27 results about "Dijkstra's algorithm" patented technology

The invention discloses a dynamic storage bin data regulation and control management system and method based on big data analysis, and belongs to the technical field of storage bin regulation and control management. The method comprises the following steps: acquiring a cargo information table, constructing a directed graph topology model, and planning an initial shipping path with the shortest path time for each cargo by adopting a Dijkstra algorithm and combining the cargo information table; calculating a congestion index of each shipment path, and when the congestion index exceeds a threshold value, searching an area with the highest cargo density in the congested cargo set, and determining the area as a congestion source area; transferring the cargos in the congestion source area to a target area; evaluating the influence of order change on the existing shipment path; carrying out local adjustment and regional transfer on the influenced goods; monitoring the cargo position, and judging whether the cargo moving track intersects with other cargo paths or not in combination with the path planning model; and when the intersection is generated, according to the cargo delivery time emergency degree and the cargo specification, the passing right of the conflict area is redistributed.

The invention discloses a battery swap station monitoring method and system based on the Internet of Things, and relates to the technical field of electric power dispatching optimization and intelligent energy management. Electric power data are collected and preprocessed, nodes are set, electric power features are extracted, an SHA-256 algorithm is used for generating electric power data blocks, electric power data error detection is carried out, and the electric power dispatching optimization and intelligent energy management are realized. A block chain system based on a threshold editable hash function is constructed, and log recording and responsibility tracking are edited; the method comprises the following steps: constructing a road network diagram, calculating the dynamic weight of each edge of the road network diagram, calculating an optimal power path by using a Dijkstra algorithm, taking an MILP model as an optimization target, defining three layers of constraint conditions, constructing an XGBoost prediction model to predict the load of a battery swap station, and adjusting a power distribution strategy. The security of power data is improved by adopting an improved SHA-256 algorithm and constructing a block chain system based on a threshold editable hash function, and the intelligence of a monitoring system is enhanced by combining a road network diagram to calculate a dynamic weight, using a Dijkstra algorithm and adjusting a power distribution strategy.

The invention relates to the technical field of event-driven control, in particular to an event-driven control method for a rescue robot, which comprises the following steps of: extracting event triggering conditions, triggering time sequence data and state parameters based on identification and serial number management of various events in a rescue environment; event type classification, spatial region partitioning and triggering probability modeling are carried out in combination with an SPFA algorithm, the corresponding relation between an event and a response state can be accurately mapped in the space-time dimension, the structural expression and state scheduling capability of a complex environment is enhanced, path optimization is carried out in combination with weights, and the path optimization efficiency is improved. Quantitative allocation of response resources and quantitative evaluation of response execution efficiency can be realized, a response path cost model is constructed by adopting a Dijkstra algorithm, and a mapping relation between path suitability and a triggering probability is compared, so that dynamic screening of an optimal path can be realized under an environment constraint condition; and the execution accuracy of the response path and the task completion collaboration are improved.

The application relates to the technical field of supply chain logistics scheduling, and particularly discloses a chemical supply chain intelligent logistics scheduling optimization system based on big data analysis, which belongs to the technical field of supply chain logistics scheduling. A data acquisition module is arranged in the system to obtain a multi-agent multi-scheduling cycle supply chain correlation data set, a supply and demand analysis module generates a supply and demand gap distribution map according to order demand and inventory state data, and a road condition evaluation module generates a road traffic cost matrix based on vehicle positioning and road traffic data. An improved Dijkstra algorithm with a node weight dynamic updating mechanism and an edge cost time-varying attenuation mechanism is adopted in a scheduling optimization module, joint path optimization is carried out on the two types of graph matrices, an optimal logistics scheduling scheme is generated, and a command issuing module generates and issues a vehicle and path planning instruction set according to the scheduling scheme, so that the dynamic intelligent logistics scheduling demand of the chemical supply chain is adapted.

