288 results about "Interaction network" patented technology

The invention belongs to the field of combat management, and particularly relates to an air real-time combat management system based on hybrid intelligence, which is characterized in that a cross-domain thermodynamic perception situation map and a cross-domain fusion situation map are generated through sensing module multi-modal target data through situation fusion, threat analysis and target sorting; the hierarchical autonomous decision-making module analyzes combat requirements by using an enhanced hierarchical and hierarchical subsystem, constructs and generates a sub-target sequence and an interactive network through target decomposition and a constraint node network, and generates an optimal tactical control instruction set in combination with resource prediction allocation and tactical evaluation; the cross-layer intervention module dynamically adjusts an autonomous decision permission weight and an instruction according to a task risk, a communication state and a decision score, and realizes the control right of a person in a decision ring by using the instruction of a commander as the highest intervention decision instruction of each layer; the analogue simulation module integrates the subsystems and the man-machine constraint boundary, and performs collaborative decision simulation until the standard is reached; and integrated management of real-time sensing of the air combat situation, intelligent hierarchical decision under dynamic constraint, man-machine permission flexible adjustment and collaborative simulation verification is realized.

The invention relates to the technical field of medical monitoring and early warning, and discloses a postoperative care risk early warning system for neurosurgical patients. According to the system, real-time monitoring flows of electroencephalogram, intracranial pressure and body surface myoelectricity of a patient are continuously received, and a dynamic interaction network of neuroelectrophysiology and intracranial mechanical states is constructed so as to represent the coupling relation among electroencephalogram rhythm, pressure conduction and postoperative skull windowing area tissue compliance. The system analyzes three types of core parameters of cerebral cortex excitability, cerebrospinal fluid circulation load and cranial cavity compensation space from the network, further calculates a brain tissue perfusion risk and a neural structure compression risk, and generates a corresponding nursing early warning signal and a physiological regulation path. According to the scheme, deep coupling and mechanism risk early warning of multiple physiological signals are achieved, and the postoperative complication risk can be recognized earlier and more accurately.

The invention belongs to the technical field of environment regulation and control, and discloses a livestock breeding environment dynamic prediction and regulation and control method based on machine learning. Comprising the following six key steps: firstly, preprocessing multi-source data, and constructing a standardized feature data set; secondly, performing time sequence feature extraction and environment-biological response correlation analysis to obtain a multi-factor interaction network; then, constructing a dynamic coupling model of the environmental parameters and the production efficiency; a multi-target reinforcement learning regulation and control model is constructed based on the animal husbandry production efficiency prediction space; predictive environmental parameter regulation and risk assessment are carried out in combination with real-time monitoring data; and finally, intelligent environment intervention is executed, and a regulation and control strategy is iteratively optimized through production index feedback. According to the method, the breeding environment is converted from passive response to active prediction, complex correlation understanding between environmental factors and biological indexes is established, and multi-target balance optimization of production efficiency, energy consumption and animal welfare is achieved.

The invention discloses a video crowd counting method based on a cascaded cross-domain feature interaction network. The method comprises the following steps: carrying out data enhancement processing of random cutting and horizontal flipping on a current frame and front and back frames of the current frame; and constructing a cross-domain feature interaction network composed of a spatial domain branch and a frequency domain branch. The frequency domain branch extracts frequency domain feature output of different stages through a high and low frequency signal aggregation module and a feature encoder based on adjacent frames; the spatial domain branch is based on a single-frame image, and static spatial semantic features are extracted through a feature encoder. Cascade fusion is carried out on the double-branch features on multiple scales, two-way channel cross attention is utilized to reconstruct time sequence correlation frequency domain features of a current frame, and fusion and reconstruction of the two domain features are achieved through a cross-domain feature mutual modulation module. And after the reconstructed double-branch features are processed by the fusion network, outputting a crowd density map of the current frame by a density regression head. And after training is completed, storing the optimal model for video crowd counting. According to the invention, through cross-domain feature cascade and bidirectional time sequence modeling, the accuracy and robustness of crowd counting in a video scene are effectively improved.

