CN114298631A

CN114298631A - Logistics information processing method, device, equipment and medium based on RPA and AI

Info

Publication number: CN114298631A
Application number: CN202111610222.1A
Authority: CN
Inventors: 陈愫恺; 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: 2021-12-27
Filing date: 2021-12-27
Publication date: 2022-04-08

Abstract

The application provides a logistics information processing method, a logistics information processing device, logistics information processing equipment and a logistics information processing medium based on RPA and AI. Wherein, the method comprises the following steps: s1, reading the address information and the article information in the logistics list; s2, determining target address information belonging to the same geographic position from each address information; and S3, merging the article information of the same article corresponding to the same target address information in the logistics list, wherein the article information comprises the article quantity and the article price. By adopting the technical scheme, the problem of low efficiency and accuracy in manual logistics list processing is solved.

Description

Logistics information processing method, device, equipment and medium based on RPA and AI

Technical Field

The present application relates to the field of process automation technologies, and in particular, to a method, an apparatus, a device, and a medium for processing logistics information based on RPA and AI.

Background

Robot Process Automation (RPA) is a Process task that simulates human operations on a computer through specific robot software and automatically executes 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.

RPA has unique advantages: low code, non-intrusive. The low code means that the RPA can be operated without high IT level, and business personnel who do not know programming can also develop the flow; non-invasively, the RPA can simulate human operation without opening the interface with a software system. However, conventional RPA has certain limitations: can only be based on fixed rules and application scenarios are limited. With the continuous development of the AI technology, the limitation of the traditional RPA is overcome by the deep fusion of the RPA and the AI, and the RPA + AI is a Hand work + Head work, which greatly changes the value of the labor force.

At present, the logistics list is usually processed manually. For the logistics lists from various logistics suppliers, workers need to manually combine different data of the same delivery address in the lists and then perform uniform settlement. Since each Excel (electronic form) has tens of thousands of pieces of data, the same physical distribution address is not filled out adjacently in the form but is filled out according to the delivery time, and therefore, the positions of the same physical distribution information in the form may be far from each other. A large amount of time is needed for finishing the manual arrangement of all the tables, so that the efficiency is low, and the error rate is high.

Disclosure of Invention

The embodiment of the application provides a logistics information processing method, a logistics information processing device, logistics information processing equipment and a logistics information processing medium based on RPA and AI, and aims to solve the problems of low efficiency and low accuracy of manual logistics list processing, and the technical scheme is as follows:

in a first aspect, an embodiment of the present application provides a method for processing logistics information based on robot process automation RPA and artificial intelligence AI, including:

s1, reading the address information and the article information in the logistics list;

s2, determining target address information belonging to the same geographic position from each address information;

and S3, merging the article information of the same article corresponding to the same target address information in the logistics list, wherein the article information comprises the article quantity and the article price.

Optionally, step S1 specifically includes:

and calling an optical character recognition OCR component to recognize the logistics list to obtain the address information and the article information in the logistics list.

Optionally, step S2 specifically includes:

s21, converting each address information into a standard address to be matched according with the requirements of a preset map database, wherein the standard address comprises province information, city information, region information and street information;

s22, determining the position information of the standard address in a preset electronic map, wherein the position information comprises the longitude and the latitude of the standard address;

and S23, normalizing each standard address corresponding to the same position information to obtain normalized target address information.

Optionally, step S21 specifically includes:

and carrying out error correction processing on each address information to obtain a standard address to be matched, which meets the requirement of a preset map database, wherein the error correction processing comprises wrongly written character correction and address information completion.

Optionally, step S22 specifically includes:

and determining the position information of the address closest to the position of the standard address in the preset electronic map as the position information corresponding to the standard address.

Optionally, step S23 specifically includes:

s231, determining the distance between the position information and other position information for the position information corresponding to any one standard address, and if the distance is within a preset distance threshold range, taking all standard addresses with the distance relation within the preset distance threshold range as addresses to be normalized corresponding to the same position information;

and S232, taking the corresponding address information of each address to be normalized in the preset electronic map as the normalized target address information.

