118 results about "Performance model" patented technology

The invention relates to the technical field of bridge engineering detection and repair, in particular to a bridge crack repair effect prediction method and system based on performance simulation, and the method comprises the following steps: obtaining basic data and environmental load data of a target bridge, and matching at least two candidate repair schemes from a preset repair scheme library; constructing an initial performance model before repair, and calculating an initial bearing capacity loss rate and a crack propagation rate; quantifying the interaction influence of the multi-modal data through a coupling factor matrix, and iteratively calculating the repair condition of a set time period after repair to obtain an initial simulation result; and collecting real-time monitoring data in a set time period in the repairing process and after repairing, and inputting the real-time monitoring data into a preset LSTM adaptive correction model to obtain a corrected simulation result. According to the method, the long-term repair condition after repair can be accurately calculated, the accuracy of a prediction result and the environmental adaptability are remarkably improved, and a scientific basis is provided for long-term service performance pre-judgment.

The invention provides a zero-trust gateway encrypted traffic non-inductive filtering method and device based on an eBPF multi-level mapping architecture and a computer program product. The method comprises the steps that a data packet is intercepted in a network driving layer through an XDP mode of an eBPF, and quintuple and encryption protocol visible meta information are extracted; performing hierarchical strategy matching on the meta-information by adopting a multi-stage mapping architecture, and dynamically optimizing a search path through a performance model; and executing real-time verification and access decisions based on a zero-trust policy engine to realize non-perceptual filtering of encrypted traffic. According to the method, the problems of weak detection capability and high matching delay of a traditional scheme under encrypted traffic are solved, the method has the advantages of high speed, low delay and high precision, and meanwhile, data privacy and compliance are guaranteed.

The invention discloses a multi-source time sequence decomposition and fusion hydraulic structure behavior prediction method, which comprises the steps of 1, acquiring historical time sequence monitoring data of a hydraulic structure, including behavior parameters and environmental quantity parameters, the behavior parameters including dam body settlement and seepage flow, and the environmental quantity parameters including dam body water level and rainfall; 2, preprocessing the acquired monitoring data, and constructing a hydraulic structure behavior model input data set; 3, determining an optimal decomposition period of an input data set of the hydraulic structure performance model; 4, according to the determined optimal decomposition period, decomposing the preprocessed hydraulic structure performance model input data set into three component parameters according to an original time sequence, wherein the three component parameters are respectively a trend component parameter, a periodic component parameter and a residual component parameter; and 5, superposing the predicted values of the three component parameters according to time points to obtain a final performance predicted value of the hydraulic structure. According to the method, the prediction precision is remarkably improved, and the stability and reliability of long-term prediction are remarkably improved.

Medical devices and related systems and methods are provided. A method of controlling medication delivery based on sensor input involves obtaining a measurement parameter representing an electrical response of a first instance of a sensing element to a physiological condition of a person. The measurement parameter is converted into a calibrated measurement parameter using calibration data specific to the first instance of the sensing element. The method further involves determining a measurement value using the calibrated measurement parameter as input to a performance model. The performance model is derived from historical calibrated measurement parameters and corresponding reference values. The historical calibrated measurement parameters are from other instances of the sensing element. A command is then determined based on the measurement value and sent to a medical device. The command causes the medical device to deliver a dose of medication influencing the physiological condition of the person.

The invention provides a low-altitude unmanned system air node optimal deployment method based on communication-link-energy consumption joint optimization. The method comprises the following steps: initializing an unmanned aerial vehicle group; the system constructs a performance model according to the real-time channel information, constructs a multi-objective collaborative optimization problem, and solves the multi-objective optimization problem by adopting an MOPSO-HGD algorithm to obtain an optimal result; deploying the unmanned aerial vehicle group to the optimal position according to the optimal result to form a virtual distributed MIMO array structure; setting an unmanned aerial vehicle electric quantity threshold value, judging the size of each unmanned aerial vehicle remaining electric quantity and the unmanned aerial vehicle electric quantity threshold value, and if the unmanned aerial vehicle remaining electric quantity is smaller than the unmanned aerial vehicle electric quantity threshold value, recycling the unmanned aerial vehicle; otherwise, re-determining the position of the unmanned aerial vehicle group; through a closed-loop control mechanism of channel perception-multi-target optimization-dynamic adjustment, the unmanned aerial vehicle relay system can still keep a high signal-to-noise ratio and strong robustness in a complex low-altitude environment, is suitable for scenes such as low-altitude broadband communication, emergency rescue and urban information networks, and has a good engineering application prospect.