The invention discloses a fleet formation flight implementation method and device, equipment, a medium and a product, and belongs to the technical field of aeronautical communication. The method comprises the following steps: establishing an air-ground interconnection communication link to realize real-time data interaction between a ground control unit and a plurality of flying civil aircrafts and between the plurality of civil aircrafts; based on an air-ground interconnection communication link, an airborne ad hoc network with a leading aircraft as a core is constructed, and an information interaction network in the formation is formed. Acquiring formation flight environment data; determining a safe area of formation flight based on the formation flight environment data, and calculating the maximum number of aircrafts in the formation and the relative positions of the aircrafts in the formation; and issuing the maximum number, the relative position and the flight decision information to each airplane in the formation through the airborne ad hoc network so as to execute formation flight. According to the embodiment of the invention, the real-time performance and accuracy of communication between the fleet can be effectively improved, and the airspace operation efficiency and flight safety are improved through the formation flight of the fleet.

The invention discloses a multi-modal data joint embedding method based on a hierarchical progressive arithmetic interaction network, and the method comprises the following steps: obtaining text data and image data from the same semantic entity, and extracting a text local feature, a text global feature, an image local feature and an image global feature; inputting the text local feature and the image local feature into an atomic layer, and processing based on a Cartesian product to generate a first-order interaction feature; inputting the first-order interaction features into a combination layer, and carrying out nonlinear transformation processing to generate enhanced nonlinear interaction features; and inputting the nonlinear interaction features into the aggregation layer, and generating a multi-modal joint embedding vector in combination with the text global features and the image global features. The method effectively solves the problems of modal isomerism, single interaction level, lack of dynamic adaptability and the like in the prior art.

The invention discloses a method for depicting and analyzing a heavy non-aqueous phase pollution site based on microbial structure information, which comprises the following steps of: based on a historical geological survey report and a historical leakage event, defining a pollution analysis area, and constructing a site pollution conceptual model by combining high-density resistivity with a stable isotope tracer method; performing sample collection and detection on the target area to obtain area sample detection information; performing microflora analysis according to the regional sample detection information, and judging a potential pollution retention region of the target region to obtain pollution region analysis information; carrying out multi-source data coupling by combining pollution area analysis information and area sample detection information, and carrying out pollution field three-dimensional description on a target area to obtain an area DNAPLs pollution condition diagram; a degradation function gene interaction network is constructed, restoration potential grading is performed on a target area, and area restoration suggestion is performed, so that the limitation of a traditional investigation method is broken through, and accurate analysis and restoration assistance of pollution distribution are realized.

The invention discloses a large model system based on a calculation acceleration chip, and relates to the field of large models. Comprising a plurality of calculation acceleration units and a management server, and an inter-chip and off-chip data interaction network is formed and realized; a mixed video memory of the calculation acceleration unit is matched with an SSD to form a multi-source storage mode; a normalized network-on-chip, an interconnection transmission system, a storage control system and a plurality of calculation acceleration cores are arranged in the calculation chip; the normalized network-on-chip can read model parameters of a target position based on the storage control system and send the model parameters to the calculation acceleration core for calculation and recovery; and interacting with the management server and other computing acceleration units based on the interconnection transmission system, and reading and storing external model parameters and inter-chip model parameters. The technical problems of insufficient video memory capacity, too high hardware cost and limited transmission bandwidth in a traditional scheme are effectively solved through collaborative design of a mixed video memory architecture and a normalized network-on-chip in combination with a multi-stage routing control and dynamic configuration mechanism.

