9results about How to "Resolving heterogeneity" patented technology

The present application relates to the technical field of artificial intelligence and federated machine learning, and particularly relates to an asynchronous federated learning aggregation method and system based on clustering cache. The present application clusters clients by intermediate features of the clients. In global aggregation, intra-cluster aggregation is firstly performed, and then global aggregation is performed based on client clusters. The client clusters are dynamically updated. In intra-cluster aggregation, an active set and a slow set are introduced to realize an asynchronous participation mechanism of intra-cluster aggregation. Weighted aggregation of the active set and the slow set solves the problem of system heterogeneity. The present application solves the defect of insufficient timeliness of existing federated learning methods in a data heterogeneous scene, and guarantees the timeliness and model training accuracy of federated learning in the data heterogeneous scene.

The invention discloses a construction method and application of a safety evaluation risk prediction model after child medication marketing, and the method comprises the steps: constructing a three-level index system, and determining the weight of each level of index through an analytic hierarchy process; based on the three-level index system, determining a risk feature set, constructing a three-level model architecture, and performing model training by adopting a machine learning algorithm; collecting multi-source real world data, and establishing a standardized data pool; the performance of the model is improved through hyper-parameter optimization and integrated learning, the performance of the model is verified and evaluated by adopting internal cross validation and an external independent data set, and the transparency of the model is enhanced through an interpretability technology; and establishing a dynamic updating mechanism, deploying the model as a cloud API service, and realizing risk prediction and early warning functions. According to the invention, multi-source real world data can be effectively integrated, the children medication safety risk can be accurately identified, the method is used for safety evaluation after children medication is listed, and the method is of great significance to supplement clinical test insufficiency before children medication is listed, reduce medication risk and guarantee children medication safety.

The invention provides an unplanned return ICU risk prediction method, system and device, and the method comprises the steps: fusing an MIMIC-IV public data set and local private data, and carrying out the standardization preprocessing of multi-source medical data through a rule module and a clinical knowledge base; constructing a partitioned feature system comprising basic features, laboratory indexes, hemodynamic parameters and respiratory metabolism dimensions, and optimizing feature expression by adopting a sliding window and a feature interaction method; performing medical field fine tuning based on a large language model, and converting the structured features into natural language description in combination with cue words; carrying out model training by adopting optimization strategies such as label smoothing and gradient cutting, and evaluating model performance in a mode of combining internal verification and external verification; and realizing interpretability analysis of model prediction by using an SHAP framework. According to the scheme, the accuracy and generalization ability of ICU return risk prediction and the applicability of an actual scene are remarkably improved.

The invention relates to the technical field of production control, in particular to a mining instrument production line control system and method based on adaptive scheduling and multi-protocol fusion, and the method executed by the system comprises the steps: 1, triggering a production task according to an order; 2, analyzing a production task, disassembling the production task into a plurality of sub-tasks, distributing the sub-tasks to different stations to implement production, and monitoring the state of each station in real time; wherein in the production process, the production mode is adjusted in real time according to the order priority and the production state of each station. Through the adaptive task scheduling engine, the system can dynamically optimize the task allocation and execution sequence according to the real-time production state, and effectively cope with disturbances such as emergency order insertion and equipment faults.

The application discloses an enterprise risk early warning system and method based on intelligent evolution map, relates to the technical field of computer information processing, extracts time nodes and characteristic words in enterprise penalty cases, respectively forms internal characteristic vectors and external characteristic vectors, integrates into a fusion characteristic vector to construct a risk evolution sequence, carries out time normalization processing on the characteristic sequences of multiple cases, clusters to form a risk evolution map, collects internal characteristics and external characteristics of a target enterprise, compares the fusion target vector of the target enterprise with the risk evolution map, positions a similar classification cluster, calculates a risk weight to generate a characteristic word push sequence, and provides quantitative basis for enterprise risk assessment through deep mining and multi-feature fusion of enterprise penalty cases, so that management personnel can timely master enterprise risk dynamics and take measures to cope with potential risks in advance.

The application discloses a kind of based on retrieval enhancement cross-modal condition diffusion model emotion recognition method and system, the method receives text, speech, image multimodal data, detects modality integrity and extracts existing modal feature;If there is missing mode, based on existing modal feature in external sample library retrieval similar sample, weighted fusion generates retrieval enhancement representation;Cross-modal condition diffusion model is constructed, and missing modal feature is recovered by combining forward diffusion into information, reverse diffusion and iteration unified cross-modal attention mechanism;Splice complete modal feature, pass through Transform fusion network and joint loss function optimization, output emotion category and intensity.The application breaks through the limitation that existing generation model only relies on local information, fully utilizes the combination advantage of external retrieval and diffusion generation, can maintain stable performance under a variety of missing patterns, and has wide application prospect.

The application relates to the technical field of marine environment monitoring, in particular to a marine ecosystem monitoring system based on multi-source data fusion, which comprises a multi-source data acquisition module, a parameter extraction module, an ecological driving analysis module and an ecosystem anomaly monitoring module; wherein: the multi-source data acquisition module is used for acquiring multidimensional data; the parameter extraction module generates unified grid data through space-time alignment processing, and extracts dissolved inorganic phosphorus concentration time series data and diatom community spectral feature data sets; the ecological driving analysis module calculates the fluctuation rate and generates diatom community aggregation degree; the ecosystem anomaly monitoring module is used for identifying the abnormal state of the ecosystem and outputting corresponding early warning signals. Through multi-source data fusion and dynamic trend analysis, the application realizes accurate identification and timely early warning of marine ecological abnormal events, and improves the timeliness and discrimination accuracy of ecological monitoring.

The application discloses a method for intelligent identification of submerged oil pollution based on multi-source heterogeneous data fusion, comprising collecting multi-source monitoring data and meteorological data of a submerged oil area, and preprocessing the multi-source monitoring data and the meteorological data; adopting a stereoscopic perception system to perform space-time registration optimization on the multi-source monitoring data and the meteorological data to obtain spatial alignment data, and performing deep multi-modal fusion on the spatial alignment data to obtain fusion data; adopting a graph neural network to simulate oil cluster migration along a flow based on the fusion data to obtain a simulated oil cluster migration path, and performing oil cluster migration game analysis based on an actual oil cluster migration path and the simulated oil cluster migration path to obtain an oil cluster movement migration rule; and constructing an intelligent identification model for submerged oil pollution based on physical information embedding based on the oil cluster movement migration rule, inputting to-be-identified data into the intelligent identification model for submerged oil pollution, and outputting an identification result.

The application discloses an industrial internet attack-defense situation and risk early warning sensing method, and particularly relates to the field of internet risk early warning sensing, and comprises the following steps: collecting multi-source heterogeneous data, constructing a basic data set covering the attack, business and device ternary space, containing attack features, business influence and device control vectors, solving the data heterogeneity and dispersion problem; constructing a triple function based on the data set, quantifying the attack comprehensive threat degree, the attack influence degree on the business and the device malicious control risk, respectively, and retaining the characteristics of each dimension; then constructing a dynamic network topology model, defining nodes, edges and edge weights, depicting the attack propagation path and influence strength, and realizing dynamic updating of the node state; finally, fusing the quantitative indicators and topology information to construct a risk prediction model, calculating the global risk value, realizing hierarchical early warning through three dimensions, and matching the corresponding response mechanism, thereby improving the timeliness and effectiveness of the industrial internet security protection.