Information processing method and device, storage medium and electronic equipment

Abstract

The invention provides an information processing method and device, a storage medium and electronic equipment, and relates to the field of intelligent logistics and the field of artificial intelligence. The method comprises the steps that a logistics bill feedback message sent by a client side is analyzed, logistics bill defect information is obtained, and the logistics bill defect information represents the defect of a target logistics bill corresponding to the logistics bill feedback message; determining a target defect type of the target logistics order according to the logistics order defect information; determining a target server corresponding to the target defect type; generating a target logistics bill defect feedback message according to the target defect type and the logistics bill defect information; and sending the target logistics sheet defect feedback message to the target server.

Description

Technical Field

[0001] The present disclosure relates to the fields of smart logistics and artificial intelligence, and more specifically, to an information processing method, device, storage medium, electronic device and program product. Background Art

[0002] With the development of economy and commerce, logistics documents (such as logistics delivery notes) are increasingly used in logistics scenarios. Logistics documents can be attached to mailing parcels, so that logistics document scanning devices can obtain important information such as logistics information and product information of the mailing parcels by scanning the logistics documents, so as to improve the logistics efficiency of the mailing parcels.

[0003] In the process of realizing the concept disclosed in the present invention, the inventors found that there are at least the following problems in the related technology: some logistics documents affixed to mailing parcels are difficult to accurately identify information related to logistics, resulting in low logistics efficiency of mailing parcels. Summary of the invention

[0004] In view of this, the present disclosure provides an information processing method, apparatus, storage medium, electronic device, and program product.

[0005] One aspect of the present disclosure provides an information processing method, comprising:

[0006] Parse the logistics order feedback message sent by the client to obtain logistics order defect information, wherein the logistics order defect information represents the defect of the target logistics order corresponding to the logistics order feedback message;

[0007] According to the above logistics order defect information, determine the target defect type of the above target logistics order;

[0008] Determine the target server corresponding to the above target defect type;

[0009] Generate a target logistics order defect feedback message according to the target defect type and the logistics order defect information; and

[0010] Send the above-mentioned target logistics order defect feedback message to the above-mentioned target server.

[0011] According to an embodiment of the present disclosure, the above-mentioned logistics order defect information includes a logistics order defect image related to the above-mentioned target logistics order;

[0012] Wherein, determining the target defect type of the target logistics order according to the logistics order defect information includes:

[0013] Input the above-mentioned logistics order defect image into the first recognition model constructed based on the deep learning algorithm, and output the image recognition result; and

[0014] According to the above image recognition results, the above target defect type is determined.

[0015] According to an embodiment of the present disclosure, determining the target defect type according to the image recognition result includes:

[0016] In the case where the image recognition result is a first image recognition result indicating that the target logistics bill meets the standard conditions, extracting key text in the target logistics bill;

[0017] Compare the key text and the standard condition verification text corresponding to the target logistics order to obtain a comparison result; and

[0018] According to the above comparison results, the above target defect type is determined.

[0019] According to an embodiment of the present disclosure, the first recognition model includes at least one of the following:

[0020] Multilayer perceptron model, convolutional neural network model, and recurrent neural network model.

[0021] According to an embodiment of the present disclosure, the above-mentioned logistics order defect information includes a logistics order defect description text;

[0022] Wherein, determining the target defect type of the target logistics order according to the logistics order defect information includes:

[0023] Input the above logistics order defect description text into the second recognition model, and output the text recognition result; and

[0024] Based on the above text recognition results, the above target defect type is determined.

[0025] According to an embodiment of the present disclosure, the logistics order defect information includes a logistics order defect identifier, and the logistics order defect identifier is generated by the client in response to detecting a feedback operation for a logistics order defect feedback object;

[0026] Wherein, determining the target defect type of the target logistics order according to the logistics order defect information includes:

[0027] According to the above-mentioned logistics order defect identification, the above-mentioned target defect type is determined.

[0028] According to an embodiment of the present disclosure, the logistics order defect information includes a target logistics order identifier and an operation user identifier associated with the target logistics order identifier. The operation user identifier is suitable for representing a user identifier for performing logistics operations on the logistics order with the target logistics order identifier. The operation identifier includes multiple identifiers.

[0029] The above information processing method further includes:

[0030] According to the operation time attribute information corresponding to each of the plurality of operation user identifiers, a target operation user identifier is determined from the plurality of operation user identifiers.

[0031] According to an embodiment of the present disclosure, generating a target logistics order defect feedback message according to the target defect type and the logistics order defect information includes:

[0032] Determining target defect type information corresponding to the target defect type; and

[0033] According to the target protocol corresponding to the target server, the target logistics order identifier, the target operation user identifier and the target defect type information are encapsulated to obtain the target logistics order defect feedback message.

[0034] According to an embodiment of the present disclosure, the above-mentioned logistics operation includes at least one of the following:

[0035] Logistics order pasting operation, logistics order creation operation, logistics order information filling operation.

[0036] According to an embodiment of the present disclosure, the above-mentioned target defect type includes at least one of the following:

[0037] Logistics order printing defect type, logistics order pasting defect type, logistics order template defect type, system configuration defect type.

[0038] Another aspect of the present disclosure provides an information processing device, including:

[0039] A parsing module, used for parsing the logistics order feedback message sent from the client to obtain logistics order defect information, wherein the logistics order defect information represents the defect of the target logistics order corresponding to the logistics order feedback message;

[0040] A target defect type determination module is used to determine the target defect type of the target logistics order according to the logistics order defect information;

[0041] A target server determination module, used for a target server corresponding to the above target defect type;

[0042] A message generation module, used to generate a target logistics order defect feedback message according to the target defect type and the logistics order defect information; and

[0043] The sending module is used to send the above-mentioned target logistics order defect feedback message to the above-mentioned target server.

[0044] Another aspect of the present disclosure provides a computer-readable storage medium storing computer-executable instructions, which are used to implement the above method when executed.

[0045] Another aspect of the present disclosure provides an electronic device, comprising: one or more processors; and a memory for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the method as described above.

[0046] Another aspect of the present disclosure provides a computer program product, which includes computer executable instructions, and when the instructions are executed, are used to implement the method as described above.

