837 results about "Target distribution" patented technology

The invention relates to the technical field of path planning, in particular to a path planning method and system for an inspection robot, and provides the following scheme: executing an inspection task through an initial electronic map, collecting first image data and pose information, recognizing an inspection target, completing image registration, and generating second image data; inverting and calibrating the optimal observation direction based on the structural sensitivity distribution of the local visual features and the illumination variation trend, and determining the shooting position and angle to form a path node; a multi-objective optimization model is adopted, the path length, the view angle continuity and the illumination interference are comprehensively considered, and an optimal inspection path is generated; the method is suitable for outdoor inspection scenes with complex structures and non-uniform target distribution.

The invention provides an information resource distribution method and system applied to a dynamic network environment, and the method comprises the steps: firstly obtaining a real-time state feature set of a target distribution network, determining a resource distribution constraint condition set in a current network environment based on the real-time state feature set, carrying out the priority analysis of a real-time resource request sequence of a target user group, and obtaining a real-time resource distribution constraint condition set; obtaining resource timeliness grade distribution and a request path optimization feature set, generating a dynamic distribution strategy parameter set matched with a resource distribution constraint condition, triggering a resource redirection operation, and enabling the resource request response rate of the target user group to be improved to a preset threshold interval; and the information resource distribution efficiency and stability in the dynamic network are improved.

The invention discloses a UUV cluster dynamic task planning method based on a multi-target genetic algorithm, a program, equipment and a storage medium, and belongs to the field of underwater multi-UUV cooperative detection of multiple targets. According to the method, firstly, for path planning of regional task points, chromosome representation is completed through sequential coding, chromosomes are selected and ranked randomly, and the chromosome with the highest fitness value is selected from each group; then, in each group of the current population, selecting an optimal parent individual to carry out crossover and mutation operations so as to improve the quality of offspring individuals, and carrying out updating to obtain a next-generation population; and finally, obtaining a plurality of optimal individuals, and outputting a task planning path result. Dividing the search area into a plurality of task sub-areas, and completing the dynamic task planning of the UUV cluster according to the target distribution condition of the sub-areas and the condition of each UUV. According to the method, the search strategy can be adjusted in real time according to the real-time detection result and the environment change, so that the target distribution non-uniformity and the cluster efficiency difference are effectively relieved.

The invention discloses an unmanned aerial vehicle inspection control system based on binocular vision, and relates to the technical field of unmanned aerial vehicle inspection, a binocular vision acquisition module is used for acquiring a to-be-inspected target image, detecting shielding and degree, and extracting a target feature and a distribution rule when an unmanned aerial vehicle inspects according to a preset route; the information acquisition module is used for acquiring a target position, peripheral complementary target distribution, unmanned aerial vehicle pose and kinematics parameters when it is detected that the shielding degree of the target point exceeds a set value. Complete coverage of the two types of target sub-regions can be ensured, the flight trajectory curvature of the second type of target sub-regions can be minimum, and stable flight is ensured.

The invention provides a distribution agent ad hoc network method and system based on dynamic node collaboration, and the method comprises the steps: firstly obtaining a network state data set of all distribution nodes in a target network region, including real-time communication load, link stability and resource occupation characteristics, generating dynamic collaboration topology information based on the network state data set; comprising a node cooperation relation topological graph and a communication path weight table, a distribution agent strategy set is generated according to dynamic cooperation topological information, path optimization processing is carried out on a target distribution task according to an initial path distribution strategy, a dynamic load adjustment rule and a path switching trigger condition, and an optimized distribution path set is obtained; and deploying to the corresponding distribution node to execute dynamic cooperative distribution, and updating the network state data set according to the distribution real-time feedback data, so that the distribution efficiency and reliability can be improved.

