CN116401301A

CN116401301A - Information input method and device based on artificial intelligence and related equipment

Info

Publication number: CN116401301A
Application number: CN202310377795.7A
Authority: CN
Inventors: 周航; 王辉; 王炳权; 邹昊; 符尊群; 蔡禺
Original assignee: Shenzhen Pingan Integrated Financial Services Co ltd
Current assignee: Shenzhen Pingan Integrated Financial Services Co ltd
Priority date: 2023-04-06
Filing date: 2023-04-06
Publication date: 2023-07-07

Abstract

The application relates to an artificial intelligence technology, and provides an information input method, an information input device, a computer device and a storage medium based on artificial intelligence, which comprise the following steps: when receiving a supervision request, analyzing the supervision request to obtain a plurality of target text item contents; determining the importance degree of the target text item contents, and determining the combination sequence of the target text item contents according to the descending sequence of the importance degree; combining the contents of the target text items according to the combination sequence to obtain an initial supervision text; determining a target format of the initial supervision text, and adjusting the initial supervision text according to the target format to obtain a target supervision text; analyzing the target supervision text to obtain the content of the information item to be input; determining a supervision type and an information input preference corresponding to the target supervision text; and inputting the content of the information item to be input into a preset system according to the information input preference. The intelligent city intelligent management system can improve information input efficiency and promote rapid development of intelligent cities.

Description

Information input method and device based on artificial intelligence and related equipment

Technical Field

The present application relates to the field of artificial intelligence technologies, and in particular, to an information input method, device, computer device, and storage medium based on artificial intelligence.

Background

The notification, delivery and storage management of the supervision information is the key content of daily compliance management work of financial enterprises. Especially for large-scale group financial enterprises, a plurality of financial branches can receive tens of types of supervision information such as inspection, notification, assistance in checking frozen buckles, supervision functions and the like, and the quantity of the supervision information is huge and rapidly growing. The supervision information needs to be integrated and refined and is input into a supervision information platform corresponding to the enterprise.

In carrying out the present application, the applicant has found that the prior art has the following problems: in the traditional supervision information input flow, relevant responsible personnel of each institution conduct reading arrangement, refining processing, input uploading of supervision information every day, 30 minutes are consumed for each supervision information, each branch institution spends about 1 manpower full time for supervision information input, and information input efficiency is low.

Therefore, it is necessary to provide an information input method capable of improving the efficiency of information input.

Disclosure of Invention

In view of the foregoing, there is a need for an information input method based on artificial intelligence, an information input device based on artificial intelligence, a computer device, and a storage medium, which can improve the efficiency of information input.

An embodiment of the present application provides an information input method based on artificial intelligence, where the information input method based on artificial intelligence includes:

when receiving a supervision request, analyzing the supervision request to obtain a plurality of target text item contents;

determining the importance degree of each target text item content, and determining the combination sequence of the target text item content according to the descending sequence of the importance degrees;

combining the contents of the target text items according to the combination sequence to obtain an initial supervision text;

determining a target format of the initial supervision text, and adjusting the initial supervision text according to the target format to obtain a target supervision text;

analyzing the target supervision text to obtain the content of the information item to be input;

determining a supervision type corresponding to the target supervision text and an information input preference corresponding to the supervision type;

and inputting the content of the information item to be input into a preset system according to the information input preference.

Further, in the above information input method based on artificial intelligence provided in the embodiment of the present application, the determining the importance level of each target text item content includes:

Acquiring a target text item corresponding to each target text item content;

acquiring a plurality of historical text item contents corresponding to each target text item to form a historical text item content set;

determining a preset history input information item identifier corresponding to each history text item content in the history text item content set, and calculating the number of the preset history input information item identifiers to obtain the input number corresponding to each history text item content;

calculating the target entry quantity corresponding to each target text item according to a plurality of entry quantities;

and determining the importance degree of the content of each target text item according to the target input quantity.

