Ai-based intelligent customer service system for cross-border e-commerce

Abstract

The present application relates to the technical field of intelligent customer services, and particularly relates to an AI-based intelligent customer service system for cross-border e-commerce. The system comprises a multilingual real-time translation module, a cross-cultural semantic understanding module, a multi-channel unified management module, and a dynamic knowledge base update module, wherein the multilingual real-time translation module implements multilingual real-time interaction; the cross-cultural semantic understanding module interprets user requirements in combination with a cultural background database and a sentiment analysis unit; the multi-channel unified management module implements unified access and management of different channels; and the dynamic knowledge base update module ensures the real-time performance and accuracy of a knowledge base. The present system solves the problems of insufficient multilingual support, inadequate cross-cultural adaptation capabilities, imperfect multi-channel integration mechanisms and limited dynamic requirement response capabilities in existing intelligent customer service systems, thereby significantly improving the applicability and service capability of the intelligent customer service system.

Description

An AI-based intelligent customer service system for cross-border e-commerce Technical Field

[0001] This invention belongs to the field of intelligent customer service technology, specifically an AI-based intelligent customer service system for cross-border e-commerce. Background Technology

[0002] With the rapid development of the cross-border e-commerce industry, intelligent customer service systems are playing an increasingly important role in improving customer service efficiency and optimizing user experience. However, existing intelligent customer service systems still have shortcomings in cross-language support, multi-channel integration, and accurate demand matching, which affects their applicability and service quality in cross-border e-commerce scenarios.

[0003] A search revealed a patent with publication number CN105577529B, which discloses a robot customer service system and its method, as well as an intelligent customer service system, published on March 5, 2019. This patent proposes an intelligent customer service system based on a robot, which identifies customer conversation messages and determines whether it can be handled autonomously, thereby achieving automated response or transfer to human customer service. However, this technical solution is primarily designed for localized service scenarios, lacking support for multilingual environments in cross-border e-commerce, and failing to fully consider the impact of cultural differences between different countries and regions on user needs. Furthermore, the system relies on human customer service intervention when handling complex inquiries, failing to effectively improve the independent processing capabilities of the intelligent customer service system, potentially leading to decreased service efficiency.

[0004] A search revealed a patent, CN108984655B, which discloses an intelligent customer service guidance method for customer service robots, published on January 1, 2021. This patent enhances the accuracy and contextual coherence of customer service robots in specific domains by introducing product category name tags and personalized information records. However, this technical solution primarily focuses on intelligent customer service guidance in a single domain, failing to fully consider the diverse product and service needs involved in cross-border e-commerce scenarios. Furthermore, this solution lacks an effective integration mechanism when facing customer inquiries from multiple channels (such as social media, email, and instant messaging tools), potentially leading to information silos and impacting user experience. In addition, the method's understanding of user intent still relies on preset rules and training data, which may result in insufficient adaptability when facing emerging markets or new product categories. Technical issues

[0005] The aforementioned problems indicate that existing intelligent customer service systems still have many limitations in cross-border e-commerce scenarios, particularly in areas such as multilingual support, cross-cultural adaptation, multi-channel integration, and dynamic demand response. Therefore, this invention provides an AI-based intelligent customer service system for cross-border e-commerce, aiming to comprehensively improve the adaptability and service capabilities of the intelligent customer service system by introducing functions such as real-time multilingual translation, cross-cultural semantic understanding, unified multi-channel management, and dynamic knowledge base updates, thereby meeting the demands of the cross-border e-commerce sector for efficient and accurate customer service. Technical solutions

[0006] This invention provides an AI-based intelligent customer service system for cross-border e-commerce, addressing the shortcomings of existing intelligent customer service systems such as insufficient multilingual support, inadequate cross-cultural adaptability, imperfect multi-channel integration mechanisms, and limited dynamic demand response capabilities. This invention aims to construct a more intelligent, accurate, and adaptable solution for cross-border e-commerce scenarios by introducing a multilingual real-time translation module, a cross-cultural semantic understanding module, a unified multi-channel management module, and a dynamic knowledge base update module, thereby comprehensively improving the applicability and service capabilities of intelligent customer service systems.