The invention relates to the technical field of power system fault diagnosis, and discloses a distribution network line fault detection data processing method, system and device and a medium, and the method comprises the steps: obtaining load data of a target distribution line detection point, and carrying out the first preprocessing of the load data, and obtaining a fusion feature vector; performing second optimization operation on the fusion feature vector to obtain an optimized fusion feature vector; calculating a potential feature matrix according to the optimized fusion feature vector, and mapping the potential feature matrix to a low-dimensional space to obtain a low-dimensional sample set; presetting an adaptive label propagation algorithm, and performing fault category judgment on the low-dimensional sample set based on the adaptive label propagation algorithm; and storing a judgment result in a relational database. The problems that feature extraction is not accurate in a high-noise environment, and a classification model is insufficient in new fault expansion capacity are effectively solved, and fault signal processing robustness is improved.

The invention relates to the technical field of time synchronization, and discloses a hybrid time service method based on Beidou satellite navigation and a TSN, which comprises the following steps: acquiring network topology structure information and historical time service error data of each synchronization node in a target distributed system, and constructing a dynamic time delay state set for jitter identification; based on the dynamic time delay state set, a current global time reference is extracted through a time calibration algorithm, a time difference reference vector sequence is generated, and initial synchronization adjustment is carried out on the TSN main node; constructing an inter-node clock convergence function curve according to the slave node response stability, and judging whether the synchronous network is in a low jitter interval or not based on the time offset rate and the time drift trend change; in combination with the node time state after directional offset suppression, a multi-channel redundant time synchronization mechanism in a time window is applied, a weight adjustment coefficient is extracted, and an unbalanced calibration factor is introduced; and according to a regression correction result, evaluating a time service stability index in real time. The method has the advantage of improving the time service stability.

The invention relates to a check point reading and writing method and device in distributed training, a storage medium and a program product. The method comprises the steps that in response to check point file generation and the number of check point files in a memory of a training node cluster corresponding to a target distributed training task does not reach N, an idle target memory is determined in the memory of the training node cluster, and the check point files are written into the target memory, the memory of the training node cluster comprises the memory of each training node in the training node cluster; and in response to a fault of any training node in the training node cluster, reading the target check point file from the memory of the training node cluster according to the metadata of the target check point file corresponding to the training node, and recovering training based on the target check point file. According to the method and the device, the check point file can be quickly read from the memory of the training node for recovery when the training node fails.

The invention provides a real-time human body detection method and system based on a microwave radar, and is applied to the field of signal data processing. By constructing a sparse reconstruction model and combining multi-dimensional feature decomposition, the method effectively relieves the recognition difficulty caused by target signal aliasing, continuously outputs and receives radar signals, extracts distance, Doppler and angle features, constructs target distribution point clouds in a three-dimensional feature space, further utilizes angle continuity judgment and point cloud density analysis, and improves the recognition accuracy of target distribution point clouds. According to the method, the number and distribution of overlapped targets are accurately estimated, a Kalman filter is introduced for dynamic tracking for a scene in which a continuous area is not formed, continuous separation and pseudo-color image reconstruction of multiple targets are realized, and thus the resolution precision and target positioning capability of a radar in a multi-person close-range aggregation scene are remarkably improved.

The invention provides an scRNA-seq data clustering method, system and device based on ZINB distribution and graph attention. The scRNA-seq data clustering system mainly comprises three core modules: a ZINB auto-encoder, which is used for modeling scRNA-seq data based on zero-expansion negative binomial distribution, generating robust potential representation through a denoising auto-encoder, and accurately capturing sparsity, excessive discreteness and shedding events of gene expression; the residual image attention auto-encoder is used for constructing a cell relation graph by using a Pearson correlation coefficient, dynamically learning a neighborhood weight in combination with a multi-head attention mechanism, retaining original features through residual connection, and relieving the excessive smoothness problem of image convolution; and a deep clustering model is self-optimized: target distribution and soft label distribution are minimized through KL divergence, and end-to-end joint optimization of embedded learning and clustering is realized.

The invention provides an insulator defect detection method based on mixed attention and multi-scale features. By constructing a multi-scale feature extraction module and combining a bottom-up residual neural network and a top-down reverse residual neural network, the ability of the model to extract different scale features is improved. An introduced multi-head mixed attention mechanism enhances the attention of the model to details and overall information through the combination of local attention and global attention. An improved Transform framework is combined with a multi-head mixed attention and Hungary bisection matching algorithm, so that the target distribution and detection precision is optimized, and the small defect recognition capability of the model under a complex background is improved. According to the method, high-precision detection is realized at low calculation cost, and particularly, the method has a remarkable detection effect and practical application value when being used for coping with multiple targets, complex backgrounds and small defects.

