34 results about "Impact model" patented technology

The invention provides a lithium battery degradation threshold-impact model construction method and device, equipment and a storage medium. Relates to the field of lithium battery life prediction. The method comprises the following steps: establishing an inherent degradation model of the lithium battery by adopting a nonlinear Wiener process, and describing nonlinear characteristics of capacity fading; constructing an external impact damage model based on a non-homogeneous Possion process, and representing time-varying characteristics of random impact; coupling a degradation process and impact damage through a dual-threshold failure mechanism, and defining a capacity attenuation soft failure threshold and an impact accumulation hard failure threshold; the degradation parameters and the impact parameters are corrected in a dynamic linkage mode through a Bayesian updating and optimizing algorithm; and realizing probability distribution prediction of the residual life based on Monte Carlo simulation. The method can improve the prediction precision and engineering applicability of the residual life of the lithium battery under complex working conditions.

A method of evaluating impact of a change on nodes of a network, including collecting information of changes to configuration items of the network with a change management module that is executed on a computer of the network and forming a list of changes, receiving a dependency graph that defines dependencies between nodes of the network, receiving an impact model that includes an impact value that provides an indication of a risk value for different types of changes to nodes of the dependencies, executing an impact score generation module configured to receive the dependency graph, the impact model and the list of changes to determine a risk value responsive to a specific change to a node of the network and an impact of the change on other nodes of the network, repairing or predicting problems in the network based on the determined risk value for each change.

The invention discloses a wind power plant wide area safety risk early warning method, device and equipment, a medium and a program product, and the method comprises the steps: carrying out the data monitoring of a wind power plant through a space-air-ground data monitoring module, obtaining multi-source heterogeneous monitoring data, carrying out the fault feature correlation analysis of the multi-source heterogeneous monitoring data, and carrying out the fault feature correlation analysis of the multi-source heterogeneous monitoring data; obtaining a historical fault event set and characteristic factors and fault results corresponding to the historical fault events, constructing a fault data matrix according to the historical fault event set and the characteristic factors and fault results corresponding to the historical fault events, and constructing a dynamic time sequence reliability influence model of the wind power plant, risk prediction is carried out through a dynamic time sequence reliability influence model, a risk distribution structure chart is generated based on a risk prediction result, and the risk prediction result comprises a time node and a distribution area of reliability reduction of a wind power plant, so that a time sequence coupling relationship among multiple factors in a complex system is effectively captured, and the accuracy and timeliness of risk prediction are ensured.

The application provides a flood risk prediction method and system considering index and weight uncertainty, and belongs to the technical field of risk analysis, and specifically comprises the following steps: determining the weight coefficients of flood monitoring indexes corresponding to different combinations based on the flood monitoring indexes of historical flood processes in different combinations, determining stable monitoring indexes based on the analysis results of the weight coefficients, determining the construction strategy of the influence model of the flood monitoring indexes based on the variation of the weight coefficients of the flood monitoring indexes between different combinations and based on the stable monitoring index data, and determining a flood risk prediction analysis method according to the influence model and the rainfall data of the rainfall process before the current rainfall process, so that the accuracy of flood risk prediction processing is improved.

This invention relates to the field of intelligent control technology for industrial robots, and discloses a precise compliant force control method for a grinding and polishing robot under uncertain environmental conditions. The method includes establishing a dual-closed-loop control model of the environment and the robotic arm based on a set linear environment model and a robotic arm dynamics model; iteratively solving for the optimal control gain in a data-driven manner based on adaptive dynamic programming for the unknown environmental characteristics in the control model; designing a free-space control gain to achieve stable relative motion approaching the environment; designing a smooth transition mechanism for the free-contact space control gain; achieving model-free optimal control based on data iteration through adaptive dynamic programming; employing dual-closed-loop impedance control to achieve the impedance effect of the force; and utilizing contact transient impact modeling to dynamically adjust the control gain, ensuring the stability of the contact force and processing quality, making it suitable for various complex working environments.

