1212 results about "Improved algorithm" patented technology

The invention discloses a wind turbine generator data analysis and fault diagnosis method and system based on big data and artificial intelligence. According to the method, a blade image, a vibration signal, audio data and operation parameters are synchronously acquired through an unmanned aerial vehicle multi-mode sensor and a ground monitoring system, and a multi-source heterogeneous data set is constructed; after the data is classified and preprocessed, image features, vibration time-frequency domain features and operation parameter key value pairs are extracted respectively; dimensionality reduction is carried out by using an auto-encoder, feature-level space-time alignment is realized through an improved DTW algorithm, and a multi-dimensional fault feature matrix is generated; a hierarchical diagnosis model including a GRU auto-encoder, an MLP network and an attention mechanism CNN is constructed, and training is carried out by taking minimization of sub-model deviation as an optimization target; and finally, fusing multi-source features to realize fault classification, and generating a visual diagnosis report. According to the method, efficient fusion and accurate diagnosis of multi-source heterogeneous data are realized, and the accuracy and the real-time performance of fault detection of the wind turbine generator are remarkably improved.

The invention relates to the field of navigation, and more particularly discloses a gradient optimization driven unmanned aerial vehicle real-time obstacle avoidance multi-stage trajectory planning method, which comprises the following steps of: in a trajectory initialization stage, firstly, searching a collision-free geometric path considering steering limitation of an unmanned aerial vehicle on an occupied grid map by utilizing an improved algorithm to generate an initial B-spline control point; then, in a trajectory optimization stage, constructing a multi-target trajectory optimization problem, introducing an obstacle avoidance constraint based on an Euclidean distance field map, and optimizing the initial control point set in combination with unmanned aerial vehicle parameters and optimization weights to obtain a trajectory meeting an obstacle avoidance requirement; and finally, for the optimized trajectory, performing dynamic feasibility evaluation based on unmanned aerial vehicle kinematics limitation in a trajectory correction stage, and if the trajectory does not meet the constraint, performing correction based on the minimum curvature constraint on the control point to ensure that the finally generated trajectory is not only obstacle-avoiding but also feasible in dynamics, and finally, determining that the trajectory does not meet the constraint. Therefore, the real-time obstacle avoidance capability of the unmanned aerial vehicle in a complex environment is effectively improved.

The invention discloses an online prediction method for a transient frequency track of a power grid under coexistence of wind power low voltage ride through and off-grid, and belongs to the technical field of operation and control of a power system. A multi-source heterogeneous data fusion monitoring system is constructed, power grid and wind turbine generator data are collected, faults are recognized through an improved algorithm, and feature vectors are output; building an energy flow model based on a fault result, calculating a trajectory divergence index by using technologies such as phase-space reconstruction, estimating power vacancy, and obtaining a power unbalance sequence; designing a prediction algorithm by using the sequence, predicting a frequency trajectory in combination with an improved K-nearest neighbor algorithm and a trajectory feature library, and introducing a confidence coefficient to evaluate a correction error; and finally, establishing a three-level control response system, and implementing multi-time scale cooperative control according to a prediction result. The method can accurately predict the frequency trajectory, effectively deal with the wind power fault, improve the stability of the power grid and the wind power consumption capability, and provide powerful guarantee for the safe and stable operation of the power grid.

The invention belongs to the technical field of unmanned aerial vehicle navigation, and discloses an unmanned aerial vehicle exploration navigation method based on a sequence enhancement improved SAC algorithm, and the method comprises the following steps: obtaining multi-modal perception data, and enabling the multi-modal perception data to form a standardized state vector at each time step; forming a state sequence by the state vector of each time step, inputting the state sequence into an improved Transform network, and carrying out feature extraction to obtain a high-dimensional feature vector; inputting the high-dimensional feature vector into a teacher-student improved SAC network, and outputting an original action by a student network; and judging whether the distance of the nearest obstacle is smaller than a threshold value or not, if so, performing VO safety shield correction on the original action to obtain a safety action, projecting / cutting the safety action to a dynamic feasible set, and issuing and executing the safety action. According to the invention, safe and efficient autonomous exploration and flight of the unmanned aerial vehicle in an unknown environment can be realized.

