512 results about "System evaluation" patented technology

The invention discloses a hospital information intelligent analysis and decision-making system based on a multi-modal large model, and relates to the technical field of hospital information analysis and decision-making. The system comprises a data acquisition module, a preprocessing and fusion module, a large model construction and training module, an intelligent analysis module, a decision support module, a knowledge graph construction and application module, a data security and privacy protection module, a system evaluation and optimization module, a multi-hospital cooperation and data sharing module, a mobile application module and the like, and all the modules cooperate to realize hospital information intelligent processing and decision making. The system integrates multi-modal data, assists in precise diagnosis, recommends a treatment scheme, predicts resource demands, monitors medical quality, assists medical research, manages patient health and the like, comprehensively improves the intelligent level of hospitals, optimizes medical services and benefits doctors and patients.

The invention discloses a credit marketing intelligent recommendation system. According to the system, for the existing credit marketing problem, a customer portrait is constructed through multi-channel data acquisition, cleaning and preprocessing, and personalized credit product recommendation is carried out based on content and collaborative filtering. And training a recommendation model by using a neural network model and a reinforcement learning algorithm, updating recommendation in real time or at regular intervals according to real-time conditions and feedback of customers, establishing a system evaluation effect including recommendation accuracy, customer satisfaction and business indexes, and optimizing model parameters, improving the algorithm and updating data according to evaluation. The method can improve the customer satisfaction and the business conversion rate, reduces the risk, adapts to the market change, and effectively solves the defects of the existing credit marketing channel and strategy.

The invention relates to the field of photovoltaic energy storage, and discloses an off-grid photovoltaic energy storage system control method comprising the following steps: collecting photovoltaic power generation data, load demand data, meteorological prediction data and battery state data and carrying out data preprocessing; constructing a deep reinforcement learning model based on the collected data, wherein the deep reinforcement learning model is used for optimizing a charging and discharging strategy of the energy storage system; constructing a multi-objective optimization algorithm for optimizing battery management, energy efficiency and load stability and determining an optimal charging and discharging strategy; dynamically adjusting a charging and discharging strategy based on the photovoltaic generating capacity, the load demand and the battery state monitored in real time, and performing cross-seasonal energy scheduling according to meteorological prediction data; and based on real-time feedback and system evaluation, the deep reinforcement learning model is updated, and a multi-objective optimization algorithm and a discharge strategy are adjusted. By constructing a closed-loop feedback mechanism based on the state of charge and the power supply error, the dynamic correction of the energy storage scheduling strategy is realized.

The invention provides a computer big data storage control system, and relates to the technical field of computers, and the system comprises a data evaluation module which is used for comprehensively calculating the real-time popularity of data, and the real-time popularity is obtained through the comprehensive calculation of the data access frequency and the access duration; performing quantitative evaluation on the real-time popularity of the data in combination with a multi-dimensional factor to obtain a value evaluation value of the data; analyzing the system resource information of the storage device on the computer to obtain a resource analysis result; and the intelligent contract execution module is used for carrying out comprehensive analysis and judgment on the real-time popularity, the value evaluation value and the system resource analysis result of the data so as to trigger adjustment of a storage strategy. According to the method, the real-time popularity model is constructed, the analytic hierarchy process is combined to achieve multi-dimensional value evaluation, the intelligent contract dynamically adjusts the storage strategy according to the evaluation result and the resource state, the data value and the storage medium are accurately matched, the problems that a traditional system is extensive in evaluation and rigid in strategy are solved, and efficient and accurate scheduling of storage resources is achieved.

The invention discloses an aircraft braking system evaluation and prediction method based on fault simulation and geometric features, and belongs to the technical field of electric data processing. The method comprises the following steps: constructing a simulation model of an aircraft brake system, and injecting faults into the simulation model according to different degrees of fault injection control parameters to generate a degradation monitoring data set of the whole life cycle of the system; dividing the degradation monitoring data set into a training data set and a test data set; training the GoogLeNet model by using the training data set to obtain a health assessment model; evaluating the health degree of the aircraft brake system by using the test data set; constructing a topological graph according to the multi-rack operation data and the health degree of the aircraft braking system; nodes in the topological graph are clustered and divided into K clusters; establishing a residual life prediction model for each cluster; and performing similarity matching on the sample and the K clusters, and selecting the best matched residual life prediction model to perform residual life prediction on the system. The method can accurately predict the remaining life of the aircraft brake system.

