E-commerce customer service processing system with intelligent quality inspection and high-concurrency simulation capabilities

Abstract

The invention relates to the technical field of e-commerce, and discloses an e-commerce customer service processing system with intelligent quality inspection and high-concurrency simulation capabilities. According to the invention, millisecond index calculation and real-time alarm of full customer service dialogues are realized through a bypass monitoring technology, a traditional lagging mode of post sampling inspection is changed, and management response is converted from passive remedy to active intervention; a high-concurrency pressure simulation module reproduces a business scene in an e-commerce peak period by simulating a script of a real user behavior and a distributed cluster, and a real basis is provided for system capacity planning and stability guarantee; the integrated intelligent verbal skill evaluation module deeply analyzes the dialogue quality from multiple dimensions such as semantics, compliance and emotional change based on a natural language processing technology, and generates an evaluation report with guidance, so that the professional level of customer service is improved; and the service quality, the operation robustness and the management intelligence level of the e-commerce customer service system are improved.

Description

Technical Field

[0001] This invention relates to the field of e-commerce technology, specifically to an e-commerce customer service processing system with intelligent quality inspection and high-concurrency simulation capabilities. Background Technology

[0002] With the booming development of the e-commerce industry, online customer service systems, as the core bridge connecting consumers and merchants, directly impact user experience and merchant reputation through their service quality and processing efficiency. Especially during major promotional periods, the volume of inquiries explodes, placing extremely high demands on the concurrent processing capabilities, real-time quality control, and customer service script skills of customer service systems.

[0003] Currently, the e-commerce customer service systems commonly used in the industry mainly suffer from the following technical defects: Firstly, in terms of service quality monitoring, existing systems largely rely on post-event sampling mechanisms. Quality inspectors typically evaluate a portion of the recordings or chat logs manually and randomly after the session ends. This approach suffers from significant delays and biases: on the one hand, it cannot intervene in or guide the real-time service status of customer service, and by the time problems are discovered, the negative impact has already occurred; on the other hand, the low sampling rate makes it difficult to comprehensively reflect the overall service quality, and manual quality inspection is costly, lacks standardized criteria, and is highly subjective.

[0004] Secondly, regarding system performance assurance, traditional stress testing tools (such as JMeter and LoadRunner) are typically independent of the customer service business system. While these tools can simulate simple HTTP request concurrency, they struggle to simulate real, complex user consultation behavior logic and continuous session scenarios. For example, a real user consultation might involve multiple steps such as "greeting -> inquiring about products -> asking about logistics -> negotiating prices -> ending," with time intervals between these steps. Existing tools cannot accurately simulate this stateful business flow, leading to significant discrepancies between stress test results and actual system performance during peak periods, making it impossible to effectively predict and prevent system bottlenecks.

[0005] Secondly, in terms of intelligent evaluation of customer service scripts, existing technologies mostly remain at the level of keyword matching and simple rule filtering. For example, they can only detect whether certain sensitive words or prohibited language appear, but cannot deeply understand the contextual semantics of the conversation, the user's emotional changes, or the appropriateness and professionalism of the customer service response. This superficial analysis cannot accurately assess the customer service representative's communication skills and problem-solving abilities, let alone provide targeted, data-driven optimization suggestions.

[0006] Therefore, there is an urgent need in this field for a comprehensive solution that integrates real-time quality inspection, high-concurrency business simulation, and intelligent dialogue deep analysis to overcome the fragmentation, lag, and superficiality of existing technologies. This would enable a shift from "post-event remediation" to "in-process intervention" and "pre-event prevention," comprehensively improving the service quality, stability, and intelligence level of e-commerce customer service systems. To this end, an e-commerce customer service processing system with intelligent quality inspection and high-concurrency simulation capabilities is proposed. Summary of the Invention

[0007] To address the shortcomings of existing technologies, this invention provides an e-commerce customer service processing system with intelligent quality inspection and high-concurrency simulation capabilities, thereby resolving the technical problems described in the background.