The invention provides a method for determining surge margin loss of a compression part caused by production and assembly deviation. Key geometric parameters are determined; key geometric parameters are obtained through measurement; engine performance parameters and working condition parameters are obtained; correcting and confirming key geometric parameters by using the overall performance model, the performance parameters and the working condition parameters of the engine of the model; using a plurality of sub-engines to obtain probability distribution characteristics of each key geometric parameter; a Monte Carlo method is adopted, each key geometric parameter is valued according to the determined probability distribution, the engine overall performance model of the model is used for carrying out engine overall performance calculation, and a compression part common working line is obtained; determining a surge boundary line of the compression component when the key geometric parameters change; and a surge boundary line is determined, and surge margin loss caused by engine production deviation is obtained. According to the method, the surge margin loss of the compression part caused by the production and assembly deviation is determined according to a small number of engine production, assembly and test run results in the development stage.

A cognitive assessment system includes a computing device, a server coupled to the computing device, and an eye tracking device coupled to the computing device. The computing device includes a cognitive assessment program configured to perform at least one cognitive assessment task to assess cognitive function of a user. The server includes a database. The database stores historical performance data of the user, a historical performance model of the user, task performance data of a healthy population, and task performance data of a patient population. The eye tracking device is configured to capture eye movement information of the user.

The invention provides a performance analysis method and device for source address verification, equipment and a storage medium, and relates to the technical field of network security. The method comprises the following steps: receiving a source address verification processing program written through a description language; loading an algorithm configuration file; wherein the algorithm configuration file at least comprises a performance model configured for a plurality of preset algorithms, and the performance model is used for calculating the consumed time length of the operation statement based on the operand length of each operation statement; based on the source address verification processing program and the performance model, calculating the consumed time length of each operation statement; and generating a performance analysis report of the source address verification processing program according to the time-consuming duration of all the operation statements in the source address verification processing program. According to the embodiment of the invention, automatic and theoretical performance analysis can be carried out on the source address verification processing flow based on the formalized description language and the predefined algorithm configuration file under the condition of separating from the dependence of specific hardware.

The invention provides an aero-engine complete machine digital test method which comprises the following steps: firstly, establishing an engine main combustion chamber fuel flow neural network PID control model and an open-loop control rule of a guide vane angle, a channel area and a thrust augmentation fuel flow, and establishing an engine control system digital test model according to the PID control model and the open-loop control rule; and an engine performance digital twin model is constructed and used for predicting and outputting real-time performance parameters according to the engine control parameters and the test run environment parameters. In the test process, the real-time rotating speed of the engine serves as a middle bridge, a closed-loop control loop is formed between the control system model and the performance model, the whole machine dynamic test process of the aero-engine is simulated, and the aero-engine can be driven to be subjected to a high-fidelity simulation test in a digital space only through environment parameters and the angle of a throttle lever. The real physical test requirement is reduced, the engine development efficiency is improved, and compared with a traditional method, the high coincidence between the digital test process and the real test is ensured.

The application discloses a large language model parameter fine-tuning method and device based on manifold constraint multi-track adaptation and a medium. In view of the problems of unstable training and limited expression capacity of an existing low-rank adaptation technology, a plurality of parallel low-rank tracks are constructed, and a double random matrix is introduced to constrain the information flow between the tracks, so that the stability of gradient propagation is ensured. Meanwhile, the expression capacity of the adaptation module is enhanced under a limited parameter budget by dynamically fusing the outputs of the tracks through a dynamic gating mechanism. The method can realize stable, efficient and high-performance model fine-tuning by fine-tuning a small number of parameters, and reasoning has no additional overhead, and is particularly suitable for application scenarios with limited resources and high stability requirements, and has wide practical value.

