Short message exception processing method and device, computer equipment and storage medium

Abstract

The invention relates to the technical field of artificial intelligence, can be applied to service system platforms of medical health, financial science and technology and the like, and discloses a short message exception handling method and device, computer equipment and a storage medium, and the method comprises the steps: receiving a strategy configuration instruction of a target user, and configuring an exception handling strategy for short message exception according to the strategy configuration instruction; obtaining a to-be-recognized short message sent to the target user, and performing content analysis on the to-be-recognized short message by using a pre-trained semantic model to generate a content analysis result; based on the content analysis result, determining an abnormal type of the to-be-identified short message; according to the exception type of the to-be-identified short message, processing the to-be-identified short message by adopting the exception processing strategy; therefore, the method can effectively improve the individuation and accuracy of short message exception processing.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of artificial intelligence, and in particular to a short message exception processing method and device, computer equipment and a computer readable storage medium. BACKGROUND

[0002] At present, with the rapid development of mobile communication technology, short message service has become one of the indispensable communication methods in people's daily life. Short messages are not only used for information exchange between individuals, but also widely used in various fields such as business, finance, government affairs, etc. Information notification and verification. However, with the popularization of short message use, short message exception problems are increasing, which brings many inconveniences and security risks to users. Short message exceptions mainly include fraudulent short messages, marketing harassment short messages, and abnormal collection short messages. These abnormal short messages not only interfere with the normal communication of users, but also may cause economic losses and personal information security problems to users. For example, fraudulent short messages may induce users to click on abnormal links, resulting in personal information leakage or account theft; marketing harassment short messages frequently disturb users, affecting user experience.

[0003] Currently, in the prior art, there are some short message exception processing methods, but these methods have some limitations, for example, in the face of abnormal short messages, the existing short message interception function can only shield specific numbers or keywords, and cannot actively intervene or provide help, which has the following problems:

[0004] 1. Insufficient personalization: users need to manually report after receiving spam messages, and cannot actively defend against abnormal short messages according to their own needs, resulting in insufficient personalization of short message exception processing;

[0005] 2. Poor accuracy: unable to effectively distinguish the type of short message exception (such as fraud, marketing harassment or abnormal collection, etc.), resulting in poor accuracy of short message exception processing.

[0006] In the medical and health field, short message service is widely used for appointment registration, examination result notification, health reminders, etc. However, short message exception problems have also interfered with normal communication in the medical and health field, for example:

[0007] Fraudulent short messages: unscrupulous individuals may disguise as hospitals or health service agencies and send false examination results or appointment information to induce users to click on abnormal links, thereby obtaining users' personal information or committing fraud.

[0008] Marketing harassment short messages: some business agencies may send marketing short messages related to health products, which may interfere with users' attention to important medical information and affect user experience.

[0009] In the field of financial technology, SMS services are widely used for account verification, transaction notification, balance reminder, etc. However, SMS anomaly problems also pose a threat to the security and user experience of the financial technology field, for example:

[0010] Fraudulent SMS: Criminals may disguise themselves as banks or payment platforms and send false account verification information or transaction notifications to induce users to enter account passwords or click on abnormal links, resulting in account theft or financial loss.

[0011] Abnormal collection SMS: Some unscrupulous loan institutions may send abnormal collection SMS to users, harassing and threatening them, affecting their normal life and mental health.

[0012] However, in both the medical health field and the financial technology field, when facing SMS anomaly processing, there are problems of insufficient personalization and poor accuracy. Based on this, how to provide an SMS anomaly processing method, device, computer equipment and computer readable storage medium to effectively improve the personalization and accuracy of SMS anomaly processing is a problem that the technical personnel in the field are eager to solve. SUMMARY

[0013] In view of the above shortcomings of the prior art, the purpose of the present application is to provide an SMS anomaly processing method, device, computer equipment and computer readable storage medium, which aims to solve the problem of how to effectively improve the personalization and accuracy of SMS anomaly processing.

[0014] In order to achieve the above purpose, the present application adopts the following technical solutions:

[0015] In a first aspect, the present application provides an SMS anomaly processing method, comprising:

[0016] Receiving a policy configuration instruction of a target user, and configuring an abnormal processing policy for SMS anomaly according to the policy configuration instruction;

[0017] Obtaining a to-be-identified SMS sent to the target user, and using a pre-trained semantic model to analyze the content of the to-be-identified SMS to generate a content analysis result;

[0018] Based on the content analysis result, determining the abnormal type of the to-be-identified SMS;

[0019] According to the abnormal type of the to-be-identified SMS, the abnormal processing policy is used to process the to-be-identified SMS.

[0020] In a second aspect, the present application provides an SMS anomaly processing device, comprising:

[0021] The receiving module is configured to receive a policy configuration instruction of a target user, and configure an exception handling policy for short message exception according to the policy configuration instruction;

[0022] The obtaining module is configured to obtain a to-be-identified short message sent to the target user, and perform content analysis on the to-be-identified short message by using a pre-trained semantic model to generate a content analysis result;

[0023] The determining module is configured to determine an exception type of the to-be-identified short message based on the content analysis result;

[0024] The processing module is configured to perform processing on the to-be-identified short message by using the exception handling policy according to the exception type of the to-be-identified short message.

[0025] In a third aspect, the present application provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the short message exception handling method as described above when executing the computer program.

[0026] In a fourth aspect, the present application provides a computer readable storage medium, which stores a computer program, wherein the computer program is executable by a processor to implement the short message exception handling method as described above.

[0027] Compared with the prior art, the present application provides a short message exception handling method, device, computer device and computer readable storage medium, wherein a policy configuration instruction of a target user is received, an exception handling policy for short message exception is configured according to the policy configuration instruction, a to-be-identified short message sent to the target user is obtained, content analysis is performed on the to-be-identified short message by using a pre-trained semantic model to generate a content analysis result, an exception type of the to-be-identified short message is determined based on the content analysis result, and processing is performed on the to-be-identified short message by using the exception handling policy according to the exception type of the to-be-identified short message, so that the present application can effectively improve the individualization and accuracy of short message exception handling. BRIEF DESCRIPTION OF DRAWINGS

[0028] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed in the embodiments or prior art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments described in the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0029] Figure 1 An application environment schematic diagram of a short message exception handling method provided by an embodiment of the present application.

