be distinguished by the difference of colors, for example, the matching value is compared with a first threshold, and when the matching value is greater than the first threshold, the two keywords are shown by connecting a red arrow;
when the matching value is smaller than a first threshold value and larger than a second threshold value, connecting the two keywords by using a blue arrow for showing; wherein the first threshold is greater than the second threshold.
The colors are not fixed, any two different colors can be used for distinguishing, the association degree is not limited to two types, and multiple types can be set and distinguished by arrows with different colors. The distinguishing manner may be various, and is not limited to the solid line and the dotted line, and the distinguishing manner of different colors.
In other embodiments of the present application, the step of comparing the matching value with the second threshold may be omitted, that is, only by determining the size of the matching value and the first threshold, when the matching value is greater than or equal to the first threshold, a realization line or a dotted line is used to connect the two keywords; and when the matching value is smaller than the first threshold value, not processing the two keywords.
The association is carried out in the connection mode, so that the association operation among the version data is realized, and the association efficiency is improved.
It is emphasized that the product version data and the generated mind map data may also be stored in a node of a block chain in order to further ensure the privacy and security of the data.
The block chain referred by the application 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.
Extracting a document from the data of each version of a product by acquiring the data of each version of the product, and identifying a keyword in the document; refining the document data in this manner; expanding all keywords of the same version to child nodes under nodes of the same version of a preset thinking map according to the attributes of the keywords, wherein the child nodes correspond to various father nodes under the nodes of the same version; and performing pairwise matching on keywords which belong to nodes of different versions but belong to similar father nodes by using an association model, calculating a matching value, realizing association matching between different versions, associating and displaying the matching value which meets two child nodes corresponding to preset conditions in the preset thinking guide graph, and associating and visualizing the two keywords which meet the requirements, thereby realizing rapid and comprehensive display of the relationship between the versions of the product.
Fig. 4 is a functional block diagram of a device for displaying different versions of information of a product according to the present application.
The device 100 for displaying different versions of information of a product can be installed in an electronic device. According to the realized functions, the device 100 for displaying different versions of information of products can include an obtaining module 101, an extracting and identifying module 102, a filling module 103, a matching module 104 and an associated displaying module 105. A module according to the present invention, which may also be referred to as a unit, refers to a series of computer program segments that can be executed by a processor of an electronic device and that can perform a fixed function, and that are stored in a memory of the electronic device.
In the present embodiment, the functions regarding the respective modules/units are as follows:
the acquisition module 101 is used for acquiring data of each version of a product;
specifically, the obtaining module 101 is configured to obtain data of each version of a product, including obtaining a requirement document and a development document of each version.
The device 100 for displaying different versions of information of a product further comprises a request sending module and a calling module;
the sending request module is used for sending a calling request to a database, and the calling request carries a signature checking token;
the calling module is used for receiving the label checking result returned by the database and calling the data of each version of the product in the database when the label checking result is passed;
the signature verification mode is an RSA asymmetric encryption mode.
The data safety of each version of the product is ensured by the matching use of the request sending module and the calling module.
An extraction and identification module 102, configured to extract a document from data of each version of the product, and identify a keyword of the document;
the extraction and identification module 102 is configured to extract a requirement document and a development document in each version of the product, and extract keywords in the requirement document and the development document;
the extraction and identification module 102 comprises a keyword extraction submodule;
the keyword extraction submodule is used for identifying keywords in the document by using a keyword extraction model, and the keyword extraction model is obtained by training by using a historical word stock.
And the keywords are accurately extracted through the keyword extraction model.
The filling module 103 is configured to expand all keywords of the same version to child nodes under nodes of the same version of a preset mind map according to attributes of the keywords, where the child nodes correspond to various father nodes under nodes of the same version;
the filling module 103 fills the keywords of the unified version into the child nodes under the nodes of the unified version, so as to realize unified management of the keywords.
The device 100 for displaying different versions of information of a product further comprises a version number extracting module, a father node setting module and a thinking graph constructing module;
the version number extraction module is used for extracting each version number of the product as the version node;
the father node setting module is used for setting four nodes of a requirement target, a requirement background, an optional scheme and a data flow as father nodes under the version nodes;
the mind map building module is used for building the preset mind map according to the version nodes and the father nodes.
Specifically, the version number extracting module extracts the version number of each version product in each version data as the version node.
