4252 results about "Learning methods" patented technology

The invention discloses a predictive maintenance method for an optical storage and charging integrated power station based on deep learning, and the method comprises the steps: constructing an efficient equipment state evaluation and prediction model based on multi-source data fusion, an intelligent prediction algorithm and a closed-loop optimization feedback mechanism, collecting multi-source data, and carrying out the fusion processing, an improved Attention-LSTM model is utilized to evaluate and predict the state of equipment, a transfer learning method is adopted to improve generalization ability, Bayesian optimization and an adaptive sliding window technology are combined at the same time, dynamic threshold adjustment is performed, a deep reinforcement learning algorithm based on a Markov decision process is adopted to optimize a maintenance strategy, and the maintenance efficiency is improved. Weibull distribution is introduced for failure probability modeling, the maintenance cost and the fault risk are balanced, continuous optimization and dynamic adaptive adjustment of a predictive maintenance scheme are realized through a closed-loop feedback mechanism, the prediction accuracy and the intelligent level of maintenance decision are remarkably improved, planned maintenance and sudden fault maintenance are reduced, and the maintenance efficiency is improved. And the reliability of the charging station is improved.

A method is provided for improving image classification accuracy in few-shot learning scenarios, where only a limited number of training examples are available. The method combines the use of Gabor filters and convolutional neural networks (CNNs) to extract detailed texture and orientation features from images. These features are then enhanced through global average pooling, aggregated into comprehensive feature vectors, and refined using an attention mechanism that identifies and emphasizes the most relevant features for classification. Masks generated from this attention process selectively enhance critical features, which, after optional re-encoding, are used to train a classifier via a metric learning approach. This method aims to increase feature separability and classification performance, facilitating more accurate classification of new images with minimal training data.

The invention relates to the related technical field of digital data processing, in particular to a personalized federated learning method and system for a heterogeneous multi-source industrial internet, and the method comprises the steps: connecting a client, evaluating a load, time delay and modal similarity to generate a dynamic association table, deploying a hierarchical encryption protocol, and constructing a priority queue; a cache mechanism is set to coordinate distributed iterative optimization, so that the technical problem that network oscillation and computing resource waste are aggravated due to overhigh load of part of nodes caused by frequent access and exit of equipment and data volume difference in the industrial internet and repeated migration of clients and nodes is caused is solved, cross-equipment shared knowledge base vectors are extracted, and the computing efficiency is improved. The technical effects of reducing the influence of model isomerism on aggregation, dynamically scheduling high-frequency parameter local aggregation and low-frequency parameter cloud synchronization, optimizing the association weight of a client and a fog node in real time, realizing privacy protection and efficient personalized federated learning, and ensuring the privacy and security of user data in the training process are achieved.

The invention provides a student adaptive auxiliary learning method and system based on artificial intelligence, and the method comprises the steps: constructing a subject knowledge graph, and carrying out the correlation and structuralization of knowledge points; the knowledge points are associated with learning resources and test questions in the subject knowledge graph; collecting learning behavior data of students, and constructing student portraits; the student portrait comprises three dimensions of learning style, knowledge level and hobbies and interests; wherein the knowledge level is a mastering probability of each knowledge point acquired according to the subject knowledge graph; personalized learning paths, learning resources and learning strategies are recommended to the students according to the student portraits and the subject knowledge maps; and learning results of the students are automatically evaluated and fed back. According to the characteristics and requirements of each student, a personalized learning scheme is provided, the learning efficiency is improved, the limitation of time and space is broken through, and the students can obtain high-quality learning resources which are more personalized for themselves anytime and anywhere.

The invention relates to the technical field of federated learning, and discloses a federated learning method and system based on multiple agents and knowledge distillation, and a medium, and the method comprises the steps: S1, training a local model; s2, knowledge distillation based on decoupling; s3, decentralized knowledge sharing based on a block chain: packaging the standardized distillation knowledge fragments extracted in S2 and metadata thereof into block chain transactions, submitting the block chain transactions to a block chain network, verifying the legality of the transactions, and writing the transactions into a block chain account book to realize decentralized distribution; s4, performing multi-agent collaborative knowledge management based on a large language model, and updating a student model; and S5, repeatedly executing the steps S1 to S4 until a training termination condition is met. According to the method, personalized selection, adaptive filtering and efficient sharing of knowledge in the federated learning process are realized, the client drift phenomenon caused by data non-independent identical distribution characteristics is effectively relieved, the convergence speed, the overall performance and the personalized level of the model in a heterogeneous data environment are remarkably improved, and the communication overhead between clients is optimized.

