900 results about "Greedy algorithm" patented technology

The invention relates to a large model scheduling method and device based on NPU computing power, and the method specifically comprises the steps: splitting a large model into a plurality of subtasks according to layers, and analyzing the dependency relationship between the subtasks; performing operation demand prediction on the large model and the subtasks to obtain an operation amount factor and a memory demand factor; training a computing power evaluation model, and predicting real-time computing power evaluation data of each NPU node; selecting a scheduling strategy, calculating the initial priority of each to-be-scheduled sub-task according to the scheduling strategy, and allocating each sub-task to an optimal NPU node for execution by using a greedy algorithm in combination with resource demand information and NPU computing power evaluation data; and monitoring the operation condition of the subtask on each NPU node in real time, and when the execution of the subtask is abnormal, triggering an alarm and taking countermeasures. Compared with the prior art, the method has the advantages that the NPU resource utilization rate and the computing power scheduling efficiency are improved, and the equipment safety is guaranteed.

The embodiment of the invention discloses a task cooperative processing method and device based on multiple agents. The method comprises the steps that a to-be-processed task is disassembled into a plurality of sub-tasks based on a large model; determining a candidate agent corresponding to each sub-task of the plurality of sub-tasks from a preset agent library; evaluating the candidate agent corresponding to each sub-task of the plurality of sub-tasks, and determining a comprehensive score of the candidate agent corresponding to each sub-task of the plurality of sub-tasks; based on the comprehensive score of the candidate agent corresponding to each sub-task of the plurality of sub-tasks and the real-time state of the candidate agent corresponding to each sub-task of the plurality of sub-tasks, determining the agent corresponding to each sub-task of the plurality of sub-tasks by adopting a greedy algorithm; and processing the plurality of sub-tasks based on the intelligent agent corresponding to each of the plurality of sub-tasks to obtain a processing result of the to-be-processed task.

The invention provides an intelligent boxing method and system. The intelligent boxing method comprises the steps of obtaining boxing task information and generating a boxing scheme. The boxing task information comprises the size, the weight, the stacking limitation, the number and the priority of each cargo, and the size, the bearing capacity and the occupation state of each loading unit. And under the condition that the size, bearing and stacking constraint conditions are met, a plurality of candidate boxing combinations are generated, the loading utilization rate, the residual space distribution and the shape matching degree are evaluated based on a preset optimization target, and an optimal scheme is selected. The system comprises an input terminal, a task information receiving module, a data storage module, a task and scheme storage module, an operation processing module and a final boxing scheme generation module. According to the method, multi-dimensional constraint matching and weight balance are considered, and space waste and transportation risks are avoided; the maximum greedy algorithm and the adaptive large neighborhood search optimization are combined to realize the combination of automation and experience. The method can be expanded to batch task scheduling and grouping sequencing, so that the calculation complexity is reduced, and the overall boxing and transportation efficiency is improved.

The invention discloses a task scheduling method and device, equipment, a storage medium and a product, and relates to the technical field of artificial intelligence. According to the method, a large model is divided into a plurality of sub-tasks, the computing power demand is quantified in real time, and the matching score of the schedulable task and each neural network processor node is calculated in combination with the computing power evaluation information of each neural network processor node; the schedulable task with the highest priority in the scheduling queue is allocated to the neural network processor node with the highest matching score, and all subtasks of different large models are reasonably allocated through loop execution; besides, priorities are distributed to the to-be-scheduled sub-tasks in the scheduling queue based on the scheduling strategy, scheduling is carried out through a greedy algorithm, it can be guaranteed that each scheduling behavior is a real-time optimal scheduling scheme, and therefore the problem how to schedule large model tasks more efficiently is solved, and the scheduling efficiency is improved. And the computing power resource of the neural network processor is efficiently utilized to reduce the technical problem of computing power waste.

