Intelligent screening and sorting method and device for customer message board data

Abstract

The invention discloses an intelligent screening and sorting method and device for client message board data. The method comprises the following steps: regularly capturing message board data of a customer, and storing the message board data into a preset database; performing content processing on the message board data in the database by using a natural language processing algorithm, and identifying a message needing to be replied; screening messages which are not replied in a preset time limit from the replied messages to obtain summarizing and reminding information of the messages which are not replied in the preset time limit; generating mail information according to the message leaving person information, the message content and the reminding information of the unreplied message summary, and creating a reminding mail; and setting task scheduling and sending a reminding mail.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and more specifically, to a method and apparatus for intelligent filtering and processing of customer message board data. Background Technology

[0002] The information on the message board may not be clearly categorized, making it difficult for users to find relevant information or feedback. If the information on the message board is not updated in a timely manner, users may miss important feedback or suggestions. The message board may lack an effective communication mechanism, resulting in user feedback not being handled effectively. Furthermore, message board data needs to be sorted and filtered weekly; the large volume of data makes manual identification tedious and prone to omissions. Summary of the Invention

[0003] To address the shortcomings of existing technologies, this invention provides a method and apparatus for intelligent filtering and organizing customer message board data.

[0004] According to one aspect of the present invention, a method for intelligent filtering and organizing customer message board data is provided, comprising:

[0005] Regularly capture customer message board data and store it in a pre-set database;

[0006] Natural language processing algorithms are used to process the message board data in the database and identify messages that need to be replied to.

[0007] Filter the replies to find unreply messages within a preset period, and obtain a summary of unreply messages within the preset period along with reminder information;

[0008] Generate an email message based on the information of the message senders, the content of the messages, and the reminder information from the unanswered messages;

[0009] Configure task scheduling to send reminder emails.

[0010] Optionally, the crawling and storage of message board data adopts the distributed computing MapReduce model, where the expression of the MapReduce model is:

[0011] Result = Reduce(Map(Data))

[0012] In the formula, Map() represents mapping; Reduce() represents reduction.

[0013] Optionally, the Monte Carlo method is used in the natural language processing algorithm to solve the approximation problem. The expression for the Monte Carlo method is:

[0014]

[0015] In the formula, N is the number of random selections, and f(X) i ) represents the weights of each scheme.

[0016] Optionally, a deep learning algorithm is used in the natural language processing algorithm to process the content of the message board data. The loss function of the deep learning algorithm is:

[0017]

[0018] In the formula, L is a scalar; m is a positive integer; and i is an integer index. y is a probability vector; (i) It is a one-hot vector; For function

[0019] Optionally, the natural language processing algorithm employs sequence alignment methods from bioinformatics for content processing optimization of the message board data. The expression for the sequence alignment method from bioinformatics is as follows:

[0020]

[0021] In the formula, S(i,j) is the cumulative similarity score; i is the current position of sequence A; j is the current position of sequence B; a i b is the i-th character of sequence A; j Let be the j-th character of sequence B; δ() is the character matching score function; γ is the interval penalty coefficient; S(i-1,j-1) is the diagonal score; S(i-1,j) is the vertical score; S(i,j-1) is the horizontal score.

[0022] According to another aspect of the present invention, a device for intelligent filtering and organizing customer message board data is provided, comprising:

[0023] The data scraping module is used to periodically scrape customer message board data and store it in a preset database;

[0024] The recognition module is used to process the message board data in the database using natural language processing algorithms to identify messages that need to be replied to.

[0025] The filtering module is used to filter unanswered messages within a preset period from the replied messages, and obtain a summary of unanswered messages within the preset period and reminder information;

[0026] The module is used to generate email information and create reminder emails based on the information of the message senders, the content of the messages, and the reminder information of the unanswered messages.

[0027] The settings module is used to configure task scheduling and send reminder emails.

[0028] According to another aspect of the present invention, a computer-readable storage medium is provided, the storage medium storing a computer program for performing the method of any of the above aspects of the present invention.

[0029] According to another aspect of the present invention, an electronic device is provided, comprising: a processor; a memory for storing processor-executable instructions; the processor being configured to read the executable instructions from the memory and execute the instructions to implement the method described in any of the preceding aspects of the present invention.

[0030] Thus, this invention reduces the time spent on manual screening, improves work efficiency, and enhances data accuracy. Attached Figure Description

[0031] Exemplary embodiments of the present invention can be more fully understood by referring to the following figures:

[0032] Figure 1 This is a flowchart illustrating an exemplary embodiment of the present invention for a method of intelligently filtering and organizing customer message board data.

