184 results about "Context sensing" patented technology

The invention discloses a personalized computer-aided decision-making method and system fusing multi-modal data, and relates to the field of personalized computer-aided decision-making, and the method comprises the steps: mapping multi-source heterogeneous modal data to a unified semantic embedding space, and obtaining a multi-modal unified representation vector set; carrying out three-layer progressive fusion on a feature layer, a situation layer and a decision layer of the multi-modal data to generate a global decision context vector; based on a cross attention mechanism, outputting a fused context sensing personalized vector; based on the behavior cloning model, outputting probability distribution on all decision options; according to the user feedback operation data, generating a user personalized decision strategy and performing dynamic optimization; and generating a structured decision report containing visual traceability information based on the hierarchical fusion process and decision reasoning logic. End-to-end intelligent generation from multi-source heterogeneous data to personalized decisions is realized, and a standardized process is converted into personalized customized decisions.

The invention provides a YOLOv7-based photovoltaic module fault detection method and a YOLOv7-based photovoltaic module fault detection device, and relates to the technical field of target detection. According to the method, a thermal infrared and temperature information fusion module is introduced at the front end of a network, a thermal infrared image and a pixel-level temperature matrix thereof are fused through alignment, coding and attention mechanisms, and the characterization capability of multi-modal features is enhanced. A shallow layer-deep layer information aggregation module is introduced into the neck network, and the context perception and feature discrimination ability of the model to a multi-scale small target is improved. A global context sensing module is introduced in front of a detection head, a directional channel context branch and a query-key value branch of the global context sensing module capture directional global dependence and spatial context relations respectively, and global modulation of features is achieved. According to the method, the detection precision and robustness of various faults such as faults, fragmentation, hot spots and shielding of the photovoltaic module junction box are remarkably improved, the weak and small target positioning capability is optimized, the method is suitable for being deployed on mobile or embedded equipment, and efficient and intelligent inspection of a photovoltaic power station is achieved.

The invention discloses an image segmentation method and system fusing sequence modeling and a multi-scale attention mechanism, and relates to the field of computer vision, and the method comprises the steps: carrying out the extraction of multi-scale detail and space information through an encoder based on a multi-scale convolution residual fusion module, and gradually carrying out the downsampling to increase a receptive field; performing multi-scale and global context feature extraction and coding based on a multi-scale context global modeling module through a bridging layer; and through a decoder, encoder features are fused based on up-sampling, convolution and a double-guide gating fusion module, so that recovery and refinement of high-resolution semantic features are realized, and an image segmentation result of fusion sequence modeling and a multi-scale attention mechanism is obtained. According to the method, the multi-scale features are fully extracted and fused, the context sensing capability is enhanced by using sequence modeling, the features of the encoder and the decoder are dynamically fused through a gating mechanism, the segmentation precision and the edge detail performance are improved, and the problem that the model performance and generalization capability are limited in the prior art is solved.

The invention discloses a family education auxiliary method and interaction system based on an intelligent agent mode, and belongs to the technical field of data processing, and the method specifically comprises the steps: obtaining the multi-source data of family education, building an intelligent agent model of cross-dimension situation perception capability based on the multi-source data, and enabling the intelligent agent model to carry out the multi-dimension situation perception capability according to the evolution trend of three-dimensional association, the intelligent agent model automatically deduces the appearing negative education situation, when a risk node is predicted, an education intervention scheme is generated, the education intervention scheme comprises a learning task and a real-time prompt for a parent communication mode, and in the execution process of the education intervention scheme, the intelligent agent model carries out different levels of task pushing on students and parents at the same time, so that the education intervention effect is improved. The student side obtains learning tasks, the parent side obtains interactive guidance, cooperative auxiliary optimization is carried out on family education, learning progress and parent-child communication can be promoted in the same education process, and it is ensured that the education process has dynamic adaptability and systematic management ability.

The invention discloses a radiation safety management method and system based on cloud platform data driving, and relates to the technical field of cloud platform radiation management. The method comprises the following steps: collecting radiation field data and environment state data of a target area through a deployed intelligent sensing node; uploading the radiation field data, the environment state data and the context data from the service module to a cloud platform, performing energy compensation and radiation unmixing, and constructing a multi-dimensional feature vector; and processing the multi-dimensional feature vector by using a pre-trained situational radiation perception model, identifying radiation field features and an environment situation, and generating a situational radiation safety early warning signal. The technical problem that in the prior art, radiation safety monitoring depends on single-point measurement, comprehensive judgment cannot be carried out in combination with environment and service context data, and consequently the radiation field anomaly recognition capability is insufficient is solved, and the purpose that the radiation field anomaly recognition capability is improved through cloud platform data driving and context awareness model fusion is achieved. And the technical effects of high-precision identification and situational safety early warning of the radiation field state are realized.