The invention belongs to the field of submarine routing planning, particularly relates to a pipeline routing line selection method and system based on a path planning algorithm and analytic hierarchy process, and aims to improve the routing line selection efficiency and precision and reduce the routing risk in the pipeline routing line selection process. The method comprises the following steps: establishing an evaluation sample matrix X based on measured value data of evaluation indexes, and converting pixel points of a target sea area topographic map into nodes of a two-dimensional matrix to obtain a grid obstacle map; establishing a submarine pipeline routing risk quantitative evaluation model; converting the sea area topographic map into a risk level grey-scale map through the risk quantitative evaluation model of the submarine pipeline; and carrying out route planning through a route planning algorithm based on the grid obstacle map, and finding a route with the minimum accumulated gray value in the target sea area risk level gray-scale map through a greedy algorithm to serve as a final route. According to the method, risk parameters are taken into consideration for the generated path, and finally, the obtained route is a routing path with small length and low overall risk.

The invention discloses a broadcast type radio positioning system base station site selection analysis and evaluation method and system, and relates to the technical field of wireless positioning, and the method comprises the steps: building a radio ground base station deployment sample database and an elevation model database; based on a combined optimization strategy of a greedy algorithm and a genetic algorithm, selecting an optimal base station set covering a pre-working area from the database; constructing a three-dimensional evaluation system, and comprehensively evaluating the optimal base station set from three dimensions of deployment cost, system robustness and positioning precision; optimizing the base station set based on a digital twinning technology, wherein the optimization comprises multi-band signal emission modeling and machine learning recommendation of a historical case library; and dynamic presentation and parameter adjustment of a base station deployment effect are realized through a three-dimensional visual interface and an interactive sand table system. According to the method, a large-range working area can be well covered, the situation that a signal coverage blind area exists is avoided, then the working efficiency is improved, and flexible and efficient site selection evaluation on the radio ground base station in a local sudden battlefield can be achieved.

The invention discloses an AGV robot scheduling management method and system, and relates to the technical field of robot scheduling, and the method comprises the steps: calculating a task priority score Pi based on the task data of an AGV robot, generating a candidate path set Ri through employing a breadth-first search BFS in combination with an AGV robot operation environment topological graph G (V, E), carrying out the preliminary screening of the candidate path set Ri through employing a greedy algorithm, and carrying out the scheduling of the AGV robot. An initial path of the AGV is generated, an adaptive large neighborhood search ALNS is used, an objective function is set for iteration, and an optimal initial solution is output; and performing reverse annealing based on the optimal initial solution generated by the ALNS, and outputting an AGV robot scheduling management scheme in combination with neighborhood operation. According to the method, the candidate path set is preliminarily screened by using the greedy algorithm, the initial path of the AGV is generated, the ALNS is used for iterative optimization, the optimal initial solution is output, reverse annealing is combined, and an AGV scheduling management method is output, so that global optimal management of AGV robot scheduling is realized, and the intelligence and flexibility of AGV robot scheduling are improved.

The invention discloses an information retrieval system and method based on semantic normalization, and relates to the technical field of artificial intelligence information, and the method comprises the steps: collecting a semantic query record input by a user, carrying out the preliminary semantic analysis, and generating structured data; on the basis of the structured data, entity disambiguation is carried out by utilizing a knowledge graph, abstract classes are generated through a neural network, calibration and dynamic weight adjustment are carried out, and high-confidence entity abstract classes and confidence scores are generated; entity abstract classes and confidence scores are combined with user contexts, an action-value function is calculated through a value network, and an optimal action is selected by utilizing a-greedy algorithm; executing semantic normalization mapping according to the optimal action, and obtaining an intermediate expression by using a meta-symbol dynamic generator; and performing index retrieval and multi-dimensional sorting based on the intermediate expression to generate a sorted retrieval result list. According to the method, the semantic fragmentation problem of multi-modal query is solved, and deep semantic alignment and dynamic weight calibration of heterogeneous data are realized.