[0008] To achieve the above objectives, the present invention provides the following technical solution: an e-commerce customer service processing system with intelligent quality inspection and high-concurrency simulation capabilities, comprising: The customer service dialogue access module is used to connect with multiple e-commerce platforms through a predefined API interface, receive and route user inquiry messages from different platforms, including text and voice messages. The real-time quality inspection engine is connected to the customer service dialogue access module via a data bus. It is used to capture and analyze all customer service dialogue flows in real time in a bypass monitoring manner, and calculate multiple key performance indicators. The high-concurrency stress simulation module is deployed independently in the test environment. It is used to simulate a large number of virtual users initiating consultation requests that include the complete session lifecycle, based on the configured business scripts, in order to stress test the system. The intelligent dialogue evaluation module is connected to the data bus, integrates a natural language processing unit, and is associated with a dialogue rule library, which includes a high-quality dialogue library and a violation dialogue library. It is used to perform asynchronous in-depth analysis on the stored dialogue records and automatically generate a dialogue quality evaluation report. The system management and configuration platform provides a graphical user interface for administrators to configure the alarm thresholds of the real-time quality inspection engine, define the test scenarios of the high-concurrency pressure simulation module, and manage the script rule library of the intelligent script evaluation module. The data storage center uses a distributed database and object storage to store structured indicator data, unstructured dialogue transcripts and voice files, and intermediate results of script evaluation, respectively. The real-time quality inspection engine is configured to capture all conversations non-intrusively in a bypass listening manner and calculate multiple key indicators in real time, including the "commitment fulfillment rate". The "commitment fulfillment rate" is determined by identifying promise statements in customer service conversations and comparing them with subsequent system operation logs.

[0009] Preferably, the real-time quality inspection engine specifically includes: The metric calculation unit is configured to calculate and update the following 12 key metrics in real time based on a time window and session ID: initial response time, average response time, session duration, message rate, transfer rate, session end rate, customer satisfaction, number of incorrect responses, number of sensitive word triggers, promise fulfillment rate, number of recommended products, and problem resolution rate. Among them, the "promise fulfillment rate" is calculated by tracking promise statements in customer service conversations and comparing them with subsequent system operation logs. The promise statements include "I will reply to you later" and "I will ship tomorrow".

[0010] The real-time alarm unit is configured to use a multi-level alarm mechanism. When the indicator exceeds the threshold, it first prompts the customer service interface. If the situation does not improve, it sends an in-app message to the customer service supervisor and finally escalates to sending an SMS or email to the system administrator.

[0011] Preferably, the high-concurrency stress simulation module includes: The business script library stores multiple preset test scripts that simulate real user behavior. Each script contains a sequence of "event-wait time" to simulate the continuous consultation behavior of real users. The distributed pressure generator consists of multiple computing nodes in the cluster. Each node can independently execute the business script and supports dynamic expansion. It is configured to dynamically add or remove computing nodes to achieve elastic scaling of concurrent pressure, so as to simulate the extreme access load under the peak consultation scenario of e-commerce. The traffic coloring and tracing unit is used to inject a unique tracing identifier into each simulated request and pass the identifier within the system, thereby fully reproducing the entire call path and performance bottleneck of a single high-concurrency request in the distributed log.

[0012] Preferably, the natural language processing unit in the intelligent speech evaluation module includes: The semantic understanding subunit uses a BERT-based pre-trained model for fine-tuning to identify the semantic roles of user inquiry intent and customer service responses; The script compliance check subunit compares customer service responses with the high-quality script library and the non-compliant script library in the script rule base based on regular expressions and semantic similarity calculations, and identifies cases of mechanical responses, irrelevant answers, and use of prohibited words; The multimodal sentiment analysis subunit is used to analyze the changing trends of users' emotions during the conversation and, in conjunction with the context, to evaluate whether the customer service's response effectively soothes the user's negative emotions or enhances the user's positive emotions.

[0013] Preferably, the system further includes: The dialect recognition and assistance subunit is communicatively connected to the customer service dialogue access module and the intelligent dialogue evaluation module. The dialect recognition and assistance subunit is configured to: firstly, determine whether the user's voice belongs to a preset dialect type through a speech recognition model; if so, convert it into standard text and attach a dialect tag; then, on the customer service interface, dynamically push standard phrases or polite expressions applicable to the dialect area based on the tag for customer service reference.