Scanner aberration impact modeling in semiconductor manufacturing processes is described. Scanner aberration impact modeling can facilitate co-optimization of multiple scanners. Scanner aberration impact modeling can include executing a calibration model and controlling a scanner based on output from the model. The model is configured to receive patterning system aberration data. The model is calibrated with patterning system aberration calibration data and corresponding patterning process impact calibration data. New patterning process impact data for the received patterning system aberration data can be determined based on the model. The model includes a hyperdimensional function configured to associate the received patterning system aberration data with the new patterning process impact data. The hyperdimensional function is configured to associate the received patterning system aberration data with the new patterning process impact data in an approximate form, rather than full simulation, and without involving computation of aerial images or representations thereof.

Using a failure probability model, a probability of a failure in a first system within a specified time period is predicted. Using an impact model, an impact of the failure on a second system is predicted. Using a resilience model, an impact reduction of the failure is predicted. The probability, the impact, and the impact reduction are mapped into a recommended service level agreement category for the first system, the mapping performed using a classifier model.

This invention relates to the field of wind and sand protection, and discloses a vegetation protection method based on the laws of wind and sand erosion. First, historical wind and sand erosion patterns are collected, recording data on the air impact, soil erosion, and vegetation erosion. Using large-scale modeling technology, an impact model is established, and erosion simulation experiments are conducted to obtain air protection data, soil protection data, and vegetation protection data. Real-time monitoring and evaluation are performed to obtain wind and sand monitoring data. Based on this data, the wind and sand movement patterns are predicted, yielding wind and sand trajectory prediction results. The protection data are then integrated to obtain a first vegetation protection scheme, establishing a vegetation protection trajectory model. The vegetation protection trajectory model is then corrected in real time to obtain a second vegetation protection scheme. This invention solves the problem in existing technologies where vegetation selection for wind and sand protection lacks specificity and cannot be tailored to the wind and sand erosion trajectory.

The application provides a block analysis method for a transient strong impact non-stationary response signal, a storage medium and computer equipment, belongs to the field of non-stationary impact response signal processing, strong impact model test and nonlinear dynamics analysis, and solves the problems that the traditional method cannot objectively identify the stage dynamics evolution of the transient strong impact non-stationary response signal, cannot adaptively extract a local approximate stationary block in the same stage, and finally cannot reveal the system motion evolution law and cannot support subsequent dynamics characteristic extraction analysis. Through signal preprocessing, phase space reconstruction, maximum Lyapunov index stage division, critical time criterion block and stationarity test, the application extracts effective block characteristics, realizes accurate analysis of the transient strong impact non-stationary signal, and has objectivity, adaptivity and universality, and can support subsequent dynamics research.

The invention relates to the field of railway electrified power supply systems, in particular to a railway overhead line system icing form evolution characteristic quasi-steady-state numerical simulation method which comprises the steps of numerical simulation modeling, freezing rain liquid drop impact model setting, freezing rain liquid drop impact simulation, liquid film generation and phase change icing and ice layer appearance updating. According to the method, the whole process of the contact network surface from microscopic droplet impact to macroscopic ice layer formation is fully considered, the whole process comprises droplet impact, breaking, liquid film generation, phase change and ice layer form growth, compared with the prior art, prediction deviation caused by single link simplification is avoided, meanwhile, the grid is dynamically deformed through the RBF interpolation function, and the prediction accuracy is improved. Real-time updating of the ice layer appearance in the simulation process is achieved, and the limitation of a traditional fixed boundary method is overcome; in addition, the method also has the characteristics of real reproduced freezing rain intensity and environmental characteristics and high calculation efficiency.

The disclosure herein describes predicting potential impact of issues reported in incident ticket data on infrastructure element. A ticket manager component includes an impact model utilizing machine learning to analyze real-time event and metric data with incident-related data to generate predicted impact data. The predicted impact data identifies potentially impacted infrastructure elements, such as, potentially impacted users, predicted infrastructure components impacted by the issue and / or an updated time-period associated with the issue. The ticket manager component creates labeled incident tickets by updating user-generated incident tickets with additional data generated by the impact model, including predicted impact data and / or additional details associated with the issue. The labeled incident tickets are provided back to the model as training data to further refine predictions generated by the model.

