61 results about "Space optimization" patented technology

The invention discloses a vegetation carbon sink space optimization method and system based on interpretable machine learning. The method comprises the steps that firstly, multi-source environment data such as administrative division, environment factors and target variables of a target area are acquired, and a space table data set is formed; training an XGBoost regression model based on the data set, predicting the vegetation carbon sink amount and evaluating the performance of the model; quantifying the contribution of each environmental factor to the carbon sink by combining an SHAP interpreter, generating a feature importance and interpretability map, fitting through a LOWESS algorithm to obtain an environmental factor and carbon sink response relation curve, and extracting an optimal threshold interval; further predicting a carbon sink gain according to an optimization threshold value, delimiting optimization partitions, and forming a space optimization scheme; and finally outputting a vegetation carbon sink optimization suggestion report. According to the method, scientific quantification of carbon sink space optimization can be realized, and the interpretability and decision support value of a carbon sink improvement strategy are improved.

The invention relates to the technical field of space planning, in particular to an intelligent optimization method for territorial space planning fusing multi-objective evolution. The method comprises the following steps: carrying out multi-modal territorial space characterization analysis on territorial space heterogeneous data to generate multi-modal territorial grid characterization space data; performing territorial multi-modal feature analysis processing on the multi-modal territorial grid representation spatial data to generate territorial planning unit multi-modal feature data; carrying out evolution trend analysis on the territorial planning unit multi-modal characteristic data to generate territorial planning unit evolution trend data; performing territorial space planning multi-target optimization strategy analysis based on the territorial planning unit evolution trend data to generate territorial space planning multi-target optimization strategy data; and carrying out multi-objective evolution territorial space planning intelligent optimization processing through the territorial space planning multi-objective optimization strategy data to generate territorial space planning intelligent optimization data. According to the invention, intelligent land and land space optimization planning is realized.

The invention discloses a space optimization-based low-efficiency camellia oleifera forest transformation method and system. The method comprises the steps of collecting standard camellia oleifera forest data to construct a standard production database; the method comprises the following steps: acquiring stand layout data and single plant data of a camellia oleifera low-efficiency forest to be transformed, and constructing a twin digital forest of the camellia oleifera low-efficiency forest; based on data in the camellia oleifera standard production database, constructing a camellia oleifera low-efficiency forest reconstruction prediction model with maximization of forest stand expected total yield and minimization of reconstruction implementation complexity as targets, inputting data in the twin digital forest into the model, and performing optimization calculation to obtain a low-efficiency camellia oleifera low-efficiency forest reconstruction prediction model. Generating a production optimization scheme containing an optimal plant layout and a single plant transformation suggestion; based on the optimization scheme, generating a visual construction drawing or an AR guidance instruction to guide reconstruction construction; after transformation, output data are collected to verify the effect. According to the method, through digital modeling and multi-target space optimization, refinement, intelligence and scientific decision-making of camellia oleifera low-efficiency forest transformation are realized, and the overall output benefit is effectively improved.

The invention discloses a channel structure analysis method and system based on multi-modal data, and the method comprises the steps: obtaining the multi-source data of a target region, building a three-dimensional geological structure model of the target region based on the multi-source data, generating a point distribution scheme through a space optimization algorithm, generating a monitoring network based on the point distribution scheme, and carrying out the analysis of the three-dimensional geological structure model. Different types of tracers are injected into the injection points by adopting a graded injection strategy, monitoring data are collected, an underground structure model is generated based on the monitoring data, hydraulic parameters are calculated, the underground structure model is dynamically corrected based on the hydraulic parameters, and an underground pipeline three-dimensional model is obtained; and grading channel structures in the underground pipeline three-dimensional model by using a preset channel grading standard, and generating a channel three-dimensional map based on a grading result. According to the method, the three-dimensional model of the underground pipeline is generated through comprehensive analysis of the multi-modal data, and the structural analysis precision of the underground water pollution channel is improved.

