258 results about "Optimal route" patented technology

The invention discloses a multi-agent collaborative data exchange dynamic routing optimization method and system, and relates to the technical field of network communication, and the method comprises the steps: obtaining the real-time state information of each node in a network, and outputting a node state data set; constructing a historical state sequence of each node, calculating a load change trend, and outputting a routing adjustment signal when the load change trend reaches a load critical value; calculating a collaborative weight according to the network contribution degree of each agent, establishing a task allocation mapping relationship among the agents based on the collaborative weight, and outputting a collaborative scheduling result; determining a candidate path set, performing index evaluation on paths in the candidate path set, and outputting an optimal routing path; and executing data transmission, obtaining a quality difference between an actual transmission effect and an expected effect, and performing parameter correction. According to the invention, active optimization and multi-target balance of network routing are realized, and routing efficiency and system stability in a dynamic network environment are 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 relates to the technical field of computers, discloses an intelligent scheduling method and system for server cluster load balancing, and aims to solve the defects of the existing server cluster load balancing technology in response lag, non-uniform resource utilization rate, service quality guarantee, global optimization capability, fine-grained state perception and scheduling decision. The method comprises the following steps: collecting server state and request feature data, constructing a cluster state and service capability model, and predicting a load trend; and generating an optimal routing strategy by using deep reinforcement learning and multi-objective optimization, and issuing adjustment request distribution. The system comprises a data acquisition module, an application request feature acquisition module, a state sensing and modeling module, a load prediction module, an intelligent scheduling decision module and an instruction execution module. By adopting the technical scheme, the resource utilization rate can be improved, the response time can be reduced, the throughput can be improved, the system stability and elasticity can be enhanced, and the operation cost and energy consumption can be reduced.

The invention discloses a surface mine transportation real-time intelligent scheduling method and system based on multi-objective optimization. The method comprises the steps that S1, an initial data loading module initializes the system; s2, the algorithm module verifies the vehicle state after receiving the scheduling request; s3, executing a path generation strategy according to states, wherein a normal vehicle generates all feasible routes based on the global path network; when a user appoints a starting point and a terminal point, a point-to-point optimal route is intelligently generated; s4, performing dynamic optimization scheduling based on the multi-source data; optimal task allocation is solved through a multi-objective optimization model, and a vehicle and route mapping relation is output; and S5, a scheduling result is issued to the vehicle-mounted terminal through the service layer, and a terminal interface displays a recommended route and operation guidance. According to the invention, a dynamic event-driven mechanism and a multi-source data collaborative optimization intelligent scheduling framework are fused, so that efficient distribution of vehicle tasks, real-time generation of paths and quick response to abnormal conditions are realized.

The invention discloses an e-commerce storage order real-time picking path planning method and system, and relates to the technical field of intelligent storage and logistics automation, and the method comprises the following steps: training a behavior prediction model based on historical picking behavior data, and outputting a path candidate region of a picker; constructing a path game model according to the path candidate area in combination with the current order task; based on a Stackelberg mechanism, solving a game equilibrium path distribution result; according to a solving result, evaluating the candidate path scheme through a multi-objective optimization algorithm, and outputting an optimal path scheduling strategy; according to the method, by introducing path candidate region extraction based on behavior prediction, fusion graph construction, a Stackelberg hierarchical game solving mechanism and multi-target optimization strategy output, real-time performance and global collaborative optimization of order picking path selection are realized.

The invention provides a dynamic environment adaptive low-altitude route planning method and system, and the method comprises the steps: collecting multi-source environment information in real time, and constructing a flight environment model; according to flight historical data, in combination with the flight environment model, carrying out route planning; based on the battery capacity of the unmanned aerial vehicle, the endurance demand of the air route is checked, and the optimal air route is output; verifying whether the optimal route meets the requirements of safety and tasks or not, and controlling the unmanned aerial vehicle to fly according to the optimal route after verification is passed; and the unmanned aerial vehicle feeds back the flight state in real time, and dynamically optimizes the air route in combination with the flight environment model. The problems that in the prior art, the environment perception capability is insufficient, situation perception is missing, the optimization target is single, and the algorithm practicability is poor are solved. The problem of route planning of the unmanned aerial vehicle in a dynamic environment is solved.

