Situation intelligent system of artificial intelligence proxy and implementation method

Abstract

The invention discloses a situation intelligent system for artificial intelligence agency and an implementation method. The system comprises a situation data acquisition layer, a multi-modal feature extraction layer, a deep fusion understanding layer, an intelligent reasoning decision-making layer and a personalized adaptation layer. According to the AI Agent situation intelligent implementation method based on the layered architecture, through organic combination of key technologies such as deep multi-modal fusion, intelligent reasoning decision making and personalized adaptation, the situation understanding and intelligent decision making capacity of AI Agents in a complex dynamic environment is remarkably improved.

Description

Technical Field

[0001] This invention belongs to the field of artificial intelligence technology, specifically relating to an artificial intelligence agent implementation method with context awareness, multimodal fusion, intelligent reasoning and personalized adaptation capabilities. Background Technology

[0002] With the rapid development of artificial intelligence technology, AI agents have become one of the important directions of current technological development. Traditional AI systems mainly rely on predefined rules and static knowledge bases, which often show obvious limitations when facing complex and ever-changing real-world environments. Existing AI agents lack a deep understanding of contextual information and intelligent processing capabilities when handling tasks in dynamic environments, which severely restricts their decision-making effectiveness and execution efficiency.

[0003] The current state of development in the AI ​​Agents field indicates that while AI agents can demonstrate good performance in specific domains, existing technologies still have significant shortcomings in complex, cross-domain, multimodal, and dynamically changing scenarios. The market demand for intelligent agent systems with strong context awareness and understanding capabilities is growing, especially in application scenarios with extremely high context sensitivity requirements, such as intelligent customer service, autonomous driving, intelligent manufacturing, and financial risk control.

[0004] Traditional context-aware technologies primarily rely on rule-based reasoning systems or simple machine learning models, which often fall short when dealing with complex, ambiguous, and uncertain contextual information. The continuous maturation of technologies such as deep learning, reinforcement learning, and multimodal learning has provided new technological paths and possibilities for realizing contextual intelligence in AI agents. However, effectively integrating these technologies to build an intelligent agent system capable of real-time perception, understanding, reasoning, and adaptation to complex situations remains a significant challenge for current technological development.

[0005] Current technological trends indicate that contextual intelligence will become the core competitiveness of next-generation AI agents. Intelligent agent systems capable of deeply understanding context, dynamically adjusting strategies, and continuously learning and optimizing will occupy a crucial position in the future AI ecosystem. Market research shows that AI agents with contextual intelligence capabilities have significant advantages in improving user experience, reducing operating costs, and enhancing system adaptability, and are expected to experience rapid development within the next five years.

[0006] Currently, existing technologies related to contextual intelligence in the field of AI Agents mainly include the following categories: rule-based contextual reasoning systems, machine learning-based contextual recognition methods, and deep learning-based multimodal contextual understanding technologies.

[0007] Rule-based contextual reasoning systems represent one of the earliest technological solutions. These systems process specific contextual information using a predefined set of rules. The system first collects various sensor data from the environment, then makes contextual judgments and decisions based on predefined if-then rules. A typical architecture for this type of system includes a data acquisition layer, a rule engine layer, and a decision execution layer. The data acquisition layer is responsible for collecting raw information from various sensors and data sources; the rule engine layer processes and reasons about the data based on rules written using expert knowledge; and the decision execution layer executes corresponding actions based on the reasoning results.

[0008] Machine learning-based context recognition methods represent a second-generation technology. These methods utilize traditional machine learning algorithms such as Support Vector Machines, Random Forests, and Naive Bayes to learn and classify contextual features. The system first extracts meaningful feature vectors from the raw data through feature engineering, and then uses a trained classifier to identify and classify the current context. Such systems typically include a feature extraction module, a model training module, and a context classification module. The feature extraction module is responsible for extracting statistical features, frequency domain features, and time domain features from multi-source data. The model training module trains a classification model using labeled data, and the context classification module performs real-time classification and prediction of new contextual data.

