135 results about "Performance results" patented technology

The invention relates to the technical field of smart factory decision-making, and discloses a digital twin-driven smart factory AI intelligent decision-making system, which comprises a data acquisition and preprocessing module used for acquiring and preprocessing real-time and historical data of a production line; the twinborn modeling identification module is used for constructing a twinborn body and carrying out parameter identification and uncertainty quantification; the alignment evaluation credible module is used for comparing twin prediction data with real data and generating availability marks and trust scores; the strategy simulation optimization module is used for generating candidate strategies and risk evidences based on the constraints and the key performance indicators; the grayscale online publishing module is used for screening and grayscale publishing a strategy according to the trust score and the performance result; the operation evaluation auditing module is used for collecting operation data and generating an auditing packet; and the drifting detection updating module is used for detecting distribution drifting and recalibrating and updating the twinborn body and the strategy library. According to the invention, the reliability, robustness and continuous optimization of the production decision of the smart factory are realized.

The invention discloses an intelligent production and quality monitoring system and method for premixed concrete, and particularly relates to the technical field of concrete production quality monitoring, and the method comprises the following steps: constructing an initial control parameter set; environment data is collected and acts on the initial control parameter set to generate an applicable control parameter set; performing mixing production based on the applicable control parameter set, collecting process behavior data, and inputting the process behavior data into a machine learning model to predict compressive strength and setting time; comparing the prediction result with a performance target, if the tolerance is exceeded, determining that the quality is low and correcting control parameters, and if the tolerance is not exceeded, determining that the quality is high and allowing discharging; according to the method, the initial control parameter set is constructed by using the historical weighing deviation and the strength actual measurement result, the applicable control parameter set is dynamically generated in combination with the real-time environment data, the performance result is predicted through the model and compared with the preset target, and a quality judgment and parameter correction closed loop is formed. And the parameter rationality, the environmental adaptability and the quality control intelligent level of concrete production are improved.

An apparatus comprises at least one processing device comprising a processor coupled to a memory. The at least one processing device is configured to simulate a first data set stored in a first database to create a second data set, wherein the second data set maintains one or more query impacting characteristics of the first data set such that performing a query on the second data set yields a performance result similar to another performance result that would occur when the query is performed on the first data set. The at least one processing device is further configured to load the second data set into a second database, and then perform the query on the second data set stored in the second database to obtain the performance result.

Disclosed are embodiments for facilitating autonomous vehicle simulated mileage data collection-guided operational design domain testing and test generation. In some aspects, an embodiment includes receiving performance results corresponding to simulated mileage accumulation of simulated autonomous vehicles (AVs); determining, from the performance results, performance metrics and corresponding confidence intervals for the performance metrics; identifying at least one of the performance metrics that does not satisfy a performance metric threshold or at least one of the corresponding confidence intervals that does not satisfy a confidence interval threshold; determining an identified domain corresponding to the at least one of the performance metrics or the at least one of the corresponding confidence intervals; and causing at least one of on-road supervised driving of one or more AVs to be initiated or one or more synthetic tests to be generated in accordance with the identified domain.

The invention discloses a task scheduling method and device, a storage medium and electronic equipment, and relates to the field of cloud computing. The method comprises the steps that a task model is constructed according to a target training task, and the task model is used for representing attributes and resource requirements of the target training task; on the basis of the task model, predicting performance results when the target training task is executed on the N computational nodes to obtain N performance results, N being an integer greater than 1, and the performance results comprising execution efficiency and resource utilization rate of the target training task on the computational nodes; filtering the N computing nodes according to the N performance results and the network environment information to obtain S first computing nodes, and determining a target node from the S first computing nodes; and executing the target training task through the target node. The technical problem of low deep learning training task scheduling efficiency in the prior art is solved.

The invention relates to the technical field of data center resource scheduling, and discloses an intelligent data center optimization method based on distributed GPU computing power scheduling. According to the method, historical scheduling records are quantitatively evaluated, historical strategies are distributed to experience groups of different levels according to scheduling effect indexes and strategy trust levels, and a structured strategy library is formed. And for a target performance level defined in the dynamic behavior model of the data center, calculating a serialized scheduling scheme for achieving a target, and selecting candidate strategies from the structured strategy library. And carrying out adaptive correction on the serialized scheduling scheme and the candidate strategy by adopting a fusion decision-making method to generate a final scheduling strategy. And executing the final scheduling strategy, generating a control command in combination with the real-time resource data to implement resource adjustment, and recording an actual performance result. By comparing the deviation between the actual performance result and the target performance level, the deviation source is diagnosed, the experience grouping content is revised, and the structured strategy library is optimized.

