515 results about "Greedy algorithm" patented technology

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 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 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 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.

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 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.

The invention discloses a dynamic planning method and system for an intelligent patrol point location of power transformation equipment, and belongs to the technical field of intelligent patrol of power systems, the dynamic planning method for the intelligent patrol point location of the power transformation equipment comprises the following steps: mapping dynamic features and static attributes to the same feature space and carrying out cross-modal association; constructing a defect severity model, and introducing defect severity in risk quantification calculation to generate a point location priority list; a transformer substation three-dimensional point cloud model is built, path nodes are initialized according to a point location priority list, and a greedy algorithm is utilized. A closed-loop decision-making system of multi-modal data fusion is constructed; according to the method, automatic planning driven by a risk quantification model based on defect history, generation of a three-dimensional space non-blind area coverage path, dynamic adjustment triggered by two factors of equipment change and inspection effect, deep collaborative analysis of machine account-defect-real-time data and continuous inspection of a complex scene are guaranteed by a semantic compensation mechanism.

The invention relates to the technical field of the Internet of Things, and discloses a security assessment method and system based on the Internet of Vehicles, and the method comprises the steps: obtaining multi-dimensional original data, context data and a traffic data flow; attack event features are generated through data classification, cleaning, standardization and PCA feature extraction; constructing spatial and temporal distribution characteristics in combination with context data, determining a damaged object and a damage radius through a K-means clustering and morphological expansion algorithm, and outputting a damage area; analyzing abnormal behavior distribution based on the area and the traffic data flow, and generating a distribution diagram; analyzing the threat level according to the distribution map, the damaged object and the hazard radius, distributing resources by combining a greedy algorithm, and planning a time sequence to construct a dynamic defense strategy; optimizing a protection scheme according to the real-time feedback data; finally, behavior changes are continuously monitored, and a risk thermodynamic diagram and a safety gap list are generated. According to the method, the Internet of Vehicles safety assessment capability is improved.

The invention discloses an intelligent power grid load prediction and dynamic response coordinated scheduling method, and relates to the technical field of power system automation, and the method comprises the steps: accessing intelligent ammeters, distributed power controllers and other devices of Modbus, IEC61850 and DL / T645 protocols through a multi-protocol adaptive gateway, and achieving data standardization; time stamps are calibrated by means of Beidou time service and an IEEE1588PTP protocol, and it is ensured that multi-source data synchronization errors are controllable; deploying an edge computing node cluster, distributing high-priority tasks to low-load nodes through an edge coordinator in combination with a load fluctuation level and a greedy algorithm, and ensuring real-time processing efficiency; the edge nodes generate short-term load prediction, and the cloud platform outputs medium and long-term prediction based on a historical data training model; and finally, the coordinated scheduling decision module fuses the two types of prediction results and the real-time parameters of the power grid, and generates a dynamic instruction to control the output of the adjustable load and the distributed power supply.

The invention discloses an optimized node selection method based on a BGP-iSec protocol partial deployment scene, which comprises the following steps of: in a first stage, calculating three static topological characteristics of nodes according to a key node selection algorithm, weighting the three static topological characteristics and AS node levels in proportion, calculating a mixed weight of the nodes, and selecting the three static topological characteristics according to the mixed weight; preliminarily determining candidate nodes; and in the second stage, a greedy algorithm is adopted, the node with the maximum gain for the current path coverage rate is gradually selected from the candidate nodes according to the priority and added into the to-be-deployed set until the path coverage rate is stable or the number of the nodes reaches the specified deployment cost. According to the method, the key AS node selection strategy is formulated, so that the defense effect of the path manipulation / prefix hijacking attack is optimized under the limited deployment cost.

The invention relates to the technical field of computer networks, and discloses a link state sensing neural network model training flow path selection method, which comprises the following steps: collecting path information between each pair of hosts by sending a detection data packet carrying an in-band network telemetry field; calculating a priority index based on the number of sent bytes of the training task, wherein the priority index is determined by the proportion of the number of sent bytes of the training task to the total number of bytes needing to be sent and the number of sent bytes; a greedy algorithm is adopted, the training tasks serve as current training tasks according to priority indexes from high to low, paths with the lowest congestion degree are preferentially allocated to the current training tasks, and if the paths with the lowest congestion degree are occupied, suboptimal paths are allocated to the current training tasks; the congestion degree of the path is evaluated through the link utilization rate of each link in the path; according to the method, the link state and the task characteristics are monitored in real time, the path selection strategy is dynamically adjusted, and the communication efficiency of distributed neural network model training is remarkably improved.

