2228 results about "Search algorithm" patented technology

The invention relates to the technical field of low-altitude aircraft navigation, and discloses a low-altitude aircraft track real-time planning method and system. The system comprises a flight situation awareness module, a track constraint calculation module, a real-time track planning module, a conflict prediction module and a track dynamic correction module. The flight situation sensing module generates a flight situation matrix through multi-source data fusion; a track constraint calculation module extracts static obstacle contours and dynamic obstacle tracks according to the static obstacle contours and the dynamic obstacle tracks, and generates a multi-dimensional track constraint set in combination with aircraft performance parameters; the real-time flight path planning module builds a three-dimensional flight path search domain by using an adaptive space division technology, and iteratively solves an optimal flight path sequence by using an intelligent search algorithm; the conflict prediction module combines the real-time dynamic obstacle trajectory to calculate the space-time proximity, and generates a conflict early warning map; and the track dynamic correction module re-draws an obstacle avoidance constraint area according to the map, and triggers local track correction. The system improves the comprehensiveness, real-time performance and safety of flight path planning, and guarantees the stable operation of the low-altitude aircraft.

The invention discloses an intelligent substation safety measure checking method and system, and the method comprises the steps: obtaining a secondary system topological structure, equipment information and historical safety measure ticket data of a substation, and constructing a quaternary knowledge graph; according to the quaternary knowledge graph, extracting multi-modal features of the maintenance task and identifying the type of a maintenance scene by combining a memory guide reflection decision reasoning mechanism, optimizing a rule reasoning process through a distributed guide local search algorithm, and generating an optimal safety measure operation set for a specific maintenance scene; automatically identifying a correlation loop and determining a minimum safety isolation range through a memory guide decision reasoning mechanism, dynamically generating a minimum safety operation set according to the real-time state of the equipment, and adaptively adjusting operation steps; and an operation dependency relationship model is constructed in combination with unwrapping variational multi-graph representation learning and a distributed guide local search algorithm, and operation sequence compliance verification, risk level assessment and dynamic visual early warning are realized. According to the invention, accurate formulation, dynamic adjustment and risk early warning of safety measures of the intelligent substation are realized.

The invention provides a multi-stage embedded control equipment state sensing and energy cascade scheduling system. Comprising a master control decision center module, a distributed edge embedded node module, an equipment full-dimension state sensing module, an energy dynamic optimization scheduling module, a fault prediction and self-healing control module, a cross-protocol communication interconnection module and a man-machine cooperative command module. According to the invention, through constructing a three-layer time domain control chain of edge node nanosecond-level signal processing, cloud second-level optimization scheduling and equipment hour-level strategy presetting, seamless cooperation of turbine bearing pedestal micro-vibration monitoring and a power grid peak regulation strategy is realized, and real-time wavelet noise reduction preprocessing of embedded nodes is combined with cloud LSTM life prediction. A taboo search algorithm is driven to dynamically reconstruct a power supply scheme, and the pain point of control response lag in a high-fluctuation scene is solved.

The invention belongs to the field of digital delivery, and particularly relates to a digital delivery topology mapping method and system for multi-source real-time data fusion, and the method comprises the steps: obtaining factory building distribution, equipment distribution and operation control logic and preset function block operation logic, and constructing a hierarchical clustering function mapping space through combining an association analysis and clustering algorithm; in response to a target function demand, obtaining a layered response mapping path in combination with a deep search algorithm; layered synchronous response and distributed node anomaly monitoring are realized based on the path, the three-dimensional simulation model and the display system equipment performance and the network state. Tracing abnormities based on a monitoring result in combination with a hidden Markov algorithm and a forward reasoning model, performing iterative verification after conflict resolution until the function is free of abnormities, and updating a mapping space; and adjusting the demand repeating steps to obtain a complete and updated mapping space, and realizing accurate function and picture collaboration under multi-source data fusion.

