CN112632249A - Method and device for displaying different versions of information of product, computer equipment and medium - Google Patents

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

Method, device, computer equipment and medium for displaying information of different versions of products

technical field

The present application relates to the technical field of artificial intelligence, and in particular, to a method, apparatus, computer equipment and medium for displaying information of different versions of a product.

Background technique

In real life, there are many products, whether it is a hardware product or a software product, the product will be iteratively updated at regular intervals. With the continuous change of product versions, business personnel or other personnel continue to propose function construction or optimization. Aspects of the requirements, developers develop and implement according to the requirements. In the prior art, only the text description of each version stage is displayed, and there is no comprehensive display of product requirements and proposed backgrounds of different versions of products by version. The prior art cannot quickly understand the relevant information of each version and Links between versions. Therefore, how to quickly and comprehensively display the relationship between the various versions of the product has become an urgent problem to be solved.

SUMMARY OF THE INVENTION

The present application provides a method, device, computer equipment and storage medium for displaying information of different versions of a product to solve the problems in the prior art that the relationship between versions of a product cannot be displayed quickly and the displayed relationship is not comprehensive enough.

In order to solve the above problems, the present application provides methods for displaying information of different versions of products, including:

Obtain the data of each version of the product;

Extract documents from the data of each version of the product, and identify the keywords of the documents;

According to the attributes of the keywords, all keywords of the same version are expanded into the child nodes under the node of the same version of the preset mind map, and the child nodes are related to various parent nodes under the node of the same version. correspond;

Using a pre-trained association model, each child node belonging to a node of a different version but belonging to a parent node of the same type is matched in pairs and a matching value is calculated, and the association model is obtained by training historical keyword data;

The two child nodes corresponding to the matching value satisfying the preset condition are associated and displayed in the preset mind map.

Further, before obtaining the data of each version of the product, it also includes:

Send a call request to the database, where the call request carries a signature verification token;

Receive the signature verification result returned by the database, and when the signature verification result is passed, call the data of each version of the product in the database;

The signature verification method is an RSA asymmetric encryption method.

Further, the keywords for identifying the document include:

A keyword extraction model is used to identify the keywords in the document, and the keyword extraction model is trained using a historical thesaurus.

Further, before the expansion of all keywords of the same version to the sub-nodes under the node of the same version of the preset mind map, it also includes:

Extract each version number of the product as the version node;

Set four nodes of requirement target, requirement background, optional scheme and data flow as the parent node under the version node;

The preset mind map is constructed according to the version node and the parent node.

Further, according to the attributes of the keywords, all keywords of the same version are expanded into the child nodes under the node of the same version of the preset mind map, including:

Classify the keywords according to their attributes to obtain demand target keywords, demand background keywords, optional solution keywords and data process keywords;

Expand the requirement target class keyword of the same version into the child node under the requirement target parent node; expand the requirement background class keyword of the same version into the child node under the requirement background parent node; Expand the optional scheme class keyword of the same version to the child node under the optional scheme parent node; expand the data flow class keyword of the same version to the child node under the data flow parent node middle.

Further, the use of the pre-trained association model to perform pairwise matching on each child node belonging to a node of a different version but belonging to a parent node of the same type and calculate the matching value includes:

The entropy value of the keyword is calculated by the association model, and the matching value between the keywords is determined according to the entropy value, and the association model is obtained by training based on the RNN model.

Further, associating and displaying the two sub-nodes corresponding to the matching value satisfying the preset condition in the preset mind map includes:

comparing the matching value to a first threshold;

When the matching value is greater than the first threshold, use a solid line to connect the two keywords to display;

when the matching value is less than the first threshold, comparing the matching value with a second threshold;

When the matching value is less than the first threshold and greater than the second threshold, a dotted line is used to connect the two keywords to show; wherein the first threshold is greater than the second threshold.

