AI-based handheld service call quality monitoring and intelligent response system

Abstract

The invention discloses a handheld service call quality monitoring and intelligent response system based on AI, and relates to the technical field of hotel service management, the intelligent response system comprises a handheld device, a network communication module, a power management module, a real-time voice recognition module, an AI large model analysis module and a monitoring feedback module; the handheld device is used for providing a portable interface. According to the AI-based handheld service call quality monitoring and intelligent answering system, by integrating a real-time voice recognition technology and advanced AI large model analysis, service attitudes and languages of service personnel in a call process can be monitored in real time, and once a condition which does not conform to a standard is found, the service personnel can answer the call quality in real time. And a correction prompt is immediately sent to the service personnel in an equipment screen display or short message mode, such an instant feedback mechanism is helpful for the service personnel to timely adjust the service attitude and the language of the service personnel, and it is ensured that each call can reach a high-quality service standard.

Description

Technical Field

[0001] The present invention relates to the technical field of hotel service management, and in particular to an AI-based handheld service call quality monitoring and intelligent answering system. Background Art

[0002] With the development of the hotel service industry, improving customer experience has become one of the core competitiveness of the hotel industry. Faced with the diverse needs of customers, hotel service personnel need to provide answers quickly and accurately, which is also the key to improving service efficiency. Therefore, hotel service personnel need to give appropriate answers quickly when customers ask questions to ensure that the overall service level of the hotel is improved and the hotel's brand image and competitiveness are enhanced. In recent years, AI technology has made rapid progress, and key technologies such as speech recognition and natural language processing have become increasingly mature, which can provide strong support for the realization of hotel service call quality monitoring and intelligent response.

[0003] The traditional manual monitoring method of service quality has problems such as low efficiency and strong subjectivity. Moreover, the service attitude and voice of service personnel are difficult to be effectively monitored, which may lead to uneven service quality. In addition, the traditional manual monitoring method requires a lot of human resources and the monitoring effect is limited. Therefore, an AI-based handheld service call quality monitoring and intelligent answering system is proposed. Summary of the invention

[0004] To achieve the above objectives, the present invention is implemented through the following technical solutions: an AI-based handheld service call quality monitoring and intelligent response system, the intelligent response system includes a handheld device, a network communication module, a power management module, a real-time speech recognition module, an AI large model analysis module and a monitoring feedback module; The handheld device is used to provide a portable interface, forming a portable and fully functional communication tool, allowing service personnel to communicate with customers and receive system feedback; The network communication module supports Wi-Fi / 4G / 5G networks and is used to transmit call data to the cloud AI big model for analysis, realize real-time data transmission, and ensure the accuracy and real-time performance of AI analysis; The power management module is used to manage the power supply of the device, ensure long-term stable operation, extend the use time of the device, and reduce service interruptions caused by power problems; The real-time speech recognition module is used to convert the speech data in the call data into text information, provide input for subsequent AI analysis, improve the efficiency of speech-to-text conversion, and provide a basis for service attitude and voice analysis and intelligent response; The AI ​​big model analysis module is used to analyze call data to evaluate the service attitude and voice of service personnel and generate the most appropriate answer suggestions; The monitoring feedback module is used to monitor the service attitude and tone of voice of the service personnel and decide whether to send a correction reminder.

[0005] Preferably, the handheld device integrates a telephone call module, a microphone, a speaker, a display screen and a processor; The telephone call module is used to provide basic call functions, so that service personnel can communicate with customers by voice, ensuring smooth communication and providing a basis for subsequent voice recognition and analysis; The microphone and speaker are used to capture the voice in the call and convert it into electrical signals, and at the same time convert the output of the device into sound so that the service personnel can hear it, thereby ensuring the clarity and quality of the call sound and providing accurate input for real-time voice recognition; The display screen is used to display relevant information during the call, including intelligent answer suggestions, and real-time feedback on service attitude and voice, so that service personnel can intuitively see the results and suggestions of system analysis and adjust the service mode in a timely manner; The processor is used to process data from the call module, microphone, speaker and display screen, execute software-designed instructions, ensure smooth operation of the device, and quickly respond to user voice input and system analysis results.