[0047] According to the embodiments of the present disclosure, by parsing the logistics order defect information obtained from the logistics order feedback message, the target defect type of the target logistics order is determined, and the target server corresponding to the target defect type is further determined, so that the defect type of the target logistics order can be quickly classified, and the target server that needs to receive the feedback logistics order defect information can be automatically determined. By sending the target logistics order defect feedback message to the target client, the target client can obtain the logistics order defect information and the target defect type in a timely manner, at least partially reducing the operational steps for defect type classification for the logistics order defect information, saving the defect analysis time for the target logistics order, and thus achieving the technical effect of improving the overall efficiency of cargo logistics. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] The above and other objects, features and advantages of the present disclosure will become more apparent through the following description of the embodiments of the present disclosure with reference to the accompanying drawings, in which:

[0049] Figure 1 An exemplary system architecture to which the information processing method and device according to an embodiment of the present disclosure can be applied is schematically shown;

[0050] Figure 2 The flowchart of the information processing method according to the embodiment of the present disclosure is schematically shown;

[0051] Figure 3 A flowchart of determining a target defect type of a target logistics order according to logistics order defect information according to an embodiment of the present disclosure is schematically shown;

[0052] Figure 4 The application scenario diagram of the information processing method according to the embodiment of the present disclosure is schematically shown;

[0053] Figure 5 Adaptability shows an application scenario diagram of an information processing method according to another embodiment of the present disclosure;

[0054] Figure 6 A schematic diagram of a data report monitoring interface of a sorting system according to an embodiment of the present disclosure is schematically shown.

[0055] Fig. 7A The flowchart of an information processing method according to another embodiment of the present disclosure is schematically shown.

[0056] Figure 7B The flowchart of an information processing method according to another embodiment of the present disclosure is schematically shown.

[0057] Figure 8 The block diagram schematically shows an information processing device according to an embodiment of the present disclosure.

[0058] Fig. 9 A block diagram of an electronic device suitable for implementing an information processing method according to an embodiment of the present disclosure is schematically shown. DETAILED DESCRIPTION

[0059] Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the present disclosure. In the following detailed description, for ease of explanation, many specific details are set forth to provide a comprehensive understanding of the embodiments of the present disclosure. However, it is apparent that one or more embodiments may also be implemented without these specific details. In addition, in the following description, descriptions of known structures and technologies are omitted to avoid unnecessary confusion of the concepts of the present disclosure.

[0060] The terms used herein are only for describing specific embodiments and are not intended to limit the present disclosure. The terms "comprise", "include", etc. used herein indicate the existence of the features, steps, operations and / or components, but do not exclude the existence or addition of one or more other features, steps, operations or components.

[0061] All terms (including technical and scientific terms) used herein have the meanings commonly understood by those skilled in the art, unless otherwise defined. It should be noted that the terms used herein should be interpreted as having a meaning consistent with the context of this specification, and should not be interpreted in an idealized or overly rigid manner.

[0062] When using expressions such as "at least one of A, B, and C, etc.", they should generally be interpreted according to the meaning of the expression commonly understood by those skilled in the art (for example, "a system having at least one of A, B, and C" should include but is not limited to a system having A alone, B alone, C alone, A and B, A and C, B and C, and / or A, B, C, etc.).

[0063] In the embodiments of the present disclosure, the collection, updating, analysis, processing, use, transmission, provision, disclosure, storage, etc. of the data involved (for example, including but not limited to user personal information) are in compliance with the provisions of relevant laws and regulations, are used for legitimate purposes, and do not violate public order and good morals. In particular, necessary measures are taken for user personal information to prevent illegal access to user personal information data and maintain the security of user personal information, network security, and national security.

[0064] In the embodiments of the present disclosure, the user's authorization or consent is obtained before obtaining or collecting the user's personal information.

[0065] The waybill (logistics bill) plays a relatively important role in the field of logistics: the operations of each link of logistics can assist in the logistics operations such as sorting and distribution based on the information identified on the waybill. In particular, the relevant logistics automation equipment needs to identify the important information in the logistics bill to perform automated logistics operations such as inspection and sorting. For mailing users, it is also possible to realize value-added logistics services through logistics bills with value-added service content, such as privacy waybills, greeting card logistics bills and other value-added logistics bills. Therefore, the specifications and standards of logistics bills in the printing, application and other links are more important for improving the quality of logistics services. The inventor creatively discovered that it is difficult to accurately identify the defects of logistics bills that do not meet the specifications and standards in the relevant technology, and the rectification efficiency of the defects of logistics bills is low, which has a large negative impact on logistics efficiency.

[0066] The embodiments of the present disclosure provide an information processing method, device, storage medium, electronic device and program product. The information processing method includes: parsing a logistics order feedback message sent from a client to obtain logistics order defect information, wherein the logistics order defect information represents the defect of the target logistics order corresponding to the logistics order feedback message; determining the target defect type of the target logistics order according to the logistics order defect information; determining the target service end corresponding to the target defect type; generating a target logistics order defect feedback message according to the target defect type and the logistics order defect information; and sending the target logistics order defect feedback message to the target service end.

[0067] According to the embodiments of the present disclosure, by parsing the logistics order defect information obtained from the logistics order feedback message, the target defect type of the target logistics order is determined, and the target server corresponding to the target defect type is further determined, so that the defect type of the target logistics order can be quickly classified, and the target server that needs to receive the feedback logistics order defect information can be automatically determined. By sending the target logistics order defect feedback message to the target client, the target client can obtain the logistics order defect information and the target defect type in a timely manner, at least partially reducing the operational steps for defect type classification for the logistics order defect information, saving the time for defect analysis of the target logistics order, and thus improving the overall cargo logistics efficiency.

[0068] Figure 1 The exemplary system architecture 100 to which the information processing method and apparatus according to the embodiment of the present disclosure can be applied is schematically shown. It should be noted that: Figure 1 What is shown is merely an example of a system architecture to which the embodiments of the present disclosure can be applied, in order to help those skilled in the art understand the technical content of the present disclosure, but it does not mean that the embodiments of the present disclosure cannot be used in other devices, systems, environments or scenarios.

[0069] like Figure 1 As shown, the system architecture 100 according to this embodiment may include terminal devices 101, 102, 103, a network 104 and a server 105. The network 104 is used to provide a medium for communication links between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired and / or wireless communication links, etc.

[0070] Users can use terminal devices 101, 102, 103 to interact with server 105 through network 104 to receive or send messages, etc. Various communication client applications can be installed on terminal devices 101, 102, 103, such as shopping applications, web browser applications, search applications, instant messaging tools, email clients and / or social platform software, etc. (only as examples).

[0071] Terminal devices 101, 102, 103 can be various electronic devices with display screens and supporting web browsing, including but not limited to smart phones, tablet computers, laptop computers, desktop computers, etc. Among them, terminal device 103 can be a logistics order scanning device with communication function. The logistics order scanning device can scan the logistics order (face order) affixed to the mailing package to obtain logistics information related to the logistics order. Correspondingly, the logistics order scanning device can also capture images of the logistics order through the mailing package to obtain image information containing the logistics order. Alternatively, the logistics order scanning device can also detect the operations performed by the user through buttons and / or touch screens, and generate messages for interaction with the server 105 based on the operations.