A solar tracking system comprises a plurality of solar panel modules, each independently orientatable relative to a solar source. A control system determines a topography associated with the solar panel modules and generates, for each module, a performance model based at least in part on the topography and weather data. The control system independently orients each solar panel module to the solar source based on the respective performance model to optimize energy output from the array. The topography may indicate relative positions or heights of the modules and can be determined using laser site surveys, energy readings from photovoltaics, or aerial imaging. The weather data may include direct normal irradiance, global horizontal irradiance, or diffuse horizontal irradiance. The control system may periodically update the performance model for each module and account for interactions between neighboring modules due to shading.

The application provides an intelligent training acceleration method based on data dense characteristics in a neural network, which collects relevant data information and performance model information by pre-training a model, and dynamically reduces the data amount required to be transmitted between a GPU and a CPU by using the dense characteristics of a deep learning network in combination with compression and decompression algorithms, so as to improve the overall performance of the system. The application also provides an intelligent training acceleration system based on data dense characteristics in a neural network, which comprises a data acquisition module, a dense data compression rate decision module, a selective compression decision module and a training module; and the neural network training is carried out by using the system, so that the training speed and the overall performance of the system can be effectively improved.

This invention provides a task performance evaluation method based on a combination of hierarchical KAN and RBF networks. The method involves: constructing multiple KAN networks corresponding one-to-one with categories; each KAN network performs hierarchical nonlinear transformation and fusion of multidimensional feature variables of the same category, outputting low-dimensional feature variables; constructing an RBF network and inputting the low-dimensional feature variables output by each KAN network into the RBF network; the RBF network uses all low-dimensional feature variables as input, performs nonlinear mapping, and outputs the task performance evaluation result; finally, the transformation from a task performance evaluation index system to a task performance evaluation result is completed. This invention effectively fuses features from multiple categories using KAN networks to achieve feature dimensionality reduction, and uses the RBF method to quickly fit the performance model between input and output, improving the accuracy and efficiency of the evaluation. It addresses the shortcomings of existing technologies in modeling multi-category, multi-feature variable data, improving evaluation accuracy, computational efficiency, and interpretability.

The invention discloses a quantitative analysis method for deviation of an adjustable mechanism of a self-adaptive cycle engine, and relates to the field of quantitative analysis of self-adaptive cycle engines, and the method comprises the steps: obtaining part information of a self-adaptive cycle engine ACE, building an ACE overall performance model in combination with a zero-dimensional nonlinear part method, and calculating the deviation of the adjustable mechanism of the self-adaptive cycle engine; an adjustable mechanism deviation quantification module is connected into the ACE overall performance model to form an ACE adjustable mechanism adjustment deviation influence quantification model, a mode conversion control rule of the self-adaptive cycle engine is obtained, the mode conversion control rule is input into the ACE adjustable mechanism adjustment deviation influence quantification model, deviations of different adjustable mechanisms are given, and the self-adaptive cycle engine is obtained. Simulation is carried out to obtain an adjustable mechanism adjustment deviation simulation result, and influence analysis is carried out according to the adjustable mechanism adjustment deviation simulation result to obtain an analysis result. According to the method, the deviation of the adjustable mechanism can be comprehensively researched, and the influence of various factors on the adjusting effect of the adjustable mechanism is obtained.

The invention discloses a fuel gear pump unloading groove structure optimization method based on a lumped parameter framework. The method comprises the following steps: 1, establishing a fuel gear pump oil trapping mechanism model; 2, constructing a multi-cavity lumped parameter model of the fuel gear pump; 3, establishing an optimization sample library; and 4, constructing a BP neural network and optimizing a genetic algorithm. A multi-cavity performance model of the fuel gear pump is constructed by introducing a lumped parameter framework, so that the calculation complexity of the model is reduced, and a basis is provided for rapid optimization; a training sample is obtained based on a multi-cavity model, training of a neural network proxy model is carried out, a complex linear relation between unloading groove structure parameters and gear pump performance is mapped through the proxy model, and prediction precision and calculation efficiency are further improved; a genetic algorithm is adopted as an optimization engine, a neural network agent model is coupled, and multi-objective optimization of the structural parameters of the special-shaped unloading groove is carried out.

