Internet of Things industry intelligent customer service supervision and control system based on artificial intelligence

Abstract

The invention discloses an Internet of Things industry intelligent customer service supervision and control system based on artificial intelligence, and belongs to the technical field of customer service supervision. Comprising an omni-channel intelligent access and intention understanding module, an AI intelligent center and complex decision module, an intelligent supervision and ethical regulation and control module, a man-machine cooperation and humanistic care module, a data-driven optimization and edge intelligent module and a security privacy and controllable treatment module, and the omni-channel intelligent access and intention understanding module is used for integrating multiple channels. Unified request distribution is achieved, data input by a user are analyzed through voice recognition, NLU natural language understanding and image recognition technologies, composite intentions are accurately captured, interaction strategies are dynamically adjusted in combination with device state data, user historical behaviors and real-time positions, and pacified talking skills or manual intervention are triggered through voiceprint / text emotion analysis. On the basis of realizing customer service supervision and regulation, all-channel fusion and intention accurate analysis can be realized, and ethical safety integrated design can be realized.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of customer service supervision, in particular to an intelligent customer service supervision and regulation system for the Internet of Things industry based on artificial intelligence. BACKGROUND

[0002] Artificial intelligence: it is a new technical science for researching and developing theories, methods, technologies and application systems for simulating, extending and expanding human intelligence. The Internet of Things refers to real-time collection of sound, light, heat, electricity, mechanics, chemistry, biology, position and other information of objects or processes that need to be monitored, connected and interacted through various information sensors, radio frequency identification technology, global positioning systems, infrared sensors, laser scanners and other devices and technologies, and connection of the objects and processes with people through various possible networks to realize intelligent perception, identification and management of the objects and processes.

[0003] Traditional customer service relies on a single channel and cannot integrate IoT device data and multi-modal input, which increases the deviation rate of intent recognition. The existing system lacks real-time bias detection and transparent explanation mechanism, increases the algorithm discrimination complaint rate, and privacy leakage events occur frequently. Most solutions rely on cloud processing, and the edge offline capability is weak. The device fault diagnosis delay is more than 5 seconds, which cannot meet the high real-time scene of airports / power grids, the context synchronization of human and AI collaboration is poor, which increases the user's repeated explanation rate when switching, and the cultural adaptability is poor, which leads to insufficient satisfaction of cross-border services. SUMMARY

[0004] 1. Technical problem to be solved In view of the problems in the prior art, the purpose of the present application is to provide an intelligent customer service supervision and regulation system for the Internet of Things industry based on artificial intelligence. The present application can realize full-channel integration and accurate intent analysis on the basis of customer service supervision and regulation, and can be designed with ethical safety integration.

[0005] 2. Technical solution

[0006] To solve the above problems, the present application adopts the following technical solution: The intelligent customer service supervision and regulation system for the Internet of Things industry based on artificial intelligence comprises a full-channel intelligent access and intent understanding module, an AI intelligent core and complex decision module, an intelligent supervision and ethical regulation module, a man-machine cooperation and humanistic care module, a data-driven optimization and edge intelligence module, and a safe privacy and controllable governance module. The full-channel intelligent access and intent understanding module is used for integrating multiple channels, realizing unified request distribution, analyzing user input data through voice recognition, NLU natural language understanding and image recognition technology, accurately capturing complex intent, combining device state data, user historical behavior and real-time location, dynamically adjusting interaction strategy, and triggering appeasement tactics or manual intervention through voiceprint / text emotion analysis; The AI intelligent core and complex decision module is used for processing non-standard problems based on industry customized large models, correlating IoT device data streams, realizing fault root cause analysis, automatically constructing industry knowledge network, supporting reverse reasoning and literature tracing; The intelligent supervision and ethical regulation module is used for real-time monitoring of dialogue content, detecting forbidden words and privacy leakage risks, automatically triggering interception, avoiding algorithm discrimination through bias detection algorithm and manual review channel, allowing users to request explanation of AI decision logic, and supporting complaint process.

[0007] The man-machine cooperation and humanistic care module is used for real-time recommendation of dialogue, customer portrait and historical work order, assisting manual rapid response, automatically transferring to manual and synchronizing dialogue context when AI cannot handle complex emotions, supporting multi-language / dialect processing, and dynamically adapting virtual digital person expression to user emotions; The data-driven optimization and edge intelligence module is used for predicting consultation peak and device failure rate based on historical data, dynamically allocating resources, and deploying lightweight models on device end to realize offline voice navigation and fault self-checking; The security and privacy and controllable governance module is used for device access requiring MQTT protocol+certificate two-way authentication, API gateway implementing dynamic authentication, supporting private deployment, enterprise independently controlling sensitive data, defending DDoS and replay attacks, and guaranteeing service continuity of airport / power grid critical facilities.

[0008] As a preferred scheme of the application, the full-channel intelligent access and intent understanding module comprises: The multi-source heterogeneous data access engine, dynamic protocol analysis module, edge data preprocessing module, cross-channel identity fusion module, federated learning identity verification module, intelligent routing and load balancing module, reinforcement learning dynamic routing module, multi-modal fusion intent recognition module, context awareness and multi-intent management module, emotion recognition and humanistic response module and cultural adaptability enhancement module; The multi-source heterogeneous data access engine is used for unified access of telephone, WeChat, email, APP and IoT device sensor full-channel requests, supports multi-modal input of text, voice, image and device state data, the dynamic protocol analysis module is used for supporting dynamic loading of HTTP / MQTT / CoAP protocols based on a microkernel architecture, real-time adaptation to different IoT device communication specifications, the edge data preprocessing module is used for deploying a spectral subtraction noise reduction algorithm on a device end, filtering sensor noise and improving transmission data quality, the cross-channel identity fusion module is used for realizing user cross-channel identity consistency authentication through voiceprint / facial biometric feature and device ID binding, the federated learning identity authentication module is used for cross-platform joint training of an identity recognition model under the premise of protecting privacy, avoiding centralized storage of sensitive data, the intelligent routing and load balancing module is used for distributing service resources according to consultation / fault alarm request types, offline / online device states and user value classification, the reinforcement learning dynamic routing module is used for taking service response time and user satisfaction as a reward function to optimize resource allocation strategies, the multi-modal fusion intent recognition module is used for collaborative analysis of text, voice, image and device data streams, and analysis of composite intents, the context awareness and multi-intent management module is used for processing multi-intent jumps in long conversations, the sentiment recognition and humanistic response module is used for identifying user emotional states and dynamically adjusting response strategies, and the cultural adaptability enhancement module is used for identifying regional cultural differences and adjusting interaction modes.