Further, in the above information input method based on artificial intelligence provided in the embodiment of the present application, the adjusting the initial supervision text according to the target format to obtain a target supervision text includes:

determining an initial format corresponding to each target text item content to obtain an initial format set;

clustering a plurality of initial formats in the initial format set to obtain a plurality of format cluster clusters;

determining that each format cluster corresponds to a pre-trained format conversion model;

And calling the format conversion model to adjust the initial format corresponding to each target text item content in the format cluster to a target format, so as to obtain a target supervision text.

Further, in the above information input method based on artificial intelligence provided in the embodiment of the present application, the analyzing the target supervision text to obtain the content of the information item to be input includes:

acquiring preset information item keywords, and determining target positions of the preset information item keywords in the target supervision text;

acquiring a preset data format between the preset information item keywords and the information item content in the target supervision text;

and selecting the content at the target position as the content of the information item to be input according to the preset data format.

Further, in the above information input method based on artificial intelligence provided in the embodiment of the present application, the determining the supervision type corresponding to the target supervision text includes:

determining a pre-trained supervision type determination model;

and calling the supervision type determining model to process the target supervision text to obtain the supervision type corresponding to the target supervision text.

Further, in the above information input method based on artificial intelligence provided in the embodiment of the present application, the determining the information input preference corresponding to the supervision type includes:

Determining a mapping relation between a preset supervision type and information input preference;

and traversing the mapping relation to obtain the information input preference corresponding to the supervision type.

Further, in the above information input method based on artificial intelligence provided in the embodiment of the present application, the inputting the content of the information item to be input into a preset system according to the information input preference includes:

determining the component type and the component position corresponding to the information item to be input according to the information input preference;

constructing a target component at the component position in the preset system according to the component type;

and inputting the content of the information item to be input to the target component.

The second aspect of the embodiment of the application also provides an information input device based on artificial intelligence, which comprises:

the request analysis module is used for analyzing the supervision request to obtain a plurality of target text item contents when receiving the supervision request;

the order determining module is used for determining the importance degree of each target text item content and determining the combination order of the target text item content according to the descending order of the importance degrees;

The sequence combination module is used for combining the contents of the target text items according to the combination sequence to obtain an initial supervision text;

the text adjustment module is used for determining a target format of the initial supervision text and adjusting the initial supervision text according to the target format to obtain a target supervision text;

the text analysis module is used for analyzing the target supervision text to obtain the content of the information item to be input;

the type determining module is used for determining a supervision type corresponding to the target supervision text and information input preference corresponding to the supervision type;

and the information input module is used for inputting the content of the information item to be input into a preset system according to the information input preference.

A third aspect of the embodiments of the present application further provides a computer device, the computer device including a processor configured to implement the artificial intelligence based information entry method as described in any one of the above when executing a computer program stored in a memory.

The fourth aspect of the embodiments of the present application further provides a computer readable storage medium, where a computer program is stored, where the computer program is executed by a processor to implement any one of the above-mentioned information input methods based on artificial intelligence.

According to the information input method based on the artificial intelligence, the information input device based on the artificial intelligence, the computer equipment and the computer readable storage medium, the information item content to be input is obtained by analyzing the target supervision text, the information input preference corresponding to the target supervision text is determined, the information item content to be input is input into the preset system according to the information input preference, and the information input efficiency can be improved; according to the method and the device for obtaining the information item content, the initial supervision text is obtained through the importance degree of each target text item content and the combination sequence of the target text item content according to the descending sequence of the importance degrees, and then when the information item content to be input is determined, supervision texts with high importance degrees can be traversed preferentially, the information item content to be input can be found out rapidly, the problem of long query time caused by traversing the whole initial supervision text is avoided, and the information input efficiency is further improved. The intelligent city intelligent management system can be applied to various functional modules of intelligent cities such as intelligent government affairs and intelligent traffic, for example, an information input module of the intelligent city and the like, and can promote the rapid development of the intelligent city.

Drawings

Fig. 1 is a flowchart of an information input method based on artificial intelligence according to an embodiment of the present application.

Fig. 2 is a flowchart for determining importance level according to an embodiment of the present application.

FIG. 3 is a flow chart of determining target supervision text provided in an embodiment of the present application.