The invention provides an electric power inspection path planning method and device and electronic equipment, and relates to the technical field of unmanned aerial vehicle electric power inspection. The method comprises the following steps: acquiring obstacle information of an electric power facility environment, wherein the obstacle information comprises the type and position of an obstacle; based on the type of the obstacle and a preset safety distance coefficient, determining a differentiated safety distance, the type of the obstacle including a power transmission line, a transformer substation, a tower and other obstacles; based on the obstacle information and the differentiated safety distance, obtaining an initial global path through a path search algorithm; based on a preset multi-objective optimization function, the initial global path is optimized, a Pareto optimal path set is generated, and the multi-objective optimization function comprises a path length objective, a safety margin objective and an electromagnetic safety objective; and determining a target global path from the Pareto optimal path set based on a preset inspection task mode. According to the invention, the inspection efficiency and adaptability can be improved while the safety is guaranteed.

An intention-driven low-orbit satellite network SRv6 routing management and control method comprises the following steps: step 1, receiving and analyzing a user service demand from an application layer, and generating a corresponding intention label according to a service type; step 2, collecting state information of a satellite network in real time, tracking and recording satellite node position change and link state conversion, and establishing a complete network state database; 3, calculating a routing path meeting the service quality requirement by adopting an improved breadth-first search algorithm; 4, monitoring the state change of the inter-satellite link in real time; step 5, continuously monitoring fault events in the network, immediately starting a pre-calculated standby path when a fault is detected, updating a corresponding SRv6SID list, and performing rapid path switching; and step 6, dynamically adjusting the network flow. The method not only can adapt to the dynamic characteristics of the low earth orbit satellite network, but also can effectively meet the differentiated service requirements, and has the rapid fault recovery and load balancing capability.

The invention discloses a virtual power plant intelligent aggregation optimization control method for multi-type flexible resources, and the method comprises the steps: constructing a dynamic characteristic model of distributed resources, wherein the dynamic characteristic model comprises a photovoltaic output probability prediction model, an energy storage SOC-life coupling model, an electric vehicle behavior chain model, an adjustable load constraint model, and an industrial interruptible load model; an edge agent node calculates an adjustable potential interval of a resource cluster in real time and uploads the adjustable potential interval to a cloud end, a global optimization target is solved on the cloud end based on an improved sparrow search algorithm (ISSA), after a scheduling instruction is generated, model parameters are corrected in a rolling mode according to actual output deviation calculated in real time, and a scheduling result is obtained. And triggering a resource fault emergency strategy for prediction deviation and resource fault problems occurring in the operation process of the virtual power plant. Through an edge-cloud collaborative architecture and a multi-stage optimization strategy, accurate modeling, optimization aggregation and intelligent scheduling of distributed resources are realized.

The invention discloses a water quality time sequence prediction method of an SSA-VMD-LSTM-XGBoost hybrid model, and belongs to the technical field of water quality monitoring and prediction. Comprising the following steps: (1) data preparation and preprocessing; (2) optimizing the water quality time sequence decomposition of the VMD based on SSA: optimizing a penalty factor and a modal number of the VMD by adopting a sparrow search algorithm (SSA), and decomposing the water quality time sequence into a plurality of sub-components with high stability and low complexity by utilizing the optimized VMD; (3) construction and training of an LSTM-XGBoost hybrid prediction model: constructing a hybrid prediction model fusing long-short term memory (LSTM) and extreme gradient boost (XGBoost), inputting a high-frequency component into the LSTM model, inputting a low-frequency component into the XGBoost model, and finally performing superposition and integration on prediction results of the models; and (4) multi-component prediction result integration and performance verification. According to the method, adaptive optimization of VMD parameters is realized through SSA, the feature extraction and time sequence modeling capability is improved by combining the advantages of LSTM and XGBoost, and the prediction precision and stability of the water quality time sequence are effectively improved.

