31results about How to "Improve completion efficiency" patented technology

The invention discloses a multi-terminal vehicle management system based on an intelligent algorithm, relates to the technical field of vehicle management, and is used for solving the problem of insufficient real-time performance and cooperative efficiency of vehicle management. According to the method, a multi-modal sensor fusion technology is combined with Gaussian process regression, complex environment data is modeled in real time, dynamic target and static background features are accurately extracted, environment changes are analyzed and predicted in combination with a time sequence, accurate input is provided for path planning and task allocation, a path is dynamically adjusted through an incremental optimization algorithm, emergencies are coped, and the path planning and task allocation efficiency is improved. According to a distributed path optimization mechanism, multi-vehicle task path conflicts are eliminated, the cooperation efficiency is improved, a priority model is constructed based on the task emergency degree, the time constraint and the resource matching degree, the weight is dynamically adjusted, vehicle and task matching is optimized in combination with reinforcement learning, the task execution efficiency is ensured, resource competition and path conflicts are solved, and the task execution efficiency is improved. Efficient cooperation in a complex dynamic environment is realized, and the real-time performance and management efficiency of vehicle management are improved.

The invention discloses a multi-underwater-robot cooperative control system and method based on predictive control, and relates to the technical field of cooperative control. During operation of the system, underwater environment data are collected through sensor equipment, an underwater three-dimensional dynamic environment model is generated by using a data fusion technology, and the underwater three-dimensional dynamic environment model is established based on task requirements; tasks are decomposed and distributed to all the robots, through an optimization algorithm, optimal paths of the robots and the mechanical arms are planned, the motion trails of the underwater robot mechanical arms are dynamically optimized based on model predictive control MPC, a real-time control strategy is provided for task division among the multiple mechanical arms, and the real-time control strategy of the underwater robot mechanical arms is provided based on environment modeling and the real-time state. Future motion states of the robot and the mechanical arm of the robot are predicted, an optimization objective function including task completion time, energy consumption and mechanical arm operation precision is constructed, a rolling optimization method is utilized, a control instruction is updated in real time, and real-time data sharing among multiple underwater robots is supported based on an acoustic communication technology.

The invention discloses a large model parallel task scheduling method based on space-time two-dimensional segmentation and intelligent sharing, and particularly provides a model segmentation strategy based on space-time two dimensions, and based on significant differences of training and reasoning tasks in calculation properties, targeted optimization strategies are adopted respectively, so that the task execution efficiency is improved; the invention further provides a model placement strategy based on the heterogeneous resources and the interference model, the defects that a traditional method lacks intelligent sharing judgment and cannot be dynamically adjusted are overcome, the flexibility and adaptability of scheduling are improved, and therefore the maximum benefit of the shared resources is guaranteed. The method solves the problems that when an existing heterogeneous intelligent computing cluster scheduling method is used for processing large model training and reasoning tasks, the model placement optimization technology of model segmentation and heterogeneous resource adaptation is not fully utilized, intelligent sharing scheduling of computing resources in a cluster is lacked, system task scheduling is unreasonable, and the scheduling efficiency is high. And the utilization rate of computing resources is low.

The invention provides an unknown target collection-oriented mobile robot motion planning method. The method comprises the following steps of S1, synchronously constructing a grid map in a moving process of a mobile robot; s2, extracting a boundary region and a passable region; s3, the mobile robot continuously explores the environment; s4, the exploration degree of the local environment is judged according to the balance function; s5, if the exploration of the local environment is completed, the mobile robot collects targets in the task area; s6, the mobile robot completes ascertainment of the sub-regions and region coverage collection; step S7, when all unknown environments are detected and all task area targets are collected, the unknown target collection task is completed; the unknown target collection motion planning method of the mobile robot is good in performance, the mobile robot can rapidly complete exploration of an unknown environment and collection of targets in a task area, and task requirements can be effectively met.

