81 results about "Global distribution" patented technology

The invention discloses a pipeline defect magnetic flux leakage detection method and device based on multi-scale data driving deep learning, and relates to the technical field of pipeline defect detection. A multi-scale magnetic flux leakage signal data set containing defect global distribution and local details is generated, and multi-level primary features are automatically extracted by using a convolutional neural network; and the defects of poor generalization and easy key information omission of a manual method are overcome. And then dynamically enhancing and performing weighted fusion on multi-scale features by means of a multi-scale convolution branch and an attention mechanism, so that the model can learn global and local features of the defect at the same time, the problem that the global and local features of the defect are difficult to consider in traditional deep learning is solved, and through transverse connection and up-sampling fusion of a feature pyramid, a multi-scale feature is obtained. And the features have high semantic information and high spatial details. And finally, independently carrying out multi-task prediction by virtue of a task decoupling module, and calibrating result consistency by virtue of a task alignment module, so that the detection accuracy in a complex scene is improved, and more accurate and robust pipeline defect magnetic flux leakage detection is realized.

The invention discloses a target detection method and system based on dynamic memory enhancement, relates to the technical field of computer vision and target detection, and aims to solve the problems that a traditional method depends on local features of a single image, is difficult to detect a target, is low in precision and is weak in generalization ability. The method comprises the steps that after a to-be-detected image is preprocessed, multi-stage multi-scale features are extracted through a backbone network; an encoder outputs single image global features for feature interaction fusion, and a global distribution extraction module constructs and updates a data set level global feature distribution library; the decoder adopts a hierarchical structure, and optimizes the target query token layer by layer in combination with self-attention, cross attention and global distribution fusion sub-modules; and finally outputting a target category and bounding box coordinates. Through global context injection, small target and shielding target detection precision is improved, model training stability and generalization ability are enhanced, and the method is suitable for diversified detection scenes.

The invention discloses a multi-GNSS satellite signal power abnormity real-time monitoring method. The method comprises the following steps: acquiring real-time observation data of an observation station network which is uniformly distributed globally; when the linear relation between the carrier-to-noise ratios of different frequency signals of each satellite in each observation station and the dynamic monitoring threshold value related to the elevation angle of each satellite are determined, one signal is used for predicting another signal by using the linear coefficient, and the deviation between an actual value and a predicted value is calculated; calculating a satellite elevation angle of the real-time observation data, and determining a monitoring threshold value of the current satellite at the current moment according to the satellite elevation angle; and counting an average value of the deviations of all satellites monitored by all observation stations at the current moment, and marking that the power of the current satellite at the current moment is abnormal when the absolute deviation of the current satellite is greater than a monitoring threshold value. According to the invention, on the basis of the carrier-to-noise ratio linear relation of different frequency signals and the dynamic monitoring threshold, real-time monitoring of various power abnormal scenes including natural interference, man-made interference, satellite active power adjustment and the like is realized.

The invention provides a multi-Alos robot cooperative sorting method for warehouse logistics and a related device, and relates to the technical field of warehouse logistics. Receiving real-time order data, and performing analysis according to the real-time order data to generate a task set including cargo positions, target sorting areas and priorities; dynamically calculating a task matching degree by combining state information of each robot through a distributed task allocation mechanism, and optimizing global task allocation by adopting a reinforcement learning model to generate a global task allocation result; modeling a storage environment into a space-time diagram structure on the basis of a global distribution strategy; generating a conflict-free path by adopting a path-finding algorithm in combination with the space-time diagram structure, and optimizing a trajectory in combination with a task time limit constraint; and according to the target position in the conflict-free path, goods are recognized through a visual system, and grabbing and placing are completed by combining with a mechanical arm. The technical effects that the sorting efficiency and adaptability are improved, and the operation and maintenance cost is reduced are achieved.

