1. A data processing method for cognitive association ability training, the method comprising the steps of:

s1, pre-establishing a database, wherein the database comprises different types of original keywords, identifies the attributes of the original keywords, and puts the original keywords with the same attributes into the same vocabulary set to form a plurality of vocabulary sets; and

s2, randomly extracting an original keyword A1 and an original keyword A2 in the same vocabulary set, and collecting a plurality of associated words input by a user aiming at the original keyword A1 and the original keyword A2; and

s3, collecting a plurality of collected associated words input by the same user into an individual data set, collecting a plurality of collected associated words input by different users into a group data set, and analyzing the individual data set and the group data set based on a big data processing technology to obtain a vocabulary reasoning knowledge network; and

and S4, carrying out personalized content push and accurate teaching guidance on the user based on the vocabulary reasoning knowledge network.

2. The data processing method for cognitive competence training according to claim 1, wherein the step S2 specifically includes:

s21, extracting an original keyword A1 and an original keyword A2 from the vocabulary set through a computer random algorithm; and

s22, reserving an input box for filling the relevant words between the original keyword A1 and the original keyword A2 based on the html technology; and

s23, filling out relevant words forming reasonable association chains with the original keywords a1 and a2 in the input box by the user, and collecting the relevant word information input by the user.

3. The data processing method for cognitive ability training according to claim 1, wherein in step S1, based on the brain word sense map and the teaching situation requirement, a vocabulary is created in the database, the vocabulary includes unique Identification (ID), name and attributes, different types of original keywords are manually input into the vocabulary according to the attributes, and the original keywords with the same attributes form the same vocabulary set.

4. The data processing method for cognitive competence training according to claim 1, wherein the associated word in step S2 specifically includes: the similar meaning words, the synonyms, the hypernyms, the hyponyms and the expansion words, and the original keywords A1, the original keywords A2 and the plurality of the relevant words filled in the input box are mutually associated pairwise.

5. The data processing method for cognitive competence training according to claim 1, wherein in step S3, the lexical inference knowledge network is generated by obtaining deep scientific relationships between the lexical inference layer and the cognitive neural layer based on convolutional neural network analysis and using big data processing techniques to summarize, analyze, interpret and reconstruct the individual data set and the group data set, and further obtaining a tree structure diagram of the lexical hierarchical relationships from concrete to abstract.

6. The data processing method for cognitive competence training according to claim 2, wherein in step S22, the user is separated by character string separators between the associated words filled in the input box.

7. The data processing method for cognitive competence training according to claim 1, further comprising the steps of comparing the associated words of the lexical inference knowledge network with the original keywords of the database based on an application statistical algorithm, a comparison algorithm and a search algorithm, obtaining words that do not exist in the database and storing the words in the database, and continuously updating and expanding the database.

8. A data processing apparatus for cognitive association capability training, the apparatus comprising:

the system comprises a creating module, a searching module and a searching module, wherein the creating module is configured to pre-establish a database, the database comprises original keywords of different types, identifies the attributes of the original keywords, and puts the original keywords with the same attributes into the same vocabulary set to form a plurality of vocabulary sets; and

an acquisition module configured to randomly extract an original keyword A1 and an original keyword A2 in the same vocabulary set, and acquire a plurality of associated words input by a user for the original keyword A1 and the original keyword A2; and

the analysis module is configured to collect a plurality of collected associated words input by the same user into an individual data set, collect a plurality of collected associated words input by different users into a group data set, and analyze the individual data set and the group data set based on a big data processing technology to obtain a vocabulary reasoning knowledge network; and

a push module configured to perform personalized content push and precision teaching guidance for a user based on the vocabulary reasoning knowledge network.

9. The data processing apparatus for cognitive association ability training according to claim 8, wherein the obtaining module comprises:

an extraction module configured to extract original keywords a1 and original keywords a2 from the vocabulary set by a computer random algorithm; and

an input module configured to reserve an input box for filling out the associated word between the original keyword a1 and the original keyword a2 based on html technology; and

a collecting module configured to allow a user to fill out related words forming a reasonable association chain with the original keywords a1 and a2 in the input box in response to collecting related word information input by the user.

10. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the method according to any one of claims 1-7.

Background

The learning is finally the change of the brain, the cognitive neural mechanism for analyzing the learning of students from the brain science perspective is the biggest revolution of 21 st century education, and the induction of the neural education comes into birth. With the development of brain science and cognitive neurology, more and more learning cognitive neural mechanisms are disclosed, so that powerful support is provided for teacher teaching, and the brain cognitive neurology visual angle can be easily solved due to the problems which are difficult to solve in the traditional teaching. With the reform of college entrance examination, the importance of reading is getting bigger and bigger, and in the manual teaching mode, teachers generally emphasize the reading method repeatedly and do simple reading forms in the traditional classroom. However, the manual teaching mode is inefficient and difficult to be executed accurately, and in the reading process, it is often difficult to convert characters into image information, and many abstract and complex semantics are difficult to be converted into images quickly, so that a bottleneck of reading and understanding is formed, and the reading speed and the reading depth are affected.

On 27 th 4 th 2016, Nature journal published a cover article "Semantic information in natural speech information in complex maps of three-dimensional human Brain heart code", configured with "The Brain Dictionary" and depicted a "Brain vocabulary map", i.e., a scientist has drawn a map using Brain imaging technology to make it clear how 958 common English words and their meanings are represented in different areas of The Brain. Based on the research, the application provides a data processing method and device for cognitive association ability training, a word association technology is created, a plurality of words are filled between two words to form a reasonable association chain, and when a user completes the association chain, imagination of the user can be stimulated, the user can be helped to efficiently and deeply understand characters, and reading ability is improved.

Disclosure of Invention

The application aims to provide a data processing method and device for cognitive association ability training, which are used for innovating vocabulary association technology, collecting user interaction data and abstract learning data, performing structural analysis, pushing personalized contents and solving the problems in the background technology.

In a first aspect, an embodiment of the present application provides a data processing method for cognitive association ability training, where the method includes the following steps:

s1, a database is established in advance, the database comprises different types of original keywords, attributes of the original keywords are identified, and the original keywords with the same attributes are put into the same vocabulary set to form a plurality of vocabulary sets; and

s2, randomly extracting an original keyword A1 and an original keyword A2 in the same vocabulary set, and collecting a plurality of associated words input by a user aiming at the original keyword A1 and the original keyword A2; and

s3, collecting a plurality of collected associated words input by the same user into an individual data set, collecting a plurality of collected associated words input by different users into a group data set, and analyzing the individual data set and the group data set based on a big data processing technology to obtain a vocabulary reasoning knowledge network; and

and S4, carrying out personalized content push and accurate teaching guidance on the user based on the vocabulary reasoning knowledge network.

By the method, when the user fills in the associated words, different areas of the brain are activated, the connectivity between different brain areas is enhanced, the interconnection of different brain areas is realized in a thinking angle, the imagination of the user is stimulated, the user is helped to efficiently understand characters deeply, and the reading capability is improved.

In some embodiments, step S2 specifically includes:

s21, extracting an original keyword A1 and an original keyword A2 from the vocabulary set through a computer random algorithm; and

s22, reserving an input box for filling out relevant words between the original keywords A1 and the original keywords A2 based on the html technology; and

s23, filling out related words forming a reasonable association chain with the original keywords a1 and a2 in the input box by the user, and collecting related word information input by the user.

The two original keywords are extracted through a random algorithm, so that different brain areas of a user can be effectively activated, the cognitive learning interest of the user is enhanced, and associated words are filled in an input box, so that the method is simple and efficient, and is also suitable for users with smaller ages; and collecting user interaction data so as to carry out the next analysis processing operation.

