Intelligent mail processing and model training method, electronic equipment, medium and program

Abstract

The embodiment of the invention discloses an intelligent mail processing and model training method, electronic equipment, a medium and a programmer.The method is applied to an intelligent mail processing model and comprises the steps that to-be-displayed mail data of a current user is obtained; determining a current mail classification rule of the to-be-displayed mail data in real time according to the historical mail operation behavior data of the current user; and performing classified display on the to-be-displayed mail data through the current mail classification rule. According to the technical scheme of the embodiment of the invention, the mail processing efficiency and accuracy can be improved.

Description

Technical Field

[0001] The embodiments of the present invention relate to the fields of computer application and artificial intelligence technology, and in particular to a method, electronic device, medium and program for intelligent email processing and model training. Background Art

[0002] With the development of information technology, email has become an important tool for internal and external communication in enterprises. However, with the increasing number of emails, users need to classify, store and display emails to improve efficiency.

[0003] Currently, users still use rule-based classification mailboxes for email management. These mailboxes allow users to set rules based on conditions such as sender, recipient, subject and keyword, and automatically classify emails into different folders.

[0004] In the process of implementing the present invention, the inventors found that the prior art has the following defects: (1) The email classification rules need to be set by the user, which is difficult for users who are not familiar with setting rules; (2) If there is a conflict between the set rules, it will lead to inaccurate email classification; (3) If a new email type appears or the rules cannot cover the situation, it may lead to inaccurate email classification; (4) The default classification rules of the mailbox may not fully meet the needs of the user. Summary of the invention

[0005] The embodiments of the present invention provide a mail intelligent processing and model training method, electronic equipment, medium and program, which can improve the efficiency and accuracy of mail processing.

[0006] According to one aspect of the present invention, there is provided a mail intelligent processing method, which is applied to a mail intelligent processing model, and comprises:

[0007] Get the email data to be displayed for the current user;

[0008] Determine in real time the current email classification rule of the email data to be displayed according to the historical email operation behavior data of the current user;

[0009] The email data to be displayed is classified and displayed according to the current email classification rule.

[0010] According to another aspect of the present invention, a model training method is provided, comprising:

[0011] Acquire historical email data of each user and historical email-related data matched by the historical email data; wherein the historical email-related data includes user operation behavior data corresponding to the historical email data and email classification results corresponding to the historical email data;

[0012] The historical email data and the historical email-related data that matches the historical email data are used as email processing sample data, and an email intelligent processing model is trained based on the email processing sample data;

[0013] The trained email intelligent processing model is used to execute the email intelligent processing method described in any embodiment of the present invention.

[0014] According to another aspect of the present invention, there is provided an intelligent mail processing device, which is configured in an intelligent mail processing model and includes:

[0015] The module for acquiring the email data to be displayed is used to acquire the email data to be displayed of the current user;

[0016] A current email classification rule determination module, used to determine the current email classification rule of the email data to be displayed in real time according to the historical email operation behavior data of the current user;

[0017] The module for classifying and displaying the mail data to be displayed is used to classify and display the mail data to be displayed according to the current mail classification rule.

[0018] According to another aspect of the present invention, there is provided a model training device, comprising:

[0019] A historical email data acquisition module is used to acquire historical email data of each user and historical email associated data matching the historical email data; wherein the historical email associated data includes user operation behavior data corresponding to the historical email data and email classification results corresponding to the historical email data;

[0020] An email intelligent processing model training module is used to use the historical email data and the historical email associated data that matches the historical email data as email processing sample data, and train an email intelligent processing model based on the email processing sample data;

[0021] The trained email intelligent processing model is used to execute the email intelligent processing method described in any embodiment of the present invention.

[0022] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0023] at least one processor; and

[0024] a memory communicatively connected to the at least one processor; wherein,

[0025] The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the email intelligent processing method or model training method described in any embodiment of the present invention.

[0026] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the email intelligent processing method or model training method described in any embodiment of the present invention when executed.

[0027] According to another aspect of the present invention, a computer program product is provided, including a computer program, which, when executed by a processor, implements the mail intelligent processing method or model training method described in any embodiment of the present invention.

[0028] The embodiment of the present invention obtains the to-be-displayed email data of the current user by utilizing an intelligent email processing model, and further determines the current email classification rules of the to-be-displayed email data in real time according to the historical email operation behavior data of the current user, thereby classifying and displaying the to-be-displayed email data according to the current email classification rules, solving the problems of complex and conflicting settings of existing email classification rules, inability to handle new situations, and inability to meet personalized user needs, and can improve the efficiency and accuracy of email processing.