The invention discloses an unmanned aerial vehicle path planning method based on multi-objective optimization and improved particle swarm optimization. The method comprises the steps of 1, constructing a three-dimensional space map model; 2, introducing a multi-objective optimization strategy, and designing an objective function by adopting a weighted objective optimization method for evaluating the advantages and disadvantages of each path; 3, initializing particles by adopting an improved RRT algorithm in combination with a Sobol low-difference sequence, calculating a fitness value of each unmanned aerial vehicle path, and recording an optimal solution; 4, introducing a dynamic inertia weight adjustment strategy, and dynamically adjusting the inertia weight according to the number of iterations; a dive search mechanism in an eagle search algorithm is fused, and a particle restart mechanism is introduced to avoid falling into local optimum; 5, judging whether the set number of iterations is reached or not; if yes, iteration is stopped, and the optimal route of the unmanned aerial vehicle is returned to the environment model; if not, iteration is continued, and the optimal air route is searched. The invention aims to improve the path planning efficiency and robustness of the unmanned aerial vehicle in a complex environment.

The invention discloses a cable trench inspection robot path planning method and device and a medium, a cable trench three-dimensional semantic map is constructed through multi-sensor fusion, and a laser radar and a depth camera are combined to accurately identify the spatial distribution of a cable support, a suspension cable and an obstacle. An improved directional path search algorithm is adopted, firewall passing sequential logic and lifting platform kinematics constraints are integrated, and a multi-mode inspection path is generated. The environment change is sensed in real time in the inspection process, the path is adjusted online through a dynamic path optimization engine, a planning strategy is iteratively optimized based on historical data, a digital twin system is introduced to realize path pre-verification, and the firewall interaction efficiency and the exception handling capacity are optimized by adopting reinforcement learning. According to the invention, the technical problems of poor real-time performance of path planning and low reliability of facility interaction in a complex cable trench environment are solved, and the inspection efficiency and safety are significantly improved.

The invention is suitable for the technical field of robots, and provides a path planning method and a robot, and the method comprises the steps: obtaining the environment information of a target area, building a grid map according to the environment information, and enabling the grid map to comprise a starting point area, an end point area and an obstacle area; determining a global path through a path search algorithm according to the grid map; in the process of controlling the robot to move along the global path, performing local path optimization through a dynamic window algorithm; wherein the dynamic window algorithm calculates the score of each candidate local path through a trajectory cost function and determines the optimal local path, and a strategy network output score obtained by predicting the candidate paths through a pre-trained deep learning strategy model is introduced into the trajectory cost function. The adaptive capacity of the robot in a dynamic environment can be improved.

The present invention provides a dual-level thermal runaway warning method of a lithium battery based on a sound signal, comprising: obtaining a battery sound signal sequence; performing outlier identification on the battery sound signal sequence, and providing a level-1 thermal runaway warning when an abnormal data point exists; extracting a time-frequency domain feature of the abnormal data point, and identifying a presence of a thermal runaway expansion sound through a sparrow search algorithm-optimized eXtreme Gradient Boosting (SSA-XGBoost) algorithm, to providing a level-2 thermal runaway warning. In the SSA-XGBoost algorithm, optimal parameter adjustment is performed on a number of iterations, a learning rate, and a decision tree depth of the XGBoost algorithm through the SSA. A dual-level thermal runaway warning strategy is adopted to perform grading identification on general anomalies or deep anomalies, thereby effectively improving identification accuracy of a weak abnormal sound signal in an early stage.

The invention provides a joint denoising method and system based on adaptive large neighborhood search and modal decomposition, and belongs to the technical field of signal processing and nondestructive detection.The method comprises the steps that an ultrasonic signal and a vibration signal of a detected insulator are synchronously collected and preprocessed; dynamically estimating the noise level based on the preprocessed ultrasonic signal power spectral density, and optimizing decomposition parameters by adopting an adaptive large neighborhood search algorithm; on the basis of the optimized decomposition parameters, wavelet packet decomposition and ensemble empirical mode decomposition are executed in parallel, and effective intrinsic mode function components are screened through cross-correlation verification; extracting the resonance frequency of the preprocessed vibration signal, performing target frequency band weighted enhancement on the low-frequency sub-band, and dynamically adjusting the threshold parameter of the high-frequency sub-band and the low-frequency sub-band according to the resonance frequency; and generating a preliminary de-noised signal from the fused signal, performing affine projection algorithm filtering and multi-modal cross validation, and outputting the verified ultrasonic signal as a final de-noising result.

