1227 results about "Multiple Models" patented technology

A system and method for flexibly incorporating machine learning models into applications using a marketplace platform and distributed computational graph (DCG) architecture. The DCG enables dynamic selection, creation and incorporation of trained models with data sources and marketplaces for data, algorithms, simulation models, ontologies, knowledge corpora, and crowd or expert judgment. Multiple models can be used in series or parallel. An expert judgment marketplace allows human and artificial intelligence (AI) experts to score the accuracy of training data and model outputs. Consumers can select and rank AI agents or experts based on the helpfulness of their judgments. A symbolic knowledge corpora and retrieval augmented generation (RAG) marketplace enables selling access to proprietary datasets as RAGs and knowledge bases. The system includes knowledge corpora and RAG marketplaces with domain-specific components and user experience customization.

The invention discloses a financial data risk control system and method based on big data, and relates to the technical field of financial science and technology, and the system comprises a multi-source data collection module which achieves cross-mechanism safety collection through federal learning; the data cleaning and preprocessing module is used for processing abnormal values and missing values by using an improved algorithm; the knowledge graph construction module is used for constructing a dynamic knowledge network based on an innovative algorithm; the risk assessment engine fuses various models to assess risks; the real-time monitoring and early warning module is used for realizing second-level response by utilizing multi-scale analysis; the decision support module is used for optimizing a strategy based on reinforcement learning; and the audit tracking module guarantees evidence storage and privacy through zero-knowledge proof, and all the modules cooperate to improve the risk control capability. According to the financial data risk control system and method, risks are accurately recognized, real-time monitoring and early warning are achieved, data security sharing is achieved, risk control strategies are dynamically optimized, risks and business development are balanced, the risk prevention and control capacity and economic benefits of financial institutions are improved, and data privacy and risk control transparency are guaranteed.

The invention discloses a collaborative scheduling method based on a security agent, and relates to the technical field of network security design. The specific operation of the security agent collaborative scheduling method comprises the steps of system deployment and initialization, security operation task execution process, agent self-learning and capability evolution implementation, cross-domain security agent collaborative adaptation implementation and security operation visualization and traceability implementation. Connection paths between the security agent and business data, a private knowledge base, a security tool and multiple models are broken through, the problem that all elements in a traditional mode lack efficient communication and collaboration is solved, and by means of agent routing dynamic scheduling, A2A protocol interaction, MCP protocol tool calling, RAG business interface calling and multi-model combination, the security of the security agent is improved. Integration and intellectualization of the safety operation process are achieved, the continuity and the response speed of the operation process are improved, safety operation is more efficient and collaborative, and complex and variable safety requirements are met.

The invention relates to the technical field of Internet of Things, and provides a wire and cable online quality detection method and device, electronic equipment and a storage medium. The method comprises the following steps: performing multi-scale wavelet packet decomposition and signal entropy fusion on an original wire and cable signal matrix to obtain a feature tensor, and performing anomaly recognition extraction on the feature tensor through a graph attention network model to obtain a potential defect feature vector; and performing multi-physics coupling simulation inversion on the potential defect feature vector to obtain a defect quantization parameter set, performing dynamic reasoning according to the defect quantization parameter set through a Bayesian network model to obtain an online quality grade decision, and packaging the online quality grade decision according to a block chain intelligent protocol to obtain a quality traceability record. According to the invention, through organic coupling of multiple levels, multiple models and multiple technical means, real-time performance, accuracy, traceability and safety of online quality detection of wires and cables are realized.

The invention discloses a cloud data anomaly detection and safety response system based on artificial intelligence, relates to the technical field of data processing, and solves the problems that firstly, an incremental compression algorithm is difficult to store and preprocess multi-source heterogeneous data; secondly, it is difficult to fuse statistical analysis and a deep learning model, locate outliers and analyze abnormal semantics in unstructured data, and then it is difficult to effectively predict a potential attack path; then, on the premise that data security and traceability are guaranteed, correlation analysis of cross-node anomalies is difficult to achieve so as to identify distributed attacks; and finally, an attack and defense confrontation model is difficult to construct for safety response, and the safety response effect is difficult to evaluate. According to the method, cloud environment data are processed through an adaptive probe cluster and the like, multiple models are fused to generate anomaly detection features and predict attack paths, and response and evaluation are carried out through reinforcement learning and digital twinning by means of cooperative detection such as federated learning and the like.