[0030] Figure 2 A flowchart of a short message exception processing method provided by an embodiment of the present application is shown.

[0031] Figure 3 A program module diagram of a short message exception processing device provided by an embodiment of the present application is shown.

[0032] Figure 4 A structure diagram of a computer device provided by an embodiment of the present application is shown.

[0033] Figure 5 Another structure diagram of a computer device provided by an embodiment of the present application is shown. DETAILED DESCRIPTION

[0034] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are some embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of protection of the present application.

[0035] It should be understood that, when used in the specification and the appended claims of the present application, the term “comprising” indicates the presence of the described features, integers, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0036] It should also be understood that, when used in the specification and the appended claims of the present application, the term “and / or” refers to any combination of one or more of the associated listed items and all possible combinations, and includes these combinations.

[0037] As used in the specification and the appended claims of the present application, the term “if” can be interpreted as “when” or “upon” or “in response to a determination” or “in response to detecting” depending on the context. Similarly, the phrase “if it is determined” or “if [a described condition or event] is detected” can be interpreted as meaning “upon determining” or “in response to determining” or “upon detecting [a described condition or event]” or “in response to detecting [a described condition or event]” depending on the context.

[0038] In addition, in the description of the present application and the appended claims, the terms “first”, “second”, “third”, etc. are only used for differentiation in description, and cannot be understood as indicating or implying relative importance.

[0039] Reference within the specification of this document to "one embodiment" or "some embodiments" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the application. The appearances of the phrase "in one embodiment" or "in some embodiments" in various places within specified

[0040] It should be understood that the magnitude of the serial number of each step in the following embodiments does not mean the order of execution, and the execution order of each process should be determined according to its function and inherent logic, and should not constitute any limitation on the implementation process of the embodiments of the application.

[0041] In order to illustrate the technical solutions of the present application, the following will be illustrated by specific embodiments.

[0042] An embodiment of the present application provides a short message exception processing method, which can be applied in an application environment as shown in Figure 1 The client and the server communicate through the network. The client includes but is not limited to a palm computer, a desktop computer, a notebook computer, an ultra-mobile personal computer (UMPC), a netbook, a cloud computer device, a personal digital assistant (PDA), and the like. The server can be a stand-alone server, or a cloud server providing cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content distribution networks (CDN), and basic cloud computing services such as big data and artificial intelligence platforms.

[0043] Please refer to Figure 2 An embodiment of the present application provides a short message exception processing method, which includes the following steps:

[0044] S100, receiving a policy configuration instruction of a target user, and configuring an exception processing policy for short message exceptions according to the policy configuration instruction;

[0045] S200, obtaining a to-be-identified short message sent to the target user, and performing content analysis on the to-be-identified short message by using a pre-trained semantic model to generate a content analysis result;

[0046] S300, determine the abnormal type of the to-be-identified short message based on the content analysis result;

[0047] S400, process the to-be-identified short message according to the abnormal type of the to-be-identified short message using the abnormal processing strategy.

[0048] In specific implementation, the short message abnormal processing method of the embodiment realizes the individualization and accuracy of short message abnormal processing through a series of carefully designed steps, and the specific analysis is as follows:

[0049] 1. User policy configuration (S100): The method first receives the policy configuration instructions of the target user, and configures the abnormal processing strategy according to these instructions. This process allows users to set specific processing rules according to their own needs and preferences, such as specifying certain keywords (including phrases) as abnormal trigger words, and defining corresponding interception or reply strategies. This user-defined policy configuration mechanism ensures that the processing strategy accurately reflects the individual needs of the user, thereby providing customized short message abnormal processing services for the user.

[0050] 2. Content analysis (S200): The method uses a pre-trained semantic model to analyze the content of the obtained to-be-identified short message, generating a content analysis result. The pre-trained semantic model is usually trained based on a large amount of text data and can understand the semantic features of the short message content, identifying the category of the short message (such as fraud, marketing, service, etc.). This deep learning-based semantic analysis technology can more accurately understand the true intent and content of the short message compared to traditional keyword matching methods, thereby improving the accuracy of abnormal type identification.

[0051] 3. Abnormal type determination (S300): Based on the content analysis result, the method can determine the abnormal type of the to-be-identified short message. Since the content analysis result provides rich semantic information, the system can more accurately determine whether the to-be-identified short message belongs to an abnormal category and the specific abnormal type. For example, for a short message containing "verification code" and "click link to verify account", the system can identify it as a high-risk fraudulent short message, rather than an ordinary marketing short message. This accurate abnormal type determination mechanism ensures the relevance and effectiveness of subsequent processing operations.

[0052] 4. Abnormal processing (S400): Finally, according to the determined abnormal type, the user-configured abnormal processing strategy is used to process the to-be-identified short message. Since the specific processing operation is dynamically generated based on the user configuration strategy and content analysis result, the system can flexibly execute the corresponding processing operation, such as automatic interception, automatic reply or marking for archiving, according to different abnormal types and user needs. This dynamic adaptability not only improves the flexibility of processing, but also further improves the accuracy of processing results and user satisfaction.

[0053] Through the synergistic effect of the above steps, the method can not only configure an exception handling strategy according to the personalized needs of the user, but also accurately identify the exception type of the short message through advanced semantic analysis technology, and perform corresponding processing operations according to the exception type. This comprehensive processing mechanism effectively improves the personalization and accuracy of short message exception handling, and provides a high-efficiency and intelligent short message exception handling solution for users.

