34 results about "Performance results" patented technology

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

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.

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.

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.

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.

The application relates to the technical field of intelligent factory decision-making, and discloses a digital-twin-driven intelligent factory AI intelligent decision-making system, which comprises a data acquisition and preprocessing module, a twin modeling and identification module, an alignment evaluation and credibility module, a strategy simulation and optimization module, a gray online release module and a running evaluation and audit module.The data acquisition and preprocessing module is used for acquiring real-time and historical data of a production line and performing preprocessing; the twin modeling and identification module is used for constructing a twin body and performing parameter identification and uncertainty quantification; the alignment evaluation and credibility module is used for comparing twin body prediction data with real data, generating availability labels and trust scores; the strategy simulation and optimization module is used for generating candidate strategies and risk evidence based on constraints and key performance indicators; the gray online release module is used for screening and releasing strategies in gray according to the trust scores and performance results; the running evaluation and audit module is used for collecting running data and generating an audit package; and the drift detection and update module is used for detecting distribution drift and recalibrating and updating the twin body and the strategy library.The application realizes the reliability, robustness and continuous optimization of intelligent factory production decision-making.

The present invention relates to an apparatus and a method for diagnosing Parkinson's disease. The method for diagnosing Parkinson's disease according to the present embodiment may comprise: a step for presenting one or more athletic ability evaluation missions and one or more non-athletic ability evaluation missions to a user, and collecting, from the user, performance results for the one or more athletic ability evaluation missions and performance results for the one or more non-athletic ability evaluation missions; a step for extracting one or more athletic ability features and one or more non-athletic ability features from the performance results for the one or more athletic ability evaluation missions and the performance results for the one or more non-athletic ability evaluation missions; and a step for diagnosing the presence or absence of Parkinson's disease in the user on the basis of at least one of the results of extracting the one or more athletic ability features and the one or more non-athletic ability features.

A website building system (WBS) hook code optimization system includes a website session repository, an event receiver, a hook code bundler, an optimization determiner, a code creator, and a hook code executor. The website session repository stores historical performance data from a plurality of hook code executions. The event receiver receives an event associated with hook code, and the hook code bundler creates a hook code bundle based on that event. The optimization determiner analyzes the historical data to generate optimization instructions for the bundle. Following these instructions, the code creator generates execution code for an optimized hook code bundle. The hook code executor runs the optimized bundle, generating current performance results for the event. Finally, the executor logs these results back into the repository, updating the historical data to enable subsequent optimizations in a continuous feedback loop.

The invention discloses a method and a device for optimizing a scenario simulation scheme. The method comprises the following steps: S1, acquiring a factor sequence set corresponding to a preset scenario simulation file; s2, performing stratified sampling processing on the factor sequence set to obtain a selected tuple set and a residual tuple set; s3, based on the selection tuple set, performing simulation processing on the scenario simulation file to obtain a selection performance result set; s4, optimizing a preset prediction model based on the selection tuple set and the selection performance result set to obtain an agent model; s5, analyzing and processing the residual tuple set by utilizing the proxy model to obtain a residual performance matrix and Q contribution degree matrixes; and S6, performing optimal analysis on the residual tuple set, the residual performance matrix and the Q contribution degree matrixes to obtain an optimal scheme matrix. According to the method, scheme optimization can be efficiently and accurately completed in a complex high-dimensional scenario simulation scene.

The application discloses a kind of random short fiber composite structure intelligent design methods, belong to learning field.Method includes: defining fiber aspect ratio range in reinforcement learning model, defining mechanical property range in prediction model;Set target performance, find the representative volume element sample closest to target performance in prediction experience pool, compare the sample with target performance, adjust fiber aspect ratio input to reinforcement learning model;Reinforcement learning model selects the position and angle corresponding to the maximum action value mapping function to fill the current fiber, to obtain new representative volume element sample;Mechanical property of fiber and matrix is adjusted to new representative volume element sample using prediction model, compare the performance result of new representative volume element sample with target performance, carry out new round of iteration, until the representative volume element model output that meets target performance is obtained.This scheme can give consideration to calculation efficiency and design accuracy.

