786 results about "Information loss" patented technology

The invention relates to a remote sensing image change detection system and method based on multi-modal deep learning. According to the system, the detection precision is improved by constructing a twin network fusing a CNN, a Transform and an attention mechanism; a lightweight backbone network is constructed by adopting multi-size depth separable convolution, and noise is adaptively suppressed and feature expression is enhanced in combination with a soft thresholding channel space attention mechanism; harr wavelet transform downsampling is innovatively introduced to reserve high-frequency details, and traditional pooling operation is replaced to reduce information loss; the twinborn branch semantic deviation is relieved through cross-channel feature exchange, and a Transform codec is used for modeling a long-range dependency relationship; in the decoding stage, edge feature recovery is enhanced by adopting feature splicing and a progressive up-sampling strategy; according to the system, model parameters are reduced, meanwhile, the precision and the anti-interference capability of remote sensing building change detection are remarkably improved, and the system has the advantages of high efficiency and detail keeping.

The invention provides an RAG knowledge base construction method and system based on hierarchical semantic indexes. The method belongs to the cross technical field of artificial intelligence and information retrieval. The method comprises the following steps: performing multi-level semantic analysis on an input original document set to generate document semantic hierarchical structure data; constructing a hierarchical semantic index tree based on the document semantic hierarchical structure data; performing dynamic knowledge graph initialization according to the hierarchical semantic index tree to generate an initial dynamic cognitive graph; and collecting real-time interaction data through a user feedback interface and a new data access module, and performing incremental updating on the initial dynamic cognitive map to form a knowledge representation system supporting life cycle evolution. Through multi-level semantic analysis and construction of a hierarchical semantic index tree (HSIT), deep semantic analysis can be performed on an original document set, the context integrity of knowledge is ensured, structured storage is realized, the knowledge can be expressed and stored more accurately, and information loss or semantic ambiguity is avoided.

The invention discloses an electric power system short-term load prediction method based on TCN-BIGRU-CBAM, and the method comprises the steps: obtaining a standard load time series data set as input data, and carrying out the preprocessing of the input data; establishing a time convolutional network TCN model, and extracting a long-term dependency relationship in the time sequence; establishing a two-way gating cycle unit network BIGRU, and outputting two-way time characteristics; constructing a convolutional block attention module (CBAM), and obtaining a four-dimensional feature map subjected to channel and space attention enhancement; and establishing a full connection layer, mapping the time sequence characteristics to a scalar through linear transformation, and outputting a power load prediction value of a target time step. According to the method, the problems of information loss, difficulty in high-dimensional feature extraction and the like of a traditional short-term load prediction model in complex time sequence modeling are effectively solved, the precision and the stability of short-term load prediction are remarkably improved, and the method has relatively high practical value and popularization potential.

The invention relates to the technical field of image processing, in particular to an integrated image restoration method and system based on image mask modeling, and the method comprises the following steps: collecting the boundary data of the whole image, separating the boundary of a mask region from the boundary of a non-mask region, and analyzing the gradient change characteristics of the boundary of the mask region. And extracting local depth information of the mask region from the gradient change features, and constructing a mask region boundary residual distribution model. According to the method, the mask region and the non-mask region are separated, the boundary gradient features are extracted, the residual distribution model is established on the basis of gradient change, the subtle change of the boundary of the mask region is accurately captured, and the accuracy of the boundary features is improved. By constructing a depth information data set and combining local depth information with gradient features, adjustment of layered dynamic weight distribution of a mask area basic structure is realized, effective extension of area features to the inside is ensured, and the possibility of boundary information loss is reduced.

The invention relates to the technical field of deep learning and defect detection, and discloses a multi-modal fusion defect perception and identification method based on deep learning, and the method comprises the steps: 1, obtaining multi-modal data: obtaining the multi-modal data of a target object through a collection device, and obtaining the multi-modal data of the target object; comprising visual data and non-visual data, and the data format covers two-dimensional images, three-dimensional point clouds, time series data and the like. According to the multi-modal fusion defect perception and identification method based on deep learning, the advantages of visual data and non-visual data can be integrated through multi-modal data fusion, the characteristics of a target object are described more comprehensively, information loss caused by single-modal data is reduced, the accuracy of defect detection is improved, and in industrial part detection, the detection efficiency is improved. By fusing the image and the point cloud data, defects such as cracks and holes on the surface of the part can be identified more accurately, and the detection precision is obviously improved compared with that of a single-mode method.

