80 results about "Predictability" patented technology

The application discloses a kind of power energy storage online maintenance equalization management method and system, belong to power energy storage technical field.The method includes: multi-source heterogeneous data acquisition and pre-processing;Battery health state evaluation;Equalization demand evaluation based on deep reinforcement learning;Self-adapting equalization strategy generation;Equalization strategy simulation verification;External alternating current power supply circulating equalization execution and dynamic monitoring;Maintenance decision generation based on time series prediction network.The system includes multi-source heterogeneous data acquisition module, battery health state evaluation module, equalization demand intelligent evaluation module, self-adapting equalization strategy generation module, digital twin simulation verification module, external equalization execution control module, maintenance decision intelligent generation module and cloud edge collaborative communication module.The application uses external alternating current power grid power supply, realizes energy external circulation, does not consume battery pack itself energy;And the precise equalization and predictability maintenance of this energy storage system, significantly improve equalization efficiency, reduce operation and maintenance cost.

The invention belongs to the field of power system monitoring and fault diagnosis, and particularly relates to a cable branch box health early warning method based on fusion of insulation resistance and leakage current. The method comprises the following steps: acquiring insulation resistance and leakage current signals which are synchronously aligned by using a sensing monitoring unit, and extracting deterministic characteristics through nonlinear preprocessing; performing phase-space reconstruction on the leakage current signal, and calculating the maximum Lyapunov exponent, correlation dimension and other dynamic characteristic indexes so as to quantify the chaotic evolution degree responded by the insulating medium; and constructing an evaluation model based on fusion of insulation resistance and dynamic indexes, calculating a comprehensive health index, comparing the comprehensive health index with a threshold value, and determining an early warning level. According to the method, the qualitative change omen at the initial stage of insulation degradation can be sensitively captured by fusing the macroscopic electrical parameters and the microdynamics evolution characteristics, the advancement and the anti-interference capability of early warning are improved, reliable support is provided for refined operation and maintenance of the cable branch box, and the power supply reliability of a power system is guaranteed.

This invention discloses a collaborative adaptive scheduling method for pumping stations and sluice gates based on multi-source data, belonging to the field of smart water conservancy automation control technology. The method includes the following steps: pre-extraction of situational feature parameters, including real-time collection of meteorological and rainfall forecast data, upstream water level, downstream water level, inflow into the reservoir and river, equipment operating status data, and downstream water demand commands; and construction of a comprehensive evaluation function S. This invention significantly improves the predictability and timeliness of flood control scheduling, effectively avoiding flood risks caused by sudden hydrological events. Furthermore, by setting a smoothness penalty term and a stability constraint on the actuator in the objective function, it achieves gradual adjustment of gates and pumping stations, completely solving problems such as water hammer effect and river scouring damage caused by excessively rapid actions in traditional control, ensuring the stability of water flow and the service life of water conservancy facilities.

The present application relates to the technical field of sugar production, and particularly relates to a sugar juice color value online detection method and system and a process parameter control method and system. The method collects sugar juice color values and key process parameters in real time and constructs a digital feature vector; a dynamic coupling relationship model is trained using historical and real-time data, a baseline color value evolution trajectory is predicted, and a backup collaborative adjustment scheme library covering multiple process scenarios is generated in advance; real-time multi-step trend prediction is performed on the color value, when the prediction deviates from the optimal range, the scheme library is queried in real time according to the deviation characteristics, and a pre-adjustment scheme with the minimum comprehensive process cost is quickly calculated, the pre-adjustment scheme is analyzed and converted into feedforward collaborative adjustment instructions for the auxiliary material adding system and each process execution unit, active and accurate closed-loop control is realized, the lag problem of traditional offline detection and manual control is effectively solved, the predictability and collaboration of color value control are improved, and product quality stability and production efficiency are ensured.