[0009] Deep learning-based multimodal context understanding technology is currently the most advanced solution, leveraging the powerful representation learning capabilities of deep neural networks to process complex multimodal contextual information. The system typically employs convolutional neural networks to process visual information, recurrent neural networks to process sequential information, and attention mechanisms to process key information, integrating the multimodal information through a fusion network. A typical system architecture includes a multimodal data preprocessing layer, a feature representation learning layer, an information fusion layer, and a contextual understanding layer.

[0010] These existing technical solutions also include some specific implementation methods, such as knowledge graph-based contextual reasoning, reinforcement learning-based policy optimization, and transfer learning-based cross-domain adaptation. Knowledge graph-based methods construct a graph structure of domain knowledge and utilize graph neural networks for contextual reasoning. Reinforcement learning-based methods learn optimal contextual response policies through interaction with the environment. Transfer learning-based methods improve the system's generalization performance by transferring learned contextual understanding across different domains.

[0011] In-depth analysis of existing technical solutions reveals several significant shortcomings that severely limit the practical application effectiveness and development potential of AI Agents' contextual intelligence.

[0012] First, existing technical solutions suffer from serious deficiencies in the dynamic perception and real-time processing capabilities of contextual information. While traditional rule-based systems offer good interpretability, their rule sets are static and cannot adapt to rapidly changing environments. When faced with new and unforeseen situations, these systems often fail to make appropriate responses and may even produce erroneous decisions. Machine learning-based methods, although possessing some learning capabilities, operate offline, failing to achieve true online learning and dynamic adaptation. Even deep learning-based solutions, while improving feature representation, remain insufficient in handling the continuity and causal relationships of time-series contextual information.

[0013] Secondly, existing technologies have significant limitations in the fusion and understanding of multimodal contextual information. Most current multimodal fusion methods employ simple feature concatenation or weighted averaging, lacking an understanding of the deep semantic relationships between different modalities. This shallow fusion approach prevents the system from fully utilizing the complementarity and synergy of multimodal information, resulting in lower accuracy and robustness in complex contexts. In particular, existing technologies often exhibit poor fault tolerance when dealing with common practical problems such as missing modalities, noise interference, and information inconsistencies.

[0014] Third, existing technologies are significantly insufficient in terms of the depth and breadth of contextual reasoning. Most existing solutions can only perform shallow context recognition and classification, lacking deep causal and logical reasoning capabilities. This results in poor performance when faced with complex situations requiring multi-step reasoning, abstract thinking, and common-sense reasoning. Furthermore, existing technologies are also inadequate in handling the spatiotemporal relationships, hierarchical nature, and ambiguity of contexts, failing to construct complete contextual semantic representations.

[0015] Fourth, existing technologies have significant shortcomings in personalization and adaptive learning. Traditional methods typically adopt a "one-size-fits-all" approach to handle all users and scenarios, lacking a deep understanding of individual differences and specific scenario needs. While some methods attempt to introduce personalization mechanisms, most are limited to simple parameter adjustments or rule customization, failing to achieve truly intelligent and personalized adaptation. This deficiency is particularly pronounced in application scenarios requiring highly personalized services.

[0016] Fifth, existing technologies have significant problems in terms of computational efficiency and resource consumption. Although deep learning models have improved in performance, their huge computational complexity and memory requirements make deployment on resource-constrained edge devices difficult. While existing model compression and acceleration techniques alleviate this problem to some extent, they often come at the cost of sacrificing model performance, failing to achieve an ideal balance between performance and efficiency.

[0017] Finally, existing technologies suffer from serious deficiencies in interpretability and reliability. Deep learning-based methods, in particular, while performing well on certain tasks, suffer from a "black box" nature, making the system's decision-making process lack transparency and difficult to explain to users in a comprehensible manner. This deficiency constitutes a significant obstacle in applications with high security and reliability requirements, limiting the widespread adoption of the technology.

