96 results about "Algorithmic efficiency" patented technology

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.

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 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 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 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 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.

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.

The invention relates to the technical field of holography, in particular to a color hologram generation method based on an approximate secondary phase mask. The method comprises the following steps: constructing an approximate secondary phase; extracting an optimized approximate secondary phase mask from a spatial domain or a frequency domain according to the approximate secondary phase; and decomposing a target image into RGB three-channel amplitudes, and combining the optimized approximate secondary phase mask with the three-channel amplitudes to obtain a color hologram. According to the method, the calculation efficiency can be improved, a hybrid optimization strategy is provided, a high-quality initial phase is quickly generated through an approximate secondary phase mask, the phase distribution is refined by combining limited GS iteration (10-30 times), the single frame generation time is compressed to be within 5 seconds, the efficiency is improved by more than 50% compared with that of a traditional GS algorithm, high-frequency noise is inhibited through regular phase distribution, and the calculation efficiency is improved. The phase uniformity is improved in combination with double-domain optimization, so that the PSNR value of the reconstructed image exceeds 28dB, the SSIM value exceeds 0.9, and the noise power is reduced by more than 30% compared with a random phase method.

The invention relates to the field of mechanical arm path planning, in particular to a path optimization obstacle avoidance method based on an improved Inform-RRT * mechanical arm motion planner. Firstly, parameter information required by path planning is acquired, then an initial step length is determined according to an environment, a random sampling point is generated, a nearest node is found, and a new node is generated according to the step length; continuously updating path points and father nodes, and optimizing path cost; by using the rewiring process in the RRT * algorithm, the radius search of the fixed step length is changed into the dynamic step length, the path length is shortened, and the algorithm efficiency is improved. In combination with an APF function, an artificial potential field function in the APF function is fused into an Inform-RRT * algorithm, and a good convergence effect of the algorithm is used for making up the defects that the number of nodes is too large and convergence is slow before the traditional Inform-RRT * forms an elliptical convergence domain. And finally, the improved path planning obstacle avoidance algorithm is added into an ompl planning library, and a path planning obstacle avoidance experiment is completed in ROS MoveIt in combination with a UR3 mechanical arm model. The number of sampling points is effectively reduced, and the working efficiency of the mechanical arm is improved.

The application discloses a kind of based on the energy-saving method of urban rail comprehensive system optimization of multi-vehicle cooperation, comprising the following steps: according to basic input data, establish train-line-net-graph comprehensive system coupling model;Establish the time-space-speed three-dimensional state network of train operation;According to train motion characteristics, determine the train control operating mode transfer rule between adjacent time stages in time-space-speed three-dimensional state network;The train state under each time stage of time-space-speed three-dimensional state network is reduced dimension, and effective state space domain is obtained;According to multiple body dynamic programming algorithm, train-line-net-graph comprehensive system coupling model, train control operating mode transfer rule and effective state space domain, output optimal collaborative multi-train three-dimensional trajectory.The application can significantly reduce the iteration number of tidal flow calculation, greatly improve the algorithm efficiency, can simultaneously optimize the running process of all trains in multi-train system, realize system state optimization.

The invention provides a scheduling method for dual-objective optimization of a multi-agent flow shop under time-of-use electricity price, and relates to the technical field of intelligent manufacturing and production scheduling. According to the method, unified modeling is carried out on the total power cost TEC and the total customer completion time TCTA, and a dual-target mixed integer programming model is constructed; a double-node decision tree structure is constructed, and synchronous processing of task sorting and processing time interval distribution is achieved; analyzing the structural characteristics of the multi-agent flow shop under the constraint of time-of-use electricity price, designing a series of inequality constraints and multiple pruning rules, and compressing a search space on the premise of ensuring the integrity of a feasible solution; the method comprises the following steps of: performing dual-objective cooperative solution, embedding a branch and bound algorithm into an improved epsilon-constraint framework to perform iterative solution, accurately obtaining a Pareto frontier, and providing multiple optimal scheduling schemes balanced between cost and service quality for decision makers. According to the method, dual decisions of task sorting and cycle allocation can be realized, and the problems of decision model splitting, low solving algorithm efficiency and the like are solved.

The invention discloses a frame structure design method for material and thickness collaborative optimization under multiple working conditions, and the method comprises the steps: constructing a frame structure subregion cellular automaton model, and employing an internal and external loop iteration mechanism to achieve the optimal distribution solution of the structure material and quality under multiple working conditions. The outer cycle sets target cost and target quality for the inner cycle to provide optimization guidance and construct a combined target field variable function, and the inner cycle drives dynamic updating of cellular thickness and materials under multiple working conditions based on a material and thickness collaborative updating strategy and guides cellular field variables to approach target field variables, so that the structural performance is remarkably improved. In order to break through the algorithm efficiency bottleneck, the global search capability and convergence speed of the algorithm are effectively enhanced through the strategy of parallel point adding and search area expanding, and the method is particularly suitable for optimization design of a frame structure with a large number of materials and thickness design variables under the constraint of multiple working conditions. And an efficient and reliable technical path is provided for lightweight design and performance optimization of the frame structure.