The invention discloses a satellite-based routing decision making method and device and a storage medium. The method comprises the following steps: determining a plurality of reference curves in each first sampling period; determining a first distribution curve in the current time period, and broadcasting the reference curve of the current time period, the first distribution curve and the channel capacity of each area in the current time period to each satellite; determining a reference curve of the next time period, calculating a deformation relation between the reference curve of the second sampling time period and the reference curve of the third sampling time period, and performing deformation processing on the first distribution curve to obtain a plurality of second distribution curves of the next time period; predicting the satellite congestion degree of each region in the next time period according to the channel capacity of each region in the current time period on the basis of the plurality of second distribution curves; and based on each region, constructing a plurality of corresponding routing paths, and determining an optimal routing path in the plurality of routing paths by using a genetic algorithm. Therefore, the technical effect of reliably transmitting data is achieved.

The invention relates to the technical field of intelligent traffic systems, in particular to a personalized traffic travel route recommendation method based on multi-source data. The method mainly solves the problem that in the prior art, real-time collaborative optimization of a dynamic traffic environment and user personalized requirements is difficult. Real-time information is collected through a multi-source heterogeneous traffic data acquisition module, and a standardized data set is generated through data fusion processing; a dynamic user portrait is constructed in combination with machine learning modeling; adopting a multi-target constraint path to search and calculate personalized candidate routes; dynamically re-planning an optimal route based on the real-time traffic flow; and finally, continuously updating the user portrait through an interactive feedback closed loop. The collaborative optimization of traffic state perception and user preference dynamic matching is realized, and the accuracy and timeliness of route recommendation are remarkably improved.

The invention relates to an edge computing network-oriented micro-service and distributed database collaborative deployment system. The system comprises a database copy number dynamic optimization unit which obtains a dynamic optimization strategy of the copy number of a database based on a queuing gradient projection elastic scaling algorithm; a deployment strategy dynamic adjustment unit calculates end-to-end time delay based on the micro-service routing path and the database routing path; calculating a data inconsistency measurement index based on the number of each copy of the database; dynamically adjusting the deployment strategy based on the resource constraint between the end-to-end time delay and the data inconsistency measurement index and the dynamic optimization strategy of the number of copies of the database; the deployment strategy comprises micro-service instance deployment, database copy deployment and optimal routing path selection; according to the MEC scene-oriented distributed database design and fine-grained collaborative deployment optimization method, a message queue, a queuing theory and a Canal database incremental updating mechanism are integrated, and the micro-service application performance and the data query efficiency / reliability are improved.

The invention relates to the field of modern intelligent manufacturing and automation, and discloses a method for planning a path of an intelligent robot with a body based on visual inspection, which comprises the following steps: acquiring real-time image data, preprocessing and extracting position coordinates of an obstacle, and generating an obstacle distribution diagram; an A star algorithm is adopted to generate a preliminary optimal route sequence, and map information is updated; uncertain interference in the path is judged, and a path alternative set is generated through coordinate calibration; a time-varying parameter and a security constraint optimization path strategy are integrated through adaptive path optimization; key node coordinates are extracted and are fused with obstacle boundary identification to obtain a path execution sequence; real-time performance is checked, if calculation is overtime, a rollback mechanism is triggered, and an initial path is loaded and fused with real-time obstacle information to generate a final feasible route; a control instruction sequence is generated according to the final route and transmitted to the robot for execution. According to the method, efficient and adaptive path planning in a dynamic environment can be realized, and the real-time response capability and task execution reliability of the robot are improved.