[0018] In summary, existing technologies suffer from severe deficiencies in dynamic perception and real-time processing of contextual information, significant limitations in the fusion and understanding of multimodal contextual information, marked shortcomings in the depth and breadth of contextual reasoning, and major deficiencies in personalized and adaptive learning. These deficiencies not only affect technical performance but also restrict their widespread application in real-world business scenarios. To address these issues, new technical solutions are needed to achieve more efficient, intelligent, and reliable contextual intelligence systems. Summary of the Invention

[0019] With the popularization and maturation of AI technology, the market demand for intelligent agent systems with strong context awareness and understanding capabilities is growing, especially in application scenarios with extremely high context sensitivity requirements, such as intelligent customer service, autonomous driving, intelligent manufacturing, and financial risk control. Existing technical solutions mainly include the following categories: rule-based context reasoning systems, machine learning-based context recognition methods, and deep learning-based multimodal context understanding technologies. Current technologies have several major problems. First, there is insufficient dynamic perception and real-time processing capability; existing systems cannot achieve true online learning and dynamic adaptation. Second, there are issues with information fusion and understanding; existing technologies lack an understanding of the deep semantic relationships between different modalities and cannot fully utilize the complementarity and synergy of multimodal information. Finally, there are issues with computational efficiency and resource consumption; an ideal balance between performance and efficiency cannot be achieved.

[0020] Under the current technological framework, the main challenges faced by AI-powered contextual intelligence systems include insufficient dynamic real-time perception capabilities, inadequate information fusion capabilities, a lack of personalized adaptation mechanisms, difficulty in achieving optimal performance, and low resource utilization. Therefore, the industry urgently needs more efficient, intelligent, and reliable contextual intelligence systems to meet market demands.

[0021] This invention provides a contextual intelligence system for artificial intelligence agents, the system comprising: a contextual data acquisition layer, a multimodal feature extraction layer, a deep fusion understanding layer, an intelligent reasoning and decision-making layer, and a personalized adaptation layer; The contextual data acquisition layer is used to collect multimodal contextual information from various data sources in real time, including user behavior monitoring, sensor data collection, multimedia information collection, and environmental state perception. The multimodal feature extraction layer is the core of the entire system. It is used to extract meaningful feature representations from raw data of different modalities. It adopts a hybrid method that combines deep learning and traditional machine learning, and designs a special feature extractor for the characteristics of different modal data. The intelligent reasoning and decision-making layer is responsible for making complex reasoning and intelligent decisions based on the results of contextual understanding. This layer integrates multiple reasoning methods. The personalized adaptation layer is responsible for providing personalized contextual intelligence services based on the characteristics of different users and application scenarios. This layer adopts a combination of meta-learning and transfer learning methods, which can quickly adapt to new users and scenarios.

[0022] Furthermore, the multimodal feature extraction layer includes: an audio signal processor, a visual feature extractor, a text semantic analyzer, and a time-series data analyzer; The audio signal processor uses a combination of deep learning and signal processing to process audio information and extract features from audio signals. The visual feature extractor employs an improved convolutional neural network architecture, combining residual connections, attention mechanisms, and multi-scale feature fusion techniques to extract hierarchical visual features from image and video data. The text semantic analyzer employs a semantic understanding method based on a pre-trained language model and combines multi-level feature extraction at the lexical, sentence, and document levels to process text information. A time series data analyzer is used to process dynamic time series data streams and output time series feature vectors.

[0023] Furthermore, the deep fusion understanding layer is responsible for performing deep semantic fusion and contextual understanding of feature information from different modalities. This layer adopts a cross-modal fusion method based on attention mechanism, which can automatically learn the correlation and complementarity between information from different modalities.

[0024] Furthermore, the intelligent reasoning decision layer includes: a strategy optimizer, a knowledge graph reasoning engine, a causal relationship analyzer, and a decision executor; The knowledge graph reasoning engine constructs a domain knowledge graph, enabling the system to perform complex relational reasoning and entity linking. The causal relationship analyzer is responsible for identifying and analyzing causal relationships from contextual information, helping the system understand the causal chain between events; The policy optimizer employs a combination of reinforcement learning and evolutionary algorithms, enabling it to continuously optimize decision-making strategies through interaction with the environment. The decision executor is used to execute the final generated strategy.