In some embodiments, in step S1, based on the brain word sense map and the teaching context requirement, a vocabulary is created in the database, the vocabulary includes unique Identification (ID), name and attributes, different types of original keywords are manually entered into the vocabulary according to the attributes, and the original keywords with the same attributes form the same vocabulary set. The related original keywords close to the learning and living of the user can be input into the vocabulary table so as to obtain more accurate teaching guidance analysis and personalized content push in the following; the vocabulary sets are partitioned by attributes to efficiently provide services to users.

In some embodiments, the relevant word in step S2 specifically includes: the similar meaning words, the synonyms, the hypernyms, the hyponyms and the expansion words, and the original keywords A1, the original keywords A2 and the plurality of associated words filled in the input box are mutually associated in pairs.

In some embodiments, in step S3, the vocabulary inference knowledge network is generated based on convolutional neural network analysis and big data processing techniques to summarize, analyze, interpret and reconstruct the individual data sets and the group data sets to obtain deep scientific relationships between the vocabulary inference layers and the cognitive neural layers, and further to obtain a tree structure diagram of the vocabulary hierarchical relationships from concrete to abstract. According to the classification of the brain vocabulary map, or corresponding grade teaching outline, or other classification requirements, the individual data set and the group data set are analyzed and processed, and the obtained vocabulary reasoning knowledge network is more reasonable and scientific through the operation.

In some embodiments, in step S22, the user fills in the input box and separates the associated words with character string separators. The character string separators are used for separation, and the operation is simple and efficient.

In some embodiments, the method further comprises the following steps of comparing the associated words of the lexical inference knowledge network with the original keywords of the database based on the application of a statistical algorithm, a comparison algorithm and a search algorithm, obtaining words which do not exist in the database, storing the words into the database, and continuously updating and expanding the database. The database is gradually improved by continuously expanding the vocabulary of the original keywords, so that the field covered by the original keywords is relatively comprehensive.

In a second aspect, the present application provides a data processing apparatus for cognitive association ability training, the apparatus comprising:

the system comprises a creating module, a searching module and a searching module, wherein the creating module is configured to pre-establish a database, the database comprises different types of original keywords, identifies the attributes of the original keywords, and puts the original keywords with the same attributes into the same vocabulary set to form a plurality of vocabulary sets; and

the acquisition module is configured to randomly extract an original keyword A1 and an original keyword A2 in the same vocabulary set, and acquire a plurality of associated words input by a user aiming at the original keyword A1 and the original keyword A2; and

the analysis module is configured to collect a plurality of collected associated words input by the same user into an individual data set, collect a plurality of collected associated words input by different users into a group data set, and analyze the individual data set and the group data based on a big data processing technology to obtain a vocabulary reasoning knowledge network; and

a push module configured to perform personalized content push and precise teaching guidance for a user based on the vocabulary reasoning knowledge network.

In some embodiments, the obtaining module comprises:

an extraction module configured to extract an original keyword a1 and an original keyword a2 from the vocabulary set by a computer random algorithm; and

an input module configured to reserve an input box for filling out a relevant word between the original keyword a1 and the original keyword a2 based on html technology; and

and the acquisition module is configured to enable the user to fill out relevant words forming reasonable association chains with the original keywords A1 and A2 in the input boxes, and respond to the acquired relevant word information input by the user.

The combination of a plurality of modules in the acquisition module can improve the operation speed and more efficiently acquire user interaction data, so that the personalized content and the guidance analysis which are finally pushed to the user based on the vocabulary reasoning knowledge network are more accurate.

In a third aspect, the present application provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, performs the method of any of the first aspects described above.

The data processing method and device for cognitive association ability training provided by the embodiment of the application have the following advantages: (1) based on brain semantic maps and teaching situation requirements, forming vocabulary sets from some original keywords, randomly extracting 2 original keywords from a certain vocabulary set, filling a plurality of other different words and forming a reasonable associated chain with the original keywords in the middle, activating different brain areas when a user completes the associated chain, enhancing the connectivity among the different brain areas, and realizing the interconnection of the different brain areas in a thinking angle, thereby exciting the imagination of the user, helping the user to efficiently and deeply understand characters and improving the reading capability; (2) and collecting a data set formed by user interaction, re-analyzing and integrating according to the difference of the repeatability and the part of speech of the word frequency, constructing an individual data set and a group data set, and pushing personalized learning materials and carrying out accurate teaching analysis and guidance on the basis of the data set.