An axial flow compressor rapid prediction method based on self-adaptive correction comprises the following steps that a one-dimensional performance rapid prediction model of an axial flow compressor is constructed based on compressor geometric parameters and a one-dimensional average pitch diameter theory; based on a one-dimensional performance rapid prediction model, taking a relative error between a calculation result and experimental performance as an optimization target, introducing a correction factor into a lag angle and total pressure loss empirical model as an optimization parameter, and carrying out optimization of the correction factor; establishing a mapping relation among the compressor inlet speed coefficient, the inlet relative Mach number and the optimal correction factor under different working conditions; based on an existing compressor experimental performance result, a correction factor optimization method is adopted, a mapping relation between the multiple compressor inlet working condition parameters and the optimal correction factor is constructed, and a data set is formed; a BP neural network is utilized to train a data set, so that the data set has generalization ability; and calling the trained BP neural network in the one-dimensional performance rapid prediction model to realize adaptive correction of the empirical model and rapid prediction of the performance.

The invention is suitable for the technical field of automobile commutator reinforcing rings, and particularly relates to an automobile commutator reinforcing ring production control method and equipment, and the method comprises the steps: obtaining all to-be-processed order information in a reinforcing ring order management system in real time; order analysis is carried out on the to-be-processed order information to obtain priority processing information; performing pre-production analysis based on the priority processing information to obtain production line model information of the reinforcing ring in the production workshop; performing performance simulation analysis according to the production line model information to obtain performance result information; performing control parameter optimization on the performance result information to obtain production control information; wherein the production control information is used for controlling the commutator reinforcing ring production equipment to produce the reinforcing ring. According to the automobile commutator reinforcing ring production control method provided by the invention, the performance of the automobile commutator reinforcing ring can be predicted, so that the utilization rate and the production speed of automobile commutator reinforcing ring production equipment are increased, and the structural stability and durability are improved.

The invention discloses a pavement anti-slide performance prediction method and system based on hyperspectral image texture features. The method comprises the following steps: acquiring an airborne hyperspectral image of a pavement; performing wavelet transformation on each wave band of the hyperspectral image to obtain a high-frequency component, calculating Shannon entropy and energy on the high-frequency component, and further constructing a wave band score by using the Shannon entropy and the energy to screen out a spectral characteristic wave band reflecting road surface characteristics; screening texture parameters based on the texture features of the spectral feature wavebands to form a texture parameter data set; and finally, inputting the texture parameter data set into the CGS-XGBoost model for training, and predicting anti-skid performance results of other unknown pavements. According to the technical scheme, the limitation of existing evaluation indexes on road surface texture characterization is expanded, the anti-skid performance is predicted from the perspective of macroscopic image texture, the defect that the measurement area of a traditional method is small is overcome, and the accuracy and universality of road surface anti-skid performance prediction are remarkably improved.

The invention belongs to the technical field of supply chains, and provides a supply chain intelligent contract fulfillment method and device based on a block chain. The method comprises the steps of obtaining electronic contract data in a supply chain and a preset data verification mode, wherein the electronic contract data comprises a to-be-performed electronic contract, a contract ID and a first contract hash value; clause data are extracted from the electronic contract data, and a to-be-performed task is generated according to the clause data and the contract ID; generating a to-be-performed task smart contract in the block chain network according to the to-be-performed task and the data verification mode; and obtaining a performance confirmation message, executing the to-be-performed task through the to-be-performed task smart contract, and outputting a performance result. According to the invention, the performance condition of the electronic contract in the supply chain is automatically verified through the intelligent contract, the effectiveness of the performance execution result is ensured, and the verification efficiency of the performance result is improved.

The invention relates to a talent data analysis method and device based on multi-dimensional data fusion and electronic equipment, and the method comprises the steps: carrying out the clustering of multi-dimensional talent data, obtaining a data set, extracting target data from the data set, and obtaining the performance result of the target data; generating an internal driving force result of the target data, performing first-order motivation correlation analysis based on the performance result and the internal driving force result to obtain a first-order motivation analysis result, and generating a second-order motivation analysis result corresponding to the target data according to the performance result and the performance level average score; and generating an evaluation result of the multi-dimensional talent data based on the first-order motivation analysis result and the second-order motivation analysis result. Quantitative analysis is carried out through value output withdrawn from a first-order motivation analysis result and value change withdrawn from a second-order motivation analysis result, a complete quantitative evaluation model is provided, multi-dimensional and multi-level analysis can be carried out on talent data on the whole, and the limitation of talent data analysis in the prior art is avoided.