[0054] It can be understood that the short message exception handling method provided by the embodiment of the present application can be applied to the short message exception handling scene related to the medical health field. The following is a specific example:

[0055] Scenario description

[0056] In the medical health field, short message services are widely used for appointment registration, examination result notification, health reminders, etc. However, short message exception problems also interfere with normal communication in the medical health field. For example, criminals may disguise as hospitals or health service agencies to send false examination results or appointment information, induce users to click on abnormal links, and thus obtain personal information of the users or commit fraud.

[0057] Specific application example

[0058] 1. User policy configuration:

[0059] The user sets a policy configuration instruction through the user interaction interface of the short message platform. For example, the user can set to intercept all short messages containing "examination result" but not from a specified hospital number, or set to automatically reply "please query the examination result through the official channel".

[0060] The user can also set keywords such as "physical examination report" and "appointment confirmation", and specify the interception policy as "automatic interception" or "automatic reply".

[0061] 2. Content analysis:

[0062] The system obtains the to-be-identified short message sent to the user, and performs content analysis using a pre-trained semantic model. For example, a short message content is: "Dear user, your physical examination report has been issued. Please click the link to view the detailed results."

[0063] The semantic model generates a content analysis result after analysis, and identifies that the short message may belong to the "fraud" category because its content is similar to known fraud short message patterns.

[0064] 3. Exception type determination:

[0065] Based on the content analysis result, the system determines that the abnormal type of the short message is "fraud". The system further checks the sender information of the short message and finds that it is not from the user-trusted hospital number, further confirming that it is an abnormal short message.

[0066] 4. Abnormal processing:

[0067] According to the user-configured abnormal processing strategy, the system automatically intercepts the short message and sends a notification to the user: "A possible fraudulent short message has been intercepted, the content involves a fake physical examination report, please pay attention to personal information security."

[0068] If the user has previously set an automatic reply strategy, the system will also automatically reply to the sender: "Please query the test results through the official channel, thank you for your understanding."

[0069] It can be understood that the short message abnormal processing method provided by the embodiments of the present application can also be applied to the short message abnormal processing scene related to the field of financial technology. The following is a specific example:

[0070] Scenario description

[0071] In the field of financial technology, short message services are widely used for account verification, transaction notification, balance reminder, etc. However, short message abnormal problems also pose a threat to the safety and user experience of the field of financial technology. For example, criminals may pretend to be a bank or a payment platform and send false account verification information or transaction notifications to induce users to input account passwords or click on abnormal links, resulting in account theft or loss of funds.

[0072] Specific application example

[0073] 1. User policy configuration:

[0074] The user sets the policy configuration instruction through the user interaction interface of the short message platform. For example, the user can set to intercept all short messages containing "account verification" but not from the specified bank number, or set to automatically reply "Please verify the account through the official channel".

[0075] The user can also set keywords such as "transaction notification" and "balance change", and specify the interception strategy as "automatic interception" or "automatic reply".

[0076] 2. Content analysis:

[0077] The system obtains the to-be-recognized short message sent to the user and uses a pre-trained semantic model to perform content analysis. For example, a short message content is: "Dear user, your account needs to be re-verified, please click the link to input the verification code."

[0078] The semantic model analyzes the content and generates a content analysis result, identifying that the short message may belong to the "fraud" category because its content is similar to known fraud short message patterns.

[0079] 3. Abnormality type determination:

[0080] Based on the content analysis result, the system determines that the abnormality type of the short message is "fraud". The system further checks the sender information of the short message and finds that it is not from a bank number trusted by the user, further confirming that it is an abnormal short message.

[0081] 4. Abnormality processing:

[0082] According to the abnormality processing strategy configured by the user, the system automatically intercepts the short message and sends a notification to the user: "A possible fraud short message has been intercepted, the content involves fake account verification, please pay attention to personal information security."

[0083] If the user has previously set an automatic reply strategy, the system will also automatically reply to the sender: "Please verify your account through official channels, thank you for your understanding."

[0084] Through the above specific application examples, it can be seen that the short message abnormality processing method provided by the embodiments of the present application can effectively identify and process abnormal short messages in the medical and health field and the financial technology field, and can improve the personalization and accuracy of short message abnormality processing. This method not only protects the user's personal information security, but also improves the user experience, ensuring that the user can receive important information in a timely and accurate manner.

[0085] Further, in one embodiment, the short message abnormality processing method, wherein the receiving of the policy configuration instruction of the target user, according to the policy configuration instruction, configuring the abnormality processing strategy for short message abnormality, specifically comprising steps of:

[0086] receiving the policy configuration instruction sent by the target user through the user interaction interface of the short message platform;

[0087] parsing the policy configuration instruction, and configuring the original processing strategy for short message abnormality according to the parsing result;

[0088] optimizing the original processing strategy according to the interaction behavior data of the target user on its historical short message information, and generating the abnormality processing strategy.

[0089] Further, the short message abnormality processing method, wherein the optimizing the original processing strategy according to the interaction behavior data of the target user on its historical short message information, and generating the abnormality processing strategy, specifically comprising steps of:

[0090] Extracting interaction behavior data of the target user on historical short message information from the message cloud database of the short message platform;

[0091] Cleaning and formatting the interaction behavior data, and analyzing the cleaned and formatted interaction behavior data to identify the target user's processing preference information for different types of abnormal short messages;

[0092] Adjusting the original processing strategy according to the processing preference information to generate the abnormal processing strategy.

[0093] In specific implementation, the specific implementation process of the steps of the embodiment is as follows:

[0094] Step 1: Receiving strategy configuration instruction

[0095] 1. User interaction interface design:

[0096] Design the user interaction interface of the short message platform, providing multiple ways for users to send strategy configuration instructions. These ways can include short messages, web forms, mobile application interfaces, etc.

[0097] For example, the user can send the instruction "SET block key words: verification code; intercept strategy: automatic reply 'Please do not disclose personal information'" through a short message.

[0098] In the web or mobile application, a configuration page is provided, and the user can set the strategy by filling out the form.

[0099] 2. Instruction receiving:

[0100] Through the user interaction interface of the short message platform, receive the strategy configuration instruction sent by the target user.

