3325 results about "Quantitative Evaluations" patented technology

The invention relates to the technical field of robot decision making, and provides a robot multi-modal fusion autonomous decision making method and system based on a large language model.The method comprises the steps that a robot obtains multi-modal environment information through a visual sensor, a touch sensor, an auditory sensor and a laser radar which are carried by the robot; performing preliminary filtering and noise reduction processing on the original sensor data, and synchronously recording all the sensor data by timestamps; performing space-time semantic alignment on the preprocessed multi-modal data, mapping pixel coordinates of a target in a visual target coordinate quantization original image to a robot coordinate system, performing uncertainty evaluation on a multi-modal signal through a dynamic Bayesian network, and taking entropy or variance as an uncertainty quantitative evaluation index. According to the method, the information quality is improved from a data fusion source, accurate and reliable basic support is provided for subsequent decision making, and decision making errors caused by data deviation are greatly reduced.

The invention belongs to the technical field of semiconductor testing, and discloses a novel automatic detection system and method for the electrostatic protection performance of a power device. A distributed parasitic parameter network model is constructed by acquiring packaging structure parameters of a to-be-tested power device and physical layout data of a test fixture, key parasitic parameter distribution is identified in combination with a time domain reflection measurement technology, a waveform distortion coefficient is calculated, and a spatio-temporal evolution model of electrostatic pulse propagation is constructed. Based on the model, a compensatory predistortion waveform is generated to counteract the parasitic effect of a transmission path, a device port response signal is collected in real time, an energy dissipation characteristic spectrum is extracted through wavelet packet decomposition, and quantitative evaluation of the electrostatic protection performance is achieved. According to the invention, the test precision and repeatability are improved, and the method is suitable for electrostatic protection tests of various novel power devices, especially high-frequency wide-band gap devices.

The invention discloses an ocean multi-mode environment perception and intelligent ship navigation decision-making method based on a double-branch vision-semantic encoder. The method comprises the following steps: S1, acquiring a multi-source data image containing a ship and a surrounding environment thereof from an existing public maritime data set or platform; s2, training a double-branch vision-semantic encoder by using the multi-source data image, and inputting a to-be-processed image extracted in real time into a multi-modal feature matrix in the trained double-branch vision-semantic encoder; s3, based on the multi-modal feature matrix, obtaining positioning information of the ship and surrounding environment elements, and constructing a dynamic security domain model; and S4, in combination with the dynamic security domain model and the multi-ship relative position relationship, carrying out quantitative evaluation on the navigation risk, and generating a self-adaptive navigation strategy based on an evaluation result. According to the invention, high-precision ship positioning and environment element identification under complex weather and illumination conditions are realized by using all-weather characteristics and multi-scale visual feature coding of SAR imaging.

The invention relates to a model training method and device. The method comprises the steps of obtaining a first training image set; performing first fine tuning training on the pre-trained artificial intelligence model by using the first training image set to obtain a preliminary optimization model; acquiring a second training image set; constructing a composite reward function based on the second training image set; wherein the composite reward function is used for performing multi-dimensional quantitative evaluation on the quality output by the model; and performing second fine tuning training on the preliminary optimization model based on the second training image set and the composite reward function to obtain a final optimization model. Therefore, a two-stage differential fine-tuning model strategy is realized, so that the output result of the final optimization model is highly matched with the expectation of a real scene, the problem of insufficient model practicability is fundamentally solved, and the reliability and value of the model after deployment are greatly improved.

The invention provides a multi-dimensional electrical equipment insulation analysis and evaluation method, and relates to the technical field of electrical equipment insulation online detection and intelligent diagnosis. According to the method, an electrically isolated direct current detection channel is constructed in a neutral point small resistance grounding system, controllable direct current detection signals are injected, micro direct current leakage response signals are collected, and after zero drift correction, temperature compensation and power frequency ripple suppression are carried out, layered attribution is carried out in combination with primary system topology and an equipment ledger, and a multi-dimensional response matrix is formed. According to the method, environment and working condition normalization is realized through factor rejection and robust pull-back, a mechanism model containing leakage channel equivalent parameters and path constraints is established, multi-working-condition data is fused to calculate equivalent insulation parameters and credibility indication quantity, and a multi-dimensional insulation representation vector is generated. And performing time sequence analysis on the representation vector, extracting degradation and abrupt change characteristics, outputting a health index and a risk level, and realizing quantitative evaluation and intelligent early warning of the insulation state.