The invention relates to a point cloud registration method based on improved ISS-TOLDI features in combination with ICP, and belongs to the technical field of laser point cloud application. The method comprises the steps of obtaining a source point cloud and a target point cloud, and performing preprocessing; extracting feature points from the point cloud data by using an internal shape descriptor ISS algorithm; performing feature description on the extracted feature point set by using an improved TOLDI algorithm; performing coarse registration on the point cloud by using a sampling consistency initial registration algorithm; and performing fine registration on the coarsely registered point cloud data by using an iterative closest point ICP algorithm to complete point cloud registration. According to the method, the TOLDI algorithm is improved, so that the calculation complexity is reduced, the feature integrity is ensured, and the point cloud registration precision is higher.

The invention discloses an AGV path planning algorithm and a conflict-free strategy based on a Dlite algorithm. The algorithm and the strategy are suitable for multi-AGV collaborative operation in dynamic complex environments such as track equipment assembly workshops. The method comprises the following steps: (1) adopting an improved Dlite algorithm to realize dynamic path planning, and shortening the time consumed for updating a single AGV path through an incremental re-planning mechanism to adapt to the real-time change of dynamic obstacles such as personnel flow and equipment displacement; (2) constructing a space-time joint constraint model, dividing a workshop channel into limited grid units, and accurately marking the space-time occupancy state of the AGV through a time window; and (3) designing a hierarchical conflict resolution strategy, and when path crossing is detected, dynamically allocating priorities based on the task emergency degree and the AGV cargo carrying value, and realizing conflict avoidance through a speed adjustment window and path fine adjustment.

The invention provides a cylinder processing cutter path adaptive optimization generation method, which belongs to the technical field of cylinder processing, and adopts fractal dimension analysis to identify surface geometric complexity and determine a minimum repetitive unit, and applies a four-color theorem to carry out non-intersection area division on the surface, so as to improve the processing precision of the cylinder processing cutter path. Multi-axis linkage machining parameters are initialized, global path planning and cutter axis vector optimization and collision detection are performed based on an improved Di jkstra algorithm, path adaptive adjustment is performed according to local curvature change of a minimum fractal matrix, a game model is established, and coevolution of a double-layer game model is realized by adopting a cross optimization rule. And finally, an optimized tool path NC code containing the position coordinates, the attitude angle and the feeding speed is generated, and the technical problem that the machining efficiency and the surface quality are difficult to consider in tool path planning under the complex geometrical characteristics of the cylinder surface is solved.

The invention relates to an improved NSGA-III-based multi-target geographic site selection optimization method and system, belongs to the technical field of optimization in business management and resource planning, and particularly relates to resource allocation and site selection decision making by using a calculation model. The technology aims to solve the problems of slow scheme convergence, non-uniform solution set distribution and low calculation efficiency when multi-target site selection is carried out under limited resources. According to the scheme, geographic space data is preprocessed, and an initial population representing different site selection schemes is generated; carrying out optimization iteration by adopting an improved NSGA-III algorithm, and calculating a plurality of objective functions such as coverage rate, normalized population density and medical service accessibility; and performing individual selection and population updating by utilizing improved reference point generation and vertical distance measurement. The method is mainly used for improving the efficiency and effect of commercial and municipal decisions such as urban planning, medical resource configuration, energy station layout and logistics network optimization.

The invention belongs to the technical field of ship navigation and port automation, and particularly discloses a dock ship guiding path planning and dynamic obstacle avoidance system, which is characterized in that a data acquisition module acquires ship parameters, environmental data and obstacle information through a ship end sensing unit and a dock end monitoring unit and standardizes the ship parameters, the environmental data and the obstacle information; the initial path planning module generates an optimal initial ship guiding path based on an improved A * algorithm in combination with ship physical constraints, environmental constraints and operation rules; the dynamic obstacle avoidance module monitors the movement of an obstacle in real time, predicts the trajectory of a ship and the obstacle, and triggers local path re-planning and smoothing processing when a collision risk occurs; and the control output module converts the path into a control instruction, issues and receives ship feedback through two-way communication, and forms closed-loop control according to a deviation fine adjustment instruction or re-planning. The system realizes full-process automation of ship guiding, can dynamically adapt to environmental changes, improve path precision and operation safety, adapt to different docks and ship types, and ensure that ships are accurately and safely parked to target berths.