The invention discloses a personalized federated learning method based on parameter decoupling. The personalized federated learning method comprises the following steps: S1, constructing a federated learning framework including model parameter decoupling and personalized cooperative training; s2, in a model parameter decoupling module, a local model of the end side equipment is decoupled into global shared model parameters and local personalized model parameters; s3, calculating a difference coefficient between the personalized model parameters and the global shared model parameters, wherein the difference coefficient is used for guiding updating of the personalized model; s4, in the personalized collaborative training module, the end-side equipment performs local model training based on the difference coefficient, and performs dynamic weighting according to the similarity with personalized models of other equipment to realize selective fusion of knowledge; s5, the cloud server aggregates the global shared model parameters uploaded by the end side devices, and finally a complete personalized model is formed. According to the method, the performance of the personalized model is improved, the collaborative effect of personalized training is enhanced, and the generalization ability of the whole model is improved.

The invention relates to the technical field of microprocessors, and discloses a safety debugging method of an embedded microprocessor and the embedded microprocessor, the method comprises the following steps: implanting a random module in the microprocessor, constructing a resistance network array through random distribution of silicon substrate lattice defects to generate a disordered sequence, and if microprocessor data access is carried out, starting the random module; if physical intrusion is detected, a debugging session is established, three types of physical characteristics are synchronously generated to construct a composite key, a triple dynamic confusion layer is constructed in the microprocessor, the dynamic confusion layer and the composite key construct physical logic linkage protection, a probe access instruction logic address is called, if physical intrusion is detected, a probe triggers an invisible check point, and the probe accesses the invisible check point. The integrity of an execution environment is verified, if abnormal debugging is detected, a hardware defense mechanism is triggered, an access attack model is established, confusion parameters are trained and optimized, and a module interconnection mode is changed, so that the learning cycle of an attacker is broken, and the risk of attacking a processor in a machine learning mode is reduced.

The invention belongs to the technical field of robot learning, and particularly discloses a robot continuous imitation learning method and system based on a double diffusion model. Comprising the following steps: generating old task data in a submerged space by utilizing a pre-trained latent diffusion model, generating a model through guidance of a task identifier in combination with conditional diffusion, generating an image and state data related to an old task, modeling to construct a diffusion model, realizing multi-modal mapping from a state to an action, and generating a diversified action sequence; high-quality images and state data are generated through a pre-trained variational auto-encoder and a conditional diffusion model, and the consistency of the generated data in time and the accuracy of global semantics are ensured through time sequence continuity constraint and semantic consistency constraint; and performing feedback optimization training on the latent diffusion model. According to the method, the loss weight is dynamically adjusted, and the model is ensured to show good generalization ability and stability in a complex task in combination with collaborative training of an imitation learning strategy and a generator.

The invention discloses a multi-modal deep learning method for artistic work analysis, and relates to the technical field of computer vision, and the method comprises the steps: multi-modal data preprocessing: obtaining a visual image, text description and metadata of an artistic work, the method has the advantages that valuable feature vectors are extracted from visual images, text description and metadata of artistic works through multi-modal data preprocessing, the importance of all modal features is dynamically adjusted by means of an attention mechanism in the cross-modal dynamic fusion step, style information of different sources can be effectively integrated, and the effect of improving the artistic work quality is achieved. In the style migration stage, the multi-scale feature pyramid network is used for fusing feature information of different levels, real-time feedback optimization is continuously evaluated and parameters are adjusted through a discriminator, and compared with an existing neural style migration method, the method has the advantages that complex artistic styles are better processed, core elements of specific styles are accurately reserved in the migration process, and the method is suitable for popularization and application. And the accuracy of style migration and the artistic expressive force are improved.

The invention belongs to the related technical field of artificial intelligence, and discloses a robot time sequence imitation learning method and system based on a Mama coding complete history, and the method comprises the steps: receiving a multi-modal observation sequence in a task execution process of a robot, the multi-modal observation sequence comprising observation data of at least one sensor; processing the multi-modal observation sequence by using a sequence processing module based on a state space model, and updating a time sequence output of complete historical information of one code up to the current time step at each time step; and predicting the next step or a series of future actions of the robot based on the time sequence output of the current time step so as to control the robot to simulate. The time sequence processing module based on the state space model is utilized to process and encode the complete observation history in the task execution process of the robot, so that a non-Markov decision-making imitation learning method is realized, and the learning efficiency and the execution success rate of the robot in a complex and state-dependent long time sequence operation task are improved.

