39 results about "D algorithm" patented technology

The invention discloses a remote cooperative diagnosis task allocation method. Aiming at complex diagnosis tasks, an RCFD (Remote Collaboration Fault Diagnostic) center is used for decomposing the complex diagnosis tasks into a plurality of executable diagnosis tasks and allocating the executable diagnosis tasks for allowing each diagnosis resource participating in the cooperative diagnosis to execute; and each executable diagnosis task can also carry out diagnosis task allocation on the basis of a method for expanding a contract net. The remote cooperative diagnosis task allocation method disclosed by the invention comprises three links, namely diagnosis task model establishment, diagnosis task path planning and diagnosis resource configuration and has the advantages of modularity, favorable expansibility, wide application occasions and the like, wherein in the diagnosis task model establishment link, an intersection set of different types of models is paid attention to from different decomposition granularities and weights of the models are optimized by applying a Bayesian network method; in the diagnosis task path planning link, a unified key path planning algorithm is established on the basis of a D algorithm; and in the diagnosis resource configuration link, a configuration algorithm is established by fusing multi-constraint indicators from a view of service.

The invention provides an MOEA / D algorithm based aluminum electrolysis production optimization method. The method comprises: at first, modeling an aluminum electrolysis production process by utilizing a BP neural network; and then, optimizing a production process model by utilizing an MOEA / D algorithm to obtain a group of optimal solutions of decision variables as well as current efficiency, ton aluminum energy consumption and perfluoro-compound discharge amount corresponding to the optimal solutions, wherein the MOEA / D algorithm decomposes multi-target optimization into a plurality of single-target optimization sub-problems, and a core idea of the MOEA / D algorithm is that information of other sub-problems adjacent to one sub-problem is obtained and then cooperative optimization is performed, so that the optimization speed of multi-target optimization is greatly increased. The method determines an optimal value of process parameters in the aluminum electrolysis production process, effectively improves the current efficiency, reduces the greenhouse gas discharge amount, and truly achieves the purposes of energy conservation and discharge reduction.

The invention provides an intelligent plant breeding method and system based on data analysis, wherein the method compromises: collecting the varieties of plants, growing season, soil moisture, soil PH value, light intensity, environment temperature, environment humidity, watering quantity, fertilizer quantity, fertilizer types; forming a matrix X of influential factors and uploading the matrix X to the server, wherein the decision variables are formed by the watering quantity, the fertilizer quantity and the fertilizer types; by utilizing Elman Neural Network in the server, setting up a nonlinear relationship between the matrix X of influential factors and plant health index to get a plant breeding model; optimizing the plant breeding model by utilizing the MOEA / D algorithm to attain a set of optimal solutions of the decision variables; regarding the set of the optimal solutions as the recommended decisions for plans and sending the decisions to the terminal equipment of users through the server to display. Users plant according to the recommended decisions displayed by the terminal equipment. The intelligent plant breeding method and system based on data analysis can ensure the optimal plant breeding scheme and create a better living environment for plants.

A generalized rebinning method transforms SPECT projection data acquired with a first type of collimator into projection data as if it were acquired with a second type of collimator, for subsequent reconstruction using a 3-D algorithm devised for the second type of collimator. The method includes use of a non-stationary point-spread function in the conversion to account for image blurring.

The invention discloses a time delay and energy consumption-oriented electric power Internet of Things workload distribution method. The method comprises the steps of constructing an electric power Internet of Things workload distribution model based on edge calculation; establishing a multi-objective optimization function of power Internet of Things workload allocation based on the constructed power Internet of Things workload allocation model by taking the time delay and energy consumption of the unmanned aerial vehicle terminal UE as a common optimization objective; and solving the established multi-objective optimization function through an improved MOEA / D algorithm. According to the invention, the workload distribution method based on the improved MOEA / D algorithm is provided, and theoptimal solution searching capability of the algorithm is improved; in the neighborhood updating strategy, the bad individuals in the neighborhood are updated by using the maximum fitness increment,so that the survival time of the excellent individuals is prolonged, the final result is closer to the optimal workload allocation mode, the energy consumption under the same time delay index is smaller, and the time delay under the same energy consumption index is smaller.