The invention relates to the technical field of integrated learning, in particular to an intelligent livestock disease prevention system which comprises a local geometric representation module, a performance model preliminary screening module, a decision logic verification module and an individual risk early warning module. According to the method, a plurality of base learners are evaluated and screened by mining a local geometrical relationship among physiological data and constructing a weight vector, a candidate model set adapted to current data features is formed, secondary preferential selection is performed according to the feature contribution degree concentration degree of each model during prediction, and a model with the most decision logic certainty is selected to execute a prediction task. The dynamic construction and preferential mechanism enhances the adaptability of the model to data fluctuation, avoids the limitation of generalization ability of a single model, and significantly improves the accuracy of disease risk identification and the stability of system operation.

The application relates to an independent hybrid frequency modulation energy storage power station capacity configuration method and device, computer equipment, a computer readable storage medium and a computer program product, relates to the field of electric power energy storage, and can improve the resource utilization rate of a hybrid energy storage system. The method comprises the following steps: acquiring total power control instructions of a hybrid energy storage system in a target frequency modulation scene and operation characteristic parameters of each energy storage unit; based on the operation characteristic parameters and the total power control instructions, determining first charge and discharge instructions and second charge and discharge instructions of different energy storage units according to a real-time power distribution strategy; based on the first charge and discharge instructions and a life attenuation model, determining a device replacement cycle of an energy type energy storage unit; based on system frequency modulation response and periodic resource consumption, determining a comprehensive performance model in a full life cycle; under the premise of meeting preset operation constraint conditions, taking the comprehensive performance value of the comprehensive performance model as a target, and determining capacity configuration parameters through a Bayesian optimization algorithm.

This invention belongs to the field of electronic digital data processing and discloses a propulsion modeling method for FPSOs based on Modelica, comprising the following steps: S1: Modeling the propulsion coefficient model of the propeller, including calculating the following parameters: thrust coefficient, thrust torque coefficient, thrust gain coefficient, and thrust torque gain coefficient; S2: Modeling the propeller performance model, including calculating the following parameters: wake coefficient, advance number, thrust loss coefficient, and relative rotational efficiency; S3: Based on the results of steps S1 and S2, outputting the thrust and thrust torque of the propeller, thereby obtaining the propulsion efficiency of the propeller. This method can comprehensively simulate the thrust and torque variation characteristics of the propeller under different operating conditions through a discrete dynamic modeling approach, thus providing accurate simulation support for the dynamic response of FPSO systems in complex marine environments.

The invention provides a PI controller parameter optimization method, a PI controller and a grid-connected inverter, and the method comprises the steps: firstly, determining an allowable range of a current loop bandwidth, taking the value range of controller parameters as a constraint, and taking the minimum overshoot, the highest response speed and the minimum reactive current change as targets; and parameters of the current loop dynamic performance model are optimized according to a simple gradient method. According to the method, high accuracy can be achieved through less test data through the current loop dynamic performance model, the relation between controller parameters and output variables can be accurately reflected, a reliable basis is provided for parameter optimization, the parameter optimization process is more efficient due to application of the simple gradient algorithm, and the method is suitable for large-scale popularization and application. The optimal solution can be found in a short time, the optimized controller can achieve the good decoupling performance of a current loop, and especially when the active current changes greatly, the optimized controller can remarkably reduce the change of the reactive current, so that the dynamic performance of the system is improved.

The invention provides an equipment type and management model association method in an airship platform OMC system, and the method comprises the steps: setting a corresponding equipment type label for the managed equipment type of the airship platform OMC system; constructing an equipment type definition table according to the data of the managed equipment types and the equipment types; for each equipment type, setting a corresponding configuration model, an alarm model and a performance model for configuration, alarm and performance monitoring in a database; associating the configuration model, the alarm model and the performance model with the equipment type definition table through equipment type labels; and obtaining a to-be-managed equipment type, and performing further management through the associated configuration model, alarm model and performance model according to the to-be-managed equipment type.