The invention relates to the technical field of data expression, in particular to a GIS-based geological disaster monitoring point data expression method and system, and the method comprises the following steps: obtaining the detection data of geological disaster monitoring points, calling the space coordinates of the monitoring points and the corresponding monitoring data, calculating the spatial correlation according to the spatial distance between the monitoring points, and calculating the spatial correlation; and calculating monitoring data of the uncollected points according to the spatial correlation, and generating an interpolation monitoring value. According to the method, the monitoring data of the non-collection points are calculated according to the spatial correlation among the monitoring points, so that the integrity of the spatial data is improved, monitoring information loss caused by incomplete data collection is avoided, the local density of the monitoring points is analyzed, different grid scales are divided according to density characteristics, and the partition expression precision of the data is improved; based on the space coordinates, the timestamps and the disaster intensity data of the monitoring points, the multi-level visual expression is constructed, so that the monitoring information is intuitive, and the interpretability of the disaster monitoring data is improved.

The invention discloses a Transform remote sensing semantic segmentation method based on an adaptive dynamic attention mechanism, and relates to the technical field of the adaptive dynamic attention mechanism, and the method comprises the following steps: obtaining a remote sensing image data set, and carrying out the data marking and data enhancement; adjusting the remote sensing image into a model input size and a pixel value unified specification, and generating a standardized data set; feature embedding is generated based on a preset embedding module, and position coding is added to enhance position information for decoding; correcting the decoding process by using a self-attention and cross attention module; combining different scale features with continuously iterated updated adaptive weights to obtain a fusion feature value; constructing a multi-task joint loss function based on the to-be-solved problem; refining the fusion feature value through a segmentation module, and generating predicted values of different sub-tasks; according to the method, the problems of information loss and inconsistency possibly occurring during feature fusion in a multi-scale task in a traditional method are solved.

The invention discloses a micro posture recognition method based on multi-modal feature fusion and fine adjustment, and relates to the field of computer vision and action recognition. The method is characterized in that a universal cross-modal knowledge fusion framework is provided, and multi-modal features are respectively extracted through a fine tuning network by using three kinds of modal information of video, skeleton and text. Meanwhile, a video-skeleton and text-skeleton fusion module is introduced to enhance the interactivity between modals. Compared learning is adopted to align feature distribution, and model training is optimized in combination with a freezing-fine tuning strategy, so that the calculation complexity is reduced, and the recognition efficiency is improved. According to the method provided by the invention, the problem of single-mode information loss can be solved, the perception capability of tiny attitude change is enhanced, the recognition precision and robustness are improved, and the method is suitable for multiple application scenes such as behavior monitoring, human-computer interaction and safety protection.

The invention discloses a document knowledge base LLM intelligent question and answer method, device and equipment and a storage medium. The method comprises the steps that dynamic partitioning is conducted on a to-be-processed document, and vectorization and index storage are conducted on knowledge blocks; vector retrieval and keyword retrieval are carried out according to the natural language question, a vector retrieval result and a keyword retrieval result are fused, an answer is obtained through LLM, and the answer is fed back to a user after being safely filtered; when it is detected that the to-be-processed document is updated, the vector library and the index are synchronously updated, and the cue word template and the partitioning strategy are periodically optimized, so that the problem of form picture information loss can be solved, the integrity of document information analysis is guaranteed, the situation that the partitioning strategy is single is avoided, the multi-hop recall rate is increased, and the problem of semantic missing is avoided; a partitioning mechanism is reasonable, retrieval precision is improved, updating cost is reduced, data security is improved, implementation is convenient, universality is good, and the speed and efficiency of LLM intelligent question answering of the document knowledge base are improved.

The invention discloses a pavement throwing detection method and device based on deep learning, electronic equipment and a program product. The method is realized through a trained detection model, the model adopts an LMSADet detection head, a multi-scale feature extraction and space attention mechanism is introduced into a task branch, and multi-scale modeling is decoupled from a backbone network and a neck network and integrated to the detection head so as to fit a detection task to directly optimize local details and scale differences of a throwing target. In order to suppress background interference and improve the recognition effect of fuzzy boundaries, the neck network is added into an MSHA module so as to efficiently capture the semantic relation between the thrown object and the background and enhance the regional understanding ability. A C3ESP module is introduced into the backbone network, deep features are extracted through stacking depth separable convolution, and information loss is avoided in combination with residual optimization fusion; meanwhile, a PEMA attention mechanism is introduced, the importance of different receptive field features is dynamically adjusted, the model focuses on key features, data information is captured more comprehensively, and therefore the detection performance is remarkably improved.