The invention relates to the technical field of pipe gallery fire analysis, in particular to an underground pipe gallery fire feature correlation analysis system based on an improved Apriori algorithm, and aims to comprehensively collect underground pipe gallery data through multi-modal sensing equipment and pre-process the underground pipe gallery data into a standardized three-dimensional time sequence data set to provide a high-quality data basis for subsequent analysis; a multi-dimensional feature association matrix is constructed based on the three-dimensional time sequence data set, feature association strength is quantified, and a multi-dimensional feature association basis is provided for fire risk mining; a fire risk rule knowledge base is constructed through a space-time weighted support degree optimization Apriori algorithm, effective association rules are accurately extracted, and the accuracy and reliability of fire risk early warning are improved; based on the fire risk rule knowledge base, a decision tree model is established, association rule weights of branches of the decision tree are analyzed, fire risk grades are divided, a composite early warning instruction set is generated, different coping strategies can be adopted for risks of different grades, and the pertinence and effectiveness of emergency response are improved.

The invention discloses an SOP defect detection method based on lightweight multi-scale feature fusion, and the method specifically comprises the steps: obtaining an SOP defect image, and marking the defect image; a YOLO-DBS model is provided on the basis of the YOLOv8n; replacing a C2f module in a backbone network with a DCFB module, designing an efficient multi-scale feature fusion pyramid network BiFPN, and adding an SENetV2 module in an input layer of a detection head; training the improved algorithm model by adopting an SOP packaging chip surface defect image training set; and transmitting a defect image test set into the trained model, recording a detection result and evaluating the performance of the model. According to the method, the balance between the precision and the model complexity is realized while the high detection precision is maintained.

The invention discloses a truck-mounted crane group operation safety monitoring system and method based on multi-source perception and collaborative decision, and particularly relates to the field of safety monitoring, the truck-mounted crane group operation safety monitoring system comprises a distributed perception module, an edge calculation module, a graded early warning and execution module, a digital twin platform module and a man-machine interaction module; collected multi-source data are fused through Kalman filtering and then transmitted to an edge computing node; generating a dynamic safety envelope region based on an adaptive kinematics model, predicting a group conflict risk through a graph theory algorithm, and calculating collision time; the digital twin platform fuses a BIM model and real-time data, hoisting path rehearsal, load swing prediction and foundation bearing capacity visualization are carried out, an improved RRT * algorithm is adopted to plan a collision-free path, and a Lagrange dynamics model is adopted to predict a swing track; the grading early warning module triggers differential response according to the risk grade; and risk warning and path guidance are provided through the AR-HUD and the three-dimensional situation billboard.

The invention discloses a path planning method and system based on an improved A * algorithm and an improved DWA algorithm, and relates to the technical field of path planning, and the method comprises the steps: outputting a dynamically optimized heuristic function weight; generating a complete path of the current navigation task in combination with the heuristic function weight; executing self-adaptive smoothing processing, and outputting a smoothed global path; performing dynamic trajectory prediction according to the dynamic window parameters to generate a plurality of motion trajectories; performing comprehensive evaluation on each motion track, generating an evaluation value of each motion track, and selecting the motion track with the optimal evaluation value as a final track; and inputting the path coefficient recorded by the navigation task to the path coefficient optimization network based on the hierarchical attention mechanism. According to the method, the planning time of the A * algorithm in global path planning is shortened, the search efficiency of the A * algorithm in a large-scale scene is improved by optimizing the algorithm structure, and the problem that the DWA algorithm is prone to falling into local optimum is greatly solved.