Fig. 4 is a flowchart for determining the content of an information item to be entered according to an embodiment of the present application.

Fig. 5 is a block diagram of an information input device based on artificial intelligence according to an embodiment of the present application.

Fig. 6 is a schematic structural diagram of a computer device according to an embodiment of the present application.

The following detailed description will further illustrate the application in conjunction with the above-described figures.

Detailed Description

In order that the above-recited objects, features and advantages of the present application will be more clearly understood, a more particular description of the application will be rendered by reference to specific embodiments thereof which are illustrated in the appended drawings. It should be noted that, in the case of no conflict, the embodiments of the present application and the features in the embodiments may be combined with each other.

In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present application, the described embodiments are some, but not all, of the embodiments of the present application.

Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein in the description of the application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application.

The embodiment of the application can acquire and process the related data based on the artificial intelligence technology. Among these, artificial intelligence (Artificial Intelligence, AI) is the theory, method, technique and application system that uses a digital computer or a digital computer-controlled machine to simulate, extend and extend human intelligence, sense the environment, acquire knowledge and use knowledge to obtain optimal results.

Artificial intelligence infrastructure technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technologies, operation/interaction systems, mechatronics, and the like. The artificial intelligence software technology mainly comprises a computer vision technology, a robot technology, a biological recognition technology, a voice processing technology, a natural language processing technology, machine learning/deep learning and other directions.

The information input method based on artificial intelligence provided by the embodiment of the invention is executed by the computer equipment, and correspondingly, the information input device is operated in the computer equipment. Fig. 1 is a flowchart of an information input method based on artificial intelligence according to an embodiment of the present application. As shown in fig. 1, the information input method based on artificial intelligence may include the following steps, the order of the steps in the flowchart may be changed according to different requirements, and some may be omitted:

And S11, when receiving the supervision request, analyzing the supervision request to obtain a plurality of target text item contents.

In at least one embodiment of the present application, the supervision request refers to a request sent by a supervision authority to assist in performing a relevant supervision task, for example, the supervision request may be that the supervision authority sends a supervision task requesting that a financial institution assist in performing a freeze deduction, a supervision labor arbitration, a supervision system complaint, and the like. The supervision request may be output in the form of a sms, mail, etc., which is not limited herein. The content of the target text item refers to the content of the target text item carried by the supervision request, and the number of the target text items can be 1 or more. Taking the output of the supervision request in a mail form as an example, the target text item may be a mail title, a mail body and a mail attachment, and the target text item content refers to the content of the mail title, the mail body and the mail attachment.

Optionally, the parsing the supervision request to obtain a plurality of target text item contents includes:

s111, determining text item keywords contained in the supervision request;

and S112, traversing the corresponding relation between the preset text item keywords and the text item content to obtain target text item content corresponding to the text item keywords.

The text item keywords are preset keywords used for uniquely identifying the text item. For example, the text item keywords may be "title", "text", and "enclosure", where "title" is used to identify a mail title, "text" is used to identify a mail body, and "enclosure" is used to identify a mail attachment, which is not limited herein. And determining the text item keywords in the supervision request, so as to obtain the target text item contained in the supervision request and the target text item content.

And S12, determining the importance degree of each target text item content, and determining the combination sequence of the target text item content according to the descending sequence of the importance degrees.

In at least one embodiment of the present application, each of the target text item contents includes a corresponding importance level, where the importance level may be set according to actual requirements. For example, taking the content of the target text item as an example, the content of a mail title, a mail body and a mail attachment, the importance of the content of the mail body is the highest, and the importance of the content of the mail title is the lowest.

The determination flow of the importance degree provided in the embodiment of the present application is described with reference to fig. 2. Optionally, the determining the importance degree of each target text item content includes:

S121, obtaining the target text item corresponding to each target text item content.

In an embodiment, for each of the target text item contents, there is a unique corresponding target text item. The text item keywords corresponding to the target text item are contained in the target text item content, and the text item keywords include but are not limited to "title", "text" and "close". By determining the text item keywords in the target text item content, a target text item can be obtained.

S122, acquiring a plurality of historical text item contents corresponding to each target text item to form a historical text item content set.