The invention relates to the technical field of computer mode recognition, and discloses a multi-source heterogeneous data fusion processing and key feature extraction method and system, and the method comprises the steps: achieving the adaptive caching and granularity normalization of streaming data through a dynamic buffering queue and a time alignment window; generating a structured vector of a unified space-time reference by using a structured analysis module; a high-dimensional fusion feature tensor is constructed through two-stage convolutional coding and a cross-source attention interaction network; and a key feature channel is screened based on gradient sensitivity through a differentiable channel pruning module. The system comprises a multi-source data access unit, a dynamic buffer management unit, a time alignment unit, a synchronous resampling unit, a structured analysis unit, a primary fusion coding unit, a cross-source attention interaction unit, a time sequence dependence modeling unit, a feature importance evaluation unit, a key feature screening unit and the like. According to the method, efficient, accurate and low-overhead multi-source heterogeneous data real-time fusion and task-oriented key feature extraction can be realized.

The invention provides a QoS prediction method, system and device based on a multilayer graph attention mechanism, and the method comprises the steps: inputting a user embedding matrix generated based on a user context graph and a service embedding matrix generated based on a service context graph into a QoS prediction model, performing feature aggregation on the user embedding matrix through a first multilayer graph attention network to obtain a first feature vector of a to-be-tested user node, and performing feature aggregation on the service embedding matrix through a second multilayer graph attention network to obtain a second feature vector of a to-be-tested service node; fusing the first feature vector and the second feature vector through a feature interaction network to obtain a first fused feature vector, and predicting the first fused feature vector through a feature prediction network to obtain a first QoS prediction value between the to-be-tested user node and the to-be-tested service node; and performing error correction on the first QoS predicted value according to the first fusion feature vector and the high-error sample memory bank. The QoS prediction accuracy can be improved.

The invention discloses a diabetes cognitive impairment method based on metabonomics analysis and prediction, and relates to the technical field of biological information, and the method comprises the following steps: S1, obtaining metabonomics data and immunomics data from a peripheral blood sample of a diabetic patient, extracting relevant time sequence data aiming at glucose metabolism, and calculating the glucose metabolism related time sequence data; processing the sequence data by adopting a time sequence analysis algorithm to obtain time sequence change characteristics; s2, constructing a cross-omics interaction network according to time sequence change characteristics, integrating an incidence relation between metabolite concentration and immune factor expression, and setting a dynamic interaction mode; according to the diabetes cognitive impairment method based on metabonomics analysis and prediction, through multi-omics data integration and dynamic network analysis, the precision and reliability of diabetes cognitive impairment mechanism analysis are remarkably improved, and a theoretical basis is provided for precise intervention.

The invention discloses a graph contrast learning drug recommendation method and system fusing clinical features, and relates to the technical field of intelligent medical treatment. The method comprises the following steps: acquiring a clinical medical record database of a patient, extracting diagnosis, operation and medicine information of each treatment, and constructing a historical medical record heterogeneous graph of the patient; in heterogeneous graph propagation, a structure guidance matrix (mask) is generated by using a conditional probability matrix to guide node connection and attention generation, and the expression stability is improved in combination with Hellinger distance constraint; in the aspect of drug modeling, from three perspectives of a drug co-occurrence network, an interaction network and a molecular structure diagram, representation consistency and interpretability are improved through diagram contrast learning. On the basis, four types of loss function optimization recommendation results are designed, and a medicine combination with the lowest comprehensive medicine use risk is screened out for clinical auxiliary diagnosis and treatment. According to the method, multi-view medical knowledge is fused, efficient drug combination screening and recommendation are realized, meanwhile, the drug interaction risk is reduced, and the recommendation accuracy and safety are improved.

The invention provides a multi-modal cerebral arterial thrombosis medical image segmentation method, device and equipment, and the method comprises the steps: extracting the independent features of different modal medical images through combining a ViT encoder branch and a CNN encoder branch which are finely adjusted by a hybrid expert as a multi-modal image double-branch coding network; further integrating complementary information of different modes by using a mode missing adaptive fusion network, and performing global-local information interaction between CNN features and ViT features by using an encoder branch interaction network, so that specific features and cross-mode invariant features of different available modes can be decoupled under the condition of mode missing; and meanwhile, the advantages of different types of features are fully utilized, and the value information of the multi-modal image features is deeply mined, so that accurate multi-modal cerebral arterial thrombosis medical image segmentation and imaging are realized, and the method is high in reliability, good in accuracy and good in practicability.