The invention discloses a document generation system and method based on multi-agent collaboration, and the method comprises the steps: understanding and extracting the high-level semantic information of a text input by a user, and dynamically generating a problem to guide the user to define a demand, so as to form a detailed document demand; recognizing the theme and key elements of the document from the detailed document requirements; the optimal sequence of task execution is determined based on a shortest path algorithm, potential conflicts in task execution are predicted and solved through parallel processing and a search algorithm, and optimized task planning is formed through feedback circulation; generating a document outline according to user requirements, retrieving associated contents of document chapters so as to write chapter contents, and performing grammar and format verification to form a preliminary document; defining a document quality evaluation index according to user requirements and industry standards, performing document quality evaluation on the preliminary document, and feeding back the document quality evaluation result to the user; and generating a complete final document and presenting the complete final document to the user. According to the invention, the document generation quality is improved.

The invention discloses a dynamic path optimization method based on response time domain attenuation, and relates to the technical field of artificial intelligence and intelligent path planning, and the method comprises the steps: generating a node network composed of navigation points, terrain units or interaction regions, and forming a node network topology structure; generating a dynamic state feature set used for describing game scene changes, and forming input data used for follow-up node priority dynamic adjustment; converting the dynamic state feature set into node-level standardized event data, and distributing the node-level standardized event data to a node state management module through an event bus; setting a current node priority for each node in a node state management module based on the node-level standardized event data; forming a time domain attenuation model of the node priority; the priority recovery coefficient is improved; in the path planning stage, executing a dynamic path search algorithm to generate a passing path with the minimum total cost and the optimal path smoothness; when it is detected that player operation or scene change causes node response mutation, a local re-planning mechanism is triggered, smooth path transition is achieved, and overall jumping is avoided. According to the method, the problems of path congestion, frequent switching and unsmoothness caused by lack of dynamic node state and event response calculation in the prior art are solved. Through node priority time domain attenuation and dynamic path optimization, the technical effects of smooth path, efficient passing and node load balancing are achieved.

The invention discloses an automatic planning method and system for a curved surface path of a grinding robot, and the method comprises the following steps: S1, constructing a to-be-machined three-dimensional curved surface model and a triangular mesh model, and relates to the technical field of automatic control of robots for surface machining in the manufacturing industry. According to the method, a complex three-dimensional surface coverage path planning problem of a workpiece is converted into two low-complexity sub-problems of key point sampling of a two-dimensional path planning domain and three-dimensional shortest path searching on a triangular mesh model, a small number of sampling points are sampled only on the two-dimensional path planning domain, and the sampling process depends on a spatial index framework; according to the method, global traversal of disordered point clouds is avoided, three-dimensional coordinates are positioned through nearest neighbor search of a KD tree algorithm, adjacent three-dimensional ordered key points are sequentially connected through a graph search algorithm, and a continuous machining path is formed, so that the operand is greatly reduced, real-time response of path planning is facilitated, and the problems that traditional online planning is long in time consumption and difficult in real-time response are solved.

The invention provides a lithium battery health state estimation method based on a long short-term memory network, and relates to the technical field of battery health state estimation. The method comprises the following steps: firstly, carrying out a charge-discharge cycle test on a high-health-degree sample battery, collecting operation and working condition data, extracting indirect and composite health factors including voltage differential, charging time, temperature change rate and the like, and constructing a health factor set; key health factors strongly related to the health state of the battery are screened out by adopting a dynamic double-threshold correlation analysis method, and a time sequence sample set is formed; a multi-output prediction model combining a double-layer gating circulation unit, an attention mechanism and output uncertainty estimation is constructed, and a sparrow search algorithm is introduced to carry out adaptive global optimization on model hyper-parameters. And predicting the health state of the target lithium battery by using the optimized model, and analyzing the deviation between a predicted value and a true value to complete the judgment of the final health state of the target lithium battery.