In an embodiment, for each target text item, there is a history text item content corresponding to the target text item, and the number of the history text item contents may be plural, and the plurality of history text item contents corresponding to each target text item form a history text item content set.

For example, when the target text item is a mail header, the history text item content refers to a history mail header content, and the number of the history mail header contents may be multiple, and mail headers corresponding to different history mail header contents may be the same or different, which is not limited herein. When the target text item is a mail text, the historical text item content refers to historical mail text content, the number of the historical mail text content can be multiple, and different historical mail text content is generally different. When the target text item is a mail attachment, the content of the history text item refers to the content of a history mail attachment, and the number of the content of the history mail attachment may be multiple, and different content of the history mail attachment is generally different.

S123, determining a preset history entry information item identifier corresponding to each history text item content in the history text item content set, and calculating the number of the preset history entry information item identifiers to obtain the entry number corresponding to each history text item content.

In an embodiment, the preset history entry information item identifier refers to preset content for marking information item content selected from the history text item content and entered into a preset system, where the preset history entry information item identifier may be a letter identifier, a number identifier or a color identifier, and is not limited herein.

S124, calculating the target entry quantity corresponding to each target text item according to the entry quantity.

In an embodiment, the target entry number may be averaged over a number of the entry numbers. Illustratively, when the target text item is a mail body, the history text item content a, the history text item content B, and the history text item content C are corresponding. The number of preset history entry information item identifiers selected from the history text item content A and entered into a preset system is 3, the number of preset history entry information item identifiers selected from the history text item content B and entered into the preset system is 4, the number of preset history entry information item identifiers selected from the history text item content C and entered into the preset system is 2, and at this time, the target entry number of mail text corresponding to the preset history entry information item identifiers may be an average number of 3.

S125, determining the importance degree of the content of each target text item according to the target input quantity.

It will be appreciated that the importance of each target text item content is determined in descending order of the number of entries, i.e., the greater the number of entries, the greater the importance of the corresponding target text item; the smaller the number of entries, the lower the importance of the corresponding target text item.

And S13, combining the contents of the target text items according to the combination sequence to obtain an initial supervision text.

According to the method and the device for obtaining the information input, the initial supervision text is obtained through the importance degree of each target text item content and the combination sequence of the target text item content according to the descending order of the importance degrees, and then when the information input to be input is determined, supervision texts with high importance degrees can be traversed preferentially, the information input to be input can be found out rapidly, the problem of long query time caused by traversing the whole initial supervision text is avoided, and the information input efficiency is further improved.

S14, determining a target format of the initial supervision text, and adjusting the initial supervision text according to the target format to obtain the target supervision text.

In at least one embodiment of the present application, the initial supervision text includes a plurality of target text items, and for each target text item, there is an initial format corresponding to the target text item. Before information is input into the initial supervision text, the initial format of each target text item in the initial supervision text is required to be adjusted to be a uniform target format, and the target supervision text is obtained, so that the information input efficiency is improved.

The determination flow of the target supervision text provided in the embodiment of the present application is described with reference to fig. 3. Optionally, the adjusting the initial supervision text according to the target format to obtain a target supervision text includes:

s141, determining an initial format corresponding to each target text item content to obtain an initial format set.

In an embodiment, for each of the target text item contents, there is a corresponding initial format, which may or may not be the same. Illustratively, when the target text item content is mail header content, the corresponding initial format is doc text format. When the target text item content is mail text content, the corresponding initial format is doc text format. When the target text item content is mail attachment content, the corresponding initial format includes, but is not limited to, doc text format, xls table format, pdf file format, and jpg picture format.

S142, clustering a plurality of initial formats in the initial format set to obtain a plurality of format cluster clusters;

in an embodiment, when the initial formats in the initial format set are different, when the initial format of each target text item content is adjusted to the target format, a format conversion model needs to be trained in advance for each initial format in the initial format set, and then the format conversion model is called to adjust the initial format of the target text item content to the target format.

In an embodiment, when the initial formats in the initial format set exist the same, clustering the initial formats in the initial format set, wherein the format cluster contains at least one same initial format.