This invention belongs to the field of unmanned aerial vehicle (UAV) control technology, specifically relating to a method and system for attitude stabilization control of UAVs under special environmental disturbances. The method includes the following steps: S1: Establishing a wind field disturbance model, a dust particle impact model, and an electromagnetic interference model, and dynamically coupling these three models into a total disturbance vector experienced by the UAV; S2: Constructing a six-degree-of-freedom dynamic model incorporating the influence of the total disturbance vector. This model describes the translational and rotational motion of the UAV's center of mass under multi-source disturbances in complex environments; S3: Based on traditional aerodynamic models and incorporating environmental disturbance parameters, constructing a variable structure model with aerodynamic coefficients. This invention effectively improves the UAV's adaptability to multi-source coupled disturbances by constructing a unified multi-layered architecture for environmental identification, disturbance observation, Lyapunov robust control, and actuator compensation, thereby ensuring mission accuracy and safety.

This invention provides a big data-based intelligent matching method for upstream and downstream enterprises in the chemical industry chain, belonging to the technical field of matching upstream and downstream enterprises in the chemical industry chain. The method includes collecting multi-dimensional feature data of the chemical industry chain; analyzing basic enterprise data using an industry chain attribute matching algorithm; analyzing the dynamic changes in supply and demand relationships with market cycles; constructing a quantitative model of supply and demand impact, analyzing the influence weight of dynamic supply and demand data on initial enterprise matching data; and building an intelligent matching model of the industry chain to evaluate the matching process of upstream and downstream enterprises in real time. By integrating multi-dimensional feature data of enterprises and utilizing industry chain attribute matching algorithms, dynamic supply and demand analysis methods, quantitative supply and demand impact models, and intelligent matching models of the industry chain, this invention achieves accurate matching and dynamic optimization of upstream and downstream enterprises in the chemical industry chain, solving problems such as reliance on manual experience in matching, untimely response to dynamic changes in supply and demand, and insufficient risk management capabilities in existing technologies.

The application discloses a method and system for designing parameters of a ground surface concrete cushion layer for protecting a buried pipeline based on rockfall influence, and the method comprises the following steps: constructing a rockfall impact model, a buried pipeline model and a cushion layer model; collecting strain data and velocity of rockfall, variation of earth pressure in an undisturbed soil layer and strain of the buried pipeline model by using a data testing device; analyzing the collected data, and retaining data not exceeding a preset safety range; changing parameters of the cushion layer model by using a control variable method, and keeping other model parameters unchanged until analysis of all parameters of the cushion layer model is completed, and then stopping; further analyzing the retained data to obtain a relationship between strain and the parameters of the cushion layer model, and completing parameter design of the buried pipeline. The application effectively simulates the protection effect of the concrete cushion layer on the buried pipeline under rockfall impact, is closer to an actual engineering, and provides a reference value for the actual engineering.

The invention discloses a method for establishing a multi-particle multi-azimuth impact model for a glass cabinet surface. The method comprises the following steps: establishing a glass substrate model, a plurality of micro-nano super-hydrophobic CNT / SiO2 particle models and an Euler domain model; assembling the glass substrate model, the Euler domain model and the plurality of micro-nano CNT / SiO2 super-hydrophobic particle models; performing grid division on the glass substrate model and the Euler domain model; establishing a temperature-displacement coupling analysis step; setting contact interaction attributes; and setting boundary conditions of all the models, speed parameters of the micro-nano CNT / SiO2 super-hydrophobic particle model and initial temperatures of the micro-nano CNT / SiO2 super-hydrophobic particle model and the glass substrate model, so that the micro-nano CNT / SiO2 super-hydrophobic particle model impacts the glass substrate model in multiple directions at different incident angles. The method lays a foundation for research on particle deformation, equivalent plastic strain, residual stress and the like of cold spraying particle impact.