The invention relates to the technical field of space optimization, and discloses a container loading space optimization method based on robot collaboration, and the method comprises the steps: building a multi-dimensional physical model of a package through obtaining the point cloud data and weight distribution of the package; generating a loading scheme containing a target pose, a pressure tolerance threshold and a loading sequence based on a reinforcement learning algorithm; the mechanical arm generates a motor current-position composite control instruction according to a pre-trained grabbing dynamical model, and accurate grabbing is achieved; in the loading process, the pose of the parcel is monitored in real time through a Kalman filtering algorithm, when deviation exceeds a threshold value, a local space KD tree is constructed, a collision probability gradient field is calculated, and a compensation scheme meeting stability constraints is generated; and finally, updating the quad-tree index and feeding back to the reinforcement learning algorithm for online optimization. The problem that in the robot loading process, the grabbing force control is not accurate, and the efficiency is reduced due to execution deviation accumulation is effectively solved, and efficient and accurate automatic loading is achieved.

The invention belongs to the technical field of computer vision and model generation, and particularly relates to a diffusion generation method for hierarchical condition control and dynamic jump sampling, and the method is realized through a hierarchical control architecture: macroscopic time scheduling adopts a bidirectional LSTM dynamic combination de-noising step, and the equivalence is ensured through differential transition constraint; in microscopic space optimization, a key area is positioned through an uncertainty thermodynamic diagram, only local attention is executed on the key area, and an implicit network is used for background generation; according to the multi-modal fusion, dynamic routing gating is adopted to calculate the weight and adversarial training is combined, according to the diffusion generation method of hierarchical condition control and dynamic jump sampling, the sampling step number is compressed from 1000 + to 50-80, 95% of non-critical area calculation is reduced, the multi-modal guide quality loss is reduced by 37%, the peak video memory occupation is reduced by 60%, the efficiency is improved while the quality is guaranteed, and the method is suitable for large-scale popularization and application. And mobile terminal deployment is supported.

The invention provides a balance calibration modeling method and system based on sample errors and space optimization. The method comprises the steps of obtaining a calibration data set and dividing the calibration data set into a sample set and a verification set; calculating a load space weight and a load complexity weight of the sample set, and obtaining a comprehensive weight based on the load space weight and the load complexity weight; and constructing a polynomial model of the sample set, and solving a coefficient matrix in the polynomial model based on the comprehensive weight to obtain a final balance calibration model. The method further comprises the following steps: verifying and evaluating the balance calibration model by using the sample set and the verification set, and optimizing the balance calibration model according to an evaluation result. According to the balance calibration modeling method cooperating with the sample space distance and the load complexity weight provided by the invention, nonlinear system errors introduced by calibration load complexity are fully considered, so that the influence of error distribution of calibration samples under various load combinations on modeling is more uniform and reasonable; and meanwhile, the problem of sample space occupation ratio is also solved.

This invention discloses a method and system for spatial optimization of vegetation carbon sinks based on interpretable machine learning. The method first acquires multi-source environmental data, including administrative divisions, environmental factors, and target variables for the target area, forming a spatial tabular dataset. Based on this dataset, an XGBoost regression model is trained to predict vegetation carbon sink capacity and evaluate model performance. A SHAP interpreter is then used to quantify the contribution of each environmental factor to carbon sink, generating feature importance and interpretability maps. The Lowes algorithm is used to fit the relationship curve between environmental factors and carbon sink response, extracting the optimal threshold interval. Further, based on the optimization threshold, carbon sink gain is predicted, optimization zones are delineated, and a spatial optimization scheme is formed. Finally, a vegetation carbon sink optimization recommendation report is output. This method enables the scientific quantification of carbon sink spatial optimization, improving the interpretability and decision support value of carbon sink enhancement strategies.