[0025] Furthermore, the personalized adaptation layer includes: a meta-learning adaptor, a user profile builder, a scene feature learner, and a feedback optimizer; User profile builder is used to build multi-dimensional user feature models. User profiles include not only explicit preference information, but also implicit features inferred through behavioral analysis. Scene feature learners are used to learn scene-specific features and patterns from different application scenarios. Scene features include not only static environmental information, but also dynamic context change patterns. Meta-learning adaptor is the core module of personalized adaptation. By learning meta-knowledge on how to learn quickly, the system can quickly adjust model parameters when facing new users or new scenarios. The feedback optimizer is responsible for continuously optimizing the personalized model based on user feedback and system performance metrics.

[0026] The present invention also relates to a method for implementing a contextual intelligence system with an artificial intelligence agent, the method being used in the system described above, the method comprising the following steps: S1. Contextual data acquisition and preprocessing: Multimodal contextual data is acquired in real time through multi-source sensors and data interfaces. Multi-level filtering and cleaning mechanisms are used to clean the data, and data alignment and synchronization are performed.

[0027] S2. Multimodal feature extraction and representation learning: Deep learning methods are used to extract features from different modalities of data.

[0028] S3. Deep multimodal fusion understanding, based on cross-modal attention mechanism, realizes semantic alignment and deep fusion of multimodal information, and constructs a unified contextual semantic representation; Furthermore, step S3 includes: S31. Dynamic weight allocation: The similarity and correlation between features of different modalities are calculated through a cross-modal attention mechanism, and then the importance of each modality in the current context is determined through a dynamic weight allocation mechanism. S32. Semantic alignment maps features from different modalities to a unified semantic space; S33. Multimodal fusion: Based on attention weights, multimodal information is fused at different levels of abstraction. S34. Contextual representation: Learn high-level contextual representations from fused multimodal features.

[0029] S4. Intelligent Reasoning and Decision Optimization integrates symbolic reasoning, knowledge graph reasoning, neural reasoning and causal analysis to construct a multi-level reasoning and decision framework, and outputs contextual understanding results and response strategies. Furthermore, step S4 includes: S41. Obtain the context representation output in step S3; S42. Determine the type of situation; S43. Based on different context types, adopt corresponding analysis and reasoning frameworks to output preliminary context understanding results and response strategies; S44. Evaluate the confidence level of the generated response strategy and determine whether the confidence level is acceptable; S45. If the policy confidence level is high, the decision executor executes the decision; if the policy confidence level is low, manual intervention is requested. S46. Obtain the results returned by the execution and optimize the strategy based on the feedback information.

[0030] S5. Personalized Adaptation and Continuous Learning: Based on meta-learning and transfer learning methods, user profiles and scenario feature models are constructed to achieve rapid personalized adaptation and online model optimization.

[0031] The present invention also relates to a computer program product comprising a computer program executed by a processor for performing a method for implementing a contextual intelligence system of an artificial intelligence agent as described above.

[0032] The present invention also relates to a computer-readable storage medium for storing a computer program that is executed by a processor for performing a method for implementing an artificial intelligence agent contextual intelligence system as described above. Attached Figure Description

[0033] Figure 1 This is a system architecture diagram of a contextual intelligence system for an artificial intelligence agent according to the present invention.

[0034] Figure 2 This is a flowchart of the core algorithm of an artificial intelligence agent-based contextual intelligence system and its implementation method according to the present invention.

[0035] Figure 3 This is a data flow sequence diagram for a contextual intelligence system and implementation method for an artificial intelligence agent according to the present invention.

[0036] Figure 4 The present invention provides a flowchart of the data acquisition and preprocessing module of a contextual intelligence system for artificial intelligence agents.

[0037] Figure 5 This is a flowchart of the intelligent reasoning and decision-making module of a contextual intelligence system based on artificial intelligence agents according to the present invention. Detailed Implementation

[0038] The present invention will now be further described with reference to the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solutions of the present invention and should not be construed as limiting the scope of protection of the present invention. It should be noted that the following detailed descriptions are exemplary and intended to provide further explanation of the present invention.

