1316 results about "Performance estimation" patented technology

The invention relates to the technical field of computer networks, and discloses a computing power network real-time performance evaluation method based on an analytic hierarchy process, and the method comprises the steps: collecting a node operation state and task demand data through a sensor, and generating a local performance index in combination with an edge quantum algorithm; simulating a future network state by using digital twinning, and fusing to generate a multi-dimensional performance data set; the AHP weight is dynamically adjusted based on resource deviation and a geological classification model, high-frequency updating is started for high load / fault, and the weight range is expanded for low load; introducing a risk assessment algorithm to quantify a performance-cost-carbon effect conflict level, and triggering resource recovery, optimization prompt or single index suggestion; scheduling strategies are triggered in a grading mode according to evaluation results, and active intervention is started in combination with anomaly detection; the AHP weight is dynamically updated through reinforcement learning, and quantum-classical hybrid algorithm parameters and block chain verification weight are automatically optimized. The real-time response efficiency and the resource utilization rate of the computing power network can be improved.

The invention discloses an automatic code generation and optimization system based on multiple modes, which relates to the technical field of automatic programming, and comprises a task analysis module for analyzing task description and constraint conditions in combination with a CLIPS rule engine and a knowledge graph and original data, identifying task targets and requirements, and outputting a task risk assessment report and a task intention set; the optimization decision module is used for selecting an optimal modal data subset by using a particle swarm optimization algorithm and a path planning algorithm, performing optimization adjustment according to task requirements, and outputting a code generation strategy; and the test evaluation module is used for evaluating and improving the unit test and the integration test by utilizing the variation test, carrying out quality and performance evaluation on the code through continuous integration and continuous delivery, and outputting a code evaluation report and an optimization suggestion. According to the method, the optimal modal data subset is dynamically screened, and the data transmission path is optimized, so that the code performance is ensured, the computing resource consumption is reduced, and the global optimization of the code generation strategy is realized.

The invention provides an intelligent short message scheduling method and device based on multi-dimensional dynamic optimization, and the method comprises the steps: obtaining the performance data of a plurality of short message channels, and calculating a channel health score based on a weight dynamic adjustment model; determining a scheduling strategy according to the priority identifier of the to-be-sent message, and performing channel screening and optimal matching; executing message sending and monitoring a sending state; terminal state detection is carried out on the failure message through operator base station signaling, and a decision tree model is applied to determine a retry strategy; and performing Huffman coding compression processing on the P2-level marketing messages which fail in retry, and performing batch sending in an idle window. According to the method, a comprehensive performance evaluation index and reward function model is also constructed, and parameter optimization is performed by applying a reinforcement learning algorithm. According to the invention, multi-dimensional dynamic channel scoring, intelligent retry decision making based on terminal state perception, batch processing with balanced cost-time efficiency and a closed-loop self-optimization system are realized, the short message delivery rate is obviously improved, and the invalid retry rate and the sending cost are reduced.

The invention discloses a process automation execution method based on a large language model, and belongs to the technical field of artificial intelligence and process automation. User intention is analyzed through multi-modal input, and a structured task definition is constructed; the semantic reasoning layer is used for performing task layering, complexity evaluation and sorting optimization; the task execution layer completes subtask scheduling and execution; and the feedback and optimization layer performs performance evaluation and model updating based on execution data to realize closed loop and continuous optimization of the process, so that the technical problems of dynamically analyzing unstructured instructions, automatically optimizing a complex task dependency relationship and adapting to business changes in real time by a process automation tool are solved; according to the method, end-to-end conversion from an unstructured instruction to a structured task is realized, a subtask execution path is dynamically optimized, cross-platform tool calling is supported, the existing system integration cost of an enterprise is reduced, visual display task decomposition logic and prediction and execution time consumption comparison are provided, and the system credibility is enhanced.