The invention relates to the technical field of intelligent power distribution system state monitoring, in particular to a cable connection state online monitoring method for a low-voltage cable branch box, which comprises the following steps of: applying a disturbance excitation signal to a connection node of the cable branch box, and acquiring transient voltage, current and phase response parameters of the node in different excitation periods; establishing a mapping relation between the node electrical characteristics and the connection state; performing dynamic reference calibration on the acquired signals based on a multi-node space-time coupling matrix to form an adaptive judgment reference of electrical coupling between nodes; a connection degradation sensitive index is extracted through feature sequence differential calculation, a connection state quantitative evaluation result is generated, and real-time updating and anomaly recognition are carried out according to the state change trend; according to the method, the problems of misjudgment and delay caused by a monitoring mode depending on indirect parameters such as temperature and current are solved, high environmental adaptability and degradation identification precision are achieved, and the operation safety and monitoring reliability of the low-voltage power distribution system are greatly improved.

The invention discloses a crop environment quantitative evaluation and decision making system based on growth stage self-adaption, and relates to the technical field of agricultural intelligent decision making. Acquiring an environment original data sequence of field multi-type sensors through a data acquisition module; the dynamic identification module analyzes the sequence by using a preset crop growth stage discrimination model, identifies the current growth stage and outputs a corresponding key environment parameter weight template; the feature fusion module performs weighted fusion on the original data according to the template to generate an environment feature vector with stage adaptability; the state evaluation module converts the vector into a growth state quantitative evaluation value through a growth state evaluation model; and the decision trigger judgment module calls a corresponding preset dynamic threshold interval according to the growth stage for comparison, and generates a decision trigger instruction when the growth state quantitative evaluation value deviates. According to the invention, stage self-adaptive intelligentization of crop growth monitoring and decision making is realized, and the accuracy of environment state evaluation and the timeliness of management decision making are improved.

The invention discloses an enterprise production management method based on digital twinning and workflow simulation, and particularly relates to the technical field of industrial internet and intelligent manufacturing, and the method comprises the steps: constructing a physical production system digital twinning body and business process workflow model, and carrying out the dynamic association through a model fusion engine to form an integrated digital twinning model; real-time event driving is used for deducing and simulating a future production process, and bottleneck and conflict prediction is output; based on the prediction result, utilizing a multi-objective optimization engine to generate a plurality of alternative scheduling schemes; performing parallel simulation quantitative evaluation on the KPI of each scheme, selecting an optimal scheme, analyzing the optimal scheme into a control instruction, and issuing and executing the control instruction; and model self-correction and closed-loop optimization are realized through real-time monitoring and feedback. According to the method, the problem of service and physical state disjunction caused by digital twinning and workflow independence is solved, the whole-process closed-loop management from prediction to execution is realized, and the production self-adaption and intelligent level is improved.

The invention provides a slope displacement monitoring data processing system based on an unmanned aerial vehicle laser radar, and relates to the technical field of data processing, and the system comprises the steps: carrying out the spatial interpolation processing of a topographic feature data set, and constructing a digital topographic surface model; the displacement field calculation module is used for performing iterative optimization based on a digital terrain surface model through spatial similarity analysis and fusion with a gradient descent algorithm, calculating a slope surface displacement vector field, identifying a potential sliding surface and a deformation abnormal region, and generating a displacement field calculation result; and the evaluation module is used for inputting a displacement field calculation result into a risk evaluation model and carrying out slope stability quantitative evaluation through a multi-source data fusion analysis platform. According to the invention, the practicability and operability of the monitoring result are improved.

The invention relates to the field of power system planning, in particular to a micro-grid boundary quantitative evaluation method and system for multi-dimensional analysis and dynamic verification. The method comprises the steps that power distribution network and micro-grid scheme data are acquired, and scene recognition and decoupling modeling are carried out after standardization processing; the method comprises the following steps: extracting end supply-preserving scene data, analyzing cost elements, and generating a critical cost threshold table through normalization processing and a threshold approximation algorithm; performing multi-dimensional parameter correlation analysis and integrated learning training based on the table, and constructing a multi-dimensional boundary index model; performing benefit matching calculation and green value accounting according to the result to generate an economic benefit decomposition structure; real-time streaming data processing and stability verification are combined to generate an economical efficiency boundary index set; and finally, generating a standardized evaluation file through matrix mapping and weight dynamic adjustment. According to the method, dynamic quantitative evaluation of the economy boundary of the micro-grid is realized, the capacity substitution benefit and the green value are effectively integrated, and an accurate basis is provided for planning decision.