[0006] Preferably, the specific process of using the handheld device includes: When a customer dials the service hotline, the handheld device is activated. After the device is started, the processor is initialized, the software program and configuration parameters are loaded, and the telephone call module, microphone, speaker, and display screen components are self-checked and initialized to ensure that they are in normal working condition; The service personnel answer the customer's call through the telephone call module on the handheld device and establish a voice communication connection; The microphone is used to capture the voice signals of the service staff and the customer during the conversation, and the captured voice signals are converted into electrical signals, which are then output through the speaker to be heard by the customer and the service staff. During a call, the display screen displays relevant information in real time, including call duration, subsequent intelligent answer suggestions, and real-time feedback on service attitude and voice, helping service personnel understand the current call status and adjust service methods according to system suggestions; The processor receives the analysis results from the AI ​​large model analysis module and displays them on the display screen of the handheld device. The service personnel can adjust the service mode according to the information on the display screen. When the call ends, the processor records the detailed information of the call, including the call duration and service quality evaluation results, for subsequent service quality analysis and improvement. If there is no call or operation within a preset time range, the handheld device automatically enters standby mode to reduce power consumption, and monitors the status of the handheld device through the processor, waking up the device when necessary to respond to new calls or operation requests.

[0007] Preferably, in the real-time speech recognition module, the process of converting speech data into text information includes: The real-time speech recognition module pre-processes the speech data in the call data, including noise reduction and speech enhancement; The speech signal is converted into a spectrum on the Mel frequency scale, and its cepstral coefficients are calculated to extract features, thereby capturing the spectral characteristics of the speech signal and extracting speech features representing the characteristics of the speech signal, including pitch and timbre; Use an acoustic model (Hidden Markov Model (HMM)) to match the extracted Mel-frequency cepstral coefficient features with known phoneme acoustic units to identify basic speech units, including syllables and words; Use the language model (N-gram model) to further process the identified speech units, integrate the context information, and combine the identified speech units into sentences that conform to grammatical rules and semantic logic; The outputs of the acoustic model and language model are combined for decoding to generate text information. After decoding, the converted text information is corrected and optimized based on the context information and known vocabulary library, including correcting spelling errors, adjusting punctuation, adjusting sentence structure, etc., to improve the accuracy and readability of the text information, and then the optimized text information is output to the display screen in real time for service personnel to view.

[0008] Preferably, the AI ​​big model analysis module includes a service attitude and voice analysis submodule and an intelligent answer suggestion submodule; The service attitude and voice analysis submodule analyzes the modal particles, speech speed, and volume features in the text based on a deep learning algorithm to evaluate the service attitude (patience, friendliness) and voice (clarity, professionalism) of the service personnel. The intelligent answer suggestion submodule uses natural language processing and semantic understanding technology to analyze customer questions and retrieve or generate the most appropriate answer suggestions from the knowledge base.

[0009] Preferably, in the service attitude and voice analysis submodule, the evaluation process of the service attitude and voice of the service personnel includes: Receive the text information converted by the real-time speech recognition module as input data, ensure the accuracy and completeness of the input data for subsequent analysis, and extract speech speed, volume, and modal particle features from the speech data and text information. The volume and speech speed features are extracted from the speech data, while the modal particles are directly extracted from the text information; The volume feature is determined by the amplitude of the speech data, the speech rate feature is determined by analyzing the number of words per unit time, and the modal particles are identified and counted from the text information using the Natural Language Processing Library (NLTK); The extracted features are input into the long short-term memory network (LSTM) model, and the LSTM model is combined with the attention mechanism to process the text information sequence after speech-to-text conversion; Output the service attitude score based on the analysis of the long short-term memory network (LSTM) model and voice score , and preset the service attitude score benchmark threshold and voice score benchmark threshold , compare the output score with the preset score threshold to determine whether the service attitude and voice of the service personnel meet the standards; The service attitude score and voice score are combined with the preset service attitude score benchmark threshold and voice score benchmark threshold to calculate the service trend index. , to evaluate the service quality trend of service personnel; The evaluation results are output to the display screen for service personnel to view in real time, and the service attitude score, voice score and service trend index are stored for service quality monitoring and management.