The invention relates to the technical field of business management, in particular to an artificial intelligence business intelligent docking service platform, in the invention, task dependency relationships are rearranged by adopting topological sorting, and waiting time and resource waste caused by improper dependency relationships among tasks are reduced through scientific execution sequence adjustment, so that the service efficiency is improved. The method comprises the following steps of: extracting a business semantic structure chart, mining tasks which can be executed in parallel, recombining an execution path, reducing redundant nodes and unnecessary delay of a process, improving the execution efficiency of the whole process, extracting key operation rules through natural language processing, establishing the business semantic structure chart by adopting a support vector machine, accurately reflecting the hierarchy and association relationship of the operation nodes, and improving the execution efficiency of the whole process. Visualization and operability of complex business logic are achieved, flow optimization is carried out through the greedy algorithm, node priorities are defined, adaptive execution paths are matched, high efficiency and execution accuracy of flow design are ensured, and the response speed, the resource utilization rate and the execution accuracy of the business flow are improved.

According to a masonry task, a feasible path of a main arm and a feasible path of a slave arm are obtained, tasks of the main arm and the slave arm are subjected to labor division, the operation mode of one-hand grabbing and one-hand gluing of a mechanical arm is achieved, the two arms can efficiently cooperate in the respective task fields, and the work efficiency is improved. The control complexity is reduced, the system efficiency is improved, the cost of each group of executable pose actions in the multiple groups of executable pose actions is solved, the pose actions to be executed are selected according to the cost, inverse kinematics solving is conducted on each masonry point, and the pose actions to be executed are obtained. And operation cost calculation is performed on the solved pose action, and the greedy algorithm is combined to take the shortest execution time as an optimization target, so that the execution efficiency of the action path of the mechanical arm can be effectively improved, the total task duration is remarkably reduced, the beat control of engineering construction is facilitated, the problem of too tight coupling of a cooperation mode of two arms is solved, and the operation efficiency is high.

The invention relates to the technical field of evolution modeling, in particular to a seabed erosion and deposition evolution analysis method and system.The method comprises the steps that the multi-dimensional adjacency relation between nodes is recognized through a graph attention network, joint judgment is conducted on the gradient direction included angle, the elevation trend and the flow direction consistency index, and the influence degree is quantized; the expression capability of complex boundary direction influence factors is improved, direction judgment is not performed according to a fixed rule, and based on quantitative comparison of flow direction and gradient components, scouring dominant path nodes are screened and direction tracks of the scouring dominant path nodes are rearranged, so that sequential sequence expression has dynamic reconstruction capability; performing clustering screening on the angle abrupt change section by means of a greedy algorithm to form a node set representing an abrupt change trend, so as to enhance the extraction stability of the local extreme scouring behavior; and the connection logic expression of the physical state between the nodes, the abnormal extraction efficiency of local evolution change, the expression continuity of the erosion and deposition trend result and the overall identification stability are effectively improved.

The invention discloses a water supply pipe network monitoring point arrangement method based on a risk assessment and optimization algorithm, and relates to the technical field of intelligent pipe network monitoring, and the method comprises the steps: collecting water supply pipe network data, carrying out the preprocessing, and storing the data in an InfluxDB time sequence database; constructing a multi-dimensional risk assessment model, and generating risk scores of the pipe sections; a greedy algorithm and genetic algorithm hybrid optimization strategy is adopted to generate a water supply network monitoring point layout scheme; the method comprises the following steps: deploying an edge computing gateway, distributing water supply network data to the edge computing gateway through an MQTT protocol, generating a pipe section risk probability by using a pipe section risk probability model, setting a four-level judgment threshold, and triggering a hierarchical alarm mechanism; a DBSCAN algorithm is used for identifying a water supply network risk high-incidence area, water supply network monitoring points are increased, and the monitoring radius of the water supply network monitoring points is dynamically adjusted. According to the method, through a greedy-genetic hybrid optimization strategy, a global optimal solution and rapid convergence of monitoring point layout are realized.