The invention discloses a transformer substation positioning and navigation method and system integrating positioning and visual auxiliary positioning. The method comprises the following steps: establishing a background electromagnetic field distribution baseline graph associated with a three-dimensional reference map; electromagnetic field data of the transformer substation in an emergency response mode is collected through the unmanned aerial vehicle, and the abnormal field intensity of a space electromagnetic field and a fault source pointing vector pointing to a fault source are calculated; constructing a risk adaptive elliptical forbidden zone; calculating a passage cost index Cto of the unmanned aerial vehicle passing through each three-dimensional grid in the three-dimensional reference map by using the fault source pointing vector and the risk adaptive elliptical forbidden zone; and searching a path which is from the current position of the unmanned aerial vehicle to a preset target point, bypasses the risk adaptive elliptical forbidden zone, faces the fault source and has the lowest total passing cost index Cto by adopting a preset path search algorithm. According to the method, the risk self-adaptive elliptical forbidden zone is constructed and is used as the three-dimensional grid forbidden assignment processing, so that the dynamic global path avoidance of the sudden dangerous zone is realized.

This invention advances power grid operational planning by introducing a mid-term scheduling framework for integrated hydro-wind-solar systems that accounts for heterogeneous daily load profiles across multiple receiving-end power grids. The proposed approach utilizes an adaptive variable-step search algorithm to segment loads into peak, flat, and valley intervals. By synthesizing five key metrics, including mean daily load, daily load factor, peak-valley differential ratio, load rates during peak / valley periods, and timing of peak / valley occurrences, the method accurately captures region-specific load patterns and peak-shaving demands. This enables a refined reconstruction of load profiles of receiving-end power grids. A nested multi-temporal scheduling model that couples medium- and short-term horizons to simultaneously maximize total energy production and minimize transmission imbalances among power grids. The model is addressed by using the mixed-integer linear programming (MILP) to obtain medium- and short-term generation schedules and power transmission schedules.

Provided in the present invention are a power-distribution-network self-healing method and system taking photovoltaic output into consideration. The method comprises: acquiring historical operation data of a photovoltaic power station and irradiance observation data from a meteorological station; on the basis of the historical operation data of the photovoltaic power station and the irradiance observation data from the meteorological station, predicting the generated power of the photovoltaic power station by using a convolutional long-short-term memory recurrent neural network model that takes sparrow search into consideration; on the basis of the generated power of the photovoltaic power station, a segment-switch state of a power distribution network and a network topology of the power distribution network, constructing an objective function and a constraint condition for a power-distribution-network self-healing model, and obtaining the power-distribution-network self-healing model; solving the power-distribution-network self-healing model by using a propagation search algorithm, so as to obtain an optimal recovery strategy; and executing the optimal recovery strategy by means of segmented switches and node loads. The present invention can realize self-healing of a power distribution network while ensuring the minimum power generation cost of a distributed power source, the minimum network loss and the minimum node voltage deviation.

The invention discloses a large model and knowledge graph dual-drive-based guided reasoning system and method, belongs to the technical field of artificial intelligence reasoning, and solves the problem of how to improve the process reasoning ability of a large language model for engineering subject courses and the reliability of solving complex engineering problems. According to the method, metadata is extracted from teaching materials, structured problem representation is constructed, and a knowledge graph is constructed to form a complete knowledge system; the method comprises the following steps: decomposing a complex engineering problem into a structured solving plan with knowledge marks, and carrying out iterative loop based on a Monte Carlo tree search algorithm to generate a search tree comprising a plurality of high-quality candidate problem solving paths; then determining an optimal answer and a corresponding reasoning path in all simulated paths in a voting mode, performing confidence evaluation on each node of each path in the candidate path set, and further selecting a path of an optimal solution; and the process reasoning capability of the large language model on engineering subject courses and the reliability of solving complex engineering problems are effectively improved.

The invention relates to a power distribution network single-phase earth fault positioning method and system based on transient traveling waves, and the method comprises the steps: 1, collecting a zero-sequence current signal at the moment when a single-phase earth fault occurs, carrying out the decomposition of the collected zero-sequence current signal through a variational mode decomposition algorithm, carrying out the feature extraction through a convolutional neural network, and carrying out the optimization through an alternating direction multiplier method; wherein the zero-sequence current simultaneously comprises a high-frequency traveling wave component and a transient abrupt change characteristic; 2, on the basis of the sampling signals optimized in the step 1, carrying out feature extraction from two dimensions of a transient abrupt change feature and a traveling wave propagation feature, and packaging the features into a fusion vector; 3, identifying a fault line by means of a Grubm angle and field method and the improved DenseNet; and 4, based on the fusion vector in the step 2 and the fault line information determined in the step 3, carrying out iterative operation by adopting an improved sparrow search algorithm, and outputting a finally determined fault section position result. According to the invention, the requirements of intelligent fault sensing and accurate response in a complex power distribution environment are met.