[0072] The server 105 may be a server that provides various services, such as a background management server (only an example) that provides support for websites browsed by users using the terminal devices 101, 102, and 103. The background management server may analyze and process the received data such as user requests, and feed back the processing results (such as web pages, information, or data obtained or generated according to user requests) to the terminal device.

[0073] It should be noted that the information processing method provided in the embodiment of the present disclosure can generally be executed by the server 105. Accordingly, the information processing device provided in the embodiment of the present disclosure can generally be set in the server 105. The information processing method provided in the embodiment of the present disclosure can also be executed by a server or server cluster that is different from the server 105 and can communicate with the terminal devices 101, 102, 103 and / or the server 105. Accordingly, the information processing device provided in the embodiment of the present disclosure can also be set in a server or server cluster that is different from the server 105 and can communicate with the terminal devices 101, 102, 103 and / or the server 105. Alternatively, the information processing method provided in the embodiment of the present disclosure can also be executed by the terminal devices 101, 102, or 103, or can also be executed by other terminal devices different from the terminal devices 101, 102, or 103. Accordingly, the information processing device provided in the embodiment of the present disclosure can also be set in the terminal devices 101, 102, or 103, or can be set in other terminal devices different from the terminal devices 101, 102, or 103.

[0074] For example, any one of the terminal devices 101, 102, or 103 (for example, terminal device 101, but not limited thereto) can receive a logistics order feedback message sent by other terminal devices (clients). Then, the terminal device 101 can locally execute the information processing method provided by the embodiment of the present disclosure, or send the logistics order feedback message to other terminal devices, servers, or server clusters, and the other terminal devices, servers, or server clusters that receive the logistics order feedback message execute the information processing method provided by the embodiment of the present disclosure.

[0075] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is only for illustration. Any number of terminal devices, networks and servers may be provided according to the implementation requirements.

[0076] Figure 2 The flowchart of the information processing method according to the embodiment of the present disclosure is schematically shown.

[0077] like Figure 2 As shown, the information processing method includes operations S210 to S250.

[0078] In operation S210, the logistics order feedback message sent from the client is parsed to obtain logistics order defect information, wherein the logistics order defect information represents the defects of the target logistics order corresponding to the logistics order feedback message.

[0079] According to the embodiments of the present disclosure, the target logistics bill may be a document used to record logistics information, such as a logistics bill affixed to mailing parcels, mailing document bags, and other mailing items. However, the target logistics bill is not limited thereto, and may also include other documents used to record logistics information, such as mailing receipts, etc. The embodiments of the present disclosure do not limit the specific file type of the target logistics bill, and those skilled in the art may select it according to actual needs, as long as it can record logistics information.

[0080] According to the embodiments of the present disclosure, the defects of the target logistics order may be any type of defects that have a negative impact on the relevant equipment or personnel reading the logistics information in the target logistics order, such as the missing or worn barcode of the logistics order, or the defect that the logistics order cannot be recognized due to the expiration of the logistics order template, etc. The embodiments of the present disclosure do not limit the specific types of logistics order defects, and those skilled in the art may select according to actual needs.

[0081] According to the embodiments of the present disclosure, the logistics order defect information may include any type of information, such as image information, text information, etc. The embodiments of the present disclosure do not limit the specific information type of the logistics order defect information. Those skilled in the art can choose according to actual needs, as long as the defects of the target logistics order can be characterized.

[0082] In operation S220, a target defect type of a target logistics order is determined according to the logistics order defect information.

[0083] According to the embodiments of the present disclosure, the logistics order defect information can be processed according to the recognition model constructed based on the deep learning algorithm to obtain the target defect type. For example, a classification model can be constructed based on the multi-layer perceptron algorithm, and the logistics order defect information can be processed through the classification model, and the regression target of the classification model is set as the defect type of the logistics order. In this way, the recognition problem of the target defect type can be converted into a classification problem, so as to obtain the target defect type corresponding to the target logistics order.

[0084] But it is not limited to this, and the target defect type can also be obtained based on other methods. For example, the logistics order defect information and candidate defect type information can be processed based on a similarity algorithm, so that the target defect type information with a higher similarity to the logistics order defect information can be determined from multiple candidate defect type information, and then the target defect type can be determined.

[0085] In operation S230, a target service end corresponding to the target defect type is determined.

[0086] In operation S240, a target logistics order defect feedback message is generated according to the target defect type and the logistics order defect information.

[0087] In operation S250, a target logistics order defect feedback message is sent to the target service end.

[0088] According to the embodiments of the present disclosure, the target service end may be a server, server cluster or terminal device for processing a target logistics order with a target defect type. Relevant personnel may obtain one or more target logistics order defect feedback messages from the target service end, and uniformly process target logistics orders with the same target defect type, so as to realize batch recording and batch rectification of target logistics orders with the same target defect type, improve the processing efficiency of target logistics order defects, and thereby reduce the probability of defects in logistics orders and improve the overall efficiency of logistics.

[0089] It should be noted that the target logistics order defect feedback message can be sent to the target server through any sending method. For example, the target logistics order defect feedback message can be sent to the message queue related to the target logistics end, so that different target servers can consume the corresponding target logistics order defect feedback message from the message queue, thereby improving the message sending efficiency.

[0090] The following is a detailed description of the embodiments of the present invention. Figure 2 The method shown is further explained.

[0091] According to an embodiment of the present disclosure, the target defect type includes at least one of the following:

[0092] Logistics order printing defect type, logistics order pasting defect type, logistics order template defect type, system configuration defect type.

[0093] According to the embodiments of the present disclosure, the type of printing defect of the logistics order may represent the printing problem of the target logistics order, such as the printing problem of the printed target logistics order such as drawing, frame breakage, etc. However, it is not limited to this, and may also include printing problems such as unclear printing and insufficient printing ink.

[0094] According to the embodiments of the present disclosure, the type of logistics order application defect can characterize the defect problem of the logistics order being applied to the mailing parcel due to irregular operation. For example, wrinkles appear on the logistics order after application, the logistics order is applied on the seam, and the same logistics order is applied to multiple mailing parcels. However, it is not limited to this, and can also include defect problems where the type of the logistics order does not match the actual required type, such as not applying the winter-exclusive logistics order in winter, not applying the exclusive express logistics order according to the user's requirements, etc.

[0095] According to an embodiment of the present disclosure, the defect type of the logistics order template can characterize the defect problem of the logistics order template for generating the target logistics order, for example, the logistics order template version is expired, the font of the logistics order template is too small, garbled characters appear in the logistics order template, the string is wrong, etc.

[0096] According to an embodiment of the present disclosure, the system configuration defect type can characterize defect problems such as system configuration information errors and missing system configuration information related to generating logistics waybills.

[0097] According to an embodiment of the present disclosure, the logistics order defect information includes a logistics order defect image related to the target logistics order.