The invention discloses a method and device for establishing a specific field modeling environment. The method comprises the following steps: constructing a three-layer abstract system for specific field modeling based on a meta-meta model; generating a domain meta-model containing entity class definition, relation class definition and enumeration class definition through an automatic instantiation rule, and configuring attributes, representation forms and constraint relations; packaging the meta-model and the constraint thereof into standardized framework data with a unique identifier; loading the frame data by a runtime analysis module, dynamically generating a visual domain model editor, and enabling a user to complete model construction conforming to constraints; triggering increment verification based on an operation-level event in a model construction process; when the meta-model is updated, executing difference comparison to identify affected model elements and giving repair suggestions; and the runtime expansion of the modeling capability is realized through a plug-in mechanism.

The invention discloses a multi-scale multi-parameter evaluation system for the puncture resistance of a fiber composite material. The multi-scale multi-parameter evaluation system comprises a pretreatment interface and a post-treatment interface which are connected in sequence, a puncture impact model is constructed through a pretreatment interface, the puncture impact model comprises a multi-scale composite material combination structure, an impactor structure and a backing material structure, and mechanical property data for testing a fiber composite material and test data for testing the type of an impactor are obtained; parameters of the composite material combination structure and the cutter structure are set according to the mechanical property data and the test data; performing simulation analysis on the set puncture impact model through a post-processing interface to obtain an anti-puncture response result, and performing effectiveness evaluation and personnel injury risk evaluation on the tested fiber composite material according to the anti-puncture response result; and according to an evaluation result, optimizing the tested fiber composite material to obtain an optimal scheme of the tested fiber composite material. According to the scheme, multi-parameter accurate evaluation and design guidance can be carried out on the puncture-proof performance of the fiber composite material.

The invention discloses a viewpoint dynamics modeling method introducing external event impact, which comprises the following steps: (S1) constructing a multi-agent opinion dynamics model, and initializing system parameters and agent attributes with activeness; (S2) dynamically constructing a social network structure based on viewpoint homogeneity according to the current opinion difference between the agents; (S3) defining and quantifying an external event, and decoupling the influence of the external event into continuous information content and a dynamic response probability attenuating along with time; (S4) establishing a viewpoint dynamic differential equation by integrating the internal attenuation, the local social influence and the external influence of the intelligent agent; and (S5) updating opinion states of all agents through numerical integration to realize dynamic evolution of viewpoints. According to the method, the dynamic impact model in which the event information and the response intention are decoupled is constructed, so that the technical defect that an existing model simplifies external influence into deterministic impact is overcome, and a computing framework which is closer to reality and higher in interpretation is provided for simulation and prediction of complex social phenomena such as public opinion evolution and crisis response.

This invention discloses a multi-dimensional encryption method for secure data output, comprising: acquiring the data to be encrypted; establishing a quantum-resistant encryption computational model, considering the resource consumption problem that may occur during the encryption process; constructing a side effect impact model; analyzing the resource consumption contribution of the quantum-resistant encryption model; designing a resource consumption contribution mitigation strategy and a model feedback mechanism; optimizing and monitoring the side effect impact model in real time; the optimized model can not only reduce the time and space complexity in the encryption process, but also significantly reduce the computational resource consumption without sacrificing encryption strength, thereby improving encryption efficiency while ensuring data security.

The present invention provides a transaction anomaly alarm method and device, particularly relating to the financial field. The method comprises: obtaining future data of the target asset before a change at multiple future time points based on an asset data association impact model corresponding to both the target asset and its associated assets, the target asset's current target data, the associated assets' current associated data, and the current interest rate; obtaining future data of the target asset after a change at multiple future time points based on the interest rate fluctuation range, the asset data association impact model, the current target data, the current associated data, and the current interest rate; and determining whether a transaction anomaly exists for the target asset based on the future data before and after the change corresponding to the future time points, and if so, issuing a transaction anomaly alarm. The present invention can improve the accuracy of transaction anomaly alarms, thereby increasing the efficiency of transaction anomaly alarms.