[0007] The technical solution adopted by this invention to solve the above-mentioned technical problems is: an AI-based intelligent customer service system for cross-border e-commerce, including a multilingual real-time translation module, a cross-cultural semantic understanding module, a multi-channel unified management module, a dynamic knowledge base update module, and a central processing unit. The central processing unit is electrically connected to the multilingual real-time translation module, the cross-cultural semantic understanding module, the multi-channel unified management module, and the dynamic knowledge base update module. The multilingual real-time translation module includes a speech recognition unit, a text translation unit, and a speech synthesis unit. The cross-cultural semantic understanding module includes a cultural background database, a semantic analysis unit, and an intent recognition unit. The multi-channel unified management module includes a channel access unit, a message routing unit, and a session management unit. The dynamic knowledge base update module includes a data acquisition unit, a knowledge extraction unit, and a knowledge update unit.

[0008] The central processing unit includes a processor, a memory, and a communication interface. The processor is connected to the memory via a bus to store and process data transmitted from each module, and to determine user needs and issue corresponding instructions based on preset algorithms and logic. The communication interface is connected to the processor to receive and send instruction signals. The memory stores the basic data and algorithm models required for the operation of each module.

[0009] In the aforementioned multilingual real-time translation module, a speech recognition unit is installed on the client device to convert the user's speech input into text data; a text translation unit is installed on the server side, which integrates translation models for multiple languages ​​to perform real-time translation of text data; and a speech synthesis unit is installed on the client device to convert the translated text data into speech output. When the user inputs speech or text, the speech recognition unit converts the speech into text, the text translation unit translates according to the target language, and the speech synthesis unit converts the translated text into speech, thus realizing real-time multilingual interaction.

[0010] In the cross-cultural semantic understanding module, the cultural background database stores multi-national cultural background information and language habit data. The semantic analysis unit is installed on the server side to analyze the semantic content input by the user and perform semantic parsing in conjunction with the cultural background database. The intent recognition unit is also installed on the server side to identify the user's true intent based on the parsing results. When the user inputs information, the semantic analysis unit performs in-depth analysis of the input content in conjunction with the cultural background database, and the intent recognition unit generates corresponding intent tags based on the analysis results, ensuring that the system can accurately understand the user's needs.

[0011] In the aforementioned multi-channel unified management module, the channel access unit is installed on the server side to access various customer consultation channels such as social media, email, and instant messaging tools; the message routing unit is installed on the server side to allocate messages to the corresponding processing modules according to the content and priority of user requests; the session management unit is installed on the server side to record and manage the session status of different channels; when a user initiates a consultation through different channels, the channel access unit uniformly accesses the message into the system, the message routing unit allocates the message to the corresponding processing module according to preset rules, and the session management unit ensures the continuity and consistency of the session.

[0012] In the dynamic knowledge base update module, the data acquisition unit is installed on the server side to collect data from product information, user feedback, and market dynamics of the cross-border e-commerce platform; the knowledge extraction unit is installed on the server side to extract key information from the collected data and generate knowledge entries; and the knowledge update unit is installed on the server side to update the newly generated knowledge entries to the knowledge base. When the system detects a new product category or market dynamic, the data acquisition unit collects relevant data, the knowledge extraction unit extracts key information, and the knowledge update unit updates the new information to the knowledge base, ensuring that the system has dynamic response capabilities.

[0013] Preferably, the multilingual real-time translation module also includes a speech quality optimization unit, which comprises a noise suppression module, a speech enhancement module, and a volume adjustment module. The noise suppression module is installed at the front end of the speech recognition unit to reduce the impact of environmental noise on speech recognition; the speech enhancement module is installed at the back end of the speech synthesis unit to improve the clarity of the synthesized speech; and the volume adjustment module is installed at the back end of the speech synthesis unit to adjust the output volume according to the user's device volume settings. When the user inputs speech, the noise suppression module performs noise reduction processing on the input speech, the speech enhancement module enhances the synthesized speech, and the volume adjustment module adjusts the output volume according to the user's device volume settings to ensure the quality of voice interaction.