The application discloses a computing power scheduling system and method of a computing power network, relates to the technical field of computing power distribution and scheduling, and comprises the following steps: acquiring all computing power nodes in the computing power network and constructing a computing power node interaction network model; acquiring a computing power task initiating node and computing power task attributes; determining a target attribute group cluster of the computing power task based on the computing power task attributes, and recording all computing power nodes in the target attribute group cluster as demand target nodes of the computing power task; determining an execution trust target node of the computing power task based on the computing power task initiating node; finding an intersection of the demand target nodes of the computing power task and the execution trust target node of the computing power task, and recording the intersection as a comprehensive target node; and determining an execution computing power node of the computing power task from the comprehensive target node in combination with balanced load. The application has the advantages that the interaction network model of the cooperative trust relationship and the attribute group cluster is constructed, multi-dimensional node dynamic screening and optimized matching are realized, and the efficiency and reliability of the computing power scheduling are significantly improved.

The invention discloses an industrial intelligent safety risk early warning and management and control platform, and relates to the technical field of industrial safety monitoring and management and control. The industrial intelligent security risk early warning and management and control platform comprises a full-scene perception layer, a data management layer, an intelligent risk identification layer, a hierarchical management and control layer and a global operation and maintenance layer, and each layer and an edge computing node construct a distributed data interaction network through an industrial Ethernet; the scene sensing layer is used for realizing all-region and all-time security data acquisition of industrial production and comprises a multi-mode sensing terminal array, an equipment protocol adaptation module, a personnel state monitoring unit and an acquisition scheduling module; according to the industrial intelligent safety risk early warning and management and control platform, through a multi-mode terminal array and a dynamic acquisition scheduling mechanism of a full-scene sensing layer, full-area and full-time-period automatic monitoring of industrial production is realized, dependence on manual inspection is thoroughly eliminated, the problems of low manual inspection efficiency and missing inspection of hidden risk points are effectively solved, and the industrial production safety risk early warning and management and control platform is suitable for popularization and application. And the monitoring coverage rate is improved to 100%.

The invention discloses a transport capacity resource integrated intelligent scheduling method and system oriented to multiple service scenes, and relates to the field of intelligent scheduling, and the method comprises the steps: constructing a hierarchical collaborative decision-making architecture comprising a macroscopic strategic layer agent and a microscopic tactical layer optimizer; and inputting the structured feature vector into a hierarchical collaborative decision-making architecture, dynamically distributing proper solution algorithms and parameters for a microscopic tactical layer optimizer according to a real-time scheduling situation by utilizing an online element learning optimizer, and outputting a pre-scheduling scheme. According to the invention, through integrated integration of multi-service scene data, comprehensive utilization of static basic information, real-time transport capacity data and prediction environment data is realized, and the data support capability of scheduling decision is improved. The feature interaction network of the multi-layer perceptron structure can accurately extract core features and provide effective input for scheduling decisions. The hierarchical collaborative decision-making architecture is combined with an online element learning optimizer, so that a solution algorithm and parameters can be dynamically matched, and a better pre-scheduling scheme can be output.

The invention provides a blue-green algae image recognition method and system based on hierarchical self-adaption and domain driving, and the method comprises the steps: enhancing an image through employing an improved dark channel algorithm; constructing a blue-green algae biological attribute text database, and performing synonym replacement and sentence pattern recombination; multi-scale visual features are extracted through a hierarchical adaptive Swin Transform model, and key region characterization is enhanced in combination with dynamic spectrum attention; the text is input into a Bio-ALBERT model, and semantic embedding of field optimization is generated through term mask prediction and attribute relation pre-training; constructing a two-layer heterogeneous graph by using a graph attention interaction network GAIN, calculating a cross-modal association weight through a bidirectional graph attention mechanism, and outputting a cross-modal graph feature; multi-scale cross-modal association is modeled through a hierarchical graph attention fusion mechanism, and a comparison alignment loss optimization model is combined; and high-precision blue-green algae identification is realized. According to the method, through multi-scale perception, domain semantic adaptation and graph structure fusion, the accuracy of blue-green algae detection in a complex environment is improved.