The invention discloses an SAR ship instance segmentation method for self-adaptive representation alignment, and belongs to the field of SAR image instance segmentation. According to the implementation method, the problems of semantic-structural feature mismatch, global-local feature extraction mismatch and cross-dataset scale generalization mismatch are solved through modular design by constructing an adaptive representation alignment network. The method comprises an edge-guided boundary optimization module, a context sensing module and a depth adaptive feature pyramid module. The boundary optimization module introduces edge enhancement information in the feature extraction process to improve the target boundary positioning precision; the context sensing module fuses a multi-path global attention mechanism in a deep feature stage, and the semantic discrimination capability is enhanced; the depth adaptive feature pyramid module adaptively selects the optimal feature fusion depth by constructing a multi-depth fusion path in combination with scale statistical information, and improves the cross-dataset robustness. According to the method, the segmentation precision of the SAR ship instance can be remarkably improved.

The invention discloses a corn field weed detection method and device based on improved YOLOv11. The method comprises the following steps: acquiring and constructing a weed image data set for model training; replacing a backbone network of the YOLOv11n baseline network with a double-flow visual network, and taking the replaced network as a first optimized network; replacing the original pyramid pooling module with an enhanced receptive field module at the tail end of the backbone network of the first optimized network to obtain a second optimized network; in a neck feature fusion layer of the second optimization network, a self-adaptive context guide fusion module is used for replacing traditional splicing, and a third optimization network is obtained; and taking the third optimization network as a final lightweight high-precision weed detection model DEA-YOLO11, and detecting field weeds based on the model. According to the method, the problem of low detection precision caused by insufficient global context sensing capability, fine-grained feature loss and low multi-scale feature fusion efficiency of a weed detection model in a complex agricultural scene in the prior art is solved.

The invention provides an urban traffic management intelligent evaluation system and method based on a large language model, and belongs to the technical field of intelligent traffic management. The system comprises a data processing module, an MECA traffic flow analysis module, a feature engineering module, a driving behavior evaluation module, a clustering analysis module, an LLM intelligent decision module, a visualization generation module and a visualization module. The system analyzes the road traffic data collected by the unmanned aerial vehicle, adopts the MECA technology to automatically identify the road type and the traffic environment, intelligently judges the congestion level, evaluates the driving behavior, and combines a big language model to generate a targeted traffic management optimization suggestion. According to the method, the adaptive context sensing technology is innovatively combined with multi-criterion learning, adaptive congestion judgment of different road types is realized, different traffic characteristics of urban expressways, common urban roads and expressways can be accurately recognized, and differentiated management strategies are provided accordingly. The system supports real-time processing of large-scale traffic data, and provides scientific and accurate decision support for urban traffic management departments.

The invention discloses an intelligent agent planning method, system and device based on context awareness and a medium, and belongs to the technical field of artificial intelligence. The method comprises the steps that firstly, user input is analyzed, structured task requirements are generated, and meanwhile, external tool function attributes and historical interaction data are collected; according to the method, an ordered execution plan is generated in combination with task requirements and tool attributes, subsequent plans are optimized by utilizing historical interaction contexts, in an execution stage, parameters required by a target function can be accurately extracted from the task requirements and context information, corresponding tools are called to complete a task, and finally, an execution result is fed back for display, so that the execution efficiency is improved. And performing execution control based on the function calling state and the result. According to the method, more intelligent task planning and execution are realized through context sensing, and the method is particularly suitable for complex task scenes needing multi-step and multi-tool cooperation.

The invention discloses a termite remote identification method based on image processing, and aims to solve the problem that termite activities and out-of-class interference are difficult to reliably distinguish only by indirect visual clues. According to the method, visual representation of an image sequence is extracted through a context awareness framework; utilizing a time sequence diffusion model to analyze a time evolution mode of the indirect visual clues, and evaluating whether the indirect visual clues accord with long-term and continuous activity characteristics of the termites or not; aiming at the uncertain region, inferring the form or evolution trajectory of the uncertain region in combination with a conditional diffusion model, evaluating the diagnosis reasonability, and quantifying the similarity between the observation phenomenon and typical termite activities and extraclass interferents; integrating a preset knowledge graph, and introducing environment context information to carry out matching verification; a plurality of dimensions such as evolution trend, diagnosis rationality, out-of-class interference similarity and context matching degree are integrated, and termite activities are accurately identified through a situation-adaptive fusion decision model; according to the method, the reliability of remote non-destructive monitoring is effectively improved.

The invention belongs to the technical field of intelligent interaction, particularly relates to an intelligent interaction method, system and device based on AI communication and a storage medium, and aims to solve the problems that in the prior art, AI scene coverage is narrow and is separated from a business process; the system comprises four modules: a context sensing and modeling module which collects multi-source context information in real time, and generates a three-dimensional context tensor through structured alignment and time sequence normalization; the intention analysis and reasoning module performs intention level decomposition by using a deep semantic model based on the tensor, and outputs an intention recognition result and probability distribution; the response generation and adaptation module is used for generating multi-mode responses such as texts, voices, images or equipment instructions in combination with intention and context tensors, and outputting the multi-mode responses after consistency verification; and the interactive feedback and optimization module collects user explicit and implicit feedback and is used for updating model parameters and strategy weights online to realize continuous optimization of the system.

The invention discloses a low-voltage series arc fault detection method, system and device, and belongs to the technical field of low-voltage series arc fault detection, and the method comprises the steps: obtaining an original current signal, and carrying out the preprocessing through sliding window segmentation and instance normalization; performing multi-scale feature fusion on the preprocessed analysis unit, and generating fusion features through parallel feature extraction and an attention mechanism; performing context modeling on the fused features through an encoder, inputting a self-adaptive bottleneck layer containing an expert hybrid network, and routing the features to the most appropriate expert network by context sensing gating according to global information; the decoder reconstructs the signal and calculates an error, and generates a dense abnormal fraction sequence; gaussian position weighted aggregation abnormal scores are adopted, and fault judgment is carried out in combination with a self-adaptive threshold decision mechanism based on K-Means clustering. The method can be trained without a fault sample, can dynamically adapt to complex current modes under different loads, gets rid of dependence on the fault sample, and accurately detects the arc fault.

This disclosure describes an interactive augmented reality system for improving surgeon's view and context awareness during laparoscopic and video assisted surgeries. Instead of purely relying on computer vision algorithms for image registration between pre-operation (or intra-operation) images / models and later intra-operation scope images, the system can implement an interactive mechanism where surgeons may provide supervised information in initial calibration phase of the augmented reality function, thus achieving high accuracy in image registration. Besides the initialization phase before operation starts, interaction between surgeon and the system can also happens during the surgery. Specifically, patient tissue might move or deform during surgery, caused by for example cutting. The augmented reality system can re-calibrate during surgery when image registration accuracy deteriorates, by seeking additional supervised labeling from surgeons. The augmented reality system can improve surgeon's view during surgery, by utilizing surgeon's guidance sporadically to achieve high image registration accuracy.

The invention relates to an intelligent work order automatic creation and circulation system and method based on context awareness, provides dynamic modeling, and relates to the field of systems or methods specially suitable for administrative, commercial, financial, management or supervision purposes. The system comprises a work order analysis device which is used for dynamically modeling a convolutional neural network model, and intelligently analyzing a creation demand and a creation type of a skill work order of each consultation work order according to consultation associated data of each consultation work order in a recent time interval; and the automatic creation device is used for executing automatic creation of a subsequent skill work order based on the intelligent analysis result. In order to solve the technical problem that the skill work order of the tourism e-commerce is difficult to synchronously analyze the creation demand and the creation type, an artificial intelligence model obtained by dynamic modeling of the tourism e-commerce is used, and according to context sensing data of a visitor in each consultation work order, synchronous analysis of the creation demand and the creation type of the subsequent skill work order is realized; therefore, the technical problem is solved.