The invention discloses an analysis performance monitoring method and device, and the method comprises the steps that a data analysis consumer sends a second request to a network element management system, and the second request is used for requesting the analysis performance of an analysis function based on analysis granularity monitoring; the network element management system sends a first request to a first function network element, the first request is used for triggering monitoring of analysis performance of a first analysis function, and the first analysis function is used for executing data analysis; the first function network element outputs an analysis performance result of the first analysis function according to the first request; and the data analysis consumer obtains an analysis performance result of the first analysis function. According to the method provided by the embodiment of the invention, the analysis performance of the analysis function can be evaluated, and the data analysis execution capability of the analysis function can be determined according to the analysis performance of the analysis function, so that the capability of identifying and solving network problems is improved in a targeted manner, and the network service quality is guaranteed.

A system for training a student to operate an actual machine includes an electronic learning module and a simulation system for simulating operation of the actual machine. An adaptive learning artificial intelligence (ALAI) module receives student performance data to adapt training of the student. The student performance data includes instructor-graded performance results of the student based on the student operating the actual machine, simulation performance results for the student operating a simulated machine in a simulation system that simulates operation of an actual machine and electronic learning content results from an electronic learning module that delivers electronic learning content to a student computing device used by the student. The ALAI module comprises a learner profile module that profiles the student, a training task recommendation module that generates AI-generated recommendations, and an explainability and pedagogical intervention module for displaying on the instructor computing device explanations for the AI-generated recommendations.

Rather than simply providing address translation caches (ATCs) in a global approach that ignores the needs of the clients or providing static ATCs that ignore performance changes, the ATC allocation can be dynamically adjusted. The dynamic allocation optimizes overall system performance, constraining the performance of each individual client when necessary. The dynamic approach involves periodically or strategically determining whether the ATC allocation currently in use results in desired performance. The dynamic reallocation of the ATC maximizes the efficiency and benefit of the ATC by achieving the maximum performance results of the applications and / or workloads.

Rather than simply providing an address translation cache (ATC) with a global approach that ignores client needs or providing a static ATC that ignores performance changes, the ATC allocation can be dynamically adjusted. The dynamic allocation optimizes the overall system performance will constraining the performance of each individual client when necessary. The dynamic approach involves periodically, or strategically, determining whether the currently in use ATC allocation results in the performance desired. The dynamic reallocation of the ATC maximizes the efficiency and benefits of the ATC by achieving maximum performance results for the application and / or workload.

The hospital operation methodology, system, and storage media are based on value chain and data-driven approaches. The methodology includes: constructing a value chain system comprising multiple value chains: front-end settlement chain, procurement payment chain, cost control chain, performance-based compensation chain, and decision analysis chain. Each value chain includes multiple modules. The front-end settlement chain outputs settlement details to the cost control chain based on billing information and budget adjustment instructions. The procurement payment chain outputs procurement expenditures to the cost control chain based on procurement information and procurement optimization instructions. The cost control chain outputs cost accounting results to the performance-based compensation chain based on settlement details, procurement expenditures, and cost control instructions. The performance-based compensation chain outputs performance results to the decision analysis chain based on cost accounting results and performance correction instructions. The decision analysis chain outputs budget adjustment instructions, procurement optimization instructions, cost control instructions, and performance correction instructions to the remaining value chains based on performance results, and outputs analysis and decision-making results.

Provided in the embodiments of the present disclosure are information processing methods, devices, a communication system and a storage medium. An information processing method is executed by a first device, and comprises: acquiring positioning information of at least one second device; determining a performance result of an AI model on the basis of the positioning information; and determining a first operation on the basis of the performance result, wherein the first operation comprises at least one of the following: training the AI model or not training the AI model; and maintaining an AI positioning method or switching from an AI-based positioning method to a non-AI-based positioning method or switching from an AI positioning method to another AI positioning method.