The invention discloses a laser power optimization method based on a deep reinforcement learning strategy, belongs to the technical field of simulation and artificial intelligence, and is suitable for a laser powder bed melting (LPBF) process. The method comprises the steps that firstly, an LPBF simulation model is built, a molten pool three-dimensional temperature field distribution model is built through a moving heat source, and the forming process under a laser scanning path is simulated in combination with a part STL file; a state space and a reward function are constructed by extracting state features of a molten pool in real time, and a laser power adjustment strategy is optimized by using a deep reinforcement learning DQN network model. An optimal laser adjustment action is generated by adopting an epsilon-greedy algorithm, and intelligent agent interaction data is stored and updated based on an experience pool, so that network model parameters are optimized, and the stability and uniformity of the depth of a molten pool are ensured. According to the method, internal defects are effectively reduced, the quality and intelligent control level of the LPBF process are improved, and a new thought is provided for efficient and accurate process parameter optimization.

The invention discloses a monitoring system and method for working data of an AGV robot, and belongs to the technical field of artificial intelligence. The method comprises the following steps: preprocessing and integrating the driving track, freight weight, running state and task execution data of the AGV robot to form a robot working data set; clustering analysis is carried out based on driving tracks of all robots, and similar tracks are clustered into one class; constructing a dynamic driving track planning model by using an A * algorithm, and generating a plurality of candidate optimal driving tracks for each task combination; constructing a task demand list, and carding a task sequence logic relationship; performing task allocation on the AGV robot by using a greedy algorithm; generating a task allocation list by using a simulated annealing mechanism; a task instruction is issued to a corresponding AGV robot control system, a task execution process is started, and whole-course tracking is started; and after the AGV robot completes a single task and a round of task sequence, a model self-learning updating mechanism is triggered.

The invention provides an automatic identification method and system for a linear structural plane in a complex rock mass outcrop, and belongs to the field of rock mass engineering, and the method comprises the steps: obtaining a DeepLabV3 + model of a rock mass outcrop image, carrying out the semantic segmentation of an input image, outputting a binary mask image of the trace pixel of the structural plane, and carrying out the topology reconstruction; establishing a mapping relation between the image coordinate system and the three-dimensional point cloud; calculating a space included angle between the direction vector of the trace and the average occurrence of the structural plane groups, classifying the three-dimensional point cloud data according to the structural plane groups according to an angle threshold value, and performing three-dimensional line segment extraction by adopting an improved iterative Hough transform algorithm; and realizing global multi-segment connection and trace length calculation of discrete segments based on a greedy algorithm. The method breaks through the limitation of a topographic region when the rock mass structural plane data is obtained through manual measurement, and improves the precision and efficiency of structural plane extraction in the complex rock mass.

The invention relates to the technical field of medicine cold-chain logistics, and discloses a medicine cold-chain logistics dynamic data cloud platform and a route analysis method.The cloud platform comprises a data acquisition unit, a preprocessing unit, a data transmission unit, a storage management unit and a route analysis unit. Key data such as temperature, humidity and position are collected in real time through multiple sensors and are subjected to standardized preprocessing, so that the data are comprehensive, real-time and reliable, and a foundation is laid for subsequent analysis. In the aspect of storage management, partition storage is carried out based on time and space dimensions, and the data retrieval and management efficiency is greatly improved; in the aspect of route planning, factors such as time, temperature and humidity, congestion and the like are comprehensively considered, a scientific route cost evaluation function is constructed, costs such as transportation duration, temperature and humidity stability, road conditions and the like are covered, and in combination with a greedy algorithm, the medicine transportation quality can be guaranteed, the cost can be effectively reduced, and the logistics efficiency can be improved.

The invention discloses an equipment prediction maintenance framework based on probability residual life, and belongs to the technical field of fault prediction and health management (PHM). According to the framework, through combining a Bayesian neural network and a reinforcement learning technology, probability prediction and dynamic maintenance decision optimization of the residual life of equipment are realized. The method specifically comprises the following steps: acquiring sensor data in equipment operation, and preprocessing to generate a training data set; constructing a Bayesian neural network (BNN), utilizing variation reasoning to approximate posteriori distribution, and outputting probability distribution of residual life through Monte Carlo sampling; based on a probability prediction result, constructing a reinforcement learning environment model, and defining a state space containing residual life distribution, a spare part state and a maintenance action and a reward function; a Double DQN algorithm is adopted to optimize a maintenance strategy, and intelligent decision-making of the optimal maintenance time and the optimal ordering time of equipment is dynamically realized through an epsilon-greedy algorithm, so that the maintenance cost and the fault risk are minimized. According to the method, probability residual life distribution and reinforcement learning decision are innovatively combined, the problem that a traditional point estimation model ignores uncertainty is solved, an intelligent maintenance framework is constructed through a reinforcement learning method, and dynamic optimization of maintenance decision is achieved. The NASA aero-engine data set verification shows that compared with a traditional method, the optimization effects of prediction errors, uncertainty quantification, the maintenance cost rate and the like are remarkable, and the reliability and economical efficiency of equipment are effectively balanced.