A computerized system for dynamic cybersecurity policy using AI-based contextual adaptive learning includes an AI system that evaluates business contexts, risk tolerance, and productivity impact to generate threat intelligence assessments. The system includes a Contextual Adaptive Learning module that dynamically adjusts cybersecurity policies based on threat assessments to create security workflows. A Cybersecurity Mesh Development module that integrates policies across security frameworks. A Dynamic Scenario Catalog module that updates policy adjustments based on threat intelligence. An Automated Workflow Orchestration module that creates and refines security workflows for optimal efficiency. A Policy Recommendation and Automation module that generates prioritized security recommendations and automates policy changes based on organizational risk profiles and current security controls. This system harmonizes security policies while considering business context, risk, and productivity impacts.

The system (100-2) and method for identification of high ranked neighbor cells in a telecommunications network provide an efficient and accurate approach to selecting neighboring cells with superior performance for handover purposes. The system (100-2) leverages performance metrics, algorithms, and dynamic adaptation techniques to determine the suitability and ranking of potential neighbor cells. By considering factors such as signal strength, signal quality, interference levels, load balancing requirements, and operator-defined policies, the system (100-2) evaluates the performance of neighbor cells and assigns them scores or rankings to identify the higher ranked neighbor cells. The system's dynamic adaptation ensures that the rankings remain up to date and responsive to changing network conditions. The present system (100-2) and method optimize handover decisions, enhance network performance, and provide users with seamless connectivity and improved quality of service.

The invention provides an intelligent automobile test case automatic generation and evaluation system, device and product, and relates to the technical field of intelligent automobile test.According to the system, domain knowledge support is provided through a test knowledge base, and a demand understanding module is combined with a fine-tuning large model to deeply analyze demands; function points are extracted, and hidden scenes such as sensor faults and multi-system cooperation are identified; the test case generation module is based on a special prompt template and few-sample learning, a complete case containing elements such as purposes, steps and data is generated by fusing a vehicle model configuration library, and the specialty and the performability are ensured; and the evaluation module realizes objective quality evaluation through multi-dimensional quantitative scoring of demand coverage rate, scene integrity, risk coverage degree, normalization and the like. All the modules cooperate to realize intelligent generation and system evaluation of test cases, and the automation level, coverage range and reliability of intelligent automobile testing are improved.

The embodiment of the invention provides a multi-agent cooperation system evaluation method and device, electronic equipment and a computer readable storage medium, and relates to the technical field of artificial intelligence. According to the method, quantitative evaluation is carried out on a collaborative planning process based on user task description, subtask description, a subtask expected result, agent description and a relation matrix; when planning evaluation meets a preset standard, further performing execution capability evaluation according to user task description, subtask execution results, expected and actual execution efficiency and resource consumption, and performing systematic measurement on cooperation efficiency of the multi-agent system in a planning stage and an execution stage through a quantitative scoring mechanism; and closed-loop optimization is formed in combination with human preference information, so that systematic and dynamic quality control of the multi-agent cooperation system in the whole task planning and execution process is realized, the objectivity and practicability of an evaluation result are improved, and the use experience of a user can be effectively improved.

The invention, which relates to the technical field of artificial intelligence and educational informatization, discloses a college entrance examination learning-up planning system based on Ai intelligent questions and answers, the system comprises a data acquisition module, an intelligent questions and answers interaction module, a knowledge graph construction module, an intelligent recommendation module, and a risk assessment module. The system has the capability of real-time monitoring and dynamic optimization, the risk assessment module can continuously monitor data such as student score fluctuation, college enrollment plan change and professional enrollment popularity change in real time, the uncertainty of application combination is quantified according to the information entropy theory, an ascending planning scheme is dynamically adjusted according to an entropy change threshold value, and the system has the advantages of being high in reliability and high in reliability. Meanwhile, a feedback optimization unit of the intelligent recommendation module can dynamically adjust a recommendation strategy and a fusion weight according to real-time feedback of the students and a system evaluation result, and the continuous optimization and self-adaptive adjustment mechanism can enable an ascending planning scheme to always fit actual conditions of the students and external environment changes.

