Intelligent customer service quality inspection platform based on NLP technology

Abstract

The invention relates to the technical field of intelligent customer service, and discloses an intelligent customer service quality inspection platform based on an NLP technology, and the platform comprises the following steps: 1, data collection and preprocessing; step 2, data annotation; step 3, model training; and 4, deploying the model. According to the method, the accuracy and availability of the data are ensured by collecting the telephone recording data and carrying out data cleaning, conversion, standardization and feature selection, detailed labeling rules and standardized processes are formulated, and the voice data are labeled, including intention recognition, sentiment analysis, problem classification and the like of dialogues; according to the method, the NLP technology is utilized to ensure the consistency and reliability of labeling results, the RAG technology, the knowledge graph and Web search are combined, the accuracy of answering user questions by a large model in a customer service scene is improved, and the beneficial effects of utilizing the NLP technology, realizing intelligent quality inspection, reducing the manual operation time and improving the quality inspection efficiency are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent customer service, and specifically to a customer service intelligent quality inspection platform based on NLP technology. Background Art

[0002] Traditional customer service quality inspection of call recordings is usually based on manual operations and fixed rules, which leads to the following problems: 1. Low efficiency: Manual quality inspection requires listening to call recordings, which takes up a lot of staff working time; 2. Inaccuracy: When listening to recordings, manual quality inspection is prone to omissions and incomplete information acquisition, and cannot accurately obtain user tags; 3. Untimely: Manual quality inspection cannot screen out valid recordings before listening to the recordings, and cannot handle user feedback in a timely manner; 4. Non-standardization: Manual quality inspection cannot classify and manage the information contained in the voice in a standardized manner. Summary of the invention

[0003] Technical issues solved:

[0004] In view of the shortcomings of the existing technology, the present invention provides a customer service intelligent quality inspection platform based on NLP technology, which has the advantages of utilizing NLP technology to realize intelligent quality inspection, reduce manual operation time, and improve quality inspection efficiency; more accurately identify key information and problems in call recordings through NLP technology to ensure that problems are correctly classified and marked; the intelligent quality inspection system continuously optimizes quality inspection strategies through feedback learning to adapt to changes in customer needs, thereby solving the problems of the above-mentioned technologies.

[0005] Technical solution:

[0006] To achieve the above object, the present invention provides the following technical solutions: a customer service intelligent quality inspection platform based on NLP technology, the platform comprising the following steps:

[0007] Step 1: Data collection and preprocessing: Collect customer phone recording data, and perform data cleaning, data conversion, data normalization, and feature selection on the recording data;

[0008] Step 2: Data labeling: First, clarify the data types and categories that need to be labeled, focusing on the labeling of voice data, especially telephone recording data, and formulate detailed labeling rules and standardized processes to ensure the consistency and reliability of the labeling results. The labeling rules should include conversation intent recognition, sentiment analysis, and question classification;

[0009] Step 3: Model training: Model training is the core process in machine learning and deep learning. It involves how to use large amounts of data and algorithms to optimize the parameters of the model in order to make accurate predictions and classifications on new data.

[0010] Step 4: Deploy the model: Deploy the trained model to actual applications, integrate it into the customer service system, analyze customer corpus recordings in real time and provide feedback, continuously monitor the model operation status, iterate and update based on feedback to ensure that the model can dynamically adapt to new needs, collect user feedback and system operation data to continuously optimize model performance, regularly update and maintain the model to ensure that it remains efficient and stable in a changing environment.

[0011] Preferably, the detailed steps of step 1 are:

[0012] S1.1. Data cleaning: Remove noise and erroneous values: Remove noise and unexpected information in the data, including duplicate values ​​and missing values; Process missing values: Use mean interpolation, same-type mean interpolation, and modeling prediction methods to fill in missing values; Correct erroneous values: Correct erroneous values ​​in the data to ensure the accuracy of the data;

[0013] S1.2, Data conversion: Data type conversion: convert data into a format that the model can understand, including converting text data into numerical data; Data encoding: use hot encoding and label encoding methods to encode categorical data; Feature engineering: enhance the performance of the model through feature extraction and construction;

[0014] S1.3, Data normalization: Data normalization: converting data to the same range, including minimum-maximum normalization and Z-score normalization; Data standardization: converting data to a standard normal distribution to improve the accuracy and efficiency of the model;

[0015] S1.4. Feature selection: Remove irrelevant or redundant features through principal component analysis (PCA) to improve the interpretability and generalization ability of the model; extract the most important features through feature extraction techniques, including principal component analysis (PCA) and linear discriminant analysis (LDA).