[0014] Preferably, the cross-cultural semantic understanding module also includes a sentiment analysis unit, which comprises a sentiment dictionary, a sentiment classification model, and a sentiment weight calculation module. The sentiment dictionary stores sentiment terms from multiple languages ​​and their corresponding sentiment intensity values. The sentiment classification model is installed on the server side to classify sentiment based on user input. The sentiment weight calculation module is also installed on the server side to calculate sentiment weights based on the sentiment classification results. When a user inputs information, the sentiment analysis unit combines the sentiment dictionary and the sentiment classification model to perform sentiment analysis on the input content, and the sentiment weight calculation module generates sentiment weight values ​​to ensure that the system can accurately understand the user's emotional state.

[0015] Preferably, the multi-channel unified management module also includes a message priority evaluation unit, which comprises a keyword matching module, a time sensitivity analysis module, and a user value evaluation module. The keyword matching module is installed at the front end of the message routing unit to evaluate the urgency of the message based on keywords in the user's input. The time sensitivity analysis module is also installed at the front end of the message routing unit to evaluate the timeliness of the message based on the time information input by the user. The user value evaluation module is installed at the front end of the message routing unit to evaluate the user's value level based on the user's historical behavior data. When a user initiates a consultation, the message priority evaluation unit generates a message priority score based on the results of the keyword matching module, the time sensitivity analysis module, and the user value evaluation module, ensuring that the system can allocate resources reasonably.

[0016] Preferably, the dynamic knowledge base update module further includes a knowledge conflict detection unit, which comprises a knowledge comparison module, a conflict marking module, and a conflict resolution module. The knowledge comparison module is installed at the backend of the knowledge extraction unit and is used to compare the differences between newly generated knowledge entries and existing knowledge entries. The conflict marking module is installed at the backend of the knowledge comparison module and is used to mark conflicting knowledge entries. The conflict resolution module is installed at the backend of the conflict marking module and is used to resolve knowledge conflicts according to preset rules. When the knowledge extraction unit generates new knowledge entries, the knowledge comparison module compares the new knowledge entries with existing knowledge entries, the conflict marking module marks conflicting knowledge entries, and the conflict resolution module resolves conflicts according to preset rules, ensuring the consistency and accuracy of the knowledge base.

[0017] The voice quality optimization unit is connected to the voice recognition unit and the voice synthesis unit through a signal transmission line, and to the central processing unit through a communication interface. It receives instructions from the processor in real time and adjusts the voice processing parameters.

[0018] The sentiment analysis unit is connected to the semantic analysis unit and the intent recognition unit via data transmission lines, and to the central processing unit via a communication interface. It receives instructions from the processor in real time and generates sentiment analysis results.

[0019] The message priority evaluation unit is connected to the message routing unit and the session management unit through a data transmission line, and to the central processing unit through a communication interface. It receives instructions from the processor in real time and generates message priority scores.

[0020] The knowledge conflict detection unit is connected to the knowledge extraction unit and the knowledge update unit through a data transmission line, and to the central processing unit through a communication interface. It receives instructions from the processor in real time and resolves knowledge conflicts.

[0021] The structural composition, implementation method, and operating principle of this invention are as follows:

[0022] The voice quality optimization unit reduces noise in the input voice through a noise suppression module, enhances the synthesized voice through a voice enhancement module, and adjusts the output volume according to the user's device volume settings through a volume adjustment module to ensure the quality of voice interaction.

[0023] The sentiment analysis unit performs sentiment analysis on user input using a sentiment dictionary and sentiment classification model, and generates sentiment weight values ​​through the sentiment weight calculation module to ensure that the system can accurately understand the user's emotional state.

[0024] The message priority assessment unit evaluates the urgency of messages through the keyword matching module, the timeliness of messages through the time sensitivity analysis module, and the value level of users through the user value assessment module, generating a message priority score to ensure that the system can allocate resources reasonably.

[0025] The knowledge conflict detection unit compares the differences between newly generated knowledge entries and existing knowledge entries through the knowledge comparison module, marks conflicting knowledge entries through the conflict marking module, and resolves conflicts according to preset rules through the conflict resolution module, thereby ensuring the consistency and accuracy of the knowledge base. Beneficial effects

[0026] The voice quality optimization unit significantly improves the quality of voice interaction through the synergistic effect of the noise suppression module, voice enhancement module, and volume adjustment module, ensuring high-precision voice recognition and synthesis even in noisy environments.