Through the matching use of the version number extracting module, the father node setting module and the thinking graph constructing module, the keywords can be conveniently and directly filled into the corresponding child nodes subsequently, and the processing efficiency is improved.
The filling module 103 comprises a classification submodule and an expansion submodule;
the classification submodule is used for classifying the keywords according to the attributes of the keywords to obtain a demand target keyword, a demand background keyword, an optional scheme keyword and a data flow keyword;
the expansion submodule is used for expanding the requirement target key words of the same version to child nodes under the requirement target father node; expanding the requirement background class key words of the same version to child nodes under the requirement background father nodes; expanding the optional scheme type key words of the same version to child nodes under the optional scheme parent node; and expanding the data flow class key words of the same version to child nodes under the data flow father node.
Specifically, the classification sub-module classifies the keywords according to a preset database, and the words stored in the databases have different attributes.
And filling different classes of keywords into the child nodes under the corresponding father nodes by the corresponding expansion sub-modules.
And the comprehensive display of each version of data is realized through the matching of the classification submodule and the expansion submodule.
The matching module 104 is used for matching every two child nodes belonging to nodes of different versions but belonging to similar father nodes by using a pre-trained association model, and calculating a matching value, wherein the association model is obtained by training historical keyword data;
the matching module 104 includes a computation submodule;
the calculation submodule is used for calculating the entropy value of the keywords through the association model, determining the similarity among the keywords according to the entropy value, and obtaining the association model based on RNN model training;
specifically, the calculation sub-module performs entropy calculation on each keyword by using the association model, and obtains similarity, that is, a matching value, by calculating a distance between entropy values corresponding to any two keywords; by entering keywords belonging to different versions but to the same type of parent node into the association model, a matching value for any two of the keywords can be obtained.
And traversing the whole keyword, and calculating the similarity between every two keywords which belong to the same kind of father nodes and have different versions to obtain the matching value.
And calculating a matching value between the two keywords through a calculation submodule to prepare for a subsequent association step.
And the association display module 105 is configured to associate and display two child nodes corresponding to the matching values meeting a preset condition in the preset mind map.
The association display module 105 obtains the similarity of every two keywords, i.e. the matching value, and associates the two keywords with the matching value greater than the first threshold. And according to the matching value between the two keywords, the two characteristic words are connected by using the solid line and the dotted line, and finally, when a network is formed, the matching value is distinguished through the difference between the solid line and the dotted line, so that the relation between the keywords can be more clearly embodied, and the relation between products of various versions is further embodied.
The association display module 105 comprises a first comparison submodule, a first connection submodule, a second comparison submodule and a second connection submodule;
the first comparison submodule is used for comparing the matching value with a first threshold value;
the first connecting sub-module is used for connecting the two keywords to display by using a solid line when the matching value is larger than a first threshold value;
the second comparison submodule is used for comparing the matching value with a second threshold value when the matching value is smaller than the first threshold value;
the second connecting sub-module is used for connecting the two keywords to display by using a dotted line when the matching value is smaller than the first threshold and larger than the second threshold; wherein the first threshold is greater than the second threshold.
Through the cooperation of the first comparison submodule, the first connection submodule, the second comparison submodule and the second connection submodule, the association operation of the version data is realized, and the association efficiency is improved.
It is emphasized that the product version data and the generated mind map data may also be stored in a node of a block chain in order to further ensure the privacy and security of the data.
By adopting the device, the display device 100 for the information of different versions of the product can realize the rapid and comprehensive display of the relationship among the versions of the product by the matching use of the acquisition module 101, the extraction and identification module 102, the filling module 103, the matching module 104 and the association display module 105.
In order to solve the technical problem, an embodiment of the present application further provides a computer device. Referring to fig. 5, fig. 5 is a block diagram of a basic structure of a computer device according to the present embodiment.
The computer device 4 comprises a memory 41, a processor 42, a network interface 43 communicatively connected to each other via a system bus. It is noted that only computer device 4 having components 41-43 is shown, but it is understood that not all of the shown components are required to be implemented, and that more or fewer components may be implemented instead. As will be understood by those skilled in the art, the computer device is a device capable of automatically performing numerical calculation and/or information processing according to a preset or stored instruction, and the hardware includes, but is not limited to, a microprocessor, an Application Specific Integrated Circuit (ASIC), a Programmable Gate Array (FPGA), a Digital Signal Processor (DSP), an embedded device, and the like.