[0029] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0031] Figure 1 is a flow chart of an intelligent mail processing method provided by Embodiment 1 of the present invention;

[0032] Figure 2 is a flow chart of a method for intelligent email processing provided by Embodiment 2 of the present invention;

[0033] Figure 3 is a flow chart of a model training method provided in Embodiment 3 of the present invention;

[0034] Figure 4This is a flow chart of email data collection provided by Embodiment 3 of the present invention;

[0035] Figure 5 This is a mail data processing flow chart provided by Embodiment 3 of the present invention;

[0036] Figure 6 This is a flowchart of the training and application process of an email intelligent processing model provided in the third embodiment of the present invention;

[0037] Figure 7 is a schematic diagram of an intelligent mail processing device provided by Embodiment 4 of the present invention;

[0038] Figure 8 is a schematic diagram of a model training device provided in Embodiment 5 of the present invention;

[0039] Fig. 9 It is a structural schematic diagram of an electronic device provided in Embodiment 6 of the present invention. DETAILED DESCRIPTION

[0040] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0041] It should be noted that the terms "target", "initial", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0042] Embodiment 1

[0043] Figure 1This is a flowchart of an intelligent mail processing method provided by the first embodiment of the present invention. This embodiment can be applied to the situation where the mail classification rules are determined in real time according to the historical mail operation behavior data of the user to classify and process the mails in a personalized manner. The method can be executed by an intelligent mail processing device, which can be implemented by software and / or hardware and can generally be integrated in an electronic device. The electronic device can be a terminal device or a server device. As long as it can run the intelligent mail processing model to execute the intelligent mail processing method, the embodiment of the present invention does not limit the specific device type of the electronic device. Accordingly, Figure 1 As shown, the method includes the following operations:

[0044] S110: Obtain email data to be displayed of the current user.

[0045] The current user may be a personal user interacting with the mail intelligent processing model. The mail data to be displayed may be unclassified and unread mail data received by the current user through the mail intelligent processing model. It is understandable that the mail data to be displayed may include at least one mail to be displayed.

[0046] In the embodiment of the present invention, in order to determine the mail classification rules in real time to classify the mails in a personalized manner, it is necessary to first obtain the mail data to be displayed of the current user, and then determine the current mail classification rules of the mail data to be displayed. It is understandable that the current mail classification rules corresponding to the mail data to be displayed may be different depending on the mail types included in the mail data to be displayed.

[0047] The embodiment of the present invention can use an intelligent mail processing model to intelligently process the mail data of the user. The intelligent mail processing model can be a model that uses artificial intelligence technology to optimize the processing, management and use of mails. It can be understood that before applying the intelligent mail processing model to intelligently process the mail data of the user, it is first necessary to collect sample data to train the intelligent mail processing model so that the intelligent mail processing model can accurately and efficiently have the function of mail classification display processing.

[0048] When the current user's mailbox receives the email to be displayed, it can send it to the email intelligent processing model. After the email intelligent processing model obtains the email data to be displayed of the current user, it can automatically and intelligently process the email data to be displayed.

[0049] S120: Determine in real time the current email classification rule for the email data to be displayed according to the historical email operation behavior data of the current user.

[0050] The historical mail operation behavior data may be a series of behavior record data generated by the current user in the process of processing historical mails. Exemplarily, the historical mail operation behavior data may include but is not limited to the current user's search behavior, the status mark of historical mails, the subject content of historical mails, and the sending and receiving time of historical mails, etc. As long as it can reflect the current user's operation behavior on historical mails, the embodiment of the present invention does not limit the specific type of historical mail operation behavior data. The current mail classification rule may be a rule determined by the mail intelligent processing model based on the current user's historical mail operation behavior data, which is used to classify the mail data to be displayed.

[0051] In an embodiment of the present invention, after the email intelligent processing model obtains the email data to be displayed of the current user, the current email classification rules of the email data to be displayed can be determined in real time according to the historical email operation behavior data of the current user, so that the email data to be displayed can be classified and displayed according to the current email classification rules.

[0052] S130: Classify and display the email data to be displayed according to the current email classification rule.

[0053] In the embodiment of the present invention, after the mail intelligent processing model determines the current mail classification rules of the mail data to be displayed in real time based on the historical mail operation behavior data of the current user, the mail data to be displayed can be classified and displayed according to the current mail classification rules. It can be understood that the mail intelligent processing model determines the current mail classification rules of the mail data to be displayed in real time based on the historical mail operation behavior data of the current user, so that the current mail classification rules of the mail data to be displayed can meet the current user's habit of historical mail classification processing, and then the mail data to be displayed is classified and displayed according to the current mail classification rules, so that the classification display results of the mail data to be displayed are compatible with the current user's mail classification processing habits, thereby improving the efficiency and accuracy of the current user's mail processing.

[0054] The embodiment of the present invention obtains the to-be-displayed email data of the current user by utilizing an intelligent email processing model, and further determines the current email classification rules of the to-be-displayed email data in real time according to the historical email operation behavior data of the current user, thereby classifying and displaying the to-be-displayed email data according to the current email classification rules, solving the problems of complex and conflicting settings of existing email classification rules, inability to handle new situations, and inability to meet personalized user needs, and can improve the efficiency and accuracy of email processing.

[0055] Embodiment 2

[0056] Figure 2This is a flowchart of an intelligent email processing method provided by the second embodiment of the present invention. This embodiment is specific based on the above embodiment. In this embodiment, a specific optional implementation method is provided for determining the current email classification rules of the email data to be displayed in real time according to the historical email operation behavior data of the current user, generating an email classification label list, determining the email classification labels matching the email data to be displayed, and classifying and displaying the email data to be displayed according to the current email classification rules. At the same time, optional implementation operations are provided after obtaining the email data to be displayed of the current user and classifying and displaying the email data to be displayed according to the current email classification rules. Accordingly, Figure 2 As shown, the method of this embodiment may include:

[0057] S210: Obtain the email data to be displayed of the current user.