[0016] Preferably, the detailed steps of step 2 are:

[0017] S2.1. The voice data needs to be converted into text data, using a reliable speech-to-text engine or API, such as Google Cloud Speech-to-Text, IBM Watson Speech-to-Text, or Microsoft Azure Speech Service, to process and accurately transcribe the speech in real time;

[0018] S2.2. Automatically classify data using labeling systems, knowledge graphs, and artificial intelligence technologies, including: converting speech data into text data using automatic speech recognition (ASR) technology, and then using natural language processing (NLP) technology to clean, detect keywords, identify emotions, identify intent, and perform semantic analysis on text data, thereby achieving data labeling and structuring;

[0019] S2.3. Preprocess the transcribed text, including word segmentation, stop word removal, and stemming. Use recurrent neural network (RNN), long short-term memory (LSTM), and transformer models to identify subtle emotional details from speech text data and accurately classify the text into different emotional states.

[0020] S2.4. Use deep learning models with attention mechanisms, such as Attention-LSTM, and use large datasets for model training. These datasets contain transcribed conversations and their corresponding emotion labels. Through trained sentiment analysis models, understand the emotional content in the text. In addition, deep learning frameworks including TensorFlow are used to capture subtle emotional clues in speech and improve accuracy through diversified datasets.

[0021] Preferably, the detailed steps of step three are:

[0022] S3.1. Data preparation: Collect and organize customer corpus recording data, perform preprocessing, including denoising, segmentation, and label the data to ensure the accuracy and consistency of the labeling.

[0023] S3.2. Model selection: Select appropriate deep learning models, including convolutional neural networks (CNN, recurrent neural networks (RNN), long short-term memory networks (LSTM), for processing speech data, and select appropriate NLP models, including BERT and GPT, for processing text data.

[0024] S3.3, Model construction: Build the model architecture, including the input layer, hidden layer, and output layer. Use deep learning frameworks: TensorFlow and PyTorch for model construction and training. Use tf.keras to build a convolutional neural network (CNN model and configure training parameters. Use tf.keras.layers.Conv2D and tf.keras.layers.MaxPooling2D layers to build the model. Select the appropriate optimizer and loss function. Use tf.keras.optimizers.Adam as the optimizer and select tf.keras.losses.SparseCategoricalCrossentropy as the loss function.

[0025] S3.4, Training process: Divide the labeled data set into training set, validation set and test set, use the training set data to train the model, adjust the model parameters to minimize the loss function, use the validation set data to evaluate the model performance, adjust the hyperparameters to optimize the model, integrate RAG technology, and design an end-to-end retrieval enhancement generation system by integrating knowledge graph and Web search; a reasonable document segmentation strategy to improve the accuracy and efficiency of retrieval while maintaining the logical and semantic integrity of the text; index documents and encode them into vector form to improve the accuracy of the large model in answering user questions in customer service scenarios; combine Agent technology, realize human-computer interaction through NLP technology, understand and process user input, integrate data analysis functions to help improve customer service quality, and analyze historical data and user behavior patterns. Agent technology uses the model's memory ability to push user portraits, browsing records, order data, and historical conversation records to the large model to develop more accurate communication plans, predict customer needs, and provide personalized services.

[0026] S3.5, Optimization strategy: Use regularization techniques including L1 and L2 regularization to prevent overfitting, use torch.nn.DistributedDataParallel to assign each GPU to a dedicated CPU core to speed up model training, use Dropout and BatchNorm techniques to prevent overfitting, and use Early Stopping to avoid overfitting.