[0010] Preferably, the service attitude score and voice score The calculation expression is: ; ; in, Score for service attitude, Score for voice, is a sequence of text information, represents the output of the LSTM model, , is the weight matrix, , is the bias term; The calculation expression of the service trend index is: ; in, For the service trend index, Score for service attitude, Score for voice, is the service attitude score benchmark threshold, is the voice score benchmark threshold, is the square root of the relative deviation between the service attitude score and the benchmark threshold, is the square root of the relative deviation of the voice score from the benchmark threshold, is an exponential function used to convert the relative deviation into a value ranging from 0 to 1. The value range is between 0 and 1.

[0011] Preferably, in the intelligent answer suggestion submodule, the process of generating answer suggestions includes: The intelligent answer suggestion submodule receives the customer's questions in the form of text information converted by the speech recognition module, and performs word segmentation on the customer's questions, breaking down the sentences into separate words; Use part-of-speech tagging technology to determine the grammatical role of each word, understand the relationship between sentence structure and vocabulary through dependency syntax analysis, and apply named entity recognition technology to identify key information in customer questions, including names, places, and times, to determine the core intention of customer questions, such as asking for product information, seeking help, expressing dissatisfaction, etc.; Use machine learning technology to identify customer intent, determine the purpose of the customer's question, and classify the question into predefined question categories to facilitate retrieving relevant answers from the knowledge base; Retrieve the most relevant answer to the customer's question from the pre-built knowledge base, use the language representation model (BERT model) for semantic understanding, encode the customer question Q into a vector, calculate the similarity with the entries in the knowledge base, calculate the similarity score, and select the answer with the highest score As a suggested answer; If there is no relevant answer in the knowledge base, use the generative model (Seq2Seq model) to generate the answer, and use semantic understanding technology to make the generated answer consistent with the semantics of the customer's question; Optimize the retrieved or generated answers, including language polishing and logic verification, to improve the readability and accuracy of the answers, and output the suggested answers to the display screen for service personnel to refer to or use directly.

[0012] Preferably, the calculation expression of the similarity score is: ; in, is the similarity score, For customer issues, For customer issues The vector representation of The first The vector representation of the answers, The answer with the highest score.

[0013] Preferably, in the monitoring feedback module, the process of sending a correction reminder includes: Based on the service attitude and voice of the service personnel, the service attitude score and voice score are obtained, and the service attitude score and voice score obtained by the evaluation are compared with the preset benchmark threshold value; If the service attitude score or voice score is lower than the preset threshold, a correction reminder will be sent; if the score is within an acceptable range, no reminder will be sent; Generate specific correction reminders based on the specific reasons for the low score (such as too low volume, too fast speaking speed, use of inappropriate interjections, etc.), and send correction reminders to service personnel via display screens or text messages; After receiving the reminder, the service personnel adjust their service attitude and voice according to the content of the reminder, monitor the changes in the service attitude and voice of the service personnel after receiving the reminder, evaluate the effectiveness of correcting the reminder, and record the details of each reminder sent, including the content of the reminder, the sending time, and the response of the service personnel.

[0014] The present invention provides an AI-based handheld service call quality monitoring and intelligent response system. It has the following beneficial effects: 1. This AI-based handheld service call quality monitoring and intelligent answering system, by integrating real-time speech recognition technology and advanced AI large model analysis, can monitor the service attitude and voice of service personnel during the call in real time. Once a situation that does not meet the standards is found, a correction reminder will be immediately sent to the service personnel through the device screen display or SMS. This instant feedback mechanism helps service personnel to adjust their service attitude and voice in time to ensure that every call can meet high-quality service standards.