Optionally, the method provided in the embodiment of the present application further includes:

before merging the article information corresponding to the target address information, writing the target address information into a logistics list; and the number of the first and second electrodes,

and after merging the article information corresponding to the target address information, marking the merged article information.

In a second aspect, an embodiment of the present application provides a logistics information processing apparatus based on RPA and AI, including:

the information reading module is configured to read address information and article information in the logistics list;

the target address determining module is configured to determine target address information belonging to the same geographic position from each address information;

and the article information merging module is configured to merge article information of the same article corresponding to the same target address information in the logistics list, wherein the article information comprises the article quantity and the article price.

Optionally, the information reading module is specifically configured to:

and calling an optical character recognition OCR component to recognize the logistics list to obtain address information and article information in the logistics list.

Optionally, the target address determining module includes:

the standard address determining unit is configured to convert each address information into a standard address to be matched according with the requirement of a preset map database, wherein the standard address comprises province information, city information, region information and street information;

a position information determination unit configured to determine position information of a standard address in a preset electronic map, the position information including a longitude and a latitude where the standard address is located;

and the address normalization unit is configured to normalize the standard addresses corresponding to the same position information to obtain normalized target address information.

Optionally, the standard address determining unit is specifically configured to:

Optionally, the location information determining unit is specifically configured to:

Optionally, the address normalization unit is specifically configured to:

determining the distance between the position information and other position information for the position information corresponding to any one standard address, and if the distance is within a preset distance threshold range, taking all standard addresses with the distance relation within the preset distance threshold range as addresses to be normalized corresponding to the same position information;

and taking the address information corresponding to each address to be normalized in the preset electronic map as the normalized target address information.

Optionally, the apparatus provided in the embodiment of the present application further includes:

before merging the article information corresponding to the target address information, writing the target address information into the logistics list; and the number of the first and second electrodes,

In a third aspect, an embodiment of the present application provides an apparatus for processing logistics information, where the apparatus includes: a memory and a processor. Wherein the memory and the processor are in communication with each other via an internal connection path, the memory is configured to store instructions, the processor is configured to execute the instructions stored by the memory, and the processor is configured to perform the method of any of the above aspects when the processor executes the instructions stored by the memory.

In a fourth aspect, embodiments of the present application provide a computer-readable storage medium, which stores a computer program, and when the computer program runs on a computer, the method in any one of the above-mentioned aspects is executed.

According to the technical scheme, the RPA robot can determine the target address information belonging to the same geographic position from each address information by replacing the manual reading of the address information and the article information in the logistics list, so that the article information of the same article corresponding to the same target address information in the logistics list can be merged. By adopting the technical scheme, the sorting efficiency of the address information in the logistics list is improved, and the leakage rate and the error rate in the manual sorting process are reduced.

The advantages or beneficial effects in the above technical solution at least include:

1. by adopting the RPA robot to replace manual work to read, arrange and combine the address information in the logistics list, the arranging efficiency and the accuracy of the address information in the logistics list are improved.

2. The contents in the logistics list are identified by adopting a mode of combining RPA and AI, so that the identification efficiency of the contents in the logistics list is improved, and the problem of low efficiency when the contents in the logistics list are identified manually in sequence is solved.

3. By normalizing the plurality of standard addresses belonging to the same geographic position, the plurality of standard addresses can be represented in the same address description mode, namely, effective arrangement of each address information in a logistics list is realized, so that a subsequent RPA robot can merge and settle article information according to the normalized address information.

The foregoing summary is provided for the purpose of description only and is not intended to be limiting in any way. In addition to the illustrative aspects, embodiments, and features described above, further aspects, embodiments, and features of the present application will be readily apparent by reference to the drawings and following detailed description.