[0058] In an optional embodiment of the present invention, after obtaining the email data to be displayed of the current user, the method may further include: performing intelligent analysis on the email data to be displayed; wherein the intelligent analysis includes natural language processing, keyword extraction, and keyword index establishment and update.

[0059] Among them, intelligent analysis can be an analysis of the email data to be displayed by the email intelligent processing model, so as to extract the information required for model training in the email data to be displayed. For example, intelligent analysis can include but is not limited to data processing operations such as natural language processing, keyword extraction, and keyword index establishment and update. The embodiment of the present invention does not limit the specific data operation content of the intelligent analysis design.

[0060] In an embodiment of the present invention, after obtaining the email data to be displayed of the current user, the email intelligent processing model can also perform intelligent analysis on the email data to be displayed, namely, data processing operations such as natural language processing, keyword extraction, and keyword index establishment and update, thereby improving the accuracy of the model training results.

[0061] S220: Determine in real time the associated classification reference factors of the current email classification rule according to the historical email operation behavior data of the current user.

[0062] The associated classification reference factor may be a reference factor in the current user's historical email operation behavior data related to determining the current email classification rule. Exemplarily, the associated classification reference factor may include at least one of email interaction frequency, email viewing mode, email search mode, and classified email identification.

[0063] Among them, the email interaction frequency may be the frequency of communication between the current user and a specific sender or email subject. The email viewing mode may be the habits and preferences of the current user for viewing and managing emails. Exemplarily, the email viewing mode may include but is not limited to immediate viewing, batch processing, and marking and viewing later, etc., and the embodiment of the present invention does not limit the specific manner of the email viewing mode. The email search mode may be the search strategy and method used by the current user when he needs to find a specific email. Exemplarily, the email search mode may include but is not limited to the sender, subject keywords, and receipt date of the email, etc., and the embodiment of the present invention does not limit the specific manner of the email search mode. The classified email identifier may be a label or identifier used by the email intelligent processing model to mark the category to which the email belongs, which can help the current user quickly identify the nature and importance of the email, thereby effectively managing the email. Exemplarily, the classified email identifier may include but is not limited to labels, colors, highlights, and stars, etc., and the embodiment of the present invention does not limit the types of classified email identifiers.

[0064] In an embodiment of the present invention, after obtaining the email data to be displayed of the current user, the email intelligent processing model performs intelligent analysis on the email data to be displayed to obtain the historical email operation behavior data of the current user, and then can determine the associated classification reference factors of the current email classification rules in real time based on the historical email operation behavior data of the current user, so that the associated classification reference factors can be comprehensively analyzed, and the current email classification rules for the email data to be displayed can be determined based on the analysis results.

[0065] S230: Determine a current email classification rule for the email data to be displayed according to the weight of the associated classification reference factor and the email data to be displayed.

[0066] In an embodiment of the present invention, after determining the associated classification reference factors of the current email classification rules, the email intelligent processing model can determine the current email classification rules of the email data to be displayed based on the weights of the associated classification reference factors and the email data to be displayed, so that the email data to be displayed can be classified and displayed according to the current email classification rules.

[0067] It should be noted that there may be conflicts between different association classification reference factors, such as interaction frequency and email viewing mode, so it is necessary to set weights for different association classification reference factors. It is understandable that the weights of different association classification reference factors can be the same or different. The weights of the same association classification reference factors can also be different for different current users. The email intelligent processing model can determine the weights of the association classification reference factors of the current user based on the historical email operation behavior data of the current user.

[0068] In a specific example, the email intelligent processing model can classify emails according to the sender, for example, emails from leaders and important customers will be given higher weights. Since the subject of the email usually contains a summary of the email content, it helps to judge the urgency and importance of the email. The email intelligent processing model can also set a higher weight for the email subject. The size of the email usually has little impact on the classification, and the email intelligent processing model can set a lower weight for the size of the email.

[0069] S240: Classify and display the email data to be displayed according to the current email classification rule.

[0070] In the embodiment of the present invention, after determining the current mail classification rule, the mail intelligent processing model can classify and display the mail data to be displayed according to the current mail classification rule, thereby improving the efficiency and accuracy of the current user's mail processing.

[0071] In an optional embodiment of the present invention, the classification and display of the mail data to be displayed according to the current mail classification rule may include: determining an initial classification and display result of the mail data to be displayed according to the current mail classification rule; optimizing the initial classification and display result to obtain a target classification and display result; and classification and displaying the mail data to be displayed according to the target classification and display result.

[0072] The initial classification display result may be the result of classifying and displaying the mail data to be displayed according to the current mail classification rule, and the target classification display result may be the final classification display result of the mail data to be displayed obtained by optimizing the initial classification display result.

