20results about How to "Shorten the optimization cycle" patented technology

The embodiment of the invention relates to the field of large model security, and discloses a large model optimization method, a related device and a storage medium. The method comprises the steps of determining an application field of a to-be-optimized student large model; calling a preset confrontation model to generate a problem set; inputting the question set into the student large model through a preset confrontation model to obtain an output result; outputting the output result to a preset scoring model for scoring to obtain a score; calling a preset teacher model to train the student large model according to a preset high-quality data set to obtain a training result and an optimization scheme; and respectively feeding back the score, the training result and the optimization scheme to the student large model, and continuously updating the problem set to train and score the student large model until the student large model is gradually iterated to be optimal, thereby obtaining the trained student large model. According to the embodiment of the invention, the overall optimization period of the large model is remarkably shortened, the optimization efficiency of the large model is improved, and the performance of the optimized large model is further improved.

The invention discloses a directional advertisement putting system based on a rule engine, and belongs to the technical field of telecommunication networks. Through mutual cooperation and cooperation of an advertisement library management module, a user behavior construction module, an advertisement matching engine module, a flow reporting module and a real-time monitoring module, advertisement pre-putting information is processed based on a personalized label, and an advertisement is rapidly matched and selected when a terminal requests the advertisement. The problems of low efficiency and lack of flexibility caused by the fact that an existing advertisement delivery system is limited to a single terminal and a mechanical delivery mode are solved, consumed time and labor cost are reduced, advertisements are delivered more accurately according to labels, changes of consumer behaviors or changes of market trends can be responded in time, and the service life of the system is prolonged. And thus, the advertisement resource allocation accuracy and conversion rate are improved.

The invention provides a neural network multi-objective optimization and FPGA hardware acceleration collaborative design method, and belongs to the field of deep learning model compression and hardware collaborative design. The method comprises the following steps: constructing a joint optimization space containing a neural network compression parameter and an FPGA hardware design parameter; a multi-target Bayesian optimization search strategy is adopted, iterative search is carried out in the joint optimization space, model precision, FPGA resource occupation and reasoning delay are synchronously optimized, and optimal candidate configuration is obtained; matching the compressed network structure with the FPGA parallel architecture by using a hardware-perceived pruning and quantification strategy; a multi-task performance prediction model is adopted to quickly predict the precision, resource occupation and delay of the optimal candidate configuration so as to accelerate the search process; according to the optimal configuration, a hardware accelerator code facing the target FPGA is automatically generated, and integration and implementation are completed. According to the method, collaborative optimization of neural network compression and hardware design is achieved, FPGA resource occupation can be remarkably reduced, the reasoning speed can be increased, and meanwhile the model precision is kept.

The application discloses a kind of fresh stewing bird's nest process self-adapting optimization method and system, belong to food processing intelligent manufacturing technical field. Including obtaining current batch process parameters and actual sensory profile, determining expected sensory profile based on historical data retrieval similar batch, generate standardized sensory deviation vector;Knowledge graph representing the causal relationship between process parameters, product attributes and sensory evaluation is constructed, and the graph is updated in two paths according to the direction and amplitude of the deviation vector;Under the constraint of graph structure, generate candidate process adjustment scheme, and perform counterfactual reasoning to obtain predicted sensory profile, predicted deviation vector and causal effect confidence;Based on the evaluation and determination of target scheme based on the prediction result, adjust process parameters.The application combines difference driving mechanism with causal inference, realizes low trial and error cost, high explainability process self-adapting optimization, and is suitable for fresh stewing bird's nest and the production control of other high-end tonics.

The invention belongs to the technical field of simulation optimization, and particularly relates to a centrifugal pump impeller multi-physics field coupling simulation optimization method which comprises the following steps: constructing a geometric model of a centrifugal pump by adopting three-dimensional drawing software according to an actual structure of the centrifugal pump; finite element modeling is conducted, and a finite element analysis model of the centrifugal pump is obtained; hexahedral grids are divided for the impeller, a shape variable space of the impeller is built, and the impeller deformation serves as a design variable; solving the finite element analysis model after grid division through a solver; an experimental design sample is generated by adopting a Latin hypercube sampling method, a global response surface model is constructed through quadratic polynomial regression fitting, an optimal impeller deformation amount is iteratively solved by adopting a global response surface optimization algorithm, a finite element analysis model is adjusted, and an optimization model is formed. According to the method, fluid-solid coupling simulation, a global response surface method and an automatic iteration process are fused, and the efficiency of the centrifugal pump impeller is maximized and optimized on the basis of accurately matching actual working conditions.

