CN113822027A

CN113822027A - Processing method and device for business and travel data table combining AI and RPA

Info

Publication number: CN113822027A
Application number: CN202111070551.1A
Authority: CN
Inventors: 张琅虹; 汪冠春; 胡一川; 褚瑞; 李玮
Original assignee: Beijing Laiye Network Technology Co Ltd; Laiye Technology Beijing Co Ltd
Current assignee: Beijing Laiye Network Technology Co Ltd; Laiye Technology Beijing Co Ltd
Priority date: 2021-09-13
Filing date: 2021-09-13
Publication date: 2021-12-21

Abstract

The disclosure provides a business travel data table processing method and device combining an AI and an RPA, and relates to the technical field of the RPA and the AI. Wherein, the method comprises the following steps: the RPA system acquires a business travel data table to be processed under the condition of receiving a data processing instruction; calling Natural Language Processing (NLP) service, and identifying a business trip data table to be processed so as to determine the content in each cell in the business trip data table; determining a first field corresponding to each piece of data in the business trip data table according to the content in each cell, wherein the first field is used for representing department information to which each piece of data belongs; determining a splitting mode of a business trip data table to be processed according to the difference between the first fields corresponding to each piece of data; and processing the business trip data table to be processed based on the splitting mode. Therefore, the RPA system can automatically process the business trip data sheet by calling NLP service, the process does not need manual operation, time is saved, and efficiency and accuracy are improved.

Description

Processing method and device for business and travel data table combining AI and RPA

Technical Field

The present disclosure relates to the field of computer technologies, and in particular, to the fields of Artificial Intelligence (AI) and Robot Process Automation (RPA), and more particularly, to a method and an apparatus for processing a business trip data table in combination with an AI and an RPA.

Background

Robot Process Automation (RPA) is a Process task that simulates human operations on a computer by specific "robot software" and executes automatically according to rules.

Artificial Intelligence (AI) is a technical science that studies and develops theories, methods, techniques and application systems for simulating, extending and expanding human Intelligence.

In the current business, relevant personnel need regularly make statistics of the business data of each department personnel of company, need carry out the split processing according to the business trip data sheet of department to whole company earlier, obtain the business trip data sheet of each department, later carry out individual business trip data to each personnel according to the department and summarize, above-mentioned processing procedure is comparatively mechanical and loaded down with trivial details, need spend a large amount of time to operate, and the human cost is higher, but efficiency is not high.

Disclosure of Invention

The processing method, device and electronic equipment of the business trip data sheet combined with AI and RPA are used for solving the problems of high labor cost and low efficiency when the business trip data sheet is processed in the related technology.

An embodiment of the disclosure provides a method for processing a business trip data table combining an AI and an RPA, including:

the method comprises the steps that a robot process automation RPA system obtains a business trip data table to be processed under the condition that a data processing instruction is received;

calling Natural Language Processing (NLP) service, and identifying the business trip data table to be processed to determine the content in each cell in the business trip data table;

determining a first field corresponding to each piece of data in the business trip data table according to the content in each cell, wherein the first field is used for representing department information to which each piece of data belongs;

determining a splitting mode of the business trip data table to be processed according to the difference between the first fields corresponding to each piece of data;

and processing the business trip data table to be processed based on the splitting mode.

Optionally, the processing the business trip data table to be processed based on the splitting manner includes:

under the condition that the splitting mode is not split, determining a target user corresponding to the business trip data table to be processed according to the first field;

and sending the business trip data table to be processed to the terminal equipment corresponding to the target user.

determining a key character corresponding to each business trip data table to be generated according to the key character contained in the first field under the condition that the splitting mode is splitting;

and splitting each piece of data in the business trip data table to be processed according to the key character corresponding to each piece of data in the business trip data table to be processed so as to generate a business trip data table corresponding to each key character.

Optionally, the method further includes:

calling Natural Language Processing (NLP) service, and identifying a first business trip data table to determine first time information and second time information corresponding to each piece of data in the first business trip data table, wherein first fields of each piece of data in the first business trip data table are the same;

determining a time interval between the first time information and the second time information according to the first time information and the second time information;

and under the condition that the time interval is smaller than or equal to a preset time threshold, storing data corresponding to the first time information and the second time information in the first business trip data table to generate a corresponding second business trip data table.

Optionally, the method further includes:

calling Natural Language Processing (NLP) service, and identifying a first business trip data table to determine user information and resource data of each piece of data in the first business trip data table, wherein first fields of each piece of data in the first business trip data table are the same;

determining total resource data corresponding to each piece of user information according to each piece of user information and corresponding resource data;

and storing each user information and the corresponding resource total data to generate a corresponding third business trip data table.

Optionally, the method further includes:

calling Natural Language Processing (NLP) service, and identifying the business trip data table to be processed to obtain a storage address corresponding to the data to be verified contained in the business trip data table to be processed;

reading the data to be verified from the storage address so as to verify the legality of the data to be verified;

under the condition that the data to be verified are illegal, determining a target user corresponding to the data to be verified;

sending the data to be verified to the target user;

and eliminating the data to be verified in the business trip data table to be processed.