The invention relates to the technical field of fluid machinery design, and discloses a fluid machinery through-flow component intelligent design simulation system, which comprises a multi-physics field modeling module, which is used for constructing fluid mechanics, structural mechanics and heat conduction models based on preset boundary conditions and physical attributes; the intelligent optimization decision module is used for constructing and defining a through-flow performance optimization objective function; the cooperative calculation module is used for constructing a comprehensive performance evaluation model based on the candidate design parameter set; the virtual experiment feedback module is used for reconstructing a corresponding through-flow component structure model according to the multi-target evaluation result; and the scheduling control module is used for controlling algorithm path selection and computing resource allocation in the optimization process. According to the method, a method of combining multi-physics field coupling modeling and a dynamic feedback mechanism is adopted, and design parameters are continuously adjusted through simulation data fed back in real time, so that the optimization process can be dynamically updated according to actual operation conditions, and the optimization convergence speed and the overall performance are improved.

The invention relates to the technical field of cloud computing and artificial intelligence, in particular to a disaster recovery drill and performance evaluation system and method for multi-cluster distributed training. In order to solve the problem of disaster recovery and performance evaluation in multi-cluster distributed training, the method comprises the steps of environment analysis and modeling, fault injection and drill control, cross-cluster scheduling and resource monitoring, performance data acquisition and index aggregation, training result evaluation and tuning closed loop and the like. Multi-cluster environment elements are identified, a fault and index mapping relation is constructed, faults are injected by using a fault injection tool, and task scheduling, resource monitoring and performance data acquisition and analysis are realized by means of a multi-cluster management and monitoring tool, so that an adjusting and optimizing strategy is formed, and closed-loop management is realized. According to the method, faults can be dynamically simulated in a multi-cluster environment, the training performance is monitored, the disaster recovery and recovery efficiency is evaluated, the training efficiency and robustness of the system are improved, and the requirements for high availability and high performance in the fields of finance, automatic driving and the like are met.

The invention discloses a spaceborne interference source Doppler observation strategy optimization method, which comprises the following steps: acquiring an original radio frequency signal and satellite orbit data, predicting mutation characteristics in Doppler data, and generating a self-adaptive observation scheme; according to the self-adaptive observation scheme, energy-aware resource scheduling is carried out, observation is executed, and enhanced Doppler observation data is obtained; and carrying out positioning calculation and performance evaluation based on the enhanced Doppler observation data, and generating parameters for iteratively optimizing an observation strategy. According to the invention, intelligent matching of the observation resource and the signal information content is realized, and the resource utilization efficiency, the key event capturing capability and the final positioning precision are improved.

The invention discloses an integrated scheduling system for realizing a PCS, an EMS and a BMS, and relates to the technical field of power control, and the system comprises a multi-dimensional performance evaluation module which constructs a battery aging dynamic model, carries out the training, carries out the health state pre-judgment through the battery aging dynamic model based on a standardized state vector, and generates a multi-dimensional performance evaluation index; the multi-objective optimization module is used for generating a collaborative scheduling strategy set by combining a fuzzy analytic hierarchy process with a multi-objective optimization solver of an improved genetic algorithm based on the multi-dimensional performance evaluation indexes; the dynamic derating module is used for generating an executable instruction queue with security constraints by combining an industrial internet of things protocol stack with a dynamic derating coefficient algorithm based on the collaborative scheduling strategy set; according to the invention, through the physical driving characteristic layer and the dynamic parameter calibration layer, the nonlinear coupling modeling of the cyclic attenuation and calendar aging mechanism in the battery aging dynamic model is realized.

The invention relates to the technical field of defect detection, in particular to a cross-process defect root cause tracing method and system. According to the method, the data feature matrix covering multiple dimensions is formed by integrating the process parameters, the equipment state and the quality detection information, so that the performance evaluation of each process is more comprehensive, the interaction and influence paths among the processes can be clearly described by constructing the process relation graph, and the performance evaluation efficiency is improved. Meanwhile, basic data support is provided for quantifying the relation between the procedures through introduction of procedure attenuation factors, the shortest propagation path and the propagation probability of the defects can be accurately recognized by analyzing a procedure relation graph, root cause tracing of the cross-procedure defects becomes systematized in combination with construction of a knowledge graph, and the defect tracing efficiency is improved. The knowledge graph not only can effectively integrate and display data, but also is convenient for quickly positioning problems, and by utilizing an adaptive correlation analysis technology, the system can intelligently adjust an analysis model and a path and continuously optimize a defect detection and tracing process when facing new data.