The invention provides a method for determining surge margin loss of a compression part caused by performance degradation of an engine. The method comprises the steps that a first blade tip gap and a first labyrinth gap are obtained through measurement; determining an engine overall performance model; measuring a first performance parameter of the engine; obtaining a first modified engine overall performance model; the engine is in a wear state, a second performance parameter is measured, and a second corrected engine overall performance model is obtained; the engine is decomposed, and a second blade tip gap and a second labyrinth gap are measured again; obtaining a performance degradation relation equation; obtaining a third blade tip gap and a third labyrinth gap during total recession; obtaining a third modified engine overall performance model; and determining a surge boundary, obtaining a common working line of compression parts of the engine, and calculating the surge margin loss during total decline of the engine through a difference value between the common working line and the surge boundary. According to the method, the surge margin loss of the compression part caused by performance degradation can be determined according to the long-term test run result of the engine.

The invention discloses a nuclear structure material database construction and use method and system and a medium, and the method comprises the steps: obtaining original test data of a nuclear structure material, the original test data being in-pile and out-pile performance test data of the nuclear structure material; according to the original test data, first calculation information and second calculation information are obtained through calculation; the first calculation information is material calculation information for core structure material elements, and the second calculation information is material calculation information for describing core structure material components; performing data cleaning on the original test data, the first calculation information and the second calculation information, and filling the cleaned corresponding data into a database in a preset format one by one based on a Python program to form a nuclear structure material performance database; and extracting corresponding data from the database based on database use requirements of the set performance model. According to the method, construction and use of the kernel structure material database meeting the requirement of a high-accuracy kernel structure material machine learning model are realized.

The application belongs to the technical field of satellite navigation and positioning, and particularly relates to a vehicle inertial navigation auxiliary positioning and deviation rectification method in a weak network environment, which comprises the following steps: acquiring inertial measurement unit data to calculate a motion uncertainty index; combining a trend to adjust a detection frequency; embedding an inertial measurement unit pre-integration increment into a QUIC self-defined frame; optimally transmitting based on a performance model and the motion uncertainty index; solving and positioning by a server using a factor graph fusion; constructing a state transition constraint factor when a source connection identifier is changed; analyzing and propagating a pre-integration increment to predict a state using a previous state; constructing a residual term by combining a predicted state and an observed state; and outputting a position based on Mahalanobis distance and a penalty upper limit to adjust a covariance. The application combines motion perception and network prediction to optimally select a path, uses predicted information to construct a state transition constraint factor to smooth state mutation, eliminates positioning jumps, and guarantees the continuity of vehicle position output in a weak network environment.

This invention discloses an energy-saving pump system based on adaptive flow regulation, relating to the field of energy-saving control technology for fluid transport equipment. The system comprises the following components: constructing a distributed control network, establishing a distributed multi-objective optimization model based on load forecasting, an improved intelligent optimization algorithm, executing distributed collaborative optimization, and outputting optimized control commands. By constructing a multi-objective optimization model that integrates operating energy consumption costs, equipment wear costs, and start-stop penalties, combined with a dynamic adaptive penalty function and an intelligent optimization algorithm based on the optimal efficiency point, this invention significantly reduces the total energy consumption of the system while ensuring accurate fulfillment of flow requirements. Furthermore, by dynamically correcting the pump performance model and adaptively adjusting the pump combination strategy, it avoids long-term deviation of the pump from the optimal efficiency point, reduces equipment wear index, extends expected service life, and significantly reduces the total life-cycle maintenance cost.

The application provides a multi-source heterogeneous data integration management method, medium and system based on a digital main line, belongs to the technical field of industrial digital main lines, and collects various formats of data and performs standardized processing by using a digital main line platform, realizes cross-domain semantic understanding by using a deep ontology fusion model, improves the semantic understanding efficiency by using adaptive pooling feature dimension reduction and separable attention calculation, optimizes the storage space by using a similarity aggregation deduplication or distributed independent storage strategy according to the data coincidence degree, establishes a digital main line data blood relationship tracing relationship to realize all-life-cycle data link construction, optimizes the semantic understanding parameters by using a fine-tuning or coarse-tuning mode based on a semantic mapping deviation value, constructs a vertical integration mapping from a function model to a performance model and a physical model and a horizontal integration framework of multi-domain comprehensive simulation, and solves the technical problem of insufficient cross-domain model semantic understanding accuracy.