The invention discloses a detection method for printing effect verification, and the method comprises the steps: collecting scanning image information and environment sensing information, employing an improved OTSU algorithm and an illumination compensation module to process the scanning image information, and extracting a standardized image feature matrix containing the characteristics of character integrity, edge sharpness and the like; identifying a defect mode by using an EAST text detection algorithm and morphological operation, and generating a quantitative evaluation vector; the vector and environment sensing information are subjected to space-time alignment, a printing quality degradation model is constructed through a density peak value clustering algorithm, and a multi-dimensional quality index set is established; and finally, based on the graph attention network and the time sequence convolutional network, analyzing the incidence relation between the printing quality and the equipment state, and outputting a verification report containing a quality score, a defect positioning graph and a life prediction curve. According to the method, accurate quantitative evaluation of the printing quality is realized, a dynamic association model of the quality, the environmental parameters and the equipment aging is established, and an intelligent decision basis is provided for printing quality maintenance.

The invention relates to the technical field of voice processing, can be applied to business scenes such as financial science and technology and medical health, and discloses a multi-speaker dialogue voice analysis method, device and equipment and a medium, and the method comprises the steps: obtaining a to-be-analyzed multi-speaker dialogue voice, determining a naturalness score based on an acoustic feature, and obtaining a multi-speaker dialogue voice analysis result; determining a semantic consistency score based on voice embedding and semantic embedding corresponding to a preset text, determining a speaker consistency score based on embedding of a plurality of speakers of the same speaker, determining an interaction rationality score based on voice alternate overlapping duration, determining a diversity score based on a variance of voice features, and fusing the scores, the comprehensive mass fraction is obtained. According to the invention, through quantitative evaluation of five dimensions of naturalness, semantic consistency, speaker consistency, interaction rationality and diversity, a comprehensive quality scoring system is established, so that the evaluation result simultaneously reflects voice fluency, content matching degree, identity stability, interaction rhythm rationality and feature richness.

The invention relates to the technical field of SRAM (Static Random Access Memory) memory anti-radiation testing, and discloses an SRAM memory anti-radiation testing method and system based on running state injection, and the method comprises the following steps: system initialization, mapping preparation and time sequence baseline establishment; carrying out model quantitative compiling, sensitivity analysis and physical bit candidate generation; generating a running state injection plan and loading a test case; performing operation state execution, disturbance injection and synchronous monitoring acquisition; multi-source triggering, weight read-back and fault decoupling judgment are carried out; and performing quantitative evaluation on anti-radiation performance, constructing a degradation curve and outputting a report. The system corresponds to the method. According to the invention, a hardware-in-the-loop operation state injection and online evaluation technology is constructed, and the technical problems of guidance and position limited disturbance / equivalent irradiation injection based on weight bit level sensitivity and logic-physical mapping are solved.

The invention provides a factory energy safety dynamic monitoring method based on the Internet of Things and multi-mode perception, and belongs to the technical field of industrial Internet of Things. Comprising the following steps: acquiring electrical parameters, temperature data, combustible gas leakage concentration and vibration signals of equipment in real time; carrying out aggregation and protocol conversion on the electrical parameters, the temperature data, the combustible gas leakage concentration and the vibration signals through a multi-protocol intelligent gateway, and uploading the electrical parameters, the temperature data, the combustible gas leakage concentration and the vibration signals to a locally deployed edge computing node in a preset period; preprocessing the received data at the edge computing node, and generating a quantitative risk index based on a dynamic risk assessment model fusing the real-time state of the equipment, the historical aging trend and the environmental parameters; and carrying out risk grade judgment according to the quantitative risk index, and executing a corresponding grading response. Through fusion of multi-modal sensing data and edge intelligence, comprehensive sensing, real-time quantitative evaluation and hierarchical intelligent response of factory energy safety risks are realized.

The invention provides an intelligent wearable rehabilitation monitoring and evaluation device after anterior cruciate ligament injury, and relates to the technical field of exercise rehabilitation and intelligent medical monitoring, the device comprises a sensor acquisition module, a signal processing module, a compensation determination module, a difference determination module and an evaluation analysis module, a force-electricity coupling flexible sensor, an IMU and a pressure sensor, the method comprises the following steps: synchronously collecting a muscle force signal, a surface electromyogram signal, an attitude signal and a plantar pressure signal at the same position, performing dynamic filtering, confidence enhancement and personalized compensation processing, extracting a difference feature vector, inputting the difference feature vector into a muscle force prediction model, and outputting a quantitative evaluation result. The portable wearable long-term dynamic monitoring is realized, the core indexes of muscles, joints and gaits are covered in multiple dimensions, the assessment is accurate and real-time, the rehabilitation scheme can be optimized, the risk of re-injury is reduced, and the rehabilitation effect and compliance of a patient are improved.

A computing system is disclosed for generating and optimizing schema-conformant object models. The system obtains input data from multiple sources in structured, semi-structured, or unstructured formats and processes the data using parsing logic to derive a structured rule set defining spatial relationships and constraint conditions. The system generates schema-conformant object representations comprising parametric assemblies, applies the constraint conditions using machine-executed resolution logic, and produces candidate object models through iterative modification of spatial components, dimensional parameters, and adjacency relationships. Performance values are computed for the candidate models using quantitative evaluation of spatial efficiency, material usage, and constraint compliance. An optimized object model is selected based on the computed performance values and may be used to generate construction project plans including technical drawings, material specifications, and scheduling instructions.

The invention discloses a dynamic closed-loop management method for bridge-shaped contact performance, and relates to the technical field of power system protection. The method comprises the following steps: S1, a dynamic sensing process: acquiring multi-source dynamic parameters of a bridge-shaped contact in a non-intrusive mode; s2, a quantitative evaluation process: fusing the multi-source dynamic parameters and the static parameters, and calculating a health index or a health level of the contact through a preset model; s3, adaptively optimizing the process, dynamically adjusting the closing energy or protection parameters of the circuit breaker according to the health state, and compensating the performance change of the contact; and S4, a predictive maintenance process: performing trend extrapolation based on the historical data of the health index, predicting the residual life and generating a maintenance suggestion. The processes are sequentially connected and cyclically iterated to form dynamic closed-loop management, so that the problems of one-sided parameter sensing, inaccurate evaluation, fixed parameters and maintenance lag in the prior art are solved, the operation reliability of the bridge-shaped contact is improved, the service life of equipment is prolonged, and the operation and maintenance cost is reduced.

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 provides a communication network operation accident simulation drilling method and device, and solves the problem that a standardized simulation drilling process with automatic deduction and objective quantitative evaluation capabilities is difficult to construct in the prior art. The communication network operation accident simulation drilling method comprises the following steps: acquiring historical operation data of a communication network, and generating at least one scene element data set according to the historical operation data; the scene element data set comprises disposal evaluation data; determining a scene element data set corresponding to the target accident scene, and constructing an analog simulation environment of a communication network operation accident; determining a preset timeline according to the fault injection parameter, and driving a simulation environment to evolve; and acquiring drill process data, and generating a drill evaluation result by combining with analysis of the disposal evaluation data. The invention further provides a device corresponding to the method. According to the method, historical data is converted into a structured drilling process, and measurable, reproducible and continuous optimization of the emergency capability is realized.

The invention provides a method and a system for dynamically optimizing a security defense strategy based on an AI Agent, and aims to solve the problems that the traditional network security defense strategy is static and cannot adapt to a dynamic network environment and novel network threats, and the processing efficiency of massive security data is low and the response is not timely. The method comprises the following steps: firstly, realizing whole network node data acquisition through a distributed AI Agent, performing accurate identification in combination with a multi-dimensional threat perception and fusion detection mechanism, and establishing a threat parameter quantitative evaluation model; secondly, the AI Agent generates a dynamic defense strategy according to the analysis result of the intelligent threat detection and the real-time state of the network in combination with threat features and risk assessment; and finally, according to the dynamic defense response result, realizing attack path tracking, accurate vulnerability positioning, automatic repair execution and traceability information retention processing. According to the method, real-time sensing and quick response to network threats can be realized, and the hysteresis of a traditional static defense strategy is overcome.

The invention provides a manufacturing abnormality diagnosis method and system based on a production data stream, and relates to the technical field of artificial intelligence, and the method comprises the steps: collecting an initial multi-source heterogeneous data stream of a production site, and implanting virtual data anchor points corresponding to key production equipment, process nodes and quality monitoring links in the initial multi-source heterogeneous data stream; performing spatial point set construction and tetrahedron inclusion detection based on the virtual data anchor points to perform data relevance quantitative evaluation and pruning on the initial multi-source heterogeneous data stream to generate an optimized multi-source heterogeneous data stream; and based on a pre-established unified semantic metadata dictionary, performing semantic analysis and normalization processing on the optimized multi-source heterogeneous data stream, and generating a standardized production event stream with unified semantics and time sequence and product identification. According to the invention, accurate diagnosis and root cause positioning of manufacturing abnormality can be realized.