The invention discloses a health risk assessment method and early warning system based on multi-source data analysis. The health risk assessment method comprises the following steps: S1, collecting and preprocessing multi-source health data through medical detection equipment; s2, extracting key health indexes, sequence features and statistical features based on the health data set; s3, adopting a recurrent neural network model to construct a health risk assessment model; s4, optimizing structural parameters of the health risk assessment model by adopting an improved dragonfly algorithm; s5, performing performance evaluation on the health risk evaluation model by using the optimal parameter set; s6, deploying the final health risk assessment model in a health risk assessment and early warning system; and S7, when the health risk assessment result exceeds a preset risk threshold, automatically generating early warning information. According to the method, the recurrent neural network model, the improved dragonfly algorithm and the multi-source health data fusion optimization technology are combined, and health risk assessment and early warning based on multi-source data analysis are realized.

The invention relates to the technical field of dredge pump performance analysis, and discloses a numerical simulation and model test-based dredge pump performance analysis method, which comprises the following steps of: planning an analysis process, and determining numerical simulation (dredge pump internal flow field simulation) and model test contents (actually measured performance data under different working conditions); determining working condition types according to design parameters and working conditions; generating a performance analysis process set and determining an execution sequence; generating a simulation parameter setting list and a test scheme list of each working condition; and associating the data to generate an overall scheme. The method further relates to working condition analysis boundary determination, working condition type subdivision, dynamic adjustment of the process, test rule optimization, database construction and the like. According to the method, comprehensive and accurate analysis of the dredge pump performance is achieved through multi-dimensional data integration and a dynamic mechanism, the analysis efficiency and reliability are improved, and the method is suitable for evaluation and optimization of the dredge pump performance.

The invention relates to the technical field of energy storage scheduling, and discloses a distributed energy storage power system optimization scheduling method and system. The method comprises the following steps: collecting system operation data, performing normalization processing and anomaly detection, and predicting 24-hour load demand and new energy output in combination with standardized data; performing cost calculation and constraint analysis according to the prediction data, and generating a day-ahead scheduling plan; the operation state is updated according to the 15-minute cycle, and the output data is adjusted; performing multi-time-scale hierarchical processing on the power fluctuation; based on this, the energy storage capacity is optimally configured and the life characteristic is evaluated; and parameter optimization and updating are realized through operation index monitoring and analysis. According to the invention, through multi-dimensional performance evaluation and parameter optimization, efficient utilization and service life prolonging of the energy storage system are realized.

The invention discloses a method and a device for evaluating the performance of an automatic and rapid unfolding and folding tent. The method comprises the following steps: acquiring a performance index test data set of the automatic and rapid unfolding and folding tent; the performance index test data set comprises an unfolding and folding type index test data set, an efficiency type index test data set and an environment adaptation type index test data set; each type of index test data set comprises a plurality of types of index test data sequences; preprocessing the performance index test data set to obtain a preprocessed data set; performing classification evaluation processing on each type of index test data set in the preprocessed data set to obtain a sub-index test value set; and performing fusion evaluation processing on the sub-index test value set to obtain a performance evaluation result value of the automatic rapid unfolding and folding tent.

The invention provides an unmanned aerial vehicle (UAV)-based flux-inductance integrated beam forming and trajectory optimization method, on one hand, an FP-AROS method is proposed based on fractional programming and semi-definite relaxation technologies in combination with approximate rank-one solution construction to carry out optimization design on ISAC beams, the Cramer-Rao bound of AoD is adopted as a sensing performance evaluation index, and the FP-AROS method is adopted to carry out optimization design on the ISAC beams; target estimation performance can be reflected more directly, so that a collaborative optimization effect of communication and sensing performance is realized; on the other hand, the invention provides an improved continuous convex approximation method based on a trust domain, and the method is used for optimization design of a UAV track. The ITRSCA method obtains a local trend approximate solution of an original objective function by calculating a second derivative, and introduces a dynamic trust domain mechanism to control approximate precision, so as to ensure global convergence of an output solution; in addition, the method is combined with a penalty function method to process non-convex constraints, and the limitation of a trust domain method in a constrained optimization problem is effectively overcome.