The application discloses a warehouse position data dynamic regulation and management system and method based on big data analysis, and belongs to the technical field of warehouse position regulation and management. The application collects a cargo information table, constructs a directed graph topology model, adopts a Dijkstra algorithm, combines the cargo information table, plans an initial delivery path with the shortest path time for each cargo, calculates congestion indexes of various delivery paths, searches for a region with the highest cargo density in a congestion cargo set when the congestion indexes exceed a threshold value, determines the region as a congestion source region, transfers the cargo in the congestion source region to a target region, evaluates the influence of order changes on existing delivery paths, locally adjusts and regionally transfers the affected cargo, monitors the cargo position, combines a path planning model, judges whether the cargo moving track intersects with other cargo paths, and reallocates the passing right of the conflict region according to the cargo delivery time urgency and the cargo specification when the intersection occurs.

The invention discloses a hierarchical strategy-planner hybrid architecture for audio-visual navigation (AV-Nav) of a body-equipped agent, and aims to solve the challenge of tight coupling of multi-modal reasoning and precise action control in a traditional end-to-end model. According to the high-level strategy, a Transform-based network is adopted, deep fusion is carried out on real-time visual and acoustic features through a token and self-attention mechanism, and a navigation target point on a 3 * 3 local grid is output. The local target is then passed to a low-level classical planner that calculates the shortest path on the internally maintained dynamic navigation map using the Dijkstra algorithm and determines the atomic action to be performed. The method is characterized in that a planner is integrated with an interactive trial and error obstacle avoidance mechanism: once a collision signal Ct is received from the environment, the planner can immediately remove a corresponding blocked path edge from a navigation map, so that a system is forced to automatically re-plan a collision-free path in the next time step, and the navigation efficiency and robustness are remarkably improved. In addition, the architecture may perform end-to-end training in conjunction with novel audio enhancement strategies, including interfering sound sources, mixing or spectrogram masks.

The invention relates to the technical field of coal mine tunneling management and control, and discloses a coal mine tunneling face labor organization intelligent management and control system and a handheld terminal, and the system comprises an online meeting module which is used for enabling a coal mine tunneling face operator to participate in a meeting before class online; the personnel underground commuting path self-adaptive planning module is used for calculating the equivalent length of a commuting path by adopting a Dijkstra algorithm and planning an optimal commuting path according to a calculation result; the shift-over module is used for inputting key information of an on-duty state, so that a shift-over team can conveniently check the input information before getting on duty; and the operation process safety management and control module is used for positioning an operator so as to grasp the position of the underground personnel in real time, determining and delimiting a danger area according to a danger source, and persuading the operator to leave when the operator approaches or approaches the danger area. According to the invention, meeting before shift, underground commuting lines, shift change and personnel safety management and control are realized through a digital intelligent technology, and meanwhile, parallel operation of partial processes is realized, so that commuting time is shortened, labor intensity of workers is reduced, and safety is improved.

The invention discloses a distributed power distribution management method of a power distribution cabinet, and relates to the technical field of power distribution management. The method comprises the following steps: constructing a standardized data set through a three-phase linear power flow algorithm based on power operation data of a power distribution cabinet; training a graph neural network model based on a comparative learning mechanism and a generative adversarial network through the standardized data set to obtain a first power distribution neural network model; constructing a federated learning architecture, carrying out federated learning on the first power distribution neural network model based on the federated learning architecture, obtaining a second power distribution neural network model, inputting the standardized data set, obtaining a power distribution prediction result, and formulating a power dispatching basic strategy; based on the power dispatching basic strategy, the substitution method, the improved Dijkstra algorithm and the improved ant colony algorithm, optimization is carried out, a power dispatching optimization strategy is obtained and executed, feedback data is collected in real time to construct an optimization data set, the second power distribution neural network model is updated, and finally a power dispatching final strategy is obtained.