[0101] The system monitors the message queue of the short message platform in real time, captures the strategy configuration instruction sent by the user, and stores it in the temporary cache for subsequent processing.

[0102] Step 2: Analyzing strategy configuration instruction

[0103] 1. Instruction analysis:

[0104] Parse the received strategy configuration instruction, extract key information, including trigger words, interception strategies and reply content, etc.

[0105] Use natural language processing (NLP) technology to support users to send instructions in natural language form, improving user experience.

[0106] For example, parsing the user's sent instruction "SET block keyword: verification code; intercept strategy: automatic reply 'Please do not disclose personal information'", the trigger word "verification code" and the intercept strategy "automatic reply 'Please do not disclose personal information'" are extracted.

[0107] 2. Generate original processing strategy:

[0108] According to the analysis result, the original processing strategy about short message exception is generated and stored in the strategy database.

[0109] For example, the generated original processing strategy can be a record containing user ID, trigger word list, intercept strategy type and corresponding automatic reply template, etc.

[0110] Step 3: Collect user historical short message interaction behavior data

[0111] 1. Data collection:

[0112] From the message cloud database of the short message platform, the target user's historical short message information interaction behavior data is obtained.

[0113] Interaction behavior data includes user's marking of short messages (such as "spam short message" "fraud short message"), reporting records, confirmation or cancellation operations of automatic reply, and user's reading and deleting behavior of short messages, etc.

[0114] 2. Data preprocessing:

[0115] The collected interaction behavior data is cleaned to remove invalid or duplicate data records, ensuring the accuracy and consistency of the data.

[0116] The cleaned interaction behavior data is formatted for subsequent analysis and processing.

[0117] Step 4: Analyze user interaction behavior data

[0118] 1. Behavior analysis:

[0119] Use data analysis algorithms (such as clustering analysis, association rule mining, etc.) to analyze user's historical interaction behavior data, identify user's preference and processing mode for different types of short messages.

[0120] For example, analyze user's reporting frequency of marketing short messages, reading habits of service short messages, etc.

[0121] 2. Abnormal pattern recognition:

[0122] Identify abnormal patterns in user's historical short messages through machine learning algorithms (such as decision tree, support vector machine, etc.), such as frequent receipt of fraudulent short message features, short message types often ignored by users, etc.

[0123] These abnormal patterns will serve as an important basis for optimizing the original processing strategy.

[0124] Step 5: Optimize the original processing strategy

[0125] 1. Strategy adjustment:

[0126] According to the analysis results of user interaction behavior data (including behavior analysis results and abnormal pattern recognition results), the original processing strategy is adjusted.

[0127] For example, if a user frequently reports a certain type of SMS as fraudulent, the system will increase the interception threshold for that type of SMS; if a user often ignores a certain type of SMS, the system will adjust the interception strategy to avoid excessive interception of important SMS.

[0128] 2. Generate abnormal processing strategy:

[0129] Combine the adjusted strategy parameters to generate the final abnormal processing strategy and store it in the strategy database.

[0130] The system will regularly evaluate the effectiveness of the abnormal processing strategy and dynamically update it based on user feedback and new interaction data.

[0131] Through the above specific implementation process, the system can receive user's strategy configuration instructions, parse the instruction content, generate the original processing strategy, and optimize the original processing strategy according to the user's historical short message interaction behavior data, and finally generate a personalized abnormal processing strategy. This process not only improves the individualization and accuracy of short message abnormal processing, but also enhances the system's self-adaptation ability and user experience.

[0132] Further, in one embodiment, the short message abnormal processing method, wherein the short message sent to the target user is obtained, and a pre-trained semantic model is used to analyze the content of the short message to generate a content analysis result, specifically including the steps of:

[0133] Load the pre-trained semantic model;

[0134] Obtain the short message to be identified sent to the target user, and perform text extraction on the short message to be identified to obtain the text content;

[0135] Input the text content into the semantic model for content analysis to generate a content analysis result of the short message to be identified.

[0136] In specific implementation, the specific implementation process of the steps of this embodiment is roughly as follows:

[0137] Step 1: Load the pre-trained semantic model

[0138] 1. Model Selection and Loading:

[0139] Select a pre-trained semantic model suitable for SMS content analysis, such as BERT, GPT, or other deep learning models. These models are typically pre-trained on large-scale text data and can understand the semantic features of natural language.

[0140] Load the pre-trained semantic model into the memory of the SMS processing system to ensure that the model can quickly respond to SMS content analysis requests. Model caching techniques can be used to reduce the time overhead of repeated loading and improve the system's response speed.

[0141] Step 2: Acquire the SMS to be identified and perform text extraction

[0142] 1. SMS Capture:

[0143] Monitor the message queue of the SMS platform in real-time to capture the SMS to be identified sent to the target user. Ensure that the system can timely acquire each SMS for subsequent analysis and processing.

[0144] Temporarily store the captured SMS to be identified in a high-speed cache, such as an in-memory database like Redis, to quickly read and process the SMS content. At the same time, record the sending time, sender information, and other metadata of the SMS to provide a complete context for subsequent analysis.

[0145] 2. Text Extraction:

[0146] Perform text extraction on the captured SMS to be identified, removing HTML tags, special characters, and other non-text information in the SMS to extract pure text content. Ensure that the extracted text content is clean and accurate for subsequent semantic analysis.

[0147] Perform standardization processing on the extracted text content, including unifying character encoding, converting to lowercase, and removing extra spaces, to reduce text noise and improve the input quality of the semantic model.

[0148] Step 3: Content Analysis

[0149] 1. Model Input:

[0150] Input the standardized text content into the pre-trained semantic model. Ensure that the input text format meets the requirements of the model so that the model can accurately understand the SMS content.

[0151] The semantic model will perform in-depth analysis on the input text content, extract the semantic features of the text content, and generate content analysis results. Content analysis results may include the semantic category of the SMS to be identified (such as fraud, marketing, service, etc.), sentiment orientation (such as positive, negative, neutral), keyword extraction, etc.