[0009] As a preferred scheme of the application, the multi-modal fusion intent recognition module comprises a multi-modal Transformer architecture, which comprises: Text processing: BERT model extracts keywords and identifies core demands in combination with dependency syntax analysis; Voice processing: Wav2Vec2.0 end-to-end speech recognition and LSTM+attention mechanism dialect adaptation model to improve dialect recognition accuracy; Image processing: YOLOv5 target detection+OCR text extraction to realize automatic diagnosis of product fault pictures; Cross-modal alignment: aligning image-text semantic spaces through a CLIP model to solve the problem of "image-text description conflict"; The algorithm architecture of the context awareness and multi-intent management module comprises: Short-term memory layer: GRU network tracks the current conversation state; Long-term memory layer: knowledge graph associates historical work orders and device data to predict potential demands; Multi-intent segmentation: based on a Seq2Seq model, composite sentences are disassembled into independent intent units for parallel processing; The algorithm architecture of the sentiment recognition and humanistic response module comprises: Multi-modal sentiment analysis: Speech emotion recognition: Use OpenSMILE toolkit to extract acoustic features, classify emotion types by SVM; Text sentiment analysis: RoBERTa model combined with industry sentiment dictionary; Ethical regulation mechanism: Set the anger index > 0.7 as the emotional response threshold to avoid algorithm apathy response.

[0010] As a preferred scheme of the present application, the AI intelligent core and the complex decision module include: a multi-modal fusion reasoning engine, a dynamic knowledge core, an ethical regulation engine, a distributed decision network and a self-adaptive optimization center; The multi-modal fusion reasoning engine is used for collaborative processing of text, speech, image and IoT device sensor data, realizing cross-modal intent understanding, the dynamic knowledge core is used for building industry knowledge graph, supporting causal reasoning in complex scenarios, the ethical regulation engine is used for real-time monitoring of decision fairness, avoiding algorithm discrimination, and guaranteeing user's right to know, the distributed decision network is used for balanced real-time and deep analysis through edge cloud collaborative computing, and the self-adaptive optimization center is used for dynamic adjustment of decision strategy based on user feedback and scene changes.

[0011] As a preferred scheme of the present application, the algorithm architecture used by the multi-modal fusion reasoning engine includes: Cross-modal alignment module algorithm: Based on the CLIP model, the image feature and the text description are combined with the Cross-Attention cross attention mechanism to realize the collaborative reasoning of the image feature and the text description; Heterogeneous data encoder algorithm: Using Transformer-XL long context encoding, supporting dialect recognition, using visual Transformer to extract spatial features, YOLOv5 to realize fault component detection, using TCN time convolution network to process device real-time stream data; The algorithm architecture used by the dynamic knowledge core includes: Entity extraction: Use BiLSTM-CRF model to identify device and fault type entities; Relationship reasoning: Use GNN graph neural network to learn the dependency relationship between nodes; RAG enhanced engine algorithm: Retrieve RAG enhanced generation architecture, associate ticket data and knowledge base in real time, and solve non-standard problems.

[0012] As a preferred scheme of the present application, the algorithm architecture used by the ethical regulation engine includes: Bias detection module algorithm: GAN adversarial neural network generates synthetic data, trains a fairness classifier, and identifies the response bias of sensitive groups; Transparent explanation module algorithm: LIME local interpretable model visualizes the decision path; The algorithm architecture used by the distributed decision network includes: Edge lightweight inference module algorithm: using knowledge distillation model compression technology, the BERT model is reduced to MobileBERT, deployed to the IoT device end to realize offline voice navigation; Cloud joint learning module algorithm: using federated learning to train the model across enterprises to protect data privacy; The adaptive optimization center algorithm architecture includes: Reinforcement learning optimizer algorithm: using PPO proximal policy optimization algorithm, taking user satisfaction as the reward function, optimizing the dialogue strategy; Digital twin simulator algorithm: based on the parallel computing framework of Amdahl's law, simulating 100,000+ IoT device access scenarios to test system robustness.

[0013] As a preferred scheme of the present application, the intelligent supervision and ethical regulation module includes: Ethical compliance monitoring submodule, algorithm fairness guarantee submodule, transparent decision submodule, privacy security protection submodule, ethical self-learning optimization submodule and human-computer collaborative governance submodule The ethical compliance monitoring submodule is used for real-time detection of compliance of dialogue content and device operation instructions, interception of discriminatory language, privacy leakage and illegal operation, the algorithm fairness guarantee submodule is used for eliminating algorithm bias and ensuring service fairness, the transparent decision submodule is used for explaining AI decision logic to users and ensuring the right to know, the privacy security protection submodule is used for preventing user data leakage and meeting GDPR / CCPA compliance requirements, the ethical self-learning optimization submodule is used for dynamically optimizing model behavior according to user feedback and ethical events, and the human-computer collaborative governance submodule is used for coordinating AI and human supervision responsibilities and handling ethical disputes.

[0014] As a preferred scheme of the present application, the human-computer collaboration and humanistic care module includes: emotional intelligent interaction submodule, human-computer collaboration governance submodule function, cultural adaptability engine, seat assistance and care submodule and closed-loop feedback and evolution mechanism; The emotional intelligent interaction submodule is used for real-time recognition of user emotional state, dynamic adjustment of response strategy, and generation of emotional reply, the human-computer collaboration governance submodule function is used for dynamic allocation of AI and human customer service responsibilities, ensuring seamless handover of high complexity tasks and realizing responsibility traceability, the cultural adaptability engine is used for identifying regional cultural differences, dialects and etiquette taboos, customizing interaction methods, and avoiding cultural conflicts, the seat assistance and care submodule is used for assisting human customer service to respond efficiently and ensuring the health of their working state, and the closed-loop feedback and evolution mechanism is used for iterating the model based on user feedback and ethical events, ensuring that the system continuously meets humanistic values.

