653 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.

A slope safety and stability analysis method based on an integrated sensor and an intelligent algorithm comprises the following steps: acquiring initial values of geometric parameters (including height, gradient and width) of a slope and basic physical parameters (weight, internal friction angle and cohesive force) of a soil body through a method of combining in-situ static sounding and an indoor test; an improved integrated sensor slope state monitoring device is adopted to continuously obtain state parameters such as water content, matrix suction, internal three-way displacement, an inclination angle and soil pressure of a slope soil body, and multi-dimensional dynamic monitoring is achieved; basic physical parameters of a soil body are updated in real time based on data collected by a sensor, and the slope stability safety coefficient is predicted through a semi-supervised learning model; safety early warning is triggered in a grading mode according to prediction results, model prediction values and measured values are recorded and analyzed, and the system evaluates the long-term stability and applicability of the model; through multi-source data fusion and intelligent analysis, theoretical support and technical guarantee are provided for safety monitoring and early warning of the loess slope.

The invention discloses an ARVR resin wafer process which comprises the following steps: preparing a to-be-detected resin wafer and a reference test piece, and carrying out positioning marking on a micro-nano structure area of the to-be-detected resin wafer to obtain a detection sample; performing multi-dimensional spectrum characterization on the detection sample and the reference test piece, determining a material non-uniform distribution area of the detection sample through comparative analysis, and obtaining molecular structure characterization data; carrying out stress field detection and dynamic response analysis on the material non-uniform distribution area to obtain mechanical property characterization data and high-risk defect points; carrying out microstructure and morphology analysis on the high-risk defect points to obtain defect feature data; and performing system evaluation according to the molecular structure characterization data, the mechanical property characterization data and the defect characteristic data to obtain a health degree evaluation result of the resin wafer. According to the technical scheme, multi-scale characteristic characterization can be carried out on the micro-nano structure area in the resin wafer for AR / VR, and systematic detection of the characteristics such as the molecular state, the interface stress and the phase change of the material is achieved.

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.

In some implementations, a system collects data from remote devices over a communication network, including first monitoring data for a first monitoring program and second monitoring data for a second monitoring program. The system evaluates the second monitoring data obtained for the second monitoring program. Based on the evaluation, the system determines that the monitoring data indicates that one or more criteria for adjusting the first monitoring program based on the second monitoring program are satisfied. The system selects one or more elements of the second monitoring program that are different from elements of the first monitoring program. The system alters the first monitoring program to include the selected one or more elements of the second monitoring program.

Computerized methods and systems evaluate threats in a cloud environment having a plurality of assets. For each pair of one or more pairs of the assets, one or more identified paths from a first asset of the pair to a second asset of the pair is obtained. A sequence of assets that includes the first and second assets defines each path of the one or more identified paths. For each path of the one or more identified paths, a likelihood that an attacker that is at the first asset will successfully reach the second asset via the path is determined. In certain embodiments, for each pair of the one or more pairs a risk score for the pair is determined based on the determined likelihoods for the one or more identified paths. The risk score is indicative of risk the attacker will reach the second asset from the first asset.

A system generates text artifacts using a machine learned language model. The text artifacts may be provided to a search engine for providing to users along with search results. The system iteratively improves the set of text artifacts by performing the following steps. The system updates the prompt used to generate the text artifacts based on the performance of the text artifacts to obtain a new prompt. The system executes the machine learned language model using the new prompt to generate a new set of text artifacts. The system evaluates the new set of text artifacts to determine performance of each of the new set of text artifacts. These steps are repeatedly performed to improve the set of text artifacts.

An online system maintains a product catalog including products from various retailers, from which users can purchase products. Each of the products are associated with attributes such as a size value and a size unit of measure (UOM). The online system identifies products with erroneous product attributes using taxonomy attribute value homogeneity. The online system performs an inference task in conjunction with the model serving system or the interface system to infer a correct size value and size UOM of the product. The online system evaluates the accuracy of the inferred size value and size UOM of the product. Responsive to determining that the inferred data is accurate, the online system updates the product catalog with the corrected product attribute information.

The invention discloses a 4D-Flow-CFD simulation model construction method and system for quantitative evaluation of cirrhosis portal vein hydrodynamics, and belongs to the technical field of medical image processing, the construction method comprises the following steps: step 1, placing a patient on a scanner, and sequentially performing structural image and 4D-Flow MRI (four-dimensional blood flow magnetic resonance) scanning; 2, integrating the 4D-Flow MRI and CFD (computational fluid dynamics) technology, performing post-processing on a 4D-Flow MRI image by adopting Circle software, and screening out hemodynamic characteristic parameters capable of representing the liver cirrhosis process; and 3, constructing a liver portal vein system finite element model by using the screened haemodynamic characteristic parameters so as to establish a quantitative evaluation model of portal vein haemodynamics of the patients with cirrhosis, so that a new haemodynamic detection method is explored for clinical systematic evaluation of cirrhosis, and a new thought is provided for accurate evaluation of cirrhosis.

