97 results about "Performance requirement" patented technology

The invention relates to a method, a system and equipment for predicting creep stress at a joint of a concrete structure and a medium. The method comprises the following steps: testing a concrete structure material and a concrete structure joint filling material, and constructing a joint material performance database based on test data; constructing a multi-scale mechanical model comprising a macrostructure model and a microcosmic interface model based on the seam material performance database; constructing an interface constitutive relationship based on the seam material performance database and the multi-scale mechanical model; and predicting the creep stress at the joint of the concrete structure based on the material performance database at the joint, the multi-scale mechanical model and the interface constitutive relationship. The method can fully consider the sudden change of the performance of the material at the joint and the interface effect, provides reliable technical support for the safety evaluation and maintenance of the concrete structure, and is particularly suitable for engineering application of large-span and high-rise buildings and the like with higher requirements on the long-term performance of the structure.

The invention relates to an adaptive energy consumption control method, system and device based on a computing power chip and a medium. The method comprises the following steps: constructing an optimal prediction regression model based on multi-channel hardware state indexes of the computing power chip at different frequencies to predict performance data of the computing power chip at different frequencies; acquiring to-be-predicted data of the computing power chip under the current working load, predicting the to-be-predicted data based on the optimal prediction regression model, and acquiring a target optimal frequency meeting a preset performance index condition, the target optimal frequency corresponding to optimal energy consumption; and performing adaptive adjustment on the working frequency of the computing power chip under the current working load based on the target optimal frequency. According to the method and the device, the target optimal frequency which meets the performance requirement and is lowest in chip energy consumption can be accurately selected, the energy consumption is reduced while the chip performance is ensured, and the problems of low adjustment precision and high energy consumption of frequency adjustment of a computing chip in different working load performances are solved.

The invention relates to a vertical structure LED wafer thickness detection method and system and computer equipment. The method comprises the steps that a sample wafer is divided into a plurality of areas; calculating the initial wafer thickness corresponding to each region based on the motor movement data, obtaining the real wafer thickness of each region, training the initial thickness error optimization model based on the initial wafer thickness and the real wafer thickness, and obtaining a target thickness error optimization model; obtaining the final wafer thickness of each area of the to-be-measured wafer through the target thickness error optimization model; and calculating a thickness change value of the to-be-detected wafer based on each final wafer thickness, comparing the thickness change value and each final wafer thickness with a corresponding preset threshold value, and judging whether the to-be-detected wafer meets performance requirements or not based on a comparison result. According to the invention, the abnormal problems of scratch, fragment and pollution of products caused by manual measurement can be avoided, and the product yield can be improved.

The invention discloses a thermodynamic system parameter distribution method and device, and the method comprises the steps: S1, analyzing a block definition diagram and an internal block diagram from a SysML model to obtain a module structure description, a module value attribute, an interface specification of an equipment unit and a thermodynamic parameter description, and generating an analysis result, the analysis result comprises equipment unit port and connector information. And S2, based on the analysis result, mapping the equipment module to a simulation software element through name matching, and generating a steady-state thermal simulation model. And S3, associating the module value attributes with element attributes of the steady-state thermal simulation model through a parameter mapping mechanism, and generating attribute mapping data. And S4, generating a complementation parameter list according to the external design value and the heat balance demand. Therefore, the efficiency and accuracy of parameter allocation are improved, manual intervention is reduced, and it is ensured that the allocation result meets the requirements of system performance.

The invention discloses a request batch processing method and electronic equipment, and relates to the technical field of computers. When a batch processing object meets a first batch processing optimization condition, the current comprehensive error of the batch processing object is determined according to the performance requirement of the batch processing object in the current application scene and the current actual performance; when a first batch processing optimization condition is satisfied, carrying out first adjustment on the size of the current batch to obtain a first target batch size, and when a second batch processing optimization condition is satisfied, carrying out second adjustment on the size of the current batch according to the length change information of the request waiting queue to obtain a second target batch size; and finally, performing batch processing on the to-be-processed request according to the first target batch size or the second target batch size. The size of the current batch is adjusted according to the performance requirement of the application scene and the current actual performance expression or the length change information of the request waiting queue, so that the size of the target batch can adapt to the current application scene and the dynamically changing load, and the waste of hardware resources and the increase of user request response delay are avoided.

