377 results about "Interaction network" patented technology

The invention provides a hydrogen energy storage equipment leakage fault early warning method and system based on deep learning, and the method comprises the steps: collecting the multi-source sensing data of hydrogen energy storage equipment in real time, constructing a multi-dimensional feature matrix, building the nonlinear mapping of pressure fluctuation and gas concentration through a bidirectional interaction network, and carrying out the early warning of the leakage fault of the hydrogen energy storage equipment. And performing spatial modeling on the temperature gradient and the vibration spectrum to generate a fusion feature tensor. And then, inputting the fused feature tensor into a spatio-temporal reasoning model, predicting equipment leakage and energy loss, and outputting a leakage probability cloud picture and an energy loss vector. And an entropy sudden change area is identified based on an energy loss vector, when the high-density leakage area and the entropy sudden change area are overlapped in a continuous detection period, a multi-stage early warning signal is triggered, a leakage source thermodynamic diagram and a pressure balance parameter set are generated, a self-adjusting system is activated to dynamically correct the valve opening degree, network weight is updated, and a closed-loop control link is formed. The early warning accuracy and the response speed of the leakage fault of the hydrogen energy storage equipment are improved.

The invention provides a pedestrian trajectory prediction method based on window attention and a space diagram interaction network, and belongs to the technical field of computer vision. The technical problems of difficult long-time dependence modeling and complex space interaction are solved. According to the technical scheme, the method comprises the following steps: S1, acquiring data of a data set; s2, in the time dimension, designing a window mask mechanism, and adjusting the attention receptive field at each moment; s3, constructing a hierarchical heterogeneous graph convolutional network according to a spatial dimension, and combining a pedestrian dynamic interaction graph with a scene static semantic graph; and S4, inputting the time dimension features and the space dimension features into a multi-scale expansion convolutional network to generate a multi-modal trajectory. The method has the beneficial effects that the model is subjected to experimental verification on a public data set ETH / UCY, the experimental result proves the effectiveness of the model, the superior performance of the model is shown on key indexes, and the generalization ability in processing different scenes is also excellent.

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 multi-source monitoring data fusion method based on deep learning, and the method comprises the steps: carrying out the standardization and space-time dimension alignment processing of multi-source monitoring data, constructing a multi-scale data sub-sequence, inputting a multi-scale space-time feature interaction network based on a Mamba structure, extracting and interacting multi-scale space-time features, constructing a cross-modal data topological graph structure, and carrying out the fusion of the multi-source monitoring data. And dynamically calculating and adjusting attention weights of nodes and edges of the topological graph by using an adaptive cross-modal graph attention mechanism, generating dynamically optimized cross-modal fusion features, performing collaborative feature decoding, and outputting a fusion result. According to the method, the multi-source data fusion precision and generalization ability in a complex monitoring scene are improved, and the fusion feature expression ability and decision reliability are improved.

The invention provides an IBS micro-ecological transplantation intelligent prediction method and system based on multi-omics driving, and relates to the technical field of biomedicine. The method comprises the following steps: establishing a multi-omics data fusion subsystem to collect metagenome, metabolome, host genome and clinical phenotype group data of a target patient; inputting the data into a flora-metabolite combined network analysis model to construct an interaction network and extracting features; generating an incidence matrix based on the features and the host genome data and calculating indexes; generating indexes through a dynamic response algorithm in combination with the clinical phenotypic data and the indexes; and outputting a curative effect prediction result by using a transfer learning framework combined with modeling. The system comprises a data acquisition module, a network analysis module, a correlation calculation module, a dynamic response module and a joint modeling module. According to the method, multiple omics data are integrated, the flora and host relation is accurately mined, intelligent prediction of the micro-ecological transplantation curative effect is achieved, powerful support is provided for IBS personalized treatment, and meanwhile data processing and safety guarantee measures are taken.

The invention discloses a deep forgery detection method based on a multi-granularity collaborative attention mechanism, and the method comprises the following steps: extracting multi-level features through a backbone network, achieving the self-adaptive region division and weight fusion through a dynamic space grouping attention mechanism, and solving the problem that a conventional method is insufficient in attention to a distributed forgery region; a double-path channel decoupling module is designed to separate high-frequency artifacts and low-frequency semantic features, and feature coupling interference is eliminated; a cross-granularity feature interaction network is constructed, local details and global semantic features are collaboratively optimized, and the fusion efficiency of multi-scale forged clues is improved; and finally, a detection result is output through the classifier. According to the method, the defects of weak cross-domain generalization ability, low multi-granularity information utilization efficiency and the like caused by incomplete space attention coverage and channel feature interference in the prior art are overcome, the detection precision and robustness are remarkably improved, the stable performance is kept in a complex degradation scene, and the method can be widely applied to scenes such as digital content security auditing and identity authentication.