[0098] Figure 3 A flowchart for determining a target defect type of a target logistics order based on logistics order defect information according to an embodiment of the present disclosure is schematically shown.

[0099] like Figure 3 As shown, determining the target defect type of the target logistics order according to the logistics order defect information may include operations S310 to S320.

[0100] In operation S310, the defect image of the logistics order is input into a first recognition model constructed based on a deep learning algorithm, and an image recognition result is output.

[0101] In operation S320, a target defect type is determined according to the image recognition result.

[0102] According to an embodiment of the present disclosure, the logistics order defect image can be obtained by the client after performing image acquisition on the target logistics order. For example, when the user cannot read the logistics information of the target logistics order, the user can perform image acquisition on the target logistics order to obtain the logistics order defect image. It can be understood that the client can encapsulate the logistics order defect image into a logistics order feedback message, so as to facilitate the mechanical logistics feedback message according to the information processing method provided in the embodiment of the present disclosure.

[0103] According to an embodiment of the present disclosure, the first recognition model can be constructed based on a deep learning algorithm with classification and recognition capabilities. For example, the first recognition model can be constructed based on a convolutional neural network algorithm. The image recognition result output by the first recognition model can characterize the recognition probability of the logistics order defect image corresponding to the candidate defect type, and determine the target defect type from the candidate defect types based on the recognition probability.

[0104] It should be understood that the first recognition model can be trained based on training samples. The training samples can be obtained by annotating the sample logistics defect images with corresponding sample labels, and the first recognition model can be trained according to a supervised training method.

[0105] According to the embodiments of the present disclosure, by using the first recognition model to obtain the target defect type corresponding to the logistics order defect image, the classification efficiency and accuracy of defect classification for the target logistics order can be improved, thereby further improving the efficiency of generating defect feedback messages for the target logistics order, as well as improving the timeliness and intelligence level of feedback messages for target logistics order defects to the target service end, thereby improving the overall logistics efficiency.

[0106] According to an embodiment of the present disclosure, the first recognition model includes at least one of the following:

[0107] Multilayer perceptron model, convolutional neural network model, and recurrent neural network model.

[0108] According to an embodiment of the present disclosure, in operation S320, determining the target defect type based on the image recognition result includes: extracting key text in the target logistics order when the image recognition result is the first image recognition result characterizing that the target logistics order meets the standard conditions; comparing the key text and the standard condition verification text corresponding to the target logistics order to obtain a comparison result; and determining the target defect type based on the comparison result.

[0109] According to an embodiment of the present disclosure, in the recognition probability output by the first recognition model, when the recognition probability corresponding to the standard type of logistics order characterizing that the target logistics order meets the standard conditions is greater than or equal to the preset probability threshold, the image recognition result may be the first image recognition result. It should be understood that the first image recognition result cannot accurately determine the target defect type from the candidate defect types. For example, it may be difficult for the first recognition model to identify the cause of the error in the system configuration parameters, the expiration of the system configuration parameters, etc. By extracting the key text of the target logistics order, such as the configuration parameter character text, etc. from the logistics order defect image, and comparing the key text with the standard condition verification text, the difference between the key text and the standard condition verification text can be determined from the comparison result, and then the target defect type can be determined as the system configuration defect type through the comparison result.

[0110] It should be noted that the target logistics order with a system configuration defect type may be identified as a normal logistics order by the first recognition model. By combining the first recognition model and comparing the comparison results with the standard condition verification text, the accuracy of defect type identification for the target logistics order can be further improved, and the recognition efficiency of the target logistics order with a system configuration defect type can be improved, thereby achieving the technical effect of improving the rectification efficiency of the logistics order and improving the overall logistics efficiency.

[0111] Figure 4 The application scenario diagram of the information processing method according to the embodiment of the present disclosure is schematically shown.

[0112] like Figure 4 As shown, in this application scenario, the logistics order defect image 410 obtained by parsing can be input into the first recognition model 421, and the first recognition model 421 can be a neural network model constructed based on the Transformer algorithm. When the image recognition result output by the first recognition model 421 is the first recognition result 431 that characterizes that the target logistics order meets the standard conditions, the logistics order defect image 410 can be input into the text extraction model 422 to output the key text 432 in the target logistics order. The text extraction model 422 can be constructed based on the entity extraction method.

[0113] By inputting the key text 432 and the standard condition verification text 433 into the text comparison model 440, the key text 432 and the standard condition verification text 433 can be compared to obtain a comparison result, and the corresponding target defect type 450 can be output according to the comparison result.

[0114] According to an embodiment of the present disclosure, the logistics order defect information includes a logistics order defect description text.

[0115] In operation S220, determining the target defect type of the target logistics order according to the logistics order defect information includes: inputting the logistics order defect description text into the second recognition model and outputting the text recognition result; and determining the target defect type according to the text recognition result.

[0116] According to the embodiments of the present disclosure, the logistics order defect description text may be text input by the user through the client, or may be obtained by the client after performing text recognition based on other types of information such as voice and video. The embodiments of the present disclosure do not limit the specific method of obtaining the logistics order defect description text, and those skilled in the art may select it according to actual needs.

[0117] According to the embodiments of the present disclosure, the second recognition model can be constructed based on a neural network algorithm, for example, the second recognition model can be constructed based on an attention network algorithm. However, this is not limited to the above, and the second recognition model can also be constructed based on other types of algorithms. For example, the second recognition model can also be constructed by itself with other types of algorithms such as regular matching algorithms and clustering algorithms. The embodiments of the present disclosure do not limit the specific algorithm type for constructing the second recognition model, and technicians in this field can choose according to actual needs.

[0118] According to an embodiment of the present disclosure, the target defect type can be determined based on the candidate defect type corresponding to the text recognition result. Alternatively, the target defect type can be determined based on the candidate defect types corresponding to the text recognition result and the image recognition result, respectively, so as to improve the recognition accuracy of the target defect type and improve the automatic recognition efficiency of the target defect type.

[0119] According to an embodiment of the present disclosure, the logistics order defect information includes a logistics order defect identifier, which is generated by the client in response to detecting a feedback operation on a logistics order defect feedback object.

[0120] In operation S220, determining the target defect type of the target logistics order according to the logistics order defect information may include: determining the target defect type according to the logistics order defect identifier.

[0121] Figure 5 An application scenario diagram of an information processing method according to another embodiment of the present disclosure is schematically shown.