[0073] Specifically, after determining the current email classification rule, the email intelligent processing model can determine the initial classification display result of the email data to be displayed according to the current email classification rule, and further optimize the initial classification display result according to the corresponding optimization strategy to obtain the target classification display result, so as to classify and display the email data to be displayed according to the target classification display result. Exemplarily, the optimization strategy may include but is not limited to allowing the current user to provide feedback on the email classification result, such as marking the classification as correct or incorrect, analyzing the context information of the email content to adjust the email classification, and adjusting the display order of the emails according to the urgency and importance of the emails.

[0074] In an optional embodiment of the present invention, the email intelligent processing method may further include: in response to the email label setting instruction triggered by the current user, determining the email classification label that matches the email data to be displayed; predicting the arrangement order of each of the email classification labels in the email classification label list based on the email data to be displayed; sorting each of the email classification labels according to the arrangement order of each of the email classification labels in the email classification label list to generate the email classification label list.

[0075] The email label setting instruction may be an instruction for the current user to set a label for the email data to be displayed. The email classification label list may be a list composed of email classification labels. Exemplarily, the email classification label list may include but is not limited to work-related, personal life, finance, and to-do items, etc. The embodiment of the present invention does not limit the specific type of the email classification label list.

[0076] In an embodiment of the present invention, the email intelligent processing model can respond to the email label setting instruction triggered by the current user, determine the email classification label that matches the email data to be displayed, and further predict the arrangement order of each email classification label in the email classification label list based on the email data to be displayed, so that each email classification label can be sorted according to the arrangement order of each email classification label in the email classification label list, generate an email classification label list, and improve the efficiency of email processing for the current user.

[0077] In a specific example, if the email classification labels set by the current user are to be processed, completed work, meeting minutes and bill details, when the current user clicks the email label setting instruction, the email intelligent processing model can display the four email classification labels of to be processed, completed work, meeting minutes and bill details. When the email intelligent processing model detects that the attachment of the email data to be displayed contains a bill list, the email classification label of bill details can be ranked first in the email classification label classification list, the email classification label of to be processed can be ranked second in the email classification label classification list, the email classification label of completed work can be ranked third in the email classification label classification list, and the email classification label of meeting minutes can be ranked fourth in the email classification label classification list, and the final email classification label classification list is generated. When the current user classifies the email data to be displayed, he can directly click the first bill detail in the email classification label classification list to quickly classify the current email, which can improve the efficiency of the current user's email processing.

[0078] In an optional embodiment of the present invention, determining the email classification tags that match the email data to be displayed may include: obtaining historical email classification tags historically configured by the current user; predicting the current email classification tags in real time based on the email data to be displayed; and using the historical email classification tags and the current email classification tags as email classification tags that match the email data to be displayed.

[0079] The historical email classification label may be an email classification label used by the current user for historical emails. The current email classification label may be an email classification label predicted in real time by the email intelligent processing model based on the email data to be displayed. It is understandable that the current email classification label may be the same as the historical email classification label, or may be different from the historical email classification label.

[0080] In the embodiment of the present invention, in order to determine the email classification tags that match the email data to be displayed, the email intelligent processing model can obtain the historical email classification tags that the current user has historically configured, and can also predict the current email classification tags in real time based on the email data to be displayed. Furthermore, the historical email classification tags and the current email classification tags can be used as email classification tags that match the email data to be displayed, thereby realizing personalized update of the email classification tags. That is, the email classification tags included in the email classification tag list can include not only the email classification tags used by the current user, but also the email classification tags predicted in real time by the email intelligent processing model based on the email data to be displayed.

[0081] In a specific example, the email classification labels used by the current user are pending work, completed work, meeting minutes and bill details. The email intelligent processing model predicts in real time based on the email data to be displayed that the current email classification label is entertainment. At this time, the email intelligent processing model can display the five items of entertainment, pending work, completed work, meeting minutes and bill details as email classification labels matching the email data to be displayed.

[0082] In an optional embodiment of the present invention, after the to-be-displayed email data is classified and displayed according to the current email classification rule, the method may further include: real-time monitoring of the current user's operation processing behavior data on the to-be-displayed email data; and storing the operation processing behavior data of the to-be-displayed email data as the historical email operation behavior data.

[0083] The operation processing behavior data may be behavior data of a series of operations performed by the current user on the displayed email data. Exemplarily, the operation processing behavior data may include, but are not limited to, email opening frequency, reply time, and email classification, etc. As long as it is an operation performed by the current user on the displayed email data, the embodiment of the present invention does not limit the specific content of the operation processing behavior data.

[0084] Specifically, after classifying and displaying the email data to be displayed according to the current email classification rules, the email intelligent processing model can monitor the current user's operational processing behavior data of the email data to be displayed in real time and store the operational processing behavior data of the email data to be displayed, and use it as historical email operational behavior data to update the historical email operational behavior data stored by the email intelligent processing model for the current user in real time, thereby improving the accuracy of the email intelligent processing model's personalized classification of the email data to be displayed next time.

[0085] The embodiment of the present invention obtains the to-be-displayed email data of the current user by using the email intelligent processing model, and determines the associated classification reference factors of the current email classification rules in real time according to the historical email operation behavior data of the current user, and further determines the current email classification rules of the to-be-displayed email data according to the weights of the associated classification reference factors and the to-be-displayed email data, so that the to-be-displayed email data can be classified and displayed according to the current email classification rules. The above technical solution can solve the problems of complex and conflicting settings of existing email classification rules, inability to handle new situations, and inability to meet the personalized needs of users, and can improve the efficiency and accuracy of email processing.