[0014] Preferably, the user behavior sequence simulated by the test scripts in the business script library includes: entering the store, browsing products, inquiring about logistics, questioning prices, and ending the session, wherein each behavior is associated with a set waiting time.

[0015] Preferably, the real-time alarm unit is configured to trigger an alarm if, for the "commitment fulfillment rate" indicator, no corresponding fulfillment record is found in the system operation log within a preset time after the customer service representative makes a commitment.

[0016] Preferably, the data storage center is configured to use the session ID as a unique association key to associate and store the indicator data from the real-time quality inspection engine, the original dialogue records from the customer service dialogue access module, and the dialogue evaluation results from the intelligent dialogue evaluation module, so as to support full-link data traceability and joint analysis based on a single session.

[0017] Compared with the prior art, the present invention has the following beneficial effects: This invention achieves millisecond-level metric calculation and real-time alerts for all customer service conversations through bypass monitoring technology, changing the traditional delayed post-event sampling mode and transforming management response from passive remediation to proactive intervention. Its high-concurrency pressure simulation module reproduces the business scenario during peak e-commerce periods through scripts simulating real user behavior and distributed clusters, providing a realistic basis for system capacity planning and stability assurance. The integrated intelligent dialogue evaluation module, based on natural language processing technology, deeply analyzes dialogue quality from multiple dimensions such as semantics, compliance, and emotional changes, generating instructive evaluation reports and improving the professionalism of customer service. This enhances the service quality, operational robustness, and management intelligence of the e-commerce customer service system.

[0018] This invention improves service inclusiveness and experience consistency for diverse user groups through dialect recognition and real-time assistance functions.

[0019] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures pointed out in the description, claims and drawings. Attached Figure Description

[0020] Figure 1 This is a diagram illustrating the overall system architecture and data flow of the present invention. Figure 2 This is a flowchart of the real-time quality inspection engine of the present invention; Figure 3 This is a diagram illustrating the architecture of the high-concurrency stress simulation module of this invention. Figure 4 This is a flowchart of the intelligent speech evaluation module analysis process of the present invention; Figure 5 This is a schematic diagram illustrating dialect recognition and data association in this invention. Detailed Implementation

[0021] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0022] Please see Figure 1-5 This invention relates to an e-commerce customer service processing system with intelligent quality inspection and high-concurrency simulation capabilities.

[0023] 1. System Overall Architecture and Workflow This system adopts an event-driven microservice architecture, with each module communicating loosely coupled through a high-throughput distributed message queue (data bus).

[0024] The system's core workflow begins with the customer service dialogue access module, which serves as the system's unified entry point. This module connects with multiple e-commerce platforms (such as Taobao, JD.com, and Pinduoduo) through a highly configurable API gateway cluster. When a user inquiry request arrives from any platform, the gateway first performs authentication, protocol conversion (e.g., converting each platform's proprietary protocols into the internal standard JSON format), and malicious request filtering. Subsequently, based on a pre-defined load balancing strategy (such as the least connections algorithm), legitimate requests are routed to available customer service agent processing units in the backend.

[0025] All incoming conversation streams (including text messages and voice data) are synchronously replicated into two independent data streams at the gateway. The primary data stream enters the normal customer service processing flow; the replica data stream is immediately published to a specific topic on the data bus (built using Apache Kafka). This design separates data acquisition from business processing, laying the foundation for subsequent non-intrusive analysis.

[0026] The real-time quality inspection engine, as the primary consumer of the data bus, subscribes to and processes these dialog stream copies in real time. The high-concurrency stress simulation module, in the test environment, also interacts with other parts of the system by subscribing to and generating specific test messages. All processing results from all modules, including calculated metrics, generated reports, and raw dialog logs, are ultimately persisted uniformly by the data storage center. The system management and configuration platform, acting as the control hub, interacts with all modules through a graphical interface to configure and dynamically adjust system behavior. This architecture ensures the system possesses high cohesion and low coupling, allowing each module to expand independently while also collaborating through the data bus to form a complete, organic whole.

[0027] 2. Execution process of the real-time quality inspection engine The core of the real-time quality inspection engine lies in its bypass monitoring and streaming processing capabilities. Internally, the engine constructs a streaming pipeline that continuously consumes dialogue events from the data bus.