The invention relates to the technical field of logistics data processing and artificial intelligence, and discloses an intelligent warehouse management system based on logistics data processing, and the system comprises a multi-source heterogeneous data fusion preprocessing module which carries out the real-time collection of the data of a global distributed storage center, and outputs a time-space standardized multi-dimensional feature data set; the spatio-temporal correlation demand prediction module is used for constructing a spatio-temporal model and carrying out depth prediction by adopting a long-short-term memory network and an attention mechanism; the multi-constraint global inventory optimization module is used for constructing a multi-objective optimization model and solving an inventory configuration scheme through a decomposition coordination method; the reinforcement learning intelligent scheduling module is used for establishing an environment state space and performing strategy optimization by adopting a deep Q network; the digital twinborn visual decision support module is used for realizing real-time synchronization of physical storage and a digital model and outputting decision suggestions and risk early warning; according to the method, multi-source heterogeneous data can be effectively processed, intelligent prediction of cultural perception is realized, and global collaborative optimization is carried out.

The invention relates to the technical field of nanometer motion tables, in particular to a nanometer motion table design method, device and equipment and a storage medium. In the method, a structure sample library is composed of structure samples of a nanometer motion table, and a simulation result covers characteristics such as microscale stress and displacement; the trained parameter prediction model can accurately support the micro-scale performance prediction and optimization of the nano motion platform; a simulation result set of the structure sample library comprises multi-physics field data, a training data set is constructed based on a global simulation result, a proxy model can learn global distribution and coupling relations of physics fields, and optimization hidden dangers caused by single-point simplification are avoided; a proxy model is constructed and trained into a parameter prediction model, traditional finite element analysis is replaced for rapid prediction, and the calculation cost and time consumption in the optimization process are remarkably reduced; the multi-objective optimization algorithm and the parameter prediction model are combined, the optimization scheme is automatically generated, multi-objective optimization can be carried out at the same time, multiple performance indexes can be considered, and the comprehensiveness of the design scheme is improved.

The invention provides a photovoltaic output scene clustering method. High-frequency noise of an original photovoltaic output time sequence is suppressed through self-adaptive smoothing processing, fluctuation characteristics of the high-frequency noise are extracted, it is ensured that target time sequence data accurately reflect photovoltaic power generation working conditions, and high-quality input is provided for subsequent clustering; the target clustering number is determined based on joint optimization parameters (fusing local dynamic characteristics and global distribution characteristics), and scene division deviation caused by subjective setting of the clustering number is avoided; the clustering center is updated by iteratively optimizing the clustering objective function, and it is ensured that the typical scene center truly reflects the mainstream output mode. Therefore, scene representativeness is improved in the aspects of data preprocessing, clustering number decision making and central optimization of a whole link, and generalization ability of a power grid dispatching strategy is remarkably enhanced.

The invention discloses an aviation radiation dose global distribution real-time calculation system based on ground detection equipment, which belongs to the technical field of radiation detection and comprises a ground radiation detection module, a dose simulation module and a data correction module. The ground radiation detection module is used for continuously collecting ionizing radiation data of an atmospheric bottom layer in a calm state and a solar high-energy particle event so as to improve data precision and a signal-to-noise ratio under a weak dose, and the dose simulation module receives latitude and longitude, height and time parameters of an aviation flight path, and calculates a standard atmospheric model and a cosmic ray energy spectrum in combination with the standard atmospheric model and the cosmic ray energy spectrum. The data processing module is used for calculating theoretical dose rates and particle type distribution of different height levels based on a Monte Carlo method and outputting the theoretical dose rates and the particle type distribution, and the data correction module is used for dynamically correcting parameters and weights of simulation results by combining measured data based on a deep learning algorithm of deep learning, so that adaptive fusion of the simulation data and the measured data is realized; and the ground radiation detection module comprises a semiconductor radiation detector, a data acquisition and preprocessing unit, a remote control module and a network communication module. The system has the characteristics of high real-time performance and wide coverage range.