In order to solve the above problems, the present application also provides a display device for different version information of a product, and the device includes:

The acquisition module is used to acquire the data of each version of the product;

An extraction and identification module, used for extracting documents from the data of each version of the product, and identifying the keywords of the documents;

The filling module is used to expand all the keywords of the same version to the sub-nodes under the same version node of the preset mind map according to the attributes of the keywords, and the sub-nodes are the same as the sub-nodes under the same version node. Corresponding to various parent nodes;

The matching module is used for using a pre-trained association model to perform pairwise matching on each child node belonging to a node of a different version but belonging to a parent node of the same type and calculate a matching value, the association model is obtained by training from historical keyword data;

The association display module is used for associating and displaying the two sub-nodes corresponding to the matching value satisfying the preset condition in the preset mind map.

In order to solve the above problems, the present application also provides a computer equipment, including:

at least one processor; and,

a memory communicatively coupled to the at least one processor; wherein,

The memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the presentation of information on different versions of a product as described above method.

In order to solve the above problems, the present application also provides a non-volatile computer-readable storage medium, where computer-readable instructions are stored on the computer-readable storage medium, and when the computer-readable instructions are executed by a processor, the The above-mentioned display method of different version information of the product.

Compared with the prior art, the method, device, computer equipment and medium for displaying information of different versions of products provided according to the embodiments of the present application have at least the following beneficial effects: by acquiring the data of each version of the product, from the data of each version of the product Extract the document, and identify the keywords in the document; extract the document data in this way; then, according to the attributes of the keywords, expand all keywords of the same version to the same version node of the preset mind map In the child nodes, the child nodes correspond to various parent nodes under the same version node; and the associated model is used to match the keywords belonging to different version nodes but belong to the same parent node and calculate The matching value is used to realize the association matching between different versions, and the two sub-nodes corresponding to the matching value satisfying the preset condition are associated and displayed in the preset mind map. Keywords are associated and visualized, so as to quickly and comprehensively display the relationship between the various versions of the product.

Description of drawings

In order to illustrate the solutions in the present application more clearly, the following will briefly introduce the accompanying drawings used in the description of the embodiments of the present application. For those of ordinary skill, other drawings can also be obtained from these drawings without any creative effort.

1 is a schematic flowchart of a method for displaying information of different versions of a product provided by an embodiment of the present application;

FIG. 2 is a preset mind map provided by an embodiment of the present application;

FIG. 3 is a related effect diagram of a mind map provided by an embodiment of the present application;

FIG. 4 is a schematic block diagram of a display device for displaying information of different versions of a product provided by an embodiment of the present application;

FIG. 5 is a schematic structural diagram of a computer device according to an embodiment of the present application.

Detailed ways

Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the technical field of this application; the terms used herein in the specification of the application are for the purpose of describing specific embodiments only It is not intended to limit the application; the terms "comprising" and "having" and any variations thereof in the description and claims of this application and the above description of the drawings are intended to cover non-exclusive inclusion. The terms "first", "second" and the like in the description and claims of the present application or the above drawings are used to distinguish different objects, rather than to describe a specific 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 present application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are they a separate or alternative embodiment that is mutually exclusive of other embodiments. It is explicitly or implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

The present application provides a method for displaying information of different versions of a product. Referring to FIG. 1 , it is a schematic flowchart of a method for displaying information of different versions of a product according to an embodiment of the present application.

In this embodiment, the methods for displaying information of different versions of products include:

S1. Obtain the data of each version of the product;

Specifically, obtain the data of each version of the product, including obtaining the requirement document and development document of each version, among which the above requirement document records the functional requirements and optimization requirements of the business personnel for the product, that is, the requirement target and requirement background, etc., and the development document It records the description of the alternatives and the data flow.

Further, before obtaining the data of each version of the product, it also includes:

Send a call request to the database, where the call request carries a signature verification token;

Receive the signature verification result returned by the database, and when the signature verification result is passed, call the data of each version of the product in the database;

The signature verification method is an RSA asymmetric encryption method.

Specifically, since each version of the data is confidential data, a signature verification step needs to be performed when calling each version of the data to the database. to verify that the user calling the data is the correct user or an internal user.