The invention relates to the technical field of American ginseng quality detection, in particular to an American ginseng quality detection method and system based on S-transformation and multi-task deep learning. Near infrared spectrum data of American ginseng to be detected are extracted, and time-frequency conversion is performed on the near infrared spectrum data through S-transformation; obtaining a time-frequency characteristic pattern used for representing time-frequency domain information of the near infrared spectrum; and inputting the to-be-detected American ginseng time-frequency feature map into a pre-trained multi-task deep learning model, and identifying the producing area of the to-be-detected American ginseng and predicting the quality index of the to-be-detected American ginseng by using the multi-task deep learning model. The multi-task deep learning model comprises a feature extraction network used for extracting time-frequency features of each detection task of the feature map, a feature interaction network used for enhancing feature complementation between the tasks and performing feature fusion, and a multi-task head network used for executing a production place identification task and a quality index prediction task according to the time-frequency features and outputting the tasks. The method can be suitable for quality analysis and origin classification of small American ginseng samples.

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 relates to the technical field of power transmission line monitoring, and discloses a power transmission line real-time monitoring method and platform. The method comprises the following steps: establishing a multi-omics data fusion subsystem to collect metagenome, metabolome, host genome and clinical phenotype group data of a target patient; inputting the data into a flora-metabolite combined network analysis model to construct an interaction network and extracting features; generating an incidence matrix based on the features and the host genome data and calculating indexes; generating indexes through a dynamic response algorithm in combination with the clinical phenotypic data and the indexes; and outputting a curative effect prediction result by using a transfer learning framework combined with modeling. The system comprises a data acquisition module, a network analysis module, a correlation calculation module, a dynamic response module and a joint modeling module. According to the method, multiple omics data are integrated, the flora and host relation is accurately mined, intelligent prediction of the micro-ecological transplantation curative effect is achieved, powerful support is provided for IBS personalized treatment, and meanwhile data processing and safety guarantee measures are taken.

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 relates to the technical field of elevator component performance analysis, in particular to a construction method and system for an elevator component performance model and a storage medium. The method comprises the following steps that elevator use records are collected, flow frequency spectrum modeling is carried out to generate start-stop frequency spectrum features and elevator use mode features, then part response analysis is carried out on the use mode features to obtain dynamic characteristic indexes, the independent performance of parts is calculated, and the independent performance of the parts is calculated. And an elevator interaction network is reconstructed according to the independence performance and the use mode characteristics, energy consumption distribution of the elevator is calculated, overall parameters of the elevator are obtained, framework simulation is conducted, framework movement performance loss is calculated, part position projection is conducted on a simulation framework based on part independence performance, interaction performance is evaluated, and therefore an elevator part performance model is constructed. According to the elevator part performance model construction method, elevator start-stop frequency spectrum characteristics and energy consumption distribution can be accurately described, and the response characteristics and interaction performance of the parts can be dynamically evaluated.

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.

Disclosed is a wake-interaction network analysis model for wind farm optimization and method to manage and mitigate complex wake interactions in wind farms. Operationally, our method creates a dynamic network model where each wind turbine is treated as a node within a comprehensive network. The interactions between these nodes, representing the wake effects of one turbine on another, are mapped as weighted edges in the network. These weights are quantified based on sophisticated wake models, incorporating factors like wind speed, direction, and atmospheric conditions. Furthermore, our inventive method and model exhibits a dynamic adaptability to changing wind conditions. Unlike traditional static models, it recalculates the network's edges in real-time, reflecting the varying impact of wake interactions as wind direction and speed fluctuate. This dynamic adaption advantageously ensures that the model remains accurate and relevant under different environmental scenarios.

The invention relates to the technical field of classroom interaction, and discloses a classroom interaction evaluation method and system based on voice data, and the method comprises the steps: collecting classroom voice data, and converting an audio signal in the voice data into a digital signal; the collected audio signals are preprocessed, the audio is converted into characters, and voice data of different speakers are separated; analyzing the voice data of different speakers to obtain a speaker interaction sequence; carrying out statistics on the speaker interaction sequence to obtain interaction frequency characteristics among different speakers; constructing a classroom interaction network based on the speakers based on the obtained interaction durations and interaction frequencies among the different speakers; a social network analysis technology is utilized to perform deep analysis on a classroom interaction network, and classroom interaction behaviors are evaluated through quantitative evaluation parameters. According to the invention, the accuracy and efficiency of classroom interaction analysis are improved, and targeted classroom interaction organization and improvement suggestions are provided for teachers through deep analysis of interaction behaviors.