[0033] Figure 2 This is a schematic diagram of the structure of an intelligent filtering and sorting device for customer message board data provided in an exemplary embodiment of the present invention;

[0034] Figure 3 This is the structure of an electronic device provided in an exemplary embodiment of the present invention. Detailed Implementation

[0035] Hereinafter, exemplary embodiments according to the present invention will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of the present invention, and not all embodiments of the present invention. It should be understood that the present invention is not limited to the exemplary embodiments described herein.

[0036] It should be noted that, unless otherwise specifically stated, the relative arrangement, numerical expressions, and values ​​of the components and steps described in these embodiments do not limit the scope of the invention.

[0037] Those skilled in the art will understand that the terms "first," "second," etc., in the embodiments of the present invention are only used to distinguish different steps, devices, or modules, and do not represent any specific technical meaning, nor do they indicate a necessary logical order between them.

[0038] It should also be understood that in the embodiments of the present invention, "multiple" can refer to two or more, and "at least one" can refer to one, two or more.

[0039] It should also be understood that any component, data or structure mentioned in the embodiments of the present invention can generally be understood as one or more unless explicitly defined or given contrary instructions in the context.

[0040] Furthermore, the term "and / or" in this invention is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this invention generally indicates that the preceding and following related objects have an "or" relationship.

[0041] It should also be understood that the description of the various embodiments in this invention emphasizes the differences between the various embodiments, and the similarities or similarities can be referred to each other. For the sake of brevity, they will not be described in detail.

[0042] At the same time, it should be understood that, for ease of description, the dimensions of the various parts shown in the accompanying drawings are not drawn according to actual scale.

[0043] The following description of at least one exemplary embodiment is merely illustrative and is in no way intended to limit the invention or its application or use.

[0044] Techniques, methods, and equipment known to those skilled in the art may not be discussed in detail, but where appropriate, they should be considered part of the specification.

[0045] It should be noted that similar labels and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be discussed further in subsequent figures.

[0046] The embodiments of this invention can be applied to electronic devices such as terminal devices, computer systems, and servers, and can operate together with a wide range of other general-purpose or special-purpose computing system environments or configurations. Well-known examples of terminal devices, computing systems, environments, and / or configurations suitable for use with electronic devices such as terminal devices, computer systems, and servers include, but are not limited to: personal computer systems, server computer systems, thin clients, thick clients, handheld or laptop devices, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputer systems, mainframe computer systems, and distributed cloud computing environments including any of the above systems, etc.

[0047] Electronic devices such as terminal devices, computer systems, and servers can be described in the general context of computer system executable instructions (such as program modules) executed by a computer system. Typically, program modules can include routines, programs, object programs, components, logic, data structures, etc., which perform specific tasks or implement specific abstract data types. Computer systems / servers can be implemented in distributed cloud computing environments, where tasks are executed by remote processing devices linked through communication networks. In distributed cloud computing environments, program modules can reside on local or remote computing system storage media, including storage devices.

[0048] Exemplary methods

[0049] Figure 1 This is a flowchart illustrating an exemplary embodiment of the intelligent filtering and organization method for customer message board data provided by the present invention. This embodiment can be applied to electronic devices, such as... Figure 1 As shown, the intelligent filtering and organization method for customer message board data includes the following steps:

[0050] Step 101: Periodically capture customer message board data and store it in a preset database;

[0051] Step 102: Use natural language processing algorithms to process the message board data in the database and identify the messages that need to be replied to.

[0052] Step 103: Filter the replies to obtain a summary of unreply messages within a preset period and reminder information;

[0053] Step 104: Generate email information and create a reminder email based on the information of the message senders, the content of the messages, and the reminder information summarized from the unanswered messages;

[0054] Step 105: Configure task scheduling to send reminder emails.

[0055] Specifically, the solution involves regularly updating the message board, automatically identifying message content, summarizing messages that haven't been replied to in ten days, and sending email reminders.

[0056] Solution:

[0057] 1. Data Acquisition and Storage:

[0058] Use Python's requests library to periodically scrape message board data and store it in a database.

[0059] 2. Content recognition and classification:

[0060] a. Use an NLP library to process the message content and identify messages that require a reply.

[0061] b. Text similarity algorithms, such as TF-IDF, Word2Vec, or BERT, can be used to compare the similarity of message content.

[0062] 3. Filter messages that have not been replied to for ten days:

[0063] Query the database for messages that have not been replied to within ten days, and filter out the messages for which reminders need to be sent.

[0064] 4. Email notification:

[0065] a. Use the smtplib or email library to create email messages that summarize messages that have not been replied to for ten days.