The invention relates to the technical field of electric power spot transaction, and discloses an electric power spot transaction optimization system. The system comprises a data acquisition module, a dynamic pricing module, a supply and demand prediction module, a transaction optimization model construction module, a real-time scheduling module and the like. The data acquisition module acquires market data; the dynamic pricing module generates a dynamic electricity price strategy based on a deep reinforcement learning algorithm and a mixed integer programming model; the supply and demand prediction module predicts supply and demand through a federated learning framework aggregation model; the transaction optimization model construction module constructs an objective function and constraint conditions by combining the results; and the real-time scheduling module uses an improved algorithm to solve and generate an optimal scheduling scheme. In addition, the system also has the functions of risk control, abnormal transaction detection, intelligent contract management, performance evaluation and the like. The system can optimize electric power spot transaction, improve transaction efficiency, guarantee power grid stability, prevent and control market risks, and promote healthy development of the electric power market.

The invention discloses a watershed pollution prediction method based on multi-source data fusion, and belongs to the technical field of watershed pollution prediction. According to the method, water quality, runoff, land utilization and meteorological data sequences are firstly constructed, and then features of the sequences are extracted and spliced to form a fusion feature vector. And forming a training set by using a plurality of fusion feature vectors, inputting the training set into a random forest regression model for training, performing performance evaluation on the trained model by using a verification set, and finally using the model meeting evaluation conditions to predict the drainage basin pollution degree. According to the invention, through multi-source data fusion and scientific model training and evaluation processes, the accuracy of drainage basin pollution prediction is effectively improved.

The invention relates to the technical field of artificial intelligence chips, and discloses a model performance estimation method and device and computer equipment, and the method comprises the steps: determining model configuration data and candidate distributed strategies of a target model; converting the original calculation graph corresponding to the single-card deployment state based on the model configuration data and the strategy configuration data of the candidate distributed strategies to obtain a distributed overhead calculation graph corresponding to the multi-card deployment state; and performing performance estimation on the candidate distributed strategy according to the basic overhead and the additional overhead in the distributed overhead calculation graph to obtain strategy performance data of the target model under the candidate distributed strategy, thereby realizing conversion of a single-card model into a multi-card model. And based on the multi-card model, simulation calculation of the multi-card interconnection mode is realized on the premise of limited hardware resources, so that the influence of a communication operator and a topological structure corresponding to the multi-card interconnection mode in the overall operation of the model is reflected, and the upper limit of the model performance can be accurately evaluated in a simulator verification stage before silicon is applied.

The invention discloses an offshore pile foundation evaluation system construction method based on evolutionary algorithm optimization and PIML linkage. A standardized engineering data set is obtained; parameterizing the pile-soil coupling dynamics boundary condition, the pile-soil interaction specification criterion and the limit state and use state criterion, and storing the parameters as a physical constraint set; obtaining an initial performance evaluation model; generating an alpha evolutionary optimization performance evaluation model; outputting a bearing capacity-settlement relation index, a dynamic and static stiffness degradation curve index and a residual bearing capacity probability distribution index to form a structural performance index set; generating a credible structure performance index set containing confidence boundaries and extracting an influence degree sorting result; and automatically generating a structured evaluation report according to a verification result. The model output has physical consistency and engineering interpretation all the time, and the problem that a traditional pure data driving model is unreliable in result under data sparsity and environment sudden change is remarkably solved.

The invention discloses a test evaluation method and device for an industrial park integrated management system. The method comprises the following steps: acquiring a performance index data set of the industrial park integrated management system; the performance index data set comprises a performance index test data subset of each subsystem; the performance index test data subset comprises a test data sequence of each performance index; pre-processing the performance index data set to obtain a to-be-evaluated data set; and performing test evaluation processing on the to-be-evaluated data set to obtain a performance evaluation result value of the industrial park integrated management system.

The invention provides a method and a system for digital twinborn construction and multi-performance real-time evaluation of equipment, the core of the method is to construct a digital twinborn body of the equipment and develop a multi-performance real-time evaluation agent model, and the method comprises the following steps: constructing a multi-field simulation virtual prototype of the equipment based on a joint simulation technology; various performance indexes of the equipment are comprehensively presented through simulation analysis; the model of the virtual prototype is combined with sensing data, and after visualization processing, a digital twinborn body capable of reflecting direct observation information of equipment is constructed; a robust agent model construction strategy is provided, and batch joint simulation data is adopted to train the model, so that the indirect performance index of the equipment can be accurately evaluated; sensor data for collecting working conditions in real time and an agent model for evaluating performance in real time are integrated in a digital twinborn body, and a model-data dual-drive performance evaluation mechanism is formed.