The invention relates to the technical field of unmanned aerial vehicle cooperation, and particularly discloses a multi-unmanned aerial vehicle cooperation route planning method based on an improved GWO algorithm, and the method comprises the following steps: carrying out the constraint analysis of an unmanned aerial vehicle group; analyzing the flight path cost of the unmanned aerial vehicle; a GWO algorithm and improvement are carried out; and finally carrying out simulation experiment and algorithm performance analysis. According to the multi-unmanned aerial vehicle cooperative flight path planning method based on the improved GWO algorithm, aiming at the condition that the initialization effect of the GWO algorithm is poor, a greedy algorithm and a variation strategy are used, and the fitness value during unmanned aerial vehicle flight path initialization is reduced; a clustering method is introduced, the flight path of the unmanned aerial vehicle is divided into a plurality of sub-populations, each sub-population is independently optimized, and through comparison of the improved GWO algorithm, the MP-GWO algorithm and the GWO algorithm, the result shows that the flight path calculated by the improved GWO algorithm is lower in flight path cost and higher in convergence precision compared with the flight path calculated by the other two algorithms.

A multi-user multi-exit DNN reasoning partitioning and task scheduling method includes the steps that trained multi-exit DNN models are deployed on each user device and an edge server, and in a time slot, tasks reach the user devices, meet Poisson distribution and are stored in task queues of the user devices; taking the strategy feasibility interval as a constraint, taking an exit point, a partition point and a scheduling decision of the task as optimization variables, and establishing an optimization problem P taking maximization of the total completion rate and the reasoning accuracy of task reasoning in a time slot as a target; decoupling the problem into a server scheduling optimization problem P1, establishing a graph model of a server task, and solving the problem P1 by using a double-greedy algorithm; and simplifying the problem into P2, establishing a Markov decision process, and solving an optimal strategy by using a multi-agent reinforcement learning algorithm. The method is suitable for multi-user equipment to carry out multi-outlet DNN reasoning partition, and the task completion rate and reasoning accuracy within a period of time can be effectively improved.

The invention discloses a resource collaborative awareness-based Storm flow calculation dynamic scheduling method, and provides the following technical scheme aiming at the problem that the performance of a Storm default scheduling algorithm is reduced in node abnormity, resource fluctuation and rescheduling scenes: firstly, dynamically sensing node busy, downtime and data flow fluctuation events through a real-time monitoring module; triggering a rescheduling process; secondly, constructing a resource collaborative optimization model based on historical task instance resource requirements, node performance indexes and communication overhead, and generating a task instance allocation scheme by taking minimization of inter-node communication cost as a target and combining CPU / memory dynamic threshold constraints; further, a greedy algorithm is adopted to sort high-relevance task instances, the high-relevance task instances are preferentially distributed to nodes with the optimal historical performance, and it is ensured that the node resource utilization rate does not exceed a dynamic threshold value; meanwhile, node load balancing parameters are corrected in real time through time window smoothing processing, and the remaining resource state is updated; and finally, outputting an optimized topology division result, so that the system delay after rescheduling is remarkably reduced, and the throughput is improved. According to the method, rapid recovery and stable operation of the heterogeneous cluster are realized through resource collaborative modeling, historical data driven dynamic scheduling and load balancing optimization.