[0015] As a preferred scheme of the present application, the data-driven optimization and edge intelligence module comprises: a real-time data acquisition and preprocessing submodule, a multi-modal sentiment analysis engine, a dynamic knowledge graph construction submodule, an intelligent recommendation and decision optimization submodule, a closed-loop evaluation and self-optimization submodule, an edge node lightweight deployment submodule, an edge cloud collaborative computing submodule, a privacy security computing submodule, and a resource dynamic scheduling submodule; The real-time data acquisition and preprocessing submodule is used for real-time acquisition, cleaning and standardization of multi-source heterogeneous data, providing a high-quality data foundation for subsequent analysis. The multi-modal sentiment analysis engine is used for real-time analysis of emotions in user voice and text, and dynamic adjustment of response strategies. The dynamic knowledge graph construction submodule is used for real-time extraction of entity relationships in user dialog, and updating of industry knowledge base to support complex queries. The intelligent recommendation and decision optimization submodule is used for generating personalized service plans based on user historical behavior and real-time intent. The closed-loop evaluation and self-optimization submodule is used for continuously iterating the model through user feedback to ensure service effectiveness and ethical compliance. The edge node lightweight deployment submodule is used for deploying compressed AI models to edge devices to achieve near-end low-latency inference. DynaBERT dynamic pruning adjusts model complexity according to device resource status. The edge cloud collaborative computing submodule is used for dynamically allocating edge and cloud computing tasks to balance real-time performance and computational complexity. The privacy security computing submodule is used for implementing data desensitization and encryption at the edge to meet GDPR / CCPA compliance requirements. The resource dynamic scheduling submodule is used for optimizing edge device computing resource allocation to cope with high-concurrency scenarios.

[0016] As a preferred scheme of the present application, the safe privacy and controllable governance module comprises: The privacy compliance monitoring submodule is used for real-time detection of compliance in the whole process of data collection, storage and processing to ensure compliance with GDPR, CCPA and other regulations, and to intercept illegal operations. The data security protection submodule is used for ensuring data security throughout its life cycle to prevent leakage, tampering and unauthorized access. The algorithm transparency and controllable governance submodule is used for ensuring that algorithmic decisions are explainable and fair to avoid black-box discrimination and support responsibility tracing. The ethical risk assessment submodule is used for actively identifying potential ethical risks of privacy protection and algorithm application, and developing prevention strategies. The cross-domain collaborative governance submodule is used for coordinating the interests of enterprises, users and regulatory authorities to achieve a balance between data sharing and privacy protection.

[0017] 3. Beneficial effects

[0018] Compared with the prior art, the application has the advantages that: (1) The application improves the intention recognition accuracy by integrating multi-modal input, combining context and user historical behavior, solves the response delay problem caused by channel fragmentation in traditional customer service, monitors privacy leakage and algorithm discrimination in real time, guarantees service fairness through automatic interception and manual review dual mechanism, meets the GDPR / CCPA compliance requirements, and avoids the risk of data abuse of traditional systems.

[0019] (2) The application improves the customer service response speed through full-channel integration and AI hub cooperation, reduces the human cost, dynamically routes and schedules edge computing resources to effectively deal with the consultation peak, improves the fault prediction accuracy, avoids the response delay caused by the resource allocation rigidity of traditional systems, reduces the risk of violation through real-time privacy monitoring and bias interception mechanism, and improves the user satisfaction through emotion recognition and humanistic response. BRIEF DESCRIPTION OF DRAWINGS

[0020] Figure 1 It is a module schematic diagram of the intelligent customer service supervision and regulation system based on artificial intelligence of the Internet of Things industry of the application. DETAILED DESCRIPTION

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

[0022] Embodiment:

[0023] Please refer to Figure 1 , the intelligent customer service supervision and regulation system based on artificial intelligence of the Internet of Things industry, comprising: a full-channel intelligent access and intention understanding module, an AI intelligent hub and complex decision module, an intelligent supervision and ethical regulation module, a man-machine cooperation and humanistic care module, a data-driven optimization and edge intelligence module, and a security privacy and controllable governance module; The full-channel intelligent access and intention understanding module is used for integrating multiple channels to realize unified request distribution, analyzing the data input by the user through voice recognition, NLU natural language understanding and image recognition technology, accurately capturing complex intentions, combining device state data, user historical behavior and real-time location, dynamically adjusting the interaction strategy, and triggering soothing rhetoric or manual intervention through voiceprint / text emotion analysis; The AI intelligent hub and complex decision module is used for processing non-standard problems based on industry customized large models, associating IoT device data streams, realizing fault root cause analysis, automatically building an industry knowledge network, and supporting reverse reasoning and literature tracing; The intelligent supervision and ethical regulation module is used for real-time monitoring of conversation content, detection of forbidden words and privacy leakage risks, automatic triggering of interception, avoidance of algorithm discrimination through bias detection algorithms and manual review channels, user request for explanation of AI decision logic, and support for complaint procedures.

[0024] The human-computer collaboration and humanistic care module is used for real-time recommendation of dialogues, customer portraits and historical work orders, auxiliary artificial rapid response, automatic transfer to artificial work and synchronization of conversation context when AI cannot handle complex emotions, and support for multi-language / dialect processing and dynamic adaptation of virtual digital human expressions to user emotions. The data-driven optimization and edge intelligence module is used for prediction of consultation peaks and equipment failure rates based on historical data, dynamic allocation of resources, and deployment of lightweight models on the device side to realize offline voice navigation and fault self-checking. The security and privacy and controllable governance module is used for device access requiring MQTT protocol + certificate two-way authentication, dynamic authentication of API gateways, support for private deployment, enterprise self-control of sensitive data, defense against DDoS and replay attacks, and guarantee of service continuity of airport / power grid critical facilities.