[0086] Embodiment 3

[0087] Figure 3 This is a flow chart of a model training method provided in the third embodiment of the present invention. This embodiment can be applied to training the mail intelligent processing model according to the user operation behavior data so that the mail intelligent processing model can provide personalized mail intelligent processing services to different users. The method can be executed by a model training device, which can be implemented by software and / or hardware, and can generally be integrated in an electronic device. The electronic device can be a terminal device or a server device. As long as the model training method can be executed, the embodiment of the present invention does not limit the specific device type of the electronic device. Accordingly, Figure 3 As shown, the method includes the following operations:

[0088] S310, acquiring historical email data of each user and historical email associated data matching the historical email data; wherein the historical email associated data includes user operation behavior data corresponding to the historical email data and email classification results corresponding to the historical email data.

[0089] The historical email data may be email data received, sent or stored in the mailbox of the current user in the past. The historical email-related data may be data obtained by monitoring the operation behavior of the current user on the historical email data. Exemplarily, the historical email-related data may include user operation behavior data corresponding to the historical email data and email classification results corresponding to the historical email data.

[0090] S320: Use the historical email data and the historical email-related data that matches the historical email data as email processing sample data, and train an email intelligent processing model based on the email processing sample data.

[0091] In the embodiment of the present invention, the historical email data of each user and the historical email associated data matched with the historical email data can be obtained as the email processing sample data, and the email intelligent processing model can be trained according to the email processing sample data. The trained email intelligent processing model can provide an automated email intelligent processing function, thereby improving the efficiency and accuracy of email processing.

[0092] The embodiment of the present invention obtains the historical email data of each user and the historical email associated data that matches the historical email data, and further uses the historical email data and the historical email associated data that matches the historical email data as email processing sample data, so that the email processing sample data can be used to train an email intelligent processing model. The trained email intelligent processing model can provide an automated email intelligent processing function, thereby improving the efficiency and accuracy of email processing.

[0093] Specific application scenarios

[0094] In order to more clearly describe the technical solution provided by the embodiment of the present invention, the embodiment of the present invention provides a method for model training and applying the mail intelligent processing model to perform mail intelligent processing. Table 1 is a mail intelligent classification system design and function table provided by the third embodiment of the present invention.

[0095] Table 1 Design and function table of mail intelligent classification system

[0096]

[0097] In a specific example, as shown in Table 1, the mail intelligent classification system may include a data collection layer, a data processing layer, an intelligent analysis layer, and a user interaction layer. It can be understood that the data collection layer, the data processing layer, and the intelligent analysis layer can complete the model training to obtain the mail intelligent processing model, and the user interaction layer is the implementation process of applying the mail intelligent processing model to perform mail intelligent processing. Figure 4 This is a flow chart of email data collection provided by Embodiment 3 of the present invention. Figure 4As shown, in the email intelligent classification system, the email server interface module in the data collection layer can be connected to different email servers, and the further data collection module can collect email data from the email server. For example, the email data may include data such as the subject, content, sender, recipient and timestamp of the email data to be displayed, so that the data transmission module can transmit the collected data to the data processing layer for processing.

[0098] Figure 5 This is a flowchart of mail data processing provided by Embodiment 3 of the present invention. Figure 5 As shown, the data cleaning module in the data processing layer can remove duplicate, invalid and / or erroneous email data from the acquired email data, and then the data preprocessing module can perform preliminary processing on the email data, such as format conversion and text standardization. Further, the data storage module can use the database to store the processed email data for subsequent analysis and query.

[0099] Figure 6 is a flow chart of the training and application process of an intelligent mail processing model provided by the third embodiment of the present invention. In a specific example, Figure 6 As shown, the process of model training and applying the email intelligent processing model to perform the email intelligent processing method may include the following steps:

[0100] (1) First, the intelligent analysis engine receives the data that has been processed in the early stage. Then it enters the natural language processing stage. Lexical analysis breaks down the email content into words, phrases, and sentences; syntactic analysis analyzes the sentence structure; and semantic understanding digs out the deep meaning, including intent, emotion, and key topics.

[0101] (2) Then, keyword extraction is performed. Possible keywords are selected through statistical analysis and calculation of word features, and specific words are accurately extracted according to the rules. Then, an index is established, keywords are classified and organized, and an inverted index is created and updated in real time.

[0102] (3) Machine learning is then used to train a classification model based on the email data and keyword extraction results, evaluate its performance, and adjust the model based on the results and user feedback.

[0103] (4) Conduct user behavior analysis at the same time, monitor user operation behavior, analyze behavior patterns, and generate personalized classification and display rules.

[0104] (5) Finally, the analysis results are integrated and output in a specific format to provide support for email display and recommendation. The entire process uses a variety of technologies and methods to achieve comprehensive, in-depth and personalized intelligent analysis of emails.

[0105] Exemplarily, the email display interface design may include but is not limited to category view display, integration of search email recommendation lists, and application of visualization technology, etc. The embodiment of the present invention does not limit the specific content of the email display interface design.

