154 results about "Algorithmic efficiency" patented technology

The invention relates to an efficient sparse attention algorithm optimization method and device, equipment and a medium, and the method comprises the steps: obtaining to-be-processed data, and carrying out the layernorm calculation of the to-be-processed data based on importance perception, and obtaining the normalized data; performing row product sparse matrix multiplication and packet hash multi-head attention processing on the normalized data to obtain an attention score matrix; performing softmax calculation based on shift operation according to the attention score matrix to obtain a softmax calculation result; and performing row product sparse matrix multiplication on the softmax calculation result and the attention matrix median vector to obtain an attention calculation result. In the whole process, the sparse attention algorithm is optimized through improved layernorm, softmax and matrix multiplication, the data volume in the attention algorithm processing process is reduced, and the efficiency of the attention algorithm can be remarkably improved.

The invention relates to the technical field of natural language processing, and discloses a cross-domain vocabulary logical reasoning and extension method. According to the method, a multi-source cross-domain text data stream is received, cross-language alignment and semantic fusion are performed through a pre-trained semantic decoupling model, an adaptive semantic extension threshold model and a causal reasoning framework are constructed, logic interruption nodes are identified, a compensation strategy is configured, and collaborative parameters are iteratively optimized by using a distributed semantic collaborative learning framework. Multi-source data can be effectively integrated, semantic barriers can be broken, the vocabulary expansion range and mode can be accurately controlled, logic transmission coherence and accuracy are ensured, efficient collaboration of multi-language agents is achieved, algorithm efficiency and semantic accuracy are improved, the problem of semantic instability is monitored in real time, the problem of semantic instability is solved, and powerful support is provided for cross-domain natural language processing.

The invention relates to the technical field of electrolytic hydrogen production, and discloses a PEM electrolytic cell energy-economy collaborative optimization method based on digital twinning, which comprises the following steps: S1, calibrating parameters of a membrane electrode mathematical model by using a standard test for a single cell of a PEM electrolytic cell, and establishing a high-precision physical simulation model; s2, constructing a high-precision digital twin system based on the high-precision physical simulation model; s3, inputting multi-dimensional data of electricity price fluctuation, hydrogen demand prediction and equipment state, and performing collaborative optimization analysis through an optimization decision algorithm; s4, forming a PEM electrolytic cell control optimization instruction; and S5, the physical entity PEM electrolytic bath executes the instruction. According to the method, the'electricity price-demand-equipment 'dynamic relation is planned as a whole from the global perspective, the process steps cover data-driven modeling, multi-target decision, digital twinning cooperation and closed-loop feedback, balance needs to be achieved among algorithm efficiency, model precision and engineering feasibility, and energy-economy-life multi-target cooperation is achieved.

The invention relates to a power data federated learning privacy protection method based on efficient homomorphic encryption. The method comprises the following steps: step 1, a client initializes a neural network model; step 2, the server side encrypts and updates local neural network model gradient parameters uploaded by the client side; 3, the server side executes gradient average aggregation operation, the server side does not need to decrypt, the encrypted gradient parameters uploaded by the client side are directly calculated, an average value is solved, and therefore global gradient parameters are updated; step 4, after each client receives the updated global gradient, carrying out decryption calculation operation; 5, loading the updated weight parameters of the neural network model into a local neural network model, and performing neural network model training by using a local data set; the problems that an existing encryption technology is large in operand, large in computing resource overhead and low in algorithm efficiency, and privacy leakage exists in the transmission and analysis process of power data can be solved.