The data security of each version of the product is guaranteed through signature verification.

S2, extracting documents from the data of each version of the product, and identifying the keywords of the document;

Specifically, by extracting the requirements document and the development document in each version of the product, and extracting the keywords in the requirement document and the development document;

Further, the keywords for identifying the document include:

A keyword extraction model is used to identify the keywords in the document, and the keyword extraction model is trained using a historical thesaurus.

For example: in requirement 1, the process is to make a call for a scheduled task, identify the blacklist, and cancel the call;

The keywords are extracted by the keyword extraction model: timed tasks, phone calls, blacklists, and cancel-dial keywords.

In requirement 2, the process makes a call for a timed task;

Extracted through the keyword extraction model: timing tasks and calling keywords.

Specifically, the keyword extraction model first obtains N words according to a word segmentation algorithm, compares the N words with historical keywords respectively, and suggests words that are highly close to the historical keywords as keywords.

The keyword extraction model is obtained based on the TF-IDF model or the LDA model.

Accurate extraction of keywords is achieved by extracting keywords using a keyword extraction model.

S3. According to the attributes of the keywords, expand all keywords of the same version to the child nodes under the node of the same version of the preset mind map, and the child nodes are the same as various parent nodes under the node of the same version. corresponding to the node;

Specifically, the keywords of the unified version are filled into the sub-nodes under the node of the unified version, so as to realize the unified management of the keywords.

Further, before the expansion of all keywords of the same version to the sub-nodes under the node of the same version of the preset mind map, it also includes:

Extract each version number of the product as the version node;

Set four nodes of requirement target, requirement background, optional scheme and data flow as the parent node under the version node;

The preset mind map is constructed according to the version node and the parent node.

Specifically, the version number of each version of the product is extracted from the data of each version as the version node. In this embodiment, as shown in FIG. 2 , the preset mind map sets the requirement target, requirement background, optional The mind map of the four parent nodes of the scheme and data flow;

The four parent nodes of the requirement target, the requirement background, the optional solution and the data flow correspond to a version node, that is, the version 1 or version 2 node in the figure; in a complete preset mind map, there are multiple corresponding Version node, and each version node has four parent nodes: requirement target, requirement background, alternative solution, and data flow.

By presetting the mind map, it is convenient to directly fill in the keywords into the corresponding sub-nodes in the future, so as to improve the processing efficiency.

Still further, according to the attributes of the keywords, all keywords of the same version are expanded into the child nodes under the node of the same version of the preset mind map, including:

Classify the keywords according to their attributes to obtain demand target keywords, demand background keywords, optional solution keywords and data process keywords;

Expand the requirement target class keyword of the same version into the child node under the requirement target parent node; expand the requirement background class keyword of the same version into the child node under the requirement background parent node; Expand the optional scheme class keyword of the same version to the child node under the optional scheme parent node; expand the data flow class keyword of the same version to the child node under the data flow parent node middle.

Specifically, a preset database is used to classify the keywords, and the attributes of the words stored in each database are different. The optional solution word database is matched with the words in the data process word database. If it is successfully matched with the words in the demand target word database, then the keyword belongs to the demand target keyword; If the word matching is successful, then the keyword belongs to the demand background type keyword; if it matches the word in the optional scheme word database successfully, then the keyword belongs to the optional scheme category keyword; Similarly, If it is successfully matched with the words in the data process word database, then the keyword belongs to the data process type keyword.

Correspondingly, fill in different types of keywords into the child nodes under the corresponding parent node. For example, the data process type keywords will be expanded to the child nodes under the data process parent node, and the same is true for other types of keywords.

According to the attributes of the keywords, the keywords are classified and filled in the child nodes under the corresponding parent node, so as to realize a comprehensive display of the data of each version.