The invention discloses an avionics system evaluation index system construction method based on a large language model agent. The avionics system evaluation index system construction method comprises the steps that a knowledge extraction module performs knowledge extraction on a design document of an avionics system by using the large language model agent and preliminarily constructs a knowledge graph; the reflection verification module automatically checks and corrects entities and relationships in the knowledge graph based on a large language model agent in combination with a learning normal form, and finally outputs an optimized knowledge graph; and the index generation module automatically generates an evaluation index for each entity node in the optimized knowledge graph based on a large language model agent to form an avionics system evaluation index system. According to the method, extraction, induction and system construction of avionics system evaluation indexes are automatically completed, and the method has the advantages that manual decomposition is not needed, the process is traceable, the coverage is wide, and the efficiency is high.

The invention discloses an underwater focusing intense sound system and method based on phase control, and relates to the technical field of focusing intense sound, and the technical scheme is characterized in that a measurement and control upper computer is used for controlling the emission and detection of a trigger signal; the phase control signal generator is used for controlling a phase control signal of sound wave emission; the multi-channel power amplifier is used for driving the acoustic emission phased area array, and the acoustic emission phased area array is used for emitting acoustic waves; the area array calibration hydrophone is used for receiving signals of reflected sound waves; the target calibration sound source is used for transmitting a signal pulse width, the target sound field measurement hydrophone is used for receiving the signal pulse width transmitted by the target calibration sound source and forwarding the signal pulse width to the measurement and control upper computer, and the measurement and control upper computer receives the forwarded signal pulse width for system evaluation and correction. By generating the high-energy strong-focusing sound field and combining the actual underwater signal propagation process, accurate positioning and control of the underwater strong sound field can be completed, so that accurate focusing irradiation and high-resolution imaging of a long-distance small target are realized.

The invention discloses an ecological flow guarantee method based on runoff prediction and supply. Meteorological, hydrological and other data of the Tongkeng river basin are collected, an SWAT model is constructed and verified to simulate runoff, and extended rainfall is analyzed based on an R / S method and a wavelet method. And constructing a surface water and underground water coupling model, and predicting underground water volume, water level and water quality. And ecological flow safeguard measures are formulated after water resource supply and demand balance analysis, wherein the ecological flow safeguard measures comprise optimal allocation of water resources, enhanced management, ecological water supplementation, water environment treatment, ecological restoration project development and the like. The method has the advantages that measure implementation subjects and progresses are defined, monitoring system evaluation effects are established, adjustment and optimization are performed according to results, ecological flow is guaranteed, sustainable utilization of drainage basin water resources and ecological environment protection are realized, remarkable social, economic and environmental benefits are achieved, and a scientific and effective method is provided for water resource management.

A cybersecurity rule conversion system (“conversion system”) disclosed herein converts IPS / IDS rules from existing formats to target formats in a pipeline that ensures quality of resulting converted rules. First, the conversion system identifies and validates existing rule formats. For rules with validated existing formats, the conversion system maps tokens in the existing format to tokens in the target format and populates a converted rule in the target format. The conversion system then evaluates and tunes patterns in the converted rules for matching range and length. Finally, the conversion system evaluates the converted rules for performance on traffic logs and for quality of patterns and matching ranges of patterns therein, and exports the converted rules to users with indications of performance deficiencies.

The invention discloses an unmanned aerial vehicle random patrol strategy system evaluation method in a confrontation environment. The method comprises the steps of setting an unmanned aerial vehicle intelligent patrol attack and defense confrontation system and a target steady-state distribution model of the unmanned aerial vehicle intelligent patrol attack and defense confrontation system; determining the average strategy entropy of the patrol person; determining conditional probability differences of the patrol persons; determining a transfer strategy dispersion degree of the patrol person; a patrol strategy comprehensive structure difference index is determined; determining a single-time-point key state average relative error; determining the average relative error of the time average key state; and determining a time-weighted key state average relative error. According to the method, the comprehensive structure difference index of the difference between the single-state transition matrix and different matrixes is quantified, and a balanced framework is provided to evaluate the overall unpredictability of the strategy. A multi-dimensional evaluation system based on key state and time weighting is designed, and time importance is directly fused into error calculation through a key state screening mechanism.

