10results about How to "Reduce decision time" patented technology

The present application relates to the technical field of smart city, especially relates to a smart city bus stop board dynamic information accurate pushing method and system, wherein a query semantic representation structure is constructed by collecting query requests and scene state information, a joint understanding representation is obtained by modeling the scene state information multi-level semantic association, scene constraint multi-hop reasoning is executed in a bus knowledge association network, a candidate answer sequence is generated by filtering, an optimal answer is selected by calculating a scene adaptation score, pushing content is generated, interactive feedback is collected, feature fusion weights are adaptively adjusted to form a closed loop optimization, and accurate dynamic pushing of bus information and user experience improvement are realized.

The invention provides a plant protection unmanned aerial vehicle path planning method based on reinforcement learning. The plant protection unmanned aerial vehicle path planning method comprises the following steps: acquiring a starting point, a target point and static obstacle position information; establishing a two-dimensional map to obtain a line patrol path distributed in a reciprocating bow shape, and decomposing the line patrol path into a plurality of sub-target tasks; establishing an unmanned aerial vehicle model, setting an unmanned aerial vehicle initial state s and a proper reward function r based on a near-end strategy optimization algorithm, and obtaining rewards through interaction with the environment; updating the state of the unmanned aerial vehicle and taking the next action; when the unmanned aerial vehicle encounters an obstacle, the unmanned aerial vehicle completes an obstacle avoidance task according to an obstacle avoidance principle and then returns to a line patrol task, and completes a sub-target tendency task according to an algorithm process; after all obstacle avoidance behaviors are completed and n sub-target tasks are approached, the unmanned aerial vehicle arrives at a terminal point E; the optimal path planning result is provided for the unmanned aerial vehicle by accumulating the reward value, the spraying operation efficiency of the unmanned aerial vehicle is improved, the operation safety of the unmanned aerial vehicle is considered, and the labor cost is effectively reduced.

The application relates to the fields of green low-carbon technology and construction technology, and discloses a shield tunnel-based repair scheme decision method, device, equipment and medium, which comprises the following steps: generating a stiffness recovery index of a shield tunnel under each repair scheme through a preset stiffness recovery model; generating an average toughness index of the shield tunnel under each repair scheme according to the stiffness recovery index of the shield tunnel under each repair scheme, a stiffness degradation coefficient of the shield tunnel under each repair scheme, a design life of the shield tunnel under each repair scheme and a preset toughness model; generating a comprehensive evaluation value of each repair scheme according to the average toughness index of the shield tunnel under each repair scheme, a carbon emission amount corresponding to each repair scheme and a preset comprehensive evaluation model; and selecting a repair scheme with the maximum comprehensive evaluation value as an optimal repair scheme of the shield tunnel. The application is beneficial to improving the decision efficiency of the optimal repair scheme of the shield tunnel.

The application uploads the data obtained by the vehicle-mounted camera sensor of the unmanned mine car to the cloud server, carries out feature recognition on the cloud data through the cloud server, then selects the road curvature, the slope, whether there is an obstacle in the front road, whether there is a fork in the front road as the characteristic parameters according to the characteristics of the mine area to realize the data screening of the vehicle-mounted camera, so that the data input into the decision model of the unmanned mine car is reduced, and at the same time, the speed information of the unmanned mine car is referred to in the vehicle-mounted camera data screening process, and the screening weight is dynamically determined, so that the decision time of the decision model is reduced, and according to the characteristics of the mine area, the sensor data screening and the dynamic determination of the screening weight are considered, most of the interference garbage data is considered, the decision time is reduced, and the decision accuracy of the decision layer is improved.

The application discloses a multi-stage peak position detection method for satellite communication and relates to the field of satellite communication, which comprises the following steps: dividing a joint / complete three-dimensional search space into a plurality of low-dimensional independent search spaces; performing peak position detection in each independent search space by using multi-stage step-by-step decreasing steps; and performing angle jumping during the peak position detection process to avoid the influence of a power dead zone, so that beam alignment of a target satellite is completed while the total search number is reduced.

The present application relates to the technical field of logistics distribution, and discloses a package delivery method and device, computer equipment and storage medium. The package delivery method determines a delivery starting point, a target delivery community, and package size parameters and package waybill addresses of each package to be delivered according to a batch package delivery request; performs clustering analysis on all the packages to be delivered according to the package waybill addresses, and obtains a plurality of package sets; filters candidate delivery spaces of each package set according to set volume parameters; for each package set, determines a delivery comprehensive score of each candidate delivery space according to the delivery starting point, set volume parameters, and spatial attribute information of the candidate delivery space, and determines the candidate delivery space with the highest delivery comprehensive score as the target delivery space of the package set; and generates package set delivery tasks corresponding to each package set based on the target delivery space and sends them to the delivery party, thereby improving the rationality and delivery efficiency of package combination delivery.

The utility model relates to the field of household daily necessities, and provides an auxiliary positioning early warning device for a drilling platform. The auxiliary positioning early warning device for the drilling platform comprises a rectangular long frame, a flooding detection mechanism, a GPS positioning module, a 4G transmission module, a controller and an alarm mechanism, and the front end face and the rear end face of the lower end of the rectangular long frame are fixedly sleeved with U-shaped mounting plates. According to the auxiliary positioning early warning device for the drilling platform, through cooperation of the floating ball and the waterproof pressure sensor, instantaneous wave interference is avoided, the water logging condition can be accurately detected, the false alarm problem of a traditional water level sensor is avoided, meanwhile, the position of the platform is monitored in real time in combination with the GPS positioning module, and the positioning accuracy is improved. Once a safety range is exceeded, offset early warning is triggered immediately, simultaneous operation of multi-dimensional safety monitoring, flooding alarm and offset early warning is realized, risks can be analyzed in a linkage manner, whether water enters due to drifting or not is analyzed, decision time is shortened, and emergency response can be optimized.

The application relates to the technical field of fire safety, in particular to an automatic water-spraying fire extinguishing system and method based on multi-modal perception and intelligent decision-making, which comprises a perception module, a data processing and control module and an execution module; the data processing and control module is embedded with a hybrid deep learning model; the hybrid deep learning model fuses a convolutional neural network for analyzing smoke images and a long short-term memory network for analyzing temperature sequences; multi-modal sensor data can be deeply fused and intelligently understood, so that high-accuracy fire identification can be realized within 3 seconds.The beneficial effects are that, through deep fusion and interaction of hardware sensors and intelligent algorithms, the problem of slow response and high false alarm rate of traditional systems is effectively solved, and the system is particularly suitable for prevention and control scenes of new fire risks such as electric bicycles, new energy vehicles and the like.

The invention discloses a store inventory dynamic management and sales demand adaptation optimization method, and belongs to the field of data processing and supply chain optimization. According to the method, historical sales data and cost parameters of all stores are centrally collected and cleaned; based on the cleaned data, calculating a sales rhythm factor K used for representing sales volatility and a demand balance factor M used for representing demand difference between the stores; dynamically correcting the calculation result of the classic economic ordering batch EOQ model by using K and M to obtain the optimal purchase batch of the central warehouse and the optimal replenishment batch of each store; furthermore, the overall optimization of the inventory cost and the response efficiency is realized based on a standardized split inventory unit, a priority distribution unit, real-time monitoring and intelligent allocation decision and parameter closed-loop optimization. According to the method, the problems of high inventory cost and poor adaptability caused by model stiffness, poor data quality and response lag of a traditional inventory management method are effectively improved.