S4, using a pre-trained association model to perform pairwise matching on each child node belonging to a node of a different version but belonging to a parent node of the same type and calculate a matching value, the association model is obtained by training historical keyword data;

Further, the use of the pre-trained association model to perform pairwise matching on each child node belonging to a node of a different version but belonging to a parent node of the same type and calculate the matching value includes:

The entropy value of the keyword is calculated by the association model, and the matching value between the keywords is determined according to the entropy value, and the association model is obtained by training based on the RNN model.

Specifically, the association model calculates the entropy value of each keyword, and calculates the distance between the entropy values corresponding to any two of the keywords to obtain the similarity, that is, the matching value; Correlation model, the matching value of any two keywords can be obtained. The matching value of the two keywords may also be adjusted by adjusting the distance.

Traverse the entire keyword, and calculate the similarity between all keywords of different versions but belong to the same parent node, that is, the size of the matching value.

By using the association model to calculate the match value between two keywords, prepare for the subsequent association step.

S5. Associate and display the two sub-nodes corresponding to the matching value satisfying the preset condition in the preset mind map.

Specifically, the similarity between the two keywords, that is, the matching value, is calculated, and two keywords whose matching value is greater than a first threshold are associated. And according to the matching value between the two keywords, the solid line and the dotted line are used to connect the two feature words. When the network is finally formed, the matching value is distinguished by the difference between the solid line and the dotted line, which can more clearly reflect the difference between keywords. The relationship between the different versions of the product is reflected.

Further, associating and displaying the two sub-nodes corresponding to the matching value satisfying the preset condition in the preset mind map includes:

comparing the matching value to a first threshold;

When the matching value is greater than the first threshold, use a solid line to connect the two keywords to display;

when the matching value is less than the first threshold, comparing the matching value with a second threshold;

When the matching value is less than the first threshold and greater than the second threshold, a dotted line is used to connect the two keywords to show; wherein the first threshold is greater than the second threshold.

Specifically, the two keywords that meet the requirements are associated in the above manner, and the size of the association is differentiated by using the realization and the dotted line. When the matching value is less than the first threshold and greater than the second threshold, the two keywords are connected by a dotted line, and when the matching value is greater than the first threshold, the two keywords are connected by a solid line.

The keyword association step can also accept user instructions for manual association.

For example: the two ends connected by red arrows indicate that the two are related.

In other embodiments of the present application, any other distinguishable manner may also be used to associate two keywords that satisfy the preset conditions. For example, as shown in FIG. 3 , the degree of association may also be determined by different colors. Distinguish, for example, compare the matching value with a first threshold, and when the matching value is greater than the first threshold, use a red arrow to connect the two keywords to display;

When the matching value is less than the first threshold and greater than the second threshold, the two keywords are connected by blue arrows to show; wherein the first threshold is greater than the second threshold.

The above colors are not fixed, any two different colors can be used to distinguish, and the degree of association is not limited to two types, and multiple types can be set, and arrows of different colors can be used to distinguish. There may be various ways of distinguishing, and it is not limited to the above-mentioned ways of distinguishing between solid lines and dashed lines and different colors.

In other embodiments of the present application, the step of comparing the matching value with the second threshold may also be omitted, that is, only by judging the size of the matching value and the first threshold, when the matching value is greater than or equal to the first threshold When a threshold value is used, the two keywords are connected by a realization or a dotted line; when the matching value is less than the first threshold value, the two keywords are not processed.

The association is performed through the above-mentioned connection method, so as to realize the association operation between the data of each version and improve the association efficiency.

It should be emphasized that, in order to further ensure the privacy and security of the data, the data of each version of the product and the generated mind map data can also be stored in a node of a blockchain.

The blockchain referred to in this application is a new application mode of computer technologies such as distributed data storage, point-to-point transmission, consensus mechanism, and encryption algorithm. Blockchain, essentially a decentralized database, is a series of data blocks associated with cryptographic methods. Each data block contains a batch of network transaction information to verify its Validity of information (anti-counterfeiting) and generation of the next block. The blockchain can include the underlying platform of the blockchain, the platform product service layer, and the application service layer.