[0152] 2. Result generation:

[0153] The content analysis results generated by the semantic model will be stored in a database for subsequent processing and querying. At the same time, the content analysis results are associated with the metadata of the short message (such as sending time, sender information) to provide complete context information for subsequent processing steps.

[0154] For example, the content analysis result may show that the short message belongs to the "fraud" category, the sentiment orientation is negative, and the keywords include "verification code", "click link", etc.

[0155] Through the above specific implementation process, the system can load a pre-trained semantic model, capture and extract the text content of the short message to be identified, and use the semantic model to perform content analysis to generate detailed content analysis results. This process not only improves the accuracy and efficiency of short message content analysis, but also provides a solid foundation for subsequent abnormal type determination and processing.

[0156] Further, in one embodiment, the short message abnormal processing method, wherein, based on the content analysis result, determining the abnormal type of the short message to be identified, specifically comprising steps of:

[0157] Loading a pre-defined abnormal keyword library;

[0158] Standardizing the content analysis results so that the content analysis results are consistent with the vocabulary format of the abnormal keyword library;

[0159] Using a fuzzy matching algorithm to match the standardized content analysis results with the abnormal keyword library to generate an information matching result of the short message to be identified;

[0160] Determining the abnormal type of the short message to be identified according to the information matching result.

[0161] In specific implementation, the specific implementation process of the steps of the present embodiment is approximately as follows:

[0162] Step 1: Load a pre-defined abnormal keyword library

[0163] 1. Keyword library preparation:

[0164] Prepare a pre-defined abnormal keyword library that contains known abnormal keywords (including phrases) for identifying abnormal content in short messages. The abnormal keyword library can include but is not limited to fraud keywords (such as "verification code", "account freeze", "click link"), marketing keywords (such as "discount", "promotion", "member"), etc.

[0165] The abnormal keyword library should be updated regularly to include the latest abnormal patterns and keywords, ensuring the effectiveness and accuracy of the system.

[0166] 2. Abnormal keyword library loading:

[0167] At system startup or when needed, load the predefined abnormal keyword library into memory for quick matching operations. Efficient caching mechanisms such as Redis can be used to store the abnormal keyword library, improving access speed.

[0168] Step 2: Standardize the content analysis results

[0169] 1. Content extraction:

[0170] Extract key information from the content analysis results, such as semantic categories, sentiment orientation, and keywords. These information will be used for subsequent matching operations.

[0171] 2. Standardization:

[0172] Standardize the extracted content to ensure its format is consistent with the format of the words in the abnormal keyword library. Standardization includes uniform character encoding, converting to lowercase, removing extra spaces and punctuation, etc.

[0173] For example, "verification code" is standardized to "yanzhengma", and "click link" is standardized to "dianjilianjie", to reduce matching errors caused by inconsistent formats.

[0174] Step 3: Information matching using fuzzy matching algorithm

[0175] 1. Fuzzy matching algorithm selection:

[0176] Select appropriate fuzzy matching algorithms such as Levenshtein distance (edit distance), Jaro-Winkler distance, etc. to compare the key information in the content analysis results with the words or phrases in the abnormal keyword library.

[0177] Fuzzy matching algorithms can identify variations and approximate matches of keywords, improving the flexibility and accuracy of matching.

[0178] 2. Information matching:

[0179] Match the standardized content analysis results with the abnormal keyword library. Calculate the similarity of each keyword (including phrases) with the words / phrases in the keyword library to generate information matching results.

[0180] Step 4: Generate information matching results

[0181] 1. Result recording:

[0182] Record the information matching results, including the matched keywords, similarity scores, matched abnormal types, etc. These information will be used for subsequent abnormal type determination.

[0183] For example, the information matching result may show that the matched keyword is "yanzhengma", the similarity is 90%, and the abnormal type is "fraud".

[0184] 2. Result storage:

[0185] Store the information matching results in the database for subsequent processing and querying. At the same time, associate the information matching results with the metadata of the short message (such as sending time, sender information) to provide complete context information for subsequent processing steps.

[0186] Step 5: Determine the abnormal type of the short message to be identified

[0187] 1. Abnormal type determination:

[0188] According to the information matching result, determine the abnormal type of the short message to be identified. If the number of matched keywords and similarity exceed the preset threshold, determine that the short message is of abnormal type, and record the specific abnormal category (such as fraud, marketing harassment, etc.).

[0189] For example, if multiple high-similarity fraud keywords are matched, determine that the short message is of "fraud" abnormal type.

[0190] Through the above specific implementation process, the system can load the predefined abnormal keyword library, standardize the content analysis result, use the fuzzy matching algorithm for information matching, and determine the abnormal type of the short message to be identified according to the information matching result. This process not only improves the accuracy and flexibility of short message abnormal type determination, but also provides a solid foundation for subsequent processing operations.

[0191] Further, in one embodiment, the short message abnormal processing method, wherein, according to the abnormal type of the short message to be identified, the abnormal processing strategy is used to process the short message to be identified, specifically including steps:

[0192] According to the abnormal type of the short message to be identified, verify whether the abnormal processing strategy is applicable to the short message to be identified;

[0193] If the abnormal processing strategy is applicable to the short message to be identified, the abnormal processing strategy is used to process the short message to be identified, and a short message processing notification is sent to the target user.

[0194] Further, the short message exception processing method, wherein, after verifying whether the exception processing strategy is applicable to the to-be-identified short message according to the exception type of the to-be-identified short message, the method further comprises the steps of:

[0195] If the exception processing strategy is not applicable to the to-be-identified short message, recording a failure factor that the exception processing strategy is not applicable to the to-be-identified short message;

[0196] Adjusting the exception processing strategy according to the failure factor to generate a target processing strategy, processing the to-be-identified short message by using the target processing strategy, and sending a short message processing notification to the target user.