[0027] S3.6, Model fine-tuning: Fine-tune the pre-trained NLP model to adapt to specific telecommunications customer service tasks. Use transfer learning technology to migrate the parameters of the pre-trained model to the telecommunications customer service tasks to improve the generalization ability of the model.

[0028] Preferably, the calculation for removing noise and error values ​​in step 1 is:

[0029] D clean =D-Noise-Error

[0030] Among them: D is the original data set; Error is the error data;

[0031] The calculation of missing values ​​includes mean interpolation and similar mean interpolation as follows:

[0032] The mean interpolation calculation is:

[0033]

[0034] Where: D missing is the missing value part; x i Non-missing values; D imputedIt represents a data set after missing values ​​are filled or estimated after some interpolation method is used;

[0035] The same type of mean interpolation calculation is:

[0036]

[0037] Where: n class is the number of samples of the same type.

[0038] Preferably, the consistency measurement calculation in step 2 is:

[0039]

[0040] Where: p o observed agreement ratio; κ represents the kappa coefficient; p e is the expected consistency ratio.

[0041] Preferably, the annotation accuracy in step 2 is calculated as:

[0042]

[0043] The annotation recall rate is calculated as:

[0044] Preferably, the mean square error of the loss function in step 3 is calculated as:

[0045]

[0046] Where: y i : The true value of the i-th sample; n is the number of samples.

[0047] Preferably, the cross entropy loss calculation in step 3 includes a binary classification problem and a multi-classification problem, and the binary classification problem is calculated as:

[0048]

[0049] The multi-classification problem is calculated as:

[0050]

[0051] Where: y ij is the true label of the i-th sample belonging to the j-th category: 0 or 1; n is the number of samples.

[0052] Preferably, the optimization algorithm in step 3 is calculated as:

[0053]

[0054] Where: θ is the model parameter; is the gradient of the loss function L with respect to the parameter θ.

[0055] Compared with the prior art, the present invention provides a customer service intelligent quality inspection platform based on NLP technology, which has the following beneficial effects:

[0056] 1. The present invention collects telephone recording data, and performs data cleaning, conversion, normalization and feature selection to ensure the accuracy and availability of the data. Detailed annotation rules and standardized processes are formulated to annotate voice data, including conversation intent recognition, sentiment analysis, question classification, etc., to ensure the consistency and reliability of the annotation results. RAG technology, knowledge graph and Web search are combined to improve the accuracy of large models in answering user questions in customer service scenarios. Agent technology is combined to achieve human-computer interaction through NLP technology, and integrated data analysis functions help improve customer service. Automatic speech recognition (ASR) and natural language processing (NLP) technologies convert voice data into text data, automatically classify, recognize emotions and perform semantic analysis and processing to achieve data labeling and structuring, significantly reduce manual operation time, and improve quality inspection efficiency. The beneficial effects of utilizing NLP technology to achieve intelligent quality inspection, reduce manual operation time and improve quality inspection efficiency are achieved.

[0057] 2. The present invention ensures the accuracy and availability of data through data cleaning, conversion and normalization. In the data annotation stage, a reliable speech-to-text engine and automatic classification technology, combined with advanced NLP technology, accurately identify keywords, emotions and intentions in calls, and achieve accurate data annotation. In the model training stage, a suitable deep learning model is selected and constructed, combined with NLP technology, through rigorous training and optimization, to ensure that the model can accurately predict and classify in practical applications. After the model is deployed, the real-time monitoring and feedback mechanism enables the platform to dynamically adapt to new data and needs, and continuously optimize the model performance, achieving the beneficial effect of more accurately identifying key information and problems in call recordings through NLP technology, and ensuring that problems are correctly classified and labeled.