[0015] 2. The AI-based handheld service call quality monitoring and intelligent response system can accurately evaluate the service attitude and voice of service personnel through in-depth analysis of call content by AI big models, thereby effectively avoiding customer dissatisfaction caused by problems such as poor service attitude and unclear voice. At the same time, the system can intelligently generate suggested answers to questions raised by customers, helping service personnel to respond to customer needs quickly and accurately, and further improve service quality and customer satisfaction.

[0016] 3. This AI-based handheld service call quality monitoring and intelligent answering system. The traditional manual monitoring method requires a lot of human resources, and the monitoring effect is often limited by the experience and subjective judgment of the monitoring personnel. Through automated and intelligent monitoring methods, the cost of manual monitoring is greatly reduced, while the accuracy and efficiency of monitoring are improved. It can not only monitor the call quality of service personnel in real time, but also provide intelligent answer suggestions for customer questions. This intelligent auxiliary means helps service personnel quickly understand customer needs and give appropriate responses, thereby improving work efficiency and response speed. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 The overall flow chart of the AI-based handheld service call quality monitoring and intelligent answering system of the present invention; Figure 2 A flow chart for evaluating the service attitude and voice of the service personnel of the present invention; Figure 3 A flowchart for generating a suggestion for answering the present invention. DETAILED DESCRIPTION

[0018] The present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. The embodiments of the present invention are provided for the purpose of illustration and description, and are not intended to be exhaustive or to limit the present invention to the disclosed forms. Many modifications and variations will be apparent to those of ordinary skill in the art. The embodiments are selected and described in order to better illustrate the principles and practical applications of the present invention, and to enable those of ordinary skill in the art to understand the present invention and thereby design various embodiments with various modifications suitable for specific uses.