[0197] In specific implementation, the specific implementation process of the steps of the embodiment is as follows:

[0198] Step 1: Verify the applicability of the exception processing strategy

[0199] 1. Strategy retrieval:

[0200] According to the exception type of the to-be-identified short message, the corresponding exception processing strategy is retrieved from the pre-configured strategy database. It is ensured that there is a clear processing rule for each exception type, for example, for fraudulent short messages, the strategy may be "automatically intercept and notify the user"; for marketing short messages, the strategy may be "automatically mark as spam short message and archive".

[0201] 2. Strategy verification:

[0202] Verify whether the retrieved exception processing strategy is applicable to the current to-be-identified short message. The verification process includes checking the applicable range, priority and any specific conditions of the exception processing strategy, to ensure the correctness and applicability of the exception processing strategy.

[0203] For example, if the exception processing strategy is only applicable to specific sender numbers or specific keywords, the system checks whether the to-be-identified short message meets these conditions.

[0204] Step 2: Perform processing operations and notify the user

[0205] 1. Applicability confirmation:

[0206] If the verification result shows that the exception processing strategy is applicable to the current short message, the system performs the corresponding processing operation according to the exception processing strategy. The processing operation may include automatic interception, automatic reply, marking and archiving, etc.

[0207] For example, if the strategy is "automatically intercept and notify the user", the system will intercept the short message and send a notification to the user: "a possible fraudulent short message has been intercepted, please pay attention to personal information security."

[0208] 2. User notification:

[0209] Send a text message notification to the target user, including the processing result and the user's subsequent actions (such as canceling the block or reporting a misjudgment). The notification can be sent via SMS, email, or in-app message to ensure the user is promptly informed of the processing result.

[0210] Step 3: Record the factors that led to the failure.

[0211] 1. Applicability Confirmation:

[0212] If the verification results indicate that the exception handling strategy is not applicable to the current SMS message, the failure factors for the inapplicability of the exception handling strategy should be recorded. Failure factors may include exception type mismatch with strategy, insufficient strategy priority, failure to meet specific conditions, etc.

[0213] For example, if the policy only applies to a specific sender number, and the sender number of the current SMS is not within the policy's scope, then the failure factor is recorded as "sender number mismatch".

[0214] 2. Failure Factor Record:

[0215] Failure factors are recorded in a database for subsequent analysis and strategy adjustments. Records include the type of SMS anomaly, the cause of failure, and the processing time.

[0216] Step 4: Adjust the exception handling strategy

[0217] 1. Strategy Adjustment:

[0218] Based on the recorded failure factors, adjust the exception handling strategy. The adjustment process may include modifying the scope of application of the strategy, adjusting the priority, adding or modifying specific conditions, etc.

[0219] Of course, when adjusting the exception handling strategy, adjustments can be made after receiving confirmation from the user, or the system can adjust the exception handling strategy automatically, depending on the adjustment rules preset by the user.

[0220] 2. Target processing strategy:

[0221] Generate the adjusted target handling strategy and store it in the strategy database. Ensure that the new strategy can better adapt to current SMS anomalies.

[0222] For example, a new target processing strategy could be: "Automatically intercept and notify the user, applicable to all SMS messages containing 'verification codes,' regardless of the sender's number."

[0223] Step 5: Implement the target processing strategy and notify the user.

[0224] 1. Target strategy execution:

[0225] The target processing strategy is used to process the to-be-identified short message. The processing operation can include automatic interception, automatic reply, marking and archiving, etc.

[0226] 2. User notification:

[0227] A short message processing notification is sent to the target user, and the notification content includes the processing result and the subsequent action that the user can take (such as canceling interception or reporting a misjudgment). The notification can be a short message, an email or an in-application message, ensuring that the user is informed of the processing result in a timely manner.

[0228] Through the above specific implementation process, the system can verify the applicability of the abnormal processing strategy according to the abnormal type of the to-be-identified short message. If the strategy is applicable, the processing operation is performed and the user is notified; if the strategy is not applicable, the failure factor is recorded, the strategy is adjusted, and a new target processing strategy is executed. This process not only improves the accuracy and flexibility of short message abnormal processing, but also enhances the adaptive ability of the system.

[0229] As can be known from the above method embodiment, the short message abnormal processing method provided by the present application comprises: receiving a strategy configuration instruction of a target user, configuring an abnormal processing strategy for short message abnormalities according to the strategy configuration instruction; obtaining a to-be-identified short message sent to the target user, and using a pre-trained semantic model to analyze the content of the to-be-identified short message to generate a content analysis result; determining the abnormal type of the to-be-identified short message based on the content analysis result; and processing the to-be-identified short message by using the abnormal processing strategy according to the abnormal type of the to-be-identified short message. In this way, the method of the present application can effectively improve the individualization and accuracy of short message abnormal processing.

[0230] It should be understood that although the present application provides method operation steps as described in the embodiments or flowcharts, more or less operation steps can be included based on conventional or non-inventive labor, and the operation steps are not necessarily executed in the order of the embodiments or flowcharts. The order of steps listed in the embodiments or flowcharts is only one of the many execution orders, and does not represent the only execution order. It should be noted that there is no certain sequence between the above steps, and those skilled in the art can understand from the description of the embodiments of the present application that the above steps can have different execution orders in different embodiments, that is, they can be executed in parallel, or they can be exchanged and executed, etc. Moreover, at least part of the steps in the embodiments or flowcharts can include multiple sub-steps or multiple stages, which are not necessarily executed at the same time, but can be executed at different times, and the execution order of these sub-steps or stages is not necessarily sequential, but can be executed in rotation, alternation or synchronization with other steps or sub-steps or stages of other steps.

[0231] Based on the above method embodiments, please refer to Figure 3 Another embodiment of the present application also provides a short message exception processing device, wherein the device comprises:

[0232] The receiving module 11 is configured to receive a policy configuration instruction of a target user, and configure an exception handling policy for short message exceptions according to the policy configuration instruction;

[0233] The obtaining module 12 is configured to obtain a to-be-identified short message sent to the target user, and perform content analysis on the to-be-identified short message by using a pre-trained semantic model to generate a content analysis result;

[0234] The determining module 13 is configured to determine an exception type of the to-be-identified short message based on the content analysis result;

[0235] The processing module 14 is configured to process the to-be-identified short message by using the exception handling policy according to the exception type of the to-be-identified short message.

