62 results about "Performance improvement" patented technology

The invention provides a system and method for managing the lifecycle of machine learning models, from development to deployment and ongoing operation, across various environments including on-premises, cloud, and hybrid infrastructures. The system features a model build platform for data processing, feature generation, model development, training, and hyperparameter tuning. A model analytics engine extracts metadata, performs complexity analysis, and generates configuration files specifying environment settings and resource needs. A secure model repository enables version-controlled storage, while a deployment platform retrieves, validates, and deploys models in containerized environments like OpenShift or Kubernetes. The platform dynamically allocates resources, supports real-time and batch scoring, and monitors model performance with guardrails. Customizable agents provide real-time feedback and automated optimization, and the system can securely decommission models while maintaining detailed lifecycle records. The invention enhances the efficiency, security, and scalability of machine learning operations with continuous performance improvement and compliance automation.

The invention relates to a dynamic power control method and system for a storage and charging all-in-one machine, and the method comprises the following steps: data collection and monitoring: collecting and analyzing key parameters in real time through multi-source data fusion; dynamic safety threshold calculation: dynamically calculating a safety operation threshold of each subsystem based on real-time data, and ensuring that the system operates in an optimal state; multi-objective optimization control is carried out, an intelligent algorithm is adopted, and an optimal balance point is found among battery safety, power grid stability and charging efficiency; according to a fault processing and redundancy mechanism, a multi-layer fault response system is established, and the safety and continuity of the system under the abnormal working condition are ensured; according to the energy efficiency management and strategy optimization, long-term performance improvement and economical efficiency optimization are achieved based on a data closed loop, and the problem that potential safety hazards are caused due to the fact that a traditional storage and charging all-in-one machine system neglects the common influence of multiple factors such as SOC, temperature and power grid loads is solved.

The invention provides an employee performance prediction method based on multi-source heterogeneous data, and relates to the technical field of enterprise management. Structured data and time sequence data of employees are collected, the two types of data are preprocessed and then associated and fused with unique identifiers of the employees as cores, and a panoramic data wide table is constructed; then static and dynamic time sequence features are extracted from the wide table, the features are input into an artificial intelligence evaluation model, the model calculates basic performance indicators through a rule engine, and performance prediction scores are obtained through comprehensive evaluation in combination with a random forest regression model; the method is further provided with a potential prediction model based on an LSTM architecture integrated attention mechanism, and performance improvement potential scores and confidence intervals in the next three months can be output. According to the method, the problems of multi-source heterogeneous data fusion, model self-adaption, single evaluation dimension and the like are solved, and the employee performance can be predicted and evaluated comprehensively, objectively and prospectively.

The application provides a reliability analysis method for positioning accuracy of a shield tool changing robot, realizes sensitivity analysis of error parameters by constructing a kinematic model and an error proxy model, and accurately evaluates the failure probability of positioning accuracy. The assembly and machining errors are comprehensively considered, the error parameters are treated as random variables, the accuracy and efficiency of the analysis are improved. The mean and standard deviation of the random variable are used for sensitivity calculation, and a failure calculation method of positioning accuracy sensitivity is obtained, which provides an important basis for optimization design. The application not only helps to reduce the tool changing time, improve the construction efficiency and reduce the safety risk, but also provides an accurate and reliable tool changing robot manufacturing scheme for designers. The method has universality and flexibility, and can be widely applied to performance improvement and fault prevention of the shield tool changing robot. The implementation of the application will promote the development of shield construction technology and provide strong support for industrial automation and efficient production.

The invention provides a pressure sensing system compensation method and system, and the method comprises the steps: building a mathematical fitting model of an input and output pressure difference value and time under different conditions through testing and processing the data of a system in a starting time period under different input pressure and working temperature conditions, obtaining an initial environment condition when the system is started, and carrying out the calculation of the initial environment condition after the initial environment condition is obtained; and a corresponding mathematical fitting model is substituted to compensate the output pressure, so that the accurate pressure value in the system starting time period can be quickly calculated, and the performance improvement in the whole time period after the system is started is realized. According to the technical scheme, the technical problem that in the prior art, the pressure measurement precision deviation is large due to the fact that a thermal field is unbalanced in the starting period of a pressure sensing system is solved.