The invention relates to the technical field of building structure health monitoring, in particular to an unmanned aerial vehicle laser radar-based building point cloud deformation quantitative extraction method, which comprises the following steps of: based on building corner structure characteristics in point cloud data, carrying out quantitative evaluation on the overall inclination state of a building through point cloud segmentation, straight line fitting and angle calculation; based on an ideal plane hypothesis, performing plane fitting and point-to-plane distance analysis on the building facade point cloud in the point cloud data, identifying a local concave-convex deformation area of the building facade, and performing quantitative evaluation; and roof plane detection and wall surface normal vector analysis are carried out based on the point cloud data, and accurate quantification is carried out on parapet wall inclination based on region growth segmentation and PCA principal direction analysis. Through automatic processing and quantitative analysis of an algorithm, subjective errors and inconsistency possibly occurring in a traditional manual evaluation method are avoided, and objectivity and repeatability of an evaluation result are enhanced.

The invention relates to the technical field of transformer detection, and discloses a nondestructive testing optimization method and system for an oil-immersed power transformer, and the method comprises the steps: building a three-dimensional dielectric response coordinate system, and generating a preliminary defect positioning map; aging-dominated and damp-dominated defects are detected and identified through spiral frequency sweep excitation; establishing a temperature gradient excitation scheme based on the defect type to generate a defect degree quantitative evaluation index; designing a sound wave modulation excitation scheme to generate a high-resolution defect characteristic spectrum; constructing a multi-mode intelligent sensor network, and combining a defect development trend prediction model to realize defect evolution prediction and generate a graded early warning signal; according to the method, the whole-process accurate detection of the insulation defect of the transformer from positioning, classification and quantitative evaluation to evolution prediction is realized, and a reliable basis is provided for operation and maintenance.

The invention discloses a physical-data dual-drive retaining wall catastrophe robustness evaluation method, and relates to the technical field of structure catastrophe evaluation and computer science crossing. Aiming at the problems that an existing retaining wall catastrophe robustness evaluation method cannot correct physical model errors in real time and is insufficient in accuracy, the invention provides a retaining wall catastrophe process robustness evaluation method driven by fusion of a physical model and a neural network. The method comprises the following steps: 1, carrying out time discretization and parameter vectorization on retaining wall catastrophe monitoring parameters; 2, constructing a performance attenuation function based on the physical model; 3, updating an implicit damage state in combination with a neural network Cell module; 4, correcting an error through a feedforward network to obtain a total performance attenuation function; and 5, finally calculating a robustness index and outputting a corresponding robustness grade, thereby realizing quantitative evaluation of the retaining wall catastrophe robustness. Reliable quantitative support can be provided for retaining wall design optimization, operation monitoring and post-disaster recovery evaluation.

The invention relates to the technical field of bearing fault diagnosis. By providing a bearing fault CNN-Bi LSTM fusion diagnosis method, device, equipment and medium, the method comprises the steps of abstracting bearing internal components into network nodes based on preset priori knowledge of a bearing mechanical structure, defining node connection edges according to a spatial constraint relationship between the components, and constructing a bearing internal component spatial topology network model; inputting the standardized bearing visual data set into a preset convolutional neural network, and extracting a visual feature vector set of surface damage of each component; and fusing the visual feature vector set and the time sequence features of the fault propagation topological graph, inputting the visual feature vector set and the time sequence features into a preset bidirectional long-short-term memory network, predicting a fault diffusion trend, positioning a fault source, and generating a fault diagnosis quantitative evaluation report. The technical effects of improving the bearing fault propagation modeling accuracy, enhancing the adaptation capability of the complex time sequence process and promoting the collaborative optimization level of the diagnosis process are achieved.