[0025] In specific embodiments of the present application, by integrating multi-modal input, combining context and user historical behavior to improve intent recognition accuracy, solving the response delay problem caused by channel fragmentation in traditional customer service, real-time monitoring of privacy leakage and algorithm discrimination, through automatic interception and manual review dual mechanism to guarantee service fairness, meeting GDPR / CCPA compliance requirements, avoiding the risk of data abuse in traditional systems, through full-channel integration and AI hub collaboration, the system improves customer service response speed, reduces labor costs, dynamically routes and schedules edge computing resources to effectively cope with consultation peaks, improves fault prediction accuracy, avoids response delays caused by resource allocation rigidity in traditional systems, real-time privacy monitoring and bias interception mechanism reduces the risk of violations, emotion recognition and humanistic response improve user satisfaction.

[0026] Specifically, the full-channel intelligent access and intent understanding module includes: a multi-source heterogeneous data access engine, a dynamic protocol analysis module, an edge data preprocessing module, a cross-channel identity fusion module, a federated learning identity verification module, an intelligent routing and load balancing module, a reinforcement learning dynamic routing module, a multi-modal fusion intent recognition module, a context awareness and multi-intent management module, an emotion recognition and humanistic response module, and a cultural adaptability enhancement module. The multi-source heterogeneous data access engine unifies access to requests from various channels including phone, WeChat, email, apps, and IoT device sensors. It supports multimodal input of text, voice, image, and device status data. The dynamic protocol parsing module, based on a microkernel architecture, supports dynamic loading of HTTP / MQTT / CoAP protocols and adapts to different IoT device communication standards in real time. The edge data preprocessing module deploys spectral subtraction noise reduction algorithms on the device side to filter sensor noise and improve the quality of transmitted data. The cross-channel identity fusion module binds voiceprint / facial biometrics with device IDs to achieve consistent user identity authentication across channels. The federated learning authentication module enables cross-platform authentication while protecting privacy. The system employs a joint training identity recognition model to avoid centralized storage of sensitive data. An intelligent routing and load balancing module allocates service resources based on consultation / fault alarm request type, offline / online device status, and user value. A reinforcement learning dynamic routing module optimizes resource allocation strategies using service response time and user satisfaction as reward functions. A multimodal fusion intent recognition module collaboratively analyzes text, voice, image, and device data streams to parse complex intents. A context-aware and multi-intent management module handles multi-intent jumps in long dialogues. An emotion recognition and humanistic response module identifies user emotional states and dynamically adjusts response strategies. A cultural adaptability enhancement module identifies regional cultural differences and adjusts interaction methods accordingly. The formula for the spectral subtraction noise reduction algorithm in the edge data preprocessing module is as follows: ,in The amplitude spectrum of the noisy speech at the k-th frequency point in the n-th frame is given. The amplitude spectrum of the noise is estimated. This is an over-subtraction factor used to enhance noise suppression. This is the lower limit threshold for the spectrum, to prevent negative values ​​from generating musical noise. The formula for voiceprint recognition calculation in the cross-channel identity fusion module is as follows: ,in Let K be the MFCC coefficient of the t-th frame, and K be the difference order 2 to enhance the temporal dynamic characteristics. The federated learning gradient aggregation formula for the cross-channel identity fusion module is as follows: Where K is the number of clients participating in the training. For the local model parameters of the kth client, For the global model parameters, the weighted average aggregation formula is: ,in Let k be the amount of data from the kth client. N is the total amount of data from all clients. For the local model parameters trained by the Kth client in the t-th round, homomorphic encryption is used to encrypt the gradients before aggregation. The formula is as follows: , The encrypted gradient of the client k is decrypted by the aggregation server to obtain the global gradient ; The intelligent routing and reinforcement learning dynamic routing reward function design formula is: , wherein is a network performance index, is an index normalization or conversion function, is a weight coefficient, reflecting the importance of each index in the optimization target.

[0027] In specific embodiments of the present application, the multi-source heterogeneous data access engine uniformly processes full-channel requests such as telephone and WeChat, the dynamic protocol analysis module supports HTTP / MQTT / CoAP protocol to adapt to different IoT devices, the edge data preprocessing adopts spectral subtraction to reduce noise and improve data quality, cross-channel identity fusion realizes identity consistency by binding device ID through voiceprint / facial recognition, the federated learning identity verification model avoids centralized storage of sensitive data, the intelligent routing module allocates resources according to request types, the reinforcement learning dynamic routing optimizes strategies in response time and satisfaction, the multi-modal fusion intent recognition cooperatively analyzes text, voice and image, the context awareness module manages long conversation multi-intent jumping, the sentiment recognition module triggers emotional appeasement strategies, the cultural adaptability module adjusts interaction methods to avoid conflicts, solves the problem of cross-channel identity split, improves dialect recognition accuracy, and reduces response delay through dynamic routing.

[0028] Specifically, the multi-modal fusion intent recognition module has a multi-modal Transformer architecture including: Text processing: BERT model extracts keywords and identifies core demands in combination with dependency syntax analysis; Voice processing: Wav2Vec2.0 end-to-end speech recognition, LSTM+attention mechanism dialect adaptation model, improves dialect recognition accuracy; Image processing: YOLOv5 target detection + OCR text extraction, realizes automatic diagnosis of product fault pictures; Cross-modal alignment: Aligning the semantic space of text, images and speech through the CLIP model solves the problem of "text-image description conflict"; The algorithm architecture of the context awareness and multi-intent management module includes: Short-term memory layer: GRU network tracks the current conversation state; Long-term memory layer: Knowledge graph associates historical work orders and device data to predict potential needs; Multi-intent segmentation: Based on the Seq2Seq model, complex sentences are disassembled into independent intent units for parallel processing; The algorithm architecture of the sentiment recognition and humanistic response module includes: Multimodal sentiment analysis: Speech emotion recognition: extract acoustic features using OpenSMILE toolkit, classify emotion types through SVM; Text sentiment analysis: RoBERTa model combined with industry sentiment dictionary; Ethical regulation mechanism: set the anger index > 0.7 as the emotional response threshold to avoid algorithm apathy response.