The application provides a lithium battery degradation threshold-impact model construction method and device, equipment and storage medium. It relates to the field of lithium battery life prediction. The method comprises: establishing a lithium battery inherent degradation model using a nonlinear Wiener process to describe the nonlinear characteristics of capacity attenuation; constructing an external impact damage model based on a non-homogeneous Possion process to represent the time-varying characteristics of random impact; coupling the degradation process and the impact damage through a double-threshold failure mechanism to define the capacity attenuation soft failure threshold and the impact cumulative hard failure threshold; dynamically linking the degradation parameters and the impact parameters to correct them using the Bayesian update and optimization algorithm; and realizing the probability distribution prediction of the remaining life based on Monte Carlo simulation. The application can improve the accuracy and engineering applicability of lithium battery remaining life prediction under complex working conditions.

The application provides a kind of impact simulation test construction method based on Digimat / Abaqus, comprising: the setting of the impact finite element model of stab-resistant material, including geometric model, material model, contact definition, load and boundary condition and the output setting of stab-resistant impact simulation result and analysis model, including stab-resistant impact simulation result and stab-resistant impact model verification.The material model containing fabric composite material microstructure information constructed by the application using Digimat can be directly used as the material attribute input of Abaqus / CAE impact finite element model for calculation through Digimat to Abaqus interface technology, realizing the multi-scale analysis of composite material microstructure parameters and macroscopic impact load condition response;And combined with the physical test of stab-resistant impact, the effectiveness of the equivalent impact finite element model of the impact model of the stab-resistant material on the backing is established and verified.

The present invention discloses a method for rapidly assessing bullet damage to a helicopter tail drive shaft. The method comprises the following steps: establishing a finite element simulation model of a bullet-impacted tail drive shaft; conducting bullet impact tests under typical operating conditions and verifying the reliability of the bullet impact model; establishing a simulation model of the residual strength and crack propagation residual life of the damaged tail drive shaft; conducting torsional buckling tests and fatigue life tests and verifying the reliability of the model; establishing a mapping relationship between the bullet hole morphology of the damaged tail drive shaft under different operating conditions and the residual strength and residual life based on the simulation results of the model, and constructing a failure characteristic map; comparing the bullet hole morphology of the damaged shaft with the failure characteristic map to determine the bullet impact operating condition and obtain the residual strength and remaining life of the damaged shaft, and then determining whether further treatment measures are needed based on the specific subsequent mission of the helicopter. Compared with the existing technology, the present invention solves the problem of assessing a helicopter tail drive shaft after being struck by a bullet, and meets the requirement for rapid assessment in practical situations.

The invention provides a simulation analysis method and device for a negative Poisson's ratio lattice structure, equipment and a medium. The method comprises the following steps: respectively creating a plurality of simulation impact models for various negative Poisson's ratio cell elements; defining a contact relationship, a boundary constraint condition, a simulation impact speed and an output parameter type for the plurality of simulation impact models; grid division and impact simulation are carried out on the multiple simulation impact models, and output parameters corresponding to the output parameter types are obtained; calculating a multi-dimensional evaluation index of each simulation impact model according to the output parameters of each simulation impact model; and according to the multi-dimensional evaluation indexes of the various simulation impact models, carrying out performance comparative analysis on the various negative Poisson's ratio cell elements. According to the method, the performance of the lattice structure of various cell element types can be effectively analyzed.

This invention discloses an intelligent monitoring method for the construction alignment of long-span continuous rigid frames, relating to the field of long-span continuous rigid frame construction technology. The method includes the following steps: S1, data acquisition; S2, data processing; S3, multi-level intelligent early warning; S4, construction control; and S5, output of results. This invention utilizes multi-source devices to collect data synchronously, ensuring data real-time performance and integrity. Deep learning removes anomalies, and neural networks construct environmental impact models to correct data, mitigating the impact of environmental factors on alignment data. Digital twins combined with finite element analysis and neural networks predict deformation trends, improving prediction accuracy, and allowing for early prediction of alignment deviation trends and optimization of construction plans. Multi-level intelligent early warning dynamically adjusts thresholds based on multiple factors, accurately issuing early warning information and enhancing the timeliness of risk response. Construction control automatically generates decision suggestions and provides feedback on evaluation results, optimizing the construction process.