A business management system with integrated business intelligence and IT-supported optimization, consisting of: a computer device comprising at least one processor and a memory arrangement for storing executable instructions; a data acquisition and integration unit that is operationally connected to the at least one processor and is configured to receive heterogeneous enterprise data from a variety of source systems, including financial transaction systems, operational control systems, human resource information systems, supply chain systems, customer interaction systems, and IT infrastructure monitoring systems, wherein the data acquisition and integration unit is further configured to normalize syntactic formats, resolve temporal inconsistencies, and map source-specific attributes into a unified enterprise data representation stored in the storage system; a business intelligence processing unit that is operationally coupled with the data acquisition and integration unit and is configured to calculate business performance indicators through multidimensional aggregation, correlation analysis, and trend extraction over the unified business data representation, with the calculated business performance indicators being time-stamped and permanently stored; an optimization unit that is operationally coupled with the business intelligence processing unit and is configured to compare the calculated business performance indicators with stored business objectives and constraints and to determine optimized business control parameters that correspond to resource allocation, operational planning, cost efficiency or performance improvement; a control and orchestration unit that is operationally linked to the data acquisition and integration unit, the business intelligence processing unit, and the optimization unit, wherein the control and orchestration unit is configured to coordinate the execution sequence, forward the results of the business intelligence analysis to the optimization unit, and manage feedback by incorporating the effects of applied optimized business control parameters into subsequent data acquisition cycles; and a communication interface that is connected to at least one processor and configured to exchange data and control signals with external enterprise systems and user terminals.

The invention belongs to the technical field of artificial intelligence, and relates to a dynamic controllability management method and system for long-term evolution of a large model, and the method comprises the steps: S1, obtaining the current state information of the large model; s2, calculating a controllability score of the large model based on the output distribution entropy, the distribution variable quantity and the logits drift of the large model, judging whether the current state of the large model deviates from a controllability critical zone or not based on the controllability score, if the current state deviates from the controllability critical zone, entering S3, and otherwise, directly entering S4; s3, actively correcting the offset behavior of the large model in the training process through a controllable calibration head, so that the current state of the large model does not deviate from the controllable critical zone; and S4, dynamically adjusting a training strategy of the large model based on the controllability score and the task category. According to the method, the large model has the capabilities of long-term plasticity maintenance, continuous performance improvement and rapid adaptation to new tasks, and the fundamental upgrade from a static optimization model to a self-evolution model system is realized.

The invention discloses a deployment-oriented wireframe analysis method and system, and belongs to the technical field of wireframe analysis, and the method comprises the steps: collecting an original image, and carrying out the preprocessing of the original image, and obtaining a to-be-analyzed image; constructing and training a wireframe analysis framework to obtain a wireframe analysis model; the method comprises the following steps: inputting a to-be-analyzed image into a wireframe analysis model, extracting multi-scale features with long-range context dependence in the to-be-analyzed image through mobile terminal line analysis ViT, local convolution and token interactive mixing processing, aligning the multi-scale features through a multi-scale feature line decoder, and performing structure perception enhancement, and analyzing to obtain a wireframe in the original image. According to the method, the structural continuity and the intersection point positioning stability in the wireframe analysis process are enhanced, clearer performance improvement is obtained in the line segment decoding process compared with a simple splicing mode, and meanwhile the real-time processing speed is kept; the method supports plug and play, does not need an additional supervision mechanism, and keeps compatibility with standard training and reasoning processes.

This application discloses a method and related apparatus for determining a data processing model. Based on a training dataset and initial training parameters, a machine learning model is trained to obtain a target model. The performance parameters of the target model and the contribution parameters of multiple data features to the prediction results of the target model are determined. If the target model satisfies the self-optimization condition based on the performance parameters and contribution parameters, the initial training parameters are adjusted to obtain optimized training parameters. Based on the training dataset and optimized training parameters, the target model is further trained to obtain a data processing model. The data processing model is used to process financial data to obtain the corresponding prediction results. The training process considers changes in performance and contribution parameters, and automatically adjusts the model parameters to ensure that the training process is geared towards performance improvement and smooth changes in contribution parameters. The trained model has high performance and interpretability.