[0236] Further, in one embodiment, the short message exception processing device, wherein the receiving a policy configuration instruction of a target user, and configuring an exception handling policy for short message exceptions according to the policy configuration instruction, specifically comprises:

[0237] Receiving a policy configuration instruction sent by a target user through a user interaction interface of a short message platform;

[0238] Analyzing the policy configuration instruction, and configuring an original processing policy for short message exceptions according to the analysis result;

[0239] According to the interaction behavior data of the target user on the historical short message information, the original processing strategy is optimized to generate the abnormal processing strategy.

[0240] Further, the short message abnormal processing device, wherein the original processing strategy is optimized according to the interaction behavior data of the target user on the historical short message information to generate the abnormal processing strategy, specifically includes:

[0241] The interaction behavior data of the target user on the historical short message information is extracted from the message cloud database of the short message platform;

[0242] The interaction behavior data is cleaned and formatted, and the cleaned and formatted interaction behavior data is analyzed to identify the processing preference information of the target user for different types of short messages;

[0243] According to the processing preference information, the original processing strategy is adjusted to generate the abnormal processing strategy.

[0244] Further, in one embodiment, the short message abnormal processing device, wherein the short message to be identified sent to the target user is obtained, and a pre-trained semantic model is used to analyze the content of the short message to be identified to generate a content analysis result, specifically including:

[0245] Load the pre-trained semantic model;

[0246] Obtain the short message to be identified sent to the target user, and extract the text content of the short message to be identified;

[0247] The text content is input into the semantic model for content analysis to generate a content analysis result of the short message to be identified.

[0248] Further, in one embodiment, the short message abnormal processing device, wherein the abnormal type of the short message to be identified is determined based on the content analysis result, specifically including:

[0249] Load the pre-defined abnormal keyword library;

[0250] Standardize the content analysis result so that the content analysis result is consistent with the vocabulary format of the abnormal keyword library;

[0251] Using a fuzzy matching algorithm, the content analysis result after standardization is matched with the abnormal keyword library to generate an information matching result of the short message to be identified;

[0252] According to the information matching result, the abnormal type of the short message to be identified is determined.

[0253] Further, in one embodiment, the short message exception processing device, wherein the processing of the short message to be identified according to the exception type of the short message to be identified, specifically includes:

[0254] According to the exception type of the short message to be identified, verifying whether the exception processing strategy is applicable to the short message to be identified;

[0255] If the exception processing strategy is applicable to the short message to be identified, processing the short message to be identified according to the exception processing strategy, and sending a short message processing notification to the target user.

[0256] Further, the short message exception processing device, wherein after verifying whether the exception processing strategy is applicable to the short message to be identified according to the exception type of the short message to be identified, specifically includes:

[0257] If the exception processing strategy is not applicable to the short message to be identified, recording the failure factor that the exception processing strategy is not applicable to the short message to be identified;

[0258] According to the failure factor, adjusting the exception processing strategy to generate a target processing strategy, processing the short message to be identified according to the target processing strategy, and sending a short message processing notification to the target user.

[0259] It should be noted that the information interaction, execution process, etc. between the above-mentioned modules in the device embodiment of the present application are based on the same concept as the method embodiment of the present application. For specific functions and technical effects brought by them, please refer to the method embodiment part described above, which will not be repeated here.

[0260] Based on the above-mentioned method embodiment, another embodiment of the present application further provides a computer device, which can be a server. The internal structure diagram of the computer device can be as shown in Figure 4 The computer device includes a processor, a memory, a network interface and a database connected through a system bus. The processor of the computer device is used to provide calculation and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with the external terminal through the network connection. The computer program is executed by the processor to realize the functions or steps of the short message exception processing method server side in any one of the above-mentioned method embodiments.

[0261] Based on the method embodiments, another embodiment of the present application further provides a computer device which can be a client, and an internal structure diagram of the computer device can be as shown in Figure 5 The computer device includes a processor, a memory, a network interface, a display screen and an input device which are connected through a system bus. The processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for running the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is configured to communicate with an external terminal through a network connection. The computer program, when executed by the processor, implements the functions or steps of the short message exception processing method on the client side in any one of the method embodiments.

[0262] Those skilled in the art can understand that Figure 4 With Figure 5 The structural schematic diagram shown in the drawings is only a schematic diagram of part of the structure related to the present application, and does not constitute a limitation on the computer device to which the present application is applied. Specifically, the computer device can include more components than those shown in the drawings, or combine some components, or have a different component arrangement.

[0263] The processor can be a CPU, and can also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor.

[0264] The memory includes a readable storage medium, an internal memory, etc., wherein the internal memory can be a memory of the computer device, and the internal memory provides an environment for running the operating system and the computer readable instructions in the readable storage medium. The readable storage medium can be a hard disk of the computer device, and in other embodiments, can also be an external storage device of the computer device, for example, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. Further, the memory can include both an internal storage unit of the computer device and an external storage device. The memory is used to store an operating system, an application program, a boot loader, data, and other programs, such as program codes of computer programs, etc. The memory can also be used to temporarily store data that has been output or will be output.

[0265] Based on the above method embodiments, another embodiment of the present application further provides a computer readable storage medium storing a computer program, wherein the computer program is executed by a processor to implement the short message exception processing method in any one of the above method embodiments. The computer readable storage medium can be non-volatile or volatile.

[0266] It should be noted that the functions or steps that the computer readable storage medium or the computer device can achieve and the technical effects brought by the functions / steps can be referred to the related description in the foregoing method embodiments. To avoid repetition, they will not be described one by one here.

[0267] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer readable storage medium, and when the computer program is executed, the processes of the above-mentioned embodiments of the methods can be included. Any reference to memory, storage, database or other medium used in each embodiment provided in the present application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration but not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc. The disclosed memory components or memories of the operating environment described herein are intended to include one or more of these and / or any other suitable type of memory.