The invention relates to a code generation method and device, electronic equipment and a storage medium. Comprising the steps of obtaining a programming problem data set and a code generated by a target model, and obtaining a data set with rewards; based on an MSE regression training strategy and a comparison loss training strategy, training a reward model by using the data set with the reward to obtain a code reward model; and obtaining programming problem training data, sorting the programming problem training data by using the code reward model to obtain sorted training data, and obtaining a final reward according to the sorted training data, a reward model score output by the code reward model and a preset unit test score, and reinforcement learning training is carried out on a preset language model to obtain a code generation model, so that codes meeting preset performance requirements are generated through the code generation model. Therefore, the problems that in the prior art, the model reasoning cost is high, and it is difficult to capture the intermediate state and partial correctness in the code execution process to evaluate the code quality in a fine-grained mode are solved.

This application is directed to resource management in a storage system that includes a non-volatile memory and a collection of resources having one or more processing cores. The storage system allocates a first subset of resources to process queues of I / O access operations requested by a host device. The first subset of resources includes a storage controller corresponding to a subset of processing cores. The storage system obtains a first request for adjusting resource allocation of the storage device, and the first request includes a target performance requirement for processing the queues of I / O access operations. The storage system determines that the target performance requirement can be satisfied by allocation of at least a target subset of resources. In response to the first request and based on the target subset of resources, a second subset of resources is allocated for processing the one or more queues of I / O access operations.

The invention provides a composite material formula process optimization method based on a performance demand, and relates to the technical field of data processing, the method comprises the following steps: obtaining matrix material information and a performance demand, and determining a missing performance demand and a missing degree thereof; determining a reinforcing material configuration relation list; configuring formula test variables; constructing a test space, obtaining a test data set, analyzing and processing the test data set, and constructing a target data set; and optimizing the target data set to obtain a formula process optimization result. The technical problem that the performance of the composite material cannot meet the required performance due to the lack of performance fusion analysis of the reinforcing material in the prior art can be solved, after the missing performance analysis is performed on the base material, the formula processing technology of the reinforcing material is optimized and analyzed, and the performance of the composite material is improved. The technical effects that the formula process accuracy of the composite material is improved, and then the adaptation degree of the performance of the composite material and the required performance is guaranteed are achieved.

The present application relates to the technical field of computers. Provided are a frequency adjustment method and an electronic device, which make the frequency of a GPU meet GPU performance requirements or functional requirements, thereby ensuring the usage experience of a user. In the method, a load that matches a scenario corresponding to the currently rendered image can be determined from among a predicted load and a historical load, and the frequency of the GPU is then determined on the basis of the load, wherein the predicted load can be obtained on the basis of rendering information of the currently rendered image frame. A scenario of an image frame can be determined on the basis of an application state or a system state. The greater the load matching the scenario, the higher the determined GPU frequency, and thus a GPU can process more loads, thereby improving performance, reducing stuttering during image frame display, and improving the usage experience of a user; and the smaller the load matching the scenario, the lower the determined GPU frequency, and thus loads to be processed by the GPU are minimized, thereby reducing the power consumption of the GPU.

The invention belongs to the technical field of material science, and discloses a shape memory alloy component prediction method and a related device, and the method comprises the steps: processing a target stress-strain curve of a shape memory alloy through a pre-trained WCGAN-GP network, and obtaining a plurality of groups of phase field parameters; processing the multiple groups of phase field parameters through a trained LSTM model to obtain a predicted stress-strain curve corresponding to each group of phase field parameters; calculating the similarity between the target stress-strain curve and each predicted stress-strain curve, screening the predicted stress-strain curve of which the similarity with the target stress-strain curve meets the requirement, and taking the predicted stress-strain curve of which the similarity meets the requirement as the optimal predicted stress-strain curve. And taking a phase field parameter corresponding to the optimal prediction stress-strain curve as a final component of the shape memory alloy. According to the method, the components of the shape memory alloy can be predicted according to the performance requirements of the shape memory alloy, and the reverse design capability urgently needed for designing a new material is achieved.