The invention discloses an MOEA / D algorithm based multi-target optimizing method for a regression test case, and aims at solving the technical problem of complexity of the existing multi-target optimizing method for the regression test case. According to the technical scheme, the method comprises the steps of selecting three properties from all properties which influence the effect of the test case; respectively detecting the influence degree of the three properties to the effect of the test case; detecting code coverage rate through a built-in plug-in GCOV of a GCC coder; analyzing tested program source code through inserting program during detecting the branch coverage; inserting detection codes in the if-else branch, for circulation, do-while and while-do circulation; testing the case execution time through an open-source tool. According to the method, the MOEA / D algorithm is used for performing three-target optimization of a regression test case set; the test case can be effectively optimized under the condition that various test demands are completely covered; the method is simple.

The invention discloses a dynamic routing building method based on a D algorithm in a wireless body area network, and relates to a dynamic routing method. The dynamic routing building method based on the D algorithm in the wireless body area network is used for balancing energy consumption in the network and prolonging the life cycle of the whole network. The method comprises the steps that a center node obtains an initial topological structure of the network through a flooding method, distance between the center node and other nodes and energy information; a range coordinate matrix of the network is obtained; the range coordinate matrix is converted into a transmission energy consumption matrix and weighted, and a weighed transmission energy consumption matrix is obtained; a weight matrix is generated on the basis of the weighed transmission energy consumption matrix Ew through a D algorithm, and after the weight matrix is generated, each node can find the lowest-energy-consumption route to the center node; the nodes carry out data transmission according to the selected lowest-energy-consumption routes. The dynamic routing building method based on the D algorithm in the wireless body area network is suitable for signal transmission in the wireless body area network.

The invention discloses an IMU data-based user indoor movement track matching method and relates to the field of IMU data-based user indoor movement track matching. The method solves the problem that the existing indoor movement distance and movement direction have relativity and an initial position and an end position of indoor movement on the map are unknown so that the indoor movement track cannot be matched and positioned on the indoor map. The method comprises constructing an indoor reference map by nodes to obtain a track set A of the indoor reference map, wherein the track set A has the node number the same to that of an indoor movement track, removing repeated node tracks to obtain a track set B, acquiring an indoor movement track set C by a torsion angle matrix GD of the indoor reference map, acquiring a matrix R of the shortest distance between track points of the indoor movement track set C by weight matrix W and D algorithms of the indoor reference map, comparing the matrix R of the shortest distance and a distance TD between adjacent nodes, and acquiring an indoor movement track set D by a threshold value theta . The method can be used for indoor positioning navigation.

The invention discloses a cross-domain mapping method for a hybrid virtual network (HVN). In the event of having one known request of a bottom multi-domain network and the HVN, node calculation resources and link bandwidth resources in the bottom network are distributed to the HVN request based on an HVNMMD-D algorithm and a spectrogram decomposition-based mapping method; therefore, a mapping scheme, which not only satisfies the QoS (Quality of Service) but also saves the mapping cost, is found; secondly, the specificities of multicast parts in the bottom multi-domain network and the HVN are comprehensively considered; common bandwidth resources and calculation resources are optimized; and thus, cross-domain mapping development requirements of the current HVN can be satisfied.

The invention discloses an uncertainty included underground radio-frequency recognition reader high-dimensional multi-target optimization arrangement to guide users to find satisfied Radio Frequency Identify (RFID) reader arrangements and reduce the user workload. The arrangement comprises specific contents of (1) building a coal mine underground RFID reader arrangement mathematical model and giving underground roadway distribution models, constraint conditions, target functions and decision variables; (2) on the basis of user input data, giving initialization information, that is, setting multi-target evolution optimization algorithm parameter values; (3) performing decomposition based multi-target evolution operation based on the MOEA / D algorithm multi-target evolution process, that is, according to multi-target optimization of MOEA / D towards the target functions to generate evolution populations meeting user requirements.