[0029] In specific embodiments of the application, text processing uses BERT model to extract keywords and dependency syntax analysis, speech processing uses Wav2Vec2.0 and LSTM dialect adaptation model, image processing combines YOLOv5 target detection and OCR text extraction to realize fault diagnosis, cross-modal alignment uses CLIP model to solve text-image semantic conflict, context management uses GRU network to track dialogue state, knowledge graph correlates historical work order to predict demand, Seq2Seq model splits complex intent and processes in parallel, sentiment recognition uses OpenSMILE to extract acoustic features + SVM classification, text sentiment analysis uses RoBERTa model, sets the anger index threshold to trigger human response, multi-modal collaboration improves fault diagnosis accuracy, and sentiment recognition response speed is improved.

[0030] Specifically, the AI intelligent core and complex decision module include: multimodal fusion reasoning engine, dynamic knowledge core, ethical regulation engine, distributed decision network and adaptive optimization center; The multimodal fusion reasoning engine is used for collaborative processing of text, speech, image and IoT device sensor data, realizing cross-modal intent understanding, the dynamic knowledge core is used for building industry knowledge graph, supporting causal reasoning in complex scenarios, the ethical regulation engine is used for real-time monitoring of decision fairness, avoiding algorithm discrimination, and guaranteeing user's right to know, the distributed decision network is used for edge cloud collaborative computing, balancing real-time and deep analysis, and the adaptive optimization center is used for dynamically adjusting decision strategy based on user feedback and scene changes.

[0031] In specific embodiments of the application, the multi-modal fusion reasoning engine aligns the image-text semantics through the Cross-Attention mechanism, the heterogeneous data encoder uses the Transformer-XL to process long context, the visual Transformer to extract spatial features, and the TCN to process device time series data, the dynamic knowledge hub extracts entities through the BiLSTM-CRF, the GNN learns node relationships, the RAG architecture correlates work orders with the knowledge base to solve non-standard problems, the ethical regulation engine trains a fairness classifier through the GAN, the transparent explanation module visualizes the decision path with LIME, the distributed decision network realizes offline voice navigation through the edge MobileBERT, the federated learning protects data privacy, the adaptive optimization center optimizes the dialogue strategy with the PPO algorithm, the digital twin simulates 100,000+ device concurrent scenarios, the RAG engine improves the non-standard problem solving rate, and the federated learning reduces the risk of data leakage.

[0032] Specifically, the algorithm architecture used by the multi-modal fusion reasoning engine includes: Cross-modal alignment module algorithm: image-text semantic alignment technology based on the CLIP model, which realizes collaborative reasoning of image features and text descriptions through the Cross-Attention mechanism; Heterogeneous data encoder algorithm: uses Transformer-XL long context encoding, supports dialect recognition, uses visual Transformer to extract spatial features, YOLOv5 to realize fault component detection, and TCN time series convolution network to process device real-time stream data; The algorithm architecture used by the dynamic knowledge hub includes: Entity extraction: uses the BiLSTM-CRF model to identify device and fault type entities; Relationship reasoning: uses GNN graph neural network to learn node dependency relationships; RAG enhanced engine algorithm: retrieves the RAG enhanced generation architecture, correlates work order data with the knowledge base in real time, and solves non-standard problems.

[0033] In specific embodiments of the application, cross-modal alignment uses the CLIP model combined with Cross-Attention to realize collaborative reasoning of image features and text descriptions, the heterogeneous data encoder uses Transformer-XL to support dialect recognition, visual Transformer to extract spatial features, and TCN to process device real-time stream data, the dynamic knowledge hub identifies device fault entities through BiLSTM-CRF, GNN learns node dependency relationships, RAG architecture retrieves work order data and the knowledge base in real time, TCN time series convolution improves the efficiency of device stream data analysis, and GNN enhances the fault root cause inference capability.

[0034] Specifically, the algorithm architecture used by the ethical regulation engine includes: Bias detection module algorithm: GAN generates synthetic data to train a fairness classifier to identify response bias of sensitive groups; Transparent explanation module algorithm: LIME local interpretable model visualizes decision path; The algorithm architecture used by the distributed decision network includes: Edge lightweight inference module algorithm: using knowledge distillation model compression technology, BERT model is reduced to MobileBERT, deployed to IoT device end to realize offline voice navigation; Cloud joint learning module algorithm: using federated learning to train models across enterprises while protecting data privacy; The algorithm architecture of the adaptive optimization center includes: Reinforcement learning optimizer algorithm: using PPO proximal policy optimization algorithm to optimize dialogue strategy with user satisfaction as reward function; Digital twin simulator algorithm: based on Amdahl's law parallel computing framework, simulates 100,000+ IoT device access scenarios to test system robustness.

[0035] In specific embodiments of the present application, the bias detection module generates synthetic data through GAN to train a fairness classifier, the transparent explanation module uses LIME to provide visualization of decision path, the edge deployment of MobileBERT realizes offline voice navigation, the cloud federated learning trains models across enterprises, the adaptive optimization center optimizes strategy through PPO algorithm with user satisfaction, and the digital twin simulator tests high concurrency scenarios based on Amdahl's law. LIME explanation model improves user trust, and edge MobileBERT reduces delay to 200ms.

[0036] Specifically, the intelligent supervision and ethical regulation module includes: Ethical compliance monitoring submodule, algorithm fairness guarantee submodule, transparent decision submodule, privacy security protection submodule, ethical self-learning optimization submodule, and human-machine collaborative governance submodule The ethical compliance monitoring submodule is used to detect the compliance of dialogue content and device operation instructions in real time, intercept discriminatory language, privacy leakage and illegal operations, the algorithm fairness guarantee submodule is used to eliminate algorithm bias and ensure service fairness, the transparent decision submodule is used to explain AI decision logic to users and protect the right to know, the privacy security protection submodule is used to prevent user data leakage and meet GDPR / CCPA compliance requirements, the ethical self-learning optimization submodule is used to dynamically optimize model behavior according to user feedback and ethical events, and the human-machine collaborative governance submodule is used to coordinate AI and human supervision responsibilities and handle ethical disputes.

[0037] In specific embodiments of the application, the ethical compliance monitoring submodule detects the conversation content and device instructions in real time, intercepts discriminatory language, the algorithm fairness guarantee submodule eliminates response bias of sensitive groups, the transparent decision-making submodule explains the AI logic to the user, the privacy security protection submodule meets the requirements of GDPR / CCPA, the ethical self-learning submodule dynamically optimizes according to feedback, the human-machine collaborative governance submodule handles ethical disputes, real-time intercepts privacy leakage risks, and reduces the misjudgment rate of the artificial review channel.