The invention discloses a PID (Proportion Integration Differentiation) controller parameter setting method and electronic equipment. The method comprises the following steps: acquiring historical operation data of a controlled object; determining a target model of the controlled object and target model parameters of the target model according to the historical operation data; determining an initial value of a PID controller parameter based on the target model parameter; calculating a performance index of the PID controller based on the historical operation data, the target model and the current value of the PID controller parameter; determining an objective function of the PID controller according to the performance index; and based on the initial value of the PID controller parameter and the target function, carrying out multiple times of iterative optimization on the PID controller parameter to obtain an optimized target PID controller parameter. According to the method, parameter setting can be automatically realized without depending on expert experience, performance-oriented parameter setting is realized, the setting precision and efficiency are improved, different performance requirements can be self-adapted, and parameter setting optimized as required is realized.

The invention relates to training-free error compensation for compressed large language models. A Large Language Model (LLM) learns, understands and generates human-like text through machine learning, and thus is very powerful when used for various language-based tasks (e.g., text summarization, translation, and content generation). However, in order to provide superior performance, LLM typically has a considerable model scale and requires higher inference costs. In order to reduce the size and execution costs of LLMs, a method has been developed for specialized compression of LLMs. However, compared with an uncompressed model, most of the existing methods have the advantages that the accuracy is obviously reduced, or the training time is longer, and the adaptability is generally limited by a limited compression format range supported by hardware. According to the method, error compensation is provided for the compressed LLM in a training-free manner, so that flexibility is provided for various performance requirements.

An encoder includes circuitry and memory coupled to the circuitry. In operation, the circuitry determines whether a determined relationship between a total number of one or more layer groups and a total number of one or more parameters is satisfied. The one or more layer groups each include at least one output layer, and the one or more parameters each indicate a performance requirement for a decoder. When the determined relationship is satisfied, the circuitry determines, using a determined method, a correspondence relationship for associating any of the one or more parameters with each of the one or more layer groups, and omits storing information indicating the correspondence relationship in a common header shared between layers in the one or more layer groups.

The invention discloses a balancing method for an electric tool. The balancing method comprises the steps that 1, an eccentric block is decomposed into a plurality of modules capable of quantifying structural parameters; step 2, according to geometric characteristics of the modules, constructing a mass function and / or a rotational inertia function of the corresponding module and coordinate parameters of the corresponding mass center relative to the rotation center; 3, defining boundary conditions of module structure parameters based on the minimum size threshold value of each module and the correlation size constraint between the modules; 4, constructing a dynamic balance equation based on the mass function and / or the rotational inertia function and the coordinate parameters; 5, constructing an eccentric block total mass equation based on the mass function, and / or constructing an eccentric block total inertia equation based on the rotational inertia function; and step 6, seeking a specified optimal solution meeting the dynamic performance requirement. According to the method, accurate balancing of the electric tool and light weight of the eccentric block are achieved, the problems that a traditional experience trial-and-error method is low in efficiency, poor in precision and difficult in quality optimization are solved, and the design efficiency and the equipment performance are improved.

This disclosure provides a method for determining the frequency of a storage device. The method includes: determining a target performance requirement for the storage device of an electronic device, wherein the storage device performance includes a first performance and a second performance, and the target performance includes at least one of the first and second performances; determining a target frequency scheme for the storage device corresponding to the determined target performance, the target frequency scheme including a first frequency scheme corresponding to the first performance and a second frequency scheme corresponding to the second performance, wherein the first performance and the frequency of the first frequency scheme are linearly correlated, and the second performance and the frequency of the second frequency scheme are linearly correlated; and determining a frequency corresponding to the target performance requirement from the target frequency scheme based on the target frequency scheme and the current operating state of the electronic device.