The invention provides a power grid energy efficiency optimization and load prediction management method and device, relates to the field of energy and power, and solves the technical problem of data storage and processing delay caused by poor database expansibility and low algorithm efficiency. The method comprises the following steps: integrating various sensor data and intelligent sensing terminal data to form multi-dimensional data; and sending the multi-dimensional data to an edge computing node for preprocessing, and storing the multi-dimensional data in a distributed storage cluster. Load data and multi-dimensional influence factors in the multi-dimensional data stored in the historical distributed storage cluster are acquired, and a prediction model is constructed. And analyzing the multi-dimensional data stored in the distributed storage cluster through an artificial intelligence analysis engine, capturing nonlinear and non-stationary change characteristics of the load, and obtaining real-time prediction data. And acquiring real-time operation data of each link of the power grid from the distributed storage cluster, and optimizing a scheduling strategy in combination with a dynamic loss characteristic model of the equipment. The method and the device are used in the power dispatching process.

The invention relates to the technical field of the Internet of Things, in particular to an edge computing privacy task unloading method, system and device based on reinforcement learning, and the method comprises the steps: obtaining computing task information and task unloading selection generated by each terminal device, and the task unloading selection comprises task selection local execution or task selection unloading to an edge server, local execution cost or unloading cost of each task is obtained based on the calculation; performing statistics to obtain the total cost of all tasks, and establishing an initial global optimization target; the privacy entropy of the task is calculated, and the privacy entropy of the task is used as unloading additional cost to be embedded into the initial global optimization target to update the global optimization target; and searching an optimal strategy of the updated global optimization target by adopting a single-agent reinforcement learning algorithm. The privacy guarantee strength and the dynamic scene adaptability are improved while the algorithm complexity is greatly reduced and the algorithm efficiency is improved, and the method can be suitable for large-scale node, high-dynamic and strong-privacy-constraint application scenes in edge calculation.

The invention discloses an electromagnetic metasurface sparse signal modeling method and device. The method comprises the steps that S1, sparse electromagnetic signals of an electromagnetic metasurface are acquired; s2, solving through signal sparse prior knowledge under the constraint of an electromagnetic constitutive equation to obtain a sparse signal distribution function; s3, according to the sparse signal distribution function, obtaining the coordinate system representation of the sparse signal in the Riemannian manifold space; and S4, according to the coordinate system representation of the sparse signal in the Riemannian manifold space, establishing a signal electromagnetic field manifold embedding model of the sparse signal mapped in the Riemannian space based on the electromagnetic metasurface. By the adoption of the technical scheme, the problems of an existing electromagnetic metasurface sparse signal modeling method in the aspects of algorithm efficiency, model adaptability and hardware collaboration are solved.

The invention discloses a CMIES low-carbon operation scheme generation method based on variable reduction multi-objective evolutionary optimization, and belongs to the technical field of integrated energy systems. Aiming at the multi-target complex constraint characteristic of the CMIES system, a dynamic variable reduction mechanism driven by constraint correlation is provided, key variables are accurately recognized through correlation analysis and constraint gain indexes, and the number of feasible solutions and constraint processing efficiency are effectively improved; a two-stage coevolution strategy is adopted to be fused with random reduction and dynamic analysis, global exploration and local development capacity is balanced in combination with a main and auxiliary population multi-strategy cooperation mechanism, and a Pareto solution set which is high in quality and uniform in distribution is generated; by constructing a low-dimensional auxiliary optimization problem and an inherited updating mechanism, the calculation complexity of a high-dimensional space is reduced, and the algorithm efficiency is remarkably improved while the quality of a solution set is ensured.

According to the safety coprocessor hybrid encryption method based on the SM2 / 3 / 4 domestic cryptographic algorithm, a novel hybrid encryption system based on the SM2 / 3 / 4 and considering encryption safety, efficiency and key management convenience is constructed on the basis of the characteristics of the SM2 algorithm, the SM3 algorithm and the SM4 algorithm, and the encryption / decryption and signature / verification process is achieved. And aiming at the efficiency problem of a domestic SM algorithm, an encryption / decryption and signature / verification scheme is designed and realized through pure software analysis and software and hardware (SW / HW) collaboration, so that optimal division of software and hardware is realized, and the algorithm efficiency is greatly improved.