[0268] It can be clearly understood by those skilled in the art that, for the convenience and brevity of description, in the device embodiment of the present application, only the above-mentioned division of each functional unit and module is exemplified, and in actual application, the above-mentioned functions can be completed by different functional units and modules according to needs, that is, the internal structure of the device is divided into different functional units or modules to complete all or part of the above-described functions. Each functional unit and module in the embodiment can be integrated in one processing unit, or each unit can exist physically, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of software functional unit. In addition, the specific names of each functional unit and module are only for easy distinction, and do not limit the protection scope of the present application. The specific working process of the unit and module in the above-mentioned device can refer to the corresponding process in the above-mentioned method embodiment, which will not be repeated here. If the integrated unit is realized in the form of software functional unit and sold or used as an independent product, it can be stored in a computer readable storage medium.

[0269] Those skilled in the art can understand that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be realized in electronic hardware or a combination of computer software and electronic hardware. Whether the functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0270] In the embodiments provided by the present application, it should be understood that the disclosed apparatus / computer device and method can be implemented in other ways. For example, the apparatus / computer device embodiments described above are merely schematic. The division of the modules or units is merely a logical function division, and there can be another division manner in actual implementation. For example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed mutual couplings or direct couplings or communication connections between the units can be indirect couplings or communication connections through some interfaces, devices or units, and can be electrical, mechanical or in other forms.

[0271] The units described as separate components can or can not be physically separate, and the components shown as units can or can not be physical units, i.e. can be located in one place, or can be distributed on a plurality of network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the embodiments.

[0272] It should be noted that if non-company software tools or components appear in the embodiments of the present application, they are only used for example introduction and do not represent actual use. The above embodiments are only used to illustrate the technical solutions of the present application, but not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that the technical solutions recorded in the foregoing embodiments can be modified, or some technical features can be replaced by equivalents; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should be included in the protection scope of the present application.

Claims

1. A method for handling SMS anomalies, characterized in that, include: Receive the policy configuration instruction from the target user, and configure the exception handling policy for SMS anomalies according to the policy configuration instruction; The system acquires the SMS message to be identified sent to the target user, and uses a pre-trained semantic model to perform content analysis on the SMS message to be identified, generating content analysis results. Based on the content analysis results, the anomaly type of the SMS message to be identified is determined; Based on the anomaly type of the SMS message to be identified, the anomaly handling strategy is used to process the SMS message to be identified.

2. The SMS anomaly handling method according to claim 1, characterized in that, The step of receiving the policy configuration instruction from the target user and configuring an exception handling policy for SMS anomalies according to the policy configuration instruction includes: Receive policy configuration instructions sent by target users through the user interface of the SMS platform; The policy configuration instructions are parsed, and the original handling policy for SMS anomalies is configured based on the parsing results; Based on the target user's interaction data with their historical SMS messages, the original processing strategy is optimized to generate the exception handling strategy.

3. The SMS anomaly handling method according to claim 2, characterized in that, The step of optimizing the original processing strategy based on the target user's interaction behavior data with their historical SMS messages to generate the exception handling strategy includes: Extract the target user's interaction behavior data with its historical SMS messages from the SMS platform's message cloud database; The interaction behavior data is cleaned and formatted, and the cleaned and formatted interaction behavior data is analyzed to identify the target user's processing preference information for different types of abnormal text messages; The original processing strategy is adjusted based on the processing preference information to generate the exception handling strategy.

4. The SMS anomaly handling method according to claim 1, characterized in that, The step of acquiring the SMS message to be identified sent to the target user and performing content analysis on the SMS message using a pre-trained semantic model to generate content analysis results includes: Load the pre-trained semantic model; Obtain the SMS message to be identified sent to the target user, and extract the text from the SMS message to obtain the text content; The text content is input into the semantic model for content analysis, generating the content analysis results of the SMS message to be identified.

5. The SMS anomaly handling method according to claim 1, characterized in that, The determination of the anomaly type of the SMS message to be identified based on the content analysis results includes: Load the predefined exception keyword library; The content analysis results are standardized to ensure that the vocabulary format of the content analysis results is consistent with that of the abnormal keyword database; A fuzzy matching algorithm is used to match the standardized content analysis results with the abnormal keyword database to generate the information matching result of the SMS message to be identified. Based on the information matching results, the anomaly type of the SMS message to be identified is determined.

6. The SMS anomaly handling method according to claim 1, characterized in that, The step of processing the SMS message to be identified using the anomaly handling strategy according to the anomaly type of the SMS message to be identified includes: Based on the anomaly type of the SMS message to be identified, verify whether the anomaly handling strategy is applicable to the SMS message to be identified; If the exception handling strategy applies to the SMS message to be identified, then the exception handling strategy is used to process the SMS message to be identified, and an SMS processing notification is sent to the target user.

7. The SMS anomaly handling method according to claim 6, characterized in that, After verifying whether the exception handling strategy is applicable to the SMS to be identified based on the exception type of the SMS to be identified, the method further includes: If the exception handling strategy is not applicable to the SMS message to be identified, then the failure factors of the exception handling strategy not being applicable to the SMS message to be identified are recorded. The exception handling strategy is adjusted based on the failure factors to generate a target handling strategy. The target handling strategy is then used to process the SMS message to be identified, and an SMS processing notification is sent to the target user.

8. A text message anomaly handling device, characterized in that, include: The receiving module is used to receive the policy configuration instruction from the target user and configure the exception handling policy for SMS exceptions according to the policy configuration instruction. The acquisition module is used to acquire the SMS message to be identified sent to the target user, and to perform content analysis on the SMS message to be identified using a pre-trained semantic model to generate content analysis results. The determination module is used to determine the anomaly type of the SMS message to be identified based on the content analysis results; The processing module is used to process the SMS message to be identified according to the anomaly type of the SMS message to be identified, using the anomaly handling strategy.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the SMS exception handling method as described in any one of claims 1-7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the SMS exception handling method as described in any one of claims 1-7.

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