The embodiment of the invention discloses a dynamic erasing method and a storage system. The method provided by the embodiment of the invention comprises the steps of determining performance demand data and a residual writable space of a target storage system, determining estimated erasing time according to the performance demand data and the residual writable space, and determining an erasing strategy corresponding to the target storage system according to the estimated erasing time, the erasure strategy comprises each block to be erased in the target storage system and a corresponding erasure mode, configuring erasure parameters corresponding to the blocks to be erased according to the erasure mode, and performing erasure operation on the corresponding blocks to be erased according to the erasure parameters. And according to the estimated erasing time, the erasing parameters corresponding to the to-be-erased blocks are adjusted, so that the reliability of the to-be-erased blocks and the system is improved on the premise of ensuring the performance of the storage system, the dynamic balance between the performance and the reliability of the system is realized, and the adaptive capacity and the overall efficiency of the system in a complex scene are enhanced.

The present disclosure relates to a model performance monitoring method, a device, a system, and a storage medium. The method can be executed by a network device. The method comprises: inputting second information into a first AI / ML model and / or a second AI / ML model to obtain third information, the second information comprising at least one piece of CSI quantization information, and the third information comprising CSIs recovered from the at least one piece of CSI quantization information; and on the basis of the third information, determining whether the first AI / ML model satisfies performance requirements, and / or determining a reason why the performance of the first AI / ML model changes. The present invention can effectively ensure that a model deployed on a network side in a bilateral model has good performance, and ensure the communication reliability.

The present application relates to the field of electronic devices, and discloses a control method and apparatus for an electronic device, a device, and a storage medium. The method comprises: acquiring current device information of an electronic device at a first moment; on the basis of the current device information, predicting a predicted state corresponding to a first system of the electronic device, wherein a state of the first system is related to the power consumption of the first system; and at a second moment, controlling the first system to switch to the predicted state, wherein the second moment is later than the first moment. Since the current device information is related to a usage scenario in which a user uses the electronic device, and the usage scenario is also related to a performance requirement of the user for the electronic device, a state that the first system having higher performance currently needs to enter is predicted on the basis of the performance requirement of the user, so that the state of the first system can be rationally controlled on the basis of the requirement of the user, thereby rationally balancing the performance and power consumption of the electronic device.

The invention relates to the field of bolt performance evaluation, and provides a bolt non-destructive mechanical property evaluation method and system based on indentation fusion correction. The bolt non-destructive mechanical property evaluation method based on indentation fusion correction comprises the steps that bolt surface and internal indentation and hardness test data are acquired, corresponding key feature parameters are extracted, and a sample data set is constructed according to the key feature parameters and corresponding target mechanical property parameters; training a double-layer fusion correction model by using the sample data set to obtain a trained double-layer fusion correction model, and predicting target mechanical property parameters of the bolt by using the trained double-layer fusion correction model to obtain a prediction confidence interval so as to judge whether the bolt meets mechanical property requirements or not; wherein the double-layer fusion correction model is formed by the weighted sum of a regression model of key characteristic parameters on the surface of the bolt and a regression model of key characteristic parameters in the bolt.

The invention provides a method for screening simulation material data through point cloud visualization, a storage medium and equipment, and the method comprises the steps: S1, obtaining a first material attribute and a second material attribute, the numerical value of the first material attribute is represented by a point on an x axis, and the numerical value of the second material attribute is represented by a point on a y axis; s2, retrieving material data with a first material attribute and a second material attribute; s3, constructing material groups according to the material data; s4, based on the material data, finding out the outer contour of the grouped material under the material group; and S5, displaying the material data and the outer contour. According to the method, two key performance indexes of the material are taken as coordinate axes, and performance data of different types of materials are marked on a graph in a point or region form, so that the performance distribution rule, the type difference and the relationship between the performance requirement and the material matching degree of the material are intuitively presented.