[0038] Specifically, the human-computer cooperation and humanistic care module includes: an emotional intelligent interaction submodule, a human-computer cooperation governance submodule function, a cultural adaptability engine, a seat assistance and care submodule, and a closed-loop feedback and evolution mechanism. The emotional intelligent interaction submodule is used for real-time recognition of user emotional state, dynamic adjustment of response strategy, and generation of emotional reply, the human-computer cooperation governance submodule function is used for dynamic allocation of AI and artificial customer service responsibilities, ensuring seamless handover of high complexity tasks, and realizing responsibility traceability, the cultural adaptability engine is used for identifying regional cultural differences, dialects and etiquette taboos, customizing interaction methods, and avoiding cultural conflicts, the seat assistance and care submodule is used for assisting artificial customer service to respond efficiently and ensuring the health of its working state, and the closed-loop feedback and evolution mechanism is used for iteration based on user feedback and ethical event models, ensuring that the system continuously meets humanistic values.

[0039] In specific embodiments of the application, the emotional intelligent interaction submodule dynamically generates emotional replies, the human-computer cooperation governance submodule realizes high complexity task handover and responsibility traceability, the cultural adaptability engine identifies dialects and etiquette taboos, the seat assistance submodule provides customer portraits and historical work orders, the closed-loop feedback mechanism iteratively optimizes the compliance of humanistic values, the virtual digital person expression adaptation improves satisfaction and reduces cultural conflict rate.

[0040] Specifically, the data-driven optimization and edge intelligence module includes: a real-time data acquisition and preprocessing submodule, a multi-modal emotion analysis engine, a dynamic knowledge graph construction submodule, an intelligent recommendation and decision optimization submodule, a closed-loop evaluation and self-optimization submodule, an edge node lightweight deployment submodule, an edge cloud collaborative computing submodule, a privacy security computing submodule, and a resource dynamic scheduling submodule. The real-time data acquisition and preprocessing submodule is used for real-time acquisition, cleaning and standardization of multi-source heterogeneous data, providing a high-quality data basis for subsequent analysis, the multi-modal sentiment analysis engine is used for real-time analysis of emotions in user voice and text, dynamically adjusting the response strategy, the dynamic knowledge graph construction submodule is used for real-time extraction of entity relationships in user dialogue, updating the industry knowledge base to support complex queries, the intelligent recommendation and decision optimization submodule is used for generating personalized service schemes based on user historical behavior and real-time intent, the closed-loop evaluation and self-optimization submodule is used for continuously iterating the model through user feedback to ensure service effectiveness and ethical compliance, the edge node lightweight deployment submodule is used for deploying the AI model to edge devices after compression, realizing near-end low-latency inference, the DynaBERT dynamic pruning adjusts the model complexity according to the device resource state, the edge cloud collaborative computing submodule is used for dynamically allocating edge and cloud computing tasks, balancing real-time performance and computational complexity, the privacy and security computing submodule is used for realizing data desensitization and encryption on the edge to meet GDPR / CCPA compliance requirements, and the resource dynamic scheduling submodule is used for optimizing edge device computing resource allocation to cope with high-concurrency scenarios.

[0041] In specific embodiments of the application, the real-time data acquisition submodule cleans multi-source heterogeneous data, the multi-modal sentiment analysis engine dynamically adjusts the response, the dynamic knowledge graph construction submodule updates the industry knowledge base, the edge node deploys the DynaBERT dynamic pruning model to adapt to device resources, the edge cloud collaborates to balance the computing load, the privacy and security computing realizes data desensitization, the edge offline voice navigation saves bandwidth, and the DynaBERT dynamic pruning improves resource utilization.

[0042] Specifically, the safe privacy and controllable governance module includes: a privacy compliance monitoring submodule, a data security protection submodule, an algorithm transparency and controllable governance submodule, an ethical risk assessment submodule, and a cross-domain collaborative governance submodule. The privacy compliance monitoring submodule is used for real-time detection of compliance in the whole process of data collection, storage and processing, to ensure compliance with GDPR, CCPA and other regulations, and to intercept illegal operations, the data security protection submodule is used to protect data security throughout its life cycle, to prevent leakage, tampering and unauthorized access, the algorithm transparency and controllable governance submodule is used to ensure that algorithmic decisions are explainable and fair, to avoid black-box discrimination, and to support responsibility tracing, the ethical risk assessment submodule is used to actively identify potential ethical risks in privacy protection and algorithm application, and to develop prevention strategies, and the cross-domain collaborative governance submodule is used to coordinate the interests of enterprises, users and regulatory authorities, to achieve a balance between data sharing and privacy protection.

[0043] In specific embodiments of the application, the privacy compliance monitoring submodule ensures that the data complies with GDPR / CCPA throughout the entire process, the data security protection submodule prevents leakage and unauthorized access, the algorithm transparent governance submodule supports responsibility tracing, the ethical risk assessment submodule formulates prevention strategies, the cross-domain collaborative governance submodule balances data sharing and privacy protection, the MQTT two-way authentication resists replay attacks, and the private deployment meets the needs of high-security scenarios such as power grids.

[0044] The above is only a preferred specific embodiment of the present application, but the protection scope of the present application is not limited thereto, any person skilled in the art can make equivalent replacements or changes within the technical range disclosed by the present application according to the technical solution and improvement concept of the present application, which should be covered within the protection scope of the present application.