By acquiring the data of each version of the product, extracting documents from the data of each version of the product, and identifying the keywords in the document; extracting the document data in this way; then according to the attributes of the keywords, the same version of the All keywords are expanded to the child nodes under the node of the same version of the preset mind map, and the child nodes correspond to various parent nodes under the node of the same version; node, but the keywords of the same parent node are matched in pairs and the matching value is calculated to realize the association matching between different versions, and the matching values satisfy the two child nodes corresponding to the preset conditions, in the preset Set up the association and display in the mind map, associate and visualize the two keywords that meet the requirements, so as to realize the rapid and comprehensive display of the relationship between the various versions of the product.

As shown in FIG. 4 , it is a functional block diagram of a display device for different version information of the product of the present application.

The display apparatus 100 for different version information of the product described in this application can be installed in an electronic device. According to the realized functions, the display device 100 for different version information of a product may include an acquisition module 101 , an extraction and identification module 102 , a filling module 103 , a matching module 104 and an associated display module 105 . The modules described in the present invention can also be called units, which refer to a series of computer program segments that can be executed by the electronic device processor and can perform fixed functions, and are stored in the memory of the electronic device.

In this embodiment, the functions of each module/unit are as follows:

an acquisition module 101, used for acquiring data of each version of the product;

Specifically, the obtaining module 101 is used to obtain data of each version of the product, including obtaining the requirement document and development document of each version.

The display device 100 for different version information of the product further includes a sending request module and a calling module;

The sending request module is configured to send a calling request to the database, where the calling request carries a signature verification token;

The calling module is used to receive the signature verification result returned by the database, and when the signature verification result is passed, the data of each version of the product in the database is invoked;

The signature verification method is an RSA asymmetric encryption method.

Through the cooperative use of the sending request module and the calling module, the data security of each version of the product is guaranteed.

Extraction and identification module 102, for extracting documents from the data of each version of the product, and identifying the keywords of the document;

The extraction and identification module 102 is used to extract the requirement document and development document in each version of the product, and extract the keywords in the requirement document and the development document;

The extraction and identification module 102 includes a keyword extraction sub-module;

The keyword extraction sub-module is used for identifying keywords in the document by using a keyword extraction model, and the keyword extraction model is trained by using a historical lexicon.

Accurate extraction of keywords is achieved through the keyword extraction model.

The filling module 103 is used to expand all the keywords of the same version into the sub-nodes under the same version node of the preset mind map according to the attributes of the keywords, and the sub-nodes are the same as those under the same version node. Corresponding to various parent nodes of ;

The filling module 103 fills the keywords of the unified version into the sub-nodes under the node of the unified version, so as to realize the unified management of the keywords.

The display device 100 for different versions of products further includes a version number extraction module, a parent node setting module and a mind map building module;

The extracting version number module is used to extract each version number of the product as the version node;

The parent node setting module is used to set four nodes of demand target, demand background, optional scheme and data flow as the parent node under the version node;

The mind map building module is configured to construct the preset mind map according to the version node and the parent node.

Specifically, the version number extracting module extracts the version number of each version product from the version data as the version node.

Through the combined use of the version number extraction module, the parent node setting module and the mind map building module, it is convenient to directly fill in the keywords into the corresponding child nodes in the future, and the processing efficiency is improved.

The filling module 103 includes a classification submodule and an expansion submodule;

The classification submodule is used for classifying the keywords according to the attributes of the keywords, so as to obtain demand target keywords, demand background keywords, optional scheme keywords and data process keywords;

The expansion submodule is used to expand the requirement target class keywords of the same version into the child nodes under the requirement target parent node; expand the requirement background class keywords of the same version to the requirement background parent node. in the child nodes under the node; expand the optional scheme class keywords of the same version into the child nodes under the optional scheme parent node; expand the data flow category keywords of the same version into the data In the child node under the parent node of the process.

Specifically, the classification sub-module classifies the keywords according to a preset database, and the attributes of the words stored in each database are different.

The corresponding expansion sub-module fills in the keywords of different categories into the child nodes under the corresponding parent node.