The method for constructing urban ventilation corridors based on local climate zone classification and remote sensing inversion belongs to the technical field of urban climate regulation and space optimization. The present application constructs a multi-scale urban thermal environment analysis framework, from macro, meso to micro level, to identify the coupling mechanism of urban form and thermal effect, and to realize the fine modeling and regulation path identification of thermal environment. At the same time, combined with machine learning and explainable analysis method, the scientificity and transparency of thermal environment driving factor identification are enhanced. Through remote sensing inversion, form factor construction and three-dimensional simulation simulation, a complete technical path of "cold source identification-resistance modeling-path optimization-microclimate response evaluation" is put forward, which effectively improves the ventilation and cooling potential identification ability of blue-green infrastructure, and is suitable for ventilation corridor planning and climate adaptive updating design of various urban spaces, and has wide application prospect.

The invention discloses a monocular depth estimation adversarial sample generation method based on a diffusion model. The monocular depth estimation adversarial sample generation method is specifically implemented according to the following steps: step 1, selecting and preprocessing an existing data set; step 2, loading a monocular depth estimation model and evaluating baseline performance; step 3, construction of a diffusion model and initialization of a back diffusion process; 4, performing disturbance-resisting submerged space optimization and constraint generation; and 5, decoding the adversarial sample, enhancing the physical realizability and verifying the attack effect. According to the method, on the premise of keeping the imperceptibility of confrontation disturbance, the prediction precision of the monocular depth estimation model in real and physical environments is remarkably reduced, so that the robustness and safety of the monocular depth estimation model are evaluated and improved.

The invention relates to the technical field of ocean space planning, in particular to an ocean function space configuration method based on landscape ecology. The method comprises the following steps: acquiring multi-source geographic space data of a target sea area, and integrating the data to generate a reference marine landscape map; calculating a group of predefined marine landscape pattern indexes according to the reference digital marine landscape map, and quantitatively evaluating the space structure of the map through the marine landscape pattern indexes; an iterative space optimization algorithm is executed, and the spatial layout of the functional area is reconstructed on the premise that constraint conditions are met by taking optimization of an ecological structure as a target; and finally, outputting a marine function space configuration diagram after ecological optimization. According to the method, the limitation of a traditional qualitative zoning method is overcome, a scientific, quantitative and repeatable process is provided for ocean space planning, and therefore the ecological integrity of ocean management planning is effectively improved.

The application provides a sea wind farm layout optimization method based on tail flow constraint random reconstruction, aiming at the problems that the tail flow effect and electrical fault control are separated in the prior art, and the aerodynamic interference is ignored in fault reconstruction. The application first establishes an initial layout model with tail flow awareness, maximizes wind energy capture efficiency through nonlinear space optimization, then constructs a probability scenario of fault and wind speed, and quantifies the influence of cable random fault on tail flow propagation. Specifically, the application designs a dynamic reconstruction optimization layer, embeds aerodynamic power constraints and tail flow sequence into electrical topology decision, has aerodynamic and electrical regulation and control capability in fault state, realizes investment planning and reconstruction optimization through progressive solution, and introduces a tail flow feasibility cutting mechanism to improve robustness. The application effectively solves the limitations of traditional layout design, improves power generation efficiency, reduces tail flow loss, shortens fault recovery period, and provides a high-precision scheme for sea wind farm layout optimization.

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.

The invention discloses a glass composition optimization method and system based on a neural network and a goat optimization algorithm, and relates to the technical field of glass composition optimization. The method comprises the following steps: optimizing hyper-parameters of a neural network by using a goat optimization algorithm, and constructing a high-performance neural network prediction model; then, the optimized neural network prediction model is embedded into a fitness evaluation link of a goat optimization algorithm, in the glass component optimization process, performance indexes of different component combinations are rapidly predicted through a neural network, the performance indexes serve as fitness values of the goat optimization algorithm, and the algorithm is guided to efficiently search for the optimal component combination. According to the method, through the high-precision prediction capability of the neural network and the global search and adaptive capability of the goat optimization algorithm, the efficiency and accuracy of glass component design are remarkably improved, an innovative solution is provided for research and development of high-performance glass materials, the separation dilemma of'prediction-design 'of glass components is broken through, and the method is suitable for large-scale popularization and application. And the core challenge of high-dimensional constraint space optimization is solved. And glass research and development is pushed from trial and error experiments to calculation design.