The invention relates to an intelligent diagnosis system for echelon utilization and recovery of a power battery, and aims to evaluate the health state of the battery with high precision and assist the echelon utilization and recovery of the battery. The system integrates five modules: the battery health state monitoring module acquires parameters such as voltage, current and temperature in real time through a high-precision sensor; the battery health state evaluation module accurately evaluates the battery state by applying algorithms such as ICA, DVA and LSTM neural networks in combination with a transfer learning technology; the echelon utilization recovery decision-making module outputs an optimal recovery scheme based on a three-level decision-making model; the intelligent disassembling and safety protection module adopts an advanced process and a monitoring system to guarantee safe and efficient disassembling; and the data storage and cloud prediction feedback module realizes system dynamic optimization by using big data analysis and machine learning. The evaluation accuracy of the system exceeds 95%, the single diagnosis time is shorter than 5 minutes, multiple types of batteries are supported, the self-learning iteration period is shorter than 24 hours, the detection cost can be remarkably reduced, the resource utilization rate is increased, carbon emission is reduced, and key technical support is provided for sustainable development of the new energy automobile industry.

The invention discloses an intelligent item selection recommendation system of a live broadcast room based on AI digital people, relates to the technical field of intelligent item selection recommendation, and solves the technical problems of generating a dynamic and high-confidence matching scheme based on highly matched commodities and adjusting a combination strategy in combination with a real-time scene and user feedback. Through the multi-dimensional weighted calculation of the feature matching score, the crowd matching score and the scene association score, the hidden integrating degree of the commodity and the user demand is quantified, the highly matched commodity is screened out, the invalid recommendation is reduced, the comprehensive confidence degree of the commodity combination is calculated based on the association rule algorithm, the personalized matching scheme conforming to the real-time scene is generated, and the user experience is improved. A high-potential scheme is screened through a self-adaptive threshold value, the matching rationality and transformation potential are improved, a comprehensive feedback processing module is introduced, a user is supported to customize combined commodities, and optimization suggestions are provided through system evaluation; and for user change feedback, generating hidden collocation based on a user portrait, and realizing real-time iteration of a recommendation scheme.

The invention discloses a power supply service intelligent management system, and relates to the technical field of intelligent power grids, and the system comprises a power grid holographic sensing module which obtains multi-dimensional unstructured data of a power system, extracts core features through cross-domain fusion analysis, and generates a power grid real-time operation state map; the equipment health twinning module is used for constructing an equipment health model in combination with a digital twinning technology and outputting an equipment fault early warning signal and a residual life prediction value; the customer energy efficiency service module is used for analyzing the association relationship and then generating a personalized energy efficiency optimization scheme and a power utilization suggestion; and the dynamic scheduling decision module is used for generating a power grid optimal scheduling strategy and an emergency response scheme. The power grid holographic sensing, digital twinning technology, dynamic scheduling, block chain and encryption technology are utilized, the power system is monitored in real time, equipment health is evaluated, faults are predicted, power utilization, energy conservation and emission reduction are optimized, data safety and stable operation of the system are ensured, and the cooperation efficiency of power enterprises and users is improved.

The embodiment of the invention relates to the technical field of power system access safety control, and provides a power system access control method and device, electronic equipment and a medium, and the method comprises the steps: collecting equipment evaluation data of target access equipment, system evaluation data of a target power system and network risk evaluation data in real time, the target access equipment is equipment of a to-be-accessed target power system; performing equipment credibility evaluation on the target access equipment based on the equipment evaluation data, performing system resource environment evaluation on the target power system based on the system evaluation data, and performing network credibility evaluation on the security state of the current access network based on the network risk evaluation data; generating a target access control strategy based on the equipment trust evaluation result, the system resource environment evaluation result and the network trust evaluation result; and performing access control on the target access equipment based on the target access control policy. The flexibility, security and real-time response capability of access control are improved.