Drawings

Other features, objects and advantages of the present application will become more apparent upon reading of the following detailed description of non-limiting embodiments thereof, made with reference to the accompanying drawings in which:

FIG. 1 is an exemplary system architecture diagram in which one embodiment of the present invention may be applied;

FIG. 2 is an exemplary basic flowchart in a data processing method for cognitive associative ability training, according to an embodiment of the present invention;

FIG. 3 is a flow chart of collecting interactive data in a data processing method for cognitive relevance ability training according to an embodiment of the invention;

FIG. 4 is a schematic diagram of interaction data collected in a data processing method for cognitive relevance training according to an embodiment of the invention;

FIG. 5 is a schematic structural diagram of a data processing apparatus for cognitive association ability training according to an embodiment of the present invention;

FIG. 6 is a schematic structural diagram of an acquisition module of a data processing apparatus for cognitive relevance ability training according to an embodiment of the present invention;

FIG. 7 is a schematic block diagram of a computer system suitable for use with the electronic device to implement an embodiment of the invention.

Detailed Description

The present application will be described in further detail with reference to the following drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the relevant invention and not restrictive of the invention. It should be noted that, for convenience of description, only the portions related to the related invention are shown in the drawings.

It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present application will be described in detail below with reference to the embodiments with reference to the attached drawings.

Fig. 1 illustrates an exemplary system architecture 100 to which a method for generating a file or an apparatus for generating a file of embodiments of the present application may be applied.

As shown in fig. 1, the system architecture 100 may include terminal devices 101, 102, 103, a network 104, and a server 105. The network 104 serves as a medium for providing communication links between the terminal devices 101, 102, 103 and the server 105. Network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.

The user may use the terminal devices 101, 102, 103 to interact with the server 105 via the network 104 to receive or send messages or the like. Various applications, such as data processing type applications, file processing type applications, etc., may be installed on the terminal apparatuses 101, 102, 103.

The terminal apparatuses 101, 102, and 103 may be hardware or software. When the terminal devices 101, 102, 103 are hardware, they may be various electronic devices including, but not limited to, smart phones, tablet computers, laptop portable computers, desktop computers, and the like. When the terminal apparatuses 101, 102, 103 are software, they can be installed in the electronic apparatuses listed above. It may be implemented as multiple pieces of software or software modules (e.g., software or software modules used to provide distributed services) or as a single piece of software or software module. And is not particularly limited herein.

The server 105 may be a server that provides various services, such as a background data processing server that processes files or data uploaded by the terminal devices 101, 102, 103. The background data processing server can process the acquired files or data to generate processing results (for example, processing an individual data set and a group data set to obtain a vocabulary reasoning knowledge network, and performing personalized content push and accurate teaching guidance on a user based on the vocabulary reasoning knowledge network).

It should be noted that the method for generating the file provided in the embodiment of the present application may be executed by the server 105, or may be executed by the terminal devices 101, 102, and 103, and accordingly, the apparatus for generating the file may be disposed in the server 105, or may be disposed in the terminal devices 101, 102, and 103.

It should be understood that the number of terminal devices, networks, and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation. In the case where the processed data does not need to be acquired from a remote location, the system architecture described above may not include a network, but only a server or a terminal device.

FIG. 2 illustrates an exemplary basic flow diagram of a data processing method for cognitive relevance ability training according to the present invention. As shown in fig. 2, the basic process includes: step 210, a database is pre-established, the database includes different types of original keywords, the attributes of the original keywords are identified, and the original keywords with the same attributes are placed into the same vocabulary set to form a plurality of vocabulary sets, such as a vocabulary set a, a vocabulary set B, a vocabulary set C, and the like.