The invention belongs to the field of equipment fault diagnosis, and discloses an unsupervised cross-equipment fault diagnosis method fusing dual alignment and pseudo-label learning. Through collaborative optimization of three mechanisms of global domain adaptation, conditional domain confrontation and pseudo-label learning, depth feature alignment and refining of three levels of global-local-instance are realized, and the accuracy, robustness and generalization ability of unsupervised cross-equipment fault diagnosis are significantly improved. The core technical problems of confusion of different fault category features on a target domain and low diagnosis precision due to the fact that an existing domain adaptation method only pays attention to global distribution alignment and ignores a category structure are solved, and the technical bottleneck that when a traditional intelligent diagnosis model is applied to new equipment or new working conditions, the performance can be guaranteed only by depending on label data is solved.

The invention discloses an action sports scoring method and device based on big data analysis, and relates to the technical field of computer vision, and the method comprises the following steps: S1, constructing a human body posture tensor manifold; s2, generating aligned action track characteristics; s3, generating symmetrical positive definite manifold features; s4, generating deep manifold distribution parameters; s5, quantifying global distribution deviation characteristics; s6, calculating a residual vector based on the deep manifold distribution parameters and the standard action distribution parameters, inputting the residual vector into the improved AGCN model for processing, constructing an adaptive topology based on the residual vector, and performing aggregation analysis to obtain a joint-level physical angle error after multi-scale space-time convolution and manifold enhancement; and S7, outputting a comprehensive score vector. According to the method, the limitation that a traditional method only depends on local geometric features and ignores global topological structure constraints and statistical distribution priori is overcome, and an efficient solution is provided for intelligent scoring of sports actions.

The invention discloses a logistics unmanned aerial vehicle material distribution energy consumption and communication collaborative optimization method. The method comprises the steps of collecting distribution task data of an unmanned aerial vehicle and communication deployment data in a flight environment; establishing an unmanned aerial vehicle distribution system model based on the distribution task data and the communication deployment data, and constructing an optimization target to optimize the total energy consumption and the total communication interruption time of the unmanned aerial vehicle for completing the distribution task; based on the unmanned aerial vehicle distribution system model, solving a global distribution sequence with the lowest energy consumption; taking the global distribution sequence as input, converting an unmanned aerial vehicle trajectory planning problem into a Markov decision process, and solving a local distribution trajectory in the unmanned aerial vehicle distribution process; on the basis of the local distribution track, a Bayesian optimization model and a deep reinforcement learning network are adopted to obtain an actual flight action of the unmanned aerial vehicle at the next moment and execute the actual flight action; and based on the real-time environment information, generating an optimal flight path satisfying energy consumption and communication collaboration, and completing a distribution task. According to the invention, an efficient and safe distribution system is realized.

The invention relates to the technical field of cold chain management, and discloses a cold chain collaborative management method and system based on cloud edge collaboration, and the method comprises the steps: obtaining the temperature, humidity and inventory data of a multi-node sensor and the global distribution point path information; according to the temperature and inventory data, edge data aggregation, noise filtering and temperature threshold monitoring are carried out, and a local inventory adjustment demand is obtained; according to the local inventory adjustment demand and the global distribution point path information, performing global path optimization analysis, multi-path variation cross set generation and comprehensive evaluation sorting to obtain a global distribution path set; and performing node conflict detection, comprehensive decision and adaptive adjustment on the global distribution path set to obtain final optimal path distribution. According to the method, global efficient collaborative management of cold-chain logistics and dynamic optimization of a distribution path can be realized.

The invention discloses a distributed chip security domain system based on an earth clone scale model, and the system comprises a modeling unit which is used for building a geographic grid according to the real latitude and longitude of the earth; the management unit is used for dividing the geographic grid into security domains corresponding to countries or regions one by one, each security domain is used for generating and storing a public and private key pair, a private key is embedded in the distributed chip security domain, and a public key is uploaded to a global distributed block chain network; and the authentication unit is used for carrying out cross-region security verification based on the security domain by utilizing a universe anchor point. The invention further provides a digital integrated authentication method and the eSIM mobile phone. According to the invention, independent COS examples are preset for each country in a distributed chip, and a total factor large Token and zero-knowledge proof technology are combined, so that identity authentication of a user at any place in the world is ensured, and integrated digital identity authentication with high safety, strong privacy and global mutual recognition is realized.