This invention discloses a site selection and capacity optimization method for electric vehicle charging stations that considers user charging accessibility, belonging to the field of electric vehicle charging infrastructure planning technology. The method includes constructing a dynamic traffic network considering road impedance, planning the shortest path using an improved Dijkstra algorithm, predicting the spatiotemporal distribution of charging demand using Latin hypercube sampling and Monte Carlo simulation, innovatively proposing an electric vehicle user charging accessibility index based on a Gaussian two-step moving search method to quantify the matching degree between charging station layout and demand, establishing a dual-objective optimization model with the goals of minimizing annual comprehensive cost and maximizing charging accessibility, and solving for the Pareto optimal solution set using the NSGA-II algorithm, and finally ranking the candidate schemes using the entropy weight method and the TOPSIS method to determine the optimal scheme. This invention balances the economic benefits for operators and the convenience for users, achieving precise allocation of charging resources and providing a scientific basis for urban charging facility planning.

The application provides a visual route path control method and device, equipment and a storage medium. The method comprises the following steps: acquiring a routing map, confirming a starting routing node and a terminating routing node in the routing map, generating a candidate path planning task based on the starting routing node and the terminating routing node, sending the candidate path planning task to a controller, confirming a target segment list that needs to be modified in response to a candidate path set sent by the controller, confirming at least one routing node as a necessary node on the routing map, generating a modified path according to the Dijkstra algorithm based on the target segment list and the necessary node, modifying the weight value of the target segment list in the modified path, and generating a routing control strategy. By directly displaying each routing node on the routing map, a user can modify the necessary node of the segment list of the candidate path on the routing map as needed, and the modification method is simple, fast and less prone to errors.

The application provides an emergency material route planning method, system, device and storage medium, the method comprising: determining each risk index related to a target emergency event and a weight corresponding to each risk index by using an analytic hierarchy process; determining a transportation path from a starting point to an ending point by using an improved Dijkstra algorithm according to the starting point and the ending point, wherein the improved Dijkstra algorithm considers a path risk cost and avoids a region with the highest risk cost when performing path planning, and the risk cost of the transportation path is calculated according to a risk severity in a specific region to which each coordinate in the transportation path belongs; and transporting the emergency material according to the transportation path. In the application, the improved Dijkstra algorithm mathematical calculation model finds a path with the lowest risk cost in a reasonable calculation time framework, and finds a relatively low-risk path, so that the improved Dijkstra algorithm can be used for time-sensitive emergency events.

The application discloses a kind of based on Internet of Things battery swap station monitoring method and system, it is related to electric power dispatching optimization and intelligent energy management technical field, collection electric power data and pre-processing, set up node, extract electric power feature, generate electric power data block using SHA-256 algorithm, carry out electric power data error detection, construct the block chain system based on threshold editable hash function, carry out editing log record and responsibility tracking;Build road network diagram, calculate the dynamic weight of each edge of road network diagram, calculate optimal electric power path using Dijkstra algorithm, with MILP model as optimization target, define three-layer constraint condition, build XGBoost prediction model to predict the load of battery swap station, adjust electric power distribution strategy, by using improved SHA-256 algorithm and constructing the block chain system based on threshold editable hash function, improve the security of electric power data, by combining road network diagram to calculate dynamic weight, using Dijkstra algorithm and adjusting electric power distribution strategy, enhance the intelligentization of monitoring system.

The present invention relates to the technical field of traffic navigation. Disclosed are a parking lot navigation method based on a directed graph, and a system and a storage medium. The method comprises: constructing a basic navigation graph of a parking lot; acquiring license plate photographs and capture times of vehicles passing through section nodes, and vehicle occupancy information of parking spaces; for any two adjacent section nodes, determining a vehicle speed weighted average value of a road between the adjacent section nodes on the basis of an average vehicle speed and a traffic flow; on the basis of the vehicle speed weighted average values, lengths, free-flow vehicle speeds and the number of lanes of roads between all adjacent section nodes, determining a traffic congestion index of the parking lot; on the basis of the traffic congestion index, optimizing the basic navigation graph, so as to obtain a weighted graph of navigation time costs; and on the basis of the weighted graph of navigation time costs and the basic navigation graph, using Dijkstra's algorithm to determine an entrance path and an exit path of the vehicles, and generating a corresponding navigation path two-dimensional code. The present invention can plan a rational traveling route for vehicles.