The application discloses a stage-decoupling-based large language model online inference service delay optimization method and system, belongs to the technical field of artificial intelligence, and solves the large language model online inference service delay problem under the premise of ensuring model accuracy. The method comprises the following steps: a pre-padding scheduling is an adaptive pre-padding task scheduling strategy, the batch processing scale is dynamically adjusted according to the current load condition, the input prompt length and the GPU resource occupation; a dynamic batch size mechanism adaptively adjusts the batch processing scale according to the length distribution of input requests, dynamically combines and allocates tasks; a decoding scheduling is a decoding scheduling algorithm based on a multi-level feedback queue (MLFQ), and the task priority and the time slice allocation are dynamically adjusted; and a KV cache management mechanism is used for automatically exchanging the KV cache to the host memory when the task is idle or waiting for scheduling, and the GPU is used for pre-fetching back when the task is about to be executed. The application is suitable for performance improvement of a large language model for inference service.

The invention provides a large model testing system, method and device and a medium, and relates to the technical field of artificial intelligence, in particular to the technical fields of machine learning, deep learning, large models and the like. The large model to be tested is configured to execute a plurality of rounds of code iterative optimization tasks. The system comprises an execution unit configured to execute a code sample generated by a large model in a current round in a sandbox environment to obtain an execution result; the index unit is configured to calculate a performance improvement index based on the execution result, and the performance improvement index represents code generation performance changes of the large model among a plurality of rounds; the feedback unit is configured to select a target level from a plurality of preset diagnosis levels based on the performance improvement index, and the plurality of diagnosis levels respectively represent different feedback information detail degrees; and feedback information is generated based on the target level and the execution result, and the large model executes the next round of code iteration optimization task based on the feedback information.

The application discloses an employee performance improvement method, device, equipment and medium, the employee performance improvement method comprises the following steps: determining the work post of the employee to be promoted, and identifying the performance evaluation type of the employee to be promoted according to the work post; according to the performance evaluation type, the performance evaluation data of the employee to be promoted is obtained; based on the performance evaluation data, the performance improvement scheme of the employee to be promoted is generated through a large model; the execution feedback data of the employee to be promoted executing the performance improvement scheme is obtained, and based on the execution feedback data, the performance improvement result of the employee to be promoted is obtained. The method dynamically adapts the post characteristics and the performance evaluation type, utilizes the real-time analysis and intelligent decision-making capability of the large model on the multi-source data, generates a personalized performance improvement scheme, so that the employee to be promoted continuously obtains accurate guidance and positive motivation in the work process, and then actively improves the employee performance.

Aspects of the disclosure are directed to enhancing RAG through a structured schema for RAG that captures a wider range of RAG techniques to serve as a foundation for improving performance and an optimization framework for efficiently serving the wider range of RAG techniques based on significant performance variability across RAG workloads. The optimization framework may achieve up to a 2× increase or more in query per second (QPS) per chip compared to alternative RAG frameworks built on LLM-system extensions.

The invention discloses a parameterization optimization method of a mixed-flow water turbine blade and a water turbine, and relates to the technical field of water turbine optimizing.According to the parameterization optimization method, a three-dimensional parameterization model based on initial geometric parameters is constructed, multiple rounds of iteration orthogonal optimization are carried out with design parameters as variables, a search space is dynamically shrunk after each round of optimization, and therefore the optimization efficiency of the mixed-flow water turbine blade is improved. The optimization efficiency and the convergence stability are greatly improved; the problem that a traditional global optimization algorithm is high in calculation cost is avoided, meanwhile, the problem that single-round optimization is prone to falling into local optimum is solved, complex and mutually coupled parameters can be efficiently explored within the limited number of simulation times, and the simulation efficiency is improved. And a closed-loop optimization path which is high in systematicness and can be really applied to engineering is provided for improving the performance of the existing water turbine.

The application provides a real-time materialized view system, the system comprising: an entry module receiving a batch query request and associating a Flink SQL engine based on the batch query request; a Flink execution module for: parsing the batch query request to obtain an SQL command; and in the case where the SQL command meets a preset rule, determining to perform a materialized view for the SQL command; and a materialized view management module performing stream calculation on the SQL command and creating a materialized view based on the result of the stream calculation. The application provides a real-time materialized view method, device, computer equipment and computer readable storage medium. The technical scheme of the application can bring great performance improvement and resource saving in the scheme of stream materialized batch, and solves the problem of low batch calculation efficiency.