S143, determining that each format cluster corresponds to a pre-trained format conversion model.

In one embodiment, the input vector of the format conversion model is text item content in an initial format, and the output vector is text item content in a target format. The format conversion model may be a neural network model. The training mode of the format conversion model is the prior art, and is not described herein.

Illustratively, when the initial format within the cluster is doc format and the target format is doc format, no format conversion model need be invoked; when the initial format in the cluster is xls table format and the target format is doc format, the format conversion model may be a table conversion model, where the table conversion model is used to adjust the text item content in the table format to the text item content in the doc format; when the initial format in the cluster is the pdf file format and the target format is the doc format, the format conversion model may be a pdf conversion model, where the pdf conversion model is used to adjust the text item content in the pdf format to the text item content in the doc format; when the initial format in the cluster is jpg file format and the target format is doc format, the format conversion model may be an image conversion model, and the image conversion model is used for adjusting the text item content in the image format to the text item content in the doc format.

S144, calling the format conversion model to adjust the initial format corresponding to each target text item content in the format cluster to a target format, and obtaining a target supervision text.

In an embodiment, the format conversion model is called to adjust an initial format corresponding to each target text item content in the format cluster to a target format, and then the text item content with the adjusted formats is combined according to the combination sequence of the target text items, so that a target supervision text is obtained.

And S15, analyzing the target supervision text to obtain the content of the information item to be input.

In at least one embodiment of the present application, the information item content to be entered refers to information item content contained in the target supervision text and used for being entered into a preset system. The content of the information item to be input can be key information of a supervision unit, a supervised unit and the like.

A flow of determining the content of an information item to be entered provided in an embodiment of the present application is described with reference to fig. 4. Optionally, the parsing the target supervision text to obtain the content of the information item to be input includes:

s151, acquiring a preset information item keyword, and determining a target position of the preset information item keyword in the target supervision text.

In an embodiment, the target supervision text includes a plurality of preset information item keywords, and the preset information item keywords are used for uniquely identifying the content of the information item to be input. And obtaining the target position of the preset information item keyword in the target supervision text by acquiring the preset information item keyword.

S152, acquiring a preset data format between the preset information item keywords and the information item content in the target supervision text.

In an embodiment, the preset information item keyword and the information item content are combined according to a preset data format, where the preset data format is preset, for example, the preset data format may be { preset information item keyword: information item content }. And obtaining the corresponding information item content to be input by obtaining the target position of the preset information item keyword in the target supervision text and the preset data format between the preset information item keyword and the information item content.

And S153, selecting the content at the target position as the content of the information item to be input according to the preset data format.

S16, determining the supervision type corresponding to the target supervision text and the information input preference corresponding to the supervision type.

In at least one embodiment of the present application, the supervision type refers to a type that a supervision authority issues to assist in performing supervision tasks, for example, the supervision type may be to assist in performing freeze deduction, supervision labor arbitration, supervision system complaints, and the like.

Optionally, the determining the supervision type corresponding to the target supervision text includes:

s161, determining a pre-trained supervision type determination model.

In an embodiment, the supervision type determining model is a pre-trained model for determining a corresponding supervision type according to the target supervision text. And the input vector of the supervision type determination model is a historical supervision text, and the output vector is a supervision type. The historical supervision text and the corresponding supervision type can be obtained by adopting a manual annotation mode, and the method is not limited. The supervision type determination model may be a neural network model. The training process of the model is prior art and is not limited herein.

S162, calling the supervision type determining model to process the target supervision text, and obtaining the supervision type corresponding to the target supervision text.

In at least one embodiment of the present application, the information input preference refers to information input requirements corresponding to different supervision types, and the information input preference may include component type preference and component position preference corresponding to a target panel of each information item to be input in a preset system. In an embodiment, the component types may include a text box type, a drop down box type, etc.; the component position preference refers to position information of the content of the information item to be entered on the target panel.

Optionally, the determining information entry preference according to the supervision type includes:

s163, determining the mapping relation between the preset supervision type and the information input preference.

