Work order generation method and device based on mail information, equipment and storage medium

Abstract

The invention relates to a work order generation method and device based on mail information, equipment and a storage medium, and the method comprises the steps: analyzing a to-be-processed mail, obtaining the text information and sender information of the to-be-processed mail, obtaining the voice description information inputted by a customer service user for the text information, converting the voice description information into target text information, and sending the target text information to a server; wherein the target text information is associated with semantics of the body information, extracting a semantic text of the to-be-processed mail based on the body information and the target text information, extracting semantic keywords corresponding to the semantic text and a question type represented by the semantic text, determining a work order template based on the sender information and the question type, and sending the work order template to the to-be-processed mail. And filling the semantic keyword and the semantic text into a work order template to generate a target work order. The work order can be accurately generated in combination with the text information of the mail and the target text information.

Description

Technical Field

[0001] The present application relates to the technical field of work order generation, and in particular to a work order generation method, device, equipment and storage medium based on email information. Background Art

[0002] Currently, work orders are usually created manually by customer service personnel. When the number of work orders is large, a lot of customer service manpower is required, and the efficiency of work order creation is low. Although there are technical solutions for automatically generating work orders in the prior art, most of these solutions generate work orders based on preset templates and fixed rules. This solution lacks flexibility, and because the rules cannot cover all scenarios, the accuracy of the automatically generated work orders is low.

[0003] Therefore, how to improve the accuracy of work order generation has become a technical problem that needs to be solved urgently by those skilled in the art. It should be noted that the information disclosed in the above background technology section is only used to enhance the understanding of the background of the present disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the invention

[0004] In view of the above, the present application provides a work order generation method, device, equipment and storage medium based on email information, which aims to solve the above technical problems.

[0005] In a first aspect, the present application provides a method for generating a work order based on email information, the method comprising:

[0006] Parsing the email to be processed to obtain the body information and sender information of the email to be processed;

[0007] Acquire voice description information input by a customer service user for the main text information, and convert the voice description information into target text information, wherein the target text information is semantically associated with the main text information;

[0008] Extracting the semantic text of the email to be processed based on the body text information and the target text information;

[0009] Extracting semantic keywords corresponding to the semantic text and the question type represented by the semantic text;

[0010] A work order template is determined based on the sender information and the problem type, and the semantic keywords and the semantic text are filled into the work order template to generate a target work order.

[0011] In a second aspect, the present application provides a work order generation device based on email information, the device comprising:

[0012] Parsing module: used to parse the email to be processed and obtain the body information and sender information of the email to be processed;

[0013] Conversion module: used for obtaining voice description information input by the customer service user for the main text information, and converting the voice description information into target text information, wherein the target text information is associated with the semantics of the main text information;

[0014] A first extraction module: used for extracting the semantic text of the email to be processed based on the body information and the target text information;

[0015] A second extraction module: used to extract semantic keywords corresponding to the semantic text and the question type represented by the semantic text;

[0016] A generation module is used to determine a work order template based on the sender information and the problem type, and fill the semantic keywords and the semantic text into the work order template to generate a target work order.

[0017] In a third aspect, the present application provides an electronic device, including a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus;

[0018] Memory, used to store computer programs;

[0019] The processor is used to implement the work order generation method based on email information as described in any embodiment of the first aspect when executing the program stored in the memory.

[0020] In a fourth aspect, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the method for generating a work order based on email information as described in any embodiment of the first aspect is implemented.

[0021] The above technical solution provided by the embodiment of the present application has the following advantages compared with the prior art:

[0022] This application parses the email to be processed to obtain the body information and sender information of the email to be processed, obtains the voice description information input by the customer service user for the body information, and converts the voice description information into target text information. Since the target text information is associated with the semantics of the body information, that is, the target text information is based on the customer service personnel's understanding of the semantics of the body information of the email, when the body information of the email contains abbreviated products, numbers instead of products, and scattered logic, it avoids the situation where the semantics of the body information of the email are recognized. The semantic text of the email to be processed is then extracted based on the body information and the target text information, and the semantic keywords corresponding to the semantic text and the problem type represented by the semantic text are extracted. Since the semantic text of the email to be processed combines the characteristics of the body information and the target text information, the work order template is determined based on the sender information and the problem type. Filling the semantic keywords and semantic text into the work order template can accurately generate the target work order, without the need for customer service personnel to manually create work orders, thereby improving the efficiency of work order generation. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0024] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0025] Figure 1 This is a flow chart of an embodiment of a method for generating a work order based on email information of the present application;