The present invention relates to the field of rock structure measurement, and disclosed are a three-dimensional pore reconstruction method and system based on a rock image, for use in solving the problem that using a machine learning method to reconstruct pores in a rock image requires relatively high mathematical or computer science expertise, involves high labor and economic costs, and is difficult to apply to small-scale rock pore reconstruction projects. The method comprises: step 1: grouping slice images of each rock sample into one set, and performing preprocessing; step 2: performing grayscale processing, histogram equalization and normalization processing on an image; step 3: using a Harris corner detection algorithm to perform corner detection, sorting Harris response values, selecting key points by setting the number of key points, and using a non-maximum suppression method to screen the selected key points; step 4: segmenting the boundaries of pores in the image by means of an adaptive threshold selection method, and extracting pore features; and step 5: using a marching cubes algorithm to construct a three-dimensional pore reconstruction model. The present invention is used for three-dimensional reconstruction of pores in a rock image.

The invention discloses a smart mine safety production violation behavior identification method, system and device and a medium, belongs to the technical field of smart mine safety identification, and aims to solve the technical problem of how to improve the accuracy and efficiency of mine safety production violation behavior identification, realize real-time and accurate safety supervision of the whole process of mine operation and improve the safety of mine safety production violation behaviors. According to the technical scheme, the method comprises the following steps: data acquisition and preprocessing: installing a camera in a key operation area of a mine to acquire video image data, and carrying out denoising, graying and normalization preprocessing operation on the video image data to obtain preprocessed video image data; and constructing a deep learning model based on a convolutional neural network: introducing an attention module into the network structure of the deep learning model, and training the deep learning model by using the marked video image data including the safety production violation behavior and the normal operation behavior, and adopting a transfer learning method in the training process.

The invention discloses a knowledge graph reasoning method based on dynamic rule perception memory. The method aims at solving the problems that an existing neural combination rule learning method is insufficient in expression ability and prone to splitting global semantics and local relation modes. The method comprises the following steps: introducing a lightweight dynamic relationship memory module, and executing self-attention on all relationships in a knowledge graph to capture global semantics; meanwhile, local relation interaction features are extracted through convolution, semantic fusion is carried out through a Transform encoder with multi-head attention and relative position coding, and unified and parallel-computing high-quality combination representation is constructed for each pair of relations in parallel. And meanwhile, a closed high-quality reasoning path is generated in combination with bidirectional breadth-first search. Through global semantic and local interactive collaborative modeling, the extendibility is ensured, the understanding of global semantics is enhanced, and the reasoning accuracy is improved. Global and local relation dependence is fused, an interpretable reasoning path is generated, and reasoning accuracy, expandability and robustness are improved.

The invention provides a machine grabbing learning method fusing shape features, contact modeling and physical constraints, and the machine grabbing learning method is a robot grabbing posture learning method capable of differentiating. According to the method, a three-dimensional point cloud serves as input, a deep neural network based on sparse voxel convolution is adopted to extract multi-scale shape features, and potential grabbing points and attitude parameters thereof are predicted. The differential contact modeling is realized by constructing a local area of direction perception and extracting geometric features reflecting the contact relationship between the mechanical claw and the object. Furthermore, the collision probability of the grabbing posture is estimated based on an implicit neural network, and a differentiable collision detection module is constructed. Meanwhile, physical constraints such as friction closing, surface alignment and geometric symmetry are introduced to serve as regular terms to jointly optimize grabbing scores, collision risks and stability; according to the method, end-to-end training is supported, efficient, robust and deployable grabbing posture estimation is achieved, and the method is suitable for various automatic grabbing tasks such as industrial robots and service robots.