The embodiment of the present application relates to the technical field of mobile robots, and discloses a path planning method and a mobile robot, the method first acquires a starting point and an ending point of movement of the mobile robot, then sets a node expansion threshold based on the starting point and the ending point, and initializes an array in a Dijkstra algorithm, then expands nodes through the Dijkstra algorithm based on the node expansion threshold and the initialized array to obtain an expanded potential field, and finally plans a path based on the expanded potential field, the path planning method provided by the embodiment of the present application expands nodes to contain nodes located in narrow channels in the node expansion process, so that path planning of the Dijkstra algorithm in a narrow channel environment is realized, and the mobile robot is ensured to pass through the narrow channel without collision.

The invention discloses a land subsidence monitoring elevation control network optimization method, which comprises the following steps: S1, carrying out bedrock mark stability evaluation by adopting a normal distribution theory to obtain a bedrock mark deformation stability evaluation index based on full historical time and space; s2: according to a multi-factor comprehensive analysis method, selecting a starting reference point to be added, optimizing the complex level network by adopting a Dijkstra algorithm, and carrying out overall consistency check and displacement significant point discrimination on the starting reference point to be added from a micro deformation detection perspective; and S3, according to result comparison of the target feature points in different adjustment systems, determining a new starting reference point of the land subsidence monitoring elevation control network. The method has the advantages that the stability evaluation effect and the datum point screening effect of the bed rock mark are good, and the method has certain practical significance in improving the ground elevation deformation monitoring precision in the later period, serving natural resource investigation, urban planning and construction and the like.

The invention discloses a construction method of a charging load prediction model, a load prediction method and related equipment, and relates to the technical field of load prediction, and the key points of the technical scheme are that based on the position information of electric vehicle charging stations, a Dijkstra algorithm is adopted to construct a dynamic adjacency matrix representing the association relationship between different electric vehicle charging stations; calculating a Spearman rank correlation coefficient between the historical meteorological data and the historical charging load data, and screening at least one meteorological factor from the historical meteorological data as a meteorological feature according to the Spearman rank correlation coefficient; determining date features based on the date data; and inputting the dynamic adjacency matrix, the meteorological features and the date features into a graph convolutional neural network-Transform model, defining a regularized mean square error loss function, training the graph convolutional neural network-Transform model by adopting an optimizer, and outputting a charging load prediction model when the training is finished. According to the invention, the problem of low load prediction precision when a plurality of charging stations are dispersedly coupled is solved.

This invention provides a path fitting method and a geographic information visualization system incorporating this method. During online billing, the Dijkstra algorithm combined with a multi-validation model is used to verify and optimize vehicle on-the-go data and nodes in the fitted path, removing gantry systems with incorrect traffic relationships or abnormal traffic conditions to ensure that the final fitted vehicle travel path more closely reflects reality. Simultaneously, by establishing a comprehensive multi-layered data and business management system, the accuracy of complex business operations is guaranteed, while reducing the system's operational load.

This application provides a method, apparatus, device, medium, and program product for obtaining the aortic centerline, comprising: preprocessing a CTA image based on a pre-trained convolutional neural network algorithm to obtain target segmentation prediction results and trunk segmentation prediction results; preprocessing the CTA image based on a pre-trained convolutional neural network algorithm to obtain target segmentation prediction results and trunk segmentation prediction results for the target aorta; obtaining a set of vessel vertices and a first set of endpoints for vessel segments in the target aorta based on the vascular skeleton line of the target segmentation prediction results; extracting the aortic sinus endpoints from the first set of endpoints based on the trunk segmentation prediction results, and determining a second set of endpoints; extracting a second shortest path set from the first shortest path set based on the Dijkstra algorithm and the set of vessel vertices, and determining the second shortest path set as the aortic centerline to improve the efficiency of obtaining the aortic centerline.