[0026] Figure 2 This is a module diagram of an embodiment of a work order generation device based on email information of the present application;

[0027] Figure 3 A schematic diagram of an electronic device embodiment of the present application;

[0028] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0029] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0030] The disclosure below provides many different embodiments or examples to realize the different structures of the present application. In order to simplify the disclosure of the present application, the parts and settings of specific examples are described below. Of course, they are only examples, and the purpose is not to limit the present application. In addition, the present application can repeat reference numbers and / or letters in different examples. This repetition is for the purpose of simplification and clarity, and does not itself indicate the relationship between the various embodiments and / or settings discussed.

[0031] This application provides a method for generating a work order based on email information. Figure 1 , which is a schematic diagram of a method flow of an embodiment of a method for generating a work order based on email information of the present application. The method can be executed by an electronic device, which is implemented by software and / or hardware. The method for generating a work order based on email information includes:

[0032] Step S10: parsing the email to be processed to obtain the body information and sender information of the email to be processed;

[0033] Step S20: obtaining voice description information input by the customer service user for the main text information, and converting the voice description information into target text information, wherein the target text information is associated with the semantics of the main text information;

[0034] Step S30: extracting the semantic text of the email to be processed based on the body text information and the target text information;

[0035] Step S40: extracting semantic keywords corresponding to the semantic text and the question type represented by the semantic text;

[0036] Step S50: determining a work order template based on the sender information and the problem type, and filling the semantic keywords and the semantic text into the work order template to generate a target work order.

[0037] In this embodiment, the emails to be processed may refer to emails received by the customer service system. Taking the customer service system as an example, the customer service system of a certain enterprise can receive emails from users such as the upstream suppliers and downstream channel merchants of the enterprise. After the customer service personnel read the emails of the customer service system, they usually need to create a corresponding work order and send the work order to the relevant processor.

[0038] Since in actual applications, a large proportion of emails in the customer service system need to generate work orders, manually creating work orders by customer service personnel requires a lot of customer service manpower, and the work order generation efficiency is low. To this end, this application provides a method for automatically generating work orders based on email information.

[0039] Specifically, after the work order generation system obtains the email from the customer system, it parses the email to be processed through the email protocol to obtain the body information and sender information of the email to be processed. For example, POP3 (Post Office Protocol) and IMAP (Internet Message Access Protocol) can obtain email data from the email server. Through these protocols, the body information and sender information of the email to be processed can be parsed, and the email can also be parsed through regular expressions.

[0040] Since the body of the email described by the user may contain abbreviations of products, or numbers may be used to replace products, or the logic of the body may be scattered, in order to accurately generate a work order based on the body of the email, the customer service staff may input the description information corresponding to the body of the email based on the body of the email. In order for the customer service staff to quickly input the description information of the body, the customer service staff may input the voice description information corresponding to the body to the work order generation system through voice input. The work order generation system may convert the voice description information into text information (recorded as target text information), wherein the target text information is associated with the semantics of the body of the email to be processed, that is, the target text information is the text with the same semantics as the body of the email input by the customer service staff based on their understanding of the semantics of the body of the email. This avoids the situation where the work order generation system has a large deviation in the recognition result when recognizing the semantics of the body of the email due to the abbreviation of products, numbers replacing products, scattered logic, etc. in the body of the email.

[0041] After obtaining the target text information, based on the body information and the target text information, the semantic text of the email to be processed can be extracted through the historical work order text set or the pre-built semantic recognition model. Specifically, the above step of "extracting the semantic text of the email to be processed based on the body information and the target text information" specifically includes:

[0042] Matching the main text information and the target text information with the historical work order text set to obtain a matching score;

[0043] If the matching score is greater than a preset value, the historical work order text corresponding to the matching score is used as the semantic text of the email to be processed;

[0044] If the matching score is less than or equal to a preset value, the body information and the target text information are input into a pre-built semantic recognition model to obtain the semantic text of the email to be processed.