The invention discloses a CKM-CNN-based power system transient stability assessment method, system, equipment and medium, and relates to the technical field of power system assessment, and the method comprises the steps: collecting time sequence electrical quantity data after power system fault disturbance, constructing a transient sample set, carrying out the standardization processing and marking of a sample, and carrying out the calculation of the transient stability of the power system; a transient sample set is divided through a clustering algorithm, based on a sample cluster division result, an adaptive oversampling technology is adopted to carry out directional enhancement on an unstable sample, an equalized sample space is generated, a multilayer convolutional neural network is utilized to carry out hierarchical feature extraction on the equalized sample space, a high-dimensional feature vector is generated, and according to the high-dimensional feature vector, a high-dimensional feature vector is generated. And outputting a transient stable state probability evaluation result through the classification decision-making layer. According to the method, a generalization sample set is constructed through standardization and labeling, the scarce instability sample identification capability is improved by combining two-stage clustering and ADASYN oversampling, and the system dynamic state is accurately captured by utilizing 1D-CNN hierarchical feature extraction, so that efficient and robust evaluation of the transient stability of the power system is realized.

The invention relates to an RAG system evaluation data set automatic synthesis method and device based on reinforcement learning, and the method comprises the steps: obtaining knowledge base data of an RAG system, and extracting keywords and related documents of a target vertical field from the knowledge base data; according to the keyword and the document, synthesizing an initial question-answer pair by using a preset cue word template; constructing a reward of the initial question-answer pair; determining training data of the initial strategy model according to the keyword, the initial question and answer pair, the reward and parameters of the initial strategy model; training the initial strategy model based on the training data to obtain a target strategy model; and generating the target question-answer pairs meeting the reward in batches based on the keywords and the documents by utilizing the target strategy model, thereby solving the problems of high cost, low efficiency and influence on the effectiveness of the assessment RAG system in the assessment data set in related technologies, and being capable of reducing the synthesis cost and improving the synthesis efficiency.

The invention belongs to the field of energy storage system evaluation, and particularly provides a compressed air energy storage system state evaluation method based on digital twinning, in the method, a multi-physics field coupling digital twinning model is constructed based on a compressed air energy storage system, and a time sequence modeling module based on an LSTM-Transformer hybrid architecture is built in the digital twinning model; based on the calibrated digital twinborn model, state evaluation is carried out on the compressed air energy storage system in combination with a preset evaluation dimension, simulation deduction data of the digital twinborn model and real-time operation data of a physical system are fused in the evaluation process, and an evaluation result is obtained and output; and based on an evaluation result, triggering an uncertainty quantification result based on an LSTM-Transform architecture to carry out graded state early warning. According to the method, the multi-physics field coupling digital twinborn model is constructed and dynamically calibrated, simulation deduction, real-time data evaluation and graded early warning based on uncertainty quantification are fused, and the compressed air energy storage system state evaluation precision, real-time performance and early warning credibility are improved.

The invention discloses an electric water heater performance testing system and method based on constant-temperature water supply control, and relates to the technical field of electric water heater testing. A dynamic behavior input model reflecting family multi-member, continuous and non-uniform water consumption behavior characteristics is established through a behavior script input flow Seq; the method comprises the following steps: constructing a structured test data set Dset by using response data collected by a multi-channel sensor, and performing pairing analysis based on the structured test data set Dset and a behavior script input stream Seq; key response performance data such as a temperature stability index, a response time delay index, an energy consumption change rate index, a flow fluctuation range index and a water outlet interruption ratio index can be quantitatively output, and a multi-dimensional performance evaluation report of the electric water heater can be finally generated. Therefore, the system evaluation of the response capability, the temperature control accuracy, the energy consumption performance and the water supply stability of the electric water heater under variable, continuous and approximately real use conditions is realized.

The invention discloses a cultivated land ecological safety comprehensive evaluation method and system based on multi-model coupling. The method comprises the steps that a future multi-source data set of cultivated land to be evaluated in a future time period is set; performing grid division on the cultivated land to be evaluated by adopting a grid calculator in ArcGIS software to obtain a plurality of grid regions, and performing grid division on the future multi-source data set based on the plurality of grid regions to obtain a future multi-source sub-data set of each grid region; and predicting the future multi-source sub-data set of each grid region by adopting the constructed multi-ecological safety comprehensive evaluation function model, the spatial classification method and the constructed ecological safety comprehensive evaluation index calculation formula to obtain an ecological safety comprehensive evaluation index corresponding to each grid region. According to the method, the ecological function of the to-be-evaluated cultivated land in the future scene is simulated by coupling the multi-ecological safety comprehensive evaluation function quantity model, so that systematic evaluation on the cultivated land ecological function of the to-be-evaluated cultivated land in the future scene is realized.