The invention relates to the technical field of electric vehicle data analysis, and discloses an electric vehicle charging demand prediction and distribution network optimization method based on data fusion. According to the method, related data of the electric vehicle are collected through a multi-source data acquisition module, feature fusion is performed by using a convolutional neural network, and charging demand parameters are predicted through a long-short-term memory network model. And constructing a multi-target distribution network optimization model, performing global optimization by adopting a genetic algorithm and introducing adaptive crossover and mutation probability, and outputting an optimal distribution network scheme. A layered charging control model is established, a strategy layer performs global planning, a scheduling layer performs local scheduling, and an execution layer realizes charging control based on fuzzy control. In addition, a differential privacy technology is adopted to protect user data privacy, a path is planned based on an A * algorithm of real-time road resistance, a prediction result is corrected in combination with weather factors, accurate prediction of the charging demand of the electric vehicle and distribution network optimization are achieved, and the resource utilization efficiency and the distribution network stability are improved.

The invention discloses a cross-platform content distribution intelligent adaptation method and system. The method comprises the following steps: acquiring multi-modal original input content; performing feature extraction on the multi-modal original input content to obtain a content feature group; obtaining platform feature parameters of the target distribution platform; obtaining a feature matching degree between the content feature group and the platform feature parameter; generating a content transformation scheme according to the platform feature parameters and the feature matching degree; and according to the content transformation scheme, carrying out transformation adjustment on the multi-modal original input content to obtain transformation content. According to the technical scheme provided by the invention, the original content is subjected to intelligent transformation adaptive to each platform; various content forms are supported, and the application scene is wide; quality evaluation and optimization are carried out on the transformation content, and the quality and the effect are improved; a publishing strategy is formulated, and content value maximization is achieved; fast access of a new platform is supported, a feature library is continuously optimized and updated, and a content modification scheme is dynamically adjusted; and the multi-platform operation efficiency is obviously improved.

The invention provides a panoramic image processing method and device suitable for an unmanned aerial vehicle and a panoramic unmanned aerial vehicle. The method comprises the following steps: acquiring multi-view images in real time through a multi-camera array of an unmanned aerial vehicle, acquiring pose data by using an inertial sensor, converting each view image into a first projection image and a second projection image, and performing dynamic target detection and recognition on the second projection image to obtain a dynamic target, geometric scene features and target distribution. Differentiated processing areas are divided, specifically, a high-precision algorithm is adopted for the first processing area, a low-precision simplified algorithm is adopted for the second processing area, and computing power focusing and processing capacity optimization are achieved. In the splicing stage, a dynamic target historical trajectory optimization splicing algorithm is combined, and processing strategies are adapted to different areas, so that the quality of the key areas is guaranteed, and the process of the non-key areas is simplified to reduce the calculation load. According to the scheme, the problem of real-time panoramic splicing under the condition that unmanned aerial vehicle hardware resources are limited is effectively solved, and the processing real-time performance and efficiency are remarkably improved.

The invention provides an unmanned aerial vehicle cluster dynamic hunting method for a complex three-dimensional scene. The method comprises the steps that the state of an escaper unmanned aerial vehicle, the state of each hunting unmanned aerial vehicle and the environment state are obtained respectively; sub-targets are generated around the escaper unmanned aerial vehicle through a Fibonacci ball algorithm, and the sub-targets are dynamically adjusted; based on the dynamically adjusted sub-targets and the state of each trapper unmanned aerial vehicle, optimal sub-target distribution is carried out on each trapper unmanned aerial vehicle through an improved market auction algorithm, wherein the improved market auction algorithm considers path cost and angle cost in a cost function; constructing a dynamic target control obstacle constraint based on the environment state, the state of the escaper unmanned aerial vehicle and the state of each trapper unmanned aerial vehicle; an escaper position change factor and a self-adaptive attenuation rate are introduced into the dynamic target control obstacle constraint; and constructing a distributed MPC optimization problem based on optimal sub-target distribution and a dynamic target control barrier function to perform trajectory optimization and motion control. According to the invention, the efficiency and the safety are ensured at the same time.