[0058] 3. After the trained model is deployed to the customer service system, the present invention continuously monitors the operating status and performance of the model, collects user feedback and system operation data, understands the performance of the model in actual applications, and identifies potential problems and room for improvement. As time goes by, customer needs and behaviors may change. Therefore, the data set is regularly updated to include the latest call recordings and related data to reflect these changes. Incremental learning technology is used to enable the model to learn new data without forgetting past knowledge, so as to adapt to new customer needs. Based on new data and feedback, the model is regularly retrained or fine-tuned to improve its accuracy and adaptability. Through methods such as cross-validation and grid search, the hyperparameters of the model are continuously adjusted to find the optimal configuration, thereby achieving the beneficial effect of the intelligent quality inspection system continuously optimizing the quality inspection strategy through feedback learning and adapting to changes in customer needs. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 It is a schematic diagram of the steps of the present invention; DETAILED DESCRIPTION

[0060] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0061] See also Figure 1 , a customer service intelligent quality inspection platform based on NLP technology, the platform includes the following steps:

[0062] Step 1: Data collection and preprocessing: Collect customer phone recording data, and perform data cleaning, data conversion, data normalization, and feature selection on the recording data;

[0063] Step 2: Data labeling: First, clarify the data types and categories that need to be labeled, focusing on the labeling of voice data, especially telephone recording data, and formulate detailed labeling rules and standardized processes to ensure the consistency and reliability of the labeling results. The labeling rules should include conversation intent recognition, sentiment analysis, and question classification;

[0064] Step 3: Model training: Model training is the core process in machine learning and deep learning. It involves how to use large amounts of data and algorithms to optimize the parameters of the model in order to make accurate predictions and classifications on new data.

[0065] Step 4: Deploy the model: Deploy the trained model to actual applications, integrate it into the customer service system, analyze customer corpus recordings in real time and provide feedback, continuously monitor the model operation status, iterate and update based on feedback to ensure that the model can dynamically adapt to new needs, collect user feedback and system operation data to continuously optimize model performance, regularly update and maintain the model to ensure that it remains efficient and stable in a changing environment.

[0066] Specifically, the detailed steps of step one are:

[0067] S1.1. Data cleaning: Remove noise and erroneous values: Remove noise and unexpected information in the data, including duplicate values ​​and missing values; Process missing values: Use mean interpolation, same-type mean interpolation, and modeling prediction methods to fill in missing values; Correct erroneous values: Correct erroneous values ​​in the data to ensure the accuracy of the data;

[0068] S1.2, Data conversion: Data type conversion: convert data into a format that the model can understand, including converting text data into numerical data; Data encoding: use hot encoding and label encoding methods to encode categorical data; Feature engineering: enhance the performance of the model through feature extraction and construction;

[0069] S1.3, Data normalization: Data normalization: converting data to the same range, including minimum-maximum normalization and Z-score normalization; Data standardization: converting data to a standard normal distribution to improve the accuracy and efficiency of the model;

[0070] S1.4. Feature selection: Remove irrelevant or redundant features through principal component analysis (PCA) to improve the interpretability and generalization ability of the model; extract the most important features through feature extraction techniques, including principal component analysis (PCA) and linear discriminant analysis (LDA).

[0071] Specifically, the detailed steps of step 2 are:

[0072] S2.1. The voice data needs to be converted into text data, using a reliable speech-to-text engine or API, such as Google Cloud Speech-to-Text, IBM Watson Speech-to-Text, or Microsoft Azure Speech Service, to process and accurately transcribe the speech in real time;

[0073] S2.2. Automatically classify data using labeling systems, knowledge graphs, and artificial intelligence technologies, including: converting speech data into text data using automatic speech recognition (ASR) technology, and then using natural language processing (NLP) technology to clean, detect keywords, identify emotions, identify intent, and perform semantic analysis on text data, thereby achieving data labeling and structuring;

[0074] S2.3. Preprocess the transcribed text, including word segmentation, stop word removal, and stemming. Use recurrent neural network (RNN), long short-term memory (LSTM), and transformer models to identify subtle emotional details from speech text data and accurately classify the text into different emotional states.

[0075] S2.4. Use deep learning models with attention mechanisms, such as Attention-LSTM, and use large datasets for model training. These datasets contain transcribed conversations and their corresponding emotion labels. Through trained sentiment analysis models, understand the emotional content in the text. In addition, deep learning frameworks including TensorFlow are used to capture subtle emotional clues in speech and improve accuracy through diversified datasets.