The invention discloses a fireman occupational health risk dynamic prediction method based on multi-modal data fusion, relates to the technical field of occupational health risk assessment, and aims to solve the problems of information loss, difficulty in capturing cross-modal complex dependence, lack of interpretability of prediction results and the like when unstructured data is processed in the prior art. The method comprises the following steps: intelligently analyzing a multi-modal document, and analyzing unstructured physical examination information into structured data; performing semantic standardization and knowledge graph dual verification of domain knowledge enhancement; the time-space-static cross-modal attention deep fusion network is used for modeling physiological, dynamic and situational data; and carrying out interpretable risk prediction and attribution. By adopting the technical scheme, the method can realize early, accurate, dynamic and high-credibility prediction of the occupational health risk of the firefighter, provides quantitative attribution, and improves the transparency and application value of the system.

The invention provides a power consumption behavior diagnosis system and method based on an anomaly detection model, and relates to the technical field of power system anomaly diagnosis, and the system comprises a data collection module which is used for collecting the data of the power consumption behavior of a user; the data preprocessing module is used for obtaining basic time sequence data; the feature extraction module is used for extracting multi-dimensional features; the feature selection module is used for screening the multi-dimensional features by adopting a feature screening function to obtain a screened feature set; the model processing module is used for inputting the screened feature set into a pre-trained anomaly detection model to generate a reconstructed feature sequence; the anomaly recognition module is used for calculating an anomaly score; and the diagnosis output module is used for outputting the abnormal level and generating an abnormal diagnosis report of the power consumption behavior of the user. The method can effectively solve the problems that in the prior art, the modeling capacity of complex multi-dimensional time series data is insufficient, feature extraction and selection depend on manual work, information loss is likely to be caused, and the anomaly detection precision and the calculation efficiency are difficult to balance.

The invention discloses a retrieval enhancement generation method and system based on a composite knowledge base, and belongs to the technical field of retrieval enhancement generation. Obtaining an original document, dividing the original document into text blocks according to paragraphs, and storing the text blocks into a vector database to form a text vector library; constructing a knowledge hypergraph based on the text blocks, and storing the knowledge hypergraph into a vector database to form a knowledge hypergraph database; obtaining cases and storing the cases into a vector database to form a case library; and obtaining a question input by a user, and generating a final answer by using a large language model based on a composite knowledge base formed by the text vector base, the knowledge hypergraph database and the case base. By constructing a composite knowledge base, the problems of context breakage, information loss, lack of professional expression and the like existing when an existing retrieval enhancement generation method is used for processing long documents are solved.

The invention discloses a multi-modal multi-scale retrieval enhancement generation method, system and device applied to external knowledge questions and answers and a medium. The method comprises question perception, multi-modal multi-scale query fusion coding, dense recall and answer generation. Analyzing a key query phrase from the question through a fine-tuned instruction language model, and accurately positioning a region of interest corresponding to the phrase in an image by using an open set visual positioning model; multi-source information is compressed and distilled into an optimal query vector through a deep fusion network integrating multi-head self-attention and an information bottleneck theory; executing a maximum inner product search to recall related knowledge; guiding the large language model to synthesize all information to generate a final answer; the system, the equipment and the medium directly perform feature fusion in the vector space based on the method, so that challenges such as information loss and cascading errors caused by a traditional normal form can be effectively dealt with, high correlation and high accuracy of retrieval knowledge are ensured, and accurate and reliable image-text questions and answers are realized.

The invention discloses a software requirement modeling method and device based on a natural language, equipment and a medium, and relates to the field of software requirement modeling. According to the software demand modeling method and device based on the natural language, the equipment and the medium, through automatic demand analysis and model generation, the manual workload is greatly reduced, and meanwhile, errors caused by manual understanding deviation are reduced; close connection among the steps is ensured by sharing intermediate representation and a unified data model, output of the previous step provides structured information for input of the next step, and information loss or distortion is avoided; a demand provider is allowed to participate in a model confirmation process through an iterative demand acquisition and modeling framework of man-machine cooperation, and the quality of a demand model is continuously improved; by means of the model consistency analysis technology based on the data flow dependency relationship, the problem of inconsistency between the requirement specification and the software model can be automatically detected, and the software product quality is improved.