The invention discloses a route determination method, system and device and a storage medium. The method comprises the steps of obtaining route notification data and a candidate route set; constructing a knowledge graph based on the route notification data, and calculating a reputation value of each autonomous domain node based on the knowledge graph; and based on the reputation value of each autonomous domain node, screening out a credible route set from the candidate route set, performing reputation weighted scoring on paths in the credible route set, and selecting the path with the highest score as the optimal route. Semantic organization is carried out on the autonomous domain, the routing prefix and the strategy relation through the knowledge graph, the data availability and analysis efficiency are improved, dynamic evaluation of the autonomous domain reputation is achieved in combination with neighbor recommendation, and the accuracy of credibility judgment is improved; a reputation evaluation result is directly applied to path selection, untrusted nodes and paths are effectively filtered, potential attack risks are reduced, and the overall security and stability of a routing system are improved.

The invention relates to the field of intelligent security and path planning, and discloses a weapon escorting route dynamic optimization and risk early warning platform, which comprises a multi-modal data acquisition module, integrates traffic flow, geographic elevation and threat public opinion data through space-time alignment and tensor filling, and constructs a multi-channel space-time feature tensor; the dynamic risk assessment module is used for solving and generating a dynamic risk field based on an adversarial game model and Nash equilibrium, and quantifying a risk gradient under an attacker strategy; the quantum collaborative optimization module is used for generating quantum parameters through tensor hidden variable decomposition and solving a multi-target optimal route in combination with a quantum annealing algorithm; the hypergraph emergency response module is used for reconstructing a mutual exclusion redundant path and feeding back and updating a strategy space when a threshold exceeding risk is detected; and the strategy collaborative updating module fuses the risk gradient and the game utility to generate a joint reward signal, and drives the reinforcement learning model to dynamically optimize the decision strategy. The safety, efficiency and reliability of the escorting task are improved.

The invention discloses a multi-constraint emergency material vehicle path optimization method and system, and belongs to the technical field of vehicle path planning. Comprising the following steps: acquiring input data from a data source, and constructing a multi-objective optimization model containing an objective function and constraint conditions based on the input data; based on the input data and the constructed multi-objective optimization model, generating an initial path scheme meeting all constraint conditions by adopting a heuristic algorithm combining nearest distance distribution and an earliest time window priority rule; and constructing a hierarchical collaborative optimization architecture consisting of an ALNS execution layer and a PPO decision layer, carrying out iterative optimization by taking the initial path scheme as a starting point, terminating the iterative optimization when a preset termination condition is met, and finally outputting an optimal path scheme. Compared with the prior art, the method has the beneficial effects that the key indexes are collaboratively optimized through the multi-objective optimization function, and the global solution quality and the scheduling precision are remarkably improved.

The invention discloses a large transport intelligent line selection implementation method fusing a graph neural network and a large language model, and the method comprises the following steps: S1, constructing multi-source features to a unified high-dimensional vector space according to large transport historical data and real-time environment data; s2, constructing a structured prompt template through multi-modal input; s3, based on the structured prompt template, generating a candidate route set in combination with a large language model; s4, dynamically optimizing the candidate route set through a reinforcement learning framework, and outputting an optimal route. Compared with the prior art, the method has the advantages that intelligent generation and dynamic decision making of a large transportation route are realized through multi-modal feature fusion, graph structure semantic compression and large model reasoning optimization, and the transportation reliability, safety and economical efficiency can be remarkably improved; traffic accidents can be responded in real time to realize route adjustment, routes are dynamically optimized to reduce passage cost, planning quality is continuously improved through historical case learning, and safe and punctual delivery of overrun goods is guaranteed.