The invention discloses a kitchen waste collection and transportation path optimization method based on an improved simulated annealing algorithm, belongs to the technical field of intelligent optimization and path planning, and mainly solves the technical bottlenecks of an existing kitchen waste collection and transportation path planning method in the aspects of dynamics, multi-objective optimization and algorithm efficiency. The method comprises an application layer, an algorithm layer and a data layer, and by fusing mixed neighborhood search, a dynamic punishment mechanism and a parallel computing architecture, the timeliness, the economical efficiency and the environmental protection property of path planning are remarkably improved. According to the method, real-time traffic and corruption risks can be dynamically responded, multi-target optimization weights are quantified, different city management strategies are adapted, the problems that a traditional algorithm is low in efficiency and prone to falling into local optimum are solved through multi-chain parallel acceleration and self-adaptive temperature control, and city-level large-scale node real-time scheduling requirements are supported.

The invention discloses a data association analysis method and system for network security, and relates to the technical field of data association, and the method comprises the steps: collecting log and event information, extracting features, forming an event feature database, enabling events of the event feature database to pass through an improved data association Apriori algorithm to obtain a frequent event set, generating an association rule according to the frequent event set, generating an effective security event association rule by using a support degree, a confidence degree and a lifting degree, and detecting a security threat or attack behavior; according to the improved Apriori algorithm, the number of scanning times of a database and generated redundant intermediate sets and candidate items are reduced, so that the efficiency of the Apriori algorithm is improved, the improved algorithm can directly perform self-connection to generate a next-level frequent item set by skipping generation of the candidate items, and the efficiency of the algorithm is improved. The time generated by scanning the target database is shortened, so that the algorithm efficiency is improved, and a data association system with network security is obtained more quickly.

The invention discloses a multi-target road transportation path planning method and system based on multiple parameters A * and NSGA-II, and belongs to the technical field of road transportation path optimization. The method comprises the following steps: constructing a large-scale road network directed graph structure; establishing a satellite orbit model and a risk distribution model, calculating a dynamic risk value of a roadside, and forming dynamic road network data; a three-stage initialization strategy is adopted to generate an initial population, transportation time and reconnaissance risks are taken as double targets, evolutionary optimization is carried out through an NSGA-II algorithm, and a Pareto optimal path set is output. The system comprises a corresponding road network module, a risk module and a planning module. According to the method, the technical problems of multi-target conflict, low initial population quality and satellite risk avoidance of a traditional method are solved, and the timeliness, the concealment and the algorithm efficiency of path planning are remarkably improved.

The invention relates to the technical field of panel processing, in particular to a panel furniture workpiece typesetting method based on an improved genetic algorithm, which comprises the following steps: acquiring parameter information of a to-be-typeset workpiece; and performing classification based on the parameter information of all the workpieces to be typeset to obtain a plurality of workpiece categories, endowing each workpiece category with a unique type code, constructing an initial population according to the category and the number of the workpieces to be typeset, and inputting the initial population into the improved genetic algorithm to obtain an optimal chromosome. According to the method, the workpieces with the same size and shape are classified into workpieces with the same specification, and a repeated gene coding mode is adopted, so that the defect that actual repeated chromosomes are generated when a plurality of workpieces with the same specification are processed in a traditional unique coding mode is effectively avoided, and the algorithm efficiency is improved. And secondly, in the gene crossover operation, an improved ordered crossover and order crossover strategy is introduced, and a random deletion mechanism is combined, so that the algorithm population diversity and the solution space coverage are effectively improved, and the search divergence and convergence efficiency are improved.

The invention discloses an unmanned aerial vehicle cluster task allocation method based on a large language model optimization genetic algorithm, and belongs to the field of computers. The method comprises the following steps: setting a specific chromosome coding mode; generating a multi-constraint initial population; calculating fitness to quantify the advantages and disadvantages of individual genes of the population; when the optimal individual meets the requirement or the maximum iteration round is reached, ending; retaining the optimal individual as a filial generation; generating a batch of new filial generation individuals by the large language model, and fusing the new filial generation individuals with the current filial generation population; calling an optimized large language model to analyze individual chromosome semantics, and outputting an evolutionary potential score; obtaining an individual comprehensive selection probability by integrating the fitness and the evolution potential score, and executing a selection operation; and selecting individuals based on the individual comprehensive selection probability to carry out crossover and mutation operation to generate offspring. The large language model is embedded into the core link of the genetic algorithm, and the algorithm efficiency is improved by improving the population diversity of the genetic algorithm in the unmanned aerial vehicle cluster task allocation scene.

The invention discloses a tractor trailer robot track generation method based on a polynomial and a symbolic distance field, and the method comprises the steps: dividing grids through a point cloud map, estimating the curvature and normal direction of each grid, extracting obstacle information, and calculating a symbolic distance value corresponding to each grid; taking the initial pose of the tractor trailer robot as a root node, and based on the obstacle information, utilizing a multi-end-point heuristic graph search algorithm for spatial search in SE (2) to generate an initial path for arriving at the target area; and in combination with the symbol distance value corresponding to each grid, modeling the trajectory generation of the tractor trailer robot as a constraint optimization problem, and solving the constraint optimization problem by taking the initial path as an initial value to obtain a final trajectory. The problem that track generation is difficult due to complex kinematics, high state space dimension and deformable structure of the traction trailer robot is solved, the problems that an existing algorithm is low in efficiency and loses solution space are solved, and efficient track generation of the traction trailer robot is achieved.

The invention discloses a low-orbit constellation measurement and control resource scheduling scheme based on an improved genetic algorithm, and the scheme comprises the steps: firstly building a model through satellite simulation software STK, obtaining resource data, building a scheduling distribution model, converting measurement and control resources and measurement and control tasks into a mathematical form, and carrying out the measurement and control of the low-orbit constellation, thereby achieving the high-dimensional and multi-constraint problems of the measurement and control of the low-orbit constellation. A profit value is specified for each measurement and control task, an arc section is used as a measurement and control main body according to constraint conditions, a function expression for evaluating the advantages and disadvantages of a scheme is specified, finally, an improved genetic algorithm is applied, improved binary coding is adopted, the measurement and control arc section is used as a gene unit, a tournament selection mechanism is utilized, and data preprocessing and multiple constraint judgment correction are carried out. And solving an optimal scheme. The scheme can efficiently process complex scheduling, reduce invalid search and improve algorithm efficiency and scheme feasibility, meets actual operation requirements, and is suitable for scenes such as commercial satellite measurement and control.

The invention discloses a test case automatic generation method and device, electronic equipment and a storage medium. The method comprises the steps of obtaining a to-be-tested program; analyzing the to-be-tested program to generate an initial population; determining a fitness function; performing iterative optimization on the initial population based on the fitness function to obtain an optimal individual; and taking the optimal individual as a target test case for the to-be-tested program. According to the technical scheme, the boundary value use case generated based on the program control flow diagram is fused with the candidate use case to generate the initial population, the coverage rate of critical paths in the population can be effectively increased, the proportion of redundant use cases can be effectively reduced, and under the same test target, the scale of the use case set is reduced, and the test execution time is shortened. The hierarchical fitness function design reduces the proportion of high-time-consumption operation cases and improves the test efficiency. All stages of operation of the genetic algorithm are combined with all stages of operation of the artificial bee colony algorithm, and algorithm efficiency is improved.