This invention discloses a remote collaborative operation system for multiple electric excavators based on the Internet of Things (IoT), comprising: a status processing module for acquiring operational status data and preprocessing it to form a status data stream; a path construction module for constructing control path data; a controlled differential prediction module for constructing an improved neural controlled differential equation and outputting prediction results; a work map update module for creating a work map and updating its status; a task charging orchestration module for determining the set of excavators requiring charging and the target charging node; a collaborative planning module for obtaining the path sequence and operation sequence of each excavator based on the Dijkstra algorithm; and a command feedback module for issuing commands to the corresponding excavators, executing the command packets, and feeding back the execution status. This invention achieves remote collaborative operation of multiple electric excavators by combining the improved neural controlled differential equation and the Dijkstra algorithm.

The invention discloses an electric energy router optimization method based on a graph theory, and relates to the technical field of energy scheduling and optimization of a multi-microgrid system. The innovation and reform of the Dijkstra algorithm with weight and the secondary subpackage strategy of congestion processing can significantly reduce the network loss: the residual capacity ratio and impedance of the line are comprehensively considered, three power distribution strategies adapted to different scenes are provided, and the network loss can be significantly reduced in different scenes; the congestion problem can be effectively processed: through a secondary subpackage strategy, the line congestion problem can be effectively solved, and stable operation of the system is ensured; the algorithm efficiency is high, the improved Dijkstra algorithm prevents the suboptimal path from being ignored due to node repetition by increasing the path weight, the path search efficiency is improved, and the efficiency and applicability of the algorithm are remarkably improved.

The invention relates to the technical field of disaster simulation surveying and mapping systems, in particular to a disaster situation-oriented rapid surveying and mapping simulation system, which is characterized in that a hidden Markov model is used for statistical modeling of a stage demarcation sequence, and a stage mean value and a duration ratio are used as statistical characteristics in a state transition matrix; through weight adjustment and change chain construction of a high-frequency transfer stage group, probability distribution self-adaptive updating of disaster intensity between stages is realized, continuity and statistical stability of a disaster evolution process are maintained, a Dijkstra algorithm is adopted in path calculation, and by superposing gradient, friction resistance and energy loss parameters between geographic nodes, the probability distribution self-adaptive updating of the disaster intensity between the stages is realized. A path cost matrix is formed, the spatial precision and energy diffusion rationality of disaster propagation direction deduction are improved, clustering division and grade calibration are performed on a fluctuation value and a distance mean value in a surveying and mapping grid system, multi-level risk spatial distribution is generated, and the level difference of high-risk nodes in space is quantitatively expressed.

The application provides a scheduling method and device of a distribution robot, computer equipment and a storage medium, which are applied to the above method, comprising: acquiring coordinate data of a storage space and position information of each robot, calculating a distribution time corresponding to a distribution task in a task priority table executed by each robot according to a Dijkstra algorithm and a pre-constructed task priority table, and establishing a time matrix table based on the distribution task and the distribution time; determining a target robot for the distribution task according to a preset scheduling strategy and the time matrix table, and issuing the distribution task to the target robot. The application can schedule the robot according to the task priority and the time matrix table to make the robot preferentially distribute the distribution task with high priority to avoid the distribution task from being unable to be distributed on time, and the robot can execute the distribution task according to the task with the shortest distribution time, thereby improving the flexibility of the scheduling system and the distribution efficiency.