Another aspect of the present disclosure provides a processing apparatus for a business trip data table combining an AI and an RPA, including:

the acquisition module is used for acquiring a business trip data table to be processed under the condition that the robot process automation RPA system receives a data processing instruction;

the identification module is used for calling Natural Language Processing (NLP) service and identifying the business trip data table to be processed so as to determine the content in each cell in the business trip data table;

the first determining module is used for determining a first field corresponding to each piece of data in the business trip data table according to the content in each cell, wherein the first field is used for representing department information to which each piece of data belongs;

the second determining module is used for determining the splitting mode of the business trip data table to be processed according to the difference between the first fields corresponding to each piece of data;

and the processing module is used for processing the business trip data table to be processed based on the splitting mode.

Optionally, the processing module is specifically configured to:

Optionally, the identification module is further configured to invoke a natural language processing NLP service, identify a first business trip data table, and determine first time information and second time information corresponding to each piece of data in the first business trip data table, where first fields of each piece of data in the first business trip data table are the same;

the first determining module is further configured to determine a time interval between the first time information and the second time information according to the first time information and the second time information;

the processing module is further configured to store data corresponding to the first time information and the second time information in the first business trip data table to generate a corresponding second business trip data table when the time interval is less than or equal to a preset time threshold.

Optionally, the identification module is further configured to invoke a natural language processing NLP service, identify a first business trip data table, and determine user information and resource data to which each piece of data in the first business trip data table belongs, where first fields of each piece of data in the first business trip data table are the same;

the first determining module is further configured to determine, according to each piece of user information and resource data corresponding to the piece of user information, total resource data corresponding to each piece of user information;

the processing module is further configured to store each piece of user information and the total resource data corresponding to the user information, so as to generate a corresponding third business trip data table.

Optionally, the method further includes:

the identification module is further configured to invoke a Natural Language Processing (NLP) service, identify the business trip data table to be processed, and acquire a storage address corresponding to data to be verified included in the business trip data table to be processed;

the processing module is further configured to read the data to be verified from the storage address to verify the validity of the data to be verified;

the first determining module is further configured to determine a target user corresponding to the data to be verified under the condition that the data to be verified is illegal;

the processing module is further configured to send the data to be verified to the target user;

the processing module is further configured to remove the data to be verified in the business trip data table to be processed.

An embodiment of another aspect of the present disclosure provides an electronic device, which includes: memory, processor and computer program stored on the memory and executable on the processor, characterized in that the processor implements the method for processing business trip data tables in combination with AI and RPA as described above when executing the program.

A further aspect of the present disclosure is directed to a computer-readable storage medium, on which a computer program is stored, wherein the computer program, when executed by a processor, implements the method for processing business trip data table combining AI and RPA as described above.

In another aspect of the present disclosure, a computer program product is provided, which includes a computer program, and when the computer program is executed by a processor, the method for processing a business trip data table combining AI and RPA according to an embodiment of the above aspect is implemented.

The processing method, device and electronic device for business trip data tables combining AI and RPA provided by the embodiments of the present disclosure, an RPA system may obtain a business trip data table to be processed under the condition of receiving a data processing instruction, and then may identify the business trip data table to be processed by invoking a natural language processing NLP service to determine the content in each cell in the business trip data table, and then determine a first field corresponding to each piece of data in the business trip data table according to the content in each cell, where the first field is used to represent information of a department to which each piece of data belongs, and then may determine a splitting manner of the business trip data table to be processed according to a difference between the first fields corresponding to each piece of data, and then process the business trip data table to be processed based on the splitting manner. From this, the RPA system can determine the corresponding split mode according to the difference between each first field through carrying out NLP to the business trip data table, and based on the split mode again, the realization is to the automatic processing of business trip data table, and this process need not manual operation, has saved the time, has improved efficiency and accuracy.

Additional aspects and advantages of the disclosure will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the disclosure.

Drawings

The foregoing and/or additional aspects and advantages of the present disclosure will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:

fig. 1 is a schematic flowchart illustrating a processing method of a business trip data table combining an AI and an RPA according to an embodiment of the present disclosure;

fig. 2 is a schematic flowchart illustrating a processing method of a business trip data table combining AI and RPA according to another embodiment of the present disclosure;

fig. 3 is a schematic flowchart illustrating a processing method of a business trip data table combining AI and RPA according to another embodiment of the present disclosure;

fig. 4 is a schematic flowchart illustrating a processing method of a business trip data table combining AI and RPA according to another embodiment of the present disclosure;

fig. 5 is a schematic structural diagram of a processing device for a business trip data table combining an AI and an RPA according to another embodiment of the present disclosure;

fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present disclosure.

Detailed Description

Reference will now be made in detail to the embodiments of the present disclosure, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the like or similar elements throughout. The embodiments described below with reference to the drawings are exemplary and intended to be illustrative of the present disclosure, and should not be construed as limiting the present disclosure.

The following describes in detail a processing method, an apparatus and an electronic device for business trip data table combining AI and RPA provided by the present disclosure with reference to the accompanying drawings.

In the description of the present disclosure, the term "data processing instructions" refers to any instructions that may represent processing data.

In the description of the present disclosure, the term "field" refers to the corresponding content of each column in the business trip data table, different fields may correspond to different information, for example, the field "department" may represent the corresponding department information.