The invention discloses a heavy-load freight direct-current electric locomotive on-board platform and a train operation curve calculation method. Step-by-step iterative optimization is carried out through a dynamic programming algorithm. Presetting operation steps and step lengths, and defining a selectable action set in each step length; and with the current state of the train as a starting point, evaluating each action in the action set in each iteration step, and calculating the next train state which may be transferred to. And evaluating all possible train state changes by adopting a target cost function, sorting according to the cost values from small to large, and preferentially selecting the action with the minimum cost value for subsequent calculation. And if all the action cost values of the current step exceed the limit, backtracking to the previous iteration step for reselection. And after iteration to the maximum step number, storing all train state changes, and finally obtaining an optimal train operation curve under the constraint of the target function. The method optimizes a control strategy through a dynamic programming algorithm, improves prediction precision, optimizes action selection, accelerates convergence, reduces redundancy, and improves operation stability, safety and algorithm efficiency.

The invention relates to the technical field of three-dimensional reconstruction, in particular to application of a non-local hierarchical adaptive propagation three-dimensional reconstruction method in cultural relic heritage protection. Through bilateral Gaussian weighted luminosity consistency cost calculation, a robust solution is provided for a low texture region, a shielding problem and an illumination change problem in multi-view matching, and technical support is provided for high-quality three-dimensional reconstruction of a complex scene. The hierarchical sampling strategy combines the advantages of coarse sampling and fine sampling, the optimal direction is screened in the coarse sampling stage, invalid calculation is reduced, precise propagation is performed in the high-confidence direction in the fine sampling stage, checkerboard division ensures the calculation independence of adjacent pixels, and the algorithm efficiency is improved; by introducing non-local operation and flexible sampling radius setting, hypothesis propagation efficiency and depth estimation accuracy are effectively improved in a low-texture area, a shielding scene and a complex scene, and a more reliable technical guarantee is provided for high-quality 3D reconstruction.

The invention discloses a distributed heterogeneous flexible flow shop batch processing scheduling method and system, relates to the technical field of distributed production scheduling in the manufacturing industry, and aims to solve the problems that an existing scheduling method is not comprehensive in constraint consideration, poor in energy consumption optimization and low in algorithm efficiency. According to the method, a mixed integer linear programming model containing multiple constraints such as release time and sequence-related preparation time is constructed, a learning-assisted dual-objective co-evolution framework is established, and the maximum completion time and the total energy consumption are synchronously optimized by combining mixed initialization, global-local search collaboration, decision reinforcement learning operator selection and a collaborative energy-saving strategy. The release time, the sequence-related preparation time, the inter-stage transportation time and the batch processing scheduling are simultaneously considered in the distributed heterogeneous flexible flow shop scheduling for the first time, the established mixed integer linear programming model better fits the actual production scene, and the method fits the actual production scene, is good in energy consumption optimization effect and can be adapted to the non-ferrous metal metallurgy aluminum production process.

The invention discloses a sparse auto-encoder-based cell type deconvolution modeling method and system, and relates to the technical field of artificial intelligence technology and bioinformatics. Single cell RNA sequencing data is input into a trained sparse auto-encoder model to obtain a predicted cell type; the sparse auto-encoder model training process comprises the following steps: generating simulated bulk transcriptome data by using single-cell RNA sequencing data to construct a training set; constructing a sparse auto-encoder model, simulating bulk transcriptome data, generating a predicted cell type proportion through an encoder, and generating reconstructed bulk transcriptome data through a decoder according to the predicted cell type proportion; constructing a total loss function based on the KL divergence function, the reconstruction error and the prediction error so as to optimize trainable parameters in the sparse auto-encoder model; according to the deconvolution modeling method and system, the problems of low cell type prediction accuracy and low algorithm efficiency in the prior art are solved, and rapid and efficient bioinformatics analysis is realized.

The invention belongs to the technical field of power grid dispatching, and discloses an energy system low-carbon dispatching method and device based on deep reinforcement learning, and the method comprises the steps: changing the input structure of a decision network through the extraction of Riemannian flow pattern features and the analysis of group distribution; the input of a global decision network in the prior art is changed from environment state information to global state information and streaming feature group distribution and prediction group distribution at the next moment. The input of a single local decision network in the prior art is changed from local state information to flow pattern feature group distribution, predicted group distribution at the next moment and a corresponding unit state; according to the method, the input dimension of the decision network and the complexity of decision search are remarkably reduced, so that the algorithm efficiency is effectively improved, and rapid scheduling of the energy system is realized.