The invention provides a geological disaster prediction method and device fusing space-time sequence analysis and causal reasoning, and belongs to the technical field of geological disaster monitoring and early warning. Aiming at the problems of non-uniform data space-time reference, lack of causal logic, poor real-time performance and weak scene adaptability of a model in the prior art, the method comprises the following steps: performing standardization processing on acquired multi-source data, processing missing values by adopting an improved K nearest neighbor algorithm in combination with stratum characteristics, and processing abnormal values through a 3 sigma criterion and geological verification; based on an information theory and an improved SURD algorithm, three types of causal entropies among variables are calculated, a time attenuation coefficient is introduced, a core causal chain is constructed, and a dynamic causal graph is constructed; a core causal variable is used as input, a multi-feature attention-multi-relation space-time diagram recursive network model is constructed, a hour-level predicted value is output through space-time diagram convolution, residual training and a geological physical constraint layer, and'causal-space-time 'fusion is realized through a causal weight adjustment model; the method can be widely applied to early warning of geological disasters such as landslide and debris flow.

The invention belongs to the technical field of injection mold detection, and discloses an injection mold surface defect detection method based on visual inspection, and the method comprises the following steps: S1, adaptively adjusting collection parameters according to mold process parameters, and ensuring clear collection of different process surface defect features; s2, a deformation matrix is output through the mold thermal deformation finite element model, image registration is guided in combination with a deformation field, and the defect position deviation of the thermal-state mold is corrected; the method comprises the following steps: constructing a vibration-fuzzy kernel mapping model based on vibration sensor data, and restoring a fuzzy image by using an improved Richardson-Lucy algorithm; s3, a process exclusive texture primitive library is constructed, and unified threshold positioning deviation is avoided; s4, converting process parameters into feature extraction weights through a process perception attention CNN model, and accurately capturing defect core features under different processes; according to the design, the detection method can adapt to the surface of a multi-process mold without replacing a model, and the debugging cost of cross-process detection is greatly reduced.

The invention provides an unmanned aerial vehicle autonomous obstacle avoidance and dynamic path planning method and system based on multi-modal perception and hybrid intelligent decision. Environmental parameters such as wind speed, illuminance, temperature and humidity and image quality indexes are collected in real time through an airborne weather station, an IMU and a visual sensor, a flight parameter-image quality coupling model is established, and multi-target optimization is achieved through support vector regression (SVR) and particle swarm optimization (PSO). And designing a dynamic strategy optimization module based on Q-learning, and designing a reward function in combination with image quality, obstacle avoidance safety and energy consumption. An obstacle three-dimensional model is constructed in real time through ORB-SLAM3, a dynamic danger coefficient is calculated, and an obstacle avoidance track is generated by adopting an improved APF-RRT * algorithm. And the online cooperative control module optimizes the control quantity by using a BFGS algorithm so as to ensure the flight stability and the task efficiency. The method realizes high-precision obstacle avoidance and path planning of the unmanned aerial vehicle in a complex environment, has the characteristics of high robustness and wide adaptability, and is suitable for practical application scenes such as routing inspection, surveying and mapping and the like.

The invention provides a rounded-corner container transportation path dynamic optimization scheduling method and system, and the method comprises the steps: quantifying three constraints of the size, the gravity center and the loading and unloading priority of a rounded-corner container, converting the three constraints into a matching formula, a path constraint threshold value and a weight rule, and constructing a weighted multi-objective optimization function in combination with the transportation cost, the time and the cargo damage risk; a genetic algorithm and ant colony algorithm mixed framework is built, a constraint adaptation layer is embedded to filter invalid solutions, and the iteration efficiency is improved; two types of algorithm operators are improved, and a constraint satisfaction degree, a loading and unloading priority and a dynamic parameter adjustment mechanism are fused; dividing multiple regions into sub-region optimization by adopting a divide-and-conquer strategy, and adapting to a large-scale dynamic scene through cross-region collaboration and local re-optimization; a full-dimension verification scheduling scheme in iteration is carried out, algorithm parameters are automatically adjusted based on constraint violation information, and iteration is terminated or constraint relaxation is started according to preset conditions; an improved algorithm is integrated to a dynamic scheduling system, real-time data are connected, parameters are optimized through a self-learning module, and a manual intervention interface is reserved.