Drawings

In the drawings, like reference numerals refer to the same or similar parts or elements throughout the several views unless otherwise specified. The figures are not necessarily to scale. It is appreciated that these drawings depict only some embodiments in accordance with the disclosure and are therefore not to be considered limiting of its scope.

Fig. 1a is a flowchart of a method for processing logistics information based on RPA and AI according to an embodiment of the present application;

fig. 1b is a screenshot illustrating an effect of a logistics list before merging logistics information according to an embodiment of the present application;

fig. 1c is a screenshot illustrating an effect of a logistics list after the logistics information is merged according to an embodiment of the present application;

fig. 2 is a flowchart of a logistics information processing method based on RPA and AI according to a second embodiment of the present application;

fig. 3 is a block diagram illustrating a device of a logistics information processing method based on RPA and AI according to a third embodiment of the present application;

fig. 4 is a block diagram of an apparatus for processing logistics information according to a fourth embodiment of the present application.

Detailed Description

Reference will now be made in detail to embodiments of the present application, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are exemplary only for the purpose of explaining the present application and are not to be construed as limiting the present application.

In the description of the present application, the term "logistics list" usually exists in the form of electronic spreadsheet (Excel), wherein the goods information and address information related to the goods are contained, and the goods information includes the name, quantity and price of the goods.

In the description of the present application, the term "predetermined map database" is a database based on digitized data of a map, which contains detailed address information of various locations in different provinces, different cities, different regions, such as streets, house numbers, etc.

In the description of the present application, the term "standard address" refers to address information conforming to the requirements of a preset map database, which includes province information, city information, region information, and street information.

In the description of the present application, the term "preset electronic map" refers to a map database for inquiring location information to which each address belongs. By calling the preset electronic map, the position information of each address can be obtained, wherein the position information comprises longitude, latitude and height.

In the description of the present application, the term "normalization" is to treat addresses whose positional relationships in a preset electronic map are close, that is, whose distances from the content in a preset range, as addresses belonging to the same geographical location.

In the description of the present application, the term "OCR" refers to Optical Character Recognition (Optical Character Recognition), and specifically refers to a process in which an electronic device examines a Character printed on paper, determines its shape by detecting dark and light patterns, and then translates the shape into a computer text by a Character Recognition method; the method is characterized in that characters in a paper document are converted into an image file with a black-white dot matrix in an optical mode aiming at print characters, and the characters in the image are converted into a text format through recognition software for further editing and processing by word processing software.

These and other aspects of embodiments of the present application will be apparent from and elucidated with reference to the following description and drawings. In the description and drawings, particular embodiments of the application are disclosed in detail as being indicative of some of the ways in which the principles of the embodiments of the application may be practiced, but it is understood that the embodiments of the application are not limited correspondingly in scope. On the contrary, the embodiments of the application include all changes, modifications and equivalents coming within the spirit and terms of the claims appended hereto.

The method, device, equipment and medium for processing logistics information based on RPA and AI provided by the embodiments of the present application are described in detail below with reference to the accompanying drawings.

Example one

Fig. 1a is a flowchart of a logistics information processing method based on RPA (robot Process Automation) and AI (Artificial Intelligence), which is provided in an embodiment of the present application and can be applied in an application scenario of settling a logistics list sent by a user. The technical scheme of the embodiment is implemented through an RPA robot, the RPA robot can be carried on a UiBot Creator platform, and the UiBot Creator platform is a powerful robot production tool in the profession and provides a good carrier for the robot. In this embodiment, the logistics list to be processed may be stored in a designated folder in advance. And the RPA robot can be set to be started regularly every day, the logistics list to be processed in the appointed folder is read, and the logistics list is processed, so that the condition of overstocking the logistics list to be processed is avoided, and the effect of improving the logistics list processing efficiency is achieved. As shown in fig. 1a, the method provided by this embodiment includes:

and S110, reading the address information and the article information in the logistics list.

For the logistics list stored in the designated folder, the list usually exists in the form of Excel (spreadsheet), in which item information and address information related to the item are contained, and the item information includes an item name, an item quantity, an item price, and the like of the item.