The invention discloses a digital twinning-based building full life periodicity performance evaluation method and device, and belongs to the field of building construction.The evaluation method comprises the following specific steps that I, in the building planning and design stage, a building foundation digital model is constructed, meanwhile, various construction data are collected in real time through a multi-modal sensor, and block chain storage is established; iI, performing real-time sensor data preprocessing and updating based on the building basic digital model so as to perform multi-physics field coupling simulation, and dynamically updating and predicting a building state; the method overcomes the limitation that traditional simulation is large in calculation amount and difficult to update in real time, improves evaluation reliability, achieves dynamic calibration and precision enhancement, enables material aging and repair prediction to be more accurate, provides a solid basis for durability evaluation, improves the economy and reliability of the whole life cycle of a building, and has a wide application prospect. And a complete closed loop of coverage planning-construction-use-maintenance is formed, so that the performance evaluation can be continuous, dynamic and traceable.

The invention provides a federated learning differential privacy method based on Rayleigh divergence and adaptive noise adjustment, and aims to balance data privacy protection and model training performance and improve model accuracy and convergence speed of federated learning on the premise of protecting user data privacy. And the contradiction between privacy protection and model performance in the existing federated learning is solved. The method comprises the following steps: step 1, constructing a privacy loss quantification model based on Rayleigh divergence; 2, deducing a tight upper bound of a Gaussian noise standard deviation; 3, initializing noise parameters of the federated learning system; 4, the client side executes local model training and noise adding; 5, updating the aggregation model of the central server and evaluating the performance; step 6, implementing a self-adaptive noise adjustment decision based on model performance; and step 7, iterating federal learning training until convergence or completion.

The invention provides a storage type XSS attack detection method and device based on a large language model, and the method comprises the steps: receiving an initial cue word, inputting the initial cue word into a cue word expander, carrying out the extension of the initial cue word, carrying out the performance evaluation to screen out a cue word meeting a preset performance index, carrying out the multi-round extension and evaluation, and obtaining a detection result. Until the optimized cue word is obtained; the method comprises the following steps: receiving a to-be-detected code, generating a program dependency graph of the to-be-detected code, decomposing the program dependency graph of the to-be-detected code into a plurality of sub-graphs with consistent program structures to obtain a plurality of sub-program dependency graphs, traversing nodes in each sub-program dependency graph, and reserving an original structure of a program; the nodes are sequentially converted into an original code format to obtain code representation fragments, the code representation fragments and the optimized cue words are input into a large language model to judge whether each code fragment is an attack vector of a storage type XSS or not, and a final speculation result is obtained. The method has the advantages of low detection cost, high accuracy and high efficiency.

The invention discloses a building composite phase change material intelligent matching and optimizing method based on a large language model, and belongs to the technical field of building energy saving and intelligent material design. Basic thermophysical property data, building environment parameter data and user demand data are collected; a deep reinforcement learning technology is utilized to construct an intelligent model based on a deep Q network strategy, and generated data samples are integrated into a thermophysical property database; outputting a candidate material combination recommendation scheme by adopting a large language model; evaluating the candidate material combination recommendation scheme by using the semantic tag vector and a sorting engine to obtain a performance evaluation result; generating a performance evaluation report according to the simulation model; a multi-agent negotiation algorithm is adopted, cross-regional thermal control performance is optimized, a multi-dimensional performance comparison diagram and tuning suggestions are generated, correction information of designers is recorded, optimization is carried out, and feedback is provided in a self-adaptive mode. According to the method, the building composite phase change material combination is screened, cross-regional thermal control is optimized, adaptive schemes and suggestions are output, and the self-adaptive optimization capability of the system is improved.

Provided in the embodiments of the present application are a large-model performance assessment method and a related device. The method comprises: acquiring structured text description of a test material, wherein the structured text description comprises information of the test material in a plurality of dimensions; inputting the structured text description and a test question into a large language model, so as to acquire an evaluation true value; inputting the test material and the test question into a large model under test, so as to acquire an answer under test of the test question; and inputting the evaluation true value, the test question and the answer under test into the large language model, so as to acquire an assessment result for the answer under test.