[0027] The sentiment analysis unit, through the combination of a sentiment dictionary, a sentiment classification model, and a sentiment weight calculation module, achieves an accurate understanding of users' emotional states and significantly improves the system's semantic parsing capabilities in cross-cultural scenarios.

[0028] The knowledge conflict detection unit, through the cooperation of the knowledge comparison module, conflict marking module, and conflict resolution module, effectively solves the conflict problem in the knowledge base update process, ensures the consistency and accuracy of the knowledge base, and significantly improves the dynamic response capability of the system. Attached Figure Description

[0029] Figure 1 is a diagram of the overall system architecture of the present invention;

[0030] Figure 2 is a schematic diagram of the multilingual real-time translation module;

[0031] Figure 3 is a schematic diagram of the cross-cultural semantic understanding module;

[0032] Figure 4 is a structural diagram of the multi-channel unified management module;

[0033] Figure 5 is a schematic diagram of the dynamic knowledge base update module;

[0034] Figure 6 is a schematic diagram of the speech quality optimization unit;

[0035] Figure 7 is a schematic diagram of the sentiment analysis unit;

[0036] Figure 8 is a schematic diagram of the message priority evaluation unit;

[0037] Figure 9 is a schematic diagram of the knowledge conflict detection unit; Attached Figure

[0038] 1. Central Processing Unit; 11. Processor; 12. Memory; 13. Communication Interface; 2. Multilingual Real-Time Translation Module; 21. Speech Recognition Unit; 22. Text Translation Unit; 23. Speech Synthesis Unit; 24. Speech Quality Optimization Unit; 241. Noise Suppression Module; 242. Speech Enhancement Module; 243. Volume Adjustment Module; 3. Cross-Cultural Semantic Understanding Module; 31. Cultural Background Database; 32. Semantic Analysis Unit; 33. Intent Recognition Unit; 34. Sentiment Analysis Unit; 341. Sentiment Dictionary; 342. Sentiment Classification Model; 3 43. Sentiment Weight Calculation Module; 4. Multi-channel Unified Management Module; 41. Channel Access Unit; 42. Message Routing Unit; 43. Session Management Unit; 44. Message Priority Evaluation Unit; 441. Keyword Matching Module; 442. Time Sensitivity Analysis Module; 443. User Value Evaluation Module; 5. Dynamic Knowledge Base Update Module; 51. Data Acquisition Unit; 52. Knowledge Extraction Unit; 53. Knowledge Update Unit; 54. Knowledge Conflict Detection Unit; 541. Knowledge Comparison Module; 542. Conflict Marking Module; 543. Conflict Resolution Module. The best embodiment of the present invention

[0039] To make the technical means, creative features, objectives and effects of this invention easier to understand, the invention will be further described below in conjunction with specific embodiments.

[0040] This invention provides an AI-based intelligent customer service system for cross-border e-commerce, addressing the shortcomings of existing intelligent customer service systems such as insufficient multilingual support, inadequate cross-cultural adaptability, imperfect multi-channel integration mechanisms, and limited dynamic demand response capabilities. This invention aims to construct a more intelligent, accurate, and adaptable solution for cross-border e-commerce scenarios by introducing a multilingual real-time translation module, a cross-cultural semantic understanding module, a unified multi-channel management module, and a dynamic knowledge base update module, thereby comprehensively improving the applicability and service capabilities of intelligent customer service systems.

[0041] Referring to Figures 1 to 9, an AI-based intelligent customer service system for cross-border e-commerce includes a central processing unit 1, a multilingual real-time translation module 2, a cross-cultural semantic understanding module 3, a multi-channel unified management module 4, and a dynamic knowledge base update module 5. The central processing unit 1 is electrically connected to the multilingual real-time translation module 2, the cross-cultural semantic understanding module 3, the multi-channel unified management module 4, and the dynamic knowledge base update module 5.

[0042] The central processing unit 1 includes a processor 11, a memory 12, and a communication interface 13. The processor 11 is connected to the memory 12 via a bus and is used to store and process data transmitted from various modules, and to determine user needs and issue corresponding instructions based on preset algorithms and logic. The communication interface 13 is connected to the processor 11 and is used to receive and send instruction signals. The memory 12 stores the basic data and algorithm models required for the operation of each module.