The application discloses a network security target-based virtual simulation and security evaluation method and system. First, a network asset knowledge graph describing a virtual target environment is constructed through information collection. Second, intelligent attack simulation is performed on the knowledge graph based on an attack strategy syntax rule library using a Monte Carlo tree search (MCTS) algorithm to discover nonlinear and multi-stage attack paths. Third, a Bayesian attack graph (BAG) is constructed based on the knowledge graph to probabilistically and systematically quantitatively evaluate the security risks of asset nodes in the network through Bayesian inference. Finally, the attack paths and quantitative risk values are integrated to generate a comprehensive evaluation report containing visualized paths, risk rankings and reinforcement suggestions. The application solves the problems of insufficient realism of existing target simulation, one-sided security evaluation and lack of predictability by combining strategic simulation of MCTS and global quantitative analysis of BAG.

Qubit predictability services for a quantum computing system are disclosed. In one example, a processor device of a computing system receives qubit utilization data that encodes a utilization history for each qubit in a set of qubits of a QCS. The processor device further performs one or more hypothesis tests for each qubit of the set of qubits based on the utilization history for the qubit and a set of quantum algorithms. The processor device further generates one or more predictability scores for each qubit of the set of qubits based on the one or more hypothesis tests for the set of qubits. The processor device further provides an indication of the one or more predictability scores for each qubit of the set of qubits to the QCS.

This invention discloses an adaptive conservation assessment system for vernacular architectural heritage based on digital narrative. The system first constructs a digital narrative knowledge graph integrating spatiotemporal attributes, linking intangible cultural information such as oral history and craft memories with a high-precision 3D model. Then, through dynamic binding of multi-source sensor data, it achieves continuous integration and updating of architectural physical state, environmental parameters, and social usage information. Based on this, a risk assessment model coupling physical risk and the degree of damage to the integrity of the cultural narrative is established, outputting a dual risk index of structural and cultural value. Finally, digital twin technology is used to simulate the long-term effects of alternative conservation strategies, supporting adaptive decision-making. This invention realizes a transformation from "static recording" to "dynamic assessment," and from "material protection" to "cultural continuity," enhancing the scientific rigor, predictability, and public participation in the conservation process.

This invention relates to the field of tunnel boring machine (TBM) construction technology and discloses an intelligent prediction method for surface settlement considering unquantifiable parameters during TBM tunneling. The method includes: selecting a main cross-section to be predicted and historical auxiliary cross-sections with approximate unquantifiable parameters based on spatial proximity; constructing a dual-branch joint architecture for the main and auxiliary cross-sections, and training it using a neural network with shared network weights through deep learning to obtain an intelligent prediction model for surface settlement; and obtaining predicted settlement data for the main cross-section based on the intelligent prediction model. By constructing a dual-branch joint architecture for the main and auxiliary cross-sections and using deep learning with shared network weights, an intelligent prediction model for surface settlement with good generalization ability is obtained. This indirectly considers unquantifiable parameters and uses reverse reasoning to obtain accurate predicted surface settlement data, improving the predictability and safety of TBM construction.

This invention discloses a disaster early warning method and system for overseas crude oil pipeline systems, belonging to the field of pipeline safety technology. The disaster early warning method for overseas crude oil pipeline systems includes constructing an equivalent sand table model and conducting experiments based on the sand table model. By acquiring the target components and attributes of the crude oil pipeline system, an equivalent sand table model corresponding to the crude oil pipeline system is constructed. Rainfall simulation experiments are then conducted based on the equivalent sand table model, simulating rainfall events of different intensities and durations to assess the pipeline's integrity under various heavy rainfall conditions, i.e., evaluating the disaster resistance capability of the crude oil pipeline system. When the evaluation result is low, corresponding early warning information is generated. This addresses the problem of current early warning methods relying too heavily on disaster data and lacking predictability, improving the reliability and accuracy of the evaluation results.

The application discloses a kind of optical fiber pressure sensor life prediction methods based on multi-factor aging.The method is oriented to interface failure mechanism, assesses the life of sensor, first by standardized test determines the initial shear strength of adhesive layer material;Then predict working shear stress by finite element analysis;Then design and execute multi-factor accelerated aging test, obtain the strength degradation data under the joint action of temperature, humidity, cyclic loading, finally combine real-time monitoring data to correct working shear stress, calculate the service life under specified reliability based on Weibull statistics.The application first improves Eyring model, and combines the improved Eyring model with strength degradation model, establishes a systematic parameter determination standard and reliability evaluation system, significantly improves the reliability and service life predictability of sensor in actual engineering.