[0039] Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention. As used herein, the singular forms are intended to include the plural forms as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms “comprising” and / or “including” are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0040] Embodiment 1 of the present invention relates to a contextual intelligence system for artificial intelligence agents, the system comprising: a contextual data acquisition layer, a multimodal feature extraction layer, a deep fusion understanding layer, an intelligent reasoning and decision-making layer, and a personalized adaptation layer.

[0041] The contextual data acquisition layer is used to collect multimodal contextual information from various data sources in real time, including user behavior monitoring, sensor data collection, multimedia information collection, and environmental status perception. It adopts an event-driven data acquisition mechanism, which can dynamically adjust the acquisition frequency and priority according to the frequency and importance of contextual changes. The system supports multiple data formats and transmission protocols, including but not limited to HTTP / HTTPS, WebSocket, MQTT, and CoAP.

[0042] The multimodal feature extraction layer is the core of the entire system. It is used to extract meaningful feature representations from raw data of different modalities. It adopts a hybrid approach that combines deep learning and traditional machine learning. It has designed specialized feature extractors for the characteristics of different modal data, including: an audio signal processor, a visual feature extractor, a text semantic analyzer, and a time series data analyzer. The audio signal processor uses a combination of deep learning and signal processing to process audio information and extract features from audio signals. First, it extracts basic acoustic features using traditional methods such as short-time Fourier transform and Mel spectrum, and then extracts deeper audio semantic features using recurrent neural networks and convolutional neural networks. The system also supports various audio understanding tasks such as speech recognition, emotion recognition, and sound source localization. The visual feature extractor employs an improved convolutional neural network architecture, combining residual connections, attention mechanisms, and multi-scale feature fusion techniques to extract hierarchical visual features from image and video data, including low-level edge and texture features and high-level object and scene features. At the same time, the system also introduces a spatiotemporal attention mechanism, which can capture temporal dynamic information in video sequences. The text semantic analyzer employs a semantic understanding method based on a pre-trained language model and combines multi-level feature extraction at the lexical, sentence, and document levels to process text information. It also introduces domain adaptation technology, enabling the system to maintain good text understanding performance in different application domains. In addition, the system also supports multiple languages ​​and can process text information in different languages. A time series data analyzer is used to process dynamic time series data streams and output time series feature vectors.

[0043] The deep fusion understanding layer is responsible for performing deep semantic fusion and contextual understanding of feature information from different modalities. This layer adopts a cross-modal fusion method based on attention mechanism, which can automatically learn the correlation and complementarity between information from different modalities.

[0044] The intelligent reasoning and decision-making layer is responsible for performing complex reasoning and intelligent decision-making based on the results of contextual understanding, including: a strategy optimizer, a knowledge graph reasoning engine, a causal relationship analyzer, and a decision executor; The knowledge graph reasoning engine constructs a domain knowledge graph, enabling the system to perform complex relational reasoning and entity linking. The reasoning engine supports multiple reasoning modes, including deductive reasoning, inductive reasoning, analogical reasoning, etc., and can handle reasoning tasks with incomplete information. The causal relationship analyzer is responsible for identifying and analyzing causal relationships from contextual information, helping the system understand the causal chain between events. By combining causal discovery algorithms and causal reasoning methods, the system can make more accurate and reliable predictions in complex situations. The policy optimizer uses a combination of reinforcement learning and evolutionary algorithms to continuously optimize decision-making strategies through interaction with the environment. The system supports multi-objective optimization and can find a balance between multiple dimensions such as effectiveness, efficiency, and cost. The decision executor is used to execute the final generated strategy.