[0045] The historical ticket text set refers to the collection of historical ticket items, which include ticket items manually created by customer service personnel and ticket items automatically generated by the ticket generation system. The body information of the email to be processed and the corresponding target text information are matched with the historical ticket text set to obtain a matching score. The matching method can be to convert the text into a vector and calculate the similarity between the texts as the matching score. The higher the matching score, the higher the similarity.

[0046] If the matching score is greater than the preset value, the historical work order text corresponding to the matching score is used as the semantic text of the email to be processed, where the preset value can be 97%. In actual applications, the preset value can be adjusted. If the matching score is less than or equal to the preset value, it means that no suitable semantic text is matched in the historical work order text set. At this time, the body information and the target text information are input into the pre-built semantic recognition model to obtain the semantic text of the email to be processed. For example, the semantic recognition model can be trained based on a neural network model. Since the customer service system may receive multiple email feedbacks on the same product problem, the body information and the target text information are matched with the historical work order text set respectively, so that the semantic text of this type of email can be quickly obtained.

[0047] Furthermore, the above step of "respectively matching the main text information and the target text information with the historical work order text set to obtain a matching score" includes:

[0048] Matching the text vectors corresponding to the main text information and the target text information with the text vectors corresponding to the historical work order text set to obtain a first score and a second score;

[0049] The average of the first score and the second score is used as the matching score.

[0050] The score obtained by matching the text vector corresponding to the text set of the historical work order is recorded as the first score, the score obtained by matching the target text information with the text vector corresponding to the text set of the historical work order is recorded as the second score, and the average of the first score and the second score is used as the matching score. It is also possible to assign corresponding weights to the first score and the second score, and perform a weighted sum of the first score and the second score to obtain the matching score. By adjusting the weight, the matched semantic text can be more biased towards the text information of the email to be processed or the target text information described by the customer service staff.

[0051] Furthermore, the above step of “respectively matching the text vectors corresponding to the main text information and the target text information with the text vectors corresponding to the historical work order text set” specifically includes:

[0052] Separately separate the main text information and the target text information into sentences to obtain the sentences of the main text information and the target text information;

[0053] Convert the sentences of the main text information and the target text information into sentence vectors of the main text information and the target text information;

[0054] Concatenating the sentence vectors of the main text information to obtain a text vector of the main text information, and concatenating the sentence vectors of the target text information to obtain a text vector of the target text information;

[0055] The text vector of the main text information and the text vector of the target text information are matched with the text vectors corresponding to the historical work order text set respectively.

[0056] Since sentence segmentation can capture the semantic information of each part of the text, the sentence vector at the sentence level can better reflect the meaning of each sentence in the text without causing information loss. Therefore, the main text information and the target text information are segmented respectively, and the sentences of the main text information and the target text information are converted into sentence vectors of the main text information and sentence vectors of the target text information. The sentence vectors of the main text information and the corresponding features of the target text information of the email to be processed can be accurately represented. The sentence vectors of the main text information are concatenated to obtain the text vector of the main text information, and the sentence vectors of the target text information are concatenated to obtain the text vector of the target text information. The text vectors of the main text information and the text vectors of the target text information are matched with the text vectors corresponding to the historical work order text set respectively. The specific matching method can be to calculate the cosine similarity between the text vectors, and use the calculated similarity as the matching score.

[0057] In one embodiment, the above-mentioned “the pre-built semantic recognition model includes an attention mechanism network, a plurality of re-parameterized networks and a recognition network, and the body information and the target text information are input into the pre-built semantic recognition model to obtain the semantic text of the email to be processed” includes:

[0058] Using multiple re-parameterized networks to extract features of the text information and perform splicing to obtain a fusion feature matrix of the text information;

[0059] Extracting initial features of the target text information, and extending the initial features to obtain an extended feature matrix of the target text information;

[0060] Using an attention mechanism network to process the extended feature matrix to obtain an attention feature matrix of the target text information;

[0061] The fusion feature matrix of the main text information and the attention feature matrix of the target text information are fused to obtain a fusion feature matrix;

[0062] The fused feature matrix is ​​input into the recognition network to obtain the semantic text of the email to be processed.