[0043] The multilingual real-time translation module 2 includes a speech recognition unit 21, a text translation unit 22, and a speech synthesis unit 23. The speech recognition unit 21 is installed on the client device and converts the user's speech input into text data. The text translation unit 22 is installed on the server side and integrates translation models for multiple languages ​​for real-time translation of text data. The speech synthesis unit 23 is installed on the client device and converts the translated text data into speech output. When the user inputs speech or text, the speech recognition unit 21 converts the speech into text, the text translation unit 22 translates according to the target language, and the speech synthesis unit 23 converts the translated text back into speech, enabling real-time multilingual interaction.

[0044] The cross-cultural semantic understanding module 3 includes a cultural background database 31, a semantic analysis unit 32, and an intent recognition unit 33. The cultural background database 31 stores multi-national cultural background information and language habit data. The semantic analysis unit 32, installed on the server, analyzes the semantic content of user input and performs semantic parsing based on the cultural background database 31. The intent recognition unit 33, also installed on the server, identifies the user's true intent based on the parsing results. When a user inputs information, the semantic analysis unit 32 performs in-depth analysis of the input content in conjunction with the cultural background database 31, and the intent recognition unit 33 generates corresponding intent tags based on the analysis results, ensuring the system can accurately understand user needs.

[0045] The multi-channel unified management module 4 includes a channel access unit 41, a message routing unit 42, and a session management unit 43. The channel access unit 41, installed on the server, is used to access various customer inquiry channels such as social media, email, and instant messaging tools. The message routing unit 42, also installed on the server, is used to allocate messages to the appropriate processing modules based on the content and priority of user requests. The session management unit 43, installed on the server, is used to record and manage the session status of different channels. When a user initiates an inquiry through different channels, the channel access unit 41 uniformly accesses the message to the system, the message routing unit 42 allocates the message to the corresponding processing module according to preset rules, and the session management unit 43 ensures the continuity and consistency of the session.

[0046] The dynamic knowledge base update module 5 includes a data acquisition unit 51, a knowledge extraction unit 52, and a knowledge update unit 53. The data acquisition unit 51, installed on the server, collects data from product information, user feedback, and market dynamics on the cross-border e-commerce platform. The knowledge extraction unit 52, also installed on the server, extracts key information from the collected data and generates knowledge entries. The knowledge update unit 53, installed on the server, updates the newly generated knowledge entries to the knowledge base. When the system detects a new product category or market dynamic, the data acquisition unit 51 collects relevant data, the knowledge extraction unit 52 extracts key information, and the knowledge update unit 53 updates the knowledge base with the new information, ensuring the system has dynamic response capabilities.

[0047] Preferably, the multilingual real-time translation module 2 further includes a speech quality optimization unit 24, which includes a noise suppression module 241, a speech enhancement module 242, and a volume adjustment module 243. The noise suppression module 241 is installed at the front end of the speech recognition unit 21 to reduce the impact of environmental noise on speech recognition; the speech enhancement module 242 is installed at the back end of the speech synthesis unit 23 to improve the clarity of the synthesized speech; and the volume adjustment module 243 is installed at the back end of the speech synthesis unit 23 to adjust the output volume according to the user's device volume settings. When the user inputs speech, the noise suppression module 241 performs noise reduction processing on the input speech, the speech enhancement module 242 enhances the synthesized speech, and the volume adjustment module 243 adjusts the output volume according to the user's device volume settings to ensure the quality of voice interaction.

[0048] Preferably, the cross-cultural semantic understanding module 3 further includes a sentiment analysis unit 34, which comprises a sentiment dictionary 341, a sentiment classification model 342, and a sentiment weight calculation module 343. The sentiment dictionary 341 stores sentiment terms from multiple languages ​​and their corresponding sentiment intensity values. The sentiment classification model 342 is installed on the server side and is used to classify sentiment based on user input. The sentiment weight calculation module 343 is also installed on the server side and is used to calculate sentiment weights based on the sentiment classification results. When a user inputs information, the sentiment analysis unit 34 combines the sentiment dictionary 341 and the sentiment classification model 342 to perform sentiment analysis on the input content, and the sentiment weight calculation module 343 generates sentiment weight values, ensuring that the system can accurately understand the user's emotional state.