The invention provides a signal type identification method based on reinforcement learning, and relates to the technical field of artificial intelligence, and the method comprises the steps: carrying out the zero-mean normalization processing and slicing operation of IQ data of a training signal, and generating IQ sample data; a data set with one-hot labels is constructed; constructing a deep learning strategy network and a deep learning target network; iteratively executing a training process: extracting one sample data from the data set as a current state; selecting an action according to an epsilon-greedy algorithm, executing the action, and obtaining an award and a next state according to a data set corresponding relation; according to the loss value calculation result and the optimizer, parameters of the deep learning strategy network are updated through a back propagation method, and a deep learning strategy network model is output; and inputting a to-be-identified signal into the trained deep learning strategy network, and outputting a final signal type through a voting algorithm. According to the method, efficient identification of communication signal types can be realized, and the method has very high signal identification accuracy and generalization ability.

The invention discloses a mechanical arm path planning method, which belongs to the technical field of mechanical arm path planning, is used for planning a mechanical arm path, and comprises the following steps: acquiring parameter information and preset parameter information of a to-be-detected area, and positions of a starting point and a target point of a cooperative manipulator at the tail end of a mechanical arm; defining a state sampling space; performing oriented random sampling in the state sampling space according to the target deviation factor to obtain a random point; gravitational search is added in calculation of new nodes of the RRT algorithm; performing optimization processing on the found path by using a greedy algorithm, and performing smoothing processing on the found path by using a B spline curve; and initial moving target point location information of the mechanical arm tail end cooperative manipulator is obtained and analyzed, and the next moving target point location of the mechanical arm tail end cooperative manipulator is obtained. Compared with the prior art, the method has the advantages that a feasible path can be quickly found in a high-dimensional space, discretization is not needed, and a dynamic environment and uncertain factors can be processed.

The invention belongs to the technical field of artificial intelligence and information retrieval, and particularly relates to a group-level core set selection method for a large model recommendation system. The method comprises the following steps: defining a core set selection task, and taking test loss after minimizing subset fine tuning as an optimization target; the method comprises the following steps: constructing an agent optimization target: based on an optimal transmission theory and gradient norm analysis, considering the distribution difference between a candidate subset and a verification set, and constructing an upper bound of agent test loss; initialization-re-refining calculation: by introducing a special cost matrix, converting an original problem into a p-median problem, and optimizing by using a greedy and sample exchange strategy; and label enhancement: according to a joint distribution category decomposition principle, further improving the category coverage capability and the performance stability of the core set in a label recommendation scene. According to the method, the data scale and computing resource consumption required by fine tuning can be remarkably compressed while the recommendation performance is kept, and key support is provided for efficient deployment of a large language model in a recommendation system.

The invention discloses an unmanned aerial vehicle area coverage flight path planning method and system based on graph segmentation, and relates to the technical field of unmanned aerial vehicle path planning. According to the method, a coverage point pool with high quality, comprehensive coverage and robustness is constructed for subsequent greedy selection through a strategy of adaptively generating candidate points; then, a greedy selection mechanism based on effective scores is adopted, the current optimal coverage point is accurately selected in each round of iteration, the new coverage area can be increased to the maximum extent, the overlapping area can be effectively controlled, and finally the whole target area is completely covered with the minimum number of circles. In the aspect of a path planning algorithm, a hybrid initialization mode of a greedy algorithm and a random generation strategy is innovatively combined, and the quality of an initial solution and population diversity are ingeniously balanced; meanwhile, in each generation of evolution of the genetic algorithm, a 2-opt local search strategy is embedded into a new non-elite individual, so that the convergence speed of the algorithm is increased, and the path optimization efficiency is remarkably improved. In conclusion, more efficient and more reliable planning of the unmanned aerial vehicle area coverage flight path can be realized.