For example, when reading the contents in the logistics list, the RPA robot may scan the logistics list in conjunction with the OCR component in the AI technology to obtain the contents in the logistics list. Specifically, the RPA robot can call a general table recognition function in the OCR component to obtain the content of each cell in the logistics list and the row and column information of each cell.

In this embodiment, the AI platform with the general form recognition function is a UiBot Mage platform, which is a tool type product that mainly provides AI capability support for an RPA robot developer. The platform and a UiBot Creator platform carried by the RPA robot both depend on the UiBot platform, and the UiBot platform is a process automation expert and a platform facing to various requirements and providing intelligent robot service for the whole business process. The AI platform is integrated with a pre-configured form information extraction template. By using the table information extraction template, table contents and corresponding row and column information can be extracted.

Optionally, the platform carried by the RPA robot may be combined with the AI platform by simultaneously logging in a target account, i.e., a UiBot account, of the RPA platform and the AI platform. After the target account is used for simultaneously logging in a platform carried by the RPA robot and an AI platform, the platform carried by the RPA robot establishes communication connection with the AI platform, namely, the RPA robot can directly call a universal form recognition function issued by the AI platform to extract contents in a logistics list. According to the arrangement, compared with the prior art that the logistics list is firstly identified by the aid of the OCR function on the AI platform, the identified data are exported manually, and then are imported into the RPA platform manually, the method combines the RPA platform with the AI platform, solves the problem that the logistics list content is time-consuming and labor-consuming in the identification process of the logistics list content in the prior art, and improves the identification efficiency of the logistics list content.

Specifically, fig. 1b is a screenshot of an effect of a logistics list before the logistics information is merged according to an embodiment of the present application. As shown in fig. 1b, the RPA robot recognizes the logistics list by calling the general table recognition function in the OCR component in the AI platform, and obtains the specific province information located in the first column of the table, the specific city information located in the second column of the table, the specific region information located in the third column of the table, the detailed address information located in the fourth column of the table, the name information of the article located in the fifth column of the table, the specific quantity information of the article located in the sixth column of the table, and the specific unit price information of the article located in the seventh column of the table.

For the address information, because the types of the address information corresponding to different columns in the logistics list are different, for example, as shown in fig. 1b, the different columns respectively correspond to province information, city information, region information, and detailed addresses, in this embodiment, the address information of different types may be spliced, and the spliced address information is used as the address information corresponding to a certain article.

And S120, determining target address information belonging to the same geographic position from each address information.

In this embodiment, the geographic locations belonging to the same geographic location means that the location information of each piece of address information in the preset electronic map is the same or the distance relationship between each piece of location information is within a set distance threshold range. The preset electronic map can provide position information including longitude, latitude, altitude and the like. The distance in this embodiment may be an euclidean distance, and may be calculated by longitude and latitude coordinates.

For example, the RPA robot may obtain the location information of each address information in the preset electronic map by calling an interface corresponding to the preset electronic map. By comparing the obtained position information pairwise, if the distance between the two is zero or the distance relationship is within a set distance threshold range, the address information with the distance within the set threshold range can be used as the target address information belonging to the same geographic position.

Further, in order to improve the accuracy of determining the target address information, before calling an interface corresponding to a preset electronic map, the RPA robot may further perform standardization on the address information, that is, convert the address information into standard address information meeting the requirement of a preset map database, where the standard address information includes province information, city information, region information, and detailed address information, and the detailed address information includes street information or house number information. In this embodiment, by converting each address information in the logistics list into standard address information meeting the requirements of a preset map database, the description mode of each address information can be standardized, so as to facilitate the subsequent determination of each address position information.

Furthermore, for a plurality of standard address information belonging to the same geographical location in the logistics list, normalization processing may be performed on the standard address information, that is, the standard address information is represented by using the same address description mode, that is, by normalizing the address. In this embodiment, the address information corresponding to each standard address information in the preset electronic map may be used as the normalized address. In this embodiment, by performing normalization processing on a plurality of standard address information belonging to the same geographic location, the plurality of standard addresses can be represented in the same address description manner, that is, effective sorting of each address information in a logistics list is realized, so that a subsequent RPA robot can merge and settle article information according to the normalized address information.