To provide a gaming machine that can enhance the excitement of pre-announcement sequences. [Solution] A gaming machine that allows a player to select an option from a set of selectable options, and in which case a variation performance can be executed in a manner corresponding to the selected option, wherein the options include a pre-notification performance that notifies the performance content to be executed before the performance result of the variation performance is displayed, and if the pre-notification performance is selected, a performance content pre-notification performance can be executed that notifies one or more performance contents from the performance content to be executed.

To provide a game machine capable of improving interest in a game.SOLUTION: A plurality of kinds of expectation degree performances suggesting an execution expectation degree of a special game can be executed during a variation performance, an emphasis performance emphasizing the expectation degree performance can be executed, and the number of kinds of the emphasis performances executed during the variation performance resulting in a winning result in a first game state tends to be larger than the number of kinds of the emphasis performances executed during the variation performance resulting in a losing result in the first game state.SELECTED DRAWING: Figure 132

Provided are a logistics robot simulating system and method, the system comprising: a performance unit for performing product production simulation using logistics robots; and an operation control unit, which determines a reference operation quantity of logistics robots on the basis of performance results of the product production simulation performed by the performance unit on the basis of a preset initial operation quantity of logistics robots, changes the reference operation quantity according to a preset change rule, and determines an optimal operation quantity of logistics robots on the basis of additional performance results of the product production simulation additionally performed by the performance unit on the basis of the changed reference operation quantity.

The application discloses a low-resistance design method for a large electric pump impeller flow passage component, and steps include: determining design parameters; adopting two-section quadratic functions to respectively define the cross-section equivalent radius variation law of front and rear flow channels, and guaranteeing smooth and continuous profile lines; setting front and rear linear segments tangent to corresponding flow channel profile lines on the hub side; defining two independent dimensionless axial length coefficients and constructing multiple groups of design parameter combinations; fixing flow channel profile line parameters to carry out simulation tests, and selecting an optimal scheme based on performance results. Through the two independent dimensionless design variables, the independent parameterization regulation and control of the blade axial form are realized, the blindness of experience design is got rid of, and the different flow characteristics of the hub and the rim can be adapted. The dimensionless system has cross-size universality, is convenient for forming a standardized design process, and at the same time, provides sufficient adjustment space for multi-target optimization, can consider multiple performance indexes such as head, efficiency, shaft power and the like, and realizes low-resistance optimization of the flow passage.

The invention discloses a load testing method and device, equipment, a storage medium and a product. A hardware channel of a PCIe dummy load card is tested to obtain a hardware test result, and the PCIe dummy load card refers to a card for simulating heating values under different loads during a heat dissipation capability test of a liquid cooling system based on a PCIe interface; testing the control instruction of the PCIe dummy load card to obtain an instruction test result; testing the heating parameter of the PCIe dummy load card to obtain a heating test result; and determining whether the performance of the PCIe dummy load card reaches the standard or not according to the hardware test result, the instruction test result and the heating test result, and obtaining a performance result. According to the method, the PCIe dummy load card is adopted to simulate the PCIe card to generate heat to complete the performance test, and a more accurate dummy load card performance result is obtained through the three tests, so that more accurate and effective PCIe heating data is obtained, and the development speed of the heat dissipation capability of the liquid cooling system is increased.

The invention discloses a slurry formula performance prediction and grade optimization method for horizontal directional drilling. The method comprises the following steps: S1, slurry experiment and data acquisition based on an HDD process; s2, establishing an HDD slurry viscosity nonlinear regression model; s3, defining a slurry performance grade for HDD construction; s4, performing Monte Carlo simulation and formula space global performance scanning; s5, visualizing the result of the step S4; S6, performing HDD field formula optimization and decision making; the method provides a standardized process and decision basis for slurry preparation. The initiative'performance grade distribution cloud picture 'enables an engineer to see all possible performance results of the formula clearly at one eye, and blind trial and error are thoroughly avoided. The lowest-cost formula meeting the requirement can be quickly locked, and the material cost is saved; meanwhile, field debugging time is shortened, and the project progress is accelerated.