And S164, traversing the mapping relation to obtain the information input preference corresponding to the supervision type.

Illustratively, taking the supervision type as an example of a supervision system complaint, the information items to be input comprise 4 information items of a supervision organization, a supervised department, a checking abstract, a supervision date selection and the like. The method comprises the steps that the preference of the component types of a supervision unit, a supervised department and a checking abstract is text box type, the preference of the component type selected by a supervision date is drop-down box type, and the component types are sequentially arranged on a target panel in a preset system according to the position sequence of the supervision date, the supervision unit, the supervised department and the checking abstract.

S17, inputting the content of the information item to be input into a preset system according to the information input preference.

In at least one embodiment of the present application, the preset system refers to a preset system for storing supervision text, and the preset system may be a target node in a blockchain in consideration of reliability and privacy of data storage. In an embodiment, the script may be run by RPA (Robotic Process Automation, robotic flow automation), and the content of the information item to be entered is entered into a preset system according to the information entry preference.

Optionally, the inputting the content of the information item to be input into a preset system according to the information input preference includes:

s171, determining the component type and the component position corresponding to the information item to be input according to the information input preference.

In an embodiment, the component types may include a text box type, a drop down box type, and the like.

S172, constructing a target component at the component position in the preset system according to the component type.

S173, inputting the content of the information item to be input to the target component.

According to the information input method based on the artificial intelligence, the information item content to be input is obtained through analyzing the target supervision text, the information input preference corresponding to the target supervision text is determined, the information item content to be input is input into the preset system according to the information input preference, and the information input efficiency can be improved; according to the method and the device for obtaining the information item content, the initial supervision text is obtained through the importance degree of each target text item content and the combination sequence of the target text item content according to the descending sequence of the importance degrees, and then when the information item content to be input is determined, supervision texts with high importance degrees can be traversed preferentially, the information item content to be input can be found out rapidly, the problem of long query time caused by traversing the whole initial supervision text is avoided, and the information input efficiency is further improved. The intelligent city intelligent management system can be applied to various functional modules of intelligent cities such as intelligent government affairs and intelligent traffic, for example, an information input module of the intelligent city and the like, and can promote the rapid development of the intelligent city.

Referring to fig. 5, fig. 5 is a block diagram of an information input device based on artificial intelligence according to an embodiment of the present application.

In some embodiments, the artificial intelligence based information entry device 20 may include a plurality of functional modules consisting of computer program segments. The computer program of the individual program segments in the artificial intelligence based information entry apparatus 20 may be stored in a memory of a computer device and executed by at least one processor to perform (see fig. 1 for details) the functions of information entry.

In this embodiment, the information input device 20 based on artificial intelligence may be divided into a plurality of functional modules according to the functions performed by the information input device. The functional module may include: a request parsing module 201, a sequence determination module 202, a sequence combination module 203, a text adjustment module 204, a text parsing module 205, a type determination module 206, and an information entry module 207. A module as referred to in this application refers to a series of computer program segments, stored in a memory, capable of being executed by at least one processor and of performing a fixed function. In the present embodiment, the functions of the respective modules will be described in detail in the following embodiments.

The request parsing module 201 may be configured to parse the supervision request to obtain a plurality of target text item contents when receiving the supervision request.

The order determination module 202 may be configured to determine a degree of importance for each of the target text item contents and determine a combined order of the target text item contents according to an order in which the degrees of importance decrease.

The sequence combination module 203 may be configured to combine the target text item contents according to the combination sequence to obtain an initial supervision text.

The text adjustment module 204 may be configured to determine a target format of the initial administrative text, and adjust the initial administrative text according to the target format to obtain a target administrative text.

The text parsing module 205 may be configured to parse the target supervision text to obtain the content of the information item to be entered.

The type determination module 206 may be configured to determine a supervision type corresponding to the target supervision text and an information entry preference corresponding to the supervision type.

The information input module 207 may be configured to input the content of the information item to be input into a preset system according to the information input preference.