[0028] The metric calculation unit is the core of this pipeline, implemented using stream processing frameworks such as Apache Flink or Spark Streaming. Each unit maintains a session context state object in memory for each active customer service session. This object, using the session ID as the primary key, records the session start time, last activity time, message sequence, and intermediate calculation results for various metrics. For each incoming message, the system triggers a complete state update cycle. Taking the calculation of the first response time as an example, when the system detects that a user has sent the first message, it records a timestamp. Record the timestamp when the customer service representative's first reply is received. Then the final value of this indicator The average response time can be calculated as follows: Where N is the number of rounds in which the user initiates the conversation in this session. and These represent the timestamps for the i-th round of customer service responses and user comments, respectively.

[0029] The promise fulfillment rate is calculated as follows: First, a lightweight promise recognition model is integrated within the unit. This model combines predefined keyword rules (such as "shipment," "reply," "resolve," etc.) with a BERT-based sequence classification model to identify whether the customer service response R contains an explicit promise C (such as "goods will be shipped tomorrow"). Once the recognition is successful, the system immediately generates a structured promise event. This information is then written to a dedicated "Pending Commitments" database table. Subsequently, a separate commitment verification service periodically (e.g., every 5 minutes) scans this table and queries relevant business system logs (such as shipping logs from the order system and follow-up records from the customer service system). It uses a semantic similarity-based matching algorithm within a time window. ( Within the tolerance period set according to the type of commitment (e.g., 48 hours for a "shipment" commitment, 2 hours for a "response" commitment), evidence of fulfillment matching commitment C is sought. Ultimately, the commitment fulfillment rate... The calculation formula for a statistical period (e.g., every 24 hours) is as follows: ,in To find the number of commitments for which evidence of fulfillment is required, This represents the total number of commitments identified during this period.

[0030] The real-time alarm unit monitors the metric stream output by the metric calculation unit. Administrators set dynamic thresholds for each metric on the system management configuration platform (e.g., a 20-second threshold for the first response time at level one alarm and a 40-second threshold at level two alarms). When the streamed metric data exceeds the threshold, the alarm unit immediately triggers an alarm event. This event enters a multi-level processing pipeline: first, a flashing notification is pushed to the customer service agent's interface via a WebSocket connection; if the metric does not recover within the next M consecutive messages (M is configurable), an in-app message containing session details is sent to the customer service agent's direct supervisor by calling the enterprise's internal IM (such as WeChat Work) API; if the problem persists and triggers a higher-level threshold, a highest-priority alarm notification is finally sent to the system operations administrator via an integrated SMS gateway and email server. This progressive alarm mechanism ensures that problems are handled promptly and appropriately.

[0031] It's important to note that the alert mechanism for the "commitment fulfillment rate" metric differs fundamentally from the instantaneous threshold-based alerts described above. Essentially, it's an asynchronous verification and triggering process based on a time window. When the system detects a customer service commitment, it doesn't immediately issue an alert; instead, it initiates a separate monitoring task for that commitment. This task runs continuously in the background until the final fulfillment deadline (i.e., the commitment time) is reached. +Tolerance period If the system still cannot find valid evidence of fulfillment in the relevant business logs at that time, a "promise not fulfilled" alarm event will be generated. This mechanism does not rely on instantaneous data streams during the dialogue process, but rather on continuous tracking of the business fulfillment status and timeout judgment, thereby achieving effective supervision of the quality of the closed-loop customer service.

[0032] 3. Testing and Implementation of the High-Concurrency Stress Simulation Module The high-concurrency stress simulation module is a completely independent, highly realistic testing platform designed to replicate the level of surge in inquiries during major sales events.

[0033] Test implementation begins with the construction of the business script library. Test engineers use the graphical script editor of the system management configuration platform to write complete sequences of user behaviors. This includes writing or combining various user behavior scripts. Each script is essentially a state machine that defines the complete sequence of virtual user behaviors. A typical "price comparison consultation user" script can be described as follows: Status 0: Entering the store (Waiting: 0s) -> Status 1: Browsing product A (Action performed: Request product details page; Waiting: 3±1s) -> Status 2: Logistics Inquiry (Action: Send message "When will it arrive?"; Waiting: 2±0.5s) -> State 3: Price Inquiry (Action: Send message "Can you lower the price a bit more?"; Waiting: 5±2s) -> Status 4: End Session (Action: Close Dialogue Window) The waiting time incorporates random perturbations to simulate the unpredictability of real user behavior.