The Brain vocabulary map, depicted by The Brain Dictionary, divides The vocabulary into 12 different types: (tactile ("finger"), visual ("yellow"), digital ("four"), location ("gym"), abstract ("natural"), time ("minute"), work ("meeting"), violence ("dead"), public facilities ("school"), mental ("sleep"), emotional ("sad"), and social relationships ("child").

Based on the brain vocabulary map and teaching context requirements described above, in a specific embodiment, different types of raw keywords are manually entered into a vocabulary that is stored in a database and that also includes unique Identifications (IDs) and names. Preferably, the specific original keywords include: words with specific situations, words according with the actual life of students and words related to subject study. For example, if words conforming to the actual life of a student are set as a word set a and words related to subject learning are set as a word set B, the pencil box a1 and the schoolbag a2 should be classified into the word set a, and the electric field B1 and the living being B2 should be classified into the word set B.

Fig. 3 is a flowchart illustrating a method for collecting user interaction data in a data processing method for cognitive relevance training according to the present invention, and with reference to fig. 2 and fig. 3, the method further includes: step 220, randomly extracting the original keyword a1 and the original keyword a2 in the same vocabulary set, and collecting a plurality of associated words input by the user aiming at the original keyword a1 and the original keyword a 2. The method specifically comprises the steps 221, extracting 2 original keywords from the same vocabulary set through a computer random algorithm, for example, randomly extracting the original keyword A1 and the original keyword A2 from the vocabulary set A; step 222, reserving an input box for filling out text information between the original keyword A1 and the original keyword A2 based on the html technology; in step 223, relevant words forming a reasonable association chain with the original keywords a1 and a2 are filled in the input box by the user, and relevant word information input by the user is collected. Specifically, in the actual operation process, after the user runs software to obtain the original keyword a1 and the original keyword a2, the user moves the mouse to click the input box so that the input box is in a trigger state, at least one associated word is filled in 2 original keywords, and text information input by the user is collected. Preferably, the related words specifically include similar words, synonyms, hypernyms, hyponyms and expansion words of the original keywords, and the filled related words are in a pairwise correlation relationship with the original keywords a1 and the original keywords a2 to form a reasonable correlation chain.

In a specific embodiment, the original keyword a1 is used as the first word, the original keyword a2 is used as the last word, and the user sequentially inputs 3 words, X, Y, Z respectively, between the original keyword a1 and the original keyword a2, and the inputted words satisfy the following relations: there is a logical link between the first word a1 and the second word X, a logical link between the second word X and the third word Y, a logical link between the third word Y and the fourth word Z, and a logical link between the fourth word Z and the last word a 2.

FIG. 4 is a schematic diagram illustrating interactive data collected in a data processing method for cognitive relevance training, as shown in FIG. 4, a computer extracts words "clothes" and "earth" according to a random algorithm, and a user can construct reasonable context links: the clothes are made of cotton, cotton cloth is associated with the clothes, cotton is associated with the cotton cloth, and the cotton is planted in the ground which belongs to the earth on the earth. The user can fill three words of 'cotton cloth', 'cotton' and 'land' in the input box, the three words are separated by a character string separator '-' and can also be separated by other character string separators, such as ','; ". When the user fills in the three relevant words, different brain areas are activated, the connectivity among the different brain areas is enhanced, the interconnection of the different brain areas is realized in a thinking angle, the imagination of the user is further stimulated, the user is helped to efficiently understand characters deeply, and the reading capability is improved.