Further, after obtaining the normalized addresses belonging to the same geographic location, the RPA robot may write the normalized addresses into a table, so as to facilitate subsequent manual inspection and review of the operations of the RPA robot.

And S130, merging the article information of the same article corresponding to the same target address information in the logistics list.

Wherein the item information includes an item quantity and an item price. In this embodiment, the RPA robot merges the item information corresponding to the same target address, specifically, merges the quantity and the price of the item, that is, the numerical value corresponding to the quantity of the item and the numerical value corresponding to the price of the item are respectively summed, so as to obtain the total quantity and the total price of the same item corresponding to the same target address, so as to facilitate the subsequent settlement of the item. Compared with the mode of manually merging the article information in the related art, the embodiment can save a large amount of time through the RPA robot, so that the article information can be accurately and quickly sorted.

Next, the logistics information processing method provided in this embodiment is described in detail with reference to a specific logistics list.

As shown in fig. 1b, the RPA robot may identify the logistics list by calling an OCR component of the AI platform, so as to obtain the content of each cell in the logistics list and the row and column information of the table where the cell is located. The RPA robot may extract address information and item information from the cell contents, i.e., specific province information located in the first column of the table, specific city information located in the second column of the table, specific region information located in the third column of the table, detailed address information located in the fourth column of the table, item name information located in the fifth column of the table, specific item number information located in the sixth column of the table, and specific item unit price information located in the seventh column of the table.

The RPA robot may splice the address information of each cell according to the position of each row in the table, for example, after splicing the province, city, district and detailed address in the first column, the second column, the third column and the fourth column of each row in the table shown in fig. 1b according to rows, a plurality of address information may be obtained. Next, the RPA robot may determine the position information of each address information according to the above method. If it is determined that the address information shown in fig. 1b all belong to the same geographical location, the addresses may be normalized, that is, the addresses are treated as the same address and expressed in the same address description manner.

Specifically, fig. 1c is a screenshot of an effect of a logistics list after the logistics information is merged according to an embodiment of the present application. As shown in fig. 1c, the RPA robot may write the normalized address into the eighth column of the table. Further, the RPA robot may merge the article information of the same article corresponding to the same normalized address in the table, and mark the merged article information, for example, may highlight the article information by adding a different color thereto. As shown in FIG. 1b, for projector X00-02, projector X10-04, in the fifth column of the table, the RPA robots can merge their numbers and unit prices, respectively. As shown in fig. 1c, the number of merged projectors X00-02 by RPA robot is 2, and the unit price is 5000; the number of the RPA robots combined with the projectors X10-04 is 2, and the unit price is 6000. The RPA robot is adopted to replace manual work to arrange the target address information belonging to the same geographic position, and the article information of the same article corresponding to the same target address is combined, so that the time of workers is saved, and the processing efficiency of the logistics list is improved.

According to the technical scheme provided by the embodiment, the RPA robot can determine the target address information belonging to the same geographic position from each address information by replacing the manual reading of the address information and the article information in the logistics list, so that the article information of the same article corresponding to the same target address information in the logistics list can be merged. By adopting the technical scheme, the sorting efficiency of the address information in the logistics list is improved, and the leakage rate and the error rate in the manual sorting process are reduced.

Example two

Fig. 2 is a flowchart of a logistics information processing method based on RPA and AI according to a second embodiment of the present application, where in this embodiment, on the basis of the foregoing embodiment, a process of converting each address information in a logistics list into a standard address and a process of normalizing the standard address are refined, and as shown in fig. 2, the method according to this embodiment includes:

and S210, reading the address information and the article information in the logistics list.

And S220, converting each address information into a standard address to be matched according with the requirement of a preset map database.