[0034] During test execution, the distributed stress generator begins operation. Its master node dynamically distributes script execution tasks to hundreds of subordinate stress generator nodes based on a preset stress curve (e.g., linearly increasing concurrent users to 10,000 within 10 minutes and maintaining that peak for 5 minutes). Each node is a lightweight container running a headless browser or a high-performance HTTP client, capable of independently executing the scripts assigned to it, simulating real user login states, cookie holding, and WebSocket long connections. The entire cluster supports elastic scaling, automatically expanding or shrinking the compute nodes in real time based on the target QPS (queries per second).

[0035] To ensure the analyzability of the test, the traffic coloring and tracing unit injects a globally unique identifier into the HTTP header of each simulated request before it is sent. .this Like DNA, requests are passed through various microservices of the customer service system (such as user authentication service, product query service, order service, and dialogue routing service) and recorded in the logs of each layer. After testing, operations personnel can use a distributed tracing system (such as SkyWalking or Zipkin) to input any... This allows for a clear visualization of the complete call chain of the request in a complex microservice network, the time spent on each hop, and potential errors or bottlenecks, thereby accurately pinpointing performance degradation points.

[0036] 4. In-depth analysis of the intelligent script evaluation module After the conversation ends, the complete dialogue record is asynchronously loaded from the data storage center to the intelligent speech evaluation module for in-depth, offline analysis. At the core of this module is a multi-stage natural language processing pipeline.

[0037] The first stage is performed by the semantic understanding subunit. It loads a BERT model finely tuned on a large-scale customer service dialogue corpus in this domain. For each sentence in the dialogue, the model converts it into a high-dimensional vector representation and performs the following tasks: 1) Intent recognition: classifying user statements into predefined intent categories, such as... (Logistics Consultation) (complaint), (After-sales service). 2) Semantic role labeling: Analyze customer service responses to identify their functional roles, such as... (greeting), (answer), (appease), (Transfer).

[0038] The second phase is the compliance check subunit. It employs a multi-strategy approach: on one hand, a high-efficiency regular expression engine is used to precisely match sensitive words and prohibited phrases (such as insults and evasive terms) in customer service replies; on the other hand, the sentence vector of the customer service reply is compared with the "high-quality reply library" and "non-compliant reply library" in the reply rule base using cosine similarity calculation. Assume the customer service reply vector is... The vector of high-quality conversational examples is The vector of the illegal verbal statements is Then calculate the similarity respectively. and .if Exceeding the threshold (e.g., 0.8), then it is marked as "high-risk violation"; if Above the threshold If it is 0.7, it is marked as "model rhetoric".

[0039] The third phase was completed by the multimodal sentiment analysis subunit, whose goal was to quantify the impact of customer service conversation techniques on user emotions. This unit analyzed each user statement in the conversation sequence. Calculate an emotion polarity score (range [-1, 1]). This is achieved by analyzing the user's emotion score sequence throughout the entire conversation. The changing trends can be used to evaluate customer service performance. For example, the frequency of each customer service response can be calculated. Subsequently, the change in the user's sentiment score If the user's sentiment was negative before the customer service representative responded ( ), and after replying A significant positive value indicates that the response effectively soothed the user's emotions. Ultimately, the analysis results from all dimensions (intent recognition accuracy, role-playing appropriateness, compliance score, and emotional reversal ability) are integrated by a comprehensive scoring algorithm to generate a structured speech quality assessment report containing specific examples of strengths and weaknesses, as well as suggestions for improvement.