With continued reference to fig. 2, the method further includes step 230 of summarizing the collected multiple associated words input by the same user into an individual data set, summarizing the collected multiple associated words input by different users into a group data set, and analyzing the individual data set and the group data set based on a big data processing technology to obtain a vocabulary reasoning knowledge network. And (3) carrying out induction, analysis, interpretation and reconstruction on the individual data set and the group data set by utilizing a big data processing technology. Specifically, associated vocabulary feature analysis is performed based on a convolutional neural network to obtain a deep scientific relationship between a vocabulary inference layer and a cognitive neural layer, i.e., a rule is found between an individual/group data set and the cognitive neural network. The convolutional neural network is a feedforward neural network and is formed by combining a convolutional layer and a downsampling layer in an additive mode, and the output of each layer is the input of the next layer. The convolutional layer is used as a feature extraction layer, and local features are extracted through a filter. And the down-sampling layer belongs to the feature mapping layer, samples the feature map generated by the convolutional layer and outputs the local optimal associated vocabulary features. Applying the emotion polarity and attribute identification of the original keyword to the representation of the associated vocabulary features, for example, knowing that the association degree of the original keyword and part of the vocabulary is high in advance, classifying the associated vocabulary types according to the associated vocabulary features, and outputting classification results such as emotion types, scientific types and creative types by using a Softmax algorithm. The related vocabulary and the psychological characteristics displayed by students can be fused, and the characteristics of excellent related vocabulary, special talents, special interests, even the related vocabulary characteristics of autism, depression and the like can be found; the associated vocabulary can be fused with a brain cognitive imaging technology, and the brain cognitive nerve function relation related to the associated vocabulary can be verified.

And acquiring a vocabulary hierarchical relation from concrete to abstract on the basis of the associated vocabulary characteristics, such as: butterfly-insect-animal-biology, thus obtaining a tree structure diagram and generating a vocabulary reasoning knowledge network. According to the classification of the brain vocabulary map, or corresponding grade teaching outline, or other classification requirements, the individual data set and the group data set are analyzed and processed, and the obtained vocabulary reasoning knowledge network is more reasonable and scientific through the operation. And the associated words can become a technology for training cognition of students and improving cognition of the students in education and teaching, and become an important technology for predicting cognitive learning tendency. In a specific embodiment, based on an application statistical algorithm, a comparison algorithm and a search algorithm, relevant words of the vocabulary reasoning knowledge network are compared with original keywords of a database, words which do not exist in the database are obtained and stored in the database, and the database is continuously updated and expanded. The database is gradually improved by continuously expanding the vocabulary of the original keywords, so that the field covered by the original keywords is relatively comprehensive.

The method further includes a step 240 of performing personalized content push and precise teaching guidance for the user based on the vocabulary reasoning knowledge network.

Specifically, the word frequency and the preference of the relevant words filled in the input box by the same user or a plurality of users are analyzed based on the vocabulary reasoning knowledge network. And in the teaching process, accurate analysis and accurate guidance can be performed according to the analysis result. For example: if the word frequency of the user 1 is less and color vocabularies appear, the user 1 can be guided to think about the related vocabularies of the color classes in the teaching process, and the activity of the color brain area can be stimulated more; the word frequency of the user 2 has more violent, pessimistic, black, melancholic and other words, and the data can be used for deducing that the user 2 has related psychological problems, so that the user 2 can be timely and effectively concerned.

Fig. 5 is a schematic structural diagram of a data processing apparatus for cognitive association capability training according to an embodiment of the present invention, as an implementation of the method shown in the above figures, the apparatus embodiment corresponds to the method embodiment shown in fig. 2, and the apparatus may be applied to various electronic devices in particular. As shown in fig. 5, the apparatus 500 includes a creation module 510, an acquisition module 520, an analysis module 530, and a push module 540. The creating module 510 is configured to pre-establish a database, where the database includes different types of original keywords, identifies attributes of the original keywords, and places the original keywords with the same attributes into the same vocabulary set to form a plurality of vocabulary sets; the obtaining module 520 is configured to randomly extract an original keyword a1 and an original keyword a2 in the same vocabulary set, and collect a plurality of associated words input by a user for the original keyword a1 and the original keyword a 2; the analysis module 530 is configured to collect a plurality of collected associated words input by the same user into an individual data set, collect a plurality of collected associated words input by different users into a group data set, and analyze the individual data set and the group data based on a big data processing technology to obtain a vocabulary reasoning knowledge network; the push module 540 is configured to perform personalized content push and precise tutoring for the user based on the lexical reasoning knowledge network.