The invention belongs to the technical field of electric power engineering digitization and intelligent drawing auditing, and particularly relates to a dynamic rule engine and multi-target auditing task decomposition method oriented to electric power drawings. Obtaining a power rule file, analyzing and verifying to generate qualified rules, and screening the qualified rules to obtain an effective rule pool; the method comprises the following steps: reserving candidate areas by preprocessing an electric power engineering drawing, generating structured cells through line segment classification and combination and table detection, extracting and cleaning a text, and affiliating the text to the corresponding cells to form a structured result; screening candidate rules from the effective rule pool, extracting auditing objects from the structured result, generating auditing points, calculating the priority of the auditing points, and packaging the auditing points into an assignment unit set capable of being executed in parallel; on the basis of the dispatching unit set, optimizing task distribution by adopting a dynamic model and a greedy algorithm in an execution environment with the maximum concurrency, and doubly optimizing task execution by quantifying benefits and verifying rules; and performing rule judgment and result measurement on a task execution result.

The invention discloses a multi-port test equipment resource allocation management system and method, particularly relates to the technical field of equipment resource allocation, and is used for solving the problem that port and resource allocation in multi-port test equipment is not clear. According to the method, the bandwidth, the frequency, the voltage demand and the signal type of the task are extracted, the feature vector is constructed and normalized, the task is classified by using a K-means clustering algorithm, the resource demand of the task is accurately identified, the adaptive capability score of each port is dynamically calculated in combination with the task feature and the port resource capability, and the port load is monitored in real time, so that the adaptive capacity of each port is improved. According to the method, the task is reasonably allocated to the most appropriate port, the port resource allocation is optimized and the port allocation task is intelligently selected by continuously updating the adaptive capability score and combining the greedy algorithm and the optimal scheduling algorithm, so that the resource conflict and waste are avoided, the task delay is reduced, and the task processing capability and the resource utilization efficiency of the system are improved.

The invention discloses an imaging satellite task scheduling automatic modeling method based on a large language model, which automatically generates different types of optimization models and corresponding greedy algorithms through LLMs, not only can efficiently solve the scheduling problem of different scales of tasks, but also can flexibly adjust when processing complex constraints, and improves the task scheduling efficiency. The method shows strong automatic correction capability, and can realize superior resolving capability and adaptivity when processing large-scale tasks.

The invention provides a wireless resource scheduling method and device used in a scene in which a mobile phone is directly connected with a satellite, and relates to the technical field of satellite communication, and the method comprises the steps: constructing an electromagnetic map which represents the corresponding relation of a geographic position, a physical resource block and ground interference power in a satellite beam coverage area; link budgeting is carried out on the multiple user terminals in combination with the real-time orbit geometric parameters of the satellite and the electromagnetic map, and the signal to interference plus noise ratio of each user terminal on each physical resource block is predicted; based on the predicted signal to interference plus noise ratio, adopting a proportional fair marginal gain greedy algorithm to allocate an optimal continuous spectrum resource block for each user terminal; on the allocated spectrum resources, optimizing and reallocating the total transmitting power of the satellites by adopting a water injection algorithm; and a final spectrum-power joint scheduling scheme is generated, and subsequent link self-adaption and data transmission are guided. According to the invention, the spectrum efficiency, throughput and connection stability of the system are obviously improved.

Embodiments of the present application relate to the technical field of computers, and provide an audience selection method and apparatus, an electronic device, and a computer-readable storage medium. The method comprises: inputting first information into prompt engineering to construct first data, wherein the first information is related to an operational issue, and the first data comprises issue features, user features and data features; inputting the first data into a pre-trained large language model (LLM) to obtain a first index and a corresponding dimension; and on the basis of the first index, the dimension, and data corresponding to the dimension, performing a greedy algorithm to obtain an audience. The embodiments of the present application achieve the objective of improving the accuracy and efficiency of audience selection.