The invention belongs to the technical field of industrial crane control, and provides a bridge crane motion path planning method and system, and the method comprises the steps: uniformly dividing the motion environment of a bridge crane into grid units, recognizing a dynamic obstacle, endowing each grid unit with a state value, carrying out the track prediction of the dynamic obstacle, and carrying out the prediction of the motion path of the bridge crane. The method comprises the following steps: generating a time grid state matrix, constructing a time sequence passing grid set sequence, exploring a shortest path by adopting an improved A * algorithm, exploring multiple feasible paths by adopting an RRT algorithm, marking change nodes, screening the multiple feasible paths based on the shortest path, marking an initial path, and sorting an initial path set and a path node sequence; and quantitatively calculating the path tortuosity of the initial path, screening based on the path tortuosity, marking an optimal path, analyzing the load swing of the load at the change node, calculating the swing envelope range of the load, marking a risk change node, and screening to obtain an optimal path.

According to the mass center self-balancing chassis system for U-rib weld defect detection and the control method, the problem that when detection equipment moves on a U-rib curved surface, the precision is reduced due to mass center deviation and view field inclination can be solved. The system integrates a Mecanum wheel omni-directional moving unit, four groups of electric telescopic supporting legs, a two-axis holder and an ECU (electronic control unit), and has the innovation point that dynamic mass center balance and posture self-correction are realized through rigid-flexible combined supporting leg design and multi-modal sensing fusion. The ECU adopts an improved Canny algorithm to process the image, extracts a U rib edge point set and fits a reference center; in combination with pressure and tilt angle sensor data, a real-time mass center is solved through a moment balance formula, and then an improved self-adaptive PID algorithm is used for driving the supporting legs to stretch out and draw back, so that the mass center returns to a reference center; the two-axis holder compensates the view field angle according to the inclination angle data to ensure that the view field angle is perpendicular to the weld joint. Through cooperation of a mechanical structure and an algorithm, the system can control centroid offset to be smaller than or equal to 5 mm and view field inclination to be smaller than or equal to 1 degree, the detection stability and precision are remarkably improved, and the algorithm is easy to deploy.

The invention provides a remote sensing semantic segmentation method and system based on an improved TransUNet algorithm, and relates to the technical field of deep learning and computer vision. The method comprises the following steps: acquiring a high-resolution remote sensing image data set, and performing multi-scale data enhancement; an improved TransUNet neural network model comprising an encoder, a Transform encoder, a context enhancement module CAEM, a decoder, a multi-scale feature fusion module MFFM and a segmentation head is constructed; wherein the CAEM intensifies a global context through multi-granularity pooling and cross-branch attention, and the MFFM fuses multi-scale features at a jump joint and introduces lightweight channel attention; and inputting the processed remote sensing image data set into a pre-trained improved TransUNet neural network model to execute semantic segmentation, and outputting a semantic segmentation result. The method effectively improves the small target recognition rate and the edge segmentation accuracy, and is suitable for fine ground feature interpretation of a complex remote sensing scene.

The invention relates to an air conditioner chilled water PID parameter optimization method based on an improved sparrow search algorithm. The method comprises the steps that population initialization, a discoverer updating formula and a follower updating formula in the sparrow search algorithm are improved; a return water temperature difference signal of the air conditioner chilled water system is detected and input to the improved sparrow search algorithm optimization PID controller, and the improved sparrow search algorithm dynamically adjusts the proportionality coefficient, the integral coefficient and the differential coefficient of the PID controller; and the improved sparrow search algorithm iteratively calculates an optimal parameter combination, and the PID controller performs optimal control of the chilled water system based on the optimal parameter combination. The improved sparrow search algorithm carries out population initialization by introducing a reverse learning strategy optimized by a refraction principle so as to enhance the diversity of an initial population; respectively introducing sine and cosine transform and a Cauchy variation mechanism in a position updating stage of the discoverer and the follower; and the PID controller dynamically adjusts Kp, Ki and Kd based on an improved algorithm so as to realize optimal parameter setting.