This invention discloses an adaptive reinforcement learning-driven multi-objective optimization method for accelerators, belonging to the technical field of accelerator optimization design. First, it analyzes the mechanism of action of key design variables such as convolution loop unrolling factor, quantization accuracy, and cache partitioning, establishes a system-level state modeling method, and designs the DDPG framework suitable for continuous action space optimization as a learner for the configuration strategy. Simultaneously, a lightweight searcher is implemented by running Python scripts on the CPU to search and allocate the block parameters of Loop-3 and Loop-4 in real time, thereby reducing the computational burden on the reinforcement learning agent. Finally, an adaptive configuration method driven by reinforcement learning and a corresponding search process are proposed to achieve joint optimization of unrolling scale, quantization accuracy, and block parameters.

The application discloses a hysteretic parameter identification method of a BWBN model based on an improved particle swarm optimization, and belongs to the field of hysteretic parameter identification. The method comprises the following steps: determining the equivalent yield point of a RC pier under a pseudo-static force reciprocating load according to a skeleton curve of a measured hysteretic curve; giving boundary constraints of hysteretic parameters to be identified; obtaining optimal values of the hysteretic parameters to be identified by using an improved particle swarm optimization; optimizing a global historical optimal position by using a Levy flight strategy and a pattern search algorithm in each step of iteration of the improved particle swarm optimization, taking the global historical optimal position output in the last step as the value of each hysteretic parameter to be identified, and completing the hysteretic parameter identification of the BWBN model. The application solves the problems that the existing method is prone to falling into a local optimal solution in the later period, premature convergence, the convergence speed and fitting precision depend on the selection of parameters, and evolution lacks sufficient exploration ability and is difficult to guarantee the diversity of a particle population when dealing with complex high-dimensional space optimization problems.

The invention discloses a robust classification model training method based on adversarial data enhancement. The method comprises the following steps: S1, actively collecting samples; s2, generating an adversarial sample model; s3, performing quality filtering and hardness scoring on the adversarial sample; s4, performing sample layering and adaptive training; s5, cross-task meta-learning enhancement is carried out; according to the robustness classification model training method based on antagonism data enhancement, through StyleGAN2 four-level potential space optimization projection and in combination with perception maintenance loss and potential code regularization, a generated confrontation sample not only meets the concealment but also has high fatality, and the robustness classification model training method based on antagonism data enhancement has the advantages that the robustness classification model training method based on antagonism data enhancement has high robustness and high robustness. Samples are divided into an easy layer, a medium layer and a difficult layer based on hardness scores, and high-hardness samples are dynamically introduced through a function increasing progressively along with training rounds, so that the model is prevented from wasting computing power on the easy samples, and training resources are ensured to focus on key samples for improving robustness.

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 invention provides a burn-up calculation method based on Grassmann manifold space mapping, and belongs to the field of reactor physical computation.The method comprises the steps that principal component analysis is conducted on nuclide density of a burn-up area, and if data points of the sum of contribution values are distributed in a relatively concentrated mode in the principal component direction, space optimization calculation is suitable for fuel rods of the part; and for a burnup area with discrete distribution of contribution value sum data points, the method is not suitable for space optimization calculation. And a k-means clustering algorithm is further adopted to specifically classify the burnup regions, so that the burnup regions under each classification have more approximate sum of contribution values of nuclide principal components. And finally, the nuclide density snapshot matrixes of all the burnup regions are projected to a Grassmann manifold, the snapshot matrixes of the same kind of burnup regions are mapped to a tangent space based on the Grassmann manifold, and the Euclidean distance between points of each burnup region on the Grassmann manifold is used as a change relation between the snapshot matrixes to carry out space optimization calculation. According to the invention, the memory overhead can be greatly reduced.