The invention discloses a concrete apparent defect detection method based on image recognition, which comprises the following steps: firstly, collecting concrete surface images with cracks, spalling and other defects, marking and classifying, establishing a data set, and dividing the data set into a training set, a test set and a verification set in proportion; secondly, a DAPF-YOLO detection model is constructed, a YOLO11 model is used as a basic framework, and an input layer, a backbone network and the like are included; the backbone network adopts a double-branch context sensing backbone network to enhance the multi-scale feature extraction capability; the feature fusion layer adopts an AFTRep module to replace an original SPPF module to realize adaptive feature fusion, and adopts a CSP-PAFNet module to improve the multi-scale feature aggregation capability; reFSC Head is used to reduce the amount of parameters and enhance feature expression. And then, training the model by using the training set, monitoring the training through the verification set, and evaluating the performance by using the test set. And finally, inputting a to-be-detected image into the trained model, and outputting defect category and position information.

The invention relates to the technical field of voice recognition intelligent home control, and discloses a non-specific person voice recognition intelligent switch control method and system based on deep learning. The non-specific person voice recognition intelligent switch control method is applied to intelligent switch control equipment, and specifically comprises the following steps of S101, receiving original audio signals continuously collected in a to-be-controlled environment, and preprocessing the collected original audio signals, and then a starting point and an ending point of an effective voice segment are positioned by adopting endpoint detection based on a double-threshold method and combining the characteristic parameters of the short-time energy and the short-time zero-crossing rate. A multi-layer hidden layer structure with Dropout regularization is adopted in a neural network model, the generalization ability of the model is enhanced, a context sensing mechanism is introduced into a semantic understanding module, a composite instruction containing azimuth information can be intelligently analyzed, crossing from recognition to understanding is achieved, and the method has the advantages of being high in practicability and easy to popularize. The system is ensured to maintain a high recognition rate for voice instructions of different users under different environment conditions.

The invention discloses a network security alarm intelligent identification method and device, and relates to the technical field of network security alarm identification, and the method comprises the steps: carrying out the preprocessing of original data from network traffic, log files, user behavior records, a firewall and an intrusion detection system, and obtaining a multi-dimensional security event data set; node modeling and edge relation modeling are carried out based on a GNN and the multi-dimensional security event data set, and cross-system and cross-time-dimension security event association features are extracted to obtain a high-dimensional context sensing feature vector set; a time sequence anomaly detection model is constructed based on a high-dimensional context sensing feature vector set and a time sequence analysis technology, the high-dimensional context sensing feature vector set is input into the time sequence anomaly detection model, and weighting calculation is performed on each time step by extracting time dependent features and combining an Attention mechanism. Outputting an abnormal score vector corresponding to each time point; and constructing a behavior deviation function based on the multi-dimensional security event data in combination with deep learning data.

The invention relates to the technical field of semiconductor detection, and discloses a method and a system for detecting chips on the surface of a semiconductor wafer. The method comprises the following steps: acquiring a high-resolution image of the surface of a wafer, and separating a defect candidate region set from a reference background region through noise filtering and contrast equalization processing; extracting defect areas to be identified one by one, and accessing the defect knowledge graph to obtain potential defect types; performing multi-feature fusion on the potential defect type and the reference background region, generating a defect semantic feature vector through a context sensing encoder, and analyzing the vector to judge the actual defect type; and processing all the candidate areas and then outputting a defect detection report. According to the method, the distinction degree of defects and backgrounds is enhanced, the defect judgment range is narrowed, similar defects are accurately recognized, missing detection and false detection are reduced, the detection efficiency and accuracy are improved, and reliable technical support is provided for wafer production quality control.

The invention discloses a psychological state dynamic evaluation and early warning system based on multi-modal behavior data, which belongs to the field of medical care informatics and comprises a multi-modal behavior data hierarchical coding module, a time sequence causal atlas construction and reasoning module, a double-stage self-adaptive early warning decision module and a context awareness intervention strategy generation module. A cross-modal association mode is extracted through a double-layer coding mechanism, a time sequence graph containing a causal relationship is constructed, causal reasoning is performed, a double-stage mechanism of short-term mutation detection and long-term trend prediction is adopted to generate graded early warning, and an optimal intervention strategy is selected based on a deep Q network according to a user situation. According to the method, the accuracy, timeliness and intervention effectiveness of psychological health assessment are improved, and dynamic monitoring and early warning of the psychological state are realized.