The invention relates to the technical field of transmission gear shifting testing, and discloses a transmission static gear selecting and shifting performance testing device and a testing method thereof.The transmission static gear selecting and shifting performance testing device comprises a gear selecting assembly, a gear shifting assembly, a detecting assembly and an adjusting assembly.The gear selecting assembly comprises a gear selecting mechanical arm and a first motor, and the gear selecting mechanical arm is fixedly connected with a rotating shaft of the first motor in a sleeving mode; the gear shifting assembly comprises a gear shifting mechanical arm and a second motor, the gear shifting mechanical arm comprises two connecting ends, one end of the gear shifting mechanical arm is fixedly connected with a rotating shaft of the second motor in a sleeved mode, and the other end of the gear shifting mechanical arm is fixedly connected with a gear shifting shaft in the transmission in a sleeved mode. The detection assembly comprises a first encoder, a second encoder, a first force sensor and a second force sensor, the first force sensor and the second force sensor are connected with the gear selecting mechanical arm and the gear shifting mechanical arm respectively, and the adjusting assembly comprises two adjusting seats which are connected with the gear selecting mechanical arm and the gear shifting mechanical arm respectively. The static gear selecting and shifting performance testing device is simple in structure, high in adaptability and low in cost, and comprehensive and objective static gear selecting and shifting performance results of the transmission can be obtained.

To provide a game machine capable of executing a highly amusing performance.SOLUTION: Performance control means can execute a first performance, a second performance, a third performance, and a winning reporting performance in turn. The second performance can suggest that a possibility that a performance result of the third performance can become a second performance result is a first degree, the first performance can suggest that a possibility of suggestion by the second performance is changed from the first degree to a second degree higher than the first degree, and there are a first case in which the second performance is executed after execution of the first performance and a second case in which the second performance is executed without executing the first performance. The second performance for suggesting that a possibility of a second performance result is the first degree is executed in the second case, and the second performance for suggesting that a possibility of a second performance result is the second degree higher than the first degree is executed in the first case.SELECTED DRAWING: Figure 12

The invention discloses a performance accounting system and method based on remote office, and belongs to the technical field of remote office systems. The system comprises five core modules: an intelligent work baseline setting module, a multi-dimensional output tracking module, a real-time cooperation value accounting module, an elastic performance calibration module, and a visual performance board and growth module. The functions of the modules are as follows: the intelligent working baseline setting module generates a dynamic performance baseline and provides an accounting basic target; the multi-dimensional output tracking module collects and quantifies employee output data and calculates an output efficiency index; the real-time collaboration value accounting module splits the collaboration chain value and calculates individual collaboration quality indexes; the elastic performance calibration module dynamically adjusts a calibration rule and outputs an environment correction coefficient; and the visual performance board and growth module records loop data, and outputs a final performance result and attribution analysis. The method has the advantages that closed-loop linkage of modules can be realized, dynamic threshold judgment is realized, and remote office scenes can be accurately adapted.

Investment portfolios are evaluated based on thematic concepts. Natural language processing may be employed to determine thematic mentions of concepts in publications. Thematic scores associated with the organizations may be generated based on the thematic mentions and publication weights. Thematic models may be determined based on the organizations and performance outcomes such that each thematic model may predict a performance outcome based on the thematic scores associated with the organizations. Thematic models may be employed to predict the predicted outcomes for the organizations based on the thematic scores such that the predicted outcomes may be displayed in a report.

The application discloses a co-entropy ceramic protective coating sintering temperature and aerodynamic thermal load cooperative control system and relates to the technical field of sintering temperature control. The system comprises an information acquisition module: acquiring previous sintering information, wherein the previous sintering information comprises sintering temperature and performance results, and the performance results are the performance of aerodynamic thermal load. The application considers coating material and structure information and environmental information to formulate a reference directory under different basic information in the whole process, and the sintering temperature is regulated according to the aerodynamic thermal load of the coating material structure and environmental manufacturing demand, and then the density, interface bonding and residual stress distribution of the coating are realized through the controllable sintering temperature curve at the preparation end, so that the aerodynamic thermal load of the coating in use is ensured, the sintering temperature is designed and optimized, the response characteristics of the coating to the aerodynamic thermal load in use are within the user demand, and the closed-loop design of optimizing the response of the service end by controlling the manufacturing end is embodied.

The invention discloses a display method, a data processing method and device, computer equipment and a storage medium, which can be applied to various competition scenes such as literature, sports and entertainment, and the display method comprises the following steps: in response to a setting operation for a performance parameter setting interface, obtaining a target course, performance parameters and a sorting rule corresponding to the performance parameters of a target competition; in response to an input operation for the performance data input interface, obtaining performance result data of the competitors; sending the target course, the performance parameters, the sorting rules corresponding to the performance parameters and the performance result data of the target event to a server, so that the server generates a performance result list based on the target course, the performance parameters, the sorting rules corresponding to the performance parameters and the performance result data of the target event; according to the embodiment of the invention, the performance result list sent by the server is obtained, and the performance result list is displayed on the performance result display interface, so that the performance parameters and the sorting rule of the match are flexibly configured, and the user experience is improved.