In the description of the present disclosure, the term "variance" refers to the degree of variance between the same field of different data in the business trip data table.

In the description of the present disclosure, the term "split mode" refers to a mode of processing business trip data tables.

In the description of the present disclosure, the term "target user" refers to a user who receives a business trip data table.

In the description of the present disclosure, the term "key character" refers to a character contained in each field in the business trip data table for characterizing the corresponding content of the field.

Fig. 1 is a schematic flowchart of a processing method of a business trip data table combining an AI and an RPA according to an embodiment of the present disclosure.

A Robotic Process Automation (RPA) system is an application that provides another way to automate the end-user's manual Process by mimicking the way end-user's manual operations at a computer.

It should be noted that the RPA technology can intelligently understand the existing application of the electronic device through the user interface, automate repeated regular operations based on rules and in large batch, such as automatically and repeatedly reading mails, reading Office components, operating databases, web pages, client software, and the like, collect data and perform complex calculations, so as to generate files and reports in large batch, thereby greatly reducing the input of labor cost and effectively improving the Office efficiency through the RPA technology.

The main executing body of the processing method of the business trip data table combining the AI and the RPA according to the embodiment of the present disclosure may be an RPA system, and may also be a processing device of the business trip data table combining the AI and the RPA according to the embodiment of the present disclosure, and the RPA system and/or the processing device of the business trip data table combining the AI and the RPA may be configured in any electronic device to execute the processing method of the business trip data table combining the AI and the RPA according to the embodiment of the present disclosure. Optionally, the RPA system may include an RPA robot.

As shown in fig. 1, the processing method of the business trip data table combining AI and RPA includes the following steps:

step 101, the RPA system obtains a business travel data table to be processed under the condition of receiving a data processing instruction.

Alternatively, the data processing instruction may be triggered manually, or may also be triggered automatically within a predetermined time according to a preset period, and the like, which is not limited in this disclosure.

And 102, calling natural language processing service, and identifying the business trip data table to be processed so as to determine the content in each cell in the business trip data table.

Natural Language Processing (NLP) is a computer used to process, understand and use human languages (such as chinese and english), which is a cross discipline between computer science and linguistics and is also commonly called computational linguistics. Since natural language is the fundamental mark that humans distinguish from other animals. Without language, human thinking has not been talk about, so natural language processing embodies the highest task and context of artificial intelligence, that is, only when a computer has the capability of processing natural language, the machine has to realize real intelligence.

It is understood that the contents in each cell in the business trip data table to be processed may be the same, or may also be different, etc., and the disclosure does not limit this.

For example, by calling the NLP service, after identifying the business trip data table to be processed, it is determined that the contents in the cells in the business trip data table may be: name: zhang III, department information: market department, departure station: beijing, arrival station: nanjing, et al, to which this disclosure is not limited.

Step 103, determining a first field corresponding to each piece of data in the business trip data table according to the content in each cell, wherein the first field is used for representing department information to which each piece of data belongs.

The field may be content corresponding to each column in the data table, for example, in the present disclosure, the first field may represent "department information", and the like, which is not limited in the present disclosure.

It is understood that the first field corresponding to each piece of data in the business trip data table may be the same or different, and this disclosure does not limit this.

In addition, the department information represented by the first field may include information of a belonging multi-level department, for example: the system comprises a research and development part, a front-end development part and a front-end development part, wherein the research and development part is a first-level department, the front-end development part is a second-level department, and the front-end development part is a third-level department; or may contain information of a certain level of department, for example, it may be legal three, etc., and this disclosure does not limit this.

For example, a certain piece of data 1 in the business trip data table to be processed is: zhang III, Ministry of China-market one, date of departure: 2020/10/1, departure station: beijing, arrival station: nanjing; the other data 2 is: zhang IV, market II, departure date: 2020/10/1, departure station: beijing, arrival station: nanjing. Then the first field corresponding to data 1 may be determined to be: the first field corresponding to data 2 is: market division two.

It should be noted that the above examples are only illustrative, and cannot be taken as limitations on the data in the pending business trip data table and the first field, etc. in the embodiments of the present disclosure.

And 104, determining a splitting mode of the business trip data table to be processed according to the difference between the first fields corresponding to each piece of data.

The difference can represent the difference degree between the first fields, for example, if the first fields corresponding to the data 1 and the data 2 are the same, it can be considered that there is no difference between the first fields corresponding to the data 1 and the data 2; or, if the first fields corresponding to the data 1 and the data 2 are different, it may be considered that there is a difference between the first fields corresponding to the data 1 and the data 2, which is not limited in this disclosure. In addition, the splitting mode refers to a mode of processing the business trip data table, for example, the business trip data table may be split or may not be split, and the disclosure does not limit this.

For example, if the first fields corresponding to each piece of data in the business trip data table to be processed are the same, it may be determined that the difference between the first fields corresponding to each piece of data is zero, that is, there is no difference, and thus it may be determined that the splitting manner is not splitting.

Or, under the condition that the first fields corresponding to part of the data are different in the business trip data table to be processed, or the first fields corresponding to each piece of data are different, it can be considered that differences exist between the first fields in the business trip data table to be processed, and therefore the splitting mode of the business trip data table to be processed can be determined as splitting.