[0063] A reparameterized network is a network that combines convolution (Conv) and normalization (BN) to reduce the amount of computation. Multiple reparameterized networks are used to extract the features of the text information, and the features extracted by each reparameterized network are spliced ​​to obtain a fused feature matrix of the text information. Since multiple reparameterized networks can extract the features of the text information from different angles, a more comprehensive feature description of the text information of the email to be processed can be obtained by splicing the features extracted by multiple reparameterized networks.

[0064] By extending the initial features of the target text information, feature information of the target text information can be extracted from different scales. The extended features can fuse information of different dimensions (for example, the sentiment features of the text), thereby more effectively capturing the connection between the contexts of the target text information. The initial features can be extended by embedding the initial features into a graph structure through a graph neural network, and using the relationship between graph nodes as feature extension.

[0065] Since the attention mechanism network can learn and highlight the important parts of the text and suppress unimportant information, using the attention mechanism network to process the extended feature matrix can improve the ability of subsequent models to capture important features in the target text information, and enable the subsequent recognition network to extract more meaningful information when faced with complex target text information.

[0066] After obtaining the fused feature matrix of the main text information and the attention feature matrix of the target text information, the fused feature matrix of the main text information and the attention feature matrix of the target text information are fused to obtain a fused feature matrix, which can characterize the features of the main text information of the email to be processed and the corresponding target text information. The fused feature matrix is ​​input into the recognition network to obtain the semantic text of the email to be processed, wherein the recognition network can be trained based on the BERT model.

[0067] The step of “using multiple re-parameterized networks to extract the features of the text information and concatenate them to obtain a fusion feature matrix of the text information” includes:

[0068] Extracting features of the text information using a first parameterized network to obtain a first text feature;

[0069] Inputting the first text feature into a second parameterized network and outputting a second text feature;

[0070] The first text feature and the second text feature are concatenated and input into a third parameterized network to obtain a third text feature;

[0071] The first text feature, the second text feature and the third text feature are fused to obtain a fused feature matrix of the text information. The first parameterized network, the second parameterized network and the third parameterized network respectively refer to networks containing different convolution kernels, and by extracting features of the text information through different levels of parameterized networks, a more comprehensive feature description of the text information of the email to be processed can be obtained.

[0072] After obtaining the semantic text of the email to be processed, the semantic keywords corresponding to the semantic text and the problem type represented by the semantic text are extracted. The semantic keywords can be extracted by the TextRank algorithm or the TF-IDF algorithm. The semantic keywords indicate the importance of the keywords to the semantic text. The problem type represented by the semantic text refers to the problem description information extracted from the semantic text. The problem description information can be the subject of the target work order generated later, for example, the subject of a work order about a product or a project.

[0073] According to the sender information and the problem type, a blank work order template can be matched from the template library. For example, the sender is usually a downstream channel merchant, and a corresponding work order template set is pre-configured for each channel merchant. The work order template set includes work orders for each product and each project. Filling semantic keywords and semantic text into the work order template can generate the target work order.

[0074] In one embodiment, after generating the target work order, the method further includes:

[0075] The target work order is sent to a target user, and the target work order is associated with the email to be processed.

[0076] The target user can be a user of the company where the customer system is located, or an upstream supplier. The target work order is associated with the pending email through a unique identification number to facilitate subsequent tracing.

[0077] Reference Figure 2 As shown, it is a functional module diagram of the work order generation device 100 based on email information of the present application.

[0078] The work order generation device 100 based on email information described in the present application can be installed in an electronic device. According to the functions implemented, the work order generation device 100 based on email information can include a parsing module 110, a conversion module 120, a first extraction module 130, a second extraction module 140 and a generation module 150. The module described in the present application can also be referred to as a unit, which refers to a series of computer program segments that can be executed by an electronic device processor and can complete fixed functions, which are stored in the memory of the electronic device.