The invention relates to the technical field of unmanned aerial vehicle autonomous decision, provides an unmanned aerial vehicle autonomous decision reinforcement learning method, system and device based on multi-objective optimization, and a storage medium, and solves the problems of limited dynamic task distribution capability and poor decision capability of an unmanned aerial vehicle. The method comprises the steps that based on infrared data and radar data, a moving track prediction sequence of a suspicious target in a target area is generated, a moving probability thermodynamic diagram is constructed, and the target area comprises a patrolled area; performing Doppler frequency shift analysis on the radar data to generate a target velocity vector field; and in combination with the position data of the unmanned aerial vehicle, outputting an unmanned aerial vehicle autonomous decision reinforcement learning result of a dynamic segmentation scheme containing an unpatrolled region through a region re-division reinforcement learning model based on a reward function for multi-objective optimization. The reward function is used for reflecting area coverage rate maximization, target tracking success probability maximization and path energy consumption minimization. According to the invention, autonomous intelligent decision-making of the unmanned aerial vehicle on the unpatrolled area is realized.

The invention discloses a structured data self-learning method based on a graph neural network, and the method comprises the following steps: S1, analyzing structured data, extracting entity fields and relation fields, and constructing a structure candidate graph; s2, generating a node embedding feature matrix, and initializing and recording the adjacency relation of candidate edges; s3, constructing a graph neural network model, inputting node features and an adjacent matrix, and defining a task loss function; s4, evaluating the gradient contribution degree of edge connection by adopting a gradient sensitive sparse adjacency self-learning algorithm, and updating the graph structure representation; s5, introducing an embedded interpretability gradient backtracking mechanism, correcting an edge connection relation and enhancing interpretability; s6, training the graph neural network by using the corrected structure, and updating the node embedding and graph structure; and S7, outputting a final graph structure and an interpretability index, and generating a graph modeling visualization result. According to the method, efficient modeling and explanatory analysis of structured data are realized through a dynamic graph structure learning and gradient backtracking mechanism.

The invention provides a personalized federal learning method and system for a shared model, and the method comprises the steps: enabling a server to store and initialize the shared model, a global model and a global class prototype of a client, and enabling the client to initialize a local learning weight vector; the server sends sharing models of other clients to one client, and the client sets a local sharing model as a global model and trains the global model, and uploads the sharing model and a local class prototype to the server; and the server calculates and obtains a global class prototype and a global model according to all the received shared models and local class prototypes. According to the method, the knowledge sharing problem in federal learning is solved, and the performance of a personalized model is improved; the problem of offset in local model training of the client is solved, and the obtained personalized prototype contains more abundant global information than the personalized prototype; while state dependence and communication overhead are reduced, effective fusion of global knowledge and local characteristics is realized so as to improve robustness and adaptability of the model in a complex scene.

The invention relates to a federated learning method, system and device for personalized differential privacy protection and a medium. The method comprises the steps that a central server initializes global model parameters and issues the global model parameters to clients; each client sets an initial value and an extreme value of a personalized privacy budget based on data characteristics of the client; the client performs local training, cuts the gradient in the training process, and adds corresponding Gaussian noise processing based on the current personalized privacy budget; the central server performs weighted aggregation on the model parameters uploaded by the clients to update a global model, and issues the updated global model parameters to the clients for a new round of local training; and the central server dynamically adjusts the personalized privacy budget of each client based on the reward factor, and then allocates the personalized privacy budget to each client for local training again until a global model meeting a preset requirement is obtained. The method can be widely applied to the field of distributed machine learning data security.

An efficient heterogeneous federated learning method based on hybrid distillation includes: initializing, by a server, global model parameters, and setting a preset total number of training rounds and a number of clients participating in each of the training rounds; loading local datasets in the clients respectively, performing random transformations on the local datasets to generate client distillation data for the clients, sampling multiple sub-networks from an original network of each client, training each sub-network on the client distillation data to obtain updated local model parameters of each client, and uploading the updated local model parameters to the server; and receiving, by the server, the updated local model parameters, performing, by the server, server distillation based on the updated local model parameters and a preset auxiliary dataset to obtain updated global model parameters and an updated global model, and sending, by the server, the updated global model to the clients.

The invention relates to the technical field of data fusion, and discloses an animal scene-oriented adaptive multi-modal data fusion method, which comprises the following steps of: extracting spatio-temporal characteristics from multi-source heterogeneous data such as visual sense, auditory sense and physiological sensing, constructing an animal-environment-group ternary spatio-temporal relation graph, and constructing an animal-environment-group ternary spatio-temporal relation graph; a pilot frequency sampling problem is solved through an adaptive interpolation algorithm, cross-modal projection alignment is completed in a public semantic space, unified space-time representation is output, and confidence coefficient weight is dynamically calculated based on uncertainty measurement of each modal feature. According to the method, accurate alignment of multi-modal data is realized through the cross-modal space-time attention network, the multi-modal feature alignment error is reduced compared with that of a traditional LSTM method, the training data volume of a federated element migration reinforcement learning framework is reduced compared with that of a traditional migration learning method, and the cross-species generalization performance of the model is improved. A multi-level causal inference engine quantitatively reveals causal association between environmental factors and animal diseases, and in combination with a dynamic decision tree visualization technology, the decision recognition degree is improved.