The invention discloses a landscape lighting intelligent control system based on distributed optical sensor data fusion, and relates to the technical field of intelligent control, and the system comprises a data collection module which is used for obtaining environment data in real time; the processing fusion module is used for performing Bayesian fusion on the environmental data to obtain final environmental data estimation; the on-off control module is used for controlling light on-off of the landscape lighting system; the brightness adjusting module is used for adjusting the brightness of light of the landscape lighting system based on a fuzzy reasoning process according to the illumination intensity data and the personnel activity state; and the system evaluation module is used for evaluating the effect of the landscape intelligent adjustment control system according to the light on-off control response analysis of the landscape illumination system and in combination with the energy-saving effect realized through brightness adjustment. According to the method, the problems that the response environment change is not rapid, and system data processing and real-time control are not efficient are solved. Therefore, the purposes of saving energy, improving the lighting effect and enhancing the user experience are achieved.

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 relates to the field of electric digital data processing, in particular to an intelligent decision-making system evaluation scene set construction method and device based on a case constraint large model, and the method comprises the following steps: constructing a scene tree framework based on domain knowledge, and extracting key elements; traversing the scene tree framework, and calculating the complexity and node weight of child nodes; assembling cue words, and generating scene description through a large model; performing matching degree judgment on the scene template and the scene description through a scene template and scene description matching measurement model; aggregating the matched scene template and scene description to generate a scene tree; and performing de-weighting and balance degree calculation on the scene tree, and if the scene tree is balanced, outputting an evaluation scene set. According to the method, large model illusion is effectively inhibited, a high-quality evaluation scene set is automatically generated, accurate matching measurement of the scene template and the scene description is realized, and the problem that the evaluation scene is difficult to realize balanced full coverage is effectively solved.

The invention discloses a coal mine tunnel support strength evaluation method and system, and the method comprises the following steps: carrying out the actual modeling of a coal mine tunnel according to a field project through FLAC3D numerical simulation software; under the condition of no ground stress, endowing elastic model parameters to the model and calculating balance; designing different supporting schemes, applying pre-tightening force and calculating balance; determining a low stress threshold value corresponding to a roadway surrounding rock unstable layer and a high stress threshold value corresponding to roof fracture instability, and calculating the low stress threshold value and the high stress threshold value by combining the volume weight of the rock sample obtained by field sampling; respectively calculating a high stress volume and a low stress volume; defining the lowest pressure stress efficiency, the lowest standard pressure stress volume and the pressure stress efficiency, and obtaining the lowest standard pressure stress volume according to the supporting strength of different schemes; the higher the lowest pressure stress efficiency, the higher the pressure stress efficiency and the lowest standard pressure stress volume are, the better the supporting effect of the anchor rod and / or the anchor cable is, and otherwise, the poorer the supporting effect is.

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 a power system adaptive evaluation system construction method based on LLM and analytic hierarchy process, comprising the following steps: step 1, collecting corpora, and constructing a knowledge base through multi-source data; step 2, knowledge enhancement and processing based on an RAG framework; step 3, professional field fine tuning and adaptive optimization of the large language model; step 4, generation of a self-adaptive power system evaluation system; step 5, subjective evaluation by human experts; and step 6, performing big language model evaluation and human expert comprehensive evaluation. The comprehensiveness, the accuracy and the adaptability of power system evaluation are remarkably improved, and the high-standard requirement in a complex evaluation environment is met.

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 belongs to the technical field of complex system evaluation, and provides a complex system evaluation method based on fuzzy multi-objective decision, which comprises the following steps: S1, constructing a multi-level and multi-dimensional complex system evaluation model; s2, performing fuzzification processing on the indexes under each evaluation target by using a fuzzy set theory; s3, introducing a dynamic weight distribution mechanism, and dynamically adjusting the weight of each target by adopting an intelligent algorithm according to the relative importance among the targets and the current system state; according to the method, a multi-level and multi-dimensional evaluation model is constructed, uncertainty and fuzziness in the evaluation process are processed by utilizing a fuzzy set theory, a dynamic weight distribution mechanism and an intelligent algorithm are introduced to optimize weights, an improved multi-objective decision algorithm is applied to perform comprehensive analysis, and finally optimization suggestions are proposed; according to the method, the overall condition of the complex system can be reflected more accurately, the timeliness and pertinence of evaluation are ensured, and the scientificity and accuracy of evaluation are improved.

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.