The above examples are merely illustrative, and are not intended to limit the splitting method and the like in the embodiments of the present disclosure.

And 105, processing the business trip data table to be processed based on the splitting mode.

Optionally, under the condition that the splitting mode is not splitting, the target user corresponding to the first business trip data table may be determined according to the first field, and then the business trip data table to be processed may be sent to the terminal device corresponding to the target user.

The target user may be a user corresponding to the first field, which may be set in advance. For example, when the first field is the market department, the corresponding target user may be a, and the to-be-processed business trip data table may be sent to the terminal device corresponding to the target user a.

Or, when the first field is the national ministry, research and development department, and front-end development department, the corresponding target user may be B, C, D, where B, C, D corresponds to the national ministry, research and development department, and front-end development department, respectively, and the business trip data sheet to be processed may be sent to the terminal device corresponding to the target user B, C, D, respectively; or, if the target user corresponding to the target user is E, the business trip data table to be processed may be sent to the terminal device corresponding to the target user E.

It should be noted that the foregoing examples are merely illustrative, and are not intended to limit the determination of target users and the like in the embodiments of the present disclosure, and the present disclosure does not limit this.

Optionally, under the condition that the splitting mode is splitting, the key character corresponding to each business trip data table to be generated may be determined according to the key character included in the first field, and then each piece of data in the business trip data table to be processed may be split according to the key character corresponding to each piece of data in the business trip data table to be processed, so as to generate the business trip data table corresponding to each key character.

The key character may represent the content of the department information in the first field, which is not limited in this disclosure.

For example, the key characters contained in the first fields corresponding to the data 1 and the data 2 are the same and are both "national interior-research and development department-front end development department", and then it can be determined that the key character corresponding to the business trip data table 1 to be generated is "national interior-research and development department-front end development department", and then the data 1 and the data 2 in the business trip data table to be processed can be separated and stored in the business trip data table 1; if the key character included in the first field corresponding to the data 3 is "national interior-research and development department — back-end development department", it may be determined that the key character corresponding to the business trip data table 2 to be generated is "national interior-research and development department — back-end development department", and then the data 3 may be split and stored in the business trip data table 2. In addition, since the key characters included in the first fields in the business trip data table 1 and the business trip data table 2 both include "department of internal medicine — department of research and development", the data in the business trip data table 1 and the business trip data table 2 may be summarized, thereby generating the business trip data table 3 corresponding to "department of internal medicine — department of research and development".

It should be noted that the foregoing example is only an example, and cannot be taken as a limitation on a manner of splitting data in a business trip data table to be processed to generate business trip data tables corresponding to each key character in the embodiment of the present disclosure.

Optionally, after the key character corresponding to each piece of data in the business trip data table to be processed is generated, the data in the business trip data table to be processed is split, so as to generate the business trip data table corresponding to each key character, the target user corresponding to each business trip data table is determined according to the first field corresponding to each business trip data table, and then each business trip data table can be sent to the terminal device corresponding to the corresponding target user, and the like.

Therefore, in the implementation of the present disclosure, each piece of data in the business trip data table to be processed can be automatically split according to the key character contained in the first field, so as to generate the business trip data table corresponding to each key character, thereby. The data business trip data sheet is split according to the department information, manual operation is not needed, time is saved, and efficiency is improved.

The RPA system can acquire the business trip data table to be processed under the condition of receiving a data processing instruction, then can identify the business trip data table to be processed by calling NLP service to determine the content in each cell in the business trip data table, and then determine the first field corresponding to each piece of data in the business trip data table according to the content in each cell, wherein the first field is used for representing department information to which each piece of data belongs, and then can determine the splitting mode of the business trip data table to be processed according to the difference between the first fields corresponding to each piece of data, and then process the business trip data table to be processed based on the splitting mode. From this, the RPA system can determine the corresponding split mode according to the difference between each first field through carrying out NLP to the business trip data table, and based on the split mode again, the realization is to the automatic processing of business trip data table, and this process need not manual operation, has saved the time, has improved efficiency and accuracy.

Fig. 2 is a schematic flowchart of a processing method of a business trip data table combining an AI and an RPA according to an embodiment of the present disclosure.

As shown in fig. 2, the processing method of the business trip data table combining AI and RPA includes the following steps:

step 201, invoking a natural language processing service, and identifying a first business trip data table to determine first time information and second time information corresponding to each piece of data in the first business trip data table, where first fields of each piece of data in the first business trip data table are the same.

It is understood that the first field may represent department information to which the piece of data belongs, which may have multiple levels of departments, or may be a certain level of departments, and the like, and this disclosure does not limit this.

For example, the first field in the first business trip data table may be "domestic", or may also be "domestic department-market department", "domestic department-market three department", and the like, which is not limited in this disclosure.

In addition, the first time information may be used to characterize a ticket purchasing date, for example, the date of purchasing a ticket, or the date of purchasing a ticket, etc., which is not limited in this disclosure.

In addition, the second time information may be used to represent a departure date, for example, the departure date of the purchased ticket may be, or the departure time date of the purchased ticket may also be, and the disclosure is not limited thereto.