The computer device can be a desktop computer, a notebook, a palm computer, a cloud server and other computing devices. The computer equipment can carry out man-machine interaction with a user through a keyboard, a mouse, a remote controller, a touch panel or voice control equipment and the like.
The memory 41 includes at least one type of readable storage medium including a flash memory, a hard disk, a multimedia card, a card type memory (e.g., SD or DX memory, etc.), a Random Access Memory (RAM), a Static Random Access Memory (SRAM), a Read Only Memory (ROM), an Electrically Erasable Programmable Read Only Memory (EEPROM), a Programmable Read Only Memory (PROM), a magnetic memory, a magnetic disk, an optical disk, etc. In some embodiments, the memory 41 may be an internal storage unit of the computer device 4, such as a hard disk or a memory of the computer device 4. In other embodiments, the memory 41 may also be an external storage device of the computer device 4, such as a plug-in 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 computer device 4. Of course, the memory 41 may also include both internal and external storage devices of the computer device 4. In this embodiment, the memory 41 is generally used for storing an operating system installed in the computer device 4 and various types of application software, such as computer readable instructions of a method for displaying different versions of information of a product. Further, the memory 41 may also be used to temporarily store various types of data that have been output or are to be output.
The processor 42 may be a Central Processing Unit (CPU), controller, microcontroller, microprocessor, or other data Processing chip in some embodiments. The processor 42 is typically used to control the overall operation of the computer device 4. In this embodiment, the processor 42 is configured to execute computer readable instructions stored in the memory 41 or process data, for example, execute computer readable instructions of a method for displaying different versions of information of the product.
The network interface 43 may comprise a wireless network interface or a wired network interface, and the network interface 43 is generally used for establishing communication connection between the computer device 4 and other electronic devices.
The present embodiment implements the steps of the method for displaying different versions of information of a product according to the above embodiments when a processor executes computer readable instructions stored in a memory, by acquiring data of each version of the product, extracting a document from the data of each version of the product, and identifying a keyword in the document; refining the document data in this manner; expanding all keywords of the same version to child nodes under nodes of the same version of a preset thinking map according to the attributes of the keywords, wherein the child nodes correspond to various father nodes under the nodes of the same version; and performing pairwise matching on keywords which belong to nodes of different versions but belong to similar father nodes by using an association model, calculating a matching value, realizing association matching between different versions, associating and displaying the matching value which meets two child nodes corresponding to preset conditions in the preset thinking guide graph, and associating and visualizing the two keywords which meet the requirements, thereby realizing rapid and comprehensive display of the relationship between the versions of the product.
The application provides another embodiment, which is to provide a computer-readable storage medium, where computer-readable instructions are stored, and the computer-readable instructions are executable by at least one processor, so that the at least one processor performs the steps of the method for displaying information of different versions of a product, as described above, by obtaining data of versions of the product, extracting a document from the data of the versions of the product, and identifying a keyword in the document; refining the document data in this manner; expanding all keywords of the same version to child nodes under nodes of the same version of a preset thinking map according to the attributes of the keywords, wherein the child nodes correspond to various father nodes under the nodes of the same version; and performing pairwise matching on keywords which belong to nodes of different versions but belong to similar father nodes by using an association model, calculating a matching value, realizing association matching between different versions, associating and displaying the matching value which meets two child nodes corresponding to preset conditions in the preset thinking guide graph, and associating and visualizing the two keywords which meet the requirements, thereby realizing rapid and comprehensive display of the relationship between the versions of the product.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solutions of the present application may be embodied in the form of a software product, which is stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk) and includes instructions for enabling a terminal device (such as a mobile phone, a computer, a server, an air conditioner, or a network device) to execute the method according to the embodiments of the present application.
It is to be understood that the above-described embodiments are merely illustrative of some, but not restrictive, of the broad invention, and that the appended drawings illustrate preferred embodiments of the invention and do not limit the scope of the invention. This application is capable of embodiments in many different forms and is provided for the purpose of enabling a thorough understanding of the disclosure of the application. Although the present application has been described in detail with reference to the foregoing embodiments, it will be apparent to one skilled in the art that the present application may be practiced without modification or with equivalents of some of the features described in the foregoing embodiments. All equivalent structures made by using the contents of the specification and the drawings of the present application are directly or indirectly applied to other related technical fields and are within the protection scope of the present application.