This invention provides a role-based multi-dimensional performance management system and method, relating to the field of enterprise information management technology. The system includes configuring functional permissions and data permission rules based on user roles; generating data filtering conditions based on the data permission rules corresponding to user roles and the organizational dimension field in core business data records to limit the data scope accessible to user roles; obtaining performance appraisal schemes configured for at least one user role; collecting business data from related business modules based on data source identifiers; and calculating and generating performance results according to scoring formulas. When key data affecting performance results changes, a change record is generated, the historical version of the original record is retained, and a confirmation process is triggered. Upon confirmation, the performance results are updated; upon rejection, the performance results before the change are maintained. This invention enables data permission control for different user roles, performance result calculation, and result management after changes to key data.

The invention discloses a mobile terminal-oriented deep learning model resource monitoring system and method. The system comprises an equipment communication layer, a data processing layer, a service logic layer and a user interface layer, data transmission and event notification are carried out among the layers through a message bus; wherein the equipment communication layer is responsible for performing communication connection and command interaction with a mobile terminal; the data processing layer is responsible for analyzing and standardizing monitoring data; the business logic layer is used as a core scheduling center to coordinate task execution and data circulation; the user interface layer displays a system operation state and a model performance result through a visual component, and provides a task control entry; the method has the advantages that instrumentation is not needed, ROM-crossing high compatibility is achieved, a continuous integration process can be integrated, and the performance evaluation efficiency and reliability of the mobile terminal deep learning model are remarkably improved.

The invention discloses a thermal power generating unit performance assessment-oriented abnormal time period data processing method. The method comprises the following steps of S1, data structure expansion; s2, abnormal time period configuration, wherein parameter configuration comprises specific data point selection, time period setting and processing mode and parameter setting; s3, writing and marking data, and after configuration is completed, writing an extended field value into the time sequence database according to a set rule; and S4, performance calculation logic integration, wherein performance calculation tasks for the corresponding sets in the specific assessment time period are triggered in a timed manner. The method at least has the following beneficial effects: by identifying and rejecting or replacing the data in the abnormal time period, the interference of data deviation caused by non-human factors such as equipment failure, control switching and test operation on the performance result is effectively avoided, and unfair scoring on teams or personnel is avoided; and the objectivity and the credibility of performance assessment are obviously improved.

The application discloses a parameter tuning method, which comprises the following steps: acquiring a performance result of a business system running under partial parameters, wherein the partial parameters are part of all parameters of the business system; performing fuzzy processing on the performance result according to multiple fuzzy sets respectively, to obtain multiple membership degrees, wherein the multiple membership degrees and the multiple fuzzy sets correspond to each other; determining multiple groups of fuzzy recommended parameters according to the multiple membership degrees, wherein the multiple membership degrees and the multiple groups of fuzzy recommended parameters correspond to each other; and performing defuzzy processing on the multiple groups of fuzzy recommended parameters, to obtain target recommended parameters, wherein the target recommended parameters are used to update the partial parameters. The application can perform parameter tuning under the condition that the parameters are incomplete, solves the problem that the system performance is not reliable due to the incomplete parameters, and finally recommends a parameter combination meeting the performance requirements of the business system.

The invention discloses a terminal performance determination method and device, a storage medium and electronic equipment. The method comprises the following steps: determining an initial capability fingerprint of a target terminal; performing fault diagnosis on the preset power grid fault scene by using the target terminal to obtain a target diagnosis result; determining a target index value of a performance index of the target terminal based on the target diagnosis result; determining a performance level of the target terminal based on the target index value and the initial capability fingerprint; based on the performance grade and the target index value, determining a performance result of the target terminal, the performance result being used for indicating whether the performance of the target terminal is qualified; and determining the target performance of the target terminal in the preset power grid fault scene based on the initial capability fingerprint, the target index value, the performance grade and the performance result. According to the invention, the technical problem that the performance determination result of the terminal is not accurate in the prior art is solved.