[0076] Specifically, the detailed steps of step three are:

[0077] S3.1. Data preparation: Collect and organize customer corpus recording data, perform preprocessing, including denoising, segmentation, and label the data to ensure the accuracy and consistency of the labeling.

[0078] S3.2. Model selection: Select appropriate deep learning models, including convolutional neural networks (CNN, recurrent neural networks (RNN), long short-term memory networks (LSTM), for processing speech data, and select appropriate NLP models, including BERT and GPT, for processing text data.

[0079] S3.3, Model construction: Build the model architecture, including the input layer, hidden layer, and output layer. Use deep learning frameworks: TensorFlow and PyTorch for model construction and training. Use tf.keras to build a convolutional neural network (CNN model and configure training parameters. Use tf.keras.layers.Conv2D and tf.keras.layers.MaxPooling2D layers to build the model. Select the appropriate optimizer and loss function. Use tf.keras.optimizers.Adam as the optimizer and select tf.keras.losses.SparseCategoricalCrossentropy as the loss function.

[0080] S3.4, Training process: Divide the labeled data set into training set, validation set and test set, use the training set data to train the model, adjust the model parameters to minimize the loss function, use the validation set data to evaluate the model performance, adjust the hyperparameters to optimize the model, integrate RAG technology, and design an end-to-end retrieval enhancement generation system by integrating knowledge graph and Web search; a reasonable document segmentation strategy to improve the accuracy and efficiency of retrieval while maintaining the logical and semantic integrity of the text; index documents and encode them into vector form to improve the accuracy of the large model in answering user questions in customer service scenarios; combine Agent technology, realize human-computer interaction through NLP technology, understand and process user input, integrate data analysis functions to help improve customer service quality, and analyze historical data and user behavior patterns. Agent technology uses the model's memory ability to push user portraits, browsing records, order data, and historical conversation records to the large model to develop more accurate communication plans, predict customer needs, and provide personalized services.

[0081] S3.5, Optimization strategy: Use regularization techniques including L1 and L2 regularization to prevent overfitting, use torch.nn.DistributedDataParallel to assign each GPU to a dedicated CPU core to speed up model training, use Dropout and BatchNorm techniques to prevent overfitting, and use Early Stopping to avoid overfitting.

[0082] S3.6, Model fine-tuning: Fine-tune the pre-trained NLP model to adapt to specific telecommunications customer service tasks. Use transfer learning technology to migrate the parameters of the pre-trained model to the telecommunications customer service tasks to improve the generalization ability of the model.

[0083] Specifically, the calculation for removing noise and error values ​​in step 1 is:

[0084] D clean =D-Noise-Error

[0085] Among them: D is the original data set; Error is the error data;

[0086] The calculation of missing values ​​includes mean interpolation and similar mean interpolation as follows:

[0087] The mean interpolation calculation is:

[0088]

[0089] Where: D missing is the missing value part; x i Non-missing values; D imputedIt represents a data set after missing values ​​are filled or estimated after some interpolation method is used;

[0090] The same type of mean interpolation calculation is:

[0091]

[0092] Where: n class is the number of samples of the same type.

[0093] Specifically, the consistency measurement calculation in step 2 is:

[0094]

[0095] Where: p o observed agreement ratio; κ represents the kappa coefficient; p e is the expected consistency ratio.

[0096] Specifically, the annotation accuracy in step 2 is calculated as:

[0097]

[0098] The annotation recall rate is calculated as:

[0099] Specifically, the mean square error of the loss function in step 3 is calculated as:

[0100]

[0101] Where: y i : The true value of the i-th sample; n is the number of samples.

[0102] Specifically, the cross entropy loss calculation in step 3 includes two-classification problems and multi-classification problems. The two-classification problem is calculated as:

[0103]

[0104] The multi-classification problem is calculated as:

[0105]

[0106] Where: y ij is the true label of the i-th sample belonging to the j-th category: 0 or 1; n is the number of samples.