The invention provides a service protection method and system in a quantum key distribution optical network. The service protection method and system in a quantum key distribution optical network includes the steps: S1, calculating a working path of service in the quantum key distribution optical network by using a D algorithm or a K algorithm, and acquiring all power-on nodes in the working path,wherein all the power-on nodes at least include a source node and a destination node, and the service at least includes data service and key service for encrypting the data service; S2, setting a protection path for the data service and the key service according to all the power-on nodes, and distributing protection resources; and S3, in the transmission process of the service, if the working path fails, switching the service to the protection path for transmission. The service protection method and system in a quantum key distribution optical network can quickly and effectively continue thetransmission of data service and key service in the network after the link fails in the network, and have the capability of maintaining security service.

The invention relates to an active power distribution network multi-target day-ahead optimization scheduling method based on wind and light randomness. The method comprises the following steps: establishing a fan output prediction model and a photovoltaic output prediction model by adopting Weibull distribution and Beta distribution respectively; respectively establishing a demand response model based on price and excitation; according to the opportunity constraint planning theory, expressing a target function containing random variables and constraint conditions in a form meeting a certain confidence coefficient, performing calculating by using a random simulation technology, and establishing an opportunity constraint planning model; generating a large number of wind and light output scenes according to the theory of a scene method, obtaining fewer scenes after sorting and reduction, and establishing a scene method model according to an expected objective function and constraint conditions; and proposing economical efficiency, safety and reliability indexes of the active power distribution network, and respectively solving the opportunity constraint programming model and the scene method model by using an MOEA / D algorithm. According to the method, the day-ahead multi-target random optimization scheduling problem of the active power distribution network containing wind, light, storage and demand responses is well solved.

Provided is an issue of processing mid-term power load predictions by using multi-target deep belief network based on decomposition. The method comprises the following steps: firstly, decomposing databy adopting empirical mode decomposition and combining decomposed data and original data into a new data set; secondly, using multi-target data mining, serving a single DBN as a candidate solution ofa multi-target optimization algorithm, iteratively evolving multiple DBNs a under the driving of an MOEA / D algorithm at the same time, so that accuracy and diversity of a model are considered, usingcross validation for samples after circulation is finished, to prevent overfitting; then, proposing a two-stage strategy to screen the prediction model, and finally allocating a weight vector to a single prediction model by utilizing ensemble learning. According to the invention, parameters of the prediction model can be optimized and selected, and the efficiency of the algorithm and the prediction accuracy are effectively improved.

The invention provides a user preference-based dynamic computing migration method and device for a smart city, and the method comprises the steps of initializing a set of input tasks; stipulating an algorithm stop standard, a population maximum iteration frequency, the number of neighborhood vector sets of each particle and a population initial migration strategy, and defining a group of weight vector sets required to be used in the algorithm; then, on the basis of an MOEA / D algorithm, continuously updating a migration strategy of the task by taking optimization of total energy consumption andtotal time delay of the mobile equipment task of the user side from generation to completion as a target; meanwhile, in order to meet the requirements of the user, adding an elitist strategy which can be changed in a directed mode according to the requirements and preferences of the user; according to the invention, an elitist strategy is adopted, energy consumption and time delay generated by task processing are comprehensively considered while user preferences are met, an appropriate calculation migration strategy is formulated for user tasks in an MEC environment, and the purpose of multi-objective optimization is achieved.