The invention discloses an intelligent log analysis system and method based on a three-program separation architecture, and relates to the technical field of log processing, the intelligent log analysis system based on the three-program separation architecture comprises a forwarding program module and an analysis program module; the forwarding program module is used for receiving the original logs and distributing the original logs to the corresponding analysis programs according to feature matching; and the analysis program module is used for analyzing the received log to obtain formatted log data and sending the formatted log data to the target platform. According to the intelligent log analysis system and method based on the three-program separation architecture, intelligent log distribution, dynamic analysis rule optimization and performance improvement based on feature recognition can be achieved.

This invention relates to the field of machine learning, and proposes a method, apparatus, device, and medium for improving the steady-state performance of a worker machine. The method includes: establishing a training set and kernel function corresponding to the worker machine; calculating the Gaussian posterior probability distribution of the worker machine on the training set based on the kernel function to obtain a local prediction model of the worker machine; sending the local prediction model to a server; aggregating the local prediction model based on an aggregation algorithm to obtain a global prediction model; and performing a fusion calculation on the worker machine of the local prediction model and the global prediction model to obtain a prediction model that approximates the objective function. The method disclosed in this invention can guarantee the steady-state performance improvement of each worker machine after fusing local and global predictions; from the perspective of steady-state performance improvement, the mean squared error can be directly used for comparison and quantification of performance improvement; and the performance improvement can be demonstrated through experiments.

The invention provides a civil aircraft air-ground information link efficiency simulation method and device, relates to the technical field of aviation systems, and is used for improving the accuracy of civil aircraft air-ground information link efficiency improvement value calculation. The method mainly comprises the following steps: constructing real communication environment attenuation disturbance in a laboratory environment, and simulating a total disturbance factor for link performance when multiple disturbance factors act together; calculating a link utility value of each type of link according to the total disturbance factor and the basic theoretical value of each type of link; determining a link utility reference value through the link utility values of all types of links; and determining the link efficiency improvement value of each type of link according to the ratio of the link utility value of each type of link to the link utility reference value.

The application provides a design method, system, medium and equipment for performance optimization of pass-through I / O virtualization, comprising: an I / O page table configuration step; a DMA buffer detection step; and a DMA buffer dynamic migration step. The application focuses on performance overhead which is not focused on by the prior art, and the design scheme well solves the overhead. The design scheme of the application is designed around I / O devices, which is more in line with the current hardware development trend compared with the design scheme around the CPU in the past work. The design scheme of the application is not sensed by the guest virtual machine, and the guest virtual machine can enjoy the performance improvement brought by the scheme without modifying the source code.

The invention relates to the technical field of cement grouting, and discloses a whole-process intelligent cement grouting system which comprises a field execution layer, a function enhancement layer and a remote decision-making layer. The field execution layer is a core execution carrier of the system and comprises various grouting operation execution devices and a local control unit; the function enhancement layer is a performance improvement module of the system and comprises a slurry storage blending device, a stratum monitoring device and an energy consumption optimization related device; the remote decision-making layer is a central management and control platform of the system and comprises a data processing server and a multi-role management platform; and the field execution layer is connected with the function enhancement layer through a pipeline and a signal line. Through a three-level architecture, data intercommunication and instruction collaboration of the whole links of slurrying, slurry conveying, slurry storage, slurry preparation and grouting are achieved, the traditional'information isolated island 'is broken through, the response efficiency of each module is improved, slurry temperature control and dynamic concentration adjustment are achieved through the intelligent slurry storage and preparation module, real-time parameter collection is combined with an intelligent grouting station, slurry adaptability is improved, and construction errors are reduced.

The invention discloses a performance improvement optimization design method and system for a movable blade adjustable axial flow fan. The method comprises the steps that firstly, the number of blade profile modeling sections of an outlet guide blade of the movable adjustable axial flow fan is selected; secondly, giving a specific blade profile section two-dimensional curve modeling method for each section; and finally, completing modeling of a three-dimensional model of the outlet guide vane of the dynamically adjustable axial flow fan, and giving the number of the outlet guide vanes. According to the method, under the condition that the overall structure of the movable blade adjustable axial flow fan is kept unchanged, the actual operation efficiency and actual output of the fan can be improved through the method for conducting optimization design on the outlet guide blades of the fan. The performance improvement optimization design method for the movable blade adjustable axial flow fan has the advantages of being low in investment cost, short in investment recovery age limit and the like, and is suitable for large-scale application and popularization in the market.