[0107] Specifically, the optimization algorithm calculation in step 3 is:

[0108]

[0109] Where: θ is the model parameter; is the gradient of the loss function L with respect to the parameter θ.

[0110] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and alterations may be made to the embodiments without departing from the principles and spirit of the invention, the scope of the invention being defined by the appended claims and their equivalents.

Claims

1. A customer service intelligent quality inspection platform based on NLP technology, characterized in that: The platform The method comprises the following steps: Step 1: Data collection and preprocessing: Collect customer phone recording data, and perform data cleaning, data conversion, data normalization, and feature selection on the recording data; Step 2: Data labeling: First, clarify the data types and categories that need to be labeled, focusing on the labeling of voice data, especially telephone recording data, and formulate detailed labeling rules and standardized processes to ensure the consistency and reliability of the labeling results. The labeling rules should include conversation intent recognition, sentiment analysis, and question classification; Step 3: Model training: Model training is the core process in machine learning and deep learning. It involves how to use large amounts of data and algorithms to optimize the parameters of the model in order to make accurate predictions and classifications on new data. Step 4: Deploy the model: Deploy the trained model to actual applications, integrate it into the customer service system, analyze customer corpus recordings in real time and provide feedback, continuously monitor the model operation status, iterate and update based on feedback to ensure that the model can dynamically adapt to new needs, collect user feedback and system operation data to continuously optimize model performance, regularly update and maintain the model to ensure that it remains efficient and stable in a changing environment.

2. According to claim 1, a customer service intelligent quality inspection platform based on NLP technology is characterized by: The detailed steps of step 1 are: S1.

1. Data cleaning: Remove noise and erroneous values: Remove noise and unexpected information in the data, including duplicate values ​​and missing values; Process missing values: Use mean interpolation, same-type mean interpolation, and modeling prediction methods to fill in missing values; Correct erroneous values: Correct erroneous values ​​in the data to ensure the accuracy of the data; S1.

2. Data conversion: Data type conversion: convert data into a format that the model can understand, including converting text data into numerical data; Data encoding: use hot encoding and label encoding methods to encode categorical data; Feature engineering: Enhance the performance of the model through feature extraction and construction; S1.3, Data normalization: Data normalization: converting data to the same range, including minimum-maximum normalization and Z-score normalization; Data standardization: converting data to a standard normal distribution to improve the accuracy and efficiency of the model; S1.

4. Feature selection: Remove irrelevant or redundant features through principal component analysis (PCA) to improve the interpretability and generalization ability of the model; extract the most important features through feature extraction techniques, including principal component analysis (PCA) and linear discriminant analysis (LDA).

3. According to claim 1, a customer service intelligent quality inspection platform based on NLP technology is characterized by: The detailed steps of step 2 are: S2.

1. The voice data needs to be converted into text data, using a reliable speech-to-text engine or API, such as Google Cloud Speech-to-Text, IBM Watson Speech-to-Text, or Microsoft Azure Speech Service, to process and accurately transcribe the speech in real time; S2.

2. Automatically classify data using labeling systems, knowledge graphs, and artificial intelligence technologies, including: converting speech data into text data using automatic speech recognition (ASR) technology, and then using natural language processing (NLP) technology to clean, detect keywords, identify emotions, identify intent, and perform semantic analysis on text data, thereby achieving data labeling and structuring; S2.

3. Preprocess the transcribed text, including word segmentation, stop word removal, and stemming. Use recurrent neural network (RNN), long short-term memory (LSTM), and transformer models to identify subtle emotional details from speech text data and accurately classify the text into different emotional states. S2.

4. Use deep learning models with attention mechanisms, such as Attention-LSTM, and use large datasets for model training. These datasets contain transcribed conversations and their corresponding emotion labels. Through trained sentiment analysis models, understand the emotional content in the text. In addition, deep learning frameworks including TensorFlow are used to capture subtle emotional clues in speech and improve accuracy through diversified datasets.

4. According to claim 1, a customer service intelligent quality inspection platform based on NLP technology is characterized by: The detailed steps of step three are: S3.