The invention discloses a missing modal learning method based on hierarchical consistency prompt distillation, relates to the field of computer vision, natural language processing and multi-modal information fusion, and aims to solve the problem that anchor drift and judgment boundary collapse are easy to occur in existing multi-modal learning under a modal missing condition. A modeling mechanism with cooperation of structure perception, anchor point guidance and layered distillation is provided, and stable alignment and high-quality prediction of the semantic space are achieved under the scene of any missing combination and high missing rate. The method comprises a structure perception representation modeling module, a double-layer semantic anchoring module, a hierarchical consistency prompt distillation module and a deletion perception classification and overall optimization module. According to the method, a three-layer semantic consistency framework is provided around anchor drifting, and modal-level consistency, fusion-level consistency and global distribution consistency are constrained in a shared semantic space at the same time, so that the class directions represented by modals and fusion are unified, boundary collapse is inhibited, and structural stability is kept and generalization robustness is improved in a modal missing scene.

The invention discloses an anomaly monitoring method for panoramic smart energy cloud edge collaboration, and the method comprises the following steps: S10, edge side credibility perception calculation: carrying out the real-time analysis of locally collected energy time sequence data through an edge side device, generating a preliminary anomaly probability Pe, and carrying out the calculation of the edge side credibility based on the probability Pe and the corresponding data; calculating an edge credibility score Re corresponding to the preliminary anomaly probability Pe by analyzing the difference with a historical normal mode and the uncertainty of local model fitting; and step S20, cloud credibility enhancement analysis: the cloud performs deep analysis according to the data uploaded by the edge side, generates a cloud anomaly probability Pc, and calculates a cloud credibility score Rc corresponding to the cloud anomaly probability Pc by analyzing the novelty of the data in global distribution and the stability of model prediction based on the probability Pc and the corresponding data. According to the invention, abnormity monitoring of panoramic smart energy cloud edge collaboration can be carried out more comprehensively.

The invention discloses a super-multi-objective optimization method and system based on dual-file coevolution and dual-distance indexes, and the method comprises the steps: generating an initial population based on the parameters of a power system, initializing the parameters and a reference vector set, and storing the population into a convergence file and a diversity file; calculating an ideal point and a worst point based on the convergence file, and updating the convergence file in combination with a double-distance index; maintaining population global distribution through a reference vector association operation; evaluating a population distribution state in combination with information entropy to determine a local neighborhood size; calculating individual local neighborhood density based on the parallel distance and updating a diversity file; constructing a mating pool based on the double archives and generating a filial generation population; and iteratively executing until the non-dominated solution set in the diversity file is output. According to the method, the convergence and diversity retention capability of the algorithm to the complex leading edge is enhanced, and a power system scheduling scheme with multi-target balance of the power generation cost, the transmission loss, the line utilization rate and the power generation adjustment amount can be efficiently generated.

The utility model discloses an insect sticking trap device and system, the device includes base body and sticking trap piece, and the base body is strip structure, and the base body includes first base plate and second base plate, and first base plate and second base plate are arranged in the included angle, and the inner surface of second base plate is equipped with sticking trap area and escape area, and escape area is equipped with the one side away from first base plate in sticking trap area, and sticking trap piece is equipped in sticking trap area, and sticking trap piece is equipped with the colloid for sticking insect. The utility model discloses compared with prior art plane formula sticking trap board single orientation's sticking trap surface, can utilize the included angle structure and enlarge effective sticking trap range, and adapt to the flight and crawl track of various insects, and improve the insect catching efficiency, and simultaneously, the inner surface of second base plate divides sticking trap area and escape area, and sticking trap area sets up the sticking trap piece with colloid, avoids the problem that the colloid of existing device global distribution is easy to stick non -target organism, and the problem of early failure due to accidental touch or dust adhesion, and can reduce the non -target organism's mistake through escape area and stick, and need not use the inducing agent, and reduce the cost.