[0122] like Figure 5 As shown, the client in this application scenario can be a logistics order scanning device 500A, and the logistics order scanning device 500A can include a touch screen 510, and the touch screen 510 can display a logistics order defect feedback object 511 representing "ink disappearance". The user can click the logistics order defect feedback object 511 in the touch screen 510 (i.e., feedback operation), and the logistics order scanning device 500A detects the feedback operation on the logistics order defect feedback object 511, and can generate a logistics order defect identification corresponding to "ink missing". By encapsulating the logistics order defect identification corresponding to "ink missing" into a logistics order feedback message, and sending the logistics order feedback message to the server 500B. The server 500B can be used to execute the information processing method provided according to the embodiment of the present disclosure, and according to the logistics order defect identification corresponding to "ink missing", determine that the target defect type of the target logistics order is the logistics order printing defect type.

[0123] According to an embodiment of the present disclosure, the server 500B can determine the target service end corresponding to the type of printing defect of the logistics order, for example, the target service end can be determined as the service end 500D of the equipment operation and maintenance department. The server 500B generates a target logistics order defect feedback message, and sends the target logistics order defect feedback message to the message queue 500C associated with the server end 500D of the equipment operation and maintenance department. When the server end 500D of the equipment operation and maintenance department detects the target logistics order defect feedback message in the message queue 500C, it can obtain the target logistics order defect feedback message from the message queue 500C, and promptly maintain or repair the equipment that prints the target logistics order to rectify the defects of the target logistics order.

[0124] According to an embodiment of the present disclosure, the logistics order defect information includes a target logistics order identifier and an operating user identifier associated with the target logistics order identifier. The operating user identifier is suitable for representing a user identifier for performing logistics operations on the logistics order with the target logistics order identifier. The operating identifier includes multiple ones.

[0125] According to an embodiment of the present disclosure, the information processing method may further include: determining a target operating user identifier from the multiple operating user identifiers according to the operating time attribute information corresponding to each of the multiple operating user identifiers.

[0126] According to an embodiment of the present disclosure, the logistics operation includes at least one of the following:

[0127] Logistics order pasting operation, logistics order creation operation, logistics order information filling operation.

[0128] According to an embodiment of the present disclosure, the logistics order preparation operation may include an operation of printing the logistics order, such as an operation in which a user uses a logistics order printer to print a target logistics order.

[0129] According to an embodiment of the present disclosure, the target logistics bill identifier may be an identifier for identifying the target logistics bill, such as a waybill number of the target logistics bill. The operating user identifier may include a user identifier of an operating user who prints the target logistics bill and / or affixes the target logistics bill. For example, a warehousing and logistics personnel may obtain an operating user identifier corresponding to the user account after logging into the user account on the client.

[0130] It should be understood that when the target logistics order is printed multiple times, each time the target logistics order is printed can be associated with a corresponding operating user identifier, so the operating time attribute information corresponding to the operating user identifier can be the time when the target logistics order is printed.

[0131] According to an embodiment of the present disclosure, a target operating user identifier is determined from multiple operating user identifiers by sorting them according to the temporal relationship of the operation time attribute information corresponding to each operating user identifier, and determining the operating user identifier at the end of the sort (i.e., the operating user identifier corresponding to the last moment of printing the target logistics order) as the target operating user identifier.

[0132] It should be noted that the user corresponding to the operating user ID may also be the user ID corresponding to the user who affixes the target logistics bill, or the user corresponding to the operating user ID may also be the user ID corresponding to the user who prints and affixes the target logistics bill. For example, in a logistics scenario, the user who prints the target physical bill and the user who affixes the target logistics bill may be the same user, for example, the user may use a logistics bill printer to print the target logistics bill, and affix the printed target logistics bill to the surface of the logistics package.

[0133] According to an embodiment of the present disclosure, in operation S240, generating a target logistics order defect feedback message according to the target defect type and the logistics order defect information may include: determining the target defect type information corresponding to the target defect type; and encapsulating the target logistics order identifier, the target operation user identifier and the target defect type information according to the target protocol corresponding to the target service end to obtain the target logistics order defect feedback message.

[0134] According to the embodiments of the present disclosure, by determining the target operating user identifier from multiple operating user identifiers, the operating user who has a negative impact on the defects of the target logistics order can be accurately determined. According to the target logistics order identifier, the target operating user identifier and the target defect type information, the target logistics order defect feedback message is encapsulated, so that the target service end can timely obtain the target operating user who has a negative impact on the defects of the target logistics order, so that the corresponding operating user can be prompted in time, or the operating user corresponding to the target operating user identifier can be trained in a targeted manner to avoid the frequency of generating defective target logistics orders and improve the overall efficiency of logistics.

[0135] According to an embodiment of the present disclosure, the target defect type information may include first-level target defect type information and second-level target defect type information, and the target logistics order defect feedback message may include any one or more of the first-level target defect type information and the second-level target defect type information, so that the corresponding target service end can clearly understand the specific type of the defect of the target logistics order and improve the efficiency of subsequent logistics order defect rectification.

[0136] For example, the primary target defect type information and the secondary target defect type information may be represented by the following Table 1.

[0137] Table 1

[0138]

[0139] According to the embodiments of the present disclosure, in the sorting system constructed for the logistics sorting site, the mailing parcels that cannot be normally identified can be processed according to the information processing method provided by the embodiments of the present disclosure, that is, the mailing parcels with the target logistics order attached can be processed. By associating and storing the obtained first-level target defect type information, second-level target defect type information, target logistics order identification, target operation user identification and other information, and displaying them in the logistics order monitoring data table, the relevant operation and maintenance personnel can relatively accurately and clearly understand the statistical information of the target logistics order defects, so as to facilitate timely and targeted defect rectification, so as to improve the operating efficiency of the logistics sorting equipment in the logistics sorting site and improve the overall logistics efficiency.

[0140] Figure 6 A schematic diagram of a data report monitoring interface of a sorting system according to an embodiment of the present disclosure is schematically shown.

[0141] like Figure 6As shown, the sorting system data report monitoring interface 600 may include a classification column suitable for displaying statistical information. The sorting system data report monitoring interface 600 may include a search input box suitable for inputting search information such as statistical time and reporting location. Accordingly, relevant packages can be retrieved according to the search information entered by the user in the search input box, and statistical information related to the reported packages, such as order numbers, package numbers, etc., can be classified and displayed. Therefore, according to the information processing method provided by the embodiment of the present disclosure, the obtained statistical information can be classified and displayed according to the information column in the sorting system data report monitoring interface 600, so as to facilitate classification processing of important information and timely query of statistical information of defects in the target logistics order, so as to facilitate timely and targeted defect rectification. At the same time, the processing status of other systems can also be connected based on the sorting system data report monitoring interface to improve information processing efficiency.

[0142] Fig. 7A The flowchart of an information processing method according to another embodiment of the present disclosure is schematically shown.

[0143] like Fig. 7A As shown, according to the information processing method provided by the embodiment of the present disclosure, information interaction can be realized between the sorting system, WMS system (Warehouse Management System), waybill system, IT system, reward and punishment system and merchant sales system to improve the efficiency of information interaction.