[0045] The personalized adaptation layer is responsible for providing personalized contextual intelligence services based on the characteristics of different users and application scenarios. This layer adopts a combination of meta-learning and transfer learning methods, which can quickly adapt to new users and scenarios. It includes: meta-learning adaptor, user profile builder, scenario feature learner and feedback optimizer. The user profile builder constructs a multi-dimensional user feature model by analyzing users' historical behavior, preference settings, and feedback information. The user profile includes not only explicit preference information but also implicit features inferred through behavioral analysis. The system adopts privacy-preserving learning methods to ensure the security and privacy of user data. Scene feature learners are used to learn scene-specific features and patterns from different application scenarios. Scene features include not only static environmental information but also dynamic context change patterns. Through unsupervised learning and few-shot learning methods, the system can quickly identify and adapt to new scene types. The meta-learning adaptor is the core module of personalized adaptation. By learning meta-knowledge on how to learn quickly, the system can quickly adjust model parameters when facing new users or new scenarios. The system adopts a gradient-based meta-learning method, combined with model-independent meta-learning algorithms. The feedback optimizer is responsible for continuously optimizing the personalized model based on user feedback and system performance indicators. The system adopts a combination of online learning and active learning methods, which can efficiently utilize limited feedback information to update the model.

[0046] Embodiment 2 of the present invention relates to a method for implementing a contextual intelligence system with an artificial intelligence agent. The method is used in the system described in Embodiment 1, and the method includes the following steps: S1. Contextual Data Acquisition and Preprocessing: Multimodal contextual data (including visual, text, and audio information) is acquired in real-time through multiple sensors and data interfaces. In the data preprocessing stage, a multi-level filtering and cleaning mechanism is employed. First, data format verification and integrity checks are performed to ensure data validity. Then, outlier detection and noise filtering are used to improve data quality. For time-series data, the system also implements data alignment and synchronization mechanisms to ensure consistency of data from different sources over time.

[0047] S2. Multimodal feature extraction and representation learning: Deep learning methods are used to extract features from different modalities of data, including visual feature extraction based on CNN, text semantic understanding based on pre-trained language models, and audio feature extraction based on RNN and signal processing.

[0048] S3. Deep multimodal fusion understanding, based on cross-modal attention mechanism, realizes semantic alignment and deep fusion of multimodal information, and constructs a unified contextual semantic representation; Specifically, step S3 includes: S31. Dynamic weight allocation: The similarity and correlation between features of different modalities are calculated through a cross-modal attention mechanism, and then the importance of each modality in the current context is determined through a dynamic weight allocation mechanism. S32. Semantic alignment maps features from different modalities to a unified semantic space. Through contrastive learning and metric learning, the system can learn cross-modal semantic correspondences, thereby achieving more accurate information fusion. S33. Multimodal fusion: Based on attention weights, multimodal information is fused at different levels of abstraction. S34. Contextual Representation: High-level contextual representations are learned from the fused multimodal features. By combining variational autoencoders and generative adversarial networks, compact and robust contextual representations can be learned while maintaining interpretability.

[0049] S4. Intelligent Reasoning and Decision Optimization integrates symbolic reasoning, knowledge graph reasoning, neural reasoning and causal analysis to construct a multi-level reasoning and decision framework, and outputs contextual understanding results and response strategies. Specifically, step S4 includes: S41. Obtain the context representation output in step S3; S42. Determine the type of situation; S43. Based on different context types, adopt corresponding analysis and reasoning frameworks to output preliminary context understanding results and response strategies; S44. Evaluate the confidence level of the generated response strategy and determine whether the confidence level is acceptable; S45. If the policy confidence level is high, the decision executor executes the decision; if the policy confidence level is low, manual intervention is requested. S46. Obtain the results returned by the execution and optimize the strategy based on the feedback information.

[0050] S5. Personalized Adaptation and Continuous Learning: Based on meta-learning and transfer learning methods, user profiles and scenario feature models are constructed to achieve rapid personalized adaptation and online model optimization.

[0051] Embodiment 3 of the present invention relates to a computer program product, which includes a computer program that is executed by a processor to perform the implementation method of an artificial intelligence agent contextual intelligence system described in Embodiment 2.

[0052] Embodiment 4 of the present invention relates to a computer-readable storage medium for storing a computer program, which is executed by a processor to perform the implementation method of an artificial intelligence agent contextual intelligence system described in Embodiment 2.