Claims

1. An artificial intelligence-based Internet of Things industry intelligent customer service supervision and regulation system, characterized in that, Comprise: Full-channel intelligent access and intent understanding module, AI intelligent hub and complex decision module, intelligent supervision and ethical regulation module, man-machine cooperation and humanistic care module, data-driven optimization and edge intelligence module, and safe privacy and controllable governance module; The full-channel intelligent access and intent understanding module is used for integrating multiple channels to realize unified request distribution, analyzing user input data through voice recognition, NLU natural language understanding and image recognition technology, accurately capturing complex intentions, combining device state data, user historical behavior and real-time location, dynamically adjusting interaction strategies, and triggering soothing rhetoric or manual intervention through voiceprint / text emotion analysis; The AI intelligent hub and complex decision module is used for processing non-standard problems based on industry customized large models, correlating IoT device data streams, realizing fault root cause analysis, automatically constructing industry knowledge network, supporting reverse reasoning and literature tracing; The intelligent supervision and ethical regulation module is used for real-time monitoring of dialogue content, detecting forbidden words and privacy leakage risks, automatically triggering interception, avoiding algorithm discrimination through bias detection algorithm and manual review channel, allowing users to request explanation of AI decision logic, and supporting complaint process; The man-machine cooperation and humanistic care module is used for real-time recommendation of rhetoric, customer portrait and historical work order, assisting manual quick response, automatically transferring to manual and synchronizing dialogue context when AI cannot handle complex emotions, supporting multi-language / dialect processing, and dynamically adapting virtual digital person expressions to user emotions; The data-driven optimization and edge intelligence module is used for predicting consultation peak and device failure rate based on historical data, dynamically allocating resources, and deploying lightweight models on device end to realize offline voice navigation and fault self-checking; The safe privacy and controllable governance module is used for device access requiring MQTT protocol + certificate two-way authentication, API gateway implementing dynamic authentication, supporting private deployment, enterprise self-control of sensitive data, defense against DDoS and replay attacks, and guaranteeing airport / power grid critical facility service continuity. 2.The AI-based IoT industry intelligent customer service supervision and regulation system according to claim 1, characterized in that, The full-channel intelligent access and intent understanding module comprises: Multi-source heterogeneous data access engine, dynamic protocol analysis module, edge data preprocessing module, cross-channel identity fusion module, federated learning identity verification module, intelligent routing and load balancing module, reinforcement learning dynamic routing module, multi-modal fusion intent recognition module, context awareness and multi-intent management module, emotion recognition and humanistic response module, and cultural adaptability enhancement module; The multi-source heterogeneous data access engine is used for unified access of telephone, WeChat, email, APP and IoT device sensor full-channel requests, supports multi-modal input of text, voice, image and device state data, the dynamic protocol analysis module is used for supporting dynamic loading of HTTP / MQTT / CoAP protocols based on a microkernel architecture, real-time adaptation to different IoT device communication specifications, the edge data preprocessing module is used for deploying a spectral subtraction noise reduction algorithm on a device end, filtering sensor noise and improving transmission data quality, the cross-channel identity fusion module is used for realizing user cross-channel identity consistency authentication through voiceprint / facial biometric feature and device ID binding, the federated learning identity authentication module is used for cross-platform joint training of an identity recognition model under the premise of protecting privacy, avoiding centralized storage of sensitive data, the intelligent routing and load balancing module is used for distributing service resources according to consultation / fault alarm request types, offline / online device states and user value classification, the reinforcement learning dynamic routing module is used for taking service response time and user satisfaction as a reward function to optimize resource allocation strategies, the multi-modal fusion intent recognition module is used for collaborative analysis of text, voice, image and device data streams, and analysis of a composite intent, the context awareness and multi-intent management module is used for processing multi-intent jumping in a long conversation, the sentiment recognition and humanistic response module is used for identifying user emotional states and dynamically adjusting response strategies, and the cultural adaptability enhancement module is used for identifying regional cultural differences and adjusting interaction modes. 3.The AI-based IoT industry intelligent customer service supervision and regulation system according to claim 2, characterized in that, The multi-modal fusion intent recognition module has a multi-modal Transformer architecture, which comprises the following: Text processing: a BERT model extracts keywords and identifies core demands in combination with dependency syntax analysis; Voice processing: a Wav2Vec2.0 end-to-end speech recognition and a LSTM+attention mechanism dialect adaptation model are used to improve dialect recognition accuracy; Image processing: a YOLOv5 target detection+OCR text extraction is used to realize automatic diagnosis of product fault pictures; Cross-modal alignment: a CLIP model is used to align the semantic spaces of text, images and voices to solve the "text-image description conflict" problem; The context awareness and multi-intent management module has an algorithm architecture, which comprises the following: Short-term memory layer: a GRU network tracks the current conversation state; Long-term memory layer: a knowledge graph is used to associate historical work orders and device data to predict potential demands; Multi-intent segmentation: a Seq2Seq model is used to decompose a composite sentence into independent intent units for parallel processing; The sentiment recognition and humanistic response module has an algorithm architecture, which comprises the following: Multi-modal sentiment analysis: voice sentiment recognition: acoustic features are extracted using an OpenSMILE toolkit, and emotion types are classified by an SVM; Text sentiment analysis: a RoBERTa model is combined with an industry sentiment dictionary; Ethical regulation mechanism: an anger index>0.7 is set as a sentiment response threshold to avoid algorithmic apathetic responses.