The invention discloses an augmented reality intelligent navigation and operation quantitative evaluation method and system in an operating room, and the method comprises the steps: collecting multispectral image data and depth point cloud data of an operation region in the operating room in real time, and generating a multidimensional perception data set; performing feature extraction and spatial registration on the multi-dimensional perception data set, and decomposing the dynamic three-dimensional augmented reality model into an anatomical structure feature layer and an instrument interaction feature layer; performing dynamic matching and real-time rendering on the anatomical structure feature layer and the instrument interaction feature layer based on a digital twinning technology to generate augmented reality navigation information and an operation guidance scheme; and performing multi-dimensional quantitative analysis on the surgical operation process according to the augmented reality navigation information and the operation guidance scheme, and outputting a quantitative evaluation result. By utilizing the embodiment of the invention, augmented reality navigation guidance which is synchronous with a real operation scene in real time can be provided, meanwhile, accurate quantitative evaluation is carried out on the operation operation, and the accuracy, the safety and the trainability of the operation are improved.

The invention relates to the technical field of nondestructive testing, and discloses a bimetal composite pipe three-dimensional reconstruction method and system based on multi-source data fusion. The method comprises the following steps: acquiring magnetic flux leakage signals and thickness data of the bimetal composite pipe in a high-pressure environment, and preprocessing the magnetic flux leakage signals and the thickness data to obtain a clean multi-source signal data set; performing time domain and frequency domain feature alignment on the data set to generate a fusion data matrix; extracting a preliminary defect feature set through multi-layer convolution processing; performing classification training on the defect features to obtain a defect type classification result containing confidence scores; if the crack exists, depth fitting is carried out to quantify the crack depth; if the preset risk threshold value is exceeded, generating a three-dimensional defect distribution model and evaluating connectivity; and finally, outputting a quantitative evaluation report of the pipeline risk level. According to the method, efficient fusion of multi-source data, intelligent identification of defect types and three-dimensional visual reconstruction are realized, and the accuracy and evaluation efficiency of pipeline defect detection are remarkably improved.

The invention discloses a fault remote diagnosis and digital process maintenance method for water treatment equipment, and relates to the field of water treatment equipment operation and maintenance. The method comprises the steps of unifying time and semantics, generating a quality marking time sequence, and performing working condition benchmark and tracing; according to self-adaptive identification mechanism parameters, constructing a digital twinning and trusted domain, and comparing to obtain a residual error and anomaly in real time; forming root cause evidence and sorting under topology and energy and material closed constraints; generating scripts and rollback, gray scale issuing, sentry monitoring and transaction records through steady / transient simulation and constraint checking; and convergence, energy consumption, chemical dosing, water quality evaluation, precipitation versioning template recharging and closed-loop updating are carried out. Through unified working condition data and digital twinning mechanism-data fusion diagnosis, simulation-driven gray reversible execution and knowledge template recharging under quantitative evaluation, a traceable, reusable and migratable full-link remote diagnosis and process maintenance closed loop is constructed.

The embodiment of the invention provides a network security validity verification and quantitative evaluation method and system, and relates to the technical field of network security, and the method comprises the steps: obtaining global dynamic threat intelligence and a multi-dimensional global network security risk data source, and carrying out the preprocessing; constructing a global feature engineering system based on heterogeneous information network atlas and sequence analysis, forming a feature vector matrix, and mapping the feature vector matrix into an index state vector; inputting the feature vector matrix, the index state vector and the external environment information vector into an evaluation model, dynamically adjusting the weight of the feature vector matrix of each dimension, and outputting the validity score of each safety control point; based on the score, calculating a safety effectiveness index based on a time decay factor; identifying a weak link based on the index, and performing simulation verification to obtain a simulation attack actual measurement result; and an error vector is constructed based on the result and the validity score, and parameter adjustment and weight calibration are carried out. According to the scheme, the accuracy and the real-time performance of network security evaluation are improved.

The invention discloses an intelligent ship steering capability multi-dimensional evaluation system based on virtual-real fusion, and relates to the technical field of intelligent ships and virtual-real fusion. The system comprises a visual platform and an integration module, wherein a simulation environment is constructed through a virtual-real data interaction engine, and ship behaviors under different sea conditions are simulated; a virtual-real data bidirectional calibration module is used for dynamically correcting simulation parameters, and it is ensured that the simulation parameters are consistent with real ship data; based on the multi-dimensional evaluation index system, the intelligent evaluation decision module comprehensively scores the steering performance; and the behavior guiding module outputs an evaluation report and a risk avoidance suggestion. According to the invention, part of real ship tests can be replaced, extreme working conditions can be simulated, the evaluation precision is guaranteed, the test period is remarkably shortened, the cost is reduced, simulation parameters are dynamically corrected through a bidirectional calibration mechanism, comprehensive and multi-dimensional quantitative evaluation on steering capability in a scene close to a real scene is realized, and a credible evaluation report is output.