The invention relates to a low-altitude intelligent air route and route combination selection method and system. The method comprises the following steps: acquiring user activity distribution data and communication intensity data, and generating real-time population distribution density based on the user activity distribution data and the communication intensity data; obtaining water network data and road network data; obtaining a starting point and a terminal point of the unmanned aerial vehicle, and generating at least one alternative route based on a Dijkstra algorithm; scoring the alternative routes based on the real-time population distribution density, the water network data and the road network data; and setting the alternative air route with the highest score as a recommended air route, and sending the recommended air route to the unmanned aerial vehicle. By adopting the method, through multi-source data fusion, the population thermodynamic diagram, the water network and the road network data are integrated, and the accuracy and the safety of route planning are improved; on the basis of a dynamic priority rule, waypoints are dynamically generated according to a'water-first-road-second-road 'principle, and the flight risk is reduced to the maximum extent; through an intelligent scoring mechanism, the population density and the waypoint priority are combined to realize intelligent optimization of the air route.

The invention discloses a charging guiding method based on Internet of Vehicles data driving, which belongs to the field of Internet of Vehicles and comprises five steps of data acquisition, path optimization, reputation management, double-layer iteration and guiding execution. Aiming at the problems that the traditional static path planning (such as the Dijkstra algorithm) does not consider actual traffic factors and the path search efficiency is low, dynamic path optimization is realized by improving an A * algorithm: a known item G (i) introduces a traffic light mechanism, traffic light waiting time is taken as a node weight to be incorporated into actual cost calculation, and the path search efficiency is improved. The problem of time consumption exceeding estimation caused by traffic light waiting is avoided; the pre-estimation item H (i) is added into a return road penalty item, the actual preference of'little turning around and no return road 'of a vehicle owner is met, and invalid path search is reduced; and finally, an optimal path is preferentially selected through a decision function, roads having no influence on an optimal result are avoided, the path searching efficiency is remarkably improved, and the total consumed time of'road driving time + charging station waiting time 'of the user is shortened.

The invention discloses a method and equipment for generating a main / standby multicast tree, and a medium, and relates to the technical field of communication. The method comprises the following steps: in an initialization stage, defining an original topological graph, a source node, a destination node set, a maximum delay constraint and an iteration threshold value, creating double trees only containing the source node, and binding independent residual graphs respectively to ensure initial resource isolation; a current growth tree is dynamically selected through a random alternate mechanism, a minimum cost path is calculated by using a super node compression technology in combination with a Dijkstra algorithm, a newly added path edge is removed in real time, and a dynamic isolation environment is constructed from another tree remainder graph; in the preferential output stage, the coverage integrity and the total cost are compared through multi-round iteration, the optimal dual-tree combination is reserved, multicast tree optimization which is free of common edges, low in delay and minimum in total cost is achieved, and the network reliability and the calculation efficiency are greatly improved.

The application provides a method and device for locating a primary fiber distribution point and an electronic device. The method comprises: determining the shortest construction distance from each candidate site of the primary fiber distribution point to a secondary fiber distribution point based on an improved Dijkstra algorithm with the constraint that the secondary fiber distribution point is allowed to pass through; selecting one of all the candidate sites that requires the least cable construction material to reach the secondary fiber distribution point as a target candidate site according to the shortest construction distance from each candidate site to the secondary fiber distribution point; and determining the actual location of the primary fiber distribution point in the region corresponding to the target candidate site. The application can intelligently determine a relatively appropriate location for the primary fiber distribution point.

The invention discloses a network information security supervision method, and relates to the technical field of network information. The method comprises the steps of obtaining access data of a user in a preset time window and performing semantic feature extraction to obtain a semantic behavior feature chain; inputting the semantic behavior feature chain into a Markov model, and outputting to obtain a network behavior risk index; judging the semantic behavior feature chain based on the network behavior risk index to obtain an abnormal behavior feature chain; constructing a time sequence behavior topological graph based on the abnormal behavior feature chain; performing risk path search on the time sequence behavior topological graph through a heuristic Dijkstra algorithm to obtain a cross-domain attack chain; and carrying out feature extraction on the cross-domain attack chain to obtain cross-domain attack chain features, inputting the cross-domain attack chain features into the multi-attribute decision model, and outputting to obtain an optimal processing strategy, thereby realizing supervision on network information security.