The standard address comprises province information, city information, region information and street information.

In this embodiment, the RPA robot may perform error correction processing on each address information to obtain a standard address to be matched that meets the requirement of the preset map database, where the error correction processing includes wrongly written character correction and address information completion.

For example, in the error correction process, the RPA robot may compare different types of address information with information in a preset map database respectively according to the types of the address information in the logistics list, such as province information, city information, region information, detailed address information, and the like, and correct unmatched address information. Or, if the address information read by the RPA robot is the address information obtained by combining the contents of the cells in the logistics list, at this time, the RPA robot may perform word segmentation on the address information, compare the word segmentation result with the information in the preset map database, and correct the unmatched address information.

As an optional implementation manner, for provincial information, city information and regional information in the logistics list, if one type of information does not match corresponding information in the preset map database, in the process of correcting the unmatched information, the unmatched information may be corrected according to other matched information. If two types of information are not matched with the corresponding information in the preset map database, the unmatched information can be corrected according to other matched information and by combining detailed address information in the logistics list in the process of correcting the unmatched information. Similarly, if the detailed address information in the logistics list is not matched with the corresponding information in the preset map database, the unmatched content can be corrected according to other matched content in the process of correcting the unmatched detailed address information.

For example, if the address information in the table is "base B of drum building area phoenix square in south jing city of jiangxi province", and the address information in the preset map database includes "base B of drum building area phoenix square in south jing city of jiangsu province", at this time, if matching province information of the RPA robot is inconsistent, it indicates that an error occurs when selecting the province information during the process of making the logistics list by the user, and at this time, the RPA robot can correct "jiangxi province" to "jiangsu province" according to other matching address information.

For another example, if the address information in the table is "B seat of drum area yellow square in south Beijing city of Jiangxi province", and the address information in the preset map database includes "B seat of drum area yellow square in south Beijing city of Jiangsu province", at this time, if the matching area information of the RPA robot is inconsistent, it indicates that an error occurs when the user fills in the area information during the process of making the logistics list, at this time, the RPA robot corrects the "drum area" into the "drum area", and corrects the "yellow square" into the "phoenix square".

As another alternative, in the error correction process, if province information, city information, or region information in the logistics list is missing, the RPA may complete the missing information based on other address information in the logistics list and according to a preset map database, so as to convert the address information into a standard address to be matched meeting the requirements of the preset map database, where the labeled address includes province information, city information, region information, and street information.

Specifically, if the address information in the table is "Nanjing city drum building area phoenix square seat B", the RPA robot can determine that Nanjing city belongs to Jiangsu province through a preset map database, and at this time, missing province information can be completed.

In this embodiment, the RPA robot corrects the address information of the user who wrongly fills in the logistics list, so as to obtain the standard address information meeting the requirement of the preset map database, i.e., standardizing the address information in the logistics list, thereby providing a basis for the judgment of the subsequent position information.

And S230, determining the position information of the standard address in the preset electronic map.

The position information comprises longitude coordinates and latitude coordinates of the standard address. And calling an interface of a preset electronic map to obtain the position information corresponding to the standard address.

For example, for a certain standard address, if a plurality of address information corresponding thereto exist in the preset electronic map, the position information of the address closest to the position of the standard address may be determined as the standard position information corresponding to the standard address.

In addition, by searching the standard address in the preset electronic map, address information closest to the position of the standard address can also be returned.

And S240, normalizing each standard address corresponding to the same position information to obtain normalized target address information.

The normalized address is an address which is processed by taking each standard address with a close position relation in a preset electronic map, namely, a distance within a preset range as an address belonging to the same geographical position.

In this embodiment, for the location information corresponding to any one standard address, the distance between the location information and other location information is determined, if the distance is within a preset distance threshold range, all standard addresses whose distance relationship is within the preset distance threshold range are used as addresses to be normalized corresponding to the same location information, and the address information corresponding to each address to be normalized in a preset electronic map is used as the normalized target address information.