Step 202, determining a time interval between the first time information and the second time information according to the first time information and the second time information.

For example, the first time information is 10 days in 2 months in 2020, the second time information is 15 days in 2 months in 2020, the time interval between the two is 5 days, and the like, which is not limited in this disclosure.

And 203, storing the data corresponding to the first time information and the second time information in the first business trip data table to generate a corresponding second business trip data table under the condition that the time interval is less than or equal to the preset time threshold.

The preset time threshold may be any value set in advance, for example, may be 3 days, or may also be 5 days, and the like, which is not limited in this disclosure.

For example, the preset time threshold is 3 days. Data 1 in the first business trip data table is: third, market department, first time information is 3 months and 1 day in 2020, second time information is 3 months and 3 days in 2020, a departure station is Nanjing, and an arrival station is Beijing, wherein the time interval between the first time information and the second time information is 2 days, which is less than a preset time threshold, and then the data 1 in the first business trip data table can be stored to generate a corresponding second business trip data table 1. It is understood that the first character in the second business trip data table 1 is "market one", and the time interval between the first time information and the second time information is less than or equal to the preset time threshold.

It should be noted that the above examples are only illustrative, and cannot be taken as limitations on the first time information, the second time information, the preset time threshold, and the like in the embodiments of the present disclosure.

Optionally, after the second business trip data table is generated, the target user corresponding to the second business trip data table may be determined according to the first field corresponding to the second business trip data table, and then the second business trip data table may be sent to the terminal device corresponding to the target user, and the like.

The RPA system calls a natural language processing service to identify a first business travel data table, so as to determine first time information and second time information corresponding to each piece of data in the first business travel data table, wherein a first field of each piece of data in the first business travel data table is the same, and then a time interval between the first time information and the second time information can be determined according to the first time information and the second time information. Therefore, the RPA system can automatically determine the temporary ticket booking condition in the time threshold according to the preset time threshold, the first time information and the second time information, and performs summary processing on the temporary ticket booking condition, manual operation is not needed in the process, time is saved, and efficiency is improved.

Fig. 3 is a flowchart illustrating a processing method of a business trip data table combining an AI and an RPA according to an embodiment of the present disclosure.

As shown in fig. 3, the processing method of the business trip data table combining AI and RPA includes the following steps:

step 301, invoking a natural language processing service, and identifying a first business trip data table to determine user information and resource data to which each piece of data in the first business trip data table belongs, wherein first fields of each piece of data in the first business trip data table are the same.

The user information may be a user name, and the resource data may be a ticket price, a ticket refund fee, and the like, which is not limited in this disclosure.

It is understood that the data belonging to the same user in the first business trip data table may be one or more, and the disclosure does not limit this.

For example, data 1 in the first business trip data table may be: zhang III, research and development department, railway ticket, ticket price of 50 Yuan, ticket refund fee of zero Yuan, departure time of 2 months and 5 days, departure station: beijing; data 2 may be: li IV, research and development department, railway tickets, ticket prices of 30 Yuan, ticket refunding and ticket changing commission charge of zero Yuan, departure time of 2 months and 5 days; data 3 may be: zhang III, research and development department, railway ticket, ticket price of 100 Yuan, ticket fee of zero Yuan of departure and change, departure time of 2 months and 7 days, etc., which are not limited by the disclosure.

Step 302, determining total resource data corresponding to each user information according to each user information and resource data corresponding to the user information.

It can be understood that the resource data belonging to the same user information may be combined to obtain the total resource data corresponding to the user information.

For example, if the same user information corresponds to multiple pieces of data, the resource data in the multiple pieces of data corresponding to the same user information may be summarized, so as to determine total resource data corresponding to the user information; if the same user information corresponds to one piece of data, the resource data in the data is the total resource data corresponding to the user information.

And step 303, storing each user information and the corresponding resource total data to generate a corresponding third business trip data table.

It can be understood that after determining each user information and its corresponding total resource data in the first business trip data table, it may be stored to generate a corresponding third business trip data table. Because the first fields of each piece of data in the first business trip data table are the same, the first fields in the third business trip data table obtained by summarizing and storing the data in the first business trip data table are also the same.

For example, the first field in the first business trip data table is "market second", and the first field corresponding to the third business trip data table is also "market second"; or the first field in the first business trip data table is "department of china-market department", the first field corresponding to the third business trip data table is also "department of china-market department", and so on, which is not limited in this disclosure.

For example, data 1 in the first business trip data table is: third, develop one, ticket price 50 yuan, refund ticket change fee zero yuan; data 2 is: li IV, developing one part, 30 Yuan of ticket price and zero Yuan of ticket refunding and altering fee; data 3 is: three, develop one, train ticket, fare 100 yuan, refund ticket change charge 5 yuan. Wherein, if the users to which the data 1 and the data 3 belong are respectively one third, the resource data of the two users can be merged to obtain the total resource data: 150 Yuan of ticket price and 5 Yuan of ticket refunding and changing. Then, the three-piece ticket, the 150 yuan ticket price, the 5 yuan ticket fee refund and change fee, the four-piece plum, the 30 yuan ticket price and the zero ticket fee refund and change fee can be respectively stored in a third business trip data table.