The invention discloses a power distribution network anti-seismic toughness enhancing method considering energy storage equipment and sequential repair, which comprises the following steps of: 1, collecting information such as a network topology structure and earthquake historical data of a power distribution network, and 2, constructing an earthquake influence model, evaluating the vulnerability of the power distribution network in an earthquake, and determining the line damage probability. And step 3, establishing a stochastic programming model, including taking the total penalty cost of the expected non-power-supply amount considering the load priority as a target function, and setting an investment cost constraint. And 4, solving the model by means of solvers such as CPLEX and the like, and optimizing algorithm parameters and a calculation process to improve the solving efficiency and precision. And step 5, outputting the optimized energy storage equipment configuration scheme, and visually displaying related results in a visual mode, such as drawing a chart and a map. The method can effectively improve the toughness of the power distribution network under earthquake disasters, reduces load loss, reduces economic loss, and provides an effective method for anti-seismic planning and operation of the power distribution network.

The present invention discloses a blind spot accuracy assessment method and system based on AI camera technology, which belongs to the technical field of automobile blind spot assessment. The method and system determine various influencing factors and identify the monitoring subjects corresponding to each influencing factor; set the comprehensive range of the vehicle's blind spot; set a comprehensive impact model corresponding to the comprehensive range of the blind spot; monitor the monitoring subjects of various influencing factors in real time to obtain the subject monitoring data of each influencing factor; determine the blind spot area, and generate an initial blind spot image according to the blind spot area; analyze the monitoring data of each subject through the comprehensive impact model to obtain the comprehensive impact value corresponding to each blind spot position; divide the initial blind spot image into a number of unit areas according to the comprehensive impact values, and set the unit accuracy value of each unit area; estimate the baseline risk value of each target object, identify the unit accuracy value corresponding to the unit area where the target object is located, correct the baseline risk value according to the unit accuracy value, and perform blind spot warning according to the corrected baseline risk value.

The invention discloses a distribution line electric pole inclination big data monitoring system and method based on an intelligent sensor, and belongs to the technical field of electric pole inclination monitoring. A wind power influence model is constructed, wind power borne by an electric pole is introduced into the wind power influence model to evaluate the influence of the wind power on electric pole inclination, and a foundation bearing model is constructed; importing the soil water content and the foundation settlement displacement value into a foundation bearing model to evaluate the influence of the foundation condition on the inclination of the electric pole, constructing a wire tension influence model, importing the wire tension data into the wire tension influence model to evaluate the influence of the wire tension on the inclination of the electric pole, constructing an inclination angle evaluation model, and evaluating the inclination angle of the electric pole at the future moment. According to the method, a building damage risk assessment model is constructed, the pulling force and the wind force borne by the electric pole are guided into the building damage risk assessment model to assess the falling speed of the electric pole, the building damage condition is assessed in combination with the building impact resistance, and the intelligent level and the risk prevention and control capacity of distribution line electric pole inclination monitoring are improved.

Systems, methods, and other embodiments described herein relate to improving the safety of vehicles by determining lane-level risks of road segments. In one embodiment, a method includes acquiring data about a segment of a roadway. The data indicates at least information about traffic on the segment. The method includes determining a lane-level risk associated with a safety event occurring on the segment using a likelihood model and an impact model. The method includes providing the lane-level risk to cause an approaching vehicle to selectively adapt operation.

The present application relates to the field of bridge health detection, and in particular to a reinforced concrete pier damage evaluation method, device, equipment and storage medium. The method comprises obtaining vehicle impact parameters, simplifying a previously obtained vehicle-pier impact model through the vehicle parameters, obtaining axial displacement, lateral displacement and rotation angle of the nodes at both ends of the fiber beam element; according to the axial displacement, the lateral displacement and the rotation angle, the displacement component of the RC section at any position of the fiber beam element is calculated; based on the displacement component of the RC section of the fiber beam element, the deformation component of the RC section is obtained; according to the RC section deformation component, the first unloading stiffness matrix and the second unloading stiffness matrix at the RC section are obtained; based on the first unloading stiffness matrix and the second unloading stiffness matrix, the third unloading stiffness matrix at the RC section is obtained, and the eigenvalue of the third unloading stiffness matrix is determined; according to the eigenvalue, the pier damage is evaluated.