10 results about "Space optimization" patented technology

This invention discloses a method for testing jailbreak attacks on large language models based on latent space optimization. First, standard rejection vectors pointing to rejection concepts at multiple network layers of the target large language model are obtained. Then, the set of key layers performing security defenses in the target large language model is located, and adaptive weights are assigned to each layer in the set of key layers. A cognitive hijacking prefix targeting malicious query commands is generated and compressed. Using the positive guiding part of the cognitive hijacking prefix as the optimization target, an initial suffix is ​​generated through a first-stage adversarial suffix optimization. Finally, using the complete cognitive hijacking prefix as the optimization target, combined with the standard rejection vectors and the adaptive weights, a second-stage adversarial suffix optimization is performed on the initial suffix to generate the final adversarial suffix. This method transforms the attack target from shallow output probability manipulation to deep cognitive state alignment, effectively eliminating the "pseudo-jailbreak" phenomenon.

This invention discloses a multi-UAV cooperative search trajectory planning method based on 3D action decoupling, aiming to solve the core problems of low efficiency in 3D action coupling learning, insufficient target uncertainty modeling, imbalance between exploration and utilization, and poor multi-UAV cooperation in multi-UAV cooperative search under 3D unknown environments. This invention models the multi-UAV cooperative search process as a partially observable Markov decision process, designs a 3D action decoupling Actor network to achieve decoupled control of horizontal plane motion and vertical altitude motion, constructs a non-sparse multi-dimensional reward function to provide dense feedback for policy learning, and introduces a dynamic noise attenuation mechanism based on target confidence to achieve spatial optimization allocation of exploration resources and complete iterative optimization of the cooperative search strategy. This method can significantly improve the target detection rate, reduce flight energy consumption and conflict rate, and can efficiently adapt to the needs of multi-UAV cooperative search trajectory planning in complex 3D unknown environments.

The application relates to an industry space adaptation optimization method and system based on a large model, which comprises the following steps: determining a target area, and obtaining a first data set and a second data set; constructing a digital twin base, and performing multidimensional analysis through a large language model; constructing an enterprise capability evaluation model and a migration risk early warning model based on the multidimensional analysis result, and obtaining enterprise capability information and migration risk grade information; obtaining interest point data and traffic network data, and determining geographic circle layer information; inputting the multidimensional analysis result, the enterprise capability information, the migration risk grade information and the geographic circle layer information into the large language model to generate an industry space optimization strategy; in summary, the application integrates multiple sources of data to construct a digital twin base, and uses a large language model to perform multidimensional analysis to generate an optimization strategy, solves the fragmentation problem in the traditional method, provides a decision basis, and has the effects of improving the scientific nature and operability of decision-making.

The application discloses a resource space optimization configuration method and system for land planning, and relates to the technical field of land planning and resource space configuration; the application collects and pre-processes space data of a planning area, constructs complete topological adjacency relations, establishes a neighborhood correlation constraint matrix and a constraint processing priority sequence, filters candidate land use types through priority, completes initial configuration based on comprehensive configuration scores, detects and resolves neighborhood conflicts, adjusts land use structure deviation, and outputs a configuration scheme after constraint compliance verification, effectively solving the problem that neighborhood correlation constraints are difficult to be effectively coupled and processed in the optimization solving process, significantly improving the scientificity and rationality of land resource space configuration, and improving the constraint compliance rate of the configuration scheme and resource utilization efficiency.

The application relates to the technical field of fault detection of industrial systems, in particular to an industrial system fault detection method and system based on distributed equivalent space optimization. The method comprises the following steps: a Hankel matrix is constructed to identify an equivalent matrix with zero projection under normal working conditions, and a residual signal is generated; based on local data of each sensor node, an average consistency algorithm is used to calculate a covariance matrix in an offline stage; parameter optimization design is performed on the equivalent matrix; the residual signal is calculated when the system is running online, and a globally consistent detection statistic is obtained through the average consistency algorithm; a detection threshold is determined in combination with normal operation data, and fault detection of a complex industrial process is realized. The application combines an equivalent space optimization method, a distributed sensor network cooperative computing mechanism and an average consistency algorithm, reduces communication and computing burden, improves system robustness and scalability, and enhances the sensitivity and reliability of fault detection.