The invention relates to the technical field of power distribution networks, and discloses a power distribution network scheduling method and system based on deep reinforcement learning, and the method comprises the steps: carrying out the clustering and partitioning of a target power distribution network, and obtaining a plurality of power distribution network sub-regions; performing numerical calculation on the operation state monitoring data to obtain state vectors of the corresponding power distribution network sub-regions; training a pre-constructed first deep reinforcement learning model to obtain a second deep reinforcement learning model corresponding to the power distribution network sub-region; evaluating each second deep reinforcement learning model to obtain an evaluation result corresponding to the second deep reinforcement learning model; according to the global aggregation parameters, updating each second deep reinforcement learning model to obtain a third deep reinforcement learning model corresponding to the power distribution network sub-region; and inputting each real-time state vector into a corresponding third deep reinforcement learning model for strategy mapping to obtain a scheduling strategy of each power distribution network sub-region. According to the invention, the dispatching reliability and real-time performance of the power distribution network are improved.

An MHCl binding peptide design method based on evolutionary information and a Transform neural network algorithm relates to the field of protein design, and comprises the following steps: S1, extracting evolutionary information features of alleles of MHCII molecules and binding core sequences of binding peptides corresponding to the alleles, S2, establishing a neural network model based on fusion of a convolution module and a Transform module, and S3, establishing a neural network model based on fusion of the convolution module and the Transform module, the method comprises the following steps: S1, extracting two frequency characteristic tensors from S11 and S12, taking the two frequency characteristic tensors extracted in S11 and S12 as double inputs, and finally obtaining probability distribution of 20 amino acids at each position of each sequence, and S3, according to an output result of a neural network model, carrying out random sampling according to the probability, and generating a binding core sequence of MHCII-peptide meeting target distribution. According to the method, evolutionary information such as sequence position amino acid frequency (first-order conservative analysis) and combined frequency (second-order conservative analysis) of amino acid pairs is introduced to design a new short peptide sequence, the problem that short peptides cannot be designed based on structures is solved, and the reliability of short peptide sequence design based on evolutionary information is provided.

The invention discloses a remote sensing target detection large model construction method based on context feature deep learning, and the method comprises the following steps: S1, dividing an original data set according to a strategy, forming a training set, a verification set and a test set, and guaranteeing that each subset has representativeness in sample number, scene diversity and target distribution; s2, constructing a remote sensing target detection large model by adopting a deep learning method based on context features, and fusing multi-level features and introducing a query optimization mechanism; and S3, performing model testing and performance evaluation, selecting optimal model parameters, performing comprehensive verification on the test set, and evaluating the comprehensive performance of the model in the aspects of detection precision, speed and robustness. According to the method, the pre-trained DINOv2 backbone network, the context feature enhancement module and the intensive supervision strategy are introduced, so that rich context information can be captured in a multi-scale and complex scene, and the detection precision is greatly improved.

The embodiment of the invention discloses an intelligent unmanned aerial vehicle food material delivery method and system, and the method comprises the following steps: obtaining to-be-delivered order information and parameter information of an unmanned aerial vehicle, determining a target delivery strategy according to the to-be-delivered order information and the parameter information of the unmanned aerial vehicle, and carrying out the food material delivery according to the target delivery strategy. Real-time environment information images of the unmanned aerial vehicle are obtained in real time in the distribution process; and comparing the real-time environment information image with an initial environment information image to obtain incremental image information, and adjusting a target distribution strategy or controlling an unmanned aerial vehicle to perform intelligent obstacle avoidance according to the incremental image information. According to the method, global distribution path planning and local intelligent obstacle avoidance are combined, manual frequent intervention and adjustment are not needed, the autonomy and efficiency of unmanned aerial vehicle distribution are improved, the method can adapt to complex scenes, the environment adaptability is higher, the safety of the unmanned aerial vehicle can be guaranteed, and food distribution can be efficiently completed.