[0106] Among them, the category view display may include but is not limited to a category navigation bar, a category mail list, category statistics, and category filtering and sorting. A category navigation bar may be set on the left or top of the mail display interface to clearly list all category options, including system default categories and user-defined categories. It should be noted that in order to save space and keep the interface tidy, the category options can be folded or expanded. After clicking a category, the mail list under the category is displayed in the main area, and the layout and mail information display method are the same as the mail list display. Furthermore, the mail sorting method can be optimized according to the characteristics of the category, for example, the "urgent" category is sorted comprehensively according to time and importance. In each category view, statistical information such as the number of mails and the number of unread mails under the category can be displayed. Filtering conditions are provided, such as only displaying unread mails and only displaying mails with attachments, and users are supported to sort by different fields (such as sender, subject, and time).

[0107] The integration of the search email recommendation list may include but is not limited to a search box, real-time search suggestions, search result display, and recommendation lists. A search box may be set in a prominent position at the top of the email display interface to support users to enter keywords for search. When users enter keywords, relevant search suggestions may be displayed in real time to help users quickly find accurate search terms. At the same time, the search results may be displayed in a list format, with each email displaying key information and the degree of match with the search terms. Furthermore, for highly matching emails, special identifiers (such as highlights) may be used to highlight them. The email intelligent processing model may display relevant recommended emails or categories below the search results based on the user's search history and intelligent analysis results. For example, if a user frequently searches for emails related to a certain project, other related emails or related categories of the project may be recommended in the recommendation list.

[0108] Visualization technology applications may include, but are not limited to, color coding, icons and badges, as well as progress bars and charts. To enhance visual distinction, different background colors may be used for emails of different categories, and eye-catching colors may be used for titles or key information of important emails. At the same time, different icons may be used to indicate email status (such as read, unread, and attachments) and categories, and badges may be added to important or urgent emails. In the category view, the proportion of read and unread emails in each category may be displayed in the form of a progress bar, and charts may be used to display statistical data such as the email sending and receiving status of users over a period of time.

[0109] In a specific example, to ensure data synchronization and consistency, when a new email arrives, it can be first written to the email metadata table (emails_metadata) of MySQL, and then written to the email content collection (emails_content) of MongoDB and the Redis cache through triggers or application logic. When the email data needs to be updated, MySQL can be updated first, and then MongoDB and Redis can be updated synchronously. When the email needs to be deleted, the data in MySQL is deleted first, and then the corresponding data in MongoDB and Redis is deleted through application logic. At the same time, the email intelligent processing model can use distributed locks or transactions to ensure consistency between multiple databases.

[0110] The MySQL database may include but is not limited to an email metadata table (emails_metadata), an email classification table (classifications), and an email and classification association table (email_categories). The email metadata table may include but is not limited to an email's unique identifier (ID), sender, recipient, subject, and whether it is a spam email. The email classification table may include but is not limited to an email classification's unique identifier ID, a classification name, and a classification description. The email and classification association table may include but is not limited to an association ID, an email's unique ID, and an email classification's unique ID.

[0111] The MongoDB database may include but is not limited to an email content collection (emails_content), etc. The email content collection (emails_content) may include but is not limited to a unique ID automatically generated by MongoDB, an email ID associated with an ID in the emails_metadata table in MySQL, and the name, size, and type of the attachment, etc.

[0112] The use of Redis data structures such as hashes, lists, and sets in the email intelligent processing model can optimize storage and access. The Redis database can include but is not limited to email metadata cache and email classification information cache, and can also set expiration times for email metadata and email classification information to improve the timeliness of data. For example, the expiration time of email metadata and email classification information can be set to 1 hour.

[0113] In a specific example, to improve the storage and query capabilities of the email intelligent processing model, MySQL can adopt a read-write separation architecture, MongoDB can be sharded according to the email ID associated with the ID of the emails_metadata table in MySQL, and Redis can be configured with a Redis database snapshot (RDB) or Append Only File (AOF) persistence strategy.

[0114] The embodiment of the present invention obtains the historical mail data of each user and the historical mail associated data that matches the historical mail data, and further uses the historical mail data and the historical mail associated data that matches the historical mail data as mail processing sample data, so that the mail intelligent processing model can be trained based on the mail processing sample data, and the trained mail intelligent processing model can provide an automated mail intelligent processing function. The mail intelligent processing model can also determine the current mail classification rules of the mail data to be displayed in real time based on the historical mail operation behavior data of the current user, so as to classify and display the mail data to be displayed according to the current mail classification rules. At the same time, by optimizing the design of the mail display interface and the synchronization and consistency of database data, the problems of complex and conflicting settings of existing mail classification rules, inability to handle new situations, and inability to meet the personalized needs of users are solved, and the efficiency and accuracy of mail processing can be improved.

[0115] In the technical solution of the present disclosure, the collection, storage, use, processing, transmission, provision and disclosure of user personal information are in compliance with the relevant laws and regulations and do not violate public order and good morals.

[0116] It should be noted that any arrangement and combination of the technical features in the above embodiments also falls within the protection scope of the present invention.