[0066] b. The email includes the message sender's information, the message content, and a reminder message.

[0067] 5. Scheduled task scheduling:

[0068] Use Python's schedule library to create a scheduled task that runs weekly to organize message board data and send reminder emails.

[0069] 6. Error handling and logging:

[0070] a. Add an exception handling mechanism to ensure stable system operation.

[0071] b. Keep logs to track and troubleshoot problems.

[0072] 7. Deployment:

[0073] Deploy the script to the server and set up a scheduled task to ensure the system runs regularly.

[0074] 1. Monte Carlo Method

[0075] The Monte Carlo method is a technique for solving mathematical problems through random sampling, particularly suitable for calculating numerical solutions to complex problems. Its basic idea is to approximate the solution to the problem through a large number of random experiments. The formula can be expressed as:

[0076]

[0077] Technology relevance: Applied to content recognition and classification.

[0078] When dealing with ambiguous semantic messages (e.g., "Reply as soon as possible" vs. "Process when available"): Simulate different interpretation schemes through random sampling (N times); calculate the weight of each scheme (f(X)). i )).

[0079] The scheme with the highest probability is used as the classification criterion.

[0080] Where N is the number of experiments, X is the result of the i-th experiment, and f(X) is the function value based on X.

[0081] 2. Qubits in quantum computing

[0082] Quantum computing is a computational model based on the principles of quantum mechanics, in which the qubit is the basic unit of computation. Unlike the bit in classical computing, a qubit can exist in a superposition of multiple states, thus enabling parallel computation. The superposition state of a qubit can be represented as:

[0083] |ψ>=α|0>+β|1>

[0084] Technical relevance: Supports distributed processing architecture.

[0085] In a server cluster deployment: a single message processing task can be in multiple states simultaneously (e.g., |0\rangle = text parsing, |1\rangle = classification judgment).

[0086] Parallel processing is achieved through a quantum computing framework.

[0087] Here, α and β are complex numbers that satisfy |α|1 + |β|2 = 1.

[0088] 3. The MapReduce model in distributed computing

[0089] MapReduce is a programming model for parallel computation of large-scale datasets. It breaks down large-scale data processing tasks into two phases: "Map" and "Reduce." The Map phase divides the input dataset into smaller chunks and maps each chunk to a series of key-value pairs; the Reduce phase then groups and reduces these key-value pairs to generate the final output. The formula can be expressed as:

[0090] Result = Reduce(Map(Data))

[0091] Technical relevance: Core implementation of data acquisition and storage.

[0092] 4. Deep Learning in Artificial Intelligence

[0093] Deep learning is a subfield of machine learning that uses multi-layered neural networks to simulate the neural network structure of the human brain and solve complex problems. A key formula in deep learning is the backpropagation algorithm, used to adjust network weights to minimize the loss function. The loss function L can be expressed as:

[0094]

[0095] Technical relevance: Driving content recognition model training;

[0096] In fine-tuning the BERT model:

[0097] y (i) Model prediction labels (reply required / no reply required); : Manually labeled with real tags.

[0098] The model parameters are dynamically adjusted using the loss function L.

[0099] 5. Sequence alignment in bioinformatics

[0100] In bioinformatics, sequence alignment is an important tool for analyzing biological sequences (such as DNA, RNA, and protein sequences). Alignment typically uses dynamic programming algorithms, the formula of which can be expressed as:

[0101]

[0102] Technical relevance: Applied to optimizing content recognition

[0103] Handling duplicate comments:

[0104] δ(a i b j ): Character similarity score; γ: Interval penalty coefficient; Duplicate messages are automatically merged when similarity > threshold.

[0105] These formulas and concepts demonstrate the application of innovation in different fields, inspiring the exploration and development of new knowledge and technologies.

[0106] Thus, this invention reduces the time spent on manual screening, improves work efficiency, and enhances data accuracy.

[0107] Exemplary device

[0108] Figure 2 This is a schematic diagram of the intelligent filtering and sorting device for customer message board data provided in an exemplary embodiment of the present invention. Figure 2 As shown, the device 200 includes:

[0109] The crawling module 210 is used to periodically crawl customer message board data and store it in a preset database;

[0110] The recognition module 220 is used to process the message board data in the database using natural language processing algorithms to identify messages that need to be replied to.

[0111] The filtering module 230 is used to filter unanswered messages within a preset period from the replied messages, and obtain a summary of unanswered messages within the preset period and reminder information;

[0112] Create module 240 to generate email information and create reminder emails based on the information of the message senders, the content of the messages, and the reminder information of the unanswered messages;

[0113] The settings module 250 is used to configure task scheduling and send reminder emails.