The present invention relates to a generative artificial intelligence agent system and a method for generating same, and, more particularly, to a system and a method for generating and organizing a plurality of agents such that a generative artificial intelligence agent can constantly derive process performance results in response to changing conditions. The generative artificial intelligence agent system and the method for generating same, according to an embodiment of the present invention, comprise: an input unit for receiving an instruction prompt for a task to be performed by generative artificial intelligence; an agent programming unit, which uses the received instruction prompt so as to generate a meta-prompt in order to increase the performance capability of the generative artificial intelligence; a task plan inference unit for selecting a task plan on the basis of the generated meta-prompt; an agent generation unit for generating a task agent and a teamwork agent on the basis of the selected task plan; and a mapping unit for mapping memory agent interface to the generated task agent or omitting same therefrom, and mapping a skill agent interface to the generated task agent.

Provided is a method for establishing a work plan based on real-time interaction between heterogeneous / multiple autonomous objects and a work supervisor. The method for establishing a work plan according to an embodiment of the present invention: establishes a plurality of candidate work plans for resolving an atypical situation when it is determined that the atypical situation has occurred; predicts performance results for the candidate work plans by performing a 3D simulation on each of the established candidate work plans; and selects, as an optimal work plan, a candidate work plan selected after a work supervisor checks the predicted candidate work plan performance results. Accordingly, the work supervisor, which is a person, supplements the processing ability of autonomous objects due to an automated work plan showing a limit in an atypical situation, thereby being able to improve productivity and service quality by quickly and smoothly resolving the situation.

The invention discloses a co-entropy ceramic protective coating sintering temperature and pneumatic heat load cooperative control system, and relates to the technical field of sintering temperature control. The information acquisition module is used for acquiring previous sintering information, the previous sintering information comprises a sintering temperature and a performance result, and the performance result is the performance of the pneumatic heat load. In the whole process, reference directories under different basic information are formulated by considering coating material and structure information and environment information, and the sintering temperature is regulated and controlled according to aerodynamic thermal loads required by the coating material structure and environment manufacturing; and the compactness, interface bonding and residual stress demand distribution of the coating are realized through a controllable sintering temperature curve at a preparation end, so that the aerodynamic thermal load of the coating during use is ensured, and the response characteristic of the coating to the aerodynamic thermal load during use is obtained within the user demand by designing and optimizing the sintering temperature. And a closed-loop design for optimizing service end response by manufacturing end control is embodied.

The application 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. The method quantitatively evaluates historical scheduling records, allocates historical strategies to experience groups of different levels according to scheduling effectiveness indexes and strategy trust levels, and forms a structured strategy library. According to a target performance level defined in a dynamic behavior model of the data center, a serialized scheduling scheme achieving the target is calculated, and candidate strategies are selected from the structured strategy library. An adaptive correction of the serialized scheduling scheme and the candidate strategies is carried out by using a fusion decision method to generate a final scheduling strategy. The final scheduling strategy is executed, control commands are generated in combination with real-time resource data to implement resource adjustment, and actual performance results are recorded. By comparing the deviation of the actual performance results from the target performance level, the deviation root cause is diagnosed, the experience group content is revised, and the structured strategy library is optimized.

Method for generating feedback for a container loading process, wherein the method comprises: Store, in a memory (154) of a computing device, a set of load stage definitions that define sequential load stages of the container loading process, each stage definition including: i) an intermediate performance target and ii) a stage duration; at a processor (150) of the computer device, in response to the arrival of a container (112) at the loading ramp (108-2), receiving a task definition that defines a performance target for the loading process; on the processor (150), retrieving the loading stages in sequence and, for each stage: i) Receiving sensor data depicting the interior of the container (112) from a sensor array (136) located at the loading ramp (108-2), ii) Determining a performance measurement of the container loading process based on the sensor data, iii) Comparing the performance measurement with the intermediate performance target corresponding to the stage, and iv) based on the comparison, generating an alarm for transmission to a client computing device (124).