The invention discloses an offshore wind power plant micro-siting optimization method considering regular layout. The method comprises the following steps: firstly, obtaining wind data, wind power plant parameters, fan parameters and wake flow parameters; performing multi-angle rotation on the wind power plant based on an equivalent rotation coordinate method, constructing a gridding mixed integer quadratic programming model, and performing rotation rule layout optimization (RRLO-1) to obtain an initial fan layout; on the basis of the initial layout, a hybrid algorithm (GA-ISSA) of a genetic algorithm and a sparrow search algorithm is adopted, the position of a draught fan and the rotation angle of the wind power plant are finely adjusted, and fine rule layout optimization (RRLO-2) is completed; and finally, the wake flow influence is calculated through a Cosine wake flow model and a quadratic sum superposition model, a fan power curve is processed in a piecewise linearization mode, and the final layout is output by taking the maximization of the total generating capacity as the target. According to the method, a fan position configuration scheme meeting the engineering rule layout requirement is provided for early-stage planning of the offshore wind plant, the wake effect is reduced, the power generation efficiency is improved, and development of the offshore wind power industry and low-carbon target implementation are facilitated.

The invention relates to the technical field of large language models, and discloses a multi-path legal inference engine based on an MCP protocol, which comprises a legal question input module, a legal question answering module and a question and answer result output module, in combination with a Monte Carlo tree search algorithm, a multi-path deduction mechanism, a retrieval enhancement mechanism and a user feedback optimization mechanism, an MCP unified management model internal state and knowledge activation and reasoning path information are relied on, and interpretable legal answers corresponding to legal query questions input by a user are generated. By means of key information flow integrated by the MCP protocol, answers, reasoning chains, reference bases and credibility evaluation are output in a visual mode, and the professionality, the interpretability and the user credibility of the whole legal reasoning process are remarkably improved.

The invention discloses an unmanned aerial vehicle three-dimensional path planning method and system based on a multi-strategy improved black-wing optimization algorithm, and the method comprises the steps: generating an initial population through a Latin hypercube sampling method, thereby improving the distribution uniformity and diversity of the population; an adaptive weight factor is introduced to realize dynamic balance of exploration and development capabilities; meanwhile, a dynamic reverse learning strategy is combined, so that the global search capability is effectively enhanced, and premature convergence is avoided; the algorithm performance is further improved by fusing a warning person position updating formula in a sparrow search algorithm. The method is used for solving the problems that a traditional path planning problem is prone to falling into local optimum, the convergence speed is low, and the path is unstable. And a shorter and safer optimal flight path can be efficiently planned.

The invention discloses an unmanned aerial vehicle cooperative inspection path planning and airport layout method and system for expressway multi-facility maintenance. According to the method, a multi-dimensional environment characterization system is constructed by collecting highway multi-source data, a comprehensive optimization model with the coverage range, the response time and the total cost as targets is established, an airport layout scheme is generated by adopting an adjustable service radius strategy and a heuristic search algorithm, and an optimal equilibrium solution set is extracted through multi-target sorting and screening. The task priority is determined based on the facility type and maintenance urgency, a multi-unmanned aerial vehicle cooperative route planning model is established, and a conflict-free circular inspection path is planned. And performing online rolling replanning by adopting a prediction control strategy, and integrating inspection data feedback to realize closed-loop self-adaptive optimization of the system. According to the invention, collaborative optimization of the airport layout and the inspection path is realized, the inspection efficiency is improved, the operation cost is reduced, and the adaptive ability of the system to deal with a dynamic environment is enhanced.