The invention provides a weather forecast learning system based on an artificial intelligence algorithm, and the system comprises a data access collection module which collects a multi-source data set; the data fusion and processing module is used for carrying out data fusion and processing to obtain a multi-source fusion data set; the AI model design module is used for constructing a multi-model collaborative architecture and carrying out multi-model parallel training and optimization; the model fusion module is used for carrying out multi-model weighted fusion to obtain an AI model; the real-time prediction module is used for updating a prediction result according to the real-time data; the visualization and interpretability module is used for designing a visualization interface and carrying out interpretability verification; and the evaluation and iteration module is used for carrying out comprehensive evaluation and continuous improvement on a prediction result in combination with evaluation indexes. According to the method, accurate and reliable observation data can be obtained, massive meteorological data are efficiently processed by combining an artificial intelligence algorithm, trend analysis and prediction are automatically carried out, and a reliable prediction result is generated.

The invention discloses an anomaly detection model selection method and system based on index portraits, and relates to the technical field of intelligent operation and maintenance of power systems. The method comprises the following steps: collecting historical data of a target monitoring index, extracting multi-dimensional features to construct an index portrait, and classifying the index portrait; screening candidate anomaly detection models from the matching rule base, performing adaptation degree scoring in combination with a model compatibility evaluation mechanism, determining an optimal anomaly detection model to perform anomaly detection, and outputting an anomaly judgment result; when a plurality of models exist, generating a final abnormal result through a confidence-driven arbitration mechanism; for multi-index abnormity, causal reasoning is carried out in combination with an electric power knowledge graph, main alarm indexes are determined, and secondary indexes are processed according to a delay strategy; meanwhile, incremental updating of index portrait features, adaptive adjustment of model parameters and dynamic optimization of matching rules are supported, and a whole-process closed-loop mechanism covering'portrait construction-model matching-result fusion-alarm decision-feedback updating 'is constructed.

The invention discloses a multi-temporal-spatial-scale cyanobacterial bloom early warning method for middle and large lake and reservoir water areas, and belongs to the field of cyanobacterial bloom prediction and risk monitoring. The method comprises the following steps: generating a pixel-level FAI index based on target water area remote sensing data, resampling meteorological data into a pixel level, then constructing a spatial-temporal distribution data set, training an Autoformer-ST-GNN time sequence model to realize FAI index prediction, dividing cyanobacterial bloom levels according to the FAI index prediction, and obtaining a global change trend; water quality monitoring points are arranged in key areas to collect data, historical water quality and meteorological data are utilized to train a DMC-PatchTST model fused with a blue-green algae migration period, and multi-time-scale prediction of the density of blue-green algae at the monitoring points is achieved; and finally, combining the global trend with a monitoring point prediction result to construct a space-time multi-scale cyanobacterial bloom comprehensive early warning system. According to the method, multi-source data and multiple models are fused, so that cyanobacterial bloom time-space multi-scale comprehensive early warning is realized, and the method is accurate and comprehensive.

The invention discloses a method for supporting fast switching of multiple models by NPU (Network Processing Unit) firmware. According to the invention, the system real-time performance and the resource utilization rate in a multi-task scene are obviously improved. Through a dynamic hierarchical caching strategy and a priority scheduling mechanism, the system can intelligently allocate cache resources and preferentially guarantee rapid loading of a high-frequency and high-urgency model, for example, in an automatic driving scene, switching delay of a path planning model can be reduced to a millisecond level, and non-perceptual switching of key tasks is guaranteed. The incremental parameter loading technology and firmware-level context management are deeply fused, only model difference data are transmitted, and hardware is utilized to accelerate and recover a calculation state, so that bandwidth waste caused by traditional full-amount loading is greatly reduced, the model switching efficiency of edge computing equipment is improved by more than 10 times when multiple tasks such as voice recognition and image processing are carried out in parallel, and the efficiency of the edge computing equipment is improved. And meanwhile, the calculation precision is kept lossless.

The invention relates to the technical field of computer science, in particular to a stamping prediction method based on a multi-machine learning model and an automatic compensation device.The stamping prediction method comprises the steps that firstly, a sheet microstructure and material characteristics are obtained through a transmission electron microscope and an atomic probe tomography technology, and then finite element simulation sampling is conducted; a data set is expanded by using an adaptive algorithm, multiple models are constructed and trained to obtain a final springback prediction model, an automatic compensation device is embedded to realize automatic compensation, and the automatic compensation device covers visual parameter input, embedded springback prediction, springback compensation calculation and an automatic control module. Parameters can be visually input, springback can be accurately predicted, a compensation value can be calculated, and the process can be adjusted. The method aims at solving the problems that in the high-strength plate stamping process, due to springback, the size and shape of a part deviate from the design requirement, and a traditional prediction and compensation technology is insufficient in precision and efficiency.