The application belongs to the field of aero-engine design, and a calculation method for calibrating engine in-flight performance parameters to the same working condition point is provided. First, a high-altitude performance reference point is selected. Then, after obtaining steady-state performance data of different test working condition points in the engine working envelope, the relative relationship of performance correction factors of each component under different working condition points and the high-altitude performance reference point is obtained through identification calculation. The relationship considers the influence of different engine states and inlet flow conditions on the performance of each component of the engine. Meanwhile, the identification calculation result of the current test data considers the influence of performance attenuation. Finally, the in-flight performance parameter calibration calculation is completed according to the high-altitude performance reference point, the relative relationship of performance correction factors of each component of different working condition points, the current test data and the steady-state performance model. The method effectively improves the parameter calculation accuracy of the in-flight performance test data calibration calculation to the same high-altitude performance reference point.

The invention discloses a computer host heat dissipation monitoring and alarming system, and relates to the technical field of computer early warning, and the system comprises a parameter collection module which is used for collecting multi-source time sequence parameters of a computer host heat dissipation system, and the multi-source time sequence parameters at least comprise the temperature of heating hardware, the rotating speed of a heat dissipation fan and the air speed of a key monitoring point in an air duct; the collaborative efficiency analysis module is used for constructing a system collaborative efficiency model based on the multi-source time sequence parameters, and construction and application of the system collaborative efficiency model comprise the steps that the pneumatic conversion efficiency of a current system is calculated according to the corresponding relation between the rotating speed of a cooling fan and the wind speed; according to the method, the collaborative efficiency model is optimized through continuously accumulated time series data, so that the expected temperature rise curve fitting is more accurate, and the risk judgment accuracy is gradually improved.

This application belongs to the field of aero-engine simulation technology, and relates to a method and apparatus for simulating the overall pulsation characteristics of a rotating detonation turbine engine. The method includes: obtaining the given total pressure pulsation characteristics of the rotating detonation combustor; calculating the total outlet pressure of the rotating detonation combustor at the current simulation moment; inputting the total outlet pressure of the rotating detonation combustor into a pre-constructed overall performance model of the turbojet engine in transition state. The overall performance model is formed by coupling various engine components through aerodynamic parameter interfaces in the order of airflow, considering rotor dynamics and volume effects during coupling; solving for independent engine variables and the characteristics of each component under the constraints of overall engine matching conditions of flow balance and power balance until the residuals converge; extracting the total inlet pressure of the rotating detonation combustor and calculating the characteristics of each engine component at the next simulation moment. This application realizes the simulation of the unsteady coupling characteristics of the rotating detonation combustor and the turbojet engine.

An energy management system for microgrid systems. The energy management system includes a controller. The controller is configured to receive forecasted weather condition data, predict a power generation profile and / or a power demand profile of a microgrid system in response to the forecasted weather condition data, determine a performance model and / or a degradation model of one or more system components of the microgrid system in response to the power generation profile and / or the power demand profile, derive an energy management optimization strategy in response to the performance model and / or the degradation model of the one or more system components of the microgrid system, and control an operation mode of the one or more system components of the microgrid system in response to the energy management optimization strategy.

This invention provides a system that allows anyone, even without experience, to easily predict egg production and shipment. [Solution] The present invention acquires daily production information that allows for the identification of the survival rate, egg-laying rate, average egg weight, and egg production volume for each size of egg produced, according to the age of laying hens. It creates a standard performance model that correlates and identifies the survival rate, egg-laying rate, and average egg weight according to the age of laying hens, and an egg weight table that identifies the egg-laying rate for each size according to the average egg weight. Using the standard performance model and the egg weight table, it is possible to predict the production quantity by size according to the age of laying hens. Furthermore, it acquires past shipment information that allows for the identification of the shipment quantity according to the shipment date for each egg size, and creates an index category that identifies a calculation method for predicting the shipment quantity according to the size of eggs based on shipment information for a predetermined period. Using the shipment information for a predetermined period and the index category, it is possible to predict the shipment quantity by size according to the scheduled shipment date.