[0117] Embodiment 4

[0118] Figure 7 Schematic diagram of a mail intelligent processing device provided by Embodiment 4 of the present invention. Figure 7 As shown, the device is configured in an email intelligent processing model, including: a module for acquiring email data to be displayed 410, a module for determining current email classification rules 420, and a module for displaying email data classification to be displayed 430, wherein:

[0119] The to-be-displayed email data acquisition module 410 is used to acquire the to-be-displayed email data of the current user.

[0120] The current email classification rule determination module 420 is used to determine the current email classification rule of the email data to be displayed in real time according to the historical email operation behavior data of the current user.

[0121] The to-be-displayed email data classification display module 430 is used to classify and display the to-be-displayed email data according to the current email classification rule.

[0122] The embodiment of the present invention obtains the to-be-displayed email data of the current user by utilizing an intelligent email processing model, and further determines the current email classification rules of the to-be-displayed email data in real time according to the historical email operation behavior data of the current user, thereby classifying and displaying the to-be-displayed email data according to the current email classification rules, solving the problems of complex and conflicting settings of existing email classification rules, inability to handle new situations, and inability to meet personalized user needs, and can improve the efficiency and accuracy of email processing.

[0123] In an optional embodiment of the present invention, the current email classification rule determination module 420 is further used to: determine in real time the associated classification reference factors of the current email classification rule based on the historical email operation behavior data of the current user; wherein the associated classification reference factors may include at least one of email interaction frequency, email viewing mode, email search mode and classified email identification; determine the current email classification rules of the email data to be displayed based on the weights of the associated classification reference factors and the email data to be displayed.

[0124] In an optional embodiment of the present invention, the to-be-displayed email data classification display module 430 is further used to: determine the initial classification display result of the to-be-displayed email data through the current email classification rule; optimize the initial classification display result to obtain a target classification display result; and classify and display the to-be-displayed email data according to the target classification display result.

[0125] In an optional embodiment of the present invention, the above-mentioned device may also include an email classification label list generation module, which is used to: determine the email classification label matching the email data to be displayed in response to the email label setting instruction triggered by the current user; predict the arrangement order of each of the email classification labels in the email classification label list according to the email data to be displayed; sort each of the email classification labels according to the arrangement order of each of the email classification labels in the email classification label list to generate the email classification label list.

[0126] In an optional embodiment of the present invention, the email classification label list generation module is further used to: obtain the historical email classification labels historically configured by the current user; predict the current email classification labels in real time according to the email data to be displayed; and use the historical email classification labels and the current email classification labels as email classification labels that match the email data to be displayed.

[0127] In an optional embodiment of the present invention, the above-mentioned device may also include an email intelligent analysis module, which is used to: perform intelligent analysis on the email data to be displayed; wherein the intelligent analysis may include natural language processing, keyword extraction, and keyword index establishment and update, a historical email operation behavior data storage module, which is used to: monitor in real time the current user's operation processing behavior data on the email data to be displayed; store the operation processing behavior data of the email data to be displayed as the historical email operation behavior data.

[0128] The above-mentioned mail intelligent processing device can execute the mail intelligent processing method provided by any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method. For technical details not described in detail in this embodiment, please refer to the mail intelligent processing method provided by any embodiment of the present invention.

[0129] Since the mail intelligent processing device introduced above is a device that can execute the mail intelligent processing method in the embodiment of the present invention, based on the mail intelligent processing method introduced in the embodiment of the present invention, the technicians in this field can understand the specific implementation of the mail intelligent processing device in this embodiment and its various variations, so how the mail intelligent processing device implements the mail intelligent processing method in the embodiment of the present invention is not described in detail here. As long as the technicians in this field implement the device used by the mail intelligent processing method in the embodiment of the present invention, it belongs to the scope of protection of this application.

[0130] Embodiment 5

[0131] Figure 8 is a schematic diagram of a model training device provided in Embodiment 5 of the present invention, such as Figure 8 As shown, the device includes: a historical email data acquisition module 510 and an email intelligent processing model training module 520, wherein:

[0132] The historical email data acquisition module 510 is used to acquire the historical email data of each user and the historical email associated data matching the historical email data; wherein the historical email associated data may include the user operation behavior data corresponding to the historical email data and the email classification result corresponding to the historical email data.

[0133] The mail intelligent processing model training module 520 is used to use the historical mail data and the historical mail associated data matching the historical mail data as mail processing sample data, and train the mail intelligent processing model according to the mail processing sample data.

[0134] The embodiment of the present invention uses the mail intelligent processing model to obtain the historical mail data of each user and the historical mail associated data that matches the historical mail data, and further uses the historical mail data and the historical mail associated data that matches the historical mail data as mail processing sample data, so that the mail intelligent processing model can be trained according to the mail processing sample data to complete mail intelligent processing. The above technical solution can solve the problems of complex and conflicting settings of existing mail classification rules, inability to handle new situations, and inability to meet user personalized needs, and can improve the efficiency and accuracy of mail processing.

[0135] The above-mentioned model training device can execute the model training method provided by any embodiment of the present invention, and has the functional modules and beneficial effects corresponding to the execution method. For technical details not described in detail in this embodiment, please refer to the model training method provided by any embodiment of the present invention.