The application discloses a tower crane detection method and system based on deep learning, and the method comprises image acquisition, initial image optimization, detail enhancement, aggregation correction, establishment of a tower crane detection model and tower crane detection. The application belongs to the field of tower crane detection, and specifically relates to a tower crane detection method and system based on deep learning. The application pertinently adjusts and attenuates channels, and improves the color authenticity of images. A global region extension and local distribution enhancement strategy is adopted, aggregation correction is based on, and false detection and missed detection caused by information loss are reduced. The feature dispersion degree in a group is quantified, an environment self-adaptive penalty is introduced to design and optimize a loss function, and the penalty for environmental interference is dynamically adjusted. A dispersion matrix and a shielding penalty coefficient are introduced to quantize the global distribution dispersion degree of tower crane features, process feature variation caused by tower crane shielding and component loss, avoid false judgment of shielding, and reduce the missed detection rate. Therefore, the detection effect is improved.

The embodiment of the specification provides a multi-node distribution method and device for a large model. The large model corresponds to a plurality of model files. A target model file is pre-divided into a plurality of file blocks, and each file block is pre-divided into a plurality of shards. The method comprises the following steps: a target node sends a download request to a scheduler, wherein the target model file is indicated; the scheduler selects a plurality of candidate nodes to join a candidate parent node set from candidate nodes in a cluster based on at least index values of state indicators of the candidate nodes and global distribution information of the shards on the candidate nodes, the candidate nodes having downloaded at least one shard, and the candidate nodes and the target node at least satisfying topology connectivity and not forming a loop; the scheduler determines a target file block to be preferentially downloaded by the target node from the plurality of file blocks; the scheduler sends the candidate parent node set and a block identifier of the target file block to the target node; and the target node preferentially downloads shards in the target file block from nodes in the candidate parent node set.

The invention discloses an unsupervised multi-modal equipment instance alignment method based on double-space embedding, and the method comprises the steps: employing a pseudo seed generation module, calculating the similarity of visual and text modal features, automatically constructing a preliminary pre-alignment instance pair, continuously expanding a high-confidence seed set in an iteration process, and carrying out the alignment of an unsupervised multi-modal equipment instance. The method comprises the following steps: firstly, carrying out data labeling on equipment instances, so as to effectively reduce the dependence on manual data labeling, then respectively carrying out embedding learning in Euclidean space and hyperbolic space by adopting a double-space embedding strategy, capturing a local structure relationship between the equipment instances, and carrying out accurate modeling on a hierarchical structure and global distribution characteristics of the equipment instances, and then, by constructing an adaptive feature fusion module, carrying out dynamic weighted integration on the embedded representations of the two spaces, and by introducing an iterative constraint mechanism and utilizing a loss function, ensuring that the representations of the same instances in the double spaces are as close as possible and the distance between different instances keeps enough discrimination.

The application relates to a global distributed computing power resource access method, device, program product and medium, and relates to the technical field of distributed computing. The method comprises the following steps: acquiring application layer response data of a computing power service endpoint, determining a target endpoint and a computing load evaluation value based on the data, and constructing a resource state table in combination with a network quality index, so that double evaluation of endpoint processing load and network transmission quality is realized; further, a candidate endpoint set is screened from the resource state table based on a service type identifier, and a comprehensive score is calculated by comprehensively considering the load evaluation value, the network quality index and a resource charging unit price, so that multidimensional trade-off of service quality and cost factors in the endpoint selection process is realized; finally, a flow type mark of a computing power request is identified, and a transmission strategy is determined according to the mark, so that differentiated transmission processing of different types of computing power requests is realized, and end-to-end response time delay is reduced.