The application discloses a kind of performance result optimization method and system of deep learning model training, method includes: in response to the end of deep learning model training, the performance result index of deep learning model training is acquired and the debugging parameter of deep learning model training is acquired;Search to debugging parameter and from the performance result index corresponding to debugging parameter in search result, the performance result index corresponding to current search is compared with the performance result index corresponding to last search, wherein the performance result index corresponding to last search includes the performance result index of deep learning model training;In response to the comparison result meets preset condition, stop searching to debugging parameter and determine the final parameter of debugging parameter;Based on each final parameter determined, deep learning model training is carried out again to obtain the performance result after optimization.Through the scheme of the application, performance evaluation cycle is shortened, and performance evaluation efficiency is improved.

A computer-implemented method for assessing autonomous vehicle performance comprising receiving, at an input, performance data of at least one autonomous driving run, the performance data comprising at least one time series of perception errors and at least one time series of driving performance results; and generating, at a rendering component, rendering data for rendering a graphical user interface, the graphical user interface for visualizing the performance data and comprising: a perception error timeline, and a driving assessment timeline, wherein the timelines are aligned in time, and divided into multiple time steps of the at least one driving run, wherein, for each time step: the perception timeline comprises a visual indication of whether a perception error occurred at that time step, and the driving assessment timeline comprises a visual indication of driving performance at that time step.

The present disclosure provides a performance monitoring method and device, electronic equipment and storage medium, and relates to the technical field of information security. The method comprises: adding performance monitoring annotations and scene annotations to a business interface in a to-be-monitored scene; obtaining object granularity performance data of the business interface for each business initiation object based on the performance monitoring annotations during processing of a business request by the business interface; and performing statistics on the object granularity performance data of the business interface based on the scene annotations to obtain scene performance result values for each business initiation object in the to-be-monitored scene. The method can distinguish and monitor the performance of the business interface in processing the business request according to the granularity of the business initiation object, and can also automatically statistically integrate the performance data of all business interfaces in a to-be-monitored scene, thereby realizing a flexible scene monitoring scheme.

This application discloses a task scheduling method, apparatus, storage medium, and electronic device, relating to the field of cloud computing. The method includes: constructing a task model based on a target training task, wherein the task model characterizes the attributes and resource requirements of the target training task; predicting the performance results of the target training task executed on N computing nodes based on the task model, obtaining N performance results, where N is an integer greater than 1, and the performance results include the execution efficiency and resource utilization of the target training task on the computing nodes; filtering the N computing nodes according to the N performance results and network environment information to obtain S first computing nodes, and determining a target node from the S first computing nodes; and executing the target training task through the target node. This application solves the technical problem of low scheduling efficiency for deep learning training tasks in the prior art.

Provided are a method and apparatus for monitoring a model in beam management by using artificial intelligence and machine learning. The method may include: in relation to a reference signal configured for a terminal, receiving second reference signal resource set configuration information of the reference signal for monitoring an AI / ML model; on the basis of the second reference signal resource set configuration information, measuring signal strength or signal quality for the reference signal; and reporting the performance result of the AI / ML model by comparing a measured value of the reference signal with a predicted value of the reference signal inferred via the AI / ML model.

The application discloses a chip performance analysis method, device and equipment and a storage medium. The method comprises the following steps: obtaining input data of a chip to be analyzed through a parameter analysis layer, analyzing the input data, and obtaining analysis parameters and timing data; performing performance index calculation according to the analysis parameters and the timing data through an operation layer, and obtaining a calculation result; and generating a performance analysis report of the chip to be analyzed according to the analysis parameters and the calculation result through a report layer. The input data is collected and analyzed through the parameter analysis layer, the output analysis parameters and the timing data are standardized, and standardized and reliable original data support is provided for performance calculation. The operation layer combines the configuration parameters and the timing data, outputs two types of performance results of a full window and a slice, considers the overall and segmented performance analysis of the chip, and accurately locates performance fluctuation. The report layer integrates various parameters and calculation data, automatically generates a complete performance analysis report, realizes regular presentation of analysis results, and facilitates reference for checking, tracing and optimization.