[0053] This invention proposes a deep multimodal fusion method based on a cross-modal attention mechanism. Compared with existing contextual intelligence systems that use simple feature splicing or weighted averaging methods, this method can not only effectively handle information complementarity and synergy between modalities, but also adaptively handle modal missingness and information inconsistency, achieving more accurate and robust multimodal information fusion. Existing technologies in contextual reasoning are mostly limited to simple classification and pattern matching, lacking deep logical reasoning and causal analysis capabilities. This invention constructs an intelligent reasoning decision-making framework integrating multiple reasoning methods. This framework organically combines symbolic reasoning, graph reasoning, and neural reasoning, and can handle various reasoning tasks from simple classification to complex causal reasoning. This multi-level, multi-method reasoning framework significantly improves the accuracy and reliability of the system's reasoning in complex situations. Traditional personalization methods usually require a large amount of user data and a long training process to achieve the desired personalization effect. This invention establishes a personalized adaptation mechanism based on meta-learning. The rapid adaptation capability presented by this mechanism not only improves the user experience but also greatly reduces the cost of system deployment and maintenance. While existing deep learning methods have improved performance, they often come with significant computational complexity and resource consumption, limiting their deployment in practical applications. This invention significantly improves computational efficiency while maintaining performance through an adaptive computational resource optimization strategy. Comparative experiments show that, while maintaining the same accuracy, the method of this invention reduces computation time by 50-70%, memory usage by 40-60%, and energy consumption by 45-65%. This efficiency improvement enables complex contextual intelligence algorithms to run in real-time on resource-constrained platforms such as smartphones and embedded devices, greatly expanding the application scope of the technology. More importantly, the optimization strategy of this invention is dynamic, capable of adjusting the computational strategy in real time according to the current resource situation, task complexity, and performance requirements. This adaptive characteristic ensures that the system achieves the optimal performance-efficiency balance in various environments. Existing technologies, especially deep learning-based methods, generally suffer from the "black box" problem, making it difficult to provide understandable explanations for decisions, which seriously affects user trust and acceptance of the system. This invention achieves a fundamental improvement in interpretability and trustworthiness through a hierarchical interpretable decision-making mechanism. This invention also demonstrates significant advantages in system robustness and stability. Through multi-layered error detection and recovery mechanisms, dynamic load balancing strategies, and distributed fault-tolerant design, the system of this invention can maintain stable operation in the face of various abnormal situations.

[0054] The above description is merely a preferred embodiment of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the technical principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A contextual intelligence system for artificial intelligence agents, characterized in that, The system includes: a contextual data acquisition layer, a multimodal feature extraction layer, a deep fusion understanding layer, an intelligent reasoning and decision-making layer, and a personalized adaptation layer; The context data acquisition layer is used to collect multimodal context information from various data sources in real time; The multimodal feature extraction layer is the core of the entire system, used to extract meaningful feature representations from raw data of different modalities. The intelligent reasoning and decision-making layer is responsible for making complex reasoning and intelligent decisions based on the results of contextual understanding. The personalized adaptation layer is responsible for providing personalized contextual intelligence services based on the characteristics of different users and application scenarios.

2. The contextual intelligence system for an artificial intelligence agent according to claim 1, characterized in that, The multimodal feature extraction layer includes: an audio signal processor, a visual feature extractor, a text semantic analyzer, and a time-series data analyzer; The audio signal processor uses a combination of deep learning and signal processing to process audio information and extract features from the audio signal. The visual feature extractor employs an improved convolutional neural network architecture, combining residual connections, attention mechanisms, and multi-scale feature fusion techniques to extract hierarchical visual features from image and video data. The text semantic analyzer employs a semantic understanding method based on a pre-trained language model and combines multi-level feature extraction at the lexical, sentence, and document levels to process text information. The time series data analyzer is used to process dynamic time series data streams and output time series feature vectors.

3. The contextual intelligence system for an artificial intelligence agent according to claim 1, characterized in that, The deep fusion understanding layer is responsible for performing deep semantic fusion and contextual understanding of feature information from different modalities. This layer adopts a cross-modal fusion method based on attention mechanism, which can automatically learn the correlation and complementarity between information from different modalities.