Certain aspects of the present disclosure provide techniques and apparatus for generating a response to a query input in a generative artificial intelligence model. An example method generally includes receiving a plurality of sets of tokens generated based on an input prompt and a first generative artificial intelligence model, each set of tokens in the plurality of sets of tokens corresponding to a candidate response to the input prompt; selecting, using a second generative artificial intelligence model and recursive adjustment of a target distribution associated with the received plurality of sets of tokens, a set of tokens from the plurality of sets of tokens; and outputting the selected set of tokens as a response to the input prompt.

The invention discloses a data distribution method and device, electronic equipment and a storage medium, the data distribution method is applied to a distribution server deployed at a network side, and comprises the following steps: obtaining distribution characteristic statistical data of a target data packet to be sent by terminal equipment; inputting the distribution characteristic statistical data of the target data packet into a first data distribution decision network to obtain a first distribution confidence coefficient of the target data packet matched with each transmission path; inputting the distribution characteristic statistical data of the target data packet into a second data distribution decision network to obtain a second distribution confidence coefficient of the target data packet matched with each transmission path; and matching the first distribution confidence coefficient and the second distribution confidence coefficient of each transmission path based on the target data packet, and determining a target distribution strategy of the target data packet. By applying the technical scheme provided by the invention, the distribution strategy of the uplink data packet of the terminal equipment can be accurately and effectively determined, and the data transmission efficiency is improved.

An example method includes determining a provider distribution by television viewing area using a provider-reported number of subscribers for a television viewing area and a sum of weights of panelist households that are located within the television viewing area. The method also includes obtaining a target distribution of a characteristic for the television viewing area. In addition, the method includes determining, using iterative proportional fitting, provider-specific distributions of the characteristic for the television viewing area. And the method includes using the provider-specific distributions of the characteristic for the television viewing area as a basis for assigning values of the characteristic to households that are subscribers of the return path data provider and located in the television viewing area.

According to one aspect, kernel-based ergodic search using a robot may include receiving a target distribution indicative of a desired ergodic search coverage, generating a kernel-based ergodic metric based on the target distribution and a candidate trajectory, generating a kernel-based ergodic gradient based on the kernel-based ergodic metric, generating a trajectory based on the kernel-based ergodic gradient, and implementing the trajectory for the robot.

The invention relates to the technical field of power system operation decision, and discloses a topological optimization reconstruction method and system based on machine learning. The method comprises the following steps: constructing a target power distribution network reconstruction model corresponding to a power distribution network; performing supervised learning on the initial XGBoost model based on historical reconstruction case data to obtain a target XGBoost model; performing line switch prediction on the real-time reconstruction case data based on a target XGBoost model to obtain a switch prediction value of each line in the power distribution network and a prediction probability corresponding to each switch prediction value; screening each switch prediction value based on the prediction probability to obtain a screening result of the corresponding switch prediction value; and solving the target power distribution network reconstruction model based on all the first switch predicted values of which the screening results are low-confidence decisions, and determining a first reconstruction strategy corresponding to the power distribution network according to the first solving result and all the second switch predicted values of which the screening results are high-confidence decisions. According to the method, the solving efficiency of the large-scale power distribution network reconstruction problem can be remarkably improved.

The invention discloses a Text2SQL (Structured Query Language) data set generation method based on dynamic difficulty adjustment. According to the method, a complexity quantification method is provided, and the complexity of SQL statements and Text texts is evaluated. A Text2SQL data set is generated based on a cue word generation template by using a large language model, and a dynamic constraint part is introduced into the cue word generation template. And performing complexity evaluation on data generated by the large language model through a complexity quantification method, comparing the data with preset difficulty target distribution, modifying dynamic constraints, and guiding the model to generate data conforming to expected target distribution. Meanwhile, a verification and correction template is also set, so that the execution correctness and semantic correctness of the generated data are ensured, the efficiency and quality of data set construction are improved, the diversity and pertinence of the data set are ensured, more comprehensive training data better fitting practical application is provided for the model, and the training efficiency is improved. And the generalization ability and the performance of the Text2SQL model in different scenes can be improved.