The invention discloses an OTA upgrade task dynamic configuration control method and system based on machine learning, and the method comprises the steps: collecting vehicle upgrade data, and carrying out the preprocessing of the vehicle upgrade data; designing a prediction model, carrying out training and effect evaluation on the model, and outputting the upgrade probability of each vehicle in a future time period and a recommended upgrade time window according to the preprocessed vehicle upgrade data; and carrying out OTA upgrade task allocation according to the output of the prediction model to complete vehicle upgrade. According to the method, the vehicle upgrading data are input into the prediction model for prediction, the predicted upgrading time window is obtained, OTA upgrading task allocation is performed, the vehicle upgrading demand can be predicted, the upgrading time window can be recommended, overlarge system load and current limitation caused by centralized task issuing are avoided, and the reliability, stability and success rate of system upgrading are improved; and a greedy algorithm is added in OTA upgrade task allocation, so that the allocation scheduling of the upgrade tasks is optimized, and the network and system loads are balanced to the maximum extent.

The invention discloses a multi-specialty and multi-nest scheduling method and related device based on electric power joint inspection, and the method comprises the steps: formulating the inspection strategy of each specialty of transmission, transformation and distribution to design an inspection plan, and integrating the inspection plan into an unmanned aerial vehicle joint inspection plan library to obtain an inspection task set; clustering a plurality of types of routing inspection tasks with similar features in the routing inspection task set by adopting a clustering algorithm to obtain a routing inspection flight task consisting of a plurality of devices; based on the established waypoint of the inspection flight task and the endurance time of the unmanned aerial vehicle, generating an inspection route of the unmanned aerial vehicle through a TSP algorithm; and based on the inspection route of the unmanned aerial vehicle, performing one-to-one matching on each inspection flight task and each unmanned aerial vehicle nest by adopting a greedy algorithm, and finally forming automatic scheduling of the unmanned aerial vehicle nest. The problems that in the prior art, routing inspection tasks are distributed unreasonably, cross-professional multi-nest routing inspection is not fully considered, and systematic and comprehensive research on the whole process of routing inspection operation is lacked are solved.

The invention discloses a distributed cooperative processing method for a weak network and full dynamic, and relates to the field of distributed computing, and the method comprises the steps: respectively obtaining network state data, computing power data and environment data of a local node through a sensing layer; the network connection indexes of the local node and the surrounding nodes are obtained by sending a probe packet; the support layer receives the data, and encodes and compresses the data through a transmission algorithm; the scheduling layer calculates measurement indexes of the nodes according to the neighborhood data support table of each node, and constructs a node scheduling vector table; the scheduling layer generates a cooperative computing node list by adopting a greedy algorithm or a dynamic programming algorithm according to a computing task of a user and a node scheduling vector table; the processing layer adopts a lightweight remote procedure call LightRPC method based on a UDP (User Datagram Protocol) to transmit an operator to a specified node in the cooperative computing node list for execution; aiming at the weak network and low distributed cooperative computing efficiency under the full-dynamic condition in the prior art, the method improves the computing efficiency.

The invention relates to the technical field of soil detection, and discloses a soil property quantitative analysis method of a spectrum-driven and greedy optimization integrated model, which comprises the following steps: preprocessing data; defining a basic model; selecting a greedy optimization model; integrating learning models; evaluating model performance; according to the method, the optimal model combination is selected through the greedy optimization method, the advantages of multiple models are integrated, and the accuracy of soil property prediction is improved. The greedy algorithm is simple and efficient, model selection and integration can be completed in a short time, and the method is suitable for processing of large-scale data sets. The method does not depend on a specific model or data set, and can be expanded and applied to prediction tasks in other fields.

The invention discloses a collinear production control method and system for instant food, and relates to the technical field of production control, and the method comprises the steps: defining a production operation process set for a collinear production line; a switching energy consumption item is defined, energy consumption needed by switching between every two production operation processes is calculated through the switching energy consumption item, a switching energy consumption set is output, and the switching energy consumption item comprises equipment repetition rate energy consumption, material transfer distance energy consumption and equipment initialization energy consumption; constructing a switching energy consumption matrix according to the switching energy consumption set; based on a greedy algorithm, performing global optimization in the production operation process set by switching the energy consumption matrix to obtain a serialized production operation process; and performing sequence switching control on the production operation process set according to the serialized production operation process. The technical problems that in the prior art, the production operation process switching efficiency is low, and the switching energy consumption is high are solved, and the technical effects of improving the production efficiency and reducing the energy consumption are achieved.