[0114] Optionally, the crawling and storage of message board data adopts the distributed computing MapReduce model, where the expression of the MapReduce model is:

[0115] Result = Reduce(Map(Data))

[0116] In the formula, Map() represents mapping; Reduce() represents reduction.

[0117] Optionally, the Monte Carlo method is used in the natural language processing algorithm to solve the approximation problem. The expression for the Monte Carlo method is:

[0118]

[0119] In the formula, N is the number of random selections, and f(X) i ) represents the weights of each scheme.

[0120] Optionally, a deep learning algorithm is used in the natural language processing algorithm to process the content of the message board data. The loss function of the deep learning algorithm is:

[0121]

[0122] In the formula, L is a scalar; m is a positive integer; and i is an integer index. y is a probability vector; (i) It is a one-hot vector; For function

[0123] Optionally, the natural language processing algorithm employs sequence alignment methods from bioinformatics for content processing optimization of the message board data. The expression for the sequence alignment method from bioinformatics is as follows:

[0124]

[0125] In the formula, S(i,j) is the cumulative similarity score; i is the current position of sequence A; j is the current position of sequence B; a i b is the i-th character of sequence A; jLet be the j-th character of sequence B; δ() is the character matching score function; γ is the interval penalty coefficient; S(i-1,j-1) is the diagonal score; S(i-1,j) is the vertical score; S(i,j-1) is the horizontal score.

[0126] Exemplary electronic devices

[0127] Figure 3 This is the structure of an electronic device provided in an exemplary embodiment of the present invention. For example... Figure 3 As shown, the electronic device 30 includes one or more processors 31 and memory 32.

[0128] The processor 31 may be a central processing unit (CPU) or other form of processing unit with data processing and / or instruction execution capabilities, and may control other components in the electronic device to perform desired functions.

[0129] The memory 32 may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory. The non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor 31 may execute the program instructions to implement the methods of the software programs of the various embodiments of the present invention described above, and / or other desired functions. In one example, the electronic device may also include an input device 33 and an output device 34, these components being interconnected via a bus system and / or other forms of connection mechanisms (not shown).

[0130] In addition, the input device 33 may also include, for example, a keyboard, a mouse, etc.

[0131] The output device 34 can output various information to the outside. The output device 34 may include, for example, a display, a speaker, a printer, and a communication network and its connected remote output devices, etc.

[0132] Of course, for the sake of simplicity, Figure 3 Only some of the components of the electronic device relevant to the present invention are shown, omitting components such as buses, input / output interfaces, etc. In addition, the electronic device may include any other suitable components depending on the specific application.

[0133] Exemplary computer program products and computer-readable storage media

[0134] In addition to the methods and apparatus described above, embodiments of the present invention may also be computer program products, which include computer program instructions that, when executed by a processor, cause the processor to perform the steps in the methods according to various embodiments of the present invention described in the "Exemplary Methods" section above.

[0135] The computer program product can be written in any combination of one or more programming languages ​​to perform the operations of the embodiments of the present invention. The programming languages ​​include object-oriented programming languages ​​such as Java and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computing device, partially on the user's computing device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0136] Furthermore, embodiments of the present invention may also be computer-readable storage media storing computer program instructions thereon, which, when executed by a processor, cause the processor to perform the steps of the methods for information mining of historical change records according to various embodiments of the present invention as described in the "Exemplary Methods" section above.

[0137] The computer-readable storage medium may be any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or any combination thereof. More specific examples (a non-exhaustive list) of readable storage media include: an electrical connection having one or more wires, a portable disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof.

[0138] The basic principles of the present invention have been described above with reference to specific embodiments. However, it should be noted that the advantages, benefits, and effects mentioned in the present invention are merely examples and not limitations, and should not be considered as essential features of each embodiment of the present invention. Furthermore, the specific details disclosed above are for illustrative and facilitative purposes only, and are not limitations. These details do not limit the present invention to the necessity of employing the aforementioned specific details.

[0139] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For system embodiments, since they largely correspond to method embodiments, the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments.

[0140] The block diagrams of devices, systems, devices, and systems involved in this invention are merely illustrative examples and are not intended to require or imply that they must be connected, arranged, or configured in the manner shown in the block diagrams. As those skilled in the art will recognize, these devices, systems, devices, and systems can be connected, arranged, and configured in any manner. Words such as “comprising,” “including,” “having,” etc., are open-ended terms meaning “including but not limited to,” and are used interchangeably with them. The terms “or” and “and” as used herein refer to the terms “and / or,” and are used interchangeably with them unless the context clearly indicates otherwise. The term “such as” as used herein refers to the phrase “such as but not limited to,” and is used interchangeably with it.