The invention discloses an emergency rescue resource scheduling method based on the Internet of Things, and the method comprises the steps: collecting disaster area environment parameters, images and personnel distribution data through Internet of Things equipment, and associating historical disaster cases to construct a multi-source heterogeneous data set; extracting disaster characteristics through preprocessing, dynamically distributing data source weights, and generating a disaster assessment matrix; outputting a resource demand peak value based on the time sequence prediction model and performing dynamic correction; constructing a deep reinforcement learning model optimization decision strategy, and generating a scheduling scheme in combination with priority matching and an improved A * algorithm; according to the method, the disaster sensing precision and the demand prediction accuracy are improved, the scheduling decision adaptability is enhanced, the response time of a high-priority region is shortened, and the efficient demand of emergency rescue in a complex disaster is met.

The invention discloses a ship dynamic collision avoidance decision-making method based on an improved DWA algorithm. The ship dynamic collision avoidance decision-making method comprises the steps of constructing sea chart environment data containing obstacle density through a grid method; establishing a dynamic elliptical ship domain based on a quaternion ship domain (QSD) model, and calculating a distance collision risk degree (NCRI) between ships based on the dynamic elliptical ship domain; improving a DWA algorithm evaluation function, and adding a path yaw evaluation function and a collision avoidance rule evaluation function on the basis of optimizing a speed and azimuth angle evaluation function; fusing the multiple evaluation functions by adopting a weight distribution method; and finally, generating a local collision avoidance optimization route based on environment data, NCRI and an improved DWA algorithm. According to the method, safety boundary dynamic adjustment, rule compliance constraint and ship control characteristic cooperation are realized, and the robustness and practicability of collision avoidance decision making under complex sea conditions are remarkably improved.

The invention belongs to the technical field of engineering data processing, and particularly relates to a digital twinning-based engineering completion delivery data information extraction method, which comprises the following steps of: acquiring completion site entity data, constructing a digital twinning body of building information model and live-action three-dimensional fusion, and fusing semantic tag information based on an improved ICP (Inductively Coupled Plasma) algorithm. Performing space coordinate alignment and attribute association on the digital twinborn body to generate a completion information dynamic twinborn mapping map; a multi-level rule inference engine is used for extracting information items conforming to delivery standards, a dynamic risk propagation model is used for simulating the influence of data deviation on a delivery result through multi-node consensus verification data of a construction party, a construction party and a supervisor, and information extraction weight parameters are corrected in real time; and dynamically generating a delivery data packet and a visual auditing report and synchronously updating the digital twin archive library in combination with engineering change records and acceptance specifications. Therefore, the problems of incapability of capturing engineering parameter fluctuation, poor transmission efficiency and the like in the prior art are solved.

The invention belongs to the technical field of power system operation control, and discloses a virtual power plant aggregation operation optimization method considering flexible and adjustable resource hierarchical and partitioned regulation, which is characterized in that dynamic clustering and clustering are performed on a distributed power supply based on an improved K-medoids algorithm, and response characteristic analysis is performed on an electric vehicle, an energy storage load and a temperature control load; by establishing a self-adaptive layering and partitioning mechanism, dynamic layer region boundary adjustment is realized. An optimization model with the lowest cost as the target is constructed, the optimal resource scheduling scheme is solved by comprehensively considering various cost factors and operation constraint conditions, and efficient configuration of resources and effective control of the cost are achieved; and the adjustable load and the energy storage resource are guided to participate in system regulation and control, so that the peak-valley difference of the system is reduced with relatively low regulation and control cost. According to the method, the resource scheduling efficiency is effectively improved, high-quality peak regulation auxiliary service is provided for the power grid, and efficient utilization of distributed energy is assisted.