In this embodiment, the RPA robot normalizes the address information belonging to the same geographical location in the logistics list, so that each address information is effectively sorted. Compared with a mode of manually sorting each address information in the logistics list in the related art, the method can effectively reduce the leakage rate and the error rate of address information processing.

And S250, writing the target address information into the logistics list.

And S260, merging the article information of the same article corresponding to the same target address information in the logistics list.

In this embodiment, the merging of the item information mainly refers to merging of quantity information and merging of price information, so that subsequent settlement of the same item is facilitated.

And S270, marking the combined article information.

For example, marking the merged article information may be adding a filling background of another color, or displaying the merged article information as a different color.

Further, after the merging and marking of the article information are completed, the RPA robot can also send the processing result of the article information to relevant staff for examination and composition, so as to further ensure the accuracy of the logistics information processing.

In the embodiment, the address information which is wrongly filled by the user in the logistics list is corrected, so that the standard address information which meets the requirement of the preset map database can be obtained, and the subsequent determination of the position information of the address information in the preset electronic map is facilitated. In addition, address information belonging to the same geographical position in the logistics list is subjected to normalization processing, so that each address information can be effectively sorted. Compared with a mode of manually sorting each address information in the logistics list in the related art, the method can effectively reduce the leakage rate and the error rate of address information processing.

EXAMPLE III

Fig. 3 is a block diagram of a device of a logistics information processing method based on RPA and AI according to a third embodiment of the present application, where the device includes: an information reading module 310, a target address determination module 320, and an item information consolidation module 330. Wherein the content of the first and second substances,

an information reading module 310 configured to read address information and article information in the logistics list;

a target address determination module 320 configured to determine target address information belonging to the same geographical location from each address information;

and an item information merging module 330 configured to merge item information of the same item corresponding to the same target address information in the logistics list, where the item information includes an item quantity and an item price.

Optionally, the information reading module 310 is specifically configured to:

Optionally, the target address determining module 320 includes:

Optionally, the address normalization unit is specifically configured to:

The functions of each module in each apparatus in the embodiment of the present application may refer to corresponding descriptions in the above method, and are not described herein again.

Example four

Fig. 4 is a block diagram of an apparatus for processing logistics information according to a fourth embodiment of the present application. As shown in fig. 4, the apparatus includes: a memory 910 and a processor 920, the memory 910 having stored therein computer programs operable on the processor 920. The processor 920 implements the method for processing logistics information based on RPA and AI in the above embodiments when executing the computer program. The number of the memory 910 and the processor 920 may be one or more.

The apparatus further comprises:

and a communication interface 930 for communicating with an external device to perform data interactive transmission.

If the memory 910, the processor 920 and the communication interface 930 are implemented independently, the memory 910, the processor 920 and the communication interface 930 may be connected to each other through a bus and perform communication with each other. The bus may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, an Extended ISA (EISA) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one thick line is shown in FIG. 4, but this does not indicate only one bus or one type of bus.

Optionally, in an implementation, if the memory 910, the processor 920 and the communication interface 930 are integrated on a chip, the memory 910, the processor 920 and the communication interface 930 may complete communication with each other through an internal interface.

Embodiments of the present application provide a computer-readable storage medium, which stores a computer program, and when the program is executed by a processor, the computer program implements the method provided in the embodiments of the present application.

The embodiment of the present application further provides a chip, where the chip includes a processor, and is configured to call and execute the instruction stored in the memory from the memory, so that the communication device in which the chip is installed executes the method provided in the embodiment of the present application.

An embodiment of the present application further provides a chip, including: the system comprises an input interface, an output interface, a processor and a memory, wherein the input interface, the output interface, the processor and the memory are connected through an internal connection path, the processor is used for executing codes in the memory, and when the codes are executed, the processor is used for executing the method provided by the embodiment of the application.

It should be understood that the processor may be a Central Processing Unit (CPU), other general purpose processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, etc. A general purpose processor may be a microprocessor or any conventional processor or the like. It is noted that the processor may be an advanced reduced instruction set machine (ARM) architecture supported processor.