The invention discloses a distributed training scheduling and communication optimization method and system of a multi-modal large model on a domestic computing power platform. The method comprises the following steps: virtualizing a heterogeneous computing unit of a preset platform into a virtual device pool, and fusing first-order gradient of a multi-modal sample and Hessian matrix information based on quantitative perception training to generate a sample sensitivity grading atlas; virtual device pool attributes and the sensitivity grading atlas are used as input, an optimal hybrid parallel configuration scheme is automatically generated through a configuration search algorithm, and a parallel combination mode, resource mapping and a high-sensitivity sample scheduling strategy are defined; a distributed training code of an integrated communication optimization strategy is automatically generated according to a configuration scheme, pipeline parallel communication and data parallel gradient synchronization constraint are executed in a topology adjacent equipment subset, and a hierarchical aggregation mechanism is adopted; and dynamically screening a core training set and scheduling a calculation task to complete distributed training. According to the method, efficient cooperative training of the multi-modal large model on the domestic computing power platform is realized.

The invention discloses a high-water-saving accurate drip irrigation optimization method based on a population algorithm. The method comprises the following steps: S1, constructing a comprehensive input data set; s2, forming a multi-dimensional collaborative vector coding set; s3, constructing a multi-objective optimization model; s4, generating an initial improved shark search algorithm population; s5, in a global exploration stage, forming an evaluation result data set; s6, in a local development stage, recording a current optimal multi-target solution; s7, decoding the multi-dimensional collaborative vector code corresponding to the current optimal multi-target solution into a drip irrigation pipe network implementation data set and an irrigation scheduling implementation data set; and S8, generating an irrigation control instruction set based on the drip irrigation pipe network implementation data set and the irrigation scheduling implementation data set, and executing partition irrigation operation. According to the method, the pipe arrangement rationality and the water resource utilization efficiency can be optimized at the same time, and higher convergence and generalization ability are shown in complex terrains and heterogeneous crop distribution environments.

The invention relates to a multi-distribution-center open type vehicle path intelligent optimization method and system, and belongs to the technical field of logistics distribution optimization and intelligent transportation, and the method comprises the steps: firstly obtaining the input data of a multi-distribution-center vehicle path optimization problem, selecting a multi-distribution-center processing strategy according to the problem scale and constraint conditions, and carrying out the optimization of the multi-distribution-center vehicle path; a vehicle path optimization model is constructed, the vehicle path optimization model comprises a single-target model and a multi-target model, a multi-algorithm collaborative optimization framework is adopted for solving, and the multi-algorithm collaborative optimization framework comprises an ant colony algorithm, a variable neighborhood search optimization ant colony algorithm and a non-dominated sorting genetic algorithm; and outputting an optimal vehicle path scheme, wherein the optimal vehicle path scheme comprises a distribution route, a distribution sequence and a corresponding objective function value of each vehicle. According to the method, strategy adaptive selection and algorithm collaborative optimization are carried out, global exploration, local optimization and multi-target equalization are carried out by combining the advantages of the ant colony algorithm, the variable neighborhood search algorithm and the non-dominated sorting genetic algorithm, and the method is good in reproducibility, high in scene adaptability and high in decision support capability.

The invention relates to a multimodal transport path selection method based on multi-agent reinforcement learning. The method comprises the following steps: constructing a multimodal transport network and a system environment; carrying out initial planning by adopting a self-adaptive large neighborhood search algorithm based on a multimodal transport network and a system environment; and when an accident occurs, acquiring accident information, optimizing the initial plan by adopting a multi-agent reinforcement learning algorithm based on the accident information in combination with a cost objective function to obtain an accident action decision, and executing a transportation task based on the accident action decision. By constructing a large neighborhood disturbance response mechanism fused with ALNS, when it is detected that node / path service time offset exceeds a threshold value, path reconstruction is intelligently triggered, a new alternative path is generated, based on an MADDPG multi-agent reinforcement learning framework, all carrier Agents can obtain global information in centralized training and make decisions independently in execution, and therefore the probability that the node / path service time offset exceeds the threshold value is lowered. And the utilization efficiency of transportation resources and the task allocation fairness are effectively improved.