[0019] The first embodiment, as Figure 1 As shown, the present invention provides a technical solution: an AI-based handheld service call quality monitoring and intelligent answering system, the intelligent answering system includes a handheld device, a network communication module, a power management module, a real-time speech recognition module, an AI large model analysis module and a monitoring feedback module; Handheld devices that provide a portable interface, forming a portable and full-featured communication tool that enables service personnel to communicate with customers and receive system feedback; Furthermore, the handheld device integrates a telephone call module, a microphone, a speaker, a display screen and a processor; wherein the telephone call module is used to provide basic call functions, so that the service personnel can communicate with the customer by voice, ensure smooth communication, and provide a basis for subsequent voice recognition and analysis; the microphone and the speaker are used to capture the voice in the call and convert it into an electrical signal, and at the same time convert the output of the device into sound so that the service personnel can hear it, ensure the clarity and quality of the call sound, and provide accurate input for real-time voice recognition; the display screen is used to display relevant information during the call, including intelligent answer suggestions, and real-time feedback on service attitude and voice, so that the service personnel can intuitively see the results and suggestions of the system analysis, and facilitate timely adjustment of the service method; the processor is used to process the data of the call module, the microphone, the speaker and the display screen, execute the instructions designed by the software, ensure the smooth operation of the device, and quickly respond to the user's voice input and system analysis results; Furthermore, the specific process of using the handheld device includes: When a customer dials the service hotline, the handheld device is activated. After the device is started, the processor is initialized, the software program and configuration parameters are loaded, and the telephone call module, microphone, speaker, and display screen components are self-checked and initialized to ensure that they are in normal working condition. The service staff answers the customer's call through the telephone call module on the handheld device, establishes a voice communication connection, uses the microphone to capture the voice signal during the call between the service staff and the customer, and converts the captured voice signal into an electrical signal, which is converted into an electrical signal through the speaker output so that the customer and the service staff can hear each other's voice. During the call, the display screen displays relevant information in real time, including the call duration, the subsequent generated intelligent answer suggestions, and the service Real-time feedback on service attitude and voice helps service personnel understand the current call status and adjust the service mode according to system recommendations. The processor receives the analysis results from the AI ​​large model analysis module and displays them on the display screen of the handheld device. Service personnel can adjust the service mode according to the information on the display screen. When the call ends, the processor records detailed information of the call, including call duration and service quality evaluation results, for subsequent service quality analysis and improvement. If there is no call or operation within the preset time range, the handheld device automatically enters standby mode to reduce power consumption, and monitors the status of the handheld device through the processor, waking up the device when necessary to respond to new calls or operation requests; Network communication module, supporting Wi-Fi / 4G / 5G networks, is used to transmit call data to the cloud AI big model for analysis, realize real-time data transmission, and ensure the accuracy and real-time performance of AI analysis; The power management module is used to manage the power supply of the device to ensure long-term stable operation, extend the use time of the device, and reduce service interruptions caused by power problems; The real-time speech recognition module is used to convert the speech data in the call data into text information, provide input for subsequent AI analysis, improve the efficiency of speech-to-text conversion, and provide a basis for service attitude and voice analysis and intelligent response. The real-time speech recognition module pre-processes the speech data in the call data, including noise reduction and speech enhancement. It performs noise reduction on the collected speech signal, removes background noise and interference, and performs speech enhancement on the basis of noise reduction to further improve the clarity and recognition of the speech signal, including enhancing the dynamic range of the speech signal, eliminating echo or reverberation, and adjusting the spectral characteristics of the speech signal to be closer to the ideal state. The speech signal is converted into a spectrum on the Mel frequency scale, and its cepstral coefficients are calculated to extract features, thereby capturing the spectral characteristics of the speech signal and extracting speech features representing the characteristics of the speech signal. Including pitch and timbre, using an acoustic model (Hidden Markov (HMM)) to match the extracted Mel frequency cepstral coefficient features with known phoneme acoustic units to identify basic speech units, including syllables and words, using a language model (N-gram model) to further process the identified speech units, integrating context information, combining the identified speech units into sentences that conform to grammatical rules and semantic logic, combining the outputs of the acoustic model and the language model for decoding, generating text information, and after decoding, correcting and optimizing the converted text information based on context information and known vocabulary, including correcting spelling errors, adjusting punctuation, adjusting sentence structure, etc., to improve the accuracy and readability of the text information, and then outputting the optimized text information to the display screen in real time for service personnel to view; AI big model analysis module, used to analyze call data to evaluate the service attitude and voice of service personnel, and generate the most appropriate answer suggestions; The monitoring feedback module is used to monitor the service attitude and tone of service personnel and decide whether to send correction reminders.