In at least one embodiment of the present application, the order determining module 202 may be further configured to obtain a target text item corresponding to each of the target text item contents; acquiring a plurality of historical text item contents corresponding to each target text item to form a historical text item content set; determining a preset history input information item identifier corresponding to each history text item content in the history text item content set, and calculating the number of the preset history input information item identifiers to obtain the input number corresponding to each history text item content; calculating the target entry quantity corresponding to each target text item according to a plurality of entry quantities; and determining the importance degree of the content of each target text item according to the target input quantity.

In at least one embodiment of the present application, the text adjustment module 204 may be further configured to determine an initial format corresponding to each of the target text item contents, to obtain an initial format set; clustering a plurality of initial formats in the initial format set to obtain a plurality of format cluster clusters; determining that each format cluster corresponds to a pre-trained format conversion model; and calling the format conversion model to adjust the initial format corresponding to each target text item content in the format cluster to a target format, so as to obtain a target supervision text.

In at least one embodiment of the present application, the text parsing module 205 may be configured to obtain a preset information item keyword, and determine a target location of the preset information item keyword in the target supervision text; acquiring a preset data format between the preset information item keywords and the information item content in the target supervision text; and selecting the content at the target position as the content of the information item to be input according to the preset data format.

In at least one embodiment of the present application, the type determination module 206 may also be configured to determine a pre-trained supervision type determination model; and calling the supervision type determining model to process the target supervision text to obtain the supervision type corresponding to the target supervision text.

In at least one embodiment of the present application, the type determining module 206 may also be configured to determine a mapping relationship between a preset supervision type and information entry preferences; and traversing the mapping relation to obtain the information input preference corresponding to the supervision type.

In at least one embodiment of the present application, the information input module 207 may be further configured to determine a component type and a component position corresponding to the information item to be input according to the information input preference; constructing a target component at the component position in the preset system according to the component type; and inputting the content of the information item to be input to the target component.

Referring to fig. 6, a schematic structural diagram of a computer device according to an embodiment of the present application is shown. In the preferred embodiment of the present application, the computer device 3 includes a memory 31, at least one processor 32, at least one communication bus 33, and a transceiver 34.

It will be appreciated by those skilled in the art that the configuration of the computer device shown in fig. 6 is not limiting of the embodiments of the present application, and that either a bus type configuration or a star type configuration is possible, and that the computer device 3 may include more or less other hardware or software than illustrated, or a different arrangement of components.

In some embodiments, the computer device 3 is a device capable of automatically performing numerical calculation and/or information processing according to preset or stored instructions, and its hardware includes, but is not limited to, a microprocessor, an application specific integrated circuit, a programmable gate array, a digital processor, an embedded device, and the like. The computer device 3 may also include a client device, which includes, but is not limited to, any electronic product that can interact with a client by way of a keyboard, mouse, remote control, touch pad, or voice control device, such as a personal computer, tablet, smart phone, digital camera, etc.

It should be noted that the computer device 3 is only used as an example, and other electronic products that may be present in the present application or may be present in the future are also included in the scope of the present application and are incorporated herein by reference.

In some embodiments, the memory 31 has stored therein a computer program which, when executed by the at least one processor 32, performs all or part of the steps in the artificial intelligence based information entry method as described. The Memory 31 includes Read-Only Memory (ROM), programmable Read-Only Memory (PROM), erasable programmable Read-Only Memory (EPROM), one-time programmable Read-Only Memory (One-time Programmable Read-Only Memory, OTPROM), electrically erasable rewritable Read-Only Memory (EEPROM), compact disc Read-Only Memory (Compact Disc Read-Only Memory, CD-ROM) or other optical disc Memory, magnetic tape Memory, or any other medium that can be used for computer-readable carrying or storing data.

Further, the computer-readable storage medium may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function, and the like; the storage data area may store data created from the use of blockchain nodes, and the like.

The blockchain referred to in the application is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, consensus mechanism, encryption algorithm and the like. The Blockchain (Blockchain), which is essentially a decentralised database, is a string of data blocks that are generated by cryptographic means in association, each data block containing a batch of information of network transactions for verifying the validity of the information (anti-counterfeiting) and generating the next block. The blockchain may include a blockchain underlying platform, a platform product services layer, an application services layer, and the like.