The application discloses a kind of incremental unsupervised domain adaptive image recognition methods based on gradual calibration, comprising: obtaining unmarked target data set and labeled source data set;Using source domain pre-training model to predict target data set to generate target class, and generate pseudo-label for the sample in target data set, store target class and pseudo-label in memory library;Target data set is divided into sample with high confidence and sample with low confidence, and target model is trained in conjunction with source data set;And a variety of constraint losses are applied in the training process to carry out target class level calibration and target level calibration simultaneously;After each iteration training, update the data in memory library until the loss function converges;Using the trained target model to process the image to be identified, the final recognition result is obtained.The method reasonably utilizes the knowledge from the source field, balances and mitigates the relationship between negative transfer and catastrophic forgetting, and improves the algorithm efficiency and accuracy.

The invention discloses a welding manipulator path planning method based on a fast expansion random forest. The method comprises the steps that a welding manipulator kinetic model is designed; an RRF * algorithm tree selection problem is formalized into an MAB problem, a more desirable tree in tree extension is actively selected, and resources are effectively allocated to the most desirable tree; designing a Bayesian updating process, wherein Bayesian updating is gradually improved through an observation result; local sampling is carried out, the local connectivity of the space is utilized through Markov chain random sampling, and the random sampling is updated through a Bayesian suggestion distribution sequence; collision detection is carried out, if a new node falls in a safe feasible region, the new node is added into the random tree, node sampling continues to be carried out, and otherwise, the node is removed and sampling is carried out again; and performing path extraction. According to the welding manipulator path planning optimization algorithm provided by the invention, the planned path can be smoother, and the algorithm efficiency is improved.

The invention relates to a laser scanning-linear growth depth synergistic pre-welding plate splicing seam visual center positioning line acquisition method, which comprises the following steps of: aiming at a pre-welding plate, acquiring laser scanning data of global scanning of a laser scanning module in real time, synchronously acquiring a visual detection image of a visual detection module, and acquiring a visual center positioning line of the pre-welding plate; obtaining global seed points by taking the scanning result as a dominant and a visual detection image as an auxiliary; determining an initial position of the seed point in the visual detection image based on the global seed point, carrying out dense growth by taking the initial position as a starting point, and carrying out cyclic tracking and repairing on a generated breakpoint to obtain a splicing seam selection point; and on the basis of the abutted seam selection points, screening processing is performed firstly, and then composite iteration trimming fitting processing is performed to obtain an abutted seam center positioning line. According to the method, the technical problems of insufficient ultra-narrow slit positioning precision, weak complex interference resistance, difficulty in breaking through meter-scale long-range one-time detection, poor algorithm efficiency and generalization adaptation and the like can be solved.

The application discloses an image up-sampling and enlarging display method in the technical field of image processing, and comprises the following steps: S100, constructing a three-dimensional scene and importing an image; S200, rendering the scene in a small size to generate an initial frame buffer as the input of the next step; S300, performing time anti-aliasing on the input frame buffer to generate a new frame buffer after eliminating jaggies as the input of the next step; S400, performing fast resampling calculation based on a CAS algorithm on the input frame buffer to generate a large-size frame buffer as a display frame; and S500, sampling and outputting the input frame to a viewport. The application has the beneficial effects that the original image sampling up-sampling algorithm is optimized, the algorithm efficiency of the image up-sampling display is improved through multi-point sampling and optimized calculation, and the display effect of the image up-sampling is optimized.

The invention discloses a transaction matchmaking algorithm based on Nash equilibrium, and aims to solve the problems of extensive demand matching, single valuation model and low algorithm efficiency in a traditional transaction mode. According to the technical scheme, the method comprises the following steps: 1) realizing accurate demand matching through NLP and domain expert knowledge; 2) constructing a multi-dimensional utility function model, and quantifying the interests of the two transaction parties on the basis of a Speak prediction model; 3) optimizing the Nash equilibrium calculation efficiency by adopting an MWU-CGD hybrid algorithm, and verifying that the convergence speed is increased by 3.2 times in a simulation environment; the Nash bargaining model is applied to knowledge achievement transactions for the first time, fair and efficient benefit distribution is achieved, experiments show that the matching success rate is increased to 68% in biological medicine asset transactions, the effectiveness of the two parties is averagely improved by 15%, and the method is suitable for real-time matching of complex transaction scenes.