The invention discloses a cross-regional municipal road maintenance resource scheduling method and system. The scheduling method comprises the following steps: collecting road disease data, maintenance historical records and real-time traffic flow data of each region; constructing a judgment matrix by applying an analytic hierarchy process, calculating a road factor weight through a root method, calculating a maintenance emergency degree score of each region according to the quantized basic data and the weight, and dividing region maintenance grades; according to the maintenance level of each area, maintenance groups with different priorities are divided; counting the existing maintenance resources of each region, and predicting the resource demand quantity of each region by combining a maintenance group division result and taking historical maintenance resource consumption data as a benchmark; for a resource shortage region, selecting a scheduling path by adopting a greedy algorithm, and formulating a preliminary cross-region resource scheduling scheme; and constructing a multi-target cost evaluation algorithm, and iteratively optimizing the scheme in combination with a particle swarm optimization algorithm. According to the invention, efficient, accurate and low-cost scheduling of cross-regional municipal road maintenance resources is realized.

The invention discloses a new energy automobile charging scheduling method and system based on deep learning, and the method comprises the steps: carrying out the preprocessing of data, and obtaining multi-source data; a self-attention mechanism of Transform is combined with a GNN graph neural network to establish a deep learning model, a time sequence is processed, a topological relation between geographic distribution of charging piles and a power grid load is modeled through GNN, and spatio-temporal joint features are output; meta-learning is introduced to dynamically adjust the weight of the spatio-temporal joint feature according to a real-time environment, and a charging demand prediction index is obtained; and solving an optimal charging pile distribution scheme through MIP mixed integer programming according to the charging demand prediction index, and generating a charging scheduling scheme by adopting a greedy algorithm. The total charging cost is reduced, the average waiting time of users is shortened, and the power grid load variance is reduced.

The invention discloses an intelligent flight path planning method for an unmanned aerial vehicle in a multi-type scene, and the method comprises the following steps: 1, building task input and task type recognition: setting the unmanned aerial vehicle to be in a fixed-height flight mode, and obtaining space information required by a task, including a take-off point position, a task point coordinate set and a task region boundary, the system identifies the task type through analysis; if the input is a discrete point set, the task is judged as a point task; if the structure is a broken line structure, judging the task as a line task; if the boundary is a task area boundary, the task is judged to be a surface task, and if the boundary width L is small, the task is still judged to be a line task. The method has the advantages that a fitness function is improved, the shortest total time of multiple unmanned aerial vehicle tasks is used as an optimization target, and task real-time weight parameters are introduced, so that a planning result meets task timeliness and energy consumption optimization at the same time; the coverage of planar tasks is realized in combination with a cattle farming algorithm, dynamic planning of electric quantity and a battery swap station is performed in combination with a greedy algorithm, and path optimization and improvement of energy management are realized.

The invention discloses a content abstract generation method based on chapter structure analysis, and belongs to the technical field of natural language processing. The method comprises the steps that firstly, an original text is preprocessed, then text structure deep analysis is carried out, the text type of the text is recognized, an explicit / implicit text relation is extracted, and special symbols are introduced through a Prompt normal form for implicit text relation extraction to strengthen logic semantics; secondly, scoring sentences by adopting a double-path scoring mechanism in combination with a deep neural network model of chapter structure features and an optimized text sorting algorithm, and fusing scores through a logistic regression model; then, screening target sentences based on a chapter relation weighted secondary modulus function and a greedy algorithm, and finally, carrying out post-processing to generate an abstract. According to the abstract generation method, the chapter structure logic is deeply utilized, so that the problems of logic unsmoothness, information redundancy or key relation missing in the existing abstract generation are solved, and the semantic coherence and information integrity of the abstract are improved.

The invention relates to a program-controlled resistor card and a program-controlled resistor smooth control method and device thereof. The method comprises the following steps: acquiring a resistor card code combination corresponding to a current resistance value and a target resistance value; a difference mask is calculated through an XOR operation, and a relay set needing to be changed is identified; on the basis of a greedy algorithm strategy, a relay enabling the instantaneous resistance value to be closest to a target value is selected from the difference mask identification set in a successive single-step switching mode for operation; iteratively executing switching and state updating until the difference mask is zero; and finally outputting a switching completion signal. The device comprises a state acquisition module, a difference analysis module, a switching decision module and a driving execution module, realizes smooth transition in a resistance change process through ordered single-step relay switching control, effectively avoids extreme states such as an open circuit or a short circuit, remarkably improves switching precision and system reliability, and is suitable for the fields of precision test measurement and automatic control.