The embodiment of the invention provides a clustering method and device, a medium and a program product. The method comprises the following steps: acquiring target data containing noise; filtering the target data to form an effective sample set; performing standardization processing on the effective sample set, and performing multi-scale noise filtering to reduce dispersive noise interference to obtain noise-reduced data; the comprehensive density score of each sample is calculated for the denoised data based on a density fusion model, the density fusion model accelerates neighbor search by constructing an index structure, the local density is calculated by fusing the spatial distance and the feature similarity, and the comprehensive density score is obtained by combining global distribution feature correction; clustering candidate centers are screened in combination with an adaptive threshold, and an initial clustering center set is obtained after redundant candidate centers are removed; and distributing the effective samples to a cluster corresponding to the nearest initial clustering center, iteratively updating the clustering center, and carrying out secondary distribution on the samples which are low in score and conform to a spatial proximity condition to generate a final clustering result.

An air travel reservation system including a digital platform system is disclosed that generates new inventory of air travel reservations. The digital platform system is operably connected to a global distribution system and a computer. The digital platform system includes a registered user memory bank, a registered user air travel reservation memory bank, a registered user air travel reservation cancellation queue storage register, and a new generated air travel itinerary inventory storage register. The air travel reservation memory bank is operatively connected to the registered user memory bank and the global distribution system. The reservation memory bank includes a booked air travel reservation register and a booking air travel itinerary register. The cancellation queue storage register is operably connected to the air travel reservation memory bank and the global distribution system. The cancellation queue storage register is configured to transfer reservation information to the new generated air travel itinerary inventory storage register. The digital platform system includes an itinerary-match register operatively connected to the air travel reservation memory bank and the air travel reservation cancellation queue storage register.

The invention provides a method and device for determining a coverage area of a centralized distribution station, and relates to the field of logistics. The method comprises the following steps: acquiring an uncovered region of interest and a candidate set distribution station set, and generating at least one candidate starting set distribution station set based on the candidate set distribution station set; for each candidate starting set distribution station set, selecting a better candidate coverage scheme as a local target coverage scheme according to the distribution related evaluation information of each candidate coverage scheme, and determining the fitness of the candidate starting set distribution station set according to the distribution related evaluation information of the local target coverage scheme; according to the fitness of each candidate starting set distribution station set, selecting a better candidate starting set distribution station set as a target starting set distribution station set; and according to the local target coverage scheme of the target enabled set distribution station set, determining a global target coverage scheme of the target enabled set distribution station to the region of interest. Therefore, a concentration and distribution station coverage scheme with relatively high global distribution related evaluation is found, and support is provided for expansion of a coverage area of the concentration and distribution station.

The invention discloses a construction method and system of a drift-compensated electronic nose gas recognition model, and relates to the technical field of electronic nose signal processing and artificial intelligence. According to the method, gas sample data of a source domain and a target domain before and after drifting are obtained and preprocessed, then a neural network model containing a feature extractor, a classifier and a domain discriminator is constructed, the feature extractor is trained through data enhancement and hidden space consistency loss regularization constraint to obtain robust feature representation, and the robust feature representation is obtained. The method comprises the following steps: generating a pseudo label for a target domain unlabeled sample, purifying based on a dynamic weighting mechanism of consistency evaluation and confidence, synchronously executing category-based local semantic alignment and adversarial training-based global distribution alignment by using the purified pseudo label, and finally, performing classification-based local semantic alignment and adversarial training-based global distribution alignment. Through a multi-objective function joint optimization model fusing classification loss, consistency loss and two kinds of alignment loss, effective sensor drift compensation is realized, and the accuracy and robustness of the model in a semi-supervised scene are significantly improved.

The invention discloses an underwater ecological monitoring task allocation method based on a dynamic multi-agent system, and the method comprises the following steps: collecting an environment and resource baseline of an operation water area, and generating an environment baseline data packet; performing attribute processing based on the environment baseline data packet to form a task priority queue; constructing a communication baseline according to the environment baseline data packet, and generating a communication scheduling table; performing feasibility judgment based on a generalized sino polyhedron to obtain an executable domain; fusing the communication scheduling table and the executable domain, carrying out asynchronous sparse synchronization, and generating an information synchronization abstract; generating a local state abstract based on the information synchronization abstract and executable domain constraints; global unification and refinement are carried out on the local state abstract, and a global distribution snapshot is output; and performing local rolling optimization according to the globally allocated snapshots in combination with an executable domain to keep security and task continuity. According to the invention, task allocation of underwater ecological monitoring is realized.