[0079] In this embodiment, the functions of each module / unit are as follows:

[0080] Parsing module 110: used to parse the email to be processed and obtain the body information and sender information of the email to be processed;

[0081] The conversion module 120 is used to obtain the voice description information input by the customer service user for the main text information, and convert the voice description information into target text information, wherein the target text information is associated with the semantics of the main text information;

[0082] The first extraction module 130 is used to extract the semantic text of the email to be processed based on the body information and the target text information;

[0083] The second extraction module 140 is used to extract the semantic keywords corresponding to the semantic text and the question type represented by the semantic text;

[0084] The generating module 150 is used to determine a work order template based on the sender information and the problem type, and fill the semantic keywords and the semantic text into the work order template to generate a target work order.

[0085] In one embodiment, extracting the semantic text of the email to be processed based on the body information and the target text information includes:

[0086] Matching the main text information and the target text information with the historical work order text set to obtain a matching score;

[0087] If the matching score is greater than a preset value, the historical work order text corresponding to the matching score is used as the semantic text of the email to be processed;

[0088] If the matching score is less than or equal to a preset value, the body information and the target text information are input into a pre-built semantic recognition model to obtain the semantic text of the email to be processed.

[0089] In one embodiment, the matching of the main text information and the target text information with the historical work order text set to obtain a matching score includes:

[0090] Matching the text vectors corresponding to the main text information and the target text information with the text vectors corresponding to the historical work order text set to obtain a first score and a second score;

[0091] The average of the first score and the second score is used as the matching score.

[0092] In one embodiment, the matching of the text vectors corresponding to the main text information and the target text information with the text vectors corresponding to the historical work order text set includes:

[0093] Separately separate the main text information and the target text information into sentences to obtain the sentences of the main text information and the target text information;

[0094] Convert the sentences of the main text information and the target text information into sentence vectors of the main text information and the target text information;

[0095] Concatenating the sentence vectors of the main text information to obtain a text vector of the main text information, and concatenating the sentence vectors of the target text information to obtain a text vector of the target text information;

[0096] The text vector of the main text information and the text vector of the target text information are matched with the text vectors corresponding to the historical work order text set respectively.

[0097] In one embodiment, the pre-built semantic recognition model includes an attention mechanism network, a plurality of re-parameterized networks and a recognition network, and the inputting of the body information and the target text information into the pre-built semantic recognition model to obtain the semantic text of the email to be processed includes:

[0098] Using multiple re-parameterized networks to extract features of the text information and perform splicing to obtain a fusion feature matrix of the text information;

[0099] Extracting initial features of the target text information, and extending the initial features to obtain an extended feature matrix of the target text information;

[0100] Using an attention mechanism network to process the extended feature matrix to obtain an attention feature matrix of the target text information;

[0101] The fusion feature matrix of the main text information and the attention feature matrix of the target text information are fused to obtain a fusion feature matrix;

[0102] The fused feature matrix is ​​input into the recognition network to obtain the semantic text of the email to be processed.

[0103] In one embodiment, the extracting features of the text information using multiple reparameterized networks and concatenating them to obtain a fused feature matrix of the text information includes:

[0104] Extracting features of the text information using a first parameterized network to obtain a first text feature;

[0105] Inputting the first text feature into a second parameterized network and outputting a second text feature;

[0106] The first text feature and the second text feature are concatenated and input into a third parameterized network to obtain a third text feature;

[0107] The first text feature, the second text feature and the third text feature are fused to obtain a fused feature matrix of the text information.

[0108] In one embodiment, the work order generating device 100 based on email information further includes an association module, and the association module is used to: send the target work order to a target user, and associate the target work order with the email to be processed.

[0109] Reference Figure 3 , which is a schematic diagram of a preferred embodiment of the electronic device of the present application.

[0110] The electronic device includes a processor 111, a communication interface 112, a memory 113 and a communication bus 114, wherein the processor 111, the communication interface 112, and the memory 113 communicate with each other via the communication bus 114;

[0111] Memory 113, for storing computer programs, for example, a work order generation program based on email information;

[0112] The processor 111 may be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chips in some embodiments. The processor 111 is generally used to control the overall operation of the electronic device, such as performing control and processing related to data interaction or communication. In this embodiment, the processor 111 is used to run the program code stored in the memory 113 or process data, such as running the program code of a work order generation program based on email information.