The embodiment of the invention provides an equipment predictive maintenance method and device based on artificial intelligence, relates to the technical field of data processing, and aims to adjust a model focus by identifying statistical characteristics of data and paying attention to characteristics with most significant changes. Moreover, the training sample set of the feature extraction model is adjusted based on the abnormal evaluation result, abnormal data can be suppressed, the model is protected from being interfered by the data, and the feature extraction effect can be optimized. Quantum state modeling can better capture a nonlinear relation in data, information loss of a traditional linear dimension reduction algorithm is avoided, and therefore the expressive power of features after dimension reduction is improved. According to the embodiment of the invention, through data compression and optimization feature extraction based on quantum state amplitude, the prediction precision and reliability of the model are improved, the quality of data input into the classifier model is ensured, and the risk of misjudgment is reduced.

The invention provides an infrared and visible light image end-to-end registration method based on phase consistency enhancement, and belongs to the technical field of electric digital data processing. The invention discloses an infrared and visible light image end-to-end registration method based on phase consistency enhancement, which comprises the following steps of: firstly, preprocessing infrared and visible light source images in an image database, and dividing the infrared and visible light source images into a training set and a test set; and constructing a feature extractor based on a phase consistency attention mask, realizing cross-modal consistency feature extraction, and focusing an important space region by using phase information. A self-defined ResSobeNet network is adopted for parameter estimation, and Haar wavelet transform is used for replacing pooling operation, so that information loss in a traditional down-sampling method is avoided. A double-branch generator structure is designed, affine parameters and an optical flow field are generated respectively, meanwhile, constraint on rigid registration and non-rigid registration is achieved, and finally a registration result is obtained through resampling of a space conversion module. According to the method, the problems that cross-modal consistent features are difficult to extract in the infrared and visible light image registration process and explicit registration steps are complicated and difficult to generalize are solved, and the method has good universality and practicability.

The invention provides a personalized health monitoring method based on multi-instrument data fusion, and aims to solve the problems of non-uniform data standards and poor interoperability of existing multiple health monitoring devices. According to the method, a data standardization protocol is automatically selected and optimized according to data characteristics (such as sampling frequency, data precision and the like) of different devices (such as oximeters, sleep monitors and the like) through a machine learning algorithm, and seamless fusion of data between the devices is ensured. Different from a traditional static data processing mode, the scheme not only improves the collaboration efficiency among the devices, but also effectively solves the problems of inconsistent data formats and information loss. For different types of data generated by multiple devices, a data fusion strategy is automatically identified and optimized by adopting an intelligent fusion algorithm based on deep learning, and information conflict or delay caused by mismatching of data features is avoided.

The invention discloses a management method, system and equipment for energy conservation prediction and a medium, and relates to the technical field of energy conservation optimization, and the method comprises the steps: collecting multi-source data of an energy use terminal, and carrying out the preprocessing of the multi-source data; and performing feature extraction and data analysis on the preprocessed data, and performing energy consumption prediction based on a deep neural network model. And generating an energy-saving strategy according to a prediction result, and performing dynamic optimization adjustment through a feedback mechanism. According to the method provided by the invention, the feature weight is adaptively adjusted through the L-VAE, the model calculation complexity is reduced, and the feature representation capability is improved, so that the energy consumption prediction can still keep efficient operation under the conditions of large data volume and complex features, and information loss is avoided. The method improves the adaptability of energy consumption prediction, enables the prediction result to be rapidly adjusted in the case of sudden load change, guarantees the prediction accuracy, and supports the making of a finer energy-saving strategy.

The invention relates to the technical field of security alarm, in particular to a home security integrated system and method based on artificial intelligence, and the system comprises a multi-source data collection module which collects environment data through an infrared sensor, a thermal imaging camera and a standard video camera, carries out the data fusion, and generates an environment data fusion result. According to the invention, the acquisition module fuses infrared, thermal imaging and video cameras, so that the monitoring coverage is expanded, the capability of capturing environmental details is enhanced, and the detection rate of abnormal behaviors is improved. Standardized monitoring data is generated through data fusion and format unified processing, high-quality input is provided for feature extraction and behavior analysis, data streams are optimized, and information loss is reduced. A pre-trained deep learning model is applied to an intelligent analysis module, recognition of complex behavior modes is enhanced, and the accuracy and timeliness of anomaly detection are improved.