[0049] Preferably, the multi-channel unified management module 4 also includes a message priority evaluation unit 44, which comprises a keyword matching module 441, a time sensitivity analysis module 442, and a user value evaluation module 443. The keyword matching module 441 is installed in front of the message routing unit 42 and is used to evaluate the urgency of the message based on keywords in the user's input. The time sensitivity analysis module 442 is installed in front of the message routing unit 42 and is used to evaluate the timeliness of the message based on the time information input by the user. The user value evaluation module 443 is installed in front of the message routing unit 42 and is used to evaluate the user's value level based on the user's historical behavior data. When a user initiates a consultation, the message priority evaluation unit 44 generates a message priority score based on the results of the keyword matching module 441, the time sensitivity analysis module 442, and the user value evaluation module 443, ensuring that the system can allocate resources reasonably.

[0050] Preferably, the dynamic knowledge base update module 5 further includes a knowledge conflict detection unit 54, which comprises a knowledge comparison module 541, a conflict marking module 542, and a conflict resolution module 543. The knowledge comparison module 541 is installed behind the knowledge extraction unit 52 and is used to compare the differences between newly generated knowledge entries and existing knowledge entries. The conflict marking module 542 is installed behind the knowledge comparison module 541 and is used to mark conflicting knowledge entries. The conflict resolution module 543 is installed behind the conflict marking module 542 and is used to resolve knowledge conflicts according to preset rules. When the knowledge extraction unit 52 generates a new knowledge entry, the knowledge comparison module 541 compares the new knowledge entry with existing knowledge entries, the conflict marking module 542 marks conflicting knowledge entries, and the conflict resolution module 543 resolves the conflict according to preset rules, ensuring the consistency and accuracy of the knowledge base.

[0051] The speech quality optimization unit 24 is connected to the speech recognition unit 21 and the speech synthesis unit 23 via a signal transmission line, and to the central processing unit 1 via a communication interface 13. It receives instructions from the processor 11 in real time and adjusts the speech processing parameters. The sentiment analysis unit 34 is connected to the semantic analysis unit 32 and the intent recognition unit 33 via a data transmission line, and to the central processing unit 1 via a communication interface 13. It receives instructions from the processor 11 in real time and generates sentiment analysis results. The message priority evaluation unit 44 is connected to the message routing unit 42 and the session management unit 43 via a data transmission line, and to the central processing unit 1 via a communication interface 13. It receives instructions from the processor 11 in real time and generates message priority scores. The knowledge conflict detection unit 54 is connected to the knowledge extraction unit 52 and the knowledge update unit 53 via a data transmission line, and to the central processing unit 1 via a communication interface 13. It receives instructions from the processor 11 in real time and resolves knowledge conflicts. Embodiments of the present invention

[0052] The voice quality optimization unit 24 performs noise reduction processing on the input voice through the noise suppression module 241, enhances the synthesized voice through the voice enhancement module 242, and adjusts the output volume according to the volume settings of the user device through the volume adjustment module 243 to ensure the quality of voice interaction.

[0053] The sentiment analysis unit 34 performs sentiment analysis on the user input content through the sentiment dictionary 341 and the sentiment classification model 342, and generates sentiment weight values ​​through the sentiment weight calculation module 343 to ensure that the system can accurately understand the user's emotional state.

[0054] The message priority assessment unit 44 assesses the urgency of the message through the keyword matching module 441, the timeliness of the message through the time sensitivity analysis module 442, and the user's value level through the user value assessment module 443, generating a message priority score to ensure that the system can allocate resources reasonably.

[0055] The knowledge conflict detection unit 54 compares the differences between newly generated knowledge entries and existing knowledge entries through the knowledge comparison module 541, marks conflicting knowledge entries through the conflict marking module 542, and resolves conflicts according to preset rules through the conflict resolution module 543, thereby ensuring the consistency and accuracy of the knowledge base.