This invention discloses a global collaborative prediction method for traffic signals based on quantum entanglement states. The method includes: establishing a quantumized model of the dynamic evolution path of the target traffic network within a future time window; constructing a spatiotemporal graph model; mapping the signal phase states of intersections to spatiotemporally integrated quantum states using a hierarchical quantum encoding scheme; constructing a Hamiltonian containing spatial coupling, temporal evolution, and cost terms based on a preset traffic optimization objective; solving for its ground state using a hybrid quantum-classical computation method to determine the optimal dynamic evolution path; encoding the current actual state of the traffic network as an initial state; decoding the collaborative signal control strategies for future time points using the optimal evolution operator and sequential measurements; and executing these strategies through a rolling time-domain control framework. This invention utilizes quantum parallelism for global optimization, enabling the acquisition of the optimal collaborative strategy across the entire spatiotemporal range in a single step, thereby improving the overall operational efficiency, predictability, and robustness of the traffic network.

The application relates to the technical field of surgical assistance, and discloses a foot deformity correction surgery planning and implementation method, which comprises the following steps: fusing multi-modal medical images and dynamic function data to construct a high-fidelity foot digital twin model; intelligently quantitatively evaluating and mechanismally analyzing deformities by using a deep learning network combined with a knowledge graph; automatically optimizing in a virtual space by using a multi-agent reinforcement learning algorithm to generate a personalized surgery plan verified by biomechanical simulation; converting the optimized scheme into augmented reality navigation information and robot control instructions to guide a surgical robot to perform adaptive and accurate osteotomy and reduction operations; dynamically evaluating execution deviations and guiding correction by using real-time three-dimensional scanning and registration technology during the operation, and intelligently matching and implanting internal fixators; immediately performing function verification after the operation, and generating a personalized digital rehabilitation scheme and long-term curative effect prediction for the patient based on the operation result. The application constructs an intelligent surgery ecological system throughout the whole process of "diagnosis, planning, implementation, evaluation and management", and significantly improves the accuracy, safety, predictability and scientific level of curative effect management of foot deformity correction surgery.

This invention discloses a method and system for fire early warning and emergency response in energy storage systems based on digital twins, belonging to the field of energy storage system safety and intelligent emergency response. The method includes: constructing a hybrid sensing network of distributed fiber optic sensors and micro IoT nodes to synchronously collect multi-dimensional data from the energy storage system; constructing a three-level, multi-scale, hierarchically coupled digital twin to simulate the coordinated response effect of electronic switches performing electrical disconnection and mechanical switches performing physical isolation; deriving the critical threshold and evolution path of thermal runaway, generating graded early warning commands; and executing the actions of electronic and mechanical switches according to risk levels. This invention employs a dual-drive logic of AI-driven predictive analysis and real-time data acquisition, combined with high-fidelity digital twin simulation and fusion algorithms, which can accurately capture the critical threshold and evolution path of thermal runaway, significantly reducing the probability of risk propagation and improving the predictability and reliability of early warning for energy storage systems.

The present application relates to the technical field of wisdom logistics, and discloses an express package automatic warehousing control method based on digital twinning, which comprises the following steps: obtaining multi-dimensional state data of the package, establishing a virtual model and giving an initial equivalent slip coefficient; generating a space-time difference when tracking the real and theoretical position time sequence; taking the space-time difference as an observation variable for closed-loop iteration, calculating the actual equivalent slip coefficient to update the model; determining the kinetic boundary constraint condition according to the state data and the slip coefficient; based on the space-time difference forward deduction, combining the constraint condition to generate a speed curve and executing it for controlling the running track. The present application updates the equivalent slip parameter through closed-loop iteration, and completes the track trial calculation in combination with the kinetic boundary before issuing the instruction, realizes the dynamic identification and predictability compensation of the nonlinear slip state of the package, helps to solve the attitude instability and cascading interference caused by the regulation lag, and is beneficial to maintaining the warehousing stability in the high-density conveying scene.