The invention relates to a retrieval routing optimization method and device based on a distributed retrieval system, and relates to the field of computer technology distributed retrieval, the method comprises the following steps: obtaining inverted metadata of a document, including routing information, fragmentation information and a document storage identifier, judging whether the document is a new document, and combining the resource state and availability of each node to optimize the retrieval routing optimization of the document. And determining an optimal routing path by adopting an optimal execution fragmentation formula. A retrieval request is split into a plurality of sub-requests and is directionally issued to a target fragment, and the coordination node integrates return results of the fragments and then returns the return results to the client. The device comprises an information integration module, an inverted metadata recorder and a retrieval routing control module, and supports a dynamic configuration routing strategy, a document state refreshing mechanism and performance monitoring parameters. By introducing the document state identifier, the dynamic routing algorithm and the node resource monitoring mechanism, accurate routing and efficient processing of the retrieval request are realized, so that the retrieval performance and the resource utilization rate of the system are improved.

The invention relates to the technical field of maritime navigation and intelligent shipping, in particular to an electronic chart shipping relation network construction and dynamic updating method and system based on a chart database, and the method comprises the steps: obtaining electronic chart static data and shipping dynamic information, and carrying out standardized preprocessing to unify a space reference and an attribute format; defining node types and edge relation types of channel space elements based on the graph database, and constructing a shipping relation network; shipping dynamic information is accessed in real time, and the weight and the attribute of the side relation are dynamically updated to reflect the real-time state of the navigation channel; and receiving a user path planning query request, executing a path planning algorithm fused with the navigation constraint condition based on the updated network, and generating and outputting an optimal route, thereby effectively solving the problems of weak data association, difficult cross-scale fusion and poor dynamic adaptability of a traditional electronic chart, realizing intelligent management and accurate path planning of a channel relationship, and improving the user experience. And the shipping efficiency and safety are improved.

Each of virtualized base stations is configured by connecting respective units of CU, DU, and RUs. An ACC is installed at least a part of computers that virtualize the respective units. The respective units notify the RAN controller of ACC information of computers and traffic information. The RAN controller comprises a route calculation unit 101 that calculates an optimal route based on the ACC information and traffic information of the respective units, and a route notification unit 102 that notifies the respective units of information on the optimal route, and the respective units switch the route between the respective units based on the notified route information.

The invention discloses an underwater multi-target routing method based on an improved non-dominated sorting genetic algorithm. The method comprises the following steps: firstly, arranging sensor nodes in a target water area, networking, and encoding a feasible path from a source node to a target node by adopting a segmented structure; secondly, constructing a routing multi-target fitness function, and introducing a dynamic weighting function to form a comprehensive fitness index; and finally, grading the population by using an improved non-dominated sorting genetic algorithm, and exploring a better path through crossover and mutation operations. And designing a reference point elite selection strategy, improving the coverage of the solution set in the target space, and outputting an optimal routing path until the algorithm converges. According to the method, the dynamic balance among different optimization targets can be realized, and the link quality is considered while the energy consumption and the time delay are reduced, so that the network routing performance is improved.

The invention belongs to the technical field of computer network, artificial intelligence and network flow optimization, and discloses a Q-Learning network path planning method based on multi-dimensional reward and adaptive exploration, which comprises the steps of 1, network environment modeling and parameter initialization, 2, multi-strategy action selection, and 3, multi-strategy action selection. Step 3, calculating a multi-dimensional reward according to a multi-dimensional reward rule in a data packet transmission process; step 4, performing adaptive exploration adjustment, monitoring the search success rate in a predetermined period in real time, and dynamically adjusting the exploration intensity epsilon according to the search success rate; step 5, performing adaptive pruning optimization to finally obtain a candidate action set; and step 6, iteratively updating and finally outputting an optimal routing path and a Q value table. According to the invention, the routing exploration efficiency can be improved, dense learning signals are provided, and the calculation overhead is reduced.

A multi-algorithm unmanned aerial vehicle intelligent route planning method, comprising: receiving a route planning task, determining the navigation target and constraint conditions of the unmanned aerial vehicle; analyzing the required route planning algorithm characteristics according to the navigation target and constraint conditions of the unmanned aerial vehicle; dynamically selecting one or more route planning algorithms suitable for the current task requirements from the preset algorithm library according to the analysis result; dynamically loading the selected route planning algorithm; generating a preliminary path of the route; defining the objective function and loss function for different navigation targets; optimizing the objective function according to the defined loss function, adjusting the weight value of the influence factors of the navigation target; determining the optimal route. The method solves the problem that a single algorithm is difficult to adapt to complex scenarios, and improves the accuracy and efficiency of route planning. Through the dynamic loading and switching mechanism, the optimal algorithm strategy can be selected according to the actual demand, the route can be dynamically adjusted, and the flight safety and task completion quality are improved.