The invention relates to an LCL filter parameter optimization method under the condition of considering harmonic stability of a VSC system. The method comprises the following steps: 1, establishing anequivalent impedance model for a VSC grid-connected system; 2, obtaining a positive network damping criterion for the equivalent impedance model of the step 1 and taking the positive network dampingcriterion as a system stability judgment basis; 3. combining the external characteristic requirements of the LCL filter and the positive network damping criterion as the constraint conditions for LCLfilter design; 4. determining the optimization objectives and establishing a multi-objective optimization model of LCL filter parameters in combination with constraint conditions; and 5. using the MOEA / D algorithm to solve the optimization model and obtaining the optimized LCL filter parameters. Compared with the methods in the prior art, the method has the advantages of high optimization rate, simple calculation and the like.

The embodiment of the invention provides a resource scheduling scheme obtaining method and device. The method comprises the steps: obtaining resource scheduling data; obtaining a first objective function and a second objective function for coping with disaster-affected resource scheduling, wherein the first target function is a function taking optimal resource scheduling time satisfaction as a target, and the second target function is a function taking optimal resource scheduling cost as a target; and optimizing the first objective function and the second objective function according to the resource scheduling data and a decomposition-based multi-objective evolutionary MOEA / D algorithm to obtain a target scheduling scheme for scheduling the to-be-scheduled material for each affected pointby each material supply point. According to the resource scheduling scheme obtained by the resource scheduling scheme obtaining method, the optimal satisfaction degree of resource scheduling time andthe optimal resource scheduling cost can be met at the same time, and the target scheduling scheme meeting a plurality of target functions at the same time is obtained.

The invention discloses a method for rapidly analyzing the clearability of a supporting structure in any direction. The method comprises the following steps of: 1, performing voxelization on a three-dimensional model for quickly judging the accessibility of a separation point; 2, quickly generating a voxel path II approaching a straight line segment based on a three-dimensional Bresenham algorithm; and 3, parallelly and efficiently detecting the accessibility of all separation points of the three-dimensional model. According to the method, the problems that the cleaning direction of the supporting structure in a 3D printing product is limited and time is consumed are solved, and the supporting structure is supported to rapidly analyze the cleanability in any direction and optimize printing direction calculation. According to the method, the Bresenham algorithm is expanded to the three-dimensional space, the voxel path approaching the straight line segment in any direction can be generated, the three-dimensional Bresenham algorithm is an integer traversal algorithm, the judgment formula is refined, the calculated amount is small, no accumulative error exists, and the searching efficiency of the voxel path in the first step is improved. Therefore, the clearability analysis of the supporting structure at any angle can be quickly supported.

The invention discloses a while-drilling electromagnetic wave logging cross-dimensional simulation method suitable for a 2D model. The method comprises the following steps: s1, inputting instrument parameters, a stratum model and other information; s2, determining a while-drilling electromagnetic wave logging forward modeling calculation domain; s3, performing variable-scale sliding windowing processing on the model; s4, calculating a model complexity factor; s5, judging whether the complexity factor of the model is greater than a threshold value or not: if so, executing a step s6, otherwise, executing a step s9; s6, carrying out dimension reduction rationality analysis on a simulation result, and calculating a model quality control factor; s7, judging whether the model quality control factor is greater than a threshold value or not, if so, executing a step s8, otherwise, executing a step s9; s8, calculating an original 2D stratum model response by adopting a 2.5 D algorithm; s9, solving the simplified 1D stratum model response by adopting an analytical solution to obtain a simulation result. According to the method, the optimal algorithm can be adaptively selected according to the stratum model to realize quick and accurate forward modeling of the electromagnetic wave logging while drilling of the 2D stratum model.

The invention belongs to the technical field of extrusion process parameter optimization, and particularly relates to an MOEA / D extrusion process parameter multi-objective optimization method and device. The extrusion process parameters are optimized by taking the energy consumption, the yield and the die wear as targets, so that the energy consumption is effectively reduced, the yield is improved, and the die wear is reduced; the provided improved MOEA / D algorithm has more excellent performance, and has better population diversity and convergence.