[0020] The second embodiment is based on the first embodiment. Figure 2 , Figure 3 As shown, the AI ​​big model analysis module includes a service attitude and voice analysis submodule and an intelligent answer suggestion submodule; Among them, the service attitude and voice analysis submodule, based on the deep learning algorithm, analyzes the modal particles, speech speed, and volume features in the text, evaluates the service attitude (patience, friendliness) and voice (clarity, professionalism) of the service personnel, receives the text information converted by the real-time speech recognition module as input data, ensures the accuracy and completeness of the input data for subsequent analysis, and extracts the speech speed, volume, and modal particle features from the voice data and text information. The volume and speech speed features are extracted from the voice data, while the modal particles are directly extracted from the text information. The volume features are determined by the amplitude of the voice data, and the speech speed features are determined by analyzing the number of words per unit time. The natural language processing library (NLTK) is used to identify and count modal particles from the text information, such as "um", "ah", "oh", etc. Modal particles can reflect the attitude and emotion of the service personnel, and the extracted features are input into the long short-term memory network (LSTM) model. The long short-term memory network (LSTM) model is combined with the attention mechanism to process the text information sequence after speech-to-text conversion. According to the analysis of the long short-term memory network (LSTM) model, the service attitude score is output. and voice score , and preset the service attitude score benchmark threshold and voice score benchmark threshold , compare the output score with the preset score threshold, judge whether the service attitude and voice of the service personnel meet the standards, and calculate the service tendency index by combining the service attitude score and voice score with the preset service attitude score benchmark threshold and voice score benchmark threshold. , to evaluate the service quality trend of service personnel, output the evaluation results to the display screen for service personnel to view in real time, and store the service attitude score, voice score and service trend index for service quality monitoring and management; Furthermore, the service attitude score and voice score The calculation expression is: ; ; in, Score for service attitude, Score for voice, is a sequence of text information, represents the output of the LSTM model, , is the weight matrix, , is the bias term; The calculation expression of service trend index is: ; in, For the service trend index, Score for service attitude, Score for voice, is the service attitude score benchmark threshold, is the voice score benchmark threshold, is the square root of the relative deviation between the service attitude score and the benchmark threshold, is the square root of the relative deviation of the voice score from the benchmark threshold, is an exponential function used to convert the relative deviation into a value ranging from 0 to 1. The value range is between 0 and 1. and are equal to their respective baseline thresholds and hour, The value of is 0.5, when and When both are above their respective baseline thresholds, The value of is close to 1, indicating that the service quality tends to be good. and When both are below their respective baseline thresholds, The value of is close to 0, indicating that the service quality tends to be poor. and Relative to their respective baseline thresholds and The increase in service trend index Increase, on the contrary, with and Relative to their respective baseline thresholds and The reduction in service trend index reduce; The intelligent answer suggestion submodule uses natural language processing and semantic understanding technology to parse customer questions and retrieve or generate the most appropriate answer suggestions from the knowledge base. The intelligent answer suggestion submodule receives customer questions in the form of text information converted by the speech recognition module, and performs word segmentation on customer questions, decomposing sentences into separate words, using part-of-speech tagging technology to determine the grammatical role of each word, understanding the relationship between sentence structure and words through dependency syntax analysis, and applying named entity recognition technology to identify key information in customer questions, including names, places, and times, and determine the core intention of customer questions, such as asking for product information, seeking help, expressing dissatisfaction, etc. Machine learning technology is used to identify customer intentions, determine the purpose of customers' questions, and classify questions into predefined question categories to facilitate retrieval of relevant answers from the knowledge base, retrieve the most relevant answers to customer questions in the pre-built knowledge base, use a language representation model (BERT (Bidirectional Encoder Representations from Transformers) model) for semantic understanding, encode customer question Q into a vector, calculate similarity with entries in the knowledge base, calculate similarity scores, and select the answer with the highest score. As a suggested answer, if there is no relevant answer in the knowledge base, use the generative model (Seq2Seq model) to generate the answer, and use semantic understanding technology to make the generated answer consistent with the semantics of the customer's question. Optimize the retrieved or generated answer, including language polishing and logic verification, to improve the readability and accuracy of the answer, and output the suggested answer to the display screen for service personnel to refer to or use directly; Furthermore, the calculation expression of the similarity score is: ; in, is the similarity score, For customer issues, For customer issues The vector representation of The knowledge base The vector representation of the answers, for the answer with the highest score; In the monitoring feedback module, the process of sending correction reminders includes: The evaluation results are based on the service attitude and voice of the service personnel to obtain the service attitude score and voice score, which are compared with the preset benchmark thresholds. If the service attitude score or voice score is lower than the preset threshold, it is decided to send a correction reminder. If the score is within an acceptable range, no reminder is sent. According to the specific reasons for the low score (such as too low volume, too fast speaking speed, use of inappropriate modal particles, etc.), specific correction reminder content is generated and sent to the service personnel via a display screen or text message. After receiving the reminder, the service personnel adjust their service attitude and voice according to the reminder content. The service attitude and voice changes of the service personnel after receiving the reminder are monitored, the effectiveness of the correction reminder is evaluated, and the details of each reminder sent are recorded, including the reminder content, sending time, and the service personnel's response.

[0021] Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field and related fields without creative work should fall within the scope of protection of the present invention. The structures, devices and operating methods not specifically described and explained in the present invention are implemented according to the conventional means in the field unless otherwise specified and limited.