This application relates to the field of wireless communication technology, providing an antenna robustness design method and apparatus based on hybrid deep learning. This invention simulates manufacturing process errors by combining sampling methods within the range of process errors, obtaining design variables and... S Sensitivity analysis was performed on the Gaussian distribution curves of the statistical mapping relationship between parameter responses to identify variables more sensitive to manufacturing errors, thereby reducing the dimensionality of variables and decreasing the complexity of subsequent antenna robustness optimization. A hybrid deep learning model was used to construct an antenna response substitution model, replacing traditional electromagnetic simulation, significantly shortening the optimization cycle while ensuring the accuracy of response prediction and avoiding distortion of optimization results due to model errors. By constructing a robustness objective function and employing a genetic algorithm to optimize antenna parameter robustness, a highly robust optimal solution was found, avoiding premature convergence of the gradient method in multi-peaked environments caused by random errors.

The invention belongs to the field of cell culture automation and intelligent regulation and control, and discloses an optogenetics-based adherent cell culture optimization method and system. The method comprises the following steps: inputting an adherent cell type and a culture target, and calling an adherent cell-optical genetic parameter joint database; performing targeted pretreatment on the target adherent cells; processing through a multi-parameter optimization algorithm hardware platform to generate an executable customized photostimulation scheme; performing accurate light stimulation on the positive adherent cells; comparing the obtained detection data with normal adherent cell parameters in a joint database, and evaluating the influence of a photostimulation scheme on the cells; updating the parameters of the multi-parameter optimization algorithm and the prediction model; and inputting the adherent cell type and the culture target to the updated prediction model, and outputting an optimal light stimulation scheme. The core problems of low photostimulation accuracy and poor targeting in the prior art are solved, and high-activity and high-consistency light-sensitive adherent cells are stably produced.

Embodiments of the present application provide a client running method, a computing device, a computer readable storage medium and a computer program product. The client running method comprises: receiving stutter information sent by a first client, the stutter information being generated in the case that the first client detects a stutter event; the stutter information comprising function information of a target function corresponding to the stutter event; analyzing the stutter information to obtain the function information of the target function; generating stutter prompt information based on the function information; and sending the stutter prompt information to a target client in a silent push manner, so that the target client adjusts an execution operation of the target function based on the stutter prompt information, wherein the target client comprises the first client and / or a second client. The technical solution provided by the embodiments of the present application improves the stutter optimization efficiency of the client.

The present invention provides a method, medium and system for optimizing the surface smoothness ratio of civil engineering concrete of substation facilities, belonging to the technical field of concrete ratio, including: firstly, collecting the physical and chemical property data of raw materials such as cement, sandstone, admixture, etc., establishing a concrete surface smoothness evaluation model, and comprehensively considering parameters such as surface roughness, flatness, glossiness, etc. On this basis, designing a small-scale indoor test to simulate engineering conditions, using computer image analysis to obtain the measured surface smoothness index under different ratios, and fitting and optimizing the model. Using computer-aided ratio design software to generate multiple groups of optimized ratios, using orthogonal test method to carry out pilot experiments, and further optimizing the fitting model. Finally, field tests are carried out in actual engineering, and the ratio with the best smoothness index is selected as the final optimization result. The technical problem that it is difficult to obtain the optimized ratio of concrete surface smoothness due to relying on the subjective experience and trial and error of engineering personnel is solved.

The invention discloses an LDMOS device electrical performance optimization method based on an SAC algorithm, and belongs to the technical field of semiconductor power device design. The method comprises the following steps: sampling in a process parameter range, and extracting electrical characteristics by utilizing TCAD simulation to construct a data set; a deep neural network agent model is constructed and trained; designing a state space, an action space and a reward function, building an SAC deep reinforcement learning framework, and accessing the proxy model to the environment; and training the SAC model to obtain optimal process parameters. According to the method, the DNN proxy model is used for replacing high-time-consumption simulation, and the SAC algorithm is combined for self-adaptive adjustment, so that the problem of difficulty in multi-target collaborative optimization in a high-dimensional parameter space is effectively solved, the optimization period is remarkably shortened, collaborative improvement of static and dynamic performance of a device is realized, and an optimization result has high reliability through circuit verification.