The invention provides a multi-modal sentiment analysis method for contrast enhancement non-isomorphic modal fusion, and relates to the technical field of sentiment analys.According to the method, a visual modal feature and an audio modal feature are fused to make up for the difference of information density with text modalities, and joint contrast learning is designed to make up for the difference of information density with text modalities. The loss in the samples, the loss between the samples and the contrast learning in the samples are obtained through the contrast learning in the samples and the contrast learning between the samples, so that the consistency between the modes and the contrast learning between the samples are enhanced, the independence and the uniqueness of each mode feature are guaranteed, and information loss caused by excessive fusion is avoided. According to the method, complementarity and difference between modes are considered, the difference of information density between the visual and audio modes and the text mode is made up, and the performance and generalization ability of the sentiment analysis model are improved.

The invention discloses a self-adaptive cross-domain remote sensing image semantic segmentation method based on an unsupervised domain, and the method comprises the steps: pre-training an encoder-decoder segmentation skeleton through a source domain image and a semantic tag of the source domain image; the difference between the domain invariant feature and the specific feature of the target domain is measured through the difference loss, and the reconstruction loss is applied to prevent information loss; the domain invariant high-level features of the source domain and the target domain are aligned based on an antagonism method; respectively processing high-layer and shallow-layer invariant features of a target domain through a main decoder and an auxiliary decoder of the decoders, and ensuring that prediction results of different levels of features are consistent by utilizing consistency loss; and fusing the domain-invariant high-level features and the specific features of the target domain to generate target domain fusion features, and optimizing the cross-domain adaptability of the decoder by aligning the prediction result of the target domain fusion features with the prediction result of the domain-invariant features. According to the method, the segmentation performance on the target domain is remarkably improved through feature decoupling and feature representation enhancement in combination with target domain self-learning based on pseudo labels.

The invention relates to the technical field of deep learning and target detection, and discloses a manhole cover hidden danger detection method based on target detection, which comprises the following steps: inputting a manhole cover image into a backbone network of a target detection model, extracting a plurality of primary feature maps of different scales through a plurality of extraction units which are sequentially connected in series, and inputting the primary feature maps into a neck network based on an FPN structure; and after outputting the fusion feature map corresponding to each scale, inputting the fusion feature map into the head network, and outputting a prediction label, a prediction bounding box and confidence corresponding to each scale of the manhole cover image. The dynamic up-sampling module generates a sampling offset to adjust a sampling network, so that small target information is better captured, and the detection and segmentation precision of details in a well lid image is improved; the inversion residual attention down-sampling module introduces an attention mechanism and a depth separable convolution and residual mechanism, effectively compresses data, reduces information loss, enhances attention on key features of a manhole cover image, improves feature expression of the model on manhole cover hidden dangers, and further improves manhole cover hidden danger detection precision.

The invention discloses a static database self-adaptive fuzzification desensitization implementation method for highly sensitive data in the power industry. The method comprises the following steps: a data preprocessing module, a sensitivity analysis module, a fuzzification algorithm module, a self-adaptive mechanism module and an effect evaluation and verification module. Compared with the prior art, the method has the advantages that 1, self-adaptive dynamic adjustment is performed: a desensitization strategy is automatically optimized according to data characteristics and use scenes, and privacy protection indexes (such as k-anonymity values) are improved by 30%-50%; 2, multi-dimensional balance: the information loss rate is reduced by 10%-20%, the query accuracy is improved by 5%-10%, and safety and service requirements are considered; 3, system expansibility: a plug-in architecture supports dynamic loading of new algorithms and rules, and a configuration management module realizes strategy real-time adjustment; and 4, compliance guarantee: through a law and regulation rule base and a compliance check algorithm, the compliance is ensured to meet the standard.

The present invention discloses a fusion method for multimodal data complementary, error correction and loss tolerance mechanisms. The steps include: collecting microwave data stream and infrared data stream under the same scene; extracting features from the microwave data and infrared data stream, adopting a multimodal feature fusion framework, and through adaptive learning of the weights of the features, performing complementary fusion of multimodal data; designing a multimodal generation network to generate lost modal data, and automatically identifying and correcting the registration error caused by the sensor by using the registration difference; performing fault tolerance processing on information loss and error correction, dynamically adjusting the weights of different modalities in the fusion, and enhancing the stability of the system in complex environments. Driven by deep learning, the complementary nature between multimodal data is fully exploited, the problems of information loss, registration error and fault tolerance are solved, the accuracy and robustness of microwave and infrared data fusion are significantly improved, and it is applicable to target detection and recognition in complex data fusion tasks.