[0040] 5. Dialect recognition and data association storage The dialect recognition and auxiliary subunit is crucial for improving the system's service inclusiveness. When the customer service dialogue access module receives a user's voice message, it synchronously sends the audio data and related metadata (such as the session ID) to this subunit. The subunit's processing flow is as follows: First, the audio is preprocessed, including noise reduction, silence removal, and frame segmentation. Then, Mel-frequency cepstral coefficients are extracted as acoustic features. These features are fed into a pre-trained dialect classifier (a multi-classification model based on a deep neural network, such as CNN or RNN), which outputs the probability distribution P(dialect|audio) of the speech belonging to each preset dialect (such as Cantonese, Shanghainese, Sichuan-Chongqing dialect). The dialect category with the highest probability is selected as the recognition result, and a dialect label is attached to the speech. .

[0041] Subsequently, the system uses the identified dialect tags It calls upon a dedicated speech recognition (ASR) engine corresponding to that dialect to transcribe the speech data into standard text. This dialect tag The transcribed text will accompany the entire conversation lifecycle. During the intelligent speech evaluation module's analysis, dialectal and cultural contexts will be considered to conduct more accurate sentiment analysis and compliance judgments. More importantly, during real-time dialogue, the system will, based on the identified... The system intelligently pushes a "dialect assistance panel" to the customer service agent interface, which contains greetings, common expressions and polite phrases applicable to the dialect region for customer service agents to refer to, thereby achieving more friendly and efficient communication.

[0042] All data generated throughout the process is ultimately managed centrally by a data storage center. It employs a hybrid storage architecture: a distributed columnar database (such as Apache Cassandra or ClickHouse) stores all structured, time-series data for rapid querying, such as KPI metrics and alarm logs; and object storage services (such as AWS S3 or MinIO) store unstructured raw data, such as conversation text, voice files, and generated evaluation report PDFs. The core of the system lies in the session ID, which serves as a unique key across all data records. This allows users to access the original transcript of the conversation, all real-time calculated metric curves, every triggered alarm, and the final in-depth conversation evaluation report in the "Conversation Tracing" function of the system management configuration platform by simply entering a session ID. This end-to-end data association based on session IDs builds insights from surface phenomena to root causes, enabling comprehensive and thorough monitoring and optimization of customer service quality.

Claims

1. An e-commerce customer service processing system with intelligent quality inspection and high-concurrency simulation capabilities, characterized in that, include: The customer service dialogue access module is used to connect with multiple e-commerce platforms through a predefined API interface, receive and route user inquiry messages from different platforms, including text and voice messages. The real-time quality inspection engine is connected to the customer service dialogue access module via a data bus. It is used to capture and analyze all customer service dialogue flows in real time in a bypass monitoring manner, and calculate multiple key performance indicators. The high-concurrency stress simulation module is deployed independently in the test environment. It is used to simulate a large number of virtual users initiating consultation requests that include the complete session lifecycle, based on the configured business scripts, in order to stress test the system. The intelligent dialogue evaluation module is connected to the data bus, integrates a natural language processing unit, and is associated with a dialogue rule library, which includes a high-quality dialogue library and a violation dialogue library. It is used to perform asynchronous in-depth analysis on the stored dialogue records and automatically generate a dialogue quality evaluation report. The system management and configuration platform provides a graphical user interface for administrators to configure the alarm thresholds of the real-time quality inspection engine, define the test scenarios of the high-concurrency pressure simulation module, and manage the script rule library of the intelligent script evaluation module. The data storage center uses a distributed database and object storage to store structured indicator data, unstructured dialogue transcripts and voice files, and intermediate results of script evaluation, respectively. The real-time quality inspection engine is configured to capture all conversations non-intrusively in a bypass listening manner and calculate multiple key indicators in real time, including the "commitment fulfillment rate". The "commitment fulfillment rate" is determined by identifying promise statements in customer service conversations and comparing them with subsequent system operation logs.