Claims (10)
1. A method for displaying different versions of information of a product is characterized by comprising the following steps:
acquiring data of each version of a product;
extracting documents from the data of each version of the product, and identifying keywords of the documents;
expanding all keywords of the same version to child nodes under nodes of the same version of a preset thinking guide graph according to the attributes of the keywords, wherein the child nodes correspond to various father nodes under the nodes of the same version;
matching every two child nodes belonging to nodes of different versions but belonging to similar father nodes by using a pre-trained association model, and calculating a matching value, wherein the association model is obtained by training historical keyword data;
and associating and displaying the two sub-nodes corresponding to the matching values meeting the preset conditions in the preset mind map.
2. The method for displaying different versions of information of a product according to claim 1, further comprising, before the obtaining data of each version of the product:
sending a calling request to a database, wherein the calling request carries a signature checking token;
receiving a signature checking result returned by the database, and calling data of each version of the product in the database when the signature checking result is passed;
the signature verification mode is an RSA asymmetric encryption mode.
3. The method for displaying different versions of information of a product according to claim 1, wherein the identifying the keywords of the document includes:
and identifying keywords in the document by using a keyword extraction model, wherein the keyword extraction model is obtained by training by using a historical word stock.
4. The method as claimed in claim 1, wherein before the expanding all the keywords of the same version into the sub-nodes under the node of the same version of the predetermined mind map, the method further comprises:
extracting each version number of the product as the version node;
setting four nodes of a demand target, a demand background, an optional scheme and a data flow as father nodes under the version nodes;
and constructing the preset mind map according to the version nodes and the father nodes.
5. The method as claimed in claim 4, wherein the expanding all the keywords of the same version to the child nodes under the node of the same version of the preset mind map according to the attributes of the keywords comprises:
classifying the keywords according to the attributes of the keywords to obtain requirement target keywords, requirement background keywords, optional keywords and data flow keywords;
expanding the requirement target class key words of the same version to child nodes under the requirement target father node; expanding the requirement background class key words of the same version to child nodes under the requirement background father nodes; expanding the optional scheme type key words of the same version to child nodes under the optional scheme parent node; and expanding the data flow class key words of the same version to child nodes under the data flow father node.
6. The method for displaying different versions of information of a product according to claim 1, wherein the using a pre-trained association model to match every two child nodes belonging to nodes of different versions but belonging to the same kind of parent nodes and calculating matching values comprises:
and calculating entropy values of the keywords through the association model, and determining matching values among the keywords according to the entropy values, wherein the association model is obtained based on RNN model training.
7. The method for displaying different versions of information of a product according to claim 1, wherein the associating and displaying two child nodes corresponding to the matching values satisfying a predetermined condition in the predetermined mind map includes:
comparing the match value to a first threshold;
connecting the two keywords with a solid line for presentation when the matching value is greater than a first threshold;
comparing the match value to a second threshold when the match value is less than a first threshold;
connecting the two keywords with a dotted line for presentation when the matching value is less than a first threshold and greater than the second threshold; wherein the first threshold is greater than the second threshold.
8. An apparatus for displaying different versions of information of a product, the apparatus comprising:
the acquisition module is used for acquiring data of each version of the product;
the extraction and identification module is used for extracting documents from the data of each version of the product and identifying keywords of the documents;
the filling module is used for expanding all keywords of the same version to child nodes under nodes of the same version of a preset thinking guide graph according to the attributes of the keywords, and the child nodes correspond to various father nodes under the nodes of the same version;
the matching module is used for pairwise matching each child node belonging to different version nodes but belonging to the same kind of father nodes by utilizing a pre-trained correlation model, and calculating a matching value, wherein the correlation model is obtained by training historical keyword data;
and the association display module is used for associating and displaying the two sub-nodes corresponding to the matching values meeting the preset conditions in the preset mind map.
9. A computer device, characterized in that the computer device comprises:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein,
the memory stores computer readable instructions, and the processor implements the method for displaying different versions of information of the product according to any one of claims 1 to 7 when executing the computer readable instructions.
10. A computer-readable storage medium, wherein the computer-readable storage medium stores thereon computer-readable instructions, and when executed by a processor, the computer-readable instructions implement the method for displaying different versions of information of a product according to any one of claims 1 to 7.
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