The application discloses a kind of optical module health degree calculation method, on the basis of comprehensively considering the characteristics of digitalized relay protection device and existing data, based on the method of analog combination, the health degree calculation scheme of layered fusion based on " threshold determination based on + analog combination trend analysis as core" is proposed, according to the monitoring data of module read by DDM technology of protection device, the health degree of optical module is calculated at the level of relay protection device, then based on the modeling of the analog combination mode of Arrhenius model+linear degradation model, the influence of interference factors on optical module degradation is comprehensively considered, the calculation of optical module health degree is optimized, the application of the application considers the change of influence parameter and real-time and predictability, effectively reduces the amount of operation, is suitable for the application of relay protection device environment development environment.

The present application belongs to the technical field of strip steel production weld detection, and provides a strip steel continuous production line laser welding machine weld quality detection method, which comprises the following steps: in the continuous production process, the trajectory position data of the weld along the width direction of the strip steel is collected in real time to form a historical trajectory sequence, the historical trajectory sequence is analyzed, and the predictability of the weld trajectory fluctuation is evaluated; according to the evaluation result of the predictability, a corresponding compensation mode is dynamically selected. When the trajectory is unpredictable, closed-loop tracking compensation is adopted, so that the sensor can follow the weld center in real time, and the complete cross section can be ensured to be collected all the time; when the trajectory is predictable, feedforward prediction compensation is adopted, the predicted offset is used to correct the profile data, and the complete cross section with the weld center as the reference is also obtained, so that the phenomenon that the complete cross section and the partial cross section appear alternately due to the weld swing is fundamentally eliminated, false positive false reports and false negative missed detections are avoided, and the reliability of the weld quality detection is improved.

This invention discloses a method for monitoring and early warning of natural gas pipeline leakage risks based on big data analysis, belonging to the field of pipeline safety monitoring technology. It includes processing multi-source operational data, generating pressure and flow feature vectors and acoustic feature matrices, and performing spatiotemporal fusion. Historical maintenance records are used to enhance the context of the fused data, and the enhanced features are input into a risk identification network along with meteorological data to calculate the comprehensive risk score and level for each location. For high-risk locations, multi-step risk evolution simulation is performed to predict future trend curves and high-risk time windows. All results are combined with spatial information to generate a dynamic distribution map, based on which graded, regional, and time-sensitive early warning commands are issued. This method achieves deep risk identification by integrating historical knowledge and realizes predictive early warning by combining dynamic simulation, improving the early identification capability of hidden dangers and the predictability of early warnings.

The present application relates to the cross field of intelligent transportation, human factors engineering, emergency management and artificial intelligence, and particularly relates to a fire driving fatigue monitoring system and method based on survival analysis, which fuses multi-modal data such as physiology, behavior, environment and task, constructs a time-dependent dynamic risk assessment model, introduces a survival analysis framework to quantitatively determine the probability of accidents caused by fatigue during task execution, and identifies fatigue critical points with intervention value in combination with an adaptive threshold mechanism, thereby providing scientific rest determination and takeover decision basis for the task management system. The present application solves the problems of existing fatigue recognition technology in special operation scenarios, such as hysteresis, limitations and lack of critical risk judgment ability; it takes into account real-time, predictability and deployability, and is suitable for high-risk driving environments such as fire fighting, police and dangerous chemical transportation.

This invention relates to the field of nonlinear optics and discloses a method and system for multi-soliton spectral tunneling based on Airy pulse parameter control. The method includes the following steps: acquiring initial parameter data of the Airy pulse and initial state data of the multi-soliton; performing Airy pulse waveform generation processing based on the initial parameter data of the Airy pulse to generate Airy pulse waveform data; constructing a coupled nonlinear Schrödinger equation for accurate simulation to ensure the predictability of nonlinear interactions; and simultaneously placing the Airy pulse and the multi-soliton in a simulation platform to simulate their interaction. The generated spectral tunneling characteristic data can be analyzed in real time to determine whether the spectral tunneling process deviates from the expected energy transfer trajectory, ensuring spectral energy transfer efficiency and tunneling accuracy, and reducing spectral distortion and efficiency loss.