The invention discloses a hybrid production flexible assembly job shop scheduling method considering multi-assembly sequence change and transportation tasks based on a Q-Learning model algorithm. The method comprises the following steps: S1, establishing a scheduling model taking total production completion time, total inventory time and total manpower cost as optimization targets; s2, providing a Q-Learning memetic algorithm to solve the scheduling model, designing a five-layer segmented mixed chromosome coding structure and a two-subgeneration chromosome updating method, and introducing a variable neighborhood search strategy and an elite retention strategy; s3, adaptively adjusting the cross range of the chromosomes through Q-Learning to improve the algorithm efficiency; and S4, through comparison with other three algorithms, validity and robustness of the proposed algorithm are verified. Based on the scheduling method provided by the invention, the processing and assembling sequences of product parts can be optimized simultaneously in the scheduling process of multi-product mixed production and consideration of transportation tasks, and meanwhile, machines, workers and AGV resources are reasonably allocated, so that the production cycle of products is effectively shortened.

The invention relates to a block cipher algorithm architecture, an algorithm calling method and device and electronic equipment. The algorithm architecture comprises a quantum security layer which is used for taking a quantum true random number generator as a key entropy source and generating an unpredictable initialization vector and a dynamic key seed based on a quantum optical effect; the hybrid encryption layer is used for encrypting a session key by adopting a hybrid encryption mechanism of post quantum cryptography and SM4 and using a public key algorithm based on lattice cryptography, generating a master key through an anti-quantum algorithm, and generating a sub-key sequence in combination with an SM4 round key expansion algorithm; the core algorithm layer is used for optimizing a round function, dynamically generating an S-box replacement table based on quantum random numbers through an implicit coding technology of dynamic S-box generation and white-box protection, carrying out implicit coding on XOR and shift operations in the round function and injecting redundant noise data; and the protocol adaptation layer is used for integrating a dynamic defense protocol. The encryption security can be improved, and the algorithm efficiency can be remarkably improved.

This application relates to the field of performance testing technology, providing an adaptive sampling method and apparatus for mixed responses based on uncertainty reduction. By constructing a Gaussian process model of multiple types of mixed responses, it models and quantifies the uncertainty of these responses and their potential correlations, thereby solving the problem of difficulty in constructing surrogate models for mixed responses. By generating a reduced candidate sample set based on ROI, it screens experimental samples, improving algorithm efficiency. Adaptive sampling based on the uncertainty reduction of the forward error of the mixed response is used to comprehensively address the uncertainty of the predicted variance and the uncertainty of the predicted mean. The sampling process follows a dynamically adjusted boundary-variance-distance criterion, ensuring the auxiliary role of the boundary-variance-distance criterion in the initial sampling stage. As the number of samples increases, the model stability improves, and the selection of samples gradually becomes dominated by the uncertainty reduction of the forward error of the mixed response with forward capability.

The present application relates to a kind of controller fatigue monitoring method based on facial features, steps are, face detection: face key point detection: eye mouth aspect ratio calculation: for the parameter calibration of individual facial feature difference: severe fatigue behavior detection: fatigue discrimination algorithm design based on dynamic attenuation time window: algorithm effectiveness analysis based on electroencephalogram signal.This method incorporates the characteristics of control work scene in fatigue discrimination algorithm, can more effectively determine and alarm the fatigue state of controller, is of great significance for preventing the occurrence of air traffic control unsafe events, provides relevant ideas for subsequent controller non-contact fatigue monitoring research, and the efficiency of algorithm is optimized, meets real-time demand.

The invention discloses a motion compensation imaging method based on Radon transformation and an SWO algorithm, and belongs to the technical field of laser radar signal processing. The method comprises the following steps: firstly, carrying out dechirp receiving and range pulse compression on an ISAL echo signal; detecting an energy peak value by using Radon transformation, and roughly estimating a target radial speed; constructing a local constraint search space based on the rough estimation speed; in the constraint space, the SWO algorithm is utilized, the image contrast is used as a fitness function, and speed and acceleration parameters are finely searched; and finally, constructing a full-aperture phase compensation function by using the optimal parameters for imaging. The problems that a traditional cross-correlation method is low in precision, and a direct optimization method is slow in iteration and prone to falling into local extremum are solved, and the imaging quality and the algorithm efficiency are remarkably improved.

The invention discloses a method and system for predicting the residual life of a fuse and a storage medium, and relates to a method for evaluating the life of the fuse, and the system comprises a data collection module, an SVM model building module, a model algorithm efficiency analysis module and a decision tree algorithm optimization module. The data is mapped to a space with a higher dimension, and an optimal hyperplane is found to divide different categories, so that prediction is carried out; meanwhile, the SVM has good generalization ability and robustness, a good prediction effect can still be obtained in the face of a small number of training samples and noise, and beneficial effects are obtained from historical data of the fuse; when the speed of the SVM algorithm is slow or the prediction precision is low, the decision tree algorithm is used to optimize the SVM algorithm, and the speed and the prediction precision of the model are improved.