[0113] The communication interface 112 may optionally include a standard wired interface, a wireless interface (such as a WI-FI interface), and the communication interface 112 may also be used to establish a communication connection between the electronic device and other electronic devices.

[0114] The memory 113 includes at least one type of readable storage medium, and the readable storage medium includes flash memory, hard disk, multimedia card, card-type memory (for example, SD or DX memory, etc.), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, disk, optical disk, etc. In some embodiments, the memory 113 can be an internal storage unit of the electronic device, such as a hard disk or memory of the electronic device. In other embodiments, the memory 113 can also be an external storage device of the electronic device, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped with the electronic device. Of course, the memory 113 can also include both the internal storage unit of the electronic device and its external storage device. In this embodiment, the memory 11 is generally used to store the operating system and various computer programs installed in the electronic device, such as the program code of the work order generation program based on email information, etc. In addition, the memory 113 can also be used to temporarily store various types of data that have been output or are to be output.

[0115] In one embodiment of the present application, the processor 111 is used to execute the program stored in the memory 113 to implement the work order generation method based on email information provided by any of the above method embodiments, including:

[0116] Parsing the email to be processed to obtain the body information and sender information of the email to be processed;

[0117] Acquire voice description information input by a customer service user for the main text information, and convert the voice description information into target text information, wherein the target text information is semantically associated with the main text information;

[0118] Extracting the semantic text of the email to be processed based on the body text information and the target text information;

[0119] Extracting semantic keywords corresponding to the semantic text and the question type represented by the semantic text;

[0120] A work order template is determined based on the sender information and the problem type, and the semantic keywords and the semantic text are filled into the work order template to generate a target work order.

[0121] For a detailed description of the above steps, please refer to the above Figure 1 Description of a flowchart of an embodiment of a method for generating a work order based on email information.

[0122] In addition, the embodiment of the present application also proposes a computer-readable storage medium, which may be non-volatile or volatile. The computer-readable storage medium includes a data storage area and a program storage area, and the program storage area stores a work order generation program based on email information. When the work order generation program based on email information is executed by a processor, the following operations are implemented:

[0123] Parsing the email to be processed to obtain the body information and sender information of the email to be processed;

[0124] Acquire voice description information input by a customer service user for the main text information, and convert the voice description information into target text information, wherein the target text information is semantically associated with the main text information;

[0125] Extracting the semantic text of the email to be processed based on the body text information and the target text information;

[0126] Extracting semantic keywords corresponding to the semantic text and the question type represented by the semantic text;

[0127] A work order template is determined based on the sender information and the problem type, and the semantic keywords and the semantic text are filled into the work order template to generate a target work order.

[0128] The specific implementation of the computer-readable storage medium of the present application is roughly the same as the specific implementation of the above-mentioned work order generation method based on email information, and will not be repeated here.

[0129] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0130] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a general hardware platform, and of course, by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the relevant technology can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0131] It should be noted that the descriptions involving "first", "second", etc. in this application are only for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In addition, the technical solutions between the various embodiments can be combined with each other, but they must be based on the ability of ordinary technicians in this field to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such combination of technical solutions does not exist and is not within the scope of protection required by this application.

[0132] It should be understood that the terms used in the text are only for the purpose of describing specific example embodiments, and are not intended to be limiting. Unless the context clearly indicates otherwise, the singular forms "one", "an" and "said" as used in the text may also be meant to include plural forms. The terms "include", "comprise", "contain", and "have" are inclusive, and therefore specify the existence of stated features, steps, operations, elements and / or parts, but do not exclude the existence or addition of one or more other features, steps, operations, elements, parts, and / or combinations thereof. The method steps, processes, and operations described herein are not interpreted as necessarily requiring them to be performed in the specific order described or illustrated, unless the execution order is clearly indicated. It should also be understood that additional or alternative steps may be used.

[0133] The above description is only a specific implementation of the present application, so that those skilled in the art can understand or implement the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest range consistent with the principles and novel features applied for herein.