It should be noted that the above examples are only illustrative, and should not be taken as limitations on user information, total resource data, and the like in the embodiments of the present disclosure.

Therefore, in the embodiment of the disclosure, the RPA system can summarize the resource data of the user according to the department information represented by the first field, so that the user data is more clear and intuitive, the process does not need manual operation, the time is saved, and the efficiency is improved.

Optionally, after the third business trip data table is generated, the target user corresponding to the third business trip data table may be determined according to the first field corresponding to the third business trip data table, and then the third business trip data table may be sent to the terminal device corresponding to the target user, and the like.

In the embodiment of the disclosure, the RPA system may invoke the natural language processing NLP service first, identify the first business trip data table, and determine the user information and the resource data to which each piece of data belongs in the first business trip data table, wherein the first field of each piece of data in the first business trip data table is the same, and then determine the total resource data corresponding to each piece of user information according to each piece of user information and the corresponding resource data, and store each piece of user information and the corresponding total resource data to generate the corresponding third business trip data table. Therefore, the RPA system can automatically realize the summarization of the resource data corresponding to the user information, the process does not need manual operation, the time is saved, and the efficiency is improved.

Fig. 4 is a flowchart illustrating a processing method of a business trip data table combining an AI and an RPA according to an embodiment of the present disclosure.

As shown in fig. 4, the processing method of the business trip data table combining AI and RPA includes the following steps:

step 401, the RPA system obtains a business trip data table to be processed under the condition of receiving the data processing instruction.

Step 402, invoking a natural language processing service, and identifying the business trip data table to be processed to obtain a storage address corresponding to the data to be verified contained in the business trip data table to be processed.

The storage address may be various, for example, a link, or a storage path, and the like, which is not limited in this disclosure.

In addition, the data to be verified may be a ticket, an air ticket, a ticket price, and the like, which is not limited in this disclosure.

And 403, reading the data to be verified from the storage address to verify the validity of the data to be verified.

It can be understood that the data to be verified can be read through the storage address, and then the validity of the data to be verified can be verified by calling the corresponding interface.

For example, if the data to be verified is a ticket, the RPA system can read the ticket information through the determined storage address, and then can perform validity verification on the ticket information. For example, whether the official seal information on the ticket is true or not can be verified by calling the corresponding interface; or may check fare data, etc., and the present disclosure is not limited thereto.

And step 404, determining a target user corresponding to the data to be verified under the condition that the data to be verified is illegal.

Step 405, sending the data to be verified to the target user.

The target user may be a certain user set in advance, or may also be a user related to the first field, and the like, which is not limited in this disclosure.

For example, the target user is user a, and the data to be verified may be sent to user a when it is determined that the data to be verified is illegal. Or, after the legitimacy of all the data to be verified in the business trip data table to be processed is verified, all the illegal data to be verified can be sent to the user a, and the like, which is not limited by the disclosure.

Or, in the case that it is determined that the data to be verified is illegal, the corresponding target user may be determined according to the first field corresponding to the data to be verified, and then the illegal generation verification data is sent to the target user, and so on, which is not limited by the present disclosure.

Optionally, under the condition that the data to be verified is legal, the business trip data table to be processed may be processed, and specific contents and processes thereof may refer to the description of each embodiment of the present disclosure, and are not described herein again.

And 406, removing illegal data to be verified in the business trip data table to be processed.

And under the condition that the data to be verified is determined to be illegal, the data to be verified in the business trip data table to be processed can be removed.

For example, if the data 1 to be verified is illegal and belongs to the data 1 in the business trip data table to be processed, all the data corresponding to the data 1 can be removed, so that the data amount can be reduced, and the data processing efficiency can be improved.

The RPA system can acquire the business trip data table to be processed under the condition of receiving a data processing instruction, then can call natural language processing service, identify the business trip data table to be processed, so as to acquire the storage address corresponding to the data to be verified contained in the business trip data table to be processed, read the data to be verified from the storage address, so as to verify the legality of the data to be verified, determine the target user corresponding to the data to be verified under the condition that the data to be verified is illegal, send the data to be verified to the target user, and remove the illegal data to be verified in the business trip data table to be processed. Therefore, the RPA system can remove illegal data to be verified by verifying the legality of the data to be verified, thereby reducing the data volume to be processed, improving the efficiency and saving the time.

In order to implement the above embodiments, the present disclosure further provides a processing device for a business trip data table combining AI and RPA.

Fig. 5 is a schematic structural diagram of a processing device for a business trip data table combining an AI and an RPA according to an embodiment of the present disclosure.

As shown in fig. 5, the processing apparatus 100 for business trip data table combining AI and RPA includes: an acquisition module 110, a recognition module 120, a first determination module 130, a second determination module 140, and a processing module 150.

The obtaining module 110 is configured to obtain a business trip data table to be processed by the robot process automation RPA system when receiving the data processing instruction.

The identifying module 120 is configured to invoke a natural language processing NLP service, and identify the business trip data table to be processed, so as to determine content in each cell in the business trip data table.

The first determining module 130 is configured to determine, according to the content in each cell, a first field corresponding to each piece of data in the business trip data table, where the first field is used to represent department information to which each piece of data belongs.