The invention provides a biological feature authentication method and system based on category awareness, the system comprises a server and a client, after the client receives an authentication request, the client synchronously collects a to-be-authenticated palm vein image and a to-be-authenticated heart rate signal of a user, generates a multi-modal fusion feature, and sends the multi-modal fusion feature to the server; in combination with a local category distribution compensation prompt vector and the optimized discriminative activity prompt vector, constructing a target sequence, and inputting the target sequence into a multi-modal biological feature authentication model to obtain an authentication result of the user; wherein the category distribution compensation prompt vector is obtained by training a global distribution reference and a local multi-modal fusion feature sample, and the global distribution reference is generated by aggregating a plurality of client sample statistics by a server; the discriminative activity prompt vector is obtained by updating a global enhancement prompt vector issued by the client based on the server, and the global enhancement prompt vector is obtained by optimizing trained discriminative activity prompt vectors of a plurality of clients by the server. Therefore, a privacy-friendly authentication scheme adaptive to multiple scenes and multiple devices is realized.

The invention provides a VR panorama generation method and a digital human VR panorama guide generation method and device, and relates to the technical field of virtual reality, in particular to the technical field of VR panorama generation and large model driven content generation. According to the specific implementation scheme, a panoramic picture processing request uploaded by a user is received; preprocessing the processing request to generate a VR processing task; distributing the VR processing task to a target computing node of a computing cluster through a global distribution layer; maintaining a local task queue in the target computing node; in the task processing process, obtaining resource consumption characteristics of the current task based on a pre-constructed resource estimation model; and performing multi-dimensional dynamic capacity expansion and shrinkage on the computing cluster according to the resource consumption characteristics and the real-time monitoring data. By means of the technical scheme, through the resource prediction model of the random forest, accurate pre-judgment and refined scheduling of task resource consumption are achieved, and the throughput and stability of the system are remarkably improved.

The application discloses a trap glycan library construction method and system based on topological graph editing and global optimization, and belongs to the technical field of glycan analysis, which comprises the following steps: analyzing original glycan into a topological structure, calculating the number of each preset type of monosaccharide to obtain a monosaccharide composition vector, applying numerical disturbance to the monosaccharide composition vector to obtain a candidate trap monosaccharide composition vector, and then calculating the difference between the monosaccharide composition vector and the candidate trap monosaccharide composition vector, so as to edit the topological structure to obtain an effective candidate trap topological structure; and performing global distribution on the original glycan group and the effective candidate trap glycan group to screen out a unique effective candidate trap glycan group, and combining all the selected effective candidate trap glycan groups to form a trap glycan library. The application aims to solve the problems of unreasonable trap glycan structure, large quality distribution deviation and difficulty in processing isomers in the prior art, so as to improve the accuracy of glycomics identification error rate estimation and quality control.

The invention relates to a satellite clock error high-precision forecasting method based on deep learning, and belongs to the field of GNSS navigation positioning. The satellite clock error is estimated in real time based on a globally distributed observation station real-time data stream and an ultra-fast orbit; calculating correlation between satellites, generating a clock difference sequence, generating a training sample, and normalizing the sample; and inputting the training sample into the neural network to generate a forecast clock error. Compared with the prior art, the method provided by the invention has the advantages that correlation information between satellites is introduced as dynamic characteristic information of the satellite clock error, so that the capability of more accurately capturing key rule information of different epoch samples by the LSTM network is improved; and by introducing a self-attention mechanism, the self-adaptive capability of dynamic adjustment weights of different parameters is improved, and finally, higher forecasting precision than other neural network models is realized.