Claims

1. A work order generation method based on email information, characterized in that: The method comprises: Parsing the email to be processed to obtain the body information and sender information of the email to be processed; Acquire voice description information input by a customer service user for the main text information, and convert the voice description information into target text information, wherein the target text information is semantically associated with the main text information; Extracting the semantic text of the email to be processed based on the body text information and the target text information; Extracting semantic keywords corresponding to the semantic text and the question type represented by the semantic text; A work order template is determined based on the sender information and the problem type, and the semantic keywords and the semantic text are filled into the work order template to generate a target work order.

2. The method according to claim 1, characterized in that: The extracting the semantic text of the email to be processed based on the body text information and the target text information includes: Matching the main text information and the target text information with the historical work order text set to obtain a matching score; If the matching score is greater than a preset value, the historical work order text corresponding to the matching score is used as the semantic text of the email to be processed; If the matching score is less than or equal to a preset value, the body information and the target text information are input into a pre-built semantic recognition model to obtain the semantic text of the email to be processed.

3. The method according to claim 2, characterized in that The matching of the main text information and the target text information with the historical work order text set to obtain a matching score includes: Matching the text vectors corresponding to the main text information and the target text information with the text vectors corresponding to the historical work order text set to obtain a first score and a second score; The average of the first score and the second score is used as the matching score.

4. The method according to claim 3, characterized in that The matching of the text vectors corresponding to the main text information and the target text information with the text vectors corresponding to the historical work order text set includes: Separately separate the main text information and the target text information into sentences to obtain the sentences of the main text information and the target text information; Convert the sentences of the main text information and the target text information into sentence vectors of the main text information and the target text information; Concatenating the sentence vectors of the main text information to obtain a text vector of the main text information, and concatenating the sentence vectors of the target text information to obtain a text vector of the target text information; The text vector of the main text information and the text vector of the target text information are matched with the text vectors corresponding to the historical work order text set respectively.

5. The method according to claim 2, characterized in that: The pre-built semantic recognition model includes an attention mechanism network, a plurality of re-parameterized networks and a recognition network. The body information and the target text information are input into the pre-built semantic recognition model to obtain the semantic text of the email to be processed, including: Using multiple re-parameterized networks to extract features of the text information and perform splicing to obtain a fusion feature matrix of the text information; Extracting initial features of the target text information, and extending the initial features to obtain an extended feature matrix of the target text information; Using an attention mechanism network to process the extended feature matrix to obtain an attention feature matrix of the target text information; The fusion feature matrix of the main text information and the attention feature matrix of the target text information are fused to obtain a fusion feature matrix; The fused feature matrix is ​​input into the recognition network to obtain the semantic text of the email to be processed.

6. The method according to claim 5, characterized in that The method of extracting the features of the text information by using multiple re-parameterized networks and concatenating them to obtain a fusion feature matrix of the text information includes: Extracting features of the text information using a first parameterized network to obtain a first text feature; Inputting the first text feature into a second parameterized network and outputting a second text feature; The first text feature and the second text feature are concatenated and input into a third parameterized network to obtain a third text feature; The first text feature, the second text feature and the third text feature are fused to obtain a fused feature matrix of the text information.

7. The method according to claim 1, characterized in that The method further comprises: The target work order is sent to a target user, and the target work order is associated with the email to be processed.

8. A work order generation device based on email information, characterized in that: The device comprises: Parsing module: used to parse the email to be processed and obtain the body information and sender information of the email to be processed; Conversion module: used for obtaining voice description information input by the customer service user for the main text information, and converting the voice description information into target text information, wherein the target text information is associated with the semantics of the main text information; A first extraction module: used for extracting the semantic text of the email to be processed based on the body information and the target text information; A second extraction module: used to extract semantic keywords corresponding to the semantic text and the question type represented by the semantic text; A generation module is used to determine a work order template based on the sender information and the problem type, and fill the semantic keywords and the semantic text into the work order template to generate a target work order.

9. An electronic device, characterized in that: It includes a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other through the communication bus; Memory, used to store computer programs; The processor is used to implement the work order generation method based on email information described in any one of claims 1 to 7 when executing the program stored in the memory.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the work order generation method based on email information according to any one of claims 1 to 7 is implemented.

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