The invention discloses a multi-dimensional collaborative counterfeit feature detection method for digital content, which comprises the following steps of: S1, receiving the digital content to be analyzed, performing format analysis and image extraction on the digital content, and performing standardization processing on the extracted image data; and S2, carrying out multi-dimensional atomic forgery feature extraction on the standardized image data, detecting potential forgery traces of each dimension, and outputting a preliminary analysis result of each dimension. The invention provides a multi-dimensional collaborative counterfeited feature detection method for digital contents, which integrates counterfeited indication information from various sources through multi-dimensional feature extraction and innovative collaborative analysis and context sensing mechanisms, and performs association analysis on the information in an innovative manner, so that the counterfeited counterfeited information is obtained. Therefore, the detection capability of digital content tampering and the interpretability of the result are effectively improved, the accuracy, robustness and interpretability of complex forgery detection are improved, and the feasibility of implementation is considered at the same time.

The invention discloses a small sample insulator infrared image fault detection method and device, and a medium. The method comprises the following steps: constructing a small sample insulator infrared image initial data set; and expanding the initial data set by using a VAE-GAN model, and constructing a mixed training set. A mixed training set is used for training by using an improved YOLOv11 target detection model, the improved YOLOv11 target detection model introduces a BMFPN into a connection layer of a neck network of an original model, and target distinguishing and context sensing capabilities are enhanced by dynamically optimizing multi-scale feature processing. And fusing a SimAM attention mechanism with an SMC3K2 module of an original model, so that the model can focus key features of a target area, and the trained model is utilized to perform automatic fault positioning and recognition on an infrared image of the insulator in a real scene. According to the method, the problem of data scarcity under the small sample condition is effectively solved through the improved generative model, and the detection precision and accuracy of the YOLOv11 model on insulator faults, especially small target faults, are remarkably improved.

The invention relates to the technical field of power equipment state monitoring, and discloses a power equipment fault detection method and device, equipment and a medium, and the method comprises the steps: obtaining a voiceprint signal of power equipment, carrying out the time-frequency transformation to obtain an original logarithmic Mel spectrogram, inputting a multi-scale context sensing auto-encoder model, and outputting a reconstructed spectrogram. According to the model, multi-scale long-range dependence features of voiceprints in time and frequency dimensions are respectively extracted by using a double-flow expansion convolutional network, and complete spectrum reconstruction is carried out based on the extracted features; and calculating an abnormal score based on a reconstruction difference degree between the original logarithmic Mel spectrogram and the reconstructed spectrogram, and when the abnormal score exceeds a dynamic threshold value, judging that the equipment has a fault. Compared with the prior art, the problems that weak fault features are difficult to extract and reconstruction details are fuzzy under strong background noise are solved, and high-robustness non-contact fault detection is achieved.

The invention discloses a full-slice image analysis method based on spatial constraint attention and context perception, and belongs to the technical field of medical image processing and computer vision. The method comprises the following steps: extracting image blocks from a full-slice image, and mapping each image block into a feature vector; constructing a directional dynamic graph; introducing constraints based on spatial position information, and measuring neighborhood influence by a triple relationship; fusing the node features and the edge features to obtain an attention weighting coefficient; updating and optimizing the node representation; and combining the graph convolutional network with instance-level clustering. According to the method, a context sensing graph representation mechanism is constructed, key histological representation is extracted from a full-slice image, and a spatial constraint attention mechanism is introduced to optimize node interaction; therefore, the stability, accuracy and efficiency of tasks such as category discrimination, subtype distinguishing and prognosis related evaluation of large-scale full-slice images are improved in a multi-data-set scene.

A robotic surgical system integrates artificial intelligence (AI) to enable dynamic inference arbitration and risk-driven autonomy. The system includes a surgeon console, robotic arms, and a control system with memory and processors configured to execute real-time surgical workflows. AI modules analyze intraoperative data, such as imaging, sensor input, and instrument telemetry, and compute context alignment scores to guide module selection, forecasting, and fallback execution. Confidence metrics are monitored, with thresholds triggering surgeon alerts, handoff, or autonomous continuation. The system supports intraoperative adaptation, surgeon fatigue detection, and real-time annotation of AI outputs for traceability. It enables improved tissue recognition, predictive planning, and context-aware adjustments through training on historical surgical data. AI-assisted decision support, deviation handling, and performance monitoring enhance safety and personalization across diverse procedures. The architecture supports modular deployment, continuous learning, and integration of multimodal data sources for precision-guided robotic surgery.