The invention discloses an interactive artificial intelligence question answering evaluation method and system oriented to the education field, and the method comprises the steps: obtaining question answering real data, and processing the question answering real data to obtain test data, the test data comprising background questions, student questions and teacher answers; according to the background questions and the student questions, performing simulation questions and answers by using the large question answering model to obtain model answers of the large question answering model; model answers and test data are input into the evaluation model, and evaluation results of different dimensions are obtained by taking teacher answers as references; and performing integration weighting on the evaluation results of all dimensions to obtain an overall score, and performing visualization processing to obtain a final evaluation report. The interactive question answering system evaluation efficiency and precision are significantly improved, and the interactive question answering system evaluation method has significant technical advantages in the aspects of cost saving, manual intervention reduction and improvement of the actual effect in education application, and meets the urgent demand of the current education industry for an intelligent evaluation tool.

The invention relates to an AI-based grade protection evaluation method and system, a terminal and a storage medium, and relates to the technical field of network information security, and the method comprises the steps: obtaining image detection data of preset network equipment; analyzing the image detection data to determine detailed equipment information of the network equipment; determining an actual grade evaluation model according to the detailed information of the equipment and a preset equipment grade evaluation model relationship; acquiring an equipment operation state of the network equipment; analyzing the equipment operation state, the equipment detailed information and the actual grade evaluation model to determine an equipment grade score value; determining an equipment score value weight according to the detailed information of the equipment and a preset equipment grade weight relationship; analyzing the equipment grade score value and the equipment score value weight to determine a system overall score value; and determining and outputting a system evaluation grade according to a relationship between the system overall score value and a preset grade score value. The method and the device have the effect of improving the accuracy of level protection evaluation.

The invention discloses an unmanned aerial vehicle cluster survivability evaluation method based on a killing chain, and relates to the technical field of unmanned combat system evaluation, and the method comprises the following steps: S1, summarizing the types and number of our unmanned aerial vehicles, forming a our unmanned aerial vehicle equipment list, and collecting enemy target information, and forming an enemy target list; s2, according to the communication relationship between the unmanned aerial vehicle clusters, analyzing the network topology structure of the unmanned aerial vehicle cluster combat, eliminating invalid network communication modes and enemy target nodes, and forming a directed unmanned aerial vehicle cluster survivability analysis network graph D = lt; e, Vgt; . According to the unmanned aerial vehicle cluster survivability evaluation method based on the killing chain, different unmanned aerial vehicle nodes are destroyed, the comprehensive survivability evaluation value change of the whole unmanned aerial vehicle cluster is calculated, a survivability change trend chart is drawn, and key equipment nodes and weak links which have the largest influence on network survivability are judged.

The invention provides an intelligent driving system evaluation method and device, electronic equipment and a storage medium, and relates to the technical field of intelligent driving, detection data of an intelligent driving system is collected, and the detection data comprises environment data and driving behavior data of each vehicle; performing causal reasoning according to the environment data and the driving behavior data in combination with a preset confrontation scene, and updating a scene key factor of each driving scene; performing user portrait processing according to the environment data and the driving behavior data, and updating the weight of the evaluation dimension decision model; and according to the scene key factors and the weights, combining with the environment data and the driving behavior data, applying an evaluation dimension decision model to carry out personalized scoring on each driving scene, and obtaining a personalized scoring result for optimizing the intelligent driving system. Accurate, dynamic and demand-fitting adaptive evaluation of the intelligent driving system is realized, so that the intelligent driving system is optimized.

The invention belongs to the field of agent system evaluation, and provides a large model application evaluation method and device based on multi-agent cooperation. The method comprises the following steps: obtaining an inference chain of the large model application according to an original problem; corresponding to each step in the reasoning chain, calling a corresponding agent to obtain reasoning data from the large model application; according to the annotation data corresponding to the reasoning data, evaluating the reasoning data to obtain evaluation results of corresponding steps in the reasoning chain; and obtaining an evaluation result of the large model application according to the evaluation result of each step in the inference chain.