This invention provides a multi-physics coupling test method and system for intelligent vehicle hybrid network architecture, belonging to the field of automotive electronics testing technology. The method includes: applying single-physics coupling, dual-physics coupling, and system-level multi-physics coupling loads through a tiered testing process, simultaneously acquiring electrical signal, optical signal performance parameters, and environmental parameters; calculating a unified signal health index (HI) and degradation rate based on a preset algorithm; analyzing the contribution of each physical field factor to performance degradation when the HI is below a health threshold or the degradation rate exceeds a rate threshold, and identifying the dominant failure factor; and intelligently matching the corresponding optimization strategy from a pre-built optimization strategy library. This invention solves the problems of media isolation, data fragmentation, and inefficient optimization in traditional testing methods, achieving unified cross-media testing, real-time health status assessment, and precise automated optimization, thus improving the reliability verification efficiency of intelligent vehicle hybrid network architecture.

The invention discloses an air conditioning system test method, system and equipment based on a genetic algorithm and a storage medium, and belongs to the technical field of automobiles, and the method comprises the steps that a thermodynamic model of a condenser is established based on basic parameters of the condenser; calculating the refrigerating capacity of the condenser based on the thermodynamic model of the condenser; establishing a fitness function based on the thermodynamic model of the condenser; optimizing the fitness function of the thermodynamic model of the condenser by using a genetic algorithm to obtain optimized parameters; and an energy-saving optimization control strategy of the air conditioning system is determined according to the optimization parameters. Through a targeted optimization algorithm, the refrigeration performance of the condenser is remarkably improved after implementation, it is ensured that the condenser can reach or exceed the design target in practical application, and therefore the overall energy efficiency ratio of an air conditioning system is improved; and by optimizing the design of the condenser, the energy consumption can be effectively reduced on the premise of ensuring the refrigeration effect, so that the energy expenditure of a user is reduced, and meanwhile, the influence on the environment is also reduced.

The application provides a material preparation method based on purification and delamination synergistic optimization of montmorillonite, and comprises the following steps: S1, pretreating natural montmorillonite raw materials; S2, centrifugal purification by using a purification parameter linear optimization model; S3, preparing a montmorillonite delamination precursor; S4, ultrasonic delamination treatment by using a delamination effect dynamic regulation model; S5, purification and delamination synergistic optimization adjustment; S6, delamination montmorillonite stabilization treatment; S7, material forming and drying; S8, material sintering and performance characterization. Through the whole-process intelligent synergy of "raw material characteristics-purification parameters-delamination effect", the application breaks through the limitations of traditional processes and realizes efficient and stable preparation of montmorillonite materials.

The embodiment of the application provides a service system optimization method, device, equipment, storage medium and program product, and relates to the field of big data. The method realizes full-process closed-loop optimization from demand understanding to strategy generation by fusing user intention recognition and system state analysis. The method replaces the traditional long process which depends on manual carding, experience judgment and manual implementation by automation technology, automatically matches user demand, analyzes system state and generates strategies by a model, reduces the labor and time cost, shortens the optimization period, and based on the deep analysis and prediction of multidimensional system data, can comprehensively identify system risks and predict performance change trends, so that the optimization strategy has real-time responsiveness and foresight, thereby improving the stability of the system while enhancing the adaptability to future business demands.

The invention provides a drilling fluid reservoir protection performance optimization method and system based on data association analysis. The method comprises the following steps: performing attribution analysis on an association relationship between an initial drilling fluid parameter and a target permeability recovery value; constructing a multiple regression model between all the target drilling fluid controllable parameters and the target permeability recovery value; calculating based on the multiple regression model to obtain an optimal multiple regression equation; respectively generating parameter diagram nodes and node edges according to the target drilling fluid controllable parameters, and obtaining a parameter diagram structure of the target drilling fluid controllable parameters; and constructing a parameter optimization graph network in combination with the parameter graph structure and the optimal multiple regression equation, inputting the initial drilling fluid parameters into the trained parameter optimization graph network to obtain optimal drilling fluid parameters output by the parameter optimization graph network, and adjusting the initial drilling fluid formula into the optimal drilling fluid formula according to the optimal drilling fluid parameters. The method has the effect of efficiently and accurately optimizing the protection performance of the drilling fluid reservoir.