The invention discloses an MOEA / D algorithm based multi-target optimizing method for a regression test case, and aims at solving the technical problem of complexity of the existing multi-target optimizing method for the regression test case. According to the technical scheme, the method comprises the steps of selecting three properties from all properties which influence the effect of the test case; respectively detecting the influence degree of the three properties to the effect of the test case; detecting code coverage rate through a built-in plug-in GCOV of a GCC coder; analyzing tested program source code through inserting program during detecting the branch coverage; inserting detection codes in the if-else branch, for circulation, do-while and while-do circulation; testing the case execution time through an open-source tool. According to the method, the MOEA / D algorithm is used for performing three-target optimization of a regression test case set; the test case can be effectively optimized under the condition that various test demands are completely covered; the method is simple.

The invention provides a Base64 extended coding method and system based on a high-frequency character substitution algorithm. On the basis that the original Base64 transcoding expansion ratio is 1 / 3,high-frequency character replacement is adopted, and if the expansion ratio is 20% and far smaller than 33.3% according to the 40% replacement rate, for data transcoding of a large data volume, the conversion economy can be greatly improved, and the expansion space is effectively saved; according to the method for changing Base64 coding shift transcoding, conversion is carried out by adopting a D* 64 + d algorithm, so that each code is independent of other codes, and the phenomenon that subsequent codes cannot be recovered due to abnormity is avoided; according to the method, the conversion range covers all ASCII data domains, the method is suitable for binary data transcoding under various applications, and the method only relates to one conversion algorithm, is irrelevant to realized languages and platforms, and is good in compatibility; end complement is not needed, and the algorithm is simpler.

The invention relates to an integrated circuit with a programmable control power device, which comprises a serial communication unit, an erasable register unit, a command decoder, an algorithm execution unit and an expanding unit. The algorithm execution unit comprises a P algorithm module, an I algorithm module, a D algorithm module and the like which are independent from each other; and the expanding unit comprises an eight-path PWM output unit which outputs control signals to a controlled power device, and a device protection unit. The integrated circuit integrates the algorithms module inside so that the systematic programmability is strong and the algorithms can be established quickly, intuitively and efficiently to achieve functions; besides, because the integrated circuit is a hardware structure and all the modules are independent from each other, the real-time processing speed of the modules is fast and the controlled power device is protected comprehensively.

The invention discloses a three-dimensional Kriging algorithm collaborative acceleration technology based on a CUDA (Compute Unified Device Architecture) and an OpenMP (Open Messaging Protocol). The method comprises the following basic steps: constructing a KD tree search structure, calculating coordinates with interpolation points, carrying out OpenMP parallel search, asynchronously constructing a Kriging equation by a CUDA (Compute Unified Device Architecture) concurrent stream, and solving a final result. The invention has the beneficial effects that the computing resources of the CPU and the GPU can be fully utilized, so that the computing parts of the GPU and the CPU are executed asynchronously in the interpolation process, and the invention has the advantages of rapidness and high efficiency; meanwhile, when more variation models participate in calculation, the invention has a better acceleration effect.

The invention discloses a multi-objective AC / DC power distribution network planning method and device, which are used for considering the economy and reliability of power distribution network planningand achieving collaborative planning of multiple objectives. The method comprises the following steps: acquiring planning parameters of a preset planning area, and determining optimization objectivesand constraint conditions of the multi-objective AC / DC power distribution network by adopting the planning parameters, wherein the optimization target comprises a construction cost target and a reliability target; establishing a multi-objective optimization planning model by adopting the planning parameters, the optimization objectives and the constraint conditions; solving the multi-objective optimization planning model by adopting a preset multi-objective evolutionary MOEA / D algorithm, and outputting an optimization variable; and determining a compromise solution of the optimization variable, and planning the multi-target AC / DC power distribution network by adopting the compromise solution. Economy and reliability of planning are both considered, and collaborative planning of multiple targets is achieved.