The second determining module 140 is configured to determine a splitting manner of the business trip data table to be processed according to differences between the first fields corresponding to each piece of data.

And the processing module 150 is configured to process the business trip data table to be processed based on the splitting manner.

Optionally, the processing module 150 is specifically configured to:

Optionally, the identifying module 120 is further configured to invoke a natural language processing NLP service, identify the first business trip data table, and determine first time information and second time information corresponding to each piece of data in the first business trip data table, where a first field of each piece of data in the first business trip data table is the same.

The first determining module 130 is further configured to determine a time interval between the first time information and the second time information according to the first time information and the second time information.

The processing module 150 is further configured to store data corresponding to the first time information and the second time information in the first business trip data table when the time interval is less than or equal to a preset time threshold, so as to generate a corresponding second business trip data table.

Optionally, the identifying module 120 is further configured to invoke a natural language processing NLP service, identify the first business trip data table, and determine user information and resource data to which each piece of data in the first business trip data table belongs, where first fields of each piece of data in the first business trip data table are the same.

The first determining module 130 is further configured to determine, according to each piece of user information and resource data corresponding to the piece of user information, total resource data corresponding to each piece of user information.

The processing module 150 is further configured to store each piece of user information and the total resource data corresponding to the user information, so as to generate a corresponding third business trip data table.

Optionally, the method further includes:

the identification module 120 is further configured to invoke a natural language processing NLP service, and identify the business trip data table to be processed, so as to obtain a storage address corresponding to the data to be verified included in the business trip data table to be processed.

The processing module 150 is further configured to read the data to be verified from the storage address, so as to verify the validity of the data to be verified;

the first determining module 130 is further configured to determine a target user corresponding to the data to be verified under the condition that the data to be verified is illegal.

The processing module 150 is further configured to send the data to be verified to the target user.

The processing module 150 is further configured to remove the to-be-verified data in the to-be-processed business trip data table.

It should be noted that, for the functions and the specific implementation principles of the modules in the embodiments of the present disclosure, reference may be made to the embodiments of the methods described above, and details are not described here again.

The processing apparatus for business trip data table combining AI and RPA provided by the embodiment of the present disclosure, an RPA system may obtain a business trip data table to be processed under a condition that a data processing instruction is received, and then may identify the business trip data table to be processed by invoking NLP service, so as to determine content in each cell in the business trip data table, and then determine a first field corresponding to each piece of data in the business trip data table according to the content in each cell, where the first field is used to represent department information to which each piece of data belongs, and then may determine a splitting manner of the business trip data table to be processed according to a difference between the first fields corresponding to each piece of data, and then process the business trip data table to be processed based on the splitting manner. From this, the RPA system can determine the corresponding split mode according to the difference between each first field through carrying out NLP to the business trip data table, and based on the split mode again, the realization is to the automatic processing of business trip data table, and this process need not manual operation, has saved the time, has improved efficiency and accuracy.

In order to implement the above embodiments, the present disclosure further provides an electronic device.

Fig. 6 is a schematic structural diagram of an electronic device incorporating a processing method of business trip data tables of an AI and an RPA according to an embodiment of the present disclosure.

As shown in fig. 6, the electronic device 200 includes:

a memory 210 and a processor 220, a bus 230 connecting different components (including the memory 210 and the processor 220), wherein the memory 210 stores a computer program, and when the processor 220 executes the program, the method for processing the business trip data table combining AI and RPA according to the embodiment of the disclosure is implemented.

Bus 230 represents one or more of any of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, and a processor or local bus using any of a variety of bus architectures. By way of example, such architectures include, but are not limited to, Industry Standard Architecture (ISA) bus, micro-channel architecture (MAC) bus, enhanced ISA bus, Video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus.

Electronic device 200 typically includes a variety of electronic device readable media. Such media may be any available media that is accessible by electronic device 200 and includes both volatile and nonvolatile media, removable and non-removable media.

Memory 210 may also include computer system readable media in the form of volatile memory, such as Random Access Memory (RAM)240 and/or cache memory 250. The electronic device 200 may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, storage system 260 may be used to read from and write to non-removable, nonvolatile magnetic media (not shown in FIG. 6, commonly referred to as a "hard drive"). Although not shown in FIG. 6, a magnetic disk drive for reading from and writing to a removable, nonvolatile magnetic disk (e.g., a "floppy disk") and an optical disk drive for reading from or writing to a removable, nonvolatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to bus 230 by one or more data media interfaces. Memory 210 may include at least one program product having a set (e.g., at least one) of program modules that are configured to carry out the functions of embodiments of the disclosure.

A program/utility 280 having a set (at least one) of program modules 270, including but not limited to an operating system, one or more application programs, other program modules, and program data, each of which or some combination thereof may comprise an implementation of a network environment, may be stored in, for example, the memory 210. The program modules 270 generally perform the functions and/or methodologies of the embodiments described in this disclosure.