[0144] For example, the sorting staff can report the waybill by sending a message to the sorting system and entering the waybill number. The sorting system determines whether it is a reprinting operation. If the judgment result is yes, the operator information and waybill information of the last reprinting of the waybill are obtained. If the judgment result is no, determine whether it is a warehouse and distribution waybill. If the judgment result is a warehouse and distribution waybill, the warehouse packing staff information and warehouse information are obtained. If the judgment result is not a warehouse and distribution waybill, the waybill printing operator information and waybill information are obtained. The printing operator information and waybill information, as well as the warehouse packing staff information and warehouse information can be sent to the sorting system. The sorting system can generate a message based on the waybill printing operator information, waybill information and report type, and send it to the message queue.

[0145] The IT system can monitor the messages generated by the message queue. When monitoring the messages generated by the message queue, it determines whether the report type is a system problem. When the result is determined to be a system problem, an IT incident ticket is generated according to the report content, and the IT incident ticket is submitted to the corresponding person in charge according to the list of persons in charge.

[0146] The reward and punishment system can monitor the messages generated by the message queue. When monitoring the messages generated by the message queue, it determines whether it is a C2C (Customer to Customer) document. If it is a C2C document, it determines whether the report type is an operation or equipment problem. If the above conditions are met, the reward and punishment system obtains the corresponding ERP department information based on the operator ERP (Enterprise Resource Planning) information in the message queue, and generates reward and punishment data for the operator and the department to which he belongs.

[0147] The merchant sales system can monitor the messages generated by the message queue. When monitoring the messages generated by the message queue, it determines whether the document type is a B2C (Business-to-Consumer) document type (the merchant sales system mainly serves merchants). If it is a B2C document, it determines whether the report type is a template problem. If the above conditions are met, the merchant sales system locates the corresponding merchant and the connected salesperson according to the merchant number in the message queue, and issues the task of improving the delivery note to the salesperson. The salesperson negotiates with the merchant to improve the delivery note and complete the closed-loop task.

[0148] Figure 7B The flowchart of an information processing method according to another embodiment of the present disclosure is schematically shown.

[0149] like Figure 7B As shown, according to the information processing method provided by the embodiment of the present disclosure, information interaction can be realized among the sorting system, AI (Artificial Intelligence) big data system, basic data system, WMS system (Warehouse Management System), waybill system, IT system, reward and punishment system and merchant sales system to improve the efficiency of information interaction.

[0150] For example, the sorting staff can report the waybill by sending a message to the sorting system and entering the waybill number. The sorting system sends the parsed photos to the AI ​​big data system, which performs photo recognition based on artificial intelligence to determine whether there is any violation. If the judgment result is not a violation, mark the suspected system problem. If the judgment result is a violation, send the big data judgment result to the sorting system. The sorting system determines whether it is a suspected system problem. If the judgment result is not a system problem, notify the basic data system to obtain the full table data to check the waybill slide number information. The basic data system determines whether there is a problem. If the judgment result is no, return the no waybill problem to the sorting system. The sorting system determines the type of problem and determines whether there is a package reprinting. If it is determined that there is a package reprinting, obtain the operator information and waybill information of the last reprinting of the waybill from the waybill system. If it is determined that there is no package reprinting, determine whether it is a warehouse and distribution waybill. If it is determined that it is a warehouse and distribution waybill, obtain the warehouse packing personnel and warehouse information from the WMS system. If it is determined that it is not a warehouse delivery bill, the delivery bill printing operator information and delivery bill information are obtained from the delivery bill system. The sorting system generates a message based on the delivery bill printing operator information, delivery bill information and report type, and sends it to the message queue.

[0151] The IT system can monitor the messages generated by the message queue. When monitoring the messages generated by the message queue, it determines whether the report type is a system problem. When the result is determined to be a system problem, an IT incident ticket is generated according to the report content, and the IT incident ticket is submitted to the corresponding person in charge according to the list of persons in charge.

[0152] The reward and punishment system can monitor the messages generated by the message queue. When monitoring the messages generated by the message queue, it determines whether it is a C2C (Customer to Customer) document. If it is a C2C document, it determines whether the report type is an operation or equipment problem. If the above conditions are met, the reward and punishment system obtains the corresponding ERP department information based on the operator ERP (Enterprise Resource Planning) information in the message queue, and generates reward and punishment data for the operator and the department to which he belongs.

[0153] The merchant sales system can monitor the messages generated by the message queue. When monitoring the messages generated by the message queue, it determines whether the document type is a B2C (Business-to-Consumer) document type (the merchant sales system mainly serves merchants). If it is a B2C document, it determines whether the report type is a template problem. If the above conditions are met, the merchant sales system locates the corresponding merchant and the connected salesperson according to the merchant number in the message queue, and issues the task of improving the delivery note to the salesperson. The salesperson negotiates with the merchant to improve the delivery note and complete the closed-loop task.

[0154] Figure 8 The block diagram schematically shows an information processing device according to an embodiment of the present disclosure.

[0155] like Figure 8 As shown, the information processing device 800 includes a parsing module 810 , a target defect type determination module 820 , a target service end determination module 830 , a message generation module 840 and a sending module 850 .

[0156] The parsing module 810 is used to parse the logistics order feedback message sent from the client to obtain the logistics order defect information, wherein the logistics order defect information represents the defects of the target logistics order corresponding to the logistics order feedback message.

[0157] The target defect type determination module 820 is used to determine the target defect type of the target logistics order according to the logistics order defect information.

[0158] The target server determination module 830 is used for determining the target server corresponding to the target defect type.

[0159] The message generation module 840 is used to generate a target logistics order defect feedback message according to the target defect type and logistics order defect information.

[0160] The sending module 850 is used to send a target logistics order defect feedback message to the target service end.

[0161] According to an embodiment of the present disclosure, the logistics order defect information includes a logistics order defect image related to the target logistics order.

[0162] The target defect type determination module includes: a first recognition unit and a first target defect type determination unit.

[0163] The first recognition unit is used to input the defect image of the logistics order into the first recognition model constructed based on the deep learning algorithm, and output the image recognition result.

[0164] The first target defect type determination unit is used to determine the target defect type according to the image recognition result.

[0165] According to an embodiment of the present disclosure, the first target defect type determination unit may include: a key text extraction subunit, a comparison subunit, and a target defect type determination subunit.

[0166] The key text extraction subunit is used to extract key text in the target logistics list when the image recognition result is the first image recognition result that indicates that the target logistics list meets the standard conditions.

[0167] The comparison subunit is used to compare the key text and the standard condition verification text corresponding to the target logistics order to obtain the comparison result.

[0168] The target defect type determination subunit is used to determine the target defect type according to the comparison result.