The present application relates to the field of auxiliary control system diagnosis of thermal power plants, and provides a dynamic diagnosis method and system for auxiliary control systems of thermal power plants based on a time sequence state machine, which slices and vectorizes real-time event streams through a sliding time window, constructs a current observation event set, and analyzes a target event set under a current state in combination with a pre-constructed state machine topology graph, performs set matching and out-of-order measurement on the observation events and the target events, quantifies the deviation degree of the event sequence, introduces an elastic tolerance mechanism, flexibly determines the state transition on the basis of considering network jitter and logical deviation, effectively distinguishes normal fluctuation, network micro-jitter and serious logical out-of-order, and finally, accumulates the trend of the diagnosis result based on the transition state pointer, updates the health degree image, generates a health degree report that can guide operation and maintenance, and effectively improves the adaptability to the state change of equipment in a complex industrial environment, and significantly enhances the predictability of the operation of the auxiliary control system and the scientificity of the operation and maintenance decision.

This invention relates to the fields of intelligent surgery and precision medicine, specifically a risk assessment method for tracheostomy cannula replacement. The method mainly includes: constructing a personalized digital twin integrating the patient's individual anatomy and respiratory mechanics; using bidirectional coupled simulation of computational fluid dynamics and solid mechanics to pre-simulate the entire surgical process in a virtual environment and quantify dynamic risk fields such as tissue damage and ventilation obstruction; based on a hierarchical reinforcement learning agent, autonomously learning and generating optimal surgical paths and strategies in the simulation environment; using augmented reality technology to overlay the pre-simulated risk heatmap and planned path onto the surgical field in real time, and achieving intraoperative adaptive adjustment through a pre-simulated strategy database; and finally, using a federated learning framework to continuously optimize system performance using multi-center desensitized surgical data. This invention significantly improves the predictability, safety, and accuracy of this high-risk procedure, realizing a paradigm shift from static assessment to dynamic navigation.

The application relates to the field of optical technology and discloses a second-order soliton trap transmission state regulation method based on an optical event horizon simulation, which comprises the following steps: preparing a second-order soliton pulse and an external probe pulse, wherein the second-order soliton pulse is generated through the balance of dispersion and nonlinearity in a nonlinear optical fiber, the external probe pulse is a weak power pulse, the group velocity parameter of the second-order soliton pulse and the external probe pulse is measured, and group velocity matching and group velocity mismatch conditions are set; through accurate setting of the group velocity matching and mismatch conditions and quantitative calculation of the refractive index change caused by the soliton pulse based on the Kerr effect, an equivalent event horizon is defined, definite physical parameter benchmarks and stable potential well environments are provided for transmission state regulation, the stability of a soliton potential well action boundary can be determined and ensured, the transmission state misjudgment problem caused by potential well drift and distortion is overcome, and the accuracy of the regulation process and the predictability of the result are ensured.

The application discloses a kind of robust prestack seismic inversion methods and devices based on coherence constraint algorithm, belong to unconventional oil and gas exploration and development field, comprising: through the extraction of seismic coherence attribute of centrifugal window of space transformation;Based on the approximate formula of azimuthal anisotropy reflection coefficient, the forward operator of fracture reservoir is deduced and constructed, and the positive problem of prestack seismic inversion prediction is clear;Based on the prestack seismic inversion prediction positive problem, under the Bayesian framework, the coherence attribute is added to the objective function of inverse problem as the prior information of seismic inversion prediction, and stable prestack and poststack joint seismic inversion prediction is carried out.The application not only considers the influence of large-scale fracture in post-stack, but also considers the azimuthal anisotropy characteristics caused by small-scale fracture, which can greatly improve the stability of anisotropy parameter inversion of fracture reservoir, and has better predictability for small-scale fracture associated with large-scale fracture.