The invention provides a federal cross-modal retrieval method and system based on interactive prompts, and relates to the field of cross-modal information retrieve.The federal cross-modal retrieval method and system based on interactive prompts retrieve similar second modal data from a second modal data set by using first modal data based on the feature similarity of two modal data comprises the steps that initial features of the two modal data are extracted respectively; performing multi-layer bidirectional interaction between the initial features of the two modals by using a cross-modal interaction network obtained by federal learning and taking a prompt vector as an intermediary to obtain final features of the two modals after cross-modal interaction; calculating feature similarity based on the final features of the two modalities, and screening similar second modal data; according to the method, federal learning and prompt learning are combined, so that the effectiveness and universality of a cross-modal retrieval technology are improved, and the problems of privacy protection and performance optimization in cross-modal retrieval are solved.

The invention discloses a bidirectional GCN-BERT scientific data classification method based on rotary coding and dynamic gating, which comprises the following steps of: firstly, preprocessing original text data, acquiring a classification mark containing global information by utilizing a pre-training model, and adding the classification mark to the beginning of an input text; secondly, feature optimization is carried out on the classification marks through a CorNet neural network, and then position enhancement and global interaction are carried out through a rotation position enhancement multi-layer feature interaction network RP-MLFIN; and then the image is transmitted to a bidirectional image convolution neural network for feature interaction and image convolution processing, and image level representation is generated. Finally, the classification marks after feature optimization and the representation of the graph level are transmitted to a classifier, BERT prediction and GCN prediction are generated, prediction results are fused through an attention mechanism, and a final classification decision is generated. According to the method, the processing capability of text data with strong context dependence and the stability and robustness of a classification result are improved.

The invention discloses a multi-dimensional performance resource dynamic configuration method, and relates to the related technical field of data processing, and the method comprises the steps: carrying out the multi-end impact factor analysis of a supply source, a control source and a configuration resource target for a resource configuration target; relevance mining is carried out based on a multi-end-multi-dimension factor library, and a factor interaction relation network is established; constructing a configuration identification path; target resources, control parameters and constraint conditions of configuration targets are obtained; performing configuration path identification search through the configuration identification path to obtain a configuration path; and performing performance evaluation on the configuration path according to the multi-dimensional performance target, obtaining the configuration path with the maximized multi-dimensional performance target, and generating a resource configuration strategy. The technical problems that in the prior art, the resource allocation mode is single, multi-dimensional factors and dynamic changes cannot be comprehensively considered, and consequently the resource allocation efficiency and flexibility are poor are solved, the allocation path is dynamically optimized, and the technical effect of improving the resource allocation efficiency and flexibility is achieved.

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 relates to the technical field of robots, and provides an image-based simplified robot visual servo control method, in the aspects of image acquisition and illumination compensation, adaptive multi-scale histogram equalization is combined with illumination simulation compensation based on a generative adversarial network (GAN), so that the problems of illumination diversity and complexity are effectively solved, image distortion is avoided, and the visual servo control precision of a robot is improved. The image feature identification degree is greatly improved in a complex illumination environment, a robot can obtain clear visual information, a multi-modal feature fusion optimization part and attention mechanism fusion automatically adjust different modal feature weights according to a scene, a cross-modal feature interaction network breaks the independence of each modal feature, and the multi-modal feature fusion optimization part and the attention mechanism fusion optimization part are integrated. The target recognition accuracy and robustness are enhanced through cooperation of the two methods, the feature fusion effect is improved, and for the shielding problem, the robot can optimize the shielding processing strategy according to the environment state and the decision result through the shielding decision based on reinforcement learning, and the adaptability is enhanced.

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 a method and system for evaluating the treatment effect of traditional Chinese medicine based on single cell and spatial transcriptome data, and the method comprises the following steps: respectively obtaining single cell data and spatial transcriptome data of a tissue sample before and after administration, and carrying out the preprocessing; classifying the cells and identifying cell types; the expressed ligand and receptor genes are paired to obtain ligand-receptor pairs, and the ligand-receptor pairs which are differentially expressed before and after administration are screened out; acquiring space coordinate information of a single cell, and constructing a cell interaction network and a differential gene network according to the cell type, the ligand-receptor pair and the space coordinate information; weighting processing is conducted on the cell interaction network and the differential gene network, and comprehensive indexes for evaluating the effect of the traditional Chinese medicine are obtained.The brand-new method for evaluating the disease treatment effect of the traditional Chinese medicine is provided, the method is scientific and reliable, and the treatment effect of the traditional Chinese medicine on complex diseases can be accurately reflected.

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%.