[0169] According to an embodiment of the present disclosure, the first recognition model includes at least one of the following:

[0170] Multilayer perceptron model, convolutional neural network model, and recurrent neural network model.

[0171] According to an embodiment of the present disclosure, the logistics order defect information includes a logistics order defect description text.

[0172] The target defect type determination module includes: a second identification unit and a second target defect type determination unit.

[0173] The second recognition unit is used to input the logistics order defect description text into the second recognition model and output the text recognition result.

[0174] The second target defect type determination unit determines the target defect type according to the text recognition result.

[0175] According to an embodiment of the present disclosure, the logistics order defect information includes a logistics order defect identifier, which is generated by the client in response to detecting a feedback operation on a logistics order defect feedback object.

[0176] The target defect type determination module includes: a third target defect type determination unit.

[0177] The third target defect type determination unit is used to determine the target defect type according to the defect identification of the logistics order.

[0178] According to an embodiment of the present disclosure, the logistics order defect information includes a target logistics order identifier and an operating user identifier associated with the target logistics order identifier. The operating user identifier is suitable for representing a user identifier for performing logistics operations on the logistics order with the target logistics order identifier. The operating identifier includes multiple ones.

[0179] The information processing device also includes a target operation user identification determination module.

[0180] The target operation user identification determination module is used to determine the target operation user identification from the multiple operation user identifications according to the operation time attribute information corresponding to each of the multiple operation user identifications.

[0181] According to an embodiment of the present disclosure, the message generation module includes: a target defect type information determination unit and a target logistics order defect feedback message acquisition unit.

[0182] The target defect type information determining unit is used to determine target defect type information corresponding to the target defect type.

[0183] The target logistics order defect feedback message obtaining unit is used to encapsulate the target logistics order identifier, the target operation user identifier and the target defect type information according to the target protocol corresponding to the target server, and obtain the target logistics order defect feedback message.

[0184] According to an embodiment of the present disclosure, the logistics operation includes at least one of the following:

[0185] Logistics order pasting operation, logistics order creation operation, logistics order information filling operation.

[0186] According to an embodiment of the present disclosure, the target defect type includes at least one of the following:

[0187] Logistics order printing defect type, logistics order pasting defect type, logistics order template defect type, system configuration defect type.

[0188] According to the embodiments of the present invention, any one or more of the modules, units, and sub-units, or at least part of the functions of any one of them can be implemented in one module. According to the embodiments of the present invention, any one or more of the modules, units, and sub-units can be split into multiple modules for implementation. According to the embodiments of the present invention, any one or more of the modules, units, and sub-units can be at least partially implemented as hardware circuits, such as field programmable gate arrays (FPGAs), programmable logic arrays (PLAs), systems on chips, systems on substrates, systems on packages, application specific integrated circuits (ASICs), or can be implemented by hardware or firmware in any other reasonable way of integrating or packaging the circuit, or in any one of the three implementation methods of software, hardware, and firmware, or in any appropriate combination of any of them. Alternatively, according to the embodiments of the present invention, one or more of the modules, units, and sub-units can be at least partially implemented as computer program modules, and when the computer program modules are run, the corresponding functions can be performed.

[0189] For example, any multiple of the parsing module 810, the target defect type determination module 820, the target server determination module 830, the message generation module 840, and the sending module 850 can be combined in one module / unit / sub-unit for implementation, or any one of the modules / units / sub-units can be split into multiple modules / units / sub-units. Alternatively, at least part of the functions of one or more of these modules / units / sub-units can be combined with at least part of the functions of other modules / units / sub-units and implemented in one module / unit / sub-unit. According to an embodiment of the present disclosure, at least one of the parsing module 810, the target defect type determination module 820, the target server determination module 830, the message generation module 840, and the sending module 850 can be at least partially implemented as a hardware circuit, such as a field programmable gate array (FPGA), a programmable logic array (PLA), a system on a chip, a system on a substrate, a system on a package, an application-specific integrated circuit (ASIC), or can be implemented by hardware or firmware such as any other reasonable way of integrating or packaging the circuit, or implemented in any one of the three implementation methods of software, hardware, and firmware, or in any appropriate combination of any of them. Alternatively, at least one of the parsing module 810, the target defect type determination module 820, the target server determination module 830, the message generation module 840, and the sending module 850 can be at least partially implemented as a computer program module, and when the computer program module is run, the corresponding function can be executed.

[0190] It should be noted that the information processing device part in the embodiments of the present disclosure corresponds to the information processing method part in the embodiments of the present disclosure. The description of the information processing device part specifically refers to the information processing method part, which will not be repeated here.

[0191] Fig. 9 A block diagram of an electronic device suitable for implementing an information processing method according to an embodiment of the present disclosure is schematically shown. Fig. 9 The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0192] like Fig. 9As shown, the electronic device 900 according to an embodiment of the present disclosure includes a processor 901, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 902 or a program loaded from a storage part 908 into a random access memory (RAM) 903. The processor 901 may include, for example, a general-purpose microprocessor (e.g., a CPU), an instruction set processor and / or a related chipset and / or a special-purpose microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 901 may also include an onboard memory for caching purposes. The processor 901 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of the present disclosure.

[0193] In RAM 903, various programs and data required for the operation of electronic device 900 are stored. Processor 901, ROM 902 and RAM 903 are connected to each other via bus 904. Processor 901 performs various operations of the method flow according to the embodiment of the present disclosure by executing the program in ROM 902 and / or RAM 903. It should be noted that the program can also be stored in one or more memories other than ROM 902 and RAM 903. Processor 901 can also perform various operations of the method flow according to the embodiment of the present disclosure by executing the program stored in the one or more memories.

[0194] According to an embodiment of the present disclosure, the electronic device 900 may further include an input / output (I / O) interface 905, which is electrically connected to the bus 904. The system 900 may further include one or more of the following components connected to the input / output (I / O) interface 905: an input portion 906 including a keyboard, a mouse, etc.; an output portion 907 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage portion 908 including a hard disk, etc.; and a communication portion 909 including a network interface card such as a LAN card, a modem, etc. The communication portion 909 performs communication processing via a network such as the Internet. A drive 910 is also connected to the input / output (I / O) interface 905 as needed. A removable medium 911, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 910 as needed, so that a computer program read therefrom is installed into the storage portion 908 as needed.

[0195] According to an embodiment of the present disclosure, the method flow according to an embodiment of the present disclosure can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product, which includes a computer program carried on a computer-readable storage medium, and the computer program contains a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 909, and / or installed from the removable medium 911. When the computer program is executed by the processor 901, the above-mentioned functions defined in the system of the embodiment of the present disclosure are executed. According to an embodiment of the present disclosure, the system, equipment, device, module, unit, etc. described above can be implemented by a computer program module.