The invention relates to the crossing field of material design and artificial intelligence, in particular to a solid electrolyte intelligent inverse design method fusing a graph neural network and confidence analysis. According to the method, a prediction framework integrating multiple models is constructed, support vector regression, gradient boosting regression, a deep neural network and a graph neural network are included, component, process and structure parameter characteristics are fully fused, and the nonlinear mapping relation between input variables and performance parameters such as resistivity and conductivity is efficiently learned. In order to improve the credibility, a Bayesian neural network and a Monte Carlo method are further introduced, a confidence interval corresponding to each group of prediction results is output, and quantitative evaluation of the credibility of the prediction value is realized. In the inverse design module, high-dimensional submerged space parameters are generated based on a variational auto-encoder, and intelligent recommendation of parameter combination driven by target performance is realized in combination with strategies such as Bayesian optimization and a genetic algorithm. The design efficiency of the solid electrolyte and the success rate of material discovery can be effectively improved.

The invention discloses an interactive simulation method for calculating aging parameters of a superconducting cable and a superconducting cable system.The system comprises the steps that a cable body electromagnetic transient model, a cooling system transient thermal model and a three-dimensional multi-field finite element model are built, and cable material characteristics, environment parameters and operation condition data are input; a model interaction interface is constructed through a simulation platform, data interaction and coupling among multiple models are realized, simulation parameters are dynamically adjusted, accurate matching of model step length and dynamic characteristics of the system is ensured, and aging parameters of the superconducting cable system under complex working conditions are calculated. According to the method, the operation state of the superconducting cable is evaluated by analyzing the mixed data, aging analysis of the superconducting cable system under multiple working conditions is carried out, and decision support is provided for system maintenance.

An embodiment includes a method of augmenting performance and compliance of language model-based copilots. The method includes receiving application-specific guidance providing instructions that restrict responses output by an application-specific copilot based on a large language model (LLM). The method includes communicating to the LLM the application-specific guidance and setting an initial set of model parameters for the LLM. The method includes sequentially optimizing model parameters related to multiple model output characteristics of the LLM to generate a final set of model parameters. The method includes communicating the final set of model parameters to the LLM such that the final set of model parameters is implemented in the LLM during operations implemented by the copilot. The method includes deploying the copilot in an environment such that the copilot receives an actual query and replies with an actual response based on the LLM implementing the final set of model parameters.

The invention relates to the technical field of road maintenance intelligence, and discloses a road intelligent maintenance decision-making system based on multi-dimensional evaluation and deep reinforcement learning, and the system comprises a geographic space data preprocessing module, a fuzzy TOPSI S evaluation module, a context awareness DQN strategy module, a credibility driving recommendation module, and a security strategy library module. The method comprises the following steps: generating a road health degree and a clustering label through multi-source data geographical weighted preprocessing and fuzzy TOPSI S dynamic weight evaluation; a reinforcement learning reward function is configured based on label differentiation, and a strategy space is explored in combination with noise; the confidence is verified through multiple models, a historical security policy is matched, and model optimization is driven through priority sampling injection samples. According to the method, the evaluation precision is improved through entropy weight-clustering dynamic weight, and flexible decision is realized in combination with reinforcement learning; and three-level security verification and closed-loop optimization are constructed to ensure that the risk is controllable, historical experience migration and multi-source data expansion are supported, and the cross-scene adaptive capacity is enhanced.

The invention provides a real-time intelligent agent dynamic scheduling method and system composed of multiple large models, and is applied to the technical field of data processing. The method comprises the following steps: performing dynamic scheduling expansion around a plurality of large-model real-time intelligent agents, firstly receiving task request data of a real-time service scene, performing grouping according to task types, priorities and resource requirements, pre-configuring a scheduling strategy, and generating a pre-scheduling queue and resource pre-planning information; and in combination with the target configuration information, scheduling task information after secondary optimization is obtained through multi-dimensional optimization such as task splitting and priority rearrangement. Dynamically adjusting and generating a target scheduling instruction based on real-time system resources and a model operation state, and cooperatively processing the target scheduling instruction with the large model capability adaptation parameters to form scheduling execution information; and finally, issuing to model nodes according to preset service quality requirements and resource constraints, generating information such as adaptive parameters and priority rules through feature extraction, association processing and the like, and supporting multi-model cooperation to efficiently complete real-time service tasks.