4. The artificial intelligence-based Internet of Things industry intelligent customer service supervision regulation and control system according to claim 3, characterized in that, The AI intelligent core and complex decision module comprises a multi-modal fusion reasoning engine, a dynamic knowledge core, an ethical regulation engine, a distributed decision network and a self-adaptive optimization center. The multi-modal fusion reasoning engine is used for cooperative processing of text, voice, image and IoT device sensor data, realizes cross-modal intent understanding, the dynamic knowledge hub is used for building an industry knowledge graph, supports causal reasoning in complex scenarios, the ethical regulation engine is used for real-time monitoring of decision fairness, avoiding algorithm discrimination, and guaranteeing user's right to know, the distributed decision network is used for cooperative calculation through edge cloud, balancing real-time and deep analysis, and the adaptive optimization center is used for dynamically adjusting decision strategy based on user feedback and scene changes. 5.The AI-based IoT industry intelligent customer service supervision regulation system of claim 4, wherein, The algorithm architecture used by the multi-modal fusion reasoning engine includes: Cross-modal alignment module algorithm: based on CLIP model's image-text semantic alignment technology, combined with Cross-Attention cross-attention mechanism to realize cooperative reasoning of image features and text description; Heterogeneous data encoder algorithm: using Transformer-XL long context encoding, supporting dialect recognition, using visual Transformer to extract spatial features, YOLOv5 to realize fault component detection, using TCN time convolution network to process device real-time stream data; The algorithm architecture used by the dynamic knowledge hub includes: Entity extraction: using BiLSTM-CRF model to identify device and fault type entities; Relationship reasoning: using GNN graph neural network to learn the dependency relationship between nodes; RAG enhancement engine algorithm: retrieve RAG enhancement generation architecture, associate work order data and knowledge base in real time, and solve non-standard problems. 6.The AI-based IoT industry intelligent customer service supervision regulation system according to claim 5, characterized in that, The algorithm architecture used by the ethical regulation engine includes: Bias detection module algorithm: GAN adversarial neural network generates synthetic data, trains fairness classifier, and identifies response bias of sensitive groups; Transparent explanation module algorithm: LIME local interpretable model visualizes decision path; The algorithm architecture used by the distributed decision network includes: Edge light reasoning module algorithm: using knowledge distillation model compression technology, reducing BERT model to MobileBERT, deploying to IoT device end to realize offline voice navigation; Cloud joint learning module algorithm: using federated learning to train models across enterprises, protecting data privacy; The adaptive optimization center algorithm architecture includes: Reinforcement learning optimizer algorithm: using PPO proximal policy optimization algorithm, taking user satisfaction as reward function, optimizing dialogue strategy; Digital twin simulator algorithm: based on Amdahl's law parallel computing framework, simulating 100,000+ IoT device access scenarios, testing system robustness. 7.The AI-based IoT industry intelligent customer service supervision regulation system of claim 6, wherein, The intelligent supervision and ethical regulation module includes: Ethical compliance monitoring submodule, algorithm fairness guarantee submodule, transparent decision submodule, privacy security protection submodule, ethical self-learning optimization submodule and human-computer collaborative governance submodule The ethical compliance monitoring submodule is used for real-time detection of compliance of conversation content and device operation instructions, and interception of discriminatory language, privacy leakage and violation of operation, the algorithm fairness guarantee submodule is used for eliminating algorithm bias and ensuring service fairness, the transparent decision submodule is used for explaining AI decision logic to users and guaranteeing informed right, the privacy security protection submodule is used for preventing user data leakage and meeting GDPR / CCPA compliance requirements, the ethical self-learning optimization submodule is used for dynamically optimizing model behavior according to user feedback and ethical events, and the human-computer collaborative governance submodule is used for coordinating AI and human supervision responsibilities and processing ethical disputes. 8.The AI-based IoT industry intelligent customer service supervision regulation system of claim 7, wherein, The human-computer collaboration and humanistic care module comprises an emotional intelligent interaction submodule, a human-computer collaboration governance submodule function, a cultural adaptability engine, a seat assistance and care submodule and a closed-loop feedback and evolution mechanism. The emotional intelligent interaction submodule is used for real-time recognition of user emotional state, dynamic adjustment of response strategy and generation of emotional reply, the human-computer collaboration governance submodule function is used for dynamic allocation of AI and manual customer service responsibilities, ensuring seamless handover of high complexity tasks and realizing responsibility traceability, the cultural adaptability engine is used for recognizing regional cultural differences, dialects and etiquette taboos, customizing interaction mode and avoiding cultural conflicts, the seat assistance and care submodule is used for assisting manual customer service to respond efficiently and guaranteeing the health of working state, and the closed-loop feedback and evolution mechanism is used for iterating the model based on user feedback and ethical events, ensuring that the system continuously meets humanistic values. 9.The AI-based IoT industry intelligent customer service supervision regulation system of claim 8, wherein, The data-driven optimization and edge intelligence module comprises: a real-time data acquisition and preprocessing submodule, a multi-modal emotion analysis engine, a dynamic knowledge graph construction submodule, an intelligent recommendation and decision optimization submodule, a closed-loop evaluation and self-optimization submodule, an edge node lightweight deployment submodule, an edge cloud collaborative computing submodule, a privacy security computing submodule and a resource dynamic scheduling submodule; The real-time data acquisition and preprocessing submodule is used for real-time acquisition, cleaning and standardization of multi-source heterogeneous data, providing a high-quality data foundation for subsequent analysis. The multi-modal sentiment analysis engine is used for real-time analysis of emotions in user voice and text, dynamically adjusting the response strategy. The dynamic knowledge graph construction submodule is used for real-time extraction of entity relationships in user dialogue, updating the industry knowledge base to support complex queries. The intelligent recommendation and decision optimization submodule is used to generate personalized service plans based on user historical behavior and real-time intent. The closed-loop evaluation and self-optimization submodule is used to continuously iterate the model through user feedback, ensuring service effectiveness and ethical compliance. The edge node lightweight deployment submodule is used to compress and deploy AI models to edge devices, enabling near-end low-latency inference. DynaBERT dynamic pruning adjusts model complexity according to device resource status. The edge cloud collaborative computing submodule is used to dynamically allocate edge and cloud computing tasks, balancing real-time performance and computational complexity. The privacy and security computing submodule is used to implement data desensitization and encryption on the edge, meeting GDPR / CCPA compliance requirements. The resource dynamic scheduling submodule is used to optimize edge device computing resource allocation and handle high-concurrency scenarios. 10.The AI-based IoT industry intelligent customer service supervision regulation system of claim 9, wherein, The security, privacy and controllable governance module includes a privacy compliance monitoring submodule, a data security protection submodule, an algorithm transparency and controllable governance submodule, an ethical risk assessment submodule and a cross-domain collaborative governance submodule. The privacy compliance monitoring submodule is used to detect compliance in data collection, storage and processing in real time, ensuring compliance with GDPR, CCPA and other regulations, and intercepting illegal operations. The data security protection submodule is used to ensure data security throughout its life cycle, preventing leaks, tampering and unauthorized access. The algorithm transparency and controllable governance submodule is used to ensure that algorithmic decisions are explainable and fair, avoiding black-box discrimination and supporting accountability. The ethical risk assessment submodule is used to proactively identify potential ethical risks in privacy protection and algorithm application, and develop prevention strategies. The cross-domain collaborative governance submodule is used to coordinate the interests of enterprises, users and regulatory authorities, achieving a balance between data sharing and privacy protection.

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