The invention discloses a short-time traffic flow prediction method based on a traffic-air interactive hybrid convolutional network, and belongs to the technical field of traffic flow prediction. According to the method, historical traffic flow and air quality data are collected and preprocessed, a fusion feature sequence is constructed through cross-modal time-space sequence interleaving recombination, and real-time traffic flow prediction is achieved through training optimization of a mean square error loss function and an RMSprop optimizer by adopting a model composed of a convolutional interaction network and a bidirectional long-short term memory network. According to the method, the space-time coupling relation of the two kinds of explicit modeling is recombined through cross-modal space-time sequence interleaving, differentiation feature extraction and two-way dependence mining are combined, the problem that real-time interactive modeling cannot be achieved through a traditional method is solved, and robustness and adaptability in the complex environment are improved.

The invention discloses a dental plaque visual detection system based on micro-ecological recognition and trend prediction. The dental plaque visual detection system comprises a data acquisition module, a prediction modeling module, a network analysis module and a risk quantification module. According to the system, a miniaturized oral cavity sensing array and a high-throughput micro-fluidic chip are used for collecting the relative abundance of dental plaque flora and dynamic characteristics such as oral cavity acidification, buffering and remineralization, and future flora data are generated through a depth time sequence prediction model. A flora interaction network is constructed by combining spatial co-localization and causal inference, kinetic parameters such as node centrality, edge weight and promotion and suppression polarity are extracted by using a graph neural network, components such as pathogen expansion amount, antagonism decline amount, network modulation amount and protection amount are calculated, and the components are fused into a dynamic pathogenic risk index (D-PRI). The method has the beneficial effects that the index can be visually presented on a time axis, personalized intervention suggestions are generated, and quantitative prediction and accurate early warning of the dental plaque risk are realized.

The invention discloses a traffic scene training data generation method and device, electronic equipment and a medium, and relates to the technical field of intelligent traffic, and the method comprises the steps: obtaining multi-modal traffic data, building a dynamic semantic interaction network according to the multi-modal traffic data, and enabling the dynamic semantic interaction network to comprise a plurality of traffic entities, the dynamic attribute of each traffic entity and the space-time relationship between the traffic entities are determined; in the dynamic semantic interaction network, labeling the target event and a core node of the target event according to a preset event specific sub-graph; performing causal chain backtracking on the labeled target event and the core node of the target event to obtain structured causal chain data corresponding to the target event; and performing question and answer pair generation processing on the structured causal chain data corresponding to the target event to obtain an instruction fine tuning data set for training the traffic large model. Therefore, automatic and high-quality generation of the traffic scene training data is realized, and the logical reasoning ability and interpretability of the model are improved.

The invention relates to the field of medical image processing and artificial intelligence, in particular to an inflammation active retinopathy prediction system and method based on artificial intelligence, and the system comprises an image processing module which comprises an image acquisition unit, a blood vessel density analysis unit, a blood-retina barrier integrity imaging analysis unit and a data integration module. The core innovation of the invention lies in that a blood vessel density analysis unit constructs a blood vessel structure analysis framework by introducing topology and differential geometry theories, comprises a topological manifold representation module, a curvature flow dynamic analysis module and a non-European interactive network module, maps fundus image data into a topological manifold structure, and extracts topological features of a blood vessel network; curvature flow evolution analysis is carried out based on the blood vessel network topology features, and blood vessel form dynamic features are generated; a non-European metric space of a vascular network and inflammatory factors is constructed, an interaction relationship between the vascular network and the inflammatory factors is analyzed, and vascular-inflammation interaction characteristics and blood-retina barrier integrity index data are integrated.