The invention relates to an ecological protection priority area identification method and system based on multi-source data and space optimization, and the method comprises the steps: collecting and preprocessing multi-source space data in a target area, and generating a theme space data set; quantifying the ecosystem service in the target area, and obtaining a standardized magnitude spatial distribution map; combining the species distribution point location in the target area with the theme space data set, and simulating to generate a suitability space distribution diagram; fusing the standardized magnitude spatial distribution map and the suitability spatial distribution map to form a regional comprehensive protection value curved surface; and carrying out iterative calculation on the regional comprehensive protection value curved surface by adopting a space optimization algorithm to obtain an ecological protection space layout scheme, and delimiting different protection level priority ecological protection regions of the target region. According to the method, the accuracy, scientificity and operability of an ecological protection priority area identification delimiting process are greatly improved, and an efficient technical approach is provided for cooperative protection of biodiversity and ecological system service.

The invention belongs to the crossing field of oil and gas field development and artificial intelligence optimization methods, and particularly discloses an old oil field well pattern reconstruction optimization method based on GSO-Niche multi-objective collaboration, which comprises the following steps: according to dynamic and static parameters of an oil reservoir and an oil-water well, determining well pattern density and well pattern type, initializing well position parameters, calculating evaluation indexes, and normalizing the evaluation indexes; on the basis of the evaluation indexes, a dominant set well pattern deployment scheme is obtained, the dominant set is continuously updated, well position parameters obtained after a follow-up process are updated through a firefly optimization algorithm, and finally the optimal well pattern deployment scheme is determined by means of the niche technology according to the dominant set. According to the method, the firefly group optimization algorithm and the niche technology are fused, five-dimensional index collaborative optimization is combined, the high-dimensional parameter space optimization problem and the blindness of multi-target solution set screening are solved, the optimization efficiency is greatly improved, the order of magnitude is improved, and the method is particularly suitable for encryption adjustment and secondary development of land high-water-content old oil fields.

The invention provides a split type flying vehicle learning planning method for obstacle areas with different densities, and belongs to the technical field of aircraft planning. Comprising the following steps: step 1, dividing an obstacle area into a dense obstacle area and a sparse obstacle area; step 2, motion space optimization is carried out; different action spaces are adopted for obstacle areas with different densities; step 3, planning a task path; according to the method, a flying vehicle adopts different action spaces and environments to perform efficient interaction, an action sequence capable of accumulating rewards to the maximum, namely an optimal action sequence, is obtained, and finally, a short air-ground cooperative task path is planned with higher learning efficiency. According to the method, the concept of an obstacle density threshold value is introduced, especially for a sparse obstacle area, the flying vehicle mainly keeps the action towards the target position direction, other redundant actions are removed, and the action space dimension is effectively reduced. Therefore, the learning efficiency is obviously improved.

The invention discloses an electricity-carbon cooperative scheduling method and system based on a target random path signature algorithm, and belongs to the field of power systems, the method comprises the steps of obtaining prediction data and a scheduling optimization problem of a power system, the scheduling optimization problem being an optimization problem of calculating a multi-stage scheduling model under a constraint condition; and inputting the prediction data into a multi-stage scheduling model, calculating the multi-stage scheduling model through a target random path signature algorithm to obtain a scheduling scheme, and scheduling the power system of the next day according to the scheduling scheme, according to the target random path signature algorithm, a function space optimization problem corresponding to the scheduling optimization problem is projected to a signature space through signature transformation, and dimension reduction processing is performed on the signature space through a vortex embedding technology, so that the function space optimization problem is converted into a nonlinear programming problem for calculation, and therefore, through implementation of the method and the system, the scheduling optimization problem can be effectively solved. And efficient and accurate power dispatching can be realized.

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 gate-pump group joint optimization scheduling method for complex plain river network areas and belongs to the technical field of flood control scheduling, which comprises the following steps: based on a long short-term memory network model, a response relationship model of a flood control point water level to meteorological and hydrological conditions and gate-pump engineering scheduling is constructed; a gate-pump group joint optimization scheduling model is constructed in combination with a target function and constraint conditions; the response relationship model and the gate-pump group joint optimization scheduling model are coupled to obtain a gate-pump group multi-objective optimization scheduling model for complex plain river network areas; a non-dominated sorting elitist algorithm for feasible search space optimization is used for multi-objective solution to obtain a non-inferior solution set; a cloud model evaluation method is used for multi-attribute decision of the non-inferior solution set, and a representative solution is preferably selected as a best gate-pump group joint scheduling scheme for complex plain river network areas. The method can solve the problem of excessive complexity caused by direct coupling of river network water dynamics simulation and optimization algorithms and realizes the optimality of gate-pump group joint flood control scheduling in complex plain river network areas.