[0136] Since the model training device introduced above is a device that can execute the model training method in the embodiment of the present invention, based on the model training method introduced in the embodiment of the present invention, the technical personnel in the field can understand the specific implementation of the model training device of this embodiment and its various variations, so how the model training device implements the model training method in the embodiment of the present invention is not introduced in detail here. As long as the technical personnel in the field implement the device used in the model training method in the embodiment of the present invention, it belongs to the scope of protection of this application.

[0137] Embodiment 6

[0138] Fig. 9 A schematic diagram of the structure of an electronic device 10 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0139] like Fig. 9As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. The input / output (I / O) interface 15 is also connected to the bus 14.

[0140] A number of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0141] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as the mail intelligent processing method or the model training method.

[0142] In some embodiments, the mail intelligent processing method or the model training method may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the mail intelligent processing method or the model training method described above may be executed. Alternatively, in other embodiments, the processor 11 may be configured to execute the mail intelligent processing method or the model training method in any other appropriate manner (e.g., by means of firmware).

[0143] Optionally, the email intelligent processing method may include: obtaining the email data to be displayed of the current user; determining the current email classification rules of the email data to be displayed in real time based on the historical email operation behavior data of the current user; and classifying and displaying the email data to be displayed according to the current email classification rules.

[0144] Optionally, the model training method may include: obtaining historical email data of each user and historical email associated data that matches the historical email data; wherein the historical email associated data includes user operation behavior data corresponding to the historical email data and email classification results corresponding to the historical email data; using the historical email data and the historical email associated data that matches the historical email data as email processing sample data, and training an email intelligent processing model based on the email processing sample data; wherein the trained email intelligent processing model is used to execute the email intelligent processing method described in any embodiment of the present invention.

[0145] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0146] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0147] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, device, or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, 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), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0148] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0149] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0150] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0151] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this disclosure can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved, and this document does not limit this.

[0152] The above specific implementations do not constitute a limitation on the protection scope of the present disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present disclosure shall be included in the protection scope of the present disclosure.

Claims

1. A mail intelligent processing method, characterized in that: Applied to the mail intelligent processing model, including: Get the email data to be displayed for the current user; Determine in real time the current email classification rule of the email data to be displayed according to the historical email operation behavior data of the current user; Classify and display the email data to be displayed according to the current email classification rule; The step of determining the current email classification rule of the email data to be displayed in real time according to the historical email operation behavior data of the current user includes: Determining in real time the associated classification reference factors of the current email classification rule according to the historical email operation behavior data of the current user; wherein the historical email operation behavior data includes at least one of the current user's search behavior, the status mark of the historical email, the subject content of the historical email, and the sending and receiving time of the historical email; wherein the associated classification reference factors include at least one of the email interaction frequency, the email viewing mode, the email search mode, and the classified email identification; The current mail classification rule of the mail data to be displayed is determined according to the weight of the associated classification reference factor and the mail data to be displayed.

2. The method according to claim 1, characterized in that The classifying and displaying the to-be-displayed email data according to the current email classification rule includes: Determining the initial classification display result of the to-be-displayed email data by using the current email classification rule; Optimizing the initial classification display result to obtain a target classification display result; The email data to be displayed is displayed in categories according to the target classification display results.

3. The method according to claim 1, characterized in that The method further comprises: In response to the email label setting instruction triggered by the current user, determining the email classification label that matches the email data to be displayed; Predicting the arrangement order of each of the email classification labels in the email classification label list according to the email data to be displayed; The mail classification labels are sorted according to the arrangement order of the mail classification labels in the mail classification label list to generate the mail classification label list.

4. The method according to claim 3, characterized in that The determining of the email classification label that matches the email data to be displayed includes: Obtain the historical email classification labels of the current user's historical configuration; Predicting the current email classification label in real time according to the email data to be displayed; The historical mail classification label and the current mail classification label are used as mail classification labels matched with the mail data to be displayed.

5. The method according to claim 1, characterized in that After obtaining the email data to be displayed of the current user, the method further includes: Performing intelligent analysis on the email data to be displayed; wherein the intelligent analysis includes natural language processing, keyword extraction, and keyword index establishment and update; After the to-be-displayed email data is classified and displayed according to the current email classification rule, the method further includes: Real-time monitoring of the current user's operation processing behavior data on the email data to be displayed; The operation processing behavior data of the to-be-displayed email data is stored as the historical email operation behavior data.

6. A model training method, characterized in that: include: Acquire historical email data of each user and historical email associated data matched by the historical email data; wherein the historical email associated data includes user operation behavior data corresponding to the historical email data and email classification results corresponding to the historical email data; The historical email data and the historical email-related data that matches the historical email data are used as email processing sample data, and an email intelligent processing model is trained based on the email processing sample data; The trained email intelligent processing model is used to execute the email intelligent processing method described in any one of claims 1-5.

7. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the email intelligent processing method described in any one of claims 1-5 or the model training method described in claim 6.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the email intelligent processing method described in any one of claims 1-5 or the model training method described in claim 6 when executed.

9. A computer program product comprising a computer program / instructions, wherein: When the computer program / instructions are executed by the processor, the intelligent email processing method described in any one of claims 1 to 5 or the model training method described in claim 6 is implemented.

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