Electronic device 200 may also communicate with one or more external devices 290 (e.g., keyboard, pointing device, display 291, etc.), with one or more devices that enable a user to interact with electronic device 200, and/or with any devices (e.g., network card, modem, etc.) that enable electronic device 200 to communicate with one or more other computing devices. Such communication may occur via input/output (I/O) interfaces 292. Also, the electronic device 200 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network such as the Internet) via the network adapter 293. As shown, the network adapter 293 communicates with the other modules of the electronic device 200 via the bus 230. It should be appreciated that although not shown in the figures, other hardware and/or software modules may be used in conjunction with the electronic device 200, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.

The processor 220 executes various functional applications and data processing by executing programs stored in the memory 210.

It should be noted that, for the implementation process and the technical principle of the electronic device of this embodiment, reference is made to the foregoing explanation of the processing method of the business trip data table combining AI and RPA according to the embodiment of the present disclosure, and details are not described here again.

The electronic device provided by the embodiment of the present disclosure, the RPA system may obtain the business trip data table to be processed under the condition that the data processing instruction is received, and then, by invoking the NLP service, may identify the business trip data table to be processed, to determine the content in each cell in the business trip data table, and then, according to the content in each cell, determine the first field corresponding to each piece of data in the business trip data table, where the first field is used to represent department information to which each piece of data belongs, and then, may determine the splitting manner of the business trip data table to be processed according to the difference between the first fields corresponding to each piece of data, and then, based on the splitting manner, process the business trip data table to be processed. From this, the RPA system can determine the corresponding split mode according to the difference between each first field through carrying out NLP to the business trip data table, and based on the split mode again, the realization is to the automatic processing of business trip data table, and this process need not manual operation, has saved the time, has improved efficiency and accuracy.

In order to implement the above embodiments, the present disclosure also proposes a computer-readable storage medium.

The computer readable storage medium stores thereon a computer program, which when executed by a processor, implements a processing method of a business trip data table combining AI and RPA according to an embodiment of the disclosure.

In order to implement the foregoing embodiments, a further embodiment of the present disclosure provides a computer program product, which includes a computer program and when being executed by a processor, implements the processing method of the business trip data table combining AI and RPA according to the embodiments of the present disclosure.

In an alternative implementation, the embodiments may be implemented in any combination of one or more computer-readable media. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable storage 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 (a non-exhaustive list) of the computer readable storage medium would include the following: 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 context of this document, a computer readable storage 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.

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 any of a variety of 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 storage 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, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

Computer program code for carrying out operations for aspects of the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the consumer electronic device, partly on the consumer electronic device, as a stand-alone software package, partly on the consumer electronic device and partly on a remote electronic device, or entirely on the remote electronic device or server. In the case of remote electronic devices, the remote electronic devices may be connected to the consumer electronic device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external electronic device (e.g., through the internet using an internet service provider).

According to the technical scheme, the RPA system can acquire the to-be-processed business trip data table under the condition of receiving the data processing instruction, then by calling NLP service, the to-be-processed business trip data table can be identified to determine the content in each cell in the business trip data table, then according to the content in each cell, a first field corresponding to each piece of data in the business trip data table is determined, wherein the first field is used for representing department information to which each piece of data belongs, then the splitting mode of the to-be-processed business trip data table can be determined according to the difference between the first fields corresponding to each piece of data, and then the to-be-processed business trip data table is processed based on the splitting mode. From this, the RPA system carries out NLP to the business trip data table, can determine corresponding split mode according to the difference between each first field, again based on split mode, realizes the automatic processing to the business trip data table, and this process need not manual operation, has saved the time, has improved efficiency and accuracy.

Other embodiments of the present disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. This disclosure is intended to cover any variations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.

It will be understood that the present disclosure is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims

1. A processing method for business travel data table combining AI and RPA is characterized by comprising the following steps:

2. The method of claim 1, wherein the processing the pending business trip data table based on the splitting comprises:

3. The method of claim 1, wherein the processing the pending business trip data table based on the splitting comprises:

4. The method of claim 1, further comprising:

5. The method of claim 1, further comprising:

6. The method of any of claims 1-5, further comprising:

sending the data to be verified to the target user;

7. A processing method and device for business travel data table combining AI and RPA is characterized by comprising the following steps:

8. The apparatus of claim 7, wherein the processing module is specifically configured to:

9. The apparatus of claim 7, wherein the processing module is specifically configured to:

10. The apparatus of claim 7,

the identification module is further configured to invoke a Natural Language Processing (NLP) service, identify a first business trip data table, and determine first time information and second time information corresponding to each piece of data in the first business trip data table, where first fields of each piece of data in the first business trip data table are the same;

11. The method of claim 7,

the identification module is further used for calling a Natural Language Processing (NLP) service and identifying a first business trip data table to determine user information and resource data to which each piece of data in the first business trip data table belongs, wherein first fields of each piece of data in the first business trip data table are the same;

12. The apparatus of any of claims 7-11, further comprising:

13. An electronic device, comprising: memory, processor and program stored on the memory and executable on the processor, which when executed implements the method of processing a business trip data sheet in conjunction with AI and RPA according to any of claims 1-6.

14. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out a method of processing a business trip data table combining AI and RPA according to any one of claims 1 to 6.

15. A computer program product comprising a computer program which, when executed by a processor, implements a method of processing a business trip data sheet in conjunction with AI and RPA according to any of claims 1-6.