The application relates to the technical field of space optimization management, and discloses a land space precise management method based on key driving factor thresholds, which adopts technical coupling and comparison of the leading ESs supply-demand types identified by SOM and the key driving factor thresholds extracted by constraint lines. The method solves the technical problem that the data collection, model analysis and policy making links are disconnected in the existing land space evaluation and monitoring, resulting in the difficulty in converting the evaluation results into specific instructions, realizes the full-chain technical closed loop from data input to evaluation suggestion output, ensures the systematicness and integrity of the evaluation and monitoring method, adopts the XGBoost model to process the complex interaction between variables, and innovatively adopts the constraint line model to analyze the constraint boundary of data distribution, so that the technical problem that the traditional evaluation method relies on a linear model and is difficult to capture nonlinear relationships and the evaluation suggestions are mostly qualitative descriptions is solved.

This invention discloses a method for optimizing agricultural spatial layout to synergistically enhance ecological and production benefits. The method includes: data collection and processing, assessment of ecosystem service and economic value, construction of a multi-objective quantitative optimization model, construction of spatial optimization rules, spatial pattern optimization based on cellular automata (CA), scenario simulation, and scheme evaluation. This invention combines quantitative and spatial optimization, overcoming the blind spots of traditional experience-based layouts and providing a systematic and scientific decision support method for industrial spatial layout. This invention fully considers the topographic heterogeneity and industrial compatibility of ecologically vulnerable areas such as karst regions, and the optimization rules are rationally designed with strong local adaptability. The spatial simulation results based on the CA model are intuitive and visual, facilitating planners' understanding and application. The optimized layout scheme can be directly used to guide actual production.

This invention discloses a low-cost, high-strength, and high-wear-resistant ductile iron design method based on few-sample machine learning, belonging to the fields of metal material preparation technology and computer science. This invention constructs an enhanced dataset by collecting the composition and mechanical properties of annealed ductile iron and extracting and screening alloy features to obtain key alloy features. Then, a multi-objective optimization function is constructed, and feature space optimization is performed using an improved NSGA-II algorithm to obtain the Pareto solution set, which is then mapped to features and experimentally verified. This invention overcomes the risk of overfitting with few samples through a hybrid enhancement strategy; it constructs an adaptive multi-scale recurrent convolutional network and a dual attention mechanism to accurately screen key alloy features; it uses an improved NSGA-II algorithm for multi-objective optimization, introducing a dynamic sharing mechanism and local search to make the Pareto front distribution more uniform; and it establishes a random forest mapping model and a closed-loop automatic retraining system to achieve accurate feature-component mapping and continuous optimization.

The present application belongs to the field of intelligent additive manufacturing quality control and fluid mechanics, and provides a flow field uncertainty correlation mapping and robust optimization method and system for high-consistency aerosol jet printing: based on the original geometric structure of the aerosol jet printing head, an aerosol printing flow field simulation model is established, and then combined with the uncertainty parameters, the flow field uncertainty space mapping data set is obtained; a flow field reasoning agent model is constructed by combining a Gaussian process regression model; a multi-objective optimization function based on velocity-pressure field stability is constructed, and the flow field reasoning agent model is used for iterative solution, and finally the optimal process parameters of aerosol jet printing are obtained through the Pareto optimal boundary identification. The present application is based on the organic combination of computational fluid dynamics simulation and uncertainty modeling based on polynomial chaos expansion, and multi-objective global robust optimization algorithm based on Pareto equilibrium, and overcomes the multi-source airflow interference from the bottom aerodynamic mechanism and global space optimization angle.