This application relates to the field of computer technology and discloses a hot-time-space task scheduling method and system for HBM in-memory computing architecture. The method includes: acquiring the thermal state information and spatiotemporal state information of a high-bandwidth in-memory computing system in real time, and performing hot spot prediction and performance prediction to obtain predicted state information; based on the thermal state information, the spatiotemporal state information, and the predicted state information, and according to preset optimization objectives and constraints, performing joint hot-time-space optimization decision-making to generate scheduling instructions including a space mapping strategy and a spatiotemporal execution strategy; and performing dynamic scheduling operations on computing tasks in the high-bandwidth in-memory computing system according to the generated scheduling instructions. This application effectively solves the performance degradation, fluctuation, and reliability risks caused by the coupling of thermal management, timing scheduling, and space resource allocation in HBM in-memory computing architecture, and improves the overall energy efficiency and stability of the system.

The application discloses a fuzzy hierarchical simulation-optimization-evaluation method for optimal management measures of non-point source pollution. The method builds a double-level intelligent algorithm model containing environmental and economic targets and corresponding constraint conditions, and couples with a hydrological model to form a hierarchical simulation-optimization algorithm. In the optimization calculation, the fuzzy expected value and fuzzy reliability are used to handle uncertainty, and the hierarchical iteration linkage optimization of sub-basins and whole basins is executed to obtain the Pareto optimal solution of the optimal management measure space configuration scheme. Then, the scheme attribute value is extracted to build a fuzzy decision matrix, the weight is calculated by combining the fuzzy maximum deviation method, and the relative closeness of each scheme is calculated by the fuzzy TOPSIS, and the optimal configuration scheme is output. The application solves the problems of long time consumption of dynamic link formed by existing effect simulation and spatial optimization of optimal management measures, and the problems of insufficient uncertainty processing, and improves the efficiency and comprehensive benefits of scientific control of basin non-point source pollution.

The invention discloses an adaptive reinforcement learning driven accelerator multi-objective optimization method, and belongs to the technical field of optimization design of accelerators. The method comprises the following steps: firstly, analyzing an action mechanism of a convolution loop expansion factor, quantization precision and cache division key design variables, establishing a system-level state modeling method, and designing a DDPG framework suitable for continuous action space optimization as a learner of a configuration strategy; and meanwhile, a Python script runs at a CPU end to realize a lightweight searcher, and real-time search and distribution are carried out on block parameters of Loop-3 and Loop-4 of convolution operation, so that the calculation burden of a reinforcement learning agent is reduced. Finally, an adaptive configuration method driven by reinforcement learning and a matched search process are provided, and joint optimization of the expansion scale, quantization precision and partitioning parameters is achieved.

This invention relates to the field of space optimization technology and discloses a method for optimizing container loading space based on robot collaboration. This method constructs a multi-dimensional physical model of the package by acquiring point cloud data and weight distribution. A loading scheme including target pose, pressure tolerance threshold, and loading sequence is generated based on a reinforcement learning algorithm. The robotic arm generates motor current-position composite control commands based on a pre-trained grasping dynamics model to achieve precise grasping. During loading, the package pose is monitored in real time using a Kalman filter algorithm. When the deviation exceeds a threshold, a local spatial KD tree is constructed and the collision probability gradient field is calculated to generate a compensation scheme that satisfies stability constraints. Finally, the quadtree index is updated and fed back to the reinforcement learning algorithm for online optimization. This method effectively solves the efficiency decline problem caused by inaccurate grasping force control and accumulated execution deviations during robot loading, achieving efficient and precise automated loading.

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.