An apparatus stores storage resource management information for managing information on a throughput performance resource and a response time of a storage apparatus in each of a main site and a remote site. The apparatus receives a performance requirement including response performance and throughput performance for a volume copy pair including a primary volume in the main site and a secondary volume in the remote site. The apparatus determines whether there is a storage apparatus satisfying the performance requirement in both the main site and the remote site with reference to the storage resource management information. When there is a storage apparatus satisfying the performance requirement in both the main site and the remote site, the apparatus determines to create the volume copy pair.

The invention provides a rapid modeling method and device for a complex functional structural member. The method comprises the steps that the complex functional structural member is divided into different stress areas; for each stress area, selecting a filling function element from a multidisciplinary function element construction library based on the performance requirement, the environmental stress load type and the space geometric constraint of the stress area; for each stress area, adjusting key parameters of the filling function element based on the environmental stress requirement and the parameterized geometric performance mapping relation of the filling function element to obtain a matched function element; and filling the corresponding stress areas with the matching function elements through a rapid reconstruction algorithm, and fusing the adjacent stress areas to obtain a reconstruction model of the complex functional structural member. The invention provides a quick modeling method and device for a complex functional structural member, and aims to solve the problems that the traditional method is difficult to solve the problems of low design efficiency and poor environmental stress adaptability and is difficult to meet the requirements of large-batch, multi-variety and quick additive manufacturing change production of the structural member.

The application provides a performance policy management method and an electronic device, relates to the technical field of electronic devices, and is applied to an electronic device. The electronic device comprises a first operation mode and a second operation mode. The processing performance of the first operation mode is superior to the processing performance of the second operation mode. The electronic device comprises at least two performance items, wherein the at least two performance items comprise a first performance item and a second performance item. Each performance item comprises a first state and a second state. When the performance item is in the first state, the electronic device is in the first operation mode. When the performance item is in the second state, the electronic device is in the second operation mode. The method comprises the following steps: in response to the first performance item switching from the first state to the second state, the electronic device detects the state of the second performance item when the electronic device is in the first operation mode; and in the case that the second performance item is in the first state, the electronic device keeps the first operation mode. The scheme can meet the performance requirement of the electronic device scene.

The invention provides an automatic modeling method and device, electronic equipment and a storage medium, and relates to the technical field of automatic modeling, and the method comprises the steps: obtaining an input parameter; the input parameters comprise a modeling surface, a front section lap joint surface, a rear section lap joint surface and a position control point; based on the input parameters, generating a basic large surface of the part through parametric modeling; presetting a plurality of features on the basic large surface; determining the number of target features based on the length of the basic large surface and the size parameters of the features; on the basis of the number of the target features, the activity states of the multiple features are controlled through a preset rule so as to dynamically activate or cancel activation of the features; and updating the model to obtain a part structure meeting design requirements. In the mode, the structure of the whole part can be controlled according to the input parameters, and it is ensured that the part can meet the performance requirement at the same time.

The invention discloses a rating method, a rating system, electronic equipment and a storage medium. The rating method comprises the following steps: presetting priority levels of various performance requirements of a to-be-rated object according to requirements; formulating an executable rating standard based on each performance requirement with the preset priority level; and based on the actual performance of the to-be-rated object, performing performance rating on the to-be-rated object according to the executable rating standard, and outputting a formal rating report. Due to the adoption of the rating method, the classification rule of the rating object such as a crystal bar in multiple dimensions can be easily realized, so that different rating standards are easy to maintain; and specific classification is realized by setting the priority level, so that the requirement of product customization can be met, and the customer satisfaction is improved.