The invention relates to the field of bridge engineering, in particular to a bridge structure toughness dynamic evaluation method and system based on digital twinning, and the system firstly collects the environment, geometry and performance information of a bridge, and then establishes an accurate bridge digital twinning model; a bridge structure toughness prediction model is systematically constructed by using a space-time diagram convolutional network, and the performance degradation process of a bridge in extreme weather can be predicted; meanwhile, toughness evaluation indexes are formulated by the system, and accurate performance evaluation of real-time monitoring data is realized; through multi-source data fusion, the system improves the comprehensiveness and accuracy of data acquisition; the prediction model based on the space-time diagram convolutional network significantly improves the accuracy of performance degradation prediction; a scientific and efficient tool is provided for bridge maintenance and management, and safe operation of the bridge in extreme weather is effectively guaranteed.

An adaptive transformation matrix generation is used simultaneous compression and encryption of data. The system continuously monitors input data streams to detect changes in distribution patterns, dynamically updating transformation matrices in response to these changes. Using performance evaluation criteria, the system selects and deploys optimal matrices that maintain both compression efficiency and cryptographic security as data characteristics evolve. The system implements configurable adaptation policies that govern when and how matrices are updated, ensuring system stability while maximizing performance. By employing sliding window analysis and distribution variance metrics, the system can quantify data drift and trigger appropriate adaptations. The system maintains compatibility with various operational modes including lossless, lossy, and modified lossless configurations, applying mode-specific optimization criteria to each scenario. This adaptive approach offers a resilient solution for data transmission and storage scenarios where both data reduction and security must be maintained across diverse and evolving data streams.

The invention discloses a robot task dynamic scheduling optimization method for an annular warehousing system, and particularly relates to the technical field of intelligent warehousing system scheduling. Task basic information is collected to construct a task state table, and robot operation states are collected to construct a robot state table R; constructing a task-robot matching graph based on T and R, and calculating a matching priority score by adopting a graph neural mechanism; generating an initial scheduling scheme according to a game optimization and state driving strategy; path conflict detection is carried out in combination with a ring topology structure and traffic rules, and a task execution path is optimized through a sliding time window and a local disturbance rearrangement algorithm; a scheduling simulation engine is constructed to perform performance evaluation, if an evaluation result does not reach a preset threshold value, structure reconstruction is performed on the scheduling graph based on a residual feedback mechanism, and the method has the advantages of being high in real-time performance, low in conflict rate and high in resource utilization efficiency and is suitable for high-reliability intelligent scheduling in a multi-robot intensive task scene.

The invention provides a train operation state estimation method and system based on a high-order extended Kalman filter considering noise correlation, and belongs to the technical field of rail transit train operation management. According to the method, correlation analysis between high-order terms is introduced, the filtering precision is remarkably improved, and particularly, excellent performance is shown under a strong nonlinear system; by establishing a multi-mode train state model, the dynamic characteristics of a train in operation modes such as traction, cruising, sliding and braking are comprehensively covered, and a more accurate input basis is provided for a filtering algorithm; by introducing a sliding window error criterion, the filter can quickly switch operation modes according to real-time errors, so that the filter is ensured to keep high adaptability in a complex and changeable operation environment; the comprehensive performance evaluation framework based on the error covariance matrix not only evaluates the mean square error and the mean absolute error, but also quantifies the contribution of the high-order correlation to the precision of the filter, and provides a more scientific and comprehensive evaluation system.

The invention discloses a building decoration carbon emission optimization decision support system. The data acquisition unit acquires real-time data through the multi-source sensing device, and the data preprocessing unit performs abnormal value filtering, format unification and standardization operation on the real-time data. And the decision optimization unit integrates an intelligent dynamic strategy adjustment mechanism and a time sequence prediction model, and generates an optimization scheme through efficiency comparison incremental learning and feedback correction. And the visual output unit converts the scheme into a graphical display interface. In addition, the system is provided with a performance evaluation unit, the carbon emission reduction rate can be counted, the decision accuracy can be analyzed, the comprehensive efficiency score can be generated, and the data acquisition frequency and algorithm parameters can be dynamically adjusted. The system provides comprehensive support for carbon emission optimization of building decoration and assists green development of the building industry.