Through the cooperation of the classification sub-module and the expansion sub-module, a comprehensive display of the data of each version is realized.

The matching module 104 is used for using a pre-trained association model to perform pairwise matching on each child node belonging to a node of a different version but belonging to a parent node of the same type and calculate a matching value, the association model is obtained by training historical keyword data;

The matching module 104 includes a calculation sub-module;

The calculation submodule is used to calculate the entropy value of the keyword through the association model, and determine the similarity between the keywords according to the entropy value, and the association model is obtained based on RNN model training;

Specifically, the calculation sub-module uses the association model to calculate the entropy value of each keyword, and obtains the similarity, that is, the matching value, by calculating the distance between the entropy values corresponding to any two of the keywords; By inputting keywords belonging to different versions but belonging to the same type of parent node into the association model, the matching value of any two keywords can be obtained.

Traverse the entire keyword, and calculate the similarity between all keywords of different versions but belong to the same parent node to obtain the matching value.

The matching value between the two keywords is calculated by the calculation sub-module to prepare for the subsequent association step.

The association display module 105 is used for associating and displaying the two sub-nodes corresponding to the matching value satisfying the preset condition in the preset mind map.

The association display module 105 obtains and calculates the similarity between the keywords, that is, the matching value, and associates the two keywords whose matching value is greater than the first threshold. And according to the matching value between the two keywords, the solid line and the dotted line are used to connect the two feature words. When the network is finally formed, the matching value is distinguished by the difference between the solid line and the dotted line, which can more clearly reflect the difference between keywords. The relationship between the different versions of the product is reflected.

The association display module 105 includes a first comparison sub-module, a first connection sub-module, a second comparison sub-module and a second connection sub-module;

a first comparison submodule for comparing the matching value with a first threshold;

The first connection sub-module is configured to use a solid line to connect the two keywords to display when the matching value is greater than the first threshold;

The second comparison submodule is configured to compare the matching value with the second threshold when the matching value is less than the first threshold;

The second connection sub-module is configured to use a dotted line to connect the two keywords for display when the matching value is less than the first threshold and greater than the second threshold; wherein the first threshold is greater than the second threshold.

Through the cooperation of the first comparison sub-module, the first connection sub-module, the second comparison sub-module and the second connection sub-module, the association operation between the data of each version is realized and the association efficiency is improved.

It should be emphasized that, in order to further ensure the privacy and security of the data, the data of each version of the product and the generated mind map data can also be stored in a node of a blockchain.

By adopting the above device, the display device 100 for the information of different versions of the product can realize the relationship between the versions of the product through the cooperation of the acquisition module 101, the extraction and identification module 102, the filling module 103, the matching module 104 and the associated display module 105. Make a quick and comprehensive presentation.

To solve the above technical problems, the embodiments of the present application also provide computer equipment. Please refer to FIG. 5 for details. FIG. 5 is a block diagram of a basic structure of a computer device according to this embodiment.

The computer device 4 includes a memory 41, a processor 42, and a network interface 43 that communicate with each other through a system bus. It should be noted that only the computer device 4 with components 41-43 is shown in the figure, but it should be understood that it is not required to implement all of the shown components, and more or less components may be implemented instead. Among them, those skilled in the art can understand that the computer device here is a device that can automatically perform numerical calculation and/or information processing according to pre-set or stored instructions, and its hardware includes but is not limited to microprocessors, special-purpose Integrated circuit (ApplicationSpecific Integrated Circuit, ASIC), programmable gate array (Field-Programmable GateArray, FPGA), digital processor (Digital Signal Processor, DSP), embedded equipment, etc.

The computer equipment may be a desktop computer, a notebook computer, a palmtop computer, a cloud server and other computing equipment. The computer device can perform human-computer interaction with the user through a keyboard, a mouse, a remote control, a touch pad or a voice control device.