The invention relates to the technical field of source network load storage cooperative control, in particular to an area source network load storage cooperative optimization control system based on an intelligent fusion terminal, and the system comprises an acquisition module which acquires energy characteristic parameters and cooperative response characteristic parameters of a target area in a historical period; analyzing an energy characteristic characterization value based on the energy characteristic parameter through an analysis module, and calculating a collaborative response characteristic characterization value based on the collaborative response characteristic parameter; determining a corresponding control strategy based on the energy feature representation value through a control module; through a verification module, whether the stability of the transformer area source network load storage meets the standard is judged based on the collaborative response characteristic representation value; and through a feedback module, based on a difference value between the energy feature representation value and a predetermined energy feature representation threshold, determining an adjustment amplitude of the cooperative response feature representation threshold, and performing collection and verification again. According to the invention, through the verification module and the feedback module, the response rate of cooperative control of each link of source network load storage is improved.

The invention discloses a fire emergency command system and method based on dynamic fire scene modeling and double-algorithm cooperation. The system comprises a fire scene intelligent map system, a fireman terminal, an unmanned fire fighting equipment terminal and external information. The method comprises the steps that the information management layer extracts the latitude and longitude of a fire point, calculates a spreading rate and a high-risk area mark and pushes the same to the display module; a commander issues a fire-fighting instruction to an AI auxiliary decision-making layer; a fire fighting task plan and a target fire point priority ranking table are generated by a target distribution module and a Hungary algorithm of the AI auxiliary decision-making layer; the AI auxiliary decision-making layer drives a fire extinguishing planning module to cooperate with an action path through an MADDPG algorithm, plans an unmanned aerial vehicle group airdrop route optimization and robot group breakthrough route, carries out data and instruction interaction to generate an end-to-end execution instruction set, and transmits the instruction set to the instruction layer; and the instruction layer sends the generated fire extinguishing instruction to each terminal to guide the firefighter and the unmanned equipment to extinguish the fire. According to the invention, full-chain optimization of fire rescue is realized through multi-dimensional technology fusion.

Disclosed are apparatuses, systems, and techniques that may use machine learning for implementing generative text-to-speech models. The techniques include identifying a mapping of speech characteristics (SC) on a target distribution of a latent variable using a non-linear transformation for at least a subset of the SC. Parameters of the non-linear transformation are determined using a neural network that approximates a statistics of the SC with a statistics predicted for the SC based on the identified mapping and the target distribution of the latent variable.

The invention provides an optimal configuration method and device for a magnetic reactance type dynamic voltage restorer, and relates to the technical field of optimal configuration, and the method specifically comprises the steps: obtaining the historical voltage sag event data of a target power distribution bus and the electrical parameters of a downstream load motor; screening a to-be-compensated event set based on the principle that the sag depth exceeds a load tolerance threshold and the duration is less than the critical stall time, and determining a compensation strategy according to a comparison result of a phase jump angle maximum value and the threshold; the maximum compensation voltage is determined according to the maximum sag depth, the rated output current is determined according to the load parameters, the theoretical energy required for compensating the longest event is calculated based on the compensation strategy, and the actually required energy is determined in combination with the energy correction coefficient of the device topology; and finally, generating an optimal configuration scheme based on the compensation strategy, the maximum compensation voltage, the rated output current and the actually required energy. According to the invention, accurate mapping from power grid measured data to device key parameters is realized.

The embodiment of the invention provides a model training method, a task processing method and a model training system. The model training method comprises the following steps: receiving state information sent by each training resource participating in a model training task; identifying abnormal training resources based on the state information of each training resource; based on task parameters of the model training task and resource parameters of each target training resource, determining a target distribution strategy under the condition that a task execution index reaches a preset index, the target training resources being training resources except abnormal training resources; and according to the target distribution strategy, distributing the model training task to each target training resource for execution, and obtaining a trained target model. According to the method, the monitoring cost, the secondary health cost and the transition cost are optimized, comprehensive, efficient and automatic recovery of model training with abnormal conditions is realized, the cost of model training is reduced, and the reliability of model training is improved.