The invention discloses a semantic retrieval generation method based on a clustering and classification model, and particularly relates to the technical field of information retrieval and natural language process.The method comprises the steps that after a text is preprocessed, semantic vectors are generated through Sension-BERT, and multi-level technical theme branches are formed through UMAP dimension reduction and HDBSCAN clustering; extracting subject terms and expanding synonyms to construct an initial retrieval formula, and iteratively optimizing through a greedy algorithm to obtain an optimal retrieval formula; defining a retrieval range in combination with the fine-tuned BERT classification model and the IPC classification number prediction model; and dividing a retrieval range according to theme hierarchy, integrating results, sorting and returning, and recording retrieval history support multiplexing at the same time. According to the semantic retrieval generation method based on the clustering and classification model, the accuracy, coverage and flexibility of semantic retrieval are improved by constructing a multi-level theme system, optimizing a retrieval formula, precisely limiting a range and intelligently integrating a result, and target information meeting requirements is efficiently returned.

The invention provides a truck-unmanned aerial vehicle cooperative dynamic scheduling method and system in emergency logistics, and relates to the technical field of unmanned aerial vehicle intelligent scheduling. Constructing a truck-unmanned aerial vehicle cooperative distribution network, wherein each truck is equipped with a cooperative unit comprising a large unmanned aerial vehicle and a small unmanned aerial vehicle; the network comprises warehouses, hub points and demand points; establishing a mixed integer programming model by taking minimization of the total operation cost as a target and taking electric quantity management, service distribution, a time window and collaborative feasibility as constraints; and solving the model by adopting a randomized greedy algorithm, and synchronously deciding a truck path and an unmanned aerial vehicle task in single iteration to obtain a truck path scheme, an unmanned aerial vehicle service distribution scheme and a dynamic electric quantity scheduling scheme. Therefore, the cost optimization and dynamic coordination of emergency logistics scheduling are realized, and the efficiency and feasibility of emergency distribution are effectively improved.

The invention discloses a multi-station robot photographing task allocation method for hub surface defect detection, and the method comprises the steps: forming a hub triangular patch model, and carrying out the Gaussian mapping of the hub triangular patch model; finding out the task area boundary of each station robot based on a simulated annealing algorithm, wherein the included angle between the normal vector of the triangular patch in the same task area and the Z axis is in a set range; generating a camera viewpoint of each station robot for completing task area shooting, and adjusting the viewpoint with collision into a collision-free viewpoint; planning a local optimal path passing through all collision-free camera viewpoints based on a greedy algorithm; and carrying out collision detection on the local optimal path, and re-planning the road section with collision. According to the multi-station wheel hub defect detection method, the task area corresponding to each station is determined according to the wheel hub curved surface features by adopting the simulated annealing algorithm, finally, automatic balanced distribution of the multi-station robot operation tasks is achieved, collision detection is conducted on the generated shooting path, and the reliability of multi-station wheel hub defect detection is improved.

The invention discloses a flexible dynamic distribution method and system for a power module based on a charging pile, and relates to the field of power distribution, and the method comprises the steps: collecting dynamic demand parameters of a charging terminal in real time, and calculating a target power demand in combination with a battery safety constraint; obtaining the total number of available power modules and rated power, and generating an effective module set; calculating the minimum module demand number according to the total demand and the module average power; if the modules are sufficient, an efficiency priority strategy is adopted, and distribution is optimized through a greedy algorithm; if not, high-power terminals are preferentially distributed, and then residual power is distributed in a balanced mode; through real-time data, distribution is dynamically adjusted when a threshold value is exceeded; starting dormancy for the module with the load rate lower than 30%, migrating the terminal to other modules, and awakening when the load rises to 40%, so as to improve the efficiency and the resource utilization rate. The method has the advantages that through real-time monitoring and dynamic distribution of the power modules, the charging efficiency and safety are optimized, and the resource utilization rate and the system reliability of the charging pile are remarkably improved.