In some embodiments, the at least one processor 32 is a Control Unit (Control Unit) of the computer device 3, connects the various components of the entire computer device 3 using various interfaces and lines, and performs various functions and processes of the computer device 3 by running or executing programs or modules stored in the memory 31, and invoking data stored in the memory 31. For example, the at least one processor 32, when executing the computer programs stored in the memory, implements all or part of the steps of the artificial intelligence based information entry methods described in embodiments of the present application; or to implement all or part of the functionality of the information entry device. The at least one processor 32 may be comprised of integrated circuits, such as a single packaged integrated circuit, or may be comprised of multiple integrated circuits packaged with the same or different functionality, including one or more central processing units (Central Processing unit, CPU), microprocessors, digital processing chips, graphics processors, combinations of various control chips, and the like.

In some embodiments, the at least one communication bus 33 is arranged to enable connected communication between the memory 31 and the at least one processor 32 or the like.

Although not shown, the computer device 3 may further comprise a power source (such as a battery) for powering the various components, preferably the power source is logically connected to the at least one processor 32 via a power management means, whereby the functions of managing charging, discharging, and power consumption are performed by the power management means. The power supply may also include one or more of any of a direct current or alternating current power supply, recharging device, power failure detection circuit, power converter or inverter, power status indicator, etc. The computer device 3 may further include various sensors, bluetooth modules, wi-Fi modules, etc., which will not be described in detail herein.

The integrated units implemented in the form of software functional modules described above may be stored in a computer readable storage medium. The software functional modules described above are stored in a storage medium and include instructions for causing a computer device (which may be a personal computer, a computer device, or a network device, etc.) or processor (processor) to perform portions of the methods described in various embodiments of the present application.

In the several embodiments provided in this application, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is merely a logical function division, and there may be other manners of division when actually implemented.

The modules described as separate components may or may not be physically separate, and components shown as modules may or may not be physical units, may be located in one place, or may be distributed over multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

In addition, each functional module in each embodiment of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units can be realized in a form of hardware or a form of hardware and a form of software functional modules.

It will be evident to those skilled in the art that the present application is not limited to the details of the foregoing illustrative embodiments, and that the present application may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the application being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned. Furthermore, it will be obvious that the term "comprising" does not exclude other elements or that the singular does not exclude a plurality. Several of the elements or devices recited in the specification may be embodied by one and the same item of software or hardware. The terms first, second, etc. are used to denote a name, but not any particular order.

Finally, it should be noted that the above embodiments are merely for illustrating the technical solution of the present application and not for limiting, and although the present application has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present application may be modified or substituted without departing from the spirit and scope of the technical solution of the present application.

Claims

1. The information input method based on the artificial intelligence is characterized by comprising the following steps of:

2. The artificial intelligence based information entry method of claim 1, wherein said determining the importance of each of said target text item contents comprises:

acquiring a target text item corresponding to each target text item content;

3. The method for inputting information based on artificial intelligence according to claim 1, wherein the adjusting the initial supervision text according to the target format to obtain a target supervision text comprises:

4. The information input method based on artificial intelligence according to claim 1, wherein the parsing the target supervision text to obtain the content of the information item to be input comprises:

5. The method for information input based on artificial intelligence according to claim 1, wherein the determining the supervision type corresponding to the target supervision text comprises:

Determining a pre-trained supervision type determination model;

6. The artificial intelligence based information entry method of claim 1, wherein the determining the information entry preference corresponding to the supervision type comprises:

7. The information input method based on artificial intelligence according to claim 1, wherein the inputting the content of the information item to be input into a preset system according to the information input preference comprises:

8. An artificial intelligence based information entry device, characterized in that the artificial intelligence based information entry device comprises:

9. A computer device, characterized in that it comprises a processor for implementing the artificial intelligence based information entry method according to any one of claims 1 to 7 when executing a computer program stored in a memory.

10. A computer readable storage medium having stored thereon a computer program, wherein the computer program when executed by a processor implements the artificial intelligence based information entry method of any of claims 1 to 7.