The memory 41 includes at least one type of readable storage medium, and the readable storage medium includes flash memory, hard disk, multimedia card, card-type memory (for example, SD or DX memory, etc.), random access memory (RAM), static Random Access Memory (SRAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), Programmable Read Only Memory (PROM), Magnetic Memory, Magnetic Disk, 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 memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, flash memory card (FlashCard) and so on. Of course, the memory 41 may also include both the internal storage unit of the computer device 4 and its external storage device. In this embodiment, the memory 41 is generally used to store the operating system and various application software installed on the computer device 4 , such as computer-readable instructions of a method for displaying information of different versions of a product. In addition, the memory 41 can also be used to temporarily store various types of data that have been output or will be output.

The processor 42 may be a central processing unit (Central Processing Unit, CPU), a controller, a microcontroller, a microprocessor, or other data processing chips in some embodiments. This 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, computer-readable instructions for executing a method for displaying information of different versions of the product.

The network interface 43 may include a wireless network interface or a wired network interface, and the network interface 43 is generally used to establish a communication connection between the computer device 4 and other electronic devices.

In this embodiment, when the processor executes the computer-readable instructions stored in the memory, the steps of the method for displaying information of different versions of the product as in the above-mentioned embodiment are realized, and the data of each version of the product is obtained, and documents are extracted from the data of each version of the product, And identify the keywords in the document; extract the document data in this way; then according to the attributes of the keywords, expand all the keywords of the same version to the child nodes under the node of the same version of the preset mind map , the child nodes correspond to various parent nodes under the same version node; and the association model is used to match keywords belonging to different version nodes but belonging to the same type of parent node in pairs and calculate the matching value, to realize the association matching between different versions, and associate and display the two sub-nodes corresponding to the matching values satisfying the preset conditions in the preset mind map, and perform the matching of the two keywords that meet the requirements. Correlate and visualize, so as to achieve a quick and comprehensive display of the relationship between the various versions of the product.

The present application also provides another embodiment, that is, to provide a computer-readable storage medium, where the computer-readable storage medium stores computer-readable instructions, and the computer-readable instructions can be executed by at least one processor to causing the at least one processor to perform the steps of the above-mentioned method for displaying information of different versions of a product, by acquiring data of each version of the product, extracting documents from the data of each version of the product, and identifying keywords in the document ; Refine the document data in this way; Then according to the attributes of the keywords, expand all keywords of the same version to the child nodes under the node of the same version of the preset mind map, and the child nodes are the same as the The various parent nodes under the version node correspond to each other; and the association model is used to match the keywords belonging to different version nodes, but belong to the same parent node, and calculate the matching value to realize the association matching between different versions. The two sub-nodes corresponding to the matching value satisfying the preset conditions are associated and displayed in the preset mind map, and the two keywords that meet the requirements are associated and visualized, so as to realize the different versions of the product. A quick and comprehensive presentation of the relationship between them.

From the description of the above embodiments, those skilled in the art can clearly understand that the method of the above embodiment can be implemented by means of software plus a necessary general hardware platform, and of course can also be implemented by hardware, but in many cases the former is better implementation. Based on this understanding, the technical solution of the present application can be embodied in the form of a software product in essence or in a part that contributes to the prior art, and the computer software product is stored in a storage medium (such as ROM/RAM, magnetic disk, CD-ROM), including several instructions to make a terminal device (which may be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) execute the methods described in the various embodiments of this application.

Obviously, the above-described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. The accompanying drawings show the preferred embodiments of the present application, but do not limit the scope of the patent of the present application. This application may be embodied in many different forms, rather these embodiments are provided so that a thorough and complete understanding of the disclosure of this application is provided. Although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing specific embodiments, or perform equivalent replacements for some of the technical features. . Any equivalent structure made by using the contents of the description and drawings of the present application, which is directly or indirectly used in other related technical fields, is also within the scope of protection of the patent of the present application.