The invention discloses an edge personalized reasoning method and system based on heterogeneous resources. The method comprises the following steps: optimizing edge deployment and reasoning through three aspects of technologies: customizing a resource-aware knowledge base, constructing a knowledge graph and converting the knowledge graph into a Pareto optimal problem, dynamically adjusting according to end-side resources, cooperatively processing tasks by the edge and the end, and compressing data; heterogeneous end side efficient query, sparse indexing for low-computing-power equipment, kNN algorithm for high computing power, weighted fusion of prediction results, and greedy algorithm optimization storage; an edge-end collaborative reasoning framework is adopted, edge nodes process intensive tasks, end-sides execute personalized tasks, a server cuts and distributes a multi-level knowledge base, a differentiated retrieval strategy is designed, the framework constructed by the method can fully utilize resource characteristics of different devices, and the overall reasoning efficiency is improved. The edge-end cooperation mechanism can improve the reasoning complexity and depth through edge enhancement while meeting the end-side resource requirements, and balance the response speed and accuracy.

The invention provides a hybrid robot path switching fairing method based on curvature and time optimization. The hybrid robot path switching fairing method comprises the following steps that a machining path is divided into a plurality of element paths; parameterization is carried out on a switching section, a remaining section and a transition section in each primitive path section; a quantum particle swarm optimization algorithm is combined with a greedy algorithm to optimize the curvature of the switching section; the quantum particle swarm optimization is combined with a moving window planning method to optimize the machining time of each section; and interpolation is conducted on the geometric path to generate a motion program, then a servo motor driving instruction of a driving joint is obtained through inverse solution of the robot, and a machining task is completed. The method has the advantages that the corner curvature and the fairing deviation of the machining path can be optimized, the machining time can be optimized, the machining path is optimized in a segmented mode by combining the greedy algorithm and the moving window planning method, complex solution of a high-dimensional problem is avoided, and the machining efficiency is improved. And the machining quality and execution efficiency of the robot are improved.

The invention discloses an AI-based unmanned aerial vehicle routing inspection path planning method and system, and relates to the technical field of unmanned aerial vehicle intelligent path planning, and the method comprises the steps: achieving the reasonable division and task distribution of an unmanned aerial vehicle routing inspection region through the combination of a K-means clustering method and a greedy algorithm; according to the method, the balance of sub-region division and the rationality of unmanned aerial vehicle load are improved while the routing inspection coverage rate is guaranteed, an initial routing inspection path conforming to energy consumption and path constraints is generated through combination of an A * algorithm and three-dimensional search space modeling, conflict detection is performed on the initial path through combination of a space-time distance analysis method and an indicator function method, and the routing inspection efficiency is improved. The security and robustness of the unmanned aerial vehicle group in a dynamic scene are enhanced, the collaborative path optimization based on the time-limited reward is realized by combining the AI model serialization action with the multi-agent DQN model, and the multi-unmanned aerial vehicle collaboration efficiency and task completion degree are effectively improved.

The invention discloses a reinforcement learning-based cable arrangement method and system in a power supply and distribution system, and a storage medium. The method comprises the following steps of S1, obtaining cable information, laying environment information and constraints required to be met by cable laying; s2, abstracting a bridge system into a weighted undirected topological graph; s3, calling a path planning algorithm based on reinforcement learning, and calculating an optimal cable laying path by taking a minimum comprehensive cost function including path length cost, plot ratio cost and precious penalty terms as an optimization target under the constraint that cable laying needs to be met; s4, calculating the center coordinate of each cable to be arranged in each bridge by adopting a greedy algorithm according to the optimal cable laying path, and arranging the cables according to the center coordinate; according to the method, the optimal laying and arrangement path can be planned on the premise of meeting various physical constraints of cable laying, and the method can automatically and dynamically adapt to different laying scenes.

This invention proposes a method, device, equipment, and medium for vehicle lane keeping and lane changing control. It detects lane lines in the road image directly in front of a target vehicle using an existing lane detection model; determines pixel reference points in the road image based on the target vehicle's speed; uses a greedy algorithm to iterate and filter the pixel groups corresponding to each lane line to find the pixels closest to the pixel reference points, obtaining the nearest pixel group; sorts the pixels in the nearest pixel group; calculates the average of adjacent nearest pixels based on the sorting result to determine the lane center pixel; and controls the target vehicle's lane keeping or lane changing based on the determined lane center pixel. This reduces the computational resource requirements of the lane keeping system and improves the system's real-time performance and stability.