The invention provides a federated learning method combining acutance perception training and knowledge distillation, and belongs to the technical field of privacy protection. The method comprises the steps that a central server initializes global model parameters and distributes the initial global model parameters to participating clients; after each participating client receives the global model parameters, acutance perception training is executed based on the local data set, and a local updated global model is obtained and serves as a teacher model; through a knowledge distillation method, on the basis of a local training set and in combination with a soft label of a teacher model, obtaining student model parameter variation; performing privacy protection operation on the parameter variation of the student model, and uploading the parameter variation to a central server; the central server aggregates the parameter variation uploaded by each participating client after privacy protection, and updates a global model; the updated global model is used as an initial global model of the next round of communication. According to the method, privacy and efficiency are balanced, sensitive information leakage can be prevented, and the practicability of a federal learning global model is maintained.

The invention relates to the technical field of die-casting optimization, and discloses a die-casting process parameter optimization method and system based on digital twinning, and the method comprises the steps: arranging a sensor to collect the operation parameters of die-casting equipment and the quality data of a die casting in real time, and forming multi-source die-casting production data; according to multi-source die-casting production data, a multi-physical field simulation model is established, and a digital twinborn model is constructed. And comparing the virtual prediction result with the actually measured quality data, and constructing a virtual-real difference compensation network to correct the parameters of the digital twin model. And performing a multi-target reinforcement learning method based on the compensated digital twin model to generate optimal die-casting process parameters. And applying the optimal die-casting process parameters to die-casting equipment for verification, and updating the virtual-real difference compensation network according to a verification result. Intelligent optimization and continuous self-evolution of the die-casting process parameters are achieved, and the casting forming precision, the energy efficiency utilization rate and the production stability are improved.

The invention relates to the technical field of federated learning, in particular to a dynamic federated mutual learning method and system for balancing individuation and generalization, and the method specifically comprises the following steps: each client carries out the preprocessing of data to be processed of a model, and carries out the strong enhancement and weak enhancement processing; inputting the data subjected to strong enhancement processing into a shared model, inputting the data subjected to weak enhancement processing into a private model, and performing iterative training on the two models; related parameters of the shared model after each round of iterative training and a difference item between two model parameters are uploaded to a federation server; the federated server adopts a multi-dimensional adaptive aggregation strategy to obtain an updated global model, and returns the updated global model to each client to replace the shared model in the next round of training; and finally generating a generalization result and a personalized result. According to the method, the private-shared model architecture is constructed, and dynamic federated mutual learning is carried out in combination with the federated server, so that balance and collaborative improvement of individuation and generalization performance can be realized.

The invention relates to the technical field of learning methods, in particular to an OPGW heterogeneous detection method based on AI interference elimination, which comprises the following steps: acquiring a vibration signal segment, dividing mutation fragments, arranging channel sequence, comparing direction extension, extracting a direction change homing path segment, analyzing an offset trend positioning response area, and extracting fluctuation trend verification interruption. And combining state feature merging numbers, identifying a jump sequence and analyzing a switching relation, and obtaining disturbance boundary features. According to the method, a path chain is constructed through sudden change signal position division and a channel response sequence, a disturbance propagation starting point and an extension direction can be subjected to structured expression, a path segment corresponding to continuous direction change supports limitation of a response interval, a fluctuation trend and direction offset are combined to form segmented reference, and identification of characteristics of boundary evolution along with time is assisted; the hopping sequence in the tail end signal is used for restoring the disturbance boundary and enhancing the trajectory separation and feature retention capability of the multipath interference behavior in the differentiated environment.

The invention provides a super-network personalized federal learning method for garbage classification. According to the method, the precision of the model is improved. The method comprises the following steps: firstly, initializing a shared layer parameter, a personalized layer parameter of each client and a super network initialization parameter, issuing the shared layer parameter to each client, then training a personalized layer on local data by each client, updating the personalized layer parameter, sending the shared layer parameter to a server by each client, and sending the shared layer parameter to the server by each client. And the server performs weighted aggregation through the weight generated by the super network, updates a shared layer parameter, optimizes the super network and updates a weight generation strategy for next aggregation. According to the method, the aggregation weight of the client sharing layer is dynamically generated by introducing the super network, and the limitation of a simple average aggregation mode is overcome.