The invention belongs to the field of tourism planning, and provides a tourism service method and system integrating local culture and tourism characteristics, and the method comprises the steps: collecting local culture data; collecting tourism characteristic data; defining each cultural activity and scenic spot as a node in the graph; establishing a multi-dimensional edge connection system for the graph; based on the constructed graph structure, fusing the geographic features and the time features into a multi-scale high-dimensional spatial-temporal feature space; constructing a hierarchical reinforcement learning framework; introducing a multi-target reward function into the reinforcement learning framework, wherein the multi-target reward function comprises a culture experience integrity index, a space-time coordination index and a risk avoidance index; and receiving a culture type and a scenic spot type which the user wants to embody, and generating an optimal route and travel time which meet the requirements of the user through the hierarchical reinforcement learning framework in the spatial-temporal feature space. The method is especially suitable for tourist route planning with rich local culture and complex geographical environment in Sichuan and Tibet areas and the like.

In large domes and stadiums, this system integrates advanced information systems, user interfaces, and real-time data to ensure that visitors are properly guided to their destinations (seats, restaurants, shops, etc.), can understand congestion levels, receive information on products and events tailored to their individual needs, and, in the face of the increasing adoption of unmanned stores, enable efficient inventory management and payment processing. [Solution] A system including a digital display device and means for the user to input a desired destination; a central control unit and means for receiving destination information from the digital display device and calculating the optimal route; means for providing the user with optimal route guidance using a geographic information system including real-time congestion information; and means for communication with the central control unit.

The present disclosure provides methods and systems for adaptively identifying an optimal route. In some examples, there is provided a method comprising: determining, by a processor, one or more pick up points based on historical data relating to a starting location, the starting location being one that is indicated in a request for a ride from the starting location to a destination location; determining, by the processor, one or more routes between each of the determined one or more pick up points and the destination location indicated in the ride request; and adaptively identifying, by the processor, an optimal route from the one or more routes based on at least one of a cheapest route, fastest route, shortest route and shortest walking distance.

A method for calculating an optimal route includes selecting first and second optimization targets; distributing optimal route calculation of the first and second optimization targets to first and second thread modules, respectively; calculating a cost matrix between nodes or receiving the cost matrix from a database when calculating an optimal route according to the LNS by applying the first and second optimization targets; randomly arranging nodes of a starting point, an arrival point and stopovers, and executing the optimal route calculation according to the LNS based on the cost matrix between the arranged nodes; and selecting an optimal route that satisfies a predetermined condition according to the first and second optimization targets calculated in the optimal route calculation. The optimal route calculation is executed by updating the cost matrix between two nodes connected by destruction and repair of the LNS into a cost matrix corresponding to the optimization target.

This invention discloses an elevator bearing safety early warning method, device, and medium based on the Internet of Things (IoT), relating to the field of control testing and monitoring technology. The method includes: acquiring raw analog signals and preprocessing them to generate synchronous data frames; decomposing fault impact sequences from the synchronous data frames and obtaining a bearing safety status index through weighted D-S evidence, while simultaneously matching a pre-diagnostic control logic library to generate a pre-diagnostic confidence identifier; based on the pre-diagnostic confidence identifier and the bearing safety status index, obtaining a dynamic health baseline through sliding retrieval and elastic tracking compensation, and dynamically extrapolating the control envelope through a stochastic control boundary evolution algorithm to generate a dynamic safety threshold; comparing the bearing safety status index with the dynamic safety threshold, evaluating link quality, and selecting a candidate route set; and obtaining the optimal route through a shortest delay tree to form an encrypted alarm stream. This invention achieves accurate generation of the dynamic safety threshold.