The invention discloses a system and method for estimating the charging capacity of an energy storage lithium battery, and particularly relates to the technical field of energy storage battery management, and the method comprises the following steps: collecting dynamic parameters in the charging process of the energy storage lithium battery, and forming a dynamic data set; calculating an environmental disturbance influence index and a charging response consistency factor based on time sequence analysis and feature decoupling; constructing a two-dimensional working condition mapping matrix, identifying a current working condition area and generating a scene label; determining parameter weights of the plurality of estimation models by adopting a probabilistic reasoning mode; according to the scene state, selecting a single model output result or fusing a plurality of model output results for estimation; according to the method, the environment disturbance influence index and the charging response consistency factor are constructed, so that the complex working condition is accurately identified; scene labels are automatically generated based on two-dimensional working condition mapping and a clustering algorithm, and a plurality of estimation models are dynamically selected or fused in combination with model confidence, so that the accuracy, robustness and intelligent level of estimation are improved.

A vehicle-mounted GNSS positioning method based on multi-motion model interaction includes: establishing a position-constant velocity (PCV) model and a position-constant steering angular velocity (PCSAV) model for two attitudes of a carrier (i.e., linear motion and turning motion) respectively to obtain a state estimation vector and a state transition matrix of the carrier of the PCV model and the PCSAV model at a previous moment, introducing an interacting multiple model (INM), establishing a heuristic position-velocity filtering (HPV)-IMM model based on the IMM model to achieve an information filtering interaction between the PCV model and the PCSAV model, and obtaining a state estimation vector and an error covariance matrix of the carrier at a current moment, so as to obtain a position and velocity of the carrier at the current moment. The present disclosure solves the problem of low accuracy of a traditional single kinematic model in multi-motion attitude vehicle positioning.

The invention relates to a multi-model time division multiplexing and parallel loading reasoning service method and system, and the method comprises the steps: receiving a user model reasoning request, and searching a corresponding model according to the user model reasoning request; deploying part of parameter layers of the model required by reasoning into a GPU (Graphics Processing Unit); inputting the user model reasoning request text into a parameter layer deployed by the GPU for calculation; when the partial parameter layers are calculated, loading the residual parameter layers of the model to the corresponding specified GPU; after reasoning calculation is completed, the parameter layer is unloaded and returned to the CPU, and a reasoning result is returned. A GPU device cluster is used for carrying out reasoning service on the models, cooperation among device clusters of multiple models is achieved, and the reasoning time delay of each model request is reduced. And under the condition of reserving and occupying fewer system video memories, the same and even better reasoning speed can be achieved. The model deployment and scheduling process is automated, and the deployment cost of the model is reduced as much as possible under the condition that the SLO requirement of each model request is met.

The invention discloses a Java-based large language model dynamic adaptation method and system, belongs to the technical field of artificial intelligence, and aims to solve the technical problems of overcoming the interface difference of multiple model interfaces, simplifying the development process and providing standardized and high-expansibility large model management. Comprising the following steps: providing a model registration and dynamic loading service, a protocol conversion and parameter standardization service and a load balancing and failover service; a streaming transmission protocol service, a function call dynamic injection service and a global error processing mechanism are provided; when a user provides a document analysis and partitioning service, a vectorization index construction service and an RAG enhanced generation service to ask questions, relevant document blocks in the Pinecone are retrieved through the RAG enhanced generation service to serve as contexts to be injected into cue words of the large language model, and answers generated by the large language model are returned.

The invention discloses a power grid maintenance plan reliability post-evaluation method based on Monte Carlo simulation and data driving, and belongs to the technical field of power system operation and reliability analysis. The method comprises the following steps: firstly, collecting multi-source data such as historical load, renewable energy output, equipment operation state and maintenance record of a power grid, constructing a time sequence database through cleaning, time alignment and feature extraction, and establishing a load and renewable energy probability model; constructing a maintenance plan model containing a state variable, a constraint condition and a peak clipping weight mechanism, and establishing a continuous time Markov chain state model for the key equipment to generate an availability sequence; generating a large-scale random operation scene set through a Monte Carlo method based on multiple models, and carrying out supply-demand balance and power flow analysis on each scene; multi-dimensional indexes of reliability, economy and safety are calculated and subjected to weighted fusion, a comprehensive post-evaluation report is generated after results are counted, and finally the maintenance plan is optimized according to the report. According to the method, the uncertainty of the power system can be comprehensively considered, multi-dimensional quantitative evaluation and closed-loop optimization of the maintenance plan are realized, intelligent support is provided for power grid maintenance decision making, and the method is suitable for a power transmission network, a power distribution network and a micro-grid.

The invention discloses a ship track prediction method based on a local wandering activity scene, and belongs to the technical field of intelligent maritime affair supervision, and the method comprises the steps: obtaining AIS data, carrying out the preprocessing, and dividing the AIS data into a training set, a verification set and a test set; constructing a hybrid neural network prediction model; inputting the training set into a hybrid neural network prediction model, and carrying out training optimization through MSE and Adam; inputting the test set into the trained hybrid neural network prediction model to obtain a prediction result, dynamically updating by using rolling prediction, and calculating an actual distance error by using a Haversine formula; and performing trajectory prediction and dynamic updating by using the trained trajectory prediction model. Advantages of multiple models are fused, data quality is guaranteed through preprocessing, prediction is accurate, dynamic updating is reliable, and maritime affair supervision efficiency is improved.

The invention relates to the technical field of financial risks, in particular to a tourism company financial risk evaluation system, which comprises a data acquisition module for acquiring and integrating internal and external multi-source data and supporting dynamic risk evaluation and early warning; the mixed algorithm evaluation module is fused with multiple models to dynamically evaluate the risk level and quantify the financial risk probability; the dynamic early warning response module identifies risk signals in real time, triggers multi-stage early warning and visualizes a conduction path; the intelligent decision support module is matched with the risk disposal scheme library and provides corresponding decisions; and the self-learning optimization module is used for evaluating parameters through case iteration optimization. According to the method, information collection and standardization are more comprehensive, a self-learning optimization module is added, a tourism company can conveniently balance the cost and the long-term influence of customer loss to make corresponding strategy adjustment by constructing a digital twin sandbox and simulating the financial influence of a rehearsal decision, and strategy hedging risks are added through measures such as customer reservation excitation.

The invention relates to the technical field of financial investment analysis and prediction, and discloses an investment market trend intelligent analysis and prediction platform based on machine learning. The data acquisition module acquires multi-dimensional financial time series data, and a space-time correlation feature matrix is generated through the feature processing module and is used for training a depth prediction model based on a hierarchical attention mechanism. The segmentation prediction and anomaly detection module carries out segmentation processing and anomaly detection, and the prediction result correction module corrects an abnormal window prediction result. The hybrid integrated model module fuses multiple models to generate market trend probability distribution, the investment strategy generation module constructs a risk-income balance strategy, and the optimization decision module determines an optimal investment portfolio. The platform can accurately analyze and predict an investment market trend, generates a reasonable investment strategy, assists investors in optimizing decisions, reduces risks, and improves benefits.

The invention provides a soil entropy prediction method and system combined with multiple models in the technical field of smart agriculture, and the method comprises the steps: S1, building a soil entropy prediction model based on a multi-modal feature extraction layer, a heterogeneous model fusion layer and an integrated prediction output layer, and setting a loss function of the soil entropy prediction model; s2, acquiring a large amount of historical monitoring data, preprocessing and labeling the historical monitoring data, and then constructing a data set; s3, dividing the data set into a training set, a verification set and a test set based on a K-fold cross validation method, and training, verifying and testing the soil entropy condition prediction model through the training set, the verification set and the test set; and S4, deploying the soil entropy condition prediction model passing the test, and performing soil entropy condition prediction through the deployed soil entropy condition prediction model. The method has the advantages that the generalization ability and accuracy of soil entropy condition prediction are greatly improved.

This invention provides a method for rapid assembly of a modular tubular beam car body. The car body is divided into several independent modules, which are manufactured in parallel at independent workstations or assembly lines. Corresponding connections between different independent modules are fitted with interlocking mortise and tenon structures for coarse positioning and fastening. The manufactured independent modules are then automatically transferred to an assembly station, where a robot grasps and rapidly assembles them. After assembly, bolts and / or structural adhesive are used to fasten the mortise and tenon joints. Compared to traditional assembly line production processes, this method significantly improves efficiency and economy, effectively reduces floor space, and overcomes the limitation of existing "unpacking processes" that are only suitable for single-model production, thus facilitating flexible manufacturing and rapid iteration across multiple models.

The invention discloses an optimal scheduling method for large model reasoning service and a related device. A plurality of reasoning service instances and a plurality of nodes used for running the reasoning service instances are deployed in the reasoning service of the target large model, firstly, hardware utilization indexes and service utilization indexes of all the reasoning service instances are collected, and collection time and instance IDs are recorded. Then, the indexes are preprocessed, and hardware preprocessing indexes and service preprocessing indexes are obtained; thirdly, multiple model parameters of the target large model and hardware computing power parameters of a target computer are obtained, and the ID, the hardware preprocessing index and the service preprocessing index of each reasoning instance are associated and integrated with the model and the computing power parameters to form an associated data set; and based on the data set, respectively calculating an HRUI, an SPHM and an HRVE of each reasoning service instance, so as to generate a capacity adjustment strategy and a request routing strategy of the target large model based on the HRUI, the SPHM and the HRVE.

The invention relates to the technical field of face recognition, and particularly discloses a high-precision face recognition method and system based on transfer learning, and the method comprises the steps: S1, selecting a plurality of universal deep learning models, and carrying out the pre-training through a universal face data set; s2, expanding the data volume by adopting a deep data enhancement technology, and introducing a generative adversarial network to carry out data enhancement; establishing an annotation optimization system; s3, performing multi-dimensional scene feature extraction on the acquired face image, performing weighted fusion on scene features of different dimensions by adopting an attention mechanism, designing an adaptive model structure, and dynamically adjusting parameters and structures of the model according to different scene features; s4, performing joint training on a plurality of models of different architectures by using the face data fused with the scene features and the enhanced face data; and iteratively optimizing the model regularly. According to the method, the accuracy and consistency of labeling are improved, so that the model can learn more accurate face features, and the recognition precision is improved.

The invention relates to the technical field of intelligent recognition, in particular to a multi-model hierarchy-based user intention classification method and device, a vehicle and a medium, and the method comprises the steps: obtaining a voice instruction of a user; decoding and converting the voice instruction into text data, inputting the text data into a pre-constructed intention classification model to obtain a matching result, and recognizing the user intention through a plurality of model layers in the intention classification model based on a confidence interval of the matching result to obtain a user intention recognition result; the target control instruction is determined according to the user intention recognition result, the vehicle is controlled to execute the corresponding operation according to the target control instruction, and the operation result is fed back, so that the problems of low user intention recognition speed and low user intention recognition accuracy in related technologies are solved, the user intention recognition speed is increased, and the user intention recognition efficiency is improved. And the accuracy of user intention recognition is improved.

The invention discloses a distribution box room environment parameter integrated measurement method, and particularly relates to the field of multi-parameter measurement, and the method comprises the steps: firstly constructing a machine room three-dimensional model and a sensor correlation degree matrix, then building a heat balance, humidity diffusion and airflow motion model, and calibrating parameters; deploying a measurement system and calibrating through dual synchronization and cross check; establishing a sensor confidence evaluation system and an adaptive threshold based on 72-hour reference data; fusing calibration data by adopting a three-level correlation calibration mechanism; four types of measurement modes and conversion logics are designed, and measurement resources are dynamically allocated through environmental risk assessment; multi-dimensional state parameters are extracted, a comprehensive evaluation value is calculated through normalization, dynamic weighting and combinatorial algorithms, and five-level early warning response is achieved; according to the method, multiple models and an intelligent algorithm are integrated, the parameter measurement precision and the environment risk identification efficiency are improved, the response delay is reduced, the fault diagnosis accuracy is improved, and reliable technical support is provided for safe operation and maintenance of the distribution box room.

The invention discloses a lung squamous cell carcinoma survival probability prediction system fusing multiple models, and relates to the technical field of medical data analysis. Comprising a data acquisition module used for acquiring a lung squamous carcinoma clinical data set and an inspection index of a patient; the data preprocessing and dynamic feature table construction module is used for preprocessing the lung squamous cell carcinoma clinical data set in existing data processing and dynamically updating a physiological feature information table; the model training and predicting module is used for carrying out training and online prediction on various survival analysis models; and the SHAP interpretation and weight calculation module is used for calling a corresponding SHAP algorithm to obtain an average absolute SHAP value of each input feature. According to the method, model interpretation conflict measurement, invalid variable elimination and pseudo high risk verification are provided, and automatic arbitration or artificial recheck is realized through weighted scoring, so that the reasonability and safety of prediction and intervention suggestions are ensured, the decision risk is reduced, and the clinical trust is enhanced.