The present application belongs to the technical field of intelligent control of livestock and poultry breeding environment, more specifically, relates to a meat duck breeding environment temperature regulation method and system fusing quantile regression prediction and reinforcement learning. The method comprises: collecting multi-dimensional data of the breeding environment and environment regulation equipment operation state information, performing feature construction to obtain a feature vector; using a multi-source data set to predict the temperature in the shed based on quantile regression, obtaining predicted temperatures at different quantiles; modeling the meat duck breeding environment temperature regulation problem as a Markov decision process, dynamically adjusting the fan power and the wet curtain equipment state; setting an online update model step threshold, and actually deploying and applying the reinforcement learning decision model obtained by training the quantile regression information fusion. The present application solves the problems of meat duck breeding environment temperature regulation technology, including insufficient predictability, lagging regulation, static control strategy, and lack of multi-objective optimization.

The application discloses a kind of wind power layered dynamic optimization scheduling methods based on high reliability guidance, belong to power grid dispatching technical field.The method includes:1. constructing node power prediction benchmark, and introducing frequency domain feature analysis module, the predictability and reliability index of each level prediction result is quantified in real time by discrete fourier transform;2. construct dynamic hybrid coordination mechanism, according to the relative strength of system level and node level reliability, dynamically switch from bottom to top aggregation and from top to bottom decomposition coordination path;3. in from top to bottom decomposition path, introduce physical constraint neural network, reconstruct node power under the premise of strictly meeting power balance equation constraint and line capacity, node voltage and other physical constraints.The application can effectively break the limitation of single coordination direction, significantly reduce voltage deviation and network loss under the premise of guaranteeing system power balance and physical safety, improve new energy consumption efficiency.

The application provides a member portrait construction and application method and system based on multi-source data. The method comprises the following steps: collecting member static attribute data and real-time dynamic behavior data at a preset frequency, obtaining core feature quantitative data after processing the RFM behavior average data of a preset period and combining the real-time dynamic behavior data; fusing the core feature quantitative data and the static attribute data, generating a member label system through a hybrid algorithm model of statistical analysis and machine learning, and constructing a structured member consumption portrait based on the label system; continuously collecting member real-time behavior data, updating the consumption portrait at a preset period, combining a preset operation strategy rule to recommend personalized marketing data for members; and thus through standardized processing and feature quantization of multi-source data, the constructed member portrait has dynamicity, predictability and business adaptability, can accurately match member features to push personalized marketing, and greatly improves member operation efficiency and marketing accuracy.

The application discloses a rail transit new energy power supply conversion control method and device, equipment and medium. It relates to the technical field of power electronics and traction power supply control. Through obtaining traffic situation information and electrical quantity information, the traction load dynamics, energy storage state and power grid working condition are unified into a hybrid prediction model, solving the defect that a single model cannot predict the change of impact load. In the multi-objective cost function, the grid comprehensive strength index term is introduced, and its weight coefficient is negatively correlated with the grid comprehensive strength index, thereby realizing real-time dynamic matching of the prediction model optimization target and the grid strength, overcoming the bottleneck that fixed parameters or physical models are difficult to balance accuracy and adaptability under complex working conditions, and ensuring voltage stability and power response under strong and weak grid wide range change scenarios, with stronger predictability and adaptability.

The application discloses a micro-grid energy management optimization method and system based on a large language model, relates to the technical field of micro-grids, and solves the technical problem that the predictability of a micro-grid prediction model is insufficient, which affects practical application. The method comprises the following steps: based on historical state data of a micro-grid, a small model agent is constructed and trained; micro-grid scheduling information is acquired, a prompt word module is designed through the scheduling information, and a large model agent is constructed based on a base large model and the prompt word module; the small model agent continuously acquires real-time information of the micro-grid, generates a small model decision, supplements the prompt word module through the small model decision and the real-time information of the micro-grid, and the large model agent generates a large model decision according to the supplemented prompt word module; and based on deviation threshold judgment between the small model decision and the large model decision, a coherent real-time decision response is output. The application ensures the explainability of decision generation and facilitates practical application in micro-grid energy management.