The invention discloses an edge connecting method for a high-rise concrete modular building, and relates to the technical field of modular integrated buildings. Comprising the following steps: obtaining structural feature data and connection performance demand data of each prefabricated template edge of the high-rise concrete modular building; the structural feature data at least comprise concrete compressive strength, steel bar spacing and edge overall dimension of the edge of each prefabricated formwork, and the connection performance demand data at least comprise uplift bearing capacity standard values, anti-sidesway rigidity requirements and interface sealing performance grades of the formwork splicing positions. According to the method, structural feature data such as concrete compressive strength and steel bar spacing of the edge of the prefabricated module are obtained, performance requirements such as uplift bearing capacity and sidesway resistance rigidity are combined, and the combined connecting piece is designed in a targeted mode, so that conflicts between the connecting piece and the module structure are avoided; precise matching of local bearing strength of concrete and compressive strength of connecting pieces with design requirements is ensured, coordination and consistency of connecting performance and performance of the module body are ensured, and safety of the whole structure is improved.

The invention discloses a high-entropy alloy design method based on machine learning and a nuclear high-entropy alloy, and belongs to the technical field of metal material design. The design method comprises the following steps: collecting historical high-entropy alloy data, and establishing an initial data set of component-process-phase structure-mechanical property; constructing an initial feature variable composed of the element features and the empirical features, and screening to obtain a key feature variable; respectively constructing a classification model for predicting a phase structure and a regression model for predicting mechanical properties; based on the key characteristic variables, a classification model and a regression model, alloy components are screened according to a target phase structure and target mechanical properties, and design components are obtained; the design components are verified through experiments. According to the design method, on the premise that the phase structure is met, collaborative optimization of strength and toughness is achieved, and therefore the high-entropy alloy meeting various performance requirements can be designed, especially the high-entropy alloy which has an FCC phase structure and is high in toughness and resistant to high temperature and is applied to an advanced nuclear energy system can be designed.

The application discloses a high-precision thread grinder ball screw pair forward design method, which is applied to the ball screw pair external thread grinder technical field and comprises the following steps: aiming at the reliability requirement of high-precision and heavy-load ball screw pair, taking the average failure-free working time as an optimization target; performing main failure mode, influence and harmfulness analysis on the ball screw pair, clearly defining the material requirement of the ball screw pair, and performing performance test based on the material requirement to select the optimal material; performing sensitivity analysis on the key design parameters of the ball screw pair, clearly defining the relative influence weight of different design parameters on the optimization target, constructing a reliability target optimization model of the ball screw pair, and solving the optimized key design parameter combination. The application realizes the whole-process connection from performance requirement to engineering realization to continuous improvement, and provides a systematic method framework and theoretical support for the design and improvement of the reliability of the ball screw pair.

The invention relates to application of a large model in the field of software testing, and discloses an application performance testing method, device and equipment and a program product. The method comprises the steps of obtaining project demand data corresponding to a target application, and performing semantic analysis on the project demand data to obtain demand keywords; if it is detected that each demand keyword is successfully matched with the risk knowledge base, obtaining a quantitative digital index corresponding to each demand keyword through a performance demand quantitative model; and obtaining a target performance test scheme according to the quantitative digital index corresponding to each demand keyword, and carrying out performance test on the target application based on the target performance test scheme to obtain a performance test result. According to the scheme of the embodiment, the demand keyword of the project demand data is extracted, the demand keyword is quantized into the digital index, and then the matched performance test scheme is determined according to the quantized digital index, so that automatic performance demand identification can be realized, and the efficiency and accuracy of application performance test can be improved.

This invention relates to the field of server technology, specifically providing a method, system, terminal, and storage medium for dynamic monitoring of hard disk performance. The method includes: setting a matching hard disk performance data collection frequency for a business scenario based on the performance requirements of that scenario; monitoring the actual business scenario of the server and obtaining a target collection frequency matching the actual business scenario; periodically collecting hard disk performance data by calling a hard disk performance data collection script according to the target collection frequency; and using pre-stored standard performance data to determine anomalies in the hard disk performance data and identifying the data as abnormal. This invention can flexibly update the hard disk performance monitoring frequency according to the server's business scenario, thereby avoiding resource waste or untimely hard disk performance monitoring.