Claims

1. An AI-based intelligent customer service system for cross-border e-commerce, characterized in that: include: A central processing unit (1) includes a processor (11), a memory (12), and a communication interface (13). The processor (11) is connected to the memory (12) via a bus, and the communication interface (13) is connected to the processor (11). A multilingual real-time translation module (2) includes a speech recognition unit (21), a text translation unit (22), and a speech synthesis unit (23). The speech recognition unit (21) is installed on the client device, the text translation unit (22) is installed on the server, and the speech synthesis unit (23) is installed on the client device. A cross-cultural semantic understanding module (3) includes a cultural background database (31), a semantic analysis unit (32), and an intent recognition unit (33). The cultural background database (31) stores multi-national cultural background information and language habit data. The semantic analysis unit (32) and the intent recognition unit (33) are installed on the server. The multi-channel unified management module (4) includes a channel access unit (41), a message routing unit (42), and a session management unit (43), which are installed on the server side; the dynamic knowledge base update module (5) includes a data acquisition unit (51), a knowledge extraction unit (52), and a knowledge update unit (53), which are installed on the server side; the central processing unit (1) is electrically connected to the multilingual real-time translation module (2), the cross-cultural semantic understanding module (3), the multi-channel unified management module (4), and the dynamic knowledge base update module (5).

2. The AI-based intelligent customer service system for cross-border e-commerce according to claim 1, characterized in that, The multilingual real-time translation module (2) further includes a speech quality optimization unit (24), which includes a noise suppression module (241), a speech enhancement module (242), and a volume adjustment module (243). The noise suppression module (241) is installed at the front end of the speech recognition unit (21), and the speech enhancement module (242) and the volume adjustment module (243) are installed at the back end of the speech synthesis unit (23).

3. The AI-based intelligent customer service system for cross-border e-commerce according to claim 1, characterized in that, The cross-cultural semantic understanding module (3) also includes a sentiment analysis unit (34), which includes a sentiment dictionary (341), a sentiment classification model (342), and a sentiment weight calculation module (343). The sentiment dictionary (341) stores sentiment words in multiple languages ​​and their corresponding sentiment intensity values. The sentiment classification model (342) and the sentiment weight calculation module (343) are installed on the server side.

4. The AI-based intelligent customer service system for cross-border e-commerce according to claim 1, characterized in that, The multi-channel unified management module (4) also includes a message priority evaluation unit (44), which includes a keyword matching module (441), a time sensitivity analysis module (442), and a user value evaluation module (443). The keyword matching module (441), the time sensitivity analysis module (442), and the user value evaluation module (443) are installed in front of the message routing unit (42).

5. The AI-based intelligent customer service system for cross-border e-commerce according to claim 1, characterized in that, The dynamic knowledge base update module (5) further includes a knowledge conflict detection unit (54), which includes a knowledge comparison module (541), a conflict marking module (542), and a conflict resolution module (543). The knowledge comparison module (541), the conflict marking module (542), and the conflict resolution module (543) are installed at the back end of the knowledge extraction unit (52).

6. The AI-based intelligent customer service system for cross-border e-commerce according to claim 2, characterized in that, The speech quality optimization unit (24) is connected to the speech recognition unit (21) and the speech synthesis unit (23) through a signal transmission line, and is connected to the central processing unit (1) through a communication interface (13).

7. The AI-based intelligent customer service system for cross-border e-commerce according to claim 3, characterized in that, The sentiment analysis unit (34) is connected to the semantic analysis unit (32) and the intent recognition unit (33) via a data transmission line, and is connected to the central processing unit (1) via a communication interface (13).

8. The AI-based intelligent customer service system for cross-border e-commerce according to claim 4, characterized in that, The message priority evaluation unit (44) is connected to the message routing unit (42) and the session management unit (43) via a data transmission line, and is connected to the central processing unit (1) via a communication interface (13).

9. The AI-based intelligent customer service system for cross-border e-commerce according to claim 5, characterized in that, The knowledge conflict detection unit (54) is connected to the knowledge extraction unit (52) and the knowledge update unit (53) through a data transmission line, and is connected to the central processing unit (1) through a communication interface (13).

10. The AI-based intelligent customer service system for cross-border e-commerce according to claim 1, characterized in that, The memory (12) stores the basic data and algorithm models required for the operation of each module.

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