2. The e-commerce customer service processing system with intelligent quality inspection and high-concurrency simulation capabilities according to claim 1, characterized in that, The real-time quality inspection engine specifically includes: The metrics calculation unit is configured to calculate and update the following 12 key metrics in real time based on a time window and session ID: initial response time, average response time, session duration, message rate, transfer rate, session end rate, customer satisfaction, number of incorrect responses, number of sensitive word triggers, promise fulfillment rate, number of recommended products, and problem resolution rate. The "promis fulfillment rate" is calculated by tracking promise statements in customer service conversations and comparing them with subsequent system operation logs. These promise statements include phrases like "I'll reply to you later" and "We'll ship tomorrow." In addition, the metrics calculation unit also simultaneously calculates several other key metrics: the "message rate" is calculated by statistically analyzing the proportion of user messages left by customer service representatives without a response; the "transfer rate" is calculated by statistically analyzing the number of times a session is transferred to other customer service representatives; the "session end rate" is determined by analyzing session ending methods; "customer satisfaction" is obtained by accessing customer evaluation data; the "number of incorrect responses" and "number of sensitive word triggers" are statistically analyzed by matching against a violation keyword database; the "number of recommended products" is statistically analyzed by identifying recommended phrases and product links; and the "problem resolution rate" is determined by combining intent recognition results with the session's final state. The real-time alarm unit is configured to use a multi-level alarm mechanism. When the indicator exceeds the threshold, it first prompts the customer service interface. If the situation does not improve, it sends an in-app message to the customer service supervisor and finally escalates to sending an SMS or email to the system administrator.

3. The e-commerce customer service processing system with intelligent quality inspection and high-concurrency simulation capabilities according to claim 1, characterized in that, The high-concurrency stress simulation module includes: The business script library stores multiple preset test scripts that simulate real user behavior. Each script contains a sequence of "event-wait time" to simulate the continuous consultation behavior of real users. The distributed pressure generator consists of multiple computing nodes in the cluster. Each node can independently execute the business script and supports dynamic expansion. It is configured to dynamically add or remove computing nodes to achieve elastic scaling of concurrent pressure, so as to simulate the extreme access load under the peak consultation scenario of e-commerce. The traffic coloring and tracing unit is used to inject a unique tracing identifier into each simulated request and pass the identifier within the system, thereby fully reproducing the entire call path and performance bottleneck of a single high-concurrency request in the distributed log.

4. The e-commerce customer service processing system with intelligent quality inspection and high-concurrency simulation capabilities according to claim 1, characterized in that, The natural language processing unit in the intelligent speech evaluation module includes: The semantic understanding subunit uses a BERT-based pre-trained model for fine-tuning to identify the semantic roles of user inquiry intent and customer service responses; The script compliance check subunit compares customer service responses with the high-quality script library and the non-compliant script library in the script rule base based on regular expressions and semantic similarity calculations, and identifies cases of mechanical responses, irrelevant answers, and use of prohibited words; The multimodal sentiment analysis subunit is used to analyze the changing trends of users' emotions during the conversation and, in conjunction with the context, to evaluate whether the customer service's response effectively soothes the user's negative emotions or enhances the user's positive emotions.

5. The e-commerce customer service processing system with intelligent quality inspection and high-concurrency simulation capabilities according to claim 4, characterized in that, The system also includes: The dialect recognition and assistance subunit is communicatively connected to the customer service dialogue access module and the intelligent dialogue evaluation module. The dialect recognition and assistance subunit is configured to: firstly, determine whether the user's voice belongs to a preset dialect type through a speech recognition model; if so, convert it into standard text and attach a dialect tag; then, on the customer service interface, dynamically push standard phrases or polite expressions applicable to the dialect area based on the tag for customer service reference.

6. The e-commerce customer service processing system with intelligent quality inspection and high-concurrency simulation capabilities according to claim 5, characterized in that, The test scripts in the business script library simulate user behavior sequences including: entering a store, browsing products, inquiring about logistics, questioning prices, and ending the session, with each behavior associated with a set waiting time.

7. The e-commerce customer service processing system with intelligent quality inspection and high-concurrency simulation capabilities according to claim 2, characterized in that, The real-time alarm unit is configured to trigger an alarm if, for the "commitment fulfillment rate" metric, no corresponding fulfillment record is found in the system operation log within a preset time after a customer service representative makes a commitment.

8. The e-commerce customer service processing system with intelligent quality inspection and high-concurrency simulation capabilities according to claim 1, characterized in that, The data storage center is configured to use the session ID as a unique association key to associate and store indicator data from the real-time quality inspection engine, original dialogue records from the customer service dialogue access module, and dialogue evaluation results from the intelligent dialogue evaluation module, so as to support end-to-end data traceability and joint analysis based on a single session.

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