Further, optionally, the memory may include a read-only memory and a random access memory, and may further include a nonvolatile random access memory. The memory may be either volatile memory or nonvolatile memory, or may include both volatile and nonvolatile memory. The non-volatile memory may include a read-only memory (ROM), a Programmable ROM (PROM), an Erasable PROM (EPROM), an electrically Erasable EPROM (EEPROM), or a flash memory. Volatile memory can include Random Access Memory (RAM), which acts as external cache memory. By way of example, and not limitation, many forms of RAM are available. For example, Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), Synchronous Dynamic Random Access Memory (SDRAM), double data rate synchronous SDRAM (DDR SDRAM), Enhanced SDRAM (ESDRAM), synchlink DRAM (SLDRAM), and direct memory bus RAM (DR RAM).

In the above embodiments, the implementation may be wholly or partially realized by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. The procedures or functions according to the present application are generated in whole or in part when the computer program instructions are loaded and executed on a computer. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium.

In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the application. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.

Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present application, "a plurality" means two or more unless specifically limited otherwise.

Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process. And the scope of the preferred embodiments of the present application includes other implementations in which functions may be performed out of the order shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved.

The logic and/or steps represented in the flowcharts or otherwise described herein, e.g., an ordered listing of executable instructions that can be considered to implement logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions.

It should be understood that portions of the present application may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. All or part of the steps of the method of the above embodiments may be implemented by hardware that is configured to be instructed to perform the relevant steps by a program, which may be stored in a computer-readable storage medium, and which, when executed, includes one or a combination of the steps of the method embodiments.

In addition, functional units in the embodiments of the present application may be integrated into one processing module, or each unit may exist alone physically, or two or more units are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The integrated module may also be stored in a computer-readable storage medium if it is implemented in the form of a software functional module and sold or used as a separate product. The storage medium may be a read-only memory, a magnetic or optical disk, or the like.

The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive various changes or substitutions within the technical scope of the present application, and these should be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims

1. A logistics information processing method based on Robot Process Automation (RPA) and Artificial Intelligence (AI) is applied to an RPA robot and is characterized by comprising the following steps:

and S3, merging the article information of the same article corresponding to the same target address information in the logistics list, wherein the article information comprises article quantity and article price.

2. The method according to claim 1, wherein the step S1 specifically includes:

and calling an Optical Character Recognition (OCR) component to recognize the logistics list to obtain address information and article information in the logistics list.

3. The method according to claim 1, wherein the step S2 specifically includes:

4. The method according to claim 3, wherein the step S21 specifically includes:

and carrying out error correction processing on each address information to obtain a standard address to be matched, which meets the requirement of a preset map database, wherein the error correction processing comprises correction of wrongly written words and completion of address information.

5. The method according to claim 3, wherein the step S22 specifically includes:

6. The method according to any one of claims 3 to 5, wherein the step S23 specifically includes:

7. The method of claim 1, further comprising:

8. A device of a logistics information processing method based on RPA and AI is characterized by comprising the following steps:

and the article information merging module is configured to merge article information of the same article corresponding to the same target address information in the logistics list, wherein the article information comprises article quantity and article price.

9. The apparatus of claim 8, wherein the information reading module is specifically configured to:

10. The apparatus of claim 8, wherein the target address determination module comprises:

a position information determining unit configured to determine position information of the standard address in a preset electronic map, wherein the position information comprises longitude and latitude of the standard address;

11. The apparatus according to claim 10, wherein the standard address determination unit is specifically configured to:

12. The apparatus according to claim 10, wherein the location information determining unit is specifically configured to:

13. The apparatus according to claim 10, wherein the address normalization unit is specifically configured to:

14. An apparatus for processing logistics information, comprising: a processor and a memory, the memory having stored therein instructions that are loaded and executed by the processor to implement the method of any of claims 1 to 7.

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