The invention provides a personalized federal learning method and system for a data heterogeneous and resource constrained environment, which is executed by a server and a plurality of clients cooperatively and used for protecting data privacy of the clients and cooperatively training a model adaptive to local data distribution of each client, and comprises the following steps: (1) initializing and distributing the server; (2) constructing and training a client personalized model; (3) uploading by a client; (4) the server receives the local parameter ranking and the local super network parameter uploaded by each client; performing discrete aggregation on the local parameter ranking by adopting a voting aggregation mode, and updating a global parameter consensus ranking; meanwhile, continuously aggregating the local super-network parameters by adopting a weighted average mode, and updating global super-network parameters; and (5) repeatedly executing the steps (1) to (4) until the model performance meets a preset convergence condition.

A federated object detection learning method based on representation enhancement and weighted aggregation under cloud-edge-terminal environment comprises the steps of: 1) building a centralized federated learning framework under cloud-edge-terminal environment; 2) locally conducting representation enhancement training to strengthen model learning for few-shot category after receiving a model from the server at the client; 3) carrying out the weighted aggregation for client models in accordance with sample distribution to obtain the global model after receiving models from all clients at the server. With regard to the problem of existing federated object detection learning on low global model accuracy and weak generalization ability, the present invention can improve the accuracy and generalization ability of global object detection model.

The invention discloses a federal map learning method and system based on feature space construction and sharing. The method and system are used for solving the problems of feature space isomerism, harmful knowledge integration and insufficient personalized performance in federal map learning in a cross-domain Internet of Things environment. The method comprises the following steps: constructing a globally consistent feature space, and realizing node feature alignment through random walk feature generation and standardization; designing a client hybrid feature encoder (CHFE), and combining global features (MLP encoding) and local features (GCN encoding) to generate hybrid representation; proposing an independent adaptive aggregation strategy (IAA), and dynamically calculating an aggregation weight based on the domain similarity, the model similarity and the gradient similarity; a personalized regularization mechanism (PPT) is introduced, and global collaboration and local optimization are balanced. The method significantly improves the convergence speed and generalization ability of the model under heterogeneous data, reduces the communication overhead, and is suitable for privacy sensitive scenes such as medical treatment and industry.

The invention relates to the technical field of power distribution network scheduling methods, and particularly provides a safety reinforcement learning power distribution network optimal scheduling method based on historical data, which comprises the following steps of: acquiring operation time sequence data of a plurality of intelligent agents in a power distribution network in a current period of time, and preprocessing the operation time sequence data; calculating the relaxation index of each agent, and classifying the agents according to the similarity between the relaxation indexes of different agents; selecting one agent group with the relaxation index corresponding to the current state mode of the power distribution network system as a strategy agent group; inputting the operation time sequence data of the intelligent agents in the strategy intelligent agent group into a reinforcement learning optimization model based on historical data, and outputting a scheduling strategy corresponding to each intelligent agent; and carrying out coordinated scheduling on the power distribution network equipment according to the plurality of scheduling strategies. According to the safety reinforcement learning power distribution network optimization scheduling method based on historical data provided by the invention, the complexity of an intensity learning method of the power distribution network can be reduced, and the calculation efficiency and the learning efficiency can be improved.

The invention relates to an intelligent customer service self-learning method and system, and the method comprises the steps: collecting the interaction data of a user and an intelligent customer service, and forming a multi-dimensional data set; based on a PID (Proportion Integration Differentiation) controller, processing the performance indexes in the multi-dimensional data set, calculating a current error signal, and generating a corresponding control instruction according to the current error signal so as to adjust the response behavior of the intelligent customer service system in real time; evaluating the system performance data adjusted by the control instruction to obtain evaluation feedback; and according to the evaluation feedback, dynamically adjusting the parameters of the PID controller through a self-adaptive control strategy, and feeding back the adjusted parameters to the PID controller in the step S2. According to the invention, by introducing a closed-loop feedback mechanism based on the PID controller and a parameter adaptive optimization strategy, real-time regulation and control of the response behavior of the intelligent customer service system are realized, and the stability, the response speed and the user satisfaction of the system are remarkably improved.