The invention relates to a lung cancer organoid and peripheral blood source immune cell co-culture model and a construction method thereof. Specifically, the invention provides a construction method of a macrophage and tumor organoid co-culture model for evaluating anti-tumor activity, monocytes of autologous or allogeneic peripheral blood of a patient are induced and differentiated into high-purity macrophages in vitro, and the high-purity macrophages and lung cancer tumor organoid are subjected to three-dimensional co-culture in matrigel; the model aims at highly reducing a core interaction network of tumor cells and macrophages in TME; the dynamic change of the polarization state of the macrophage is simulated and observed; the problem that an organ-like model is incomplete due to immune component deficiency or spatial positioning distortion of an existing model is effectively solved, an experimental platform closer to the physiological state is provided for tumor immune microenvironment research, and therefore development of the tumor immune treatment field is promoted.

The invention relates to the technical field of network security, and discloses a server data interaction network security monitoring processing method and device, in a TLS / SSL handshake process between a server and a client, non-sensitive parameters in a key negotiation process are extracted to generate a key fingerprint, pre-risk assessment is carried out in combination with a preset risk knowledge base, and the key fingerprint is obtained. Obtaining an initial risk score; according to the method, non-sensitive parameters are extracted in a TLS handshake stage to generate key fingerprints, and dynamic association analysis is performed on the key fingerprints and subsequent ciphertext data transmission behaviors, so that a novel detection normal form which does not decrypt but insight into risks is constructed; when a novel encryption attack occurs, a time-consuming traffic decryption operation does not need to be carried out, and accurate threat identification can be realized only by analyzing abnormal association of handshake features and behavior patterns, so that the problems of performance bottleneck and privacy compliance of security detection in an encrypted traffic environment are fundamentally solved, huge calculation overhead caused by decryption is avoided, and the security detection efficiency is improved. The method is suitable for high-speed network environments.

The invention relates to a data processing technology, and provides a mass distribution network industrial resource collaboration method based on load interaction and response analysis, which comprises the following steps: firstly, constructing a real-time synchronous data acquisition and processing system, and fusing multi-source data to form a unified data base; secondly, realizing flexible dynamic identification through mechanism modeling and data driving, and introducing a flexible label to quantify adjustable power, duration and response rate; constructing an interactive network based on the flexibility label, depicting a space-time coupling effect and group response characteristics, and generating an equipment, station and region level scheduling scheme through a multi-target hierarchical optimization model in combination with market electricity price and renewable output prediction; and finally, comparing the plan with the actual response in the actual scene, and performing correction. According to the invention, the cooperative scheduling and fine management capability of the power distribution network on massive industrial loads can be improved, the flexible utilization level is improved, and the economical efficiency and robustness of the system are enhanced.

The invention provides a Mini LED module service life prediction method and device based on operation data driving and a storage medium, and relates to the technical field of display.The method comprises the steps that firstly, multi-source operation data and full-period historical service life associated data of a Mini LED module are collected, and then a target service life influence factor is used as a core node to predict the service life of the Mini LED module; the method comprises the following steps: constructing a life consumption interaction network for a flexible connection edge according to a dynamic interaction relationship between factors, inputting multi-source operation data into the network, carrying out cross-dimension dynamic interaction coupling on the multi-source operation data and full-period historical life associated data to generate a real-time historical interaction coupling result, and generating a self-adaptive life consumption rate according to a dynamic adaptive node response mode; and finally, generating a residual life prediction result according to the self-adaptive life consumption rate and the current comprehensive performance state data of the module, and reversely inputting the subsequent actual data into the network to dynamically adjust the interaction relationship, so that the life of the MiniLED module can be accurately predicted.

The invention discloses a sheep infectious disease number zero individual tracing method and system based on a group interaction network, particularly relates to the technical field of agricultural informatization, and is used for solving the problem of number zero individual misjudgment caused by neglecting group dynamic behaviors in an existing static network model. The method comprises the following steps: dividing a continuous time window to construct a time sequence interaction network by acquiring dynamic contact data and high-risk medium use data of a sheep flock; virtual edges are added to individuals with the use interval smaller than the pathogen survival time of the same high-risk medium; calculating a node behavior mode mutation degree based on a medium using interval variance, and analyzing propagation fluctuation in combination with an input / output propagation flow ratio standard deviation; identifying nodes of which behavior mutation exceeds a threshold value, propagation fluctuation continuously exceeds the threshold value and output propagation flow suddenly drops as an abnormal propagation source; and extracting a first window associated sub-network, and judging the node which has the highest behavior mutation degree and uses a high-risk medium in the sub-network as a zero individual, thereby realizing accurate tracing of the sheep infectious disease source and providing a basis for accurate prevention and control of a farm.