The invention provides a method for analyzing and predicting the water stability of an asphalt mixture, which comprises the following steps of: acquiring multi-dimensional service environment, material composition and standard water stability test data, and carrying out standardization and stratified sampling division on the multi-dimensional service environment, the material composition and the standard water stability test data; a context sensing multi-layer perceptron network supporting feature parameter sharing and gradient accumulation is constructed, and the importance weight of each dimension of features under different data distributions is automatically evaluated and normalized. An attention mechanism and a graph convolutional network are introduced, association between features is quantified, and a key high-order interaction relationship is deduced; by means of K-fold cross validation and meta-learning optimization, the generalization ability of the model for new distribution data is improved, prediction accuracy and feature interpretation are improved, and intelligent analysis and proportion design of the water stability of the asphalt mixture are promoted.

The invention relates to an agent generation method and system for enterprise process automation. The method comprises the following steps: S1, obtaining a standard specification document of an enterprise; s2, knowledge extraction and knowledge graph construction; s3, semantic analysis and intention discrimination; and S4, process reasoning and automatic execution. According to the method, full-link intellectualization from enterprise system document automatic analysis to business process closed-loop execution is realized, context sensing and dynamic adaptation capabilities are realized, multiple types of tasks are supported, the analysis fine granularity and cross-system cooperation efficiency of business process rules are effectively improved, and high universality and expandability are realized.

The invention belongs to the technical field of active site prediction, and particularly relates to an enzyme active site prediction method based on a graph neural network. In order to realize high-precision prediction of active sites, the enzyme active site prediction model HFGN adopts a double-branch architecture: enzyme branches integrate a three-dimensional structure, PLM embedding, homology score and EC function annotation, and realize multi-source feature fusion through an attention mechanism; the reaction branch is based on molecular maps of a substrate and a product, chemical reaction specificity is modeled through a map neural network, and finally enzyme-reaction context sensing embedding is realized through a cross-modal attention mechanism, so that the prediction precision and generalization ability of residue-level active sites are improved.

A device and system for visual reasoning in augmented reality environments employs adaptive multi-frame capture triggered by detection of user speech. Upon detecting speech, the device or system captures image frames at an initial frame capture rate, increasing capture frequency when a hand is detected in a captured image. Timestamped frames and transcribed speech form a prompt for a multimodal large language model, which extracts relevant details with constrained output. A separate language model then generates a final response. This two-stage approach optimizes processing efficiency and accuracy while preserving privacy by limiting continuous visual data collection. The system enables more natural and context-aware interactions in AR settings without complex gesture recognition algorithms.

The invention relates to the cross technical field of Internet of Things security and artificial intelligence, in particular to an Internet of Things data security governance method based on a multi-modal large model, which comprises the following steps: S1, multi-modal Internet of Things data acquisition and preprocessing: acquiring multi-modal original data from Internet of Things equipment and platform in real time, the multi-modal original data at least comprises equipment attribute metadata, sensor time sequence data, system log event data and a network flow data packet; according to the method, a multi-modal large model oriented to the field of Internet of Things security is constructed, so that full-process closed-loop management of acquisition and preprocessing of multi-modal data, cross-modal fusion representation, security situation reasoning detection and intelligent management strategy generation is realized; according to the method, the incidence relation among multi-source heterogeneous data is deeply mined, unified context sensing representation is generated, precise threat detection and root cause positioning are achieved in combination with parallel reasoning branches, and finally a governance instruction is automatically generated and executed.

The invention provides a circuit energy efficiency automatic optimization method. The method comprises the following steps: firstly, modeling a circuit gate-level netlist into a directed graph; extracting a key path by using a static time sequence analysis tool, and extracting a context sensing key sub-graph containing the path and a first-order neighborhood node of the path; thirdly, feature coding is conducted on the key sub-graphs through a graph neural network, and state vectors representing the local state of the circuit are generated; the state vector is input to a reinforcement learning policy network to decide a drive capability adjustment action for a particular standard cell. And the system modifies the netlist according to the action, calls the static time sequence analysis tool again to evaluate the modified performance, power consumption and area indexes, and generates a reward signal to iteratively optimize the strategy network. By constructing the closed-loop optimization process, the problems of low manual optimization efficiency and unbalanced power consumption and area balance are solved, automatic and intelligent collaborative optimization of circuit energy efficiency is realized, and the design quality and efficiency are remarkably improved.