The invention discloses a cross-chain asset transfer method implemented based on a smart contract, which comprises the steps of designing an SLife structure of a main chain plus a sub-chain, designing a variant algorithm of Fts to select a sub-chain consensus node from super nodes of the SLife main chain, submitting a sub-chain verification node public key address and a domain name to a main chain contract by referring to a G.O.D algorithm, wherein the information processing module is used for a user to obtain information required by a consistent sub-chain gensis file from a contract interface, and designing an independent process visa mechanism by referring to a DKG algorithm to poll, sign and issue the on-chain state of a sub-chain contract; according to the invention, exchange functions of points and other values of different platforms are realized, real and credible point exchange of different platforms can be realized, and distributed service tracing is realized without tampering; according to the method, share exchange and transfer between different chains can be achieved under the condition of small resource deployment and consumption by means of the combination of the smart contract and the protocol, and good flexibility and expansibility can be achieved.

The invention discloses a software defect prediction method and device, electronic equipment and a storage medium. Comprising the following steps: extracting metric element features and defect information in software with known defect information to obtain training data; using a linear model as a basic model, using an improved MOEA / D algorithm for carrying out optimized training on model parameters according to training data, obtaining a Pareto solution set, and obtaining a set of defect prediction models, in which the improved MOEA / D algorithm introduces a zero parameter construction mode to optimize the model parameters, and therefore optimization of model performance is achieved; and on the basis of the optimized model, selecting one prediction model according to an application scene to perform defect prediction on the to-be-tested software to obtain defect information of the next version of to-be-tested software. Through the implementation of the method, a software defect prediction model with good sorting performance, relatively small prediction error and relatively short prediction time can be better constructed, and the application scene of the model is further expanded, and the accuracy of defect prediction is improved.

The invention belongs to the technical field of optimization of extrusion process parameters, and more specifically relates to a method and device for multi-objective optimization of MOEA / D extrusion process parameters. The extrusion process parameters were optimized with the goals of energy consumption, yield and die wear, which effectively reduced energy consumption, increased yield, and reduced die wear; the improved MOEA / D algorithm proposed has better performance and has Better population diversity and convergence.

The invention discloses an IMU data-based user indoor movement track matching method and relates to the field of IMU data-based user indoor movement track matching. The method solves the problem that the existing indoor movement distance and movement direction have relativity and an initial position and an end position of indoor movement on the map are unknown so that the indoor movement track cannot be matched and positioned on the indoor map. The method comprises constructing an indoor reference map by nodes to obtain a track set A of the indoor reference map, wherein the track set A has the node number the same to that of an indoor movement track, removing repeated node tracks to obtain a track set B, acquiring an indoor movement track set C by a torsion angle matrix GD of the indoor reference map, acquiring a matrix R of the shortest distance between track points of the indoor movement track set C by weight matrix W and D algorithms of the indoor reference map, comparing the matrix R of the shortest distance and a distance TD between adjacent nodes, and acquiring an indoor movement track set D by a threshold value theta . The method can be used for indoor positioning navigation.

The invention discloses an aircraft track rapid planning method based on an improved Dijkstra algorithm, and the method comprises the steps: building an aircraft dual-target track optimization model, and converting the dual-target track optimization model into a single-target track optimization model; and solving the single-target flight path optimization model by using an improved Dijkstra algorithm to obtain a flight path with the cumulative error smaller than a preset value, the shortest flight path distance and the minimum correction point number of the path. The method comprises the following steps: firstly, converting multi-objective optimization into single-objective optimization by using a normalization weighting method; secondly, a pre-search process is added on the basis of a classic D algorithm to realize backtracking of the algorithm, and the algorithm relaxation degree is greatly improved while the characteristics of high objectivity and good globality of the D algorithm are kept; in addition, a jump-out mechanism is added in the pre-search process, and the algorithm operation time is further shortened. The effectiveness of the improved D algorithm under complex limiting conditions is verified through MATLAB simulation.