[0196] The present disclosure also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments; or may exist independently without being assembled into the device / apparatus / system. The above computer-readable storage medium carries one or more programs, and when the above one or more programs are executed, the method according to the embodiment of the present disclosure is implemented.

[0197] According to an embodiment of the present disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium. For example, it may include, but is not limited to: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in combination with an instruction execution system, apparatus, or device.

[0198] For example, according to an embodiment of the present disclosure, the computer-readable storage medium may include the ROM 902 and / or the RAM 903 described above and / or one or more memories other than the ROM 902 and the RAM 903 .

[0199] An embodiment of the present disclosure also includes a computer program product, which includes a computer program, and the computer program contains a program code for executing the method provided by the embodiment of the present disclosure. When the computer program product runs on an electronic device, the program code is used to enable the electronic device to implement the information processing method provided by the embodiment of the present disclosure.

[0200] When the computer program is executed by the processor 901, the above functions defined in the system / device of the embodiment of the present disclosure are executed. According to the embodiment of the present disclosure, the system, device, module, unit, etc. described above can be implemented by a computer program module.

[0201] In one embodiment, the computer program may be based on a tangible storage medium such as an optical storage device, a magnetic storage device, etc. In another embodiment, the computer program may also be transmitted and distributed in the form of a signal on a network medium, and downloaded and installed through the communication part 909, and / or installed from a removable medium 911. The program code contained in the computer program may be transmitted using any appropriate network medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0202] According to an embodiment of the present disclosure, the program code for executing the computer program provided by the embodiment of the present disclosure can be written in any combination of one or more programming languages. Specifically, these computing programs can be implemented using high-level process and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages ​​include, but are not limited to, Java, C++, python, "C" language or similar programming languages. The program code can be executed entirely on the user computing device, partially on the user device, partially on the remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device can be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computing device (for example, using an Internet service provider to connect through the Internet).

[0203] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram may represent a module, a program segment, or a part of a code, and the above-mentioned module, program segment, or a part of the code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box may also occur in an order different from that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart, can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions. It can be understood by those skilled in the art that the features recorded in the various embodiments and / or claims of the present disclosure can be combined and / or combined in a variety of ways, even if such a combination or combination is not explicitly recorded in the present disclosure. In particular, without departing from the spirit and teaching of the present disclosure, the features described in the various embodiments and / or claims of the present disclosure may be combined and / or combined in a variety of ways. All of these combinations and / or combinations fall within the scope of the present disclosure.

[0204] The embodiments of the present disclosure are described above. However, these embodiments are only for illustrative purposes and are not intended to limit the scope of the present disclosure. Although the embodiments are described above separately, this does not mean that the measures in the various embodiments cannot be used in combination to advantage. The scope of the present disclosure is defined by the attached claims and their equivalents. Without departing from the scope of the present disclosure, those skilled in the art may make a variety of substitutions and modifications, which should all fall within the scope of the present disclosure.

Claims

1. An information processing method, comprising: Parse the logistics order feedback message sent by the client to obtain logistics order defect information, wherein the logistics order defect information represents the defect of the target logistics order corresponding to the logistics order feedback message; Determining a target defect type of the target logistics order according to the logistics order defect information; Determining a target service end corresponding to the target defect type; Generate a target logistics order defect feedback message according to the target defect type and the logistics order defect information; and Send the target logistics order defect feedback message to the target service end.

2. The method according to claim 1, wherein: The logistics order defect information includes a logistics order defect image related to the target logistics order; Wherein, determining the target defect type of the target logistics order according to the logistics order defect information includes: Inputting the logistics order defect image into a first recognition model constructed based on a deep learning algorithm, and outputting an image recognition result; and The target defect type is determined according to the image recognition result.

3. The method according to claim 2, wherein: Determining the target defect type according to the image recognition result includes: When the image recognition result is a first image recognition result indicating that the target logistics bill meets a standard condition, extracting key text in the target logistics bill; Comparing the key text with the standard condition verification text corresponding to the target logistics order to obtain a comparison result; and The target defect type is determined according to the comparison result.

4. The method according to claim 2, wherein: The first recognition model includes at least one of the following: Multilayer perceptron model, convolutional neural network model, and recurrent neural network model.

5. The method according to claim 1, wherein: The logistics order defect information includes a logistics order defect description text; Wherein, determining the target defect type of the target logistics order according to the logistics order defect information includes: Inputting the logistics order defect description text into a second recognition model and outputting a text recognition result; and The target defect type is determined according to the text recognition result.

6. The method according to claim 1, wherein: The logistics order defect information includes a logistics order defect identifier, where the logistics order defect identifier is generated by the client in response to detecting a feedback operation on a logistics order defect feedback object; Wherein, determining the target defect type of the target logistics order according to the logistics order defect information includes: The target defect type is determined according to the logistics order defect identifier.

7. The method according to claim 1, wherein: The logistics order defect information includes a target logistics order identifier and an operation user identifier associated with the target logistics order identifier, wherein the operation user identifier is suitable for representing a user identifier for performing logistics operations on the logistics order with the target logistics order identifier, and the operation identifier includes multiple ones; The information processing method further includes: According to the operation time attribute information corresponding to each of the plurality of operation user identifiers, a target operation user identifier is determined from the plurality of operation user identifiers.

8. The method according to claim 7, wherein: The generating a target logistics order defect feedback message according to the target defect type and the logistics order defect information includes: determining target defect type information corresponding to the target defect type; and According to the target protocol corresponding to the target service end, the target logistics order identifier, the target operation user identifier and the target defect type information are encapsulated to obtain the target logistics order defect feedback message.

9. The method according to claim 7, wherein: The logistics operation includes at least one of the following: Logistics order pasting operation, logistics order creation operation, logistics order information filling operation.

10. The method according to any one of claims 1 to 9, wherein: The target defect type includes at least one of the following: Logistics order printing defect type, logistics order pasting defect type, logistics order template defect type, system configuration defect type.

11. An information processing device, comprising: A parsing module, used for parsing the logistics order feedback message sent from the client to obtain logistics order defect information, wherein the logistics order defect information represents the defect of the target logistics order corresponding to the logistics order feedback message; A target defect type determination module, used to determine the target defect type of the target logistics order according to the logistics order defect information; A target server determination module, used for a target server corresponding to the target defect type; A message generation module, used to generate a target logistics order defect feedback message according to the target defect type and the logistics order defect information; and The sending module is used to send the target logistics order defect feedback message to the target service end.

12. An electronic device comprising: one or more processors; a memory for storing one or more programs, When the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 10.

13. A computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, enables the processor to implement the method according to any one of claims 1 to 10.

14. A computer program product comprising a computer program, which, when executed by a processor, implements the method according to any one of claims 1 to 10.

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