1. Data preparation: Collect and organize customer corpus recording data, perform preprocessing, including denoising, segmentation, and label the data to ensure the accuracy and consistency of the labeling. S3.

2. Model selection: Select appropriate deep learning models, including convolutional neural networks (CNN, recurrent neural networks (RNN), long short-term memory networks (LSTM), for processing speech data, and select appropriate NLP models, including BERT and GPT, for processing text data. S3.3, Model construction: Build the model architecture, including the input layer, hidden layer, and output layer. Use deep learning frameworks: TensorFlow and PyTorch for model construction and training. Use tf.keras to build a convolutional neural network (CNN model and configure training parameters. Use tf.keras.layers.Conv2D and tf.keras.layers.MaxPooling2D layers to build the model. Select the appropriate optimizer and loss function. Use tf.keras.optimizers.Adam as the optimizer and select tf.keras.losses.SparseCategoricalCrossentropy as the loss function. S3.4, Training process: Divide the labeled data set into training set, validation set and test set, use the training set data to train the model, adjust the model parameters to minimize the loss function, use the validation set data to evaluate the model performance, adjust the hyperparameters to optimize the model, integrate RAG technology, and design an end-to-end retrieval enhancement generation system by integrating knowledge graph and Web search; a reasonable document segmentation strategy to improve the accuracy and efficiency of retrieval while maintaining the logical and semantic integrity of the text; index documents and encode them into vector form to improve the accuracy of the large model in answering user questions in customer service scenarios; combine Agent technology, realize human-computer interaction through NLP technology, understand and process user input, integrate data analysis functions to help improve customer service quality, and analyze historical data and user behavior patterns. Agent technology uses the model's memory ability to push user portraits, browsing records, order data, and historical conversation records to the large model to develop more accurate communication plans, predict customer needs, and provide personalized services. S3.5, Optimization strategy: Use regularization techniques including L1 and L2 regularization to prevent overfitting, use torch.nn.DistributedDataParallel to assign each GPU to a dedicated CPU core to speed up model training, use Dropout and BatchNorm techniques to prevent overfitting, and use Early Stopping to avoid overfitting; S3.6, Model fine-tuning: Fine-tune the pre-trained NLP model to adapt to specific telecommunications customer service tasks. Use transfer learning technology to migrate the parameters of the pre-trained model to the telecommunications customer service tasks to improve the generalization ability of the model.

5. According to claim 1, a customer service intelligent quality inspection platform based on NLP technology is characterized by: The calculation for removing noise and error values ​​in step 1 is: D clean =D-Noise-Error Among them: D is the original data set; Error is the error data; The calculation of missing values ​​includes mean interpolation and similar mean interpolation: The mean interpolation calculation is: Where: D missing is the missing value part; x i Non-missing values; D imputed It represents a data set after missing values ​​are filled or estimated after some interpolation method is used; The same type of mean interpolation calculation is: Where: n class is the number of samples of the same type.

6. The customer service intelligent quality inspection platform based on NLP technology according to claim 1, characterized in that: The consistency measurement calculation in step 2 is: Where: p o observed agreement ratio; κ represents the kappa coefficient; p e is the expected consistency ratio.

7. The customer service intelligent quality inspection platform based on NLP technology according to claim 1, characterized in that: The annotation accuracy in step 2 is calculated as: The annotation recall rate is calculated as:

8. The customer service intelligent quality inspection platform based on NLP technology according to claim 1, characterized in that: The mean square error of the loss function in step 3 is calculated as: Where: y i : The true value of the i-th sample; n is the number of samples.

9. The customer service intelligent quality inspection platform based on NLP technology according to claim 8, characterized in that: The cross entropy loss calculation in step 3 includes a binary classification problem and a multi-classification problem. The binary classification problem is calculated as: The multi-classification problem is calculated as: Where: y ij is the true label of the i-th sample belonging to the j-th category: 0 or 1; n is the number of samples.

10. The customer service intelligent quality inspection platform based on NLP technology according to claim 1, characterized in that: The optimization algorithm in step 3 is calculated as: Where: θ is the model parameter; is the gradient of the loss function L with respect to the parameter θ.

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