The invention belongs to the technical field of target detection, and discloses an unmanned aerial vehicle image target detection method based on an improved RTDETR model, and the method introduces an FAPPA module in a backbone network, and effectively alleviates the information loss problem of small target features in a deep network. According to the method, a feedforward neural network of an AIFI module in an encoder is replaced by an EDFFN module, and high-frequency features such as edges and textures are selectively enhanced by using a patchwise FFT strategy and learnable frequency domain weights. Besides, according to the method disclosed by the invention, adaptive alignment of multi-scale features is realized by utilizing a Zoomcat module, and local details and global semantics are fused by utilizing a convolution attention double-path architecture of a CAFMFusion module, so that complementary information of different levels is fully utilized. According to the method, the unmanned aerial vehicle image target detection capability can be remarkably improved, and small target features in a complex background can be identified.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes such as robot agent decision making, financial science and technology and medical health, and discloses a data sequence generation method, device and equipment based on joint modeling and a medium. Encoding is performed as a sequence of potential visual tokens and a sequence of action tokens, a unified potential representation is generated, a prediction sequence is generated using a visual diffusion decoder and an action diffusion decoder, a random mask is applied, a loss value is determined, parameters are optimized, loop training is performed, and a target future action sequence and / or a target future video frame sequence is generated. According to the method, the visual information and the action information are fused, the unified potential representation is constructed, the visual diffusion decoder and the action diffusion decoder are jointly optimized, error transmission and information loss caused by independent prediction are avoided, and the prediction accuracy under multi-modal data and the overall reasoning efficiency of the system are improved.

The invention discloses an underground target imaging method and imaging system based on variable scale deep learning, and belongs to the technical field of geophysical exploration, and the method comprises the following steps: S1, building a multi-scale magnetic survey data set; s2, constructing a UNet + +-based variable-scale deep learning network model, and performing feature fusion on pooled information and information subjected to split convolution by a dynamic information fusion layer so as to adapt to input data of any size and make up for information loss caused by pooling; s3, designing a multi-constraint loss function formed by mean square error and loss weighting; s4, migration learning is adopted, model parameters trained in a fixed size serve as initial weights, progressive training of multi-size data is achieved by freezing and fine tuning of decoder parameters, and finally the effectiveness of the whole network model is verified according to a test set prediction effect, so that the universality and flexibility of the network are improved when data of different sizes are processed, and the network performance is improved. And large-size data features in a specific area can be quickly adapted.

The invention provides a large model-based text generation and information recovery method and system. The method comprises the following steps of: converting complex situation information into simple and accurate text description by utilizing a text generation model; converting the text description of the situation information into a semantic vector by using a text encoder of a pre-trained CLIP model, and capturing semantic information of the text; encoding the noise image into a hidden space representation by using an encoder of a variational auto-encoder, and learning a compressed representation of the image; fusing the semantic vector generated by the pre-trained CLIP model with the image hidden space representation generated by the encoder of the variational auto-encoder, and optimizing the fused hidden space representation by using the trained de-noising Unet network; the optimized hidden space representation is decoded into a high quality situational information image using a decoder of a variational auto-encoder. By applying the technical scheme of the invention, the technical problem of incomplete received information caused by inevitable information loss in the data compression process in the prior art is solved.

The invention discloses a multi-model game method for multi-scene intelligent safety management. The method comprises the following steps: acquiring multi-domain original data and generating information structure features; the heterogeneous indexes are uniformly mapped into information loss components based on the features, and stable routing of the game template is carried out in combination with sequential statistics and hysteretic decision; performing game payment-oriented decision sensitive subspace compression on the unified semantic state output after routing; a dynamic risk-to-go (dynamic risk surplus) time difference method is adopted to generate step risk signals meeting total amount conservation, and the step risk signals are injected into a compression space; and through online estimation of the spectral radius of the Jacobian matrix of the optimal response operator, an equilibrium vulnerability index is generated, and stability management and closed-loop adjustment of an equilibrium point are realized. According to the invention, the decision adaptability, stability and robustness of the intelligent system in a dynamic confrontation environment can be improved.