Claims (10)

1. a display method of product different version information, is characterized in that, described method comprises: Obtain the data of each version of the product; Extract documents from the data of each version of the product, and identify the keywords of the documents; According to the attributes of the keywords, all keywords of the same version are expanded into the child nodes under the node of the same version of the preset mind map, and the child nodes are related to various parent nodes under the node of the same version. correspond; Using a pre-trained association model, each child node belonging to a node of a different version but belonging to a parent node of the same type is matched in pairs and a matching value is calculated, and the association model is obtained by training historical keyword data; The two child nodes corresponding to the matching value satisfying the preset condition are associated and displayed in the preset mind map. 2. The method for displaying information of different versions of products according to claim 1, characterized in that, before said acquiring the data of each version of the product, further comprising: Send a call request to the database, where the call request carries a signature verification token; Receive the signature verification result returned by the database, and when the signature verification result is passed, call the data of each version of the product in the database; The signature verification method is an RSA asymmetric encryption method. 3. The method for displaying information of different versions of products according to claim 1, wherein the keywords for identifying the document include: A keyword extraction model is used to identify the keywords in the document, and the keyword extraction model is trained using a historical thesaurus. 4. the display method of product different version information according to claim 1, is characterized in that, before all keywords of the same version are expanded into the child nodes under the same version node of the preset mind map, Also includes: Extract each version number of the product as the version node; Set four nodes of requirement target, requirement background, optional scheme and data flow as the parent node under the version node; The preset mind map is constructed according to the version node and the parent node. 5. The method for displaying information of different versions of products according to claim 4, wherein, according to the attributes of the keywords, all keywords of the same version are expanded to the same version node of the preset mind map In the subnodes under , including: Classify the keywords according to their attributes to obtain demand target keywords, demand background keywords, optional solution keywords and data process keywords; Expand the requirement target class keyword of the same version into the child node under the requirement target parent node; expand the requirement background class keyword of the same version into the child node under the requirement background parent node; Expand the optional scheme class keyword of the same version to the child node under the optional scheme parent node; expand the data flow class keyword of the same version to the child node under the data flow parent node middle. 6. the display method of product different version information according to claim 1, is characterized in that, described utilizing pre-trained association model, to belong to different version nodes, but each child node that belongs to the same kind of parent node is carried out pairwise matching and Calculating matching values includes: The entropy value of the keyword is calculated by the association model, and the matching value between the keywords is determined according to the entropy value, and the association model is obtained by training based on the RNN model. 7. The method for displaying information of different versions of a product according to claim 1, characterized in that, the two sub-nodes corresponding to the matching value satisfying a preset condition are associated in the preset mind map And show includes: comparing the matching value to a first threshold; When the matching value is greater than the first threshold, use a solid line to connect the two keywords to display; when the matching value is less than the first threshold, comparing the matching value with a second threshold; When the matching value is less than the first threshold and greater than the second threshold, a dotted line is used to connect the two keywords to show; wherein the first threshold is greater than the second threshold. 8. A display device for information of different versions of a product, wherein the device comprises: The acquisition module is used to acquire the data of each version of the product; An extraction and identification module, used for extracting documents from the data of each version of the product, and identifying the keywords of the documents; The filling module is used to expand all the keywords of the same version to the sub-nodes under the same version node of the preset mind map according to the attributes of the keywords, and the sub-nodes are the same as the sub-nodes under the same version node. Corresponding to various parent nodes; The matching module is used for using a pre-trained association model to perform pairwise matching on each child node belonging to a node of a different version but belonging to a parent node of the same type and calculate a matching value, the association model is obtained by training from historical keyword data; The association display module is used for associating and displaying the two sub-nodes corresponding to the matching value satisfying the preset condition in the preset mind map. 9. A computer device, characterized in that the computer device comprises: at least one processor; and, a memory communicatively coupled to the at least one processor; wherein, The memory stores computer-readable instructions, and when the processor executes the computer-readable instructions, the method for displaying information of different versions of a product according to any one of claims 1 to 7 is implemented. 10. A computer-readable storage medium, wherein computer-readable instructions are stored on the computer-readable storage medium, and when the computer-readable instructions are executed by a processor, any one of claims 1 to 7 is implemented. The method for displaying the information of different versions of the product.

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