The invention discloses a public traffic composite traffic network key node identification method based on an improved K-shell algorithm, and the method comprises the steps: firstly extracting the line, geographic position, vehicle arrival interval and rated passenger capacity information of a subway and a bus station from multi-source data, and constructing a weighted composite traffic network considering the intra-layer transportation capacity and inter-layer transfer relation; node stripping is carried out based on a K-shell algorithm, and a shell fine position KP value is calculated in combination with a node removal sequence; further introducing a neighborhood structure overlap ratio, and constructing a node local propagation influence index; meanwhile, a comprehensive correction coefficient is constructed by considering the difference between intra-layer node strength and transfer attributes and the transportation capacity difference between subway and bus layers; and finally, a node comprehensive importance index IKA is formed and is used for sorting and identifying network key nodes. Finally, the recognition effect is verified through network efficiency, connectivity and SIR (susceptibility-infection-recovery) propagation simulation, and the result shows that the method is superior to a traditional centrality algorithm in the aspects of recognition precision and propagation influence measurement.

The invention discloses a backup task scheduling method based on load prediction and an improved SAC algorithm, and relates to the technical field of data backup and intelligent scheduling. Aiming at the problems of response lagging, insufficient key task guarantee and low strategy exploration efficiency of a traditional scheduling method under a dynamic load, a task weight monitoring matrix is constructed, server performance, a network state and data importance are fused, and a task risk coefficient is calculated in combination with an exponential smoothing algorithm; carrying out sequence decomposition on a service load by adopting an Autoformer model, identifying a periodic mode through a weighted autocorrelation mechanism, and predicting a future load change trend; on this basis, an improved SAC scheduling framework is designed, a task weight guidance and temperature coefficient adaptive adjustment mechanism is introduced, and the task starting opportunity and execution duration are optimized in combination with an elastic time window. The invention provides an intelligent scheduling method with strong adaptive capability, which can autonomously optimize the task scheduling strategy according to the dynamic change of the system and effectively reduce the peak load of the server and the task backlog risk.

The invention discloses a geological disaster emergency response path planning method based on reinforcement learning. The geological disaster emergency response path planning method comprises the following steps: S1, fusing and processing multi-source data; s2, constructing a four-dimensional space-time model based on the fused data; s3, multi-agent collaborative decision making is carried out, and a mixed agent system based on a Safe-PPO improved algorithm is deployed; a multi-objective optimization algorithm is adopted to balance the rescue efficiency and safety; deciding and outputting a leading path and a plurality of alternative paths; s4, performing conflict detection on the action of the path planning agent; s5, carrying out preferential selection on the paths subjected to conflict detection and global optimization; s6, issuing the path to a terminal for rescuing the action personnel; s7, dynamically monitoring the environment; s8, judging whether a path needs to be re-planned or not according to environment dynamic monitoring data; and S9, completing the task. According to the method, through reinforcement learning and multi-agent collaborative architecture, the risk penalty term is embedded in the reward function through the improved Safe-PPO algorithm, and the accuracy of path planning is improved.

The invention relates to an intelligent procurement collaboration method and system, and the method comprises the following steps: S1, building a supplier knowledge graph based on multi-source heterogeneous data fusion through employing a graph embedding algorithm, achieving the associated storage of industrial and commercial information, performance records and quality reports through a Neo4j graph database, calculating the weight of a supplier node through employing a PageRank improved algorithm, and carrying out the calculation of the weight of the supplier node; s2, based on the three-dimensional supplier portrait matrix, using an improved collaborative filtering algorithm to carry out demand matching, analyzing a purchase demand document through an ElasticSearch semantic analysis engine, combining TF-IDF weighted cosine similarity calculation to realize intelligent recommendation, and generating a purchase demand scheme with a weight score. The supplier knowledge graph is constructed through the multi-source heterogeneous data fusion and graph embedding algorithm, the scattered industrial and commercial information, performance records and quality reports are stored in an associated mode, traditional data island limitation is broken through, and effective integration of supplier multi-dimensional features is achieved.