The invention relates to the technical field of robot positioning, in particular to an automatic robot positioning method based on vision assistance, which comprises a vision acquisition module (1), a multi-modal data fusion module (2), a deep learning feature extraction module (3), a self-adaptive positioning optimization module (4) and an execution control module (5). The visual collection module (1) collects multi-source data through the camera (11), the infrared sensor (12) and the laser radar (13), after the multi-mode data fusion module (2) integrates the multi-source data, key features are extracted through the deep learning feature extraction module (3), then the self-adaptive positioning optimization module (4) dynamically adjusts weights and corrects errors, and finally accurate positioning and navigation are achieved through the execution control module (5). According to the invention, through the adaptive weight adjustment system (24), the adaptive feature enhancement system (34) and the dynamic error compensation system (44), the positioning precision and robustness in a complex environment are significantly improved, and meanwhile, the intelligent obstacle avoidance system (53) ensures safe operation. According to the invention, the problems of insufficient complex environment adaptability, limited positioning precision and low algorithm efficiency in the prior art can be effectively solved.

The invention belongs to the technical field of multi-unmanned aerial vehicle collaborative planning, and particularly relates to a multi-unmanned aerial vehicle intelligent avoidance target searching method based on prior probability information guidance, which comprises the following steps: step 1, establishing an unmanned aerial vehicle platform model, a sensor model and a target motion model; 2, an environment information graph model is established, wherein the environment information graph model comprises a target search probability graph, an environment uncertain information graph and a digital pheromone graph; and step 3, performing path planning by combining particle swarm optimization (PSO) and a Dubins curve under a distributed model predictive control (DMPC) framework, tracking the target by the unmanned aerial vehicle according to avoidance dynamics of the target, and stopping moving once the target is captured by the unmanned aerial vehicle until all the targets are captured. According to the method, the motion prediction model is established, the probability map is updated in real time, efficient search of multiple targets is achieved under the condition that the number of the targets, the initial positions and the motion states are known, and the problems that a collaborative search algorithm is low in efficiency and resources of multiple unmanned aerial vehicles are wasted are solved.

The invention discloses an intelligent automatic course arrangement method and system and a storage medium, relates to the technical field of informatization education, and aims to solve the problems that personalized course arrangement is insufficient, algorithm efficiency is limited and dynamic adjustment difficulty is high. Curriculum and classroom resources are accurately matched, the priority coefficient Pxs of the personalized course arrangement request is calculated based on the total available duration Zsc of the teacher, the curriculum continuity index Zkc and the student course schedule conflict value Zkb, an optimized course arrangement scheme is generated, and evaluation is carried out by calculating an optimized value Yhz; meanwhile, a resource matching and real-time monitoring mode is adopted, classroom resources and teacher load data are monitored, and a first resource matching value Rhz1 is generated; and by calculating the second resource matching value Rhz2, the matching degree of the scheme is quickly judged, dynamic adjustment and optimization are realized, adaptive change of the scheme is ensured, and the course arrangement efficiency and flexibility are improved.

The invention provides an alignment method and device for a three-dimensional model pose and a human pelvis pose, and relates to the technical field of medical image registration, and the method comprises the steps: obtaining pelvis 2D data of a patient in a pelvis minimally invasive surgery and a pre-constructed model pose of a three-dimensional model of the patient; the method comprises the following steps: determining a plurality of initial pose offset matrixes according to the positions of a plurality of interest points in pre-configured pelvis 3D data and the target position of each interest point in the body of a patient, determining a target pose offset matrix in the plurality of initial pose offset matrixes, and adjusting the model pose according to the target pose offset matrix to obtain a target pose. The model pose of the three-dimensional model is aligned with the pelvic pose of the patient. According to the method, the globally optimal solution is searched by adopting the step-by-step subdivision search strategy, the branching and trimming steps are realized, the scale of the search space is reduced, the algorithm efficiency is improved, and the method has higher adaptability and universality for the shape alignment problem.