The invention discloses an online firepower distribution method based on a battlefield situation and an improved genetic algorithm, and relates to the field of firepower distribution, and the method comprises the following steps: collecting battlefield situation core data, and obtaining input information based on the battlefield situation core data; constructing an online firepower distribution model based on the input information, and forming an initial online firepower distribution model through dominant function definition, influence factor standardization, matrix construction, total income determination and constraint condition setting; and optimizing the initial online firepower distribution model by adopting an improved genetic algorithm to obtain an optimized firepower distribution scheme. According to the invention, efficient online distribution of multi-missile cooperative combat can be realized, and the real-time performance of battlefield decision making and cooperative combat effectiveness are improved.

This invention relates to the fields of materials science and intelligent optimization technology, and discloses a multi-objective intelligent optimization method for the composition and heat treatment process of 2xxx series aluminum alloys. This method integrates the XGBoost multi-output prediction model with four intelligent optimization algorithms: Firefly Optimization (FA), Gray Wolf Optimization (GWO), Particle Swarm Optimization (PSO), and Simulated Annealing (SA). Through an automated process of data preprocessing, multi-objective prediction, constraint optimization, and result verification, it achieves the synergistic optimization of the tensile strength (UTS), yield strength (TYS), and elongation (EL) of aluminum alloys. The system uses unique heat encoding to process categorical process parameters, introduces constraint penalty mechanisms and early stopping strategies to ensure that the optimization results meet the requirements of composition ratio and process parameter range. Experimental verification shows that the comprehensive performance of the aluminum alloy optimized by this method is suitable for the performance optimization of 2xxx series aluminum alloys in aerospace, automotive manufacturing, and other fields, significantly reducing R&D costs and time.

This invention relates to the field of samarium iron nitride (SFeNi) permanent magnet material manufacturing technology, and discloses a method and system for optimizing SFeNi injection molding process parameters using digital twins. This method achieves dynamic optimization by integrating a digital twin with the physical production line. Real-time sensor data from the production line is collected and preprocessed. Based on this, a digital twin containing a particle layer, a melt layer, and a magnetic pole layer is constructed and updated. Using this twin, the agglomeration and orientation of SFeNi particles are predicted in the particle layer, temperature, shear, and magnetic flux distribution are simulated in the melt layer, and magnetic performance indicators are evaluated in the magnetic pole layer. Based on these predictions and evaluations, the material temperature, injection speed, pulsating magnetic field waveform, and holding pressure curve are optimized using an artificial intelligence proxy model. Finally, the optimized parameters are sent to the production line for execution via a programmable logic controller (PLC). This method achieves online adaptive adjustment of process parameters, improving the quality and efficiency of magnet molding.

The invention belongs to the technical field of automobile part vibration performance analysis, and particularly relates to a rear-row entertainment display screen vibration performance simulation analysis method. Establishing a vehicle body model, a seat model and a rear-row entertainment display screen model through a finite element method, establishing a finite element model based on the models, and carrying out modal and vibration transfer function performance analysis from a vehicle body key attachment point to a rear-row entertainment display screen; whether the vibration performance reaches the standard or not is judged by comparing the modal and vibration transfer function key result value with a target value; the risk is judged according to the simulation result, the rear-row entertainment display screen and other connection structures related to the rear-row entertainment display screen are optimized in the data stage, and the cost for solving later problems of a project and the cycle cost are reduced; and meanwhile, the simulation analysis period is shortened, and the iterative optimization efficiency of the scheme is greatly improved.

The invention relates to a distributed cache parameter optimization method and device, computer equipment and a storage medium. The method comprises the following steps: randomly generating a plurality of groups of distributed cache parameters as an initial group; respectively inputting the initial group into a plurality of learners of a cache performance prediction model, predicting the cache performance of the initial group, and determining a health value of the initial group according to the cache performance of the initial group; performing evolution processing on the initial group according to the health value, the mutation probability threshold and the crossover probability threshold of the initial group to generate a new group; and updating the new group into the initial group, returning to the step of respectively inputting the initial group into the plurality of learners of the cache performance prediction model, and carrying out iterative evolution processing to obtain a group of distributed cache parameters with optimal cache performance. By adopting the method, the efficiency and accuracy of distributed cache parameter tuning can be improved.