The application provides a construction method and device of an electricity load feature signature model based on attention, S1, electricity data of each user is collected according to a first set time interval; S2, the electricity data of each user is processed based on attention to obtain an electricity load feature model of each user; and S3, the electricity load feature models of all users are clustered to obtain a global electricity load feature signature module. The application obtains better performance improvement with a small time cost based on a double-layer feature extraction architecture of an attention mechanism, reduces the operation amount, avoids feature loss in a feature area, and thus guarantees the accuracy of the constructed electricity load feature signature model and the value of subsequent analysis and application.

The invention relates to the technical field of artificial intelligence, can be applied to the fields of financial science and technology and medical health, and discloses an agent efficiency improvement method, device, equipment and medium, and the method comprises the steps: obtaining a task historical execution track completed by an agent, and constructing a programmed memory library according to the task historical execution track; obtaining a query description of a new task, and retrieving a matched programmed memory from the programmed memory library according to the query description; and performing decision guidance on the intelligent agent according to the retrieved and matched programmed memory to execute the new task to obtain a task execution result. The processing efficiency of the intelligent agent in processing similar tasks is improved, and the success rate of task processing is also improved.

The invention discloses an electronic commerce management-oriented personalized learning path generation method and system, and the system comprises a multi-source data collection module which is configured to obtain multi-dimensional data through an API interface, database synchronization and user active input, the multi-dimensional data comprises user basic data including age, educational background, e-commerce service age limit, affiliated enterprise type (platform type / brand type / substitute operation type) and post level (specialist / supervisor / manager); through deep fusion of data, technologies and scenes, the accuracy, timeliness and practicability of personalized learning in the e-commerce management field are remarkably improved, powerful support is provided for practitioners to efficiently improve skills and improve operation performance, and meanwhile, a reproducible technical scheme is provided for digital transformation of vocational education.

The embodiment of the invention provides a transaction system performance improvement method and a server, and the method comprises the steps: 101, carrying out the data collection of multiple layers of links of a transaction system, and 102, carrying out the performance analysis of each layer of link through an analyzer based on the collected data; 103, positioning performance bottleneck coordinates of the transaction system according to an analysis result of the analyzer, and dynamically optimizing the transaction system through an optimizer; 104, the performance data of the optimized transaction system is counted, an optimization report is generated, optimizers comprise a method optimizer and a structured statement optimizer, the method optimizer optimizes the method calling sequence and / or service logic in the transaction system, the structured statement optimizer optimizes database operation, and the method calling sequence and / or service logic in the transaction system are / is optimized; the dynamic optimization means that the optimization result of the optimizer is actually operated in the transaction system in a dynamic scheduling and concurrent execution mode. Through the embodiment of the invention, the operation efficiency and stability of the system or the application are improved.

The application discloses an automatic management system for managing an enterprise internal elasticsearch cluster, which comprises ES cluster life cycle management, index management, account management, one-key analysis, alarm subscription and graphical monitoring and control. The ES cluster life cycle management can automatically manage the whole life cycle of the ES cluster without the intervention of operation and maintenance personnel, and can dynamically adjust the number of instances and resource configuration of the cluster according to actual resource requirements. The graphical monitoring and control can analyze and monitor various indexes of the ES through multidimensional indexes, helping operation and maintenance personnel and developers to quickly locate the cluster status, troubleshoot problems and optimize the cluster status. The application lowers the middleware use threshold of developers, enables the developers to enjoy the convenience and performance improvement brought by the management system, further liberates the operation and maintenance personnel, improves the system stability and reduces the daily workload of the operation and maintenance personnel.

A memory device includes memory cells connected to word lines. The memory cells are arranged in memory holes and configured to maintain a threshold voltage corresponding to data states. The memory holes are arranged in rows, which are grouped into strings, each containing a plurality of blocks. A control device is configured to detect a smart-verify programming voltage by programming the memory cells connected to one of the word lines and corresponding to one of the strings in a smart-verify operation that includes a plurality of smart-verify loops, starting with an initial programming voltage. The initial programming voltage is adjusted based on a cycle state of the memory cells.The storage device is also configured to program at least some of the memory cells associated with the word lines in a programming operation using the Smart Verify programming voltage based on the adapted initial programming voltage.