The invention provides a satellite constellation simulation method based on a native set model. The satellite constellation simulation method comprises the following steps that a current view angle rotation matrix is obtained through calculation according to the current moment; and calculating a satellite orbit. At the T0 moment of each circle, calculating satellite coordinate points contained at the starting and ending moments of the current circle to form a satellite orbit, calculating the satellite coordinate positions by introducing a front-end library, when the next circle arrives, calculating the satellite orbit of the current circle again at the T0 moment of the circle, and updating and displaying the satellite orbit; introducing a front-end library to calculate a satellite coordinate position; the satellite orbit and the view angle keep the same view angle rotation matrix for rotation transformation. According to the method, the algorithm efficiency is greatly improved, the interaction process of the front end and the rear end is saved, the calculation and display efficiency of the satellite orbit is improved and the rendering performance is improved on the premise that the display effect of satellite simulation is not influenced, and the native model supports dynamic updating, so that the geometrical shape and the material can be modified during operation, and re-creation is not needed.

This invention provides an iterative measurement device and method for the surface shape of a cylindrical axially stressed component, belonging to the field of component measurement technology. It solves the problems of low measurement accuracy, single measurement method, low algorithm efficiency, and insufficient precision of existing measurement devices. This measurement device can detect the perpendicularity of the cylindrical axially stressed component, ensuring its vertical placement. It can stably adjust the height of the probe detection mechanism and the laser measurement mechanism, maintaining stable positions without wobbling, thus satisfying the requirement of measuring the outer contour of the cylindrical axially stressed component's surface twice. By controlling the main unit in conjunction with the probe detection mechanism and the laser measurement mechanism, it achieves rapid and accurate measurement of the cylindrical axially stressed component's appearance shape. The measurement method of this invention does not cause errors in the measured specimen's appearance due to the irregular shape of the track itself. Utilizing an iterative algorithm, it can achieve precise measurement of the appearance of the cylindrical axially stressed component and the track.

The application provides a super block management method and device and electronic equipment, comprising: obtaining a target storage block in a storage unit in a plurality of NandFlash; the block number of the target storage block is the same; initializing a minimum heap container; constructing a super block according to the target storage block; writing a first data structure of a first sub-super block in the super block in a full data state into a first minimum heap in the minimum heap container; and writing a second data structure of a second sub-super block in the super block in an unwritten data state into a second minimum heap in the minimum heap container; and managing the write operation and the garbage collection operation of the super block through the first minimum heap and the second minimum heap. The method improves the garbage collection and wear leveling algorithm efficiency of the SSD firmware.

The invention discloses a single pixel panoramic imaging (SPPI) method based on a Walsh-Hadamard (W-H) base. The SPPI technology is widely applied to the fields of automatic driving, pipeline detection, low-altitude economy and the like, and according to a traditional scheme, although efficient panoramic perception is achieved through combination of a convex mirror and an imaging algorithm, the algorithm efficiency and precision still need to be optimized. According to the invention, a W-H orthogonal basis optimization panoramic annular modulation mode is introduced, and the SPPI technology is improved on one-dimensional and two-dimensional imaging algorithms. The method mainly comprises the following steps: 1) constructing a two-dimensional orthogonal ring modulation framework by adopting a W-H orthogonal basis to replace a random mode; 2) building a panoramic imaging system based on a passive modulation single-pixel camera; and 3) improving a single-pixel panoramic imaging model based on two-dimensional W-H base orthogonality and a V2DALM algorithm, and optimizing the panoramic imaging adaptation capability of the algorithm. According to the method, the imaging speed and quality are considered, and the optimal SPPI scheme can be selected for different application scene requirements such as high fidelity, high speed and speed-fidelity balance.