[0141] The methods and systems of the present invention may be implemented in many ways. For example, they may be implemented by software, hardware, firmware, or any combination of software, hardware, and firmware. The above-described order of steps for the methods is for illustrative purposes only, and the steps of the methods of the present invention are not limited to the order specifically described above unless otherwise specifically stated. Furthermore, in some embodiments, the present invention may also be implemented as a program recorded on a recording medium, the program comprising machine-readable instructions for implementing the methods according to the present invention. Thus, the present invention also covers recording media storing programs for performing the methods according to the present invention.

[0142] It should also be noted that in the systems, apparatus, and methods of the present invention, the components or steps can be disassembled and / or recombined. These disassemblies and / or recombinations should be considered equivalents of the present invention. The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use the invention. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein can be applied to other aspects without departing from the scope of the invention. Therefore, the invention is not intended to be limited to the aspects shown herein, but rather to be carried out within the widest scope consistent with the principles and novel features disclosed herein.

[0143] The above description has been given for purposes of illustration and description. Furthermore, this description is not intended to limit the embodiments of the invention to the forms disclosed herein. Although numerous exemplary aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, alterations, additions, and sub-combinations therein.

Claims

1. A method for intelligently filtering and organizing customer message board data, characterized in that, include: Regularly capture customer message board data and store it in a pre-set database; Natural language processing algorithms are used to process the message board data in the database and identify messages that need to be replied to. Filter the replies to find unreply messages within a preset period, and obtain a summary of unreply messages within the preset period along with reminder information; Based on the information of the message senders, the content of the messages, and the reminder information, generate an email message and create a reminder email; Configure the task scheduler to send the aforementioned reminder email.

2. The method according to claim 1, characterized in that, The data retrieval and storage for the message board uses a distributed computing MapReduce model, where the expression for the MapReduce model is: Result = Reduce(Map(Data)) In the formula, Map() represents mapping; Reduce() represents reduction.

3. The method according to claim 1, characterized in that, The natural language processing algorithm uses the Monte Carlo method to solve the approximation problem. The expression for the Monte Carlo method is: In the formula, N is the number of random selections, and f(X) i ) represents the weights of each scheme.

4. The method according to claim 1, characterized in that, The natural language processing algorithm employs a deep learning algorithm to process the content of the message board data. The loss function of the deep learning algorithm is: In the formula, L is a scalar; m is a positive integer; and i is an integer index. y is a probability vector; (i) It is a one-hot vector; For function 5. The method according to claim 1, characterized in that, The natural language processing algorithm employs sequence alignment methods from bioinformatics to optimize the content processing of the message board data. The expression for the sequence alignment method from bioinformatics is as follows: In the formula, S(i,j) is the cumulative similarity score; i is the current position of sequence A; j is the current position of sequence B; a i b is the i-th character of sequence A; j Let be the j-th character of sequence B; δ() is the character matching score function; γ is the interval penalty coefficient; S(i-1,j-1) is the diagonal score; S(i-1,j) is the vertical score; S(i,j-1) is the horizontal score.

6. A device for intelligent filtering and organizing customer message board data, characterized in that, include: The data scraping module is used to periodically scrape customer message board data and store it in a preset database; The recognition module is used to process the message board data in the database using natural language processing algorithms to identify messages that need to be replied to. The filtering module is used to filter unanswered messages within a preset period from the replied messages, and obtain a summary of unanswered messages within the preset period and reminder information; A module is created to generate email information and create reminder emails based on the summary of unanswered messages, the message content, and the reminder information. The settings module is used to configure task scheduling to send the reminder emails.

7. The apparatus according to claim 6, characterized in that, The data retrieval and storage for the message board uses a distributed computing MapReduce model, where the expression for the MapReduce model is: Result = Reduce(Map(Data)) In the formula, Map() represents mapping; Reduce() represents reduction.

8. The apparatus according to claim 6, characterized in that, The natural language processing algorithm uses the Monte Carlo method to solve the approximation problem. The expression for the Monte Carlo method is: In the formula, N is the number of random selections, and f(X) i ) represents the weights of each scheme.

9. A computer-readable storage medium, characterized in that, The storage medium stores a computer program for performing the method described in any one of claims 1-5.

10. An electronic device, characterized in that, The electronic device includes: processor; Memory used to store the processor's executable instructions; The processor is configured to read the executable instructions from the memory and execute the instructions to implement the method described in any one of claims 1-5.

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