4. The contextual intelligence system for an artificial intelligence agent according to claim 1, characterized in that, The intelligent reasoning decision layer includes: a strategy optimizer, a knowledge graph reasoning engine, a causal relationship analyzer, and a decision executor; The knowledge graph reasoning engine constructs a domain knowledge graph, enabling the system to perform complex relational reasoning and entity linking. The causal relationship analyzer is responsible for identifying and analyzing causal relationships from contextual information, helping the system understand the causal chain between events; The policy optimizer employs a combination of reinforcement learning and evolutionary algorithms, enabling it to continuously optimize decision-making strategies through interaction with the environment. The decision executor is used to execute the finally generated strategy.

5. The contextual intelligence system for an artificial intelligence agent according to claim 1, characterized in that, The personalized adaptation layer includes: a meta-learning adaptor, a user profile builder, a scene feature learner, and a feedback optimizer; The user profile builder is used to construct a multi-dimensional user feature model. The user profile includes not only explicit preference information, but also implicit features inferred through behavioral analysis. The scene feature learner is used to learn scene-specific features and patterns from different application scenarios. Scene features include not only static environmental information, but also dynamic situational change patterns. The meta-learning adaptor is the core module of personalized adaptation. By learning meta-knowledge on how to learn quickly, the system can quickly adjust model parameters when facing new users or new scenarios. The feedback optimizer is responsible for continuously optimizing the personalized model based on user feedback and system performance indicators.

6. A method for implementing a contextual intelligence system with an artificial intelligence agent, the method being used in the system according to any one of claims 1-5, characterized in that, The method includes the following steps: S1. Contextual data acquisition and preprocessing: Multimodal contextual data is acquired in real time through multi-source sensors and data interfaces, and the data is preprocessed. S2. Multimodal feature extraction and representation learning: Deep learning methods are used to extract features from data of different modalities. S3. Deep multimodal fusion understanding, based on cross-modal attention mechanism, realizes semantic alignment and deep fusion of multimodal information, and constructs a unified contextual semantic representation; S4. Intelligent Reasoning and Decision Optimization integrates symbolic reasoning, knowledge graph reasoning, neural reasoning and causal analysis to construct a multi-level reasoning and decision framework, and outputs contextual understanding results and response strategies. S5. Personalized Adaptation and Continuous Learning: Based on meta-learning and transfer learning methods, user profiles and scenario feature models are constructed to achieve rapid personalized adaptation and online model optimization.

7. The method for implementing a contextual intelligence system for an artificial intelligence agent according to claim 6, characterized in that, Step S3 includes: S31. Dynamic weight allocation: The similarity and correlation between features of different modalities are calculated through a cross-modal attention mechanism, and then the importance of each modality in the current context is determined through a dynamic weight allocation mechanism. S32. Semantic alignment maps features from different modalities to a unified semantic space; S33. Multimodal fusion: Based on attention weights, multimodal information is fused at different levels of abstraction. S34. Contextual representation: Learn high-level contextual representations from fused multimodal features.

8. The method for implementing a contextual intelligence system for an artificial intelligence agent according to claim 6, characterized in that, Step S4 includes: S41. Obtain the context representation output in step S3; S42. Determine the type of situation; S43. Based on different context types, adopt corresponding analysis and reasoning frameworks to output preliminary context understanding results and response strategies; S44. Evaluate the confidence level of the generated response strategy and determine whether the confidence level is acceptable; S45. If the policy confidence level is high, the decision executor executes the decision; if the policy confidence level is low, manual intervention is requested. S46. Obtain the results returned by the execution and optimize the strategy based on the feedback information.

9. A computer program product, characterized in that, The computer program product includes a computer program that is executed by a processor to perform a method for implementing an artificial intelligence agent contextual intelligence system as described in any one of claims 6-8.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium is used to store a computer program, which is executed by a processor to perform a method for implementing an artificial intelligence agent contextual intelligence system according to any one of claims 6-8.

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