Fig. 6 is a schematic structural diagram of an acquisition module of a data processing apparatus for cognitive relevance ability training according to an embodiment of the present invention, as an implementation of the method shown in the above figures, the apparatus embodiment corresponds to the method embodiment shown in fig. 3, and the apparatus may be applied to various electronic devices in particular. As shown in fig. 6, the obtaining module 520 includes an extracting module 521, an inputting module 522 and an acquiring module 523. Wherein the extraction module 521 is configured to extract the original keyword a1 and the original keyword a2 from the vocabulary set by a computer random algorithm; the input module 522 is configured to reserve an input box for filling out relevant words between the original keyword a1 and the original keyword a2 based on html technology; the collecting module 523 is configured to allow the user to fill out relevant words forming a reasonable association chain with the original keyword a1 and the original keyword a2 in the input box in response to collecting the relevant word information input by the user. The combination of multiple modules in the acquisition module 520 can improve the operation speed and more efficiently collect user interaction data, so that the personalized content and guidance analysis pushed to the user based on the vocabulary reasoning knowledge network are more accurate.

Referring now to FIG. 7, a block diagram of a computer system 600 suitable for use with an electronic device (e.g., the server or terminal device shown in FIG. 1) to implement an embodiment of the invention is shown. The electronic device shown in fig. 7 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present application.

As shown in fig. 7, the computer system 600 includes a Central Processing Unit (CPU)601 that can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM)602 or a program loaded from a storage section 608 into a Random Access Memory (RAM) 603. In the RAM 603, various programs and data necessary for the operation of the system 600 are also stored. The CPU 601, ROM 602, and RAM 603 are connected to each other via a bus 604. An input/output (I/O) interface 605 is also connected to bus 604.

The following components are connected to the I/O interface 605: an input portion 606 including a keyboard, a mouse, and the like; an output portion 607 including a signal such as a Liquid Crystal Display (LCD) and a speaker; a storage section 608 including a hard disk and the like; and a communication section 609 including a network interface card such as a LAN card, a modem, or the like. The communication section 609 performs communication processing via a network such as the internet. The driver 610 may also be connected to the I/O interface 605 as needed. A removable medium 611 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 610 as necessary, so that a computer program read out therefrom is mounted in the storage section 608 as necessary.

In particular, according to an embodiment of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network through the communication section 609, and/or installed from the removable medium 611. The computer program performs the above-described functions defined in the method of the present application when executed by a Central Processing Unit (CPU) 601.

It should be noted that the computer readable medium described herein can be a computer readable signal medium or a computer readable medium or any combination of the two. A computer readable medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present application, a computer readable medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In this application, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.

Computer program code for carrying out operations for aspects of the present application may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).

The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

The modules described in the embodiments of the present application may be implemented by software or hardware. The described modules may also be provided in a processor, which may be described as: a processor includes an acquisition module, a determination module, and a determination module. The names of these modules do not in some cases constitute a limitation on the module itself, and for example, the acquiring module may also be described as a "module that acquires a target file including a plurality of rows and a plurality of columns".

As another aspect, the present application also provides a computer-readable medium, which may be contained in the electronic device described in the above embodiments; or may exist separately without being assembled into the electronic device. The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to: acquiring a target file comprising a plurality of rows and a plurality of columns; determining at least one title line based on the plurality of lines, wherein the title line corresponds to a segment included in the target file, and the title line includes at least one title item; for each title line in at least one title line, determining the data type of the segment corresponding to the title line based on the title items included in the title line; acquiring a preset title library corresponding to the determined data type; matching the title line with the obtained title library; and generating a standard file which corresponds to the header line and contains the data contained in the standard header line and the corresponding segment based on the matching result.

The above description is only a preferred embodiment of the application and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the invention herein disclosed is not limited to the particular combination of features described above, but also encompasses other arrangements formed by any combination of the above features or their equivalents without departing from the spirit of the invention. For example, the above features may be replaced with (but not limited to) features having similar functions disclosed in the present application.