Claims

1. AI-based handheld service call quality monitoring and intelligent answering system, characterized by: The intelligent response system includes a handheld device, a network communication module, a power management module, a real-time speech recognition module, an AI large model analysis module, and a monitoring feedback module; The handheld device is used to provide a portable interface, forming a portable and fully functional communication tool to enable service personnel to communicate with customers; The network communication module is used to transmit the call data to the cloud AI big model for analysis; The power management module is used to manage the power supply of the device; The real-time speech recognition module is used to convert the speech data in the call data into text information; The AI ​​big model analysis module is used to analyze call data to evaluate the service attitude and voice of service personnel and generate answer suggestions; The monitoring feedback module is used to monitor the service attitude and tone of voice of the service personnel and decide whether to send a correction reminder.

2. The AI-based handheld service call quality monitoring and intelligent response system according to claim 1, characterized in that: The handheld device integrates a telephone call module, a microphone, a speaker, a display screen and a processor; The telephone call module is used to provide a call function, so that the service personnel can communicate with the customers by voice; The microphone and speaker are used to capture the voice in the call and convert it into electrical signals, and convert the output of the device into sound; The display screen is used to display relevant information during the call, including intelligent answer suggestions, and real-time feedback on service attitude and voice; The processor is used to process data from the call module, microphone, speaker and display screen, and execute instructions designed by the software.

3. The AI-based handheld service call quality monitoring and intelligent response system according to claim 2, characterized in that: The specific process of using the handheld device includes: When a customer dials the service hotline, the handheld device is activated. After the device is started, the processor is initialized, the software program and configuration parameters are loaded, and the telephone call module, microphone, speaker, and display screen components are self-checked and initialized; The service personnel answer the customer's call through the telephone call module on the handheld device and establish a voice communication connection; The microphone is used to capture the voice signals of the service staff and the customer during the conversation, and the captured voice signals are converted into electrical signals, which are then output through the speaker to be heard by the customer and the service staff. During a call, the display screen displays relevant information in real time, including call duration, subsequent intelligent answer suggestions, and real-time feedback on service attitude and voice, helping service personnel understand the current call status and adjust service methods according to system suggestions; The processor receives the analysis results from the AI ​​large model analysis module and displays them on the display screen of the handheld device. When the call ends, the processor records the detailed information of the call, including the call duration and service quality evaluation results; If there is no call or operation within a preset time range, the handheld device automatically enters standby mode, and monitors the status of the handheld device through the processor, waking up the device when necessary to respond to new calls or operation requests.

4. The AI-based handheld service call quality monitoring and intelligent response system according to claim 3 is characterized in that: In the real-time speech recognition module, the process of converting speech data into text information includes: The real-time speech recognition module pre-processes the speech data in the call data, including noise reduction and speech enhancement; The speech signal is converted into a spectrum on the Mel frequency scale, and its cepstral coefficients are calculated to extract features, thereby capturing the spectral characteristics of the speech signal and extracting speech features representing the characteristics of the speech signal, including pitch and timbre; Use the acoustic model to match the extracted Mel-frequency cepstral coefficient features with known phoneme acoustic units to identify basic speech units, including syllables and words; Use the language model to further process the recognized speech units, integrate the context information, and combine the recognized speech units into sentences that conform to grammatical rules and semantic logic; The outputs of the acoustic model and the language model are combined for decoding to generate text information. After decoding, the converted text information is corrected and optimized based on the context information and the known vocabulary library, and the optimized text information is then output to the display screen in real time for service personnel to view.

5. The AI-based handheld service call quality monitoring and intelligent response system according to claim 4 is characterized in that: The AI ​​big model analysis module includes a service attitude and voice analysis submodule and an intelligent answer suggestion submodule; The service attitude and voice analysis submodule analyzes the modal particles, speech speed, and volume features in the text based on a deep learning algorithm to evaluate the service attitude and voice of the service personnel. The intelligent answer suggestion submodule uses natural language processing and semantic understanding technology to parse customer questions and retrieve or generate answer suggestions from the knowledge base.