The invention provides a dynamic gesture recognition method based on lightweight multivariable space-time convolution, and the method comprises the steps: constructing a spatial feature extraction module based on a pseudo 3D gated attention fusion network, extracting multi-scale spatial features through the spatial feature extraction module, and injecting a guide heat map through a gated attention fusion module, enhancing features of the key region and inhibiting background interference to obtain a space refined feature sequence; and decomposing the spatial refined feature sequence into a plurality of sub-variables, parallelly capturing long-range and local time dependence by using a modern convolution module, and respectively carrying out relation modeling in the variables and between the variables through a decoupling feature interaction network to obtain a dynamic gesture recognition result. According to the method, a multivariate characteristic decomposition strategy is combined with modern convolution, the double-branch design advantage of the modern convolution is that long-range dependence and local details are taken into consideration, the limitation of traditional convolution in the aspect of capturing a long-range time dependence relationship is solved, and modeling is performed on a complex dynamic state.

The invention discloses an angle plastic fitting scheme recommendation algorithm and system fused with multi-modal ophthalmic data. The method comprises the following steps: S1, collecting ophthalmic data through multi-modal detection equipment; s2, performing feature extraction on the ophthalmology data based on a convolutional auto-encoder of the dynamic feature interaction network; s3, fusing the initial feature vectors based on a multi-scale feature aggregation module of the dynamic feature interaction network to generate a comprehensive feature matrix; s4, performing evolution trend analysis on the comprehensive feature matrix based on a time sequence prediction model of the dynamic feature interaction network; and S5, performing parameter optimization on the cornea morphological change prediction result after wearing based on a Bayesian optimization algorithm of the dynamic feature interaction network. According to the angle plastic fitting scheme recommendation algorithm and system fused with the multi-modal ophthalmologic data, the ophthalmologic data are collected through the multi-modal detection equipment, and the ophthalmologic data are input into the dynamic feature interaction network.

The invention discloses a newborn inherited metabolic disease risk assessment method based on multi-modal collaborative learning, and the method comprises the following steps: collecting and preprocessing gene, metabolism and phenotype data, and constructing normalized multi-modal input features; constructing a cross-modal interaction network, and extracting fusion features from the preprocessed data to enhance the expression ability of pathological information; constructing a metabolic pathway diagram, and modeling a structure and function relationship between metabolites by using a diagram convolutional network; and performing feature alignment on the multi-modal features and a metabolic pathway diagram representation vector, and realizing efficient multi-modal fusion reasoning based on a confidence-guided incremental attention mechanism to complete disease data processing. Through multi-modal data fusion and graph convolutional network modeling, the pathological information expression ability is enhanced, efficient fusion reasoning is realized, and the accuracy and efficiency of neonatal disease data processing are improved.

This invention provides a method and system for assessing the impact of construction disturbance on biodiversity in nature reserves, relating to the field of computer technology. The method includes: acquiring species distribution data, construction disturbance data, and environmental data within the nature reserve; constructing an ecological association network based on the species distribution data to obtain a species interaction network; extracting network structure features from the species interaction network to obtain a set of key species; modeling the disturbance propagation dynamics based on the set of key species and the construction disturbance data to obtain an impact propagation path map; predicting biodiversity responses based on the impact propagation path map and environmental data to obtain trends in changes in biological community composition; and conducting a comprehensive impact assessment based on these trends, outputting an impact level index by comparing the degree of deviation from historical baseline states. This invention effectively improves the overall accuracy and predictive precision of the assessment of the impact of construction disturbance on biodiversity in nature reserves.