The application discloses a 120 emergency center ambulance task allocation method based on multi-algorithm combination optimization, and comprises the following steps: S1, acquiring the specific positions of all patients to be treated and their illness conditions, the specific positions of all idle ambulances, the specific positions of all hospitals and the treatment ability for each patient in a current time period; S2, constructing an ambulance task allocation evaluation function and a treatment effect evaluation function obtained by each patient; S3, performing path planning in combination with an A* algorithm to ensure that the ambulance selects an optimal route; and S4, using an Ito algorithm and a Newton-Raphson optimization algorithm to iteratively calculate the optimal allocation result of each patient, so that each patient can obtain emergency treatment. The application is superior to a traditional single-algorithm scheduling scheme in response time, treatment effect and resource utilization rate through multi-algorithm combination optimization, can efficiently perform multi-ambulance task allocation, and ensures that each patient can obtain timely and effective treatment.

The application discloses an AGV optimal path automatic memory storage scheduling method and system, which comprises the following steps: receiving task information; separately threading real-time cyclically reading the task information; selecting an AGV vehicle meeting a current task; matching a path planning from a site where a current vehicle is located to a task destination site in an optimal route cache set, if the path planning exists, directly selecting an optimal planning, if the path planning does not exist, re-planning an optimal path according to a path planning method and storing the optimal path into an optimal cache for next time matching and use; converting the optimal planning into a task execution instruction; and sending the task execution instruction to the AGV through a TCP protocol.

The invention discloses a community emergency rescue remote authorization method and system based on a BIM technology, relates to the technical field of BIM positioning, and solves the problem that function modules, such as route planning, authorization management and information transmission, of a traditional community emergency rescue system are operated independently and lack collaborative linkage. According to the method, a rescue starting point (dispatching a target building B) and a terminal point (associated building A) are accurately locked through a BIM model, a road sequence is disassembled in combination with cloud map big data, then the total duration of a route is calculated in a mode of calibrating the congestion condition section by section, and finally the optimal route with the shortest consumed time is selected; compared with a traditional way of selecting routes depending on artificial experience and ignoring real-time congestion, the method can avoid route deviation caused by fuzzy positions, can dynamically adapt to road conditions, and greatly reduces the risk of delay of rescue vehicles.

The embodiment of the invention discloses a method and a device for generating closure information. In the embodiment of the invention, a first optimal route, a first score, a second optimal route and a second score are obtained, the first optimal route is generated according to first road information with road closure information, and the second optimal route is generated according to second road information without road closure information; in response to the fact that the second score is greater than the first score, inputting a first optimal route into a detour identification model, outputting a first discrimination result, inputting a second optimal route into the detour identification model, and outputting a second discrimination result; in response to the first judgment result of detour and the second judgment result of non-detour, road closure information is calculated through a road network basic route; and displaying the first optimal route and the road closure information. By means of the method, the road closure information can be accurately displayed for the user in the planned travel route, and misunderstanding of passengers is avoided.

The application discloses a route planning method and system for a large-scale vehicle path problem, belongs to the technical field of vehicle path planning, groups multiple task nodes, distributes the task nodes to corresponding groups, forms multiple populations of an upper-layer clustering scheme from the multiple groups, determines the number of vehicles and the total length of paths corresponding to each individual in the population corresponding to each group, performs non-dominated sorting on each individual, determines the non-dominated sorting level of each individual, uses the non-dominated sorting level as a label to construct a training sample of an SVM model, inputs the generated offspring population into the trained SVM model, obtains the classification result of each individual in the offspring population, selects excellent offspring, performs lower-layer path optimization, obtains the optimal route corresponding to the excellent offspring, and selects a new generation of population; the iteration is performed until the new generation of population meets a preset termination condition, and an optimal route planning scheme is output. The method can reduce the time complexity in the route planning process.