6. The AI-based handheld service call quality monitoring and intelligent response system according to claim 5, characterized in that: In the service attitude and voice analysis submodule, the evaluation process of the service attitude and voice of the service personnel includes: Receiving text information converted by a real-time speech recognition module as input data, and extracting speech speed, volume, and modal particle features from the speech data and text information; Determine the volume characteristics through the amplitude of the speech data, determine the speech speed characteristics by analyzing the number of words per unit time, and use the natural language processing library to identify and count modal particles from text information; The extracted features are input into the long short-term memory network model, and the long short-term memory network model is combined with the attention mechanism to process the text information sequence after speech-to-text conversion; Output the service attitude score based on the analysis of the long short-term memory network model and voice score , and preset the service attitude score benchmark threshold and voice score benchmark threshold , compare the output score with the preset score threshold to determine whether the service attitude and voice of the service personnel meet the standards; The service attitude score and voice score are combined with the preset service attitude score benchmark threshold and voice score benchmark threshold to calculate the service trend index. , to evaluate the service quality trend of service personnel; The evaluation results are output to the display screen for service personnel to view in real time, and the service attitude score, voice score and service tendency index are stored.

7. The AI-based handheld service call quality monitoring and intelligent response system according to claim 6, characterized in that: The service attitude score and voice score The calculation expression is: ; ; in, Score for service attitude, Score for voice, is a sequence of text information, represents the output of the LSTM model, , is the weight matrix, , is the bias term; The calculation expression of the service trend index is: ; in, For the service trend index, Score for service attitude, Score for voice, is the service attitude score benchmark threshold, is the voice score benchmark threshold, The value range is between 0 and 1.

8. The AI-based handheld service call quality monitoring and intelligent response system according to claim 7, characterized in that: In the intelligent answer suggestion submodule, the process of generating answer suggestions includes: The intelligent answer suggestion submodule receives the customer's questions in the form of text information converted by the speech recognition module, and performs word segmentation on the customer's questions, breaking down the sentences into separate words; Use part-of-speech tagging technology to determine the grammatical role of each word, understand the relationship between sentence structure and vocabulary through dependency syntax analysis, and apply named entity recognition technology to identify key information in customer questions, including names, places, and times, to determine the core intent of customer questions; Use machine learning technology to identify customer intent, determine the purpose of the customer's question, and categorize the question into predefined question categories; Retrieve the most relevant answer to the customer's question from the pre-built knowledge base, use the language representation model for semantic understanding, encode the customer question Q into a vector, calculate the similarity with the entries in the knowledge base, calculate the similarity score, and select the answer with the highest score As a suggested answer; If there is a lack of relevant answers in the knowledge base, use the generative model to generate answers, and use semantic understanding technology to make the generated answers consistent with the semantics of the customer's questions; Optimize the retrieved or generated answers, including language polishing and logic verification, and output the suggested answers to the display screen for service personnel to refer to or use directly.

9. The AI-based handheld service call quality monitoring and intelligent response system according to claim 8, characterized in that: The calculation expression of the similarity score is: ; in, is the similarity score, For customer issues, For customer issues The vector representation of The first The vector representation of the answers, The answer with the highest score.

10. The AI-based handheld service call quality monitoring and intelligent response system according to claim 9, characterized in that: In the monitoring feedback module, the process of sending a correction reminder includes: Based on the service attitude and voice of the service personnel, the service attitude score and voice score are obtained, and the service attitude score and voice score obtained by the evaluation are compared with the preset benchmark threshold value; If the service attitude score or voice score is lower than the preset threshold, a correction reminder will be sent; if the score is within an acceptable range, no reminder will be sent; Generate specific correction reminders based on the specific reasons for the low scores, and send correction reminders to service personnel via display screens or text messages; After receiving the reminder, the service personnel adjust their service attitude and voice according to the content of the reminder, monitor the changes in the service attitude and voice of the service personnel after receiving the reminder, evaluate the effectiveness of correcting the reminder, and record the details of each reminder sent, including the content of the reminder, the sending time, and the response of the service personnel.

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