The invention provides a task management method and a project management system for a scientific research platform, and relates to the technical field of data processing, and the method comprises the steps: obtaining a to-be-allocated task in the scientific research platform; splitting the to-be-allocated task into a plurality of sub-tasks according to an execution time sequence; the planned completion time of each subtask is distributed; extracting a feature vector of each subtask; obtaining the feature vector of each person; according to the feature vector of each sub-task and the feature vector of each person, distributing a plurality of execution candidates for each sub-task in combination with a collaborative filtering algorithm; determining a final executor of each sub-task from the execution candidates through a multi-objective optimization algorithm by taking minimization of task completion time and minimization of task failure factors as targets; in the execution process, the task execution progress is monitored according to the planned completion time. The task allocation efficiency can be improved, the task allocation personnel rationality is improved, and the scientific research task completion efficiency is improved.

The invention provides an intelligent mining production management and control method based on an ADP algorithm, and relates to the technical field of intelligent mining, and the method comprises the steps: firstly obtaining vehicle data and wrong vehicle point data, carrying out the road division of a two-way single lane with a wrong vehicle point as the end points of the two ends of a road section, and carrying out the position judgment and display of a running vehicle in the two-way single lane; judging passing priorities of the vehicles, and checking whether vehicles running in opposite directions exist in adjacent road sections of the current vehicle or not; carrying out data preprocessing on the collected mine car scheduling data, dividing the data into a training data set and a test data set, carrying out classification processing on the preprocessed data, and establishing a classification model; identifying and dividing a naturally formed area of the underground mining area operation area by adopting a clustering algorithm; establishing a nonlinear system model, establishing a performance index, comprehensively considering transportation efficiency, cost saving and time benefit, measuring the effect of a scheduling strategy, and setting an optimization target; and establishing an ADP algorithm, and outputting an optimal scheduling scheme.

The invention provides a real-time optimization method, device and equipment for a task allocation scheme, and relates to the technical field of data processing, and the method comprises the steps: obtaining the real-time progress and predicted completion time of a target employee for executing a target task, and the task information of the target task; determining a hysteresis evaluation result of the target employee according to the real-time progress, the predicted completion time and the execution complexity; if the hysteresis evaluation result meets a configured burden reduction rule, matching a worker with the ability to complete the target task from a plurality of workers in an idle state according to a configured task and worker matching rule, and obtaining the work attribute of the worker; and generating an optimized task allocation scheme based on the acquired work attributes, execution complexity and real-time progress of the employees. According to the method, the completion condition of the allocated task can be monitored in real time, and the task allocation scheme is optimized and adjusted in time when any employee may influence the completion of the task on schedule.

The present invention relates to the technical field of energy impact testing machines, and discloses an electric lifting and pendulum low-energy impact testing machine, including a base, a placing table is provided on the base, an impact test block is placed on the placing table, the placing table is composed of a pair of trapezoidal plates distributed front and back, a fixed card block is provided on the top of the placing table, the impact test block is placed in the fixed card block, and an automatic calibration mechanism for the test block that can be automatically raised and lowered is provided at the bottom of the placing table. Compared with the prior art, the beneficial effects of the present invention are: without the need for a manual hand-held calibrator, the calibration block can be automatically moved to the calibration position, and then the impact test piece can be quickly calibrated, the operation is convenient, and the degree of automation of the device is improved; the quick docking mechanism can make the whole process simpler, and only the push-pull rotation pressing block is required to achieve the connection and fixation of the pendulum, which improves the installation efficiency; the release and withdrawal pin and the lifting and fixing of the pendulum can be achieved through a pneumatic withdrawal pin mechanism, which enhances the overall intelligence and automation of the equipment.

The invention discloses a task scheduling method and device, equipment, a storage medium and a program product, and relates to the technical field of cloud computing. The task scheduling method comprises the following steps: constructing a third-order hypergraph by utilizing an obtained task set, a task data set and a computing platform set, the third-order hypergraph comprises a computing platform layer constructed by taking each computing platform in the computing platform set as a node, a task layer constructed by taking each task in the task set as a node, a data layer constructed by taking each task data in the task data set as a node and a hyperedge; determining task completion time of a scheduling scheme corresponding to each hyperedge in the third-order hypergraph according to the time corresponding to each hyperedge connection task, the computing platform and the task data; according to the sequence of the task completion time of the hyperedges, task scheduling schemes of the tasks in the third-order hypergraph are selected in sequence, and a task scheduling scheme of the task set is obtained. According to the embodiment of the invention, the technical problem of overlong task queuing time is solved, and the task completion efficiency can be improved.

The invention provides a deep space star catalogue multi-detector air-ground collaborative deployment method based on reinforcement learning, and belongs to the technical field of planet surface detection. According to the method, a leap device and a patroller serve as detectors to cooperatively detect an unknown area of a deep space star catalogue, three types of communication channels are established between the detectors, and the communication radius between the different detectors is calculated; the method comprises the following steps: establishing constraints in the aspects of communication range, sensing range, coverage mode and energy, and setting an optimization target to keep communication connection between detectors under limited energy and maximize the total coverage area of the detectors; a leap device and a patroller are used as two agents, and an optimal deployment strategy is determined based on a multi-agent near-end strategy optimization algorithm. According to the invention, the method optimizes the regional coverage strategy, achieves the efficient distribution of resources, improves the detection efficiency and accuracy of a leap device and a detector in extreme terrains and complex environments, reduces the energy consumption while guaranteeing the coverage efficiency, and improves the overall energy utilization efficiency of the system.

The invention provides an optical cable design intelligent drawing system and method based on fusion of multi-source acquisition data. Through combination of the large model processing layer and the core function layer, automatic analysis, association and compliance detection of the multi-source collection data are realized, the work of manually completing verification and compliance detection of the multi-source collection data in the prior art is replaced, and the manual workload is greatly reduced, so that the multi-source collection data verification and compliance detection can be realized in a complex scene, and the multi-source collection data verification and compliance detection efficiency is improved. Automatic processing can be completed based on the combination of the large model processing layer and the core function layer of the system, and the technical effect of improving the completion efficiency and precision is generated due to the reduction of the manual workload.

The invention relates to a container control method and electronic equipment. The container comprises a movable mechanical arm, and a visual assembly and an end effector located at the tail end of the mechanical arm are arranged on the mechanical arm. The container control method comprises the steps that in response to an obtained handover instruction for target goods, the photographing pose of a mechanical arm is calibrated according to a target handover position indicated by the handover instruction, a target photographing pose is obtained, and after the mechanical arm is controlled to move to the target photographing pose, a first image of a first position identifier is collected through a visual assembly; and according to the first image, the mechanical arm is controlled to execute handover operation on the target goods through the end effector. According to the method, the photographing pose of the mechanical arm is automatically calibrated based on the target handover position, the mechanical arm collects the image of the visual recognition beacon through the photographing pose calibrated in real time, the accuracy of the image used for space visual positioning is higher, and therefore the mechanical arm can be more accurately controlled to execute handover operation.

The embodiment of the invention provides a causal path completion method and device, equipment and a storage medium, and relates to the technical field of network security. In response to the path completion instruction, acquiring a path breaking point from the causal path, and determining a context data packet according to the path breaking point; performing large language model reasoning on the context data packet based on a vector database to obtain verification plan data of the candidate path, and arranging the verification plan data according to the verification platform information to obtain an instruction sequence containing at least one atomic action instruction; performing verification on the instruction sequence to obtain a verification result corresponding to each atomic action instruction; and performing decision judgment on the candidate path according to the verification plan data and the verification result to obtain a judgment result, and complementing the causal path based on the judgment result. The whole process from fracture point positioning, candidate path reasoning to verification completion is automatic, the processing efficiency is effectively improved, and the accuracy and credibility of a completion result are guaranteed.

This invention relates to a method and system for optimizing meal replacement intake time based on circadian rhythms, belonging to the field of nutrition management technology. It includes: collecting basic user information, sleep time, and wake-up time; determining a sleep-wake cycle based on sleep and wake-up times; retrieving user age and gender based on the basic information and dividing the sleep-wake cycle into physiological time periods; retrieving health goals and physical fitness parameters based on the basic information to determine the types of meal replacement foods; determining the initial time for meal replacement intake based on physiological time periods and selecting recommended food types; determining a type-based intake adjustment value based on the recommended food types; adjusting the initial time for meal replacement intake based on the type-based intake adjustment value to obtain a recommended meal replacement intake time and outputting it. This invention improves the efficiency of users in achieving their health goals during nutrition management.

The invention relates to a data acquisition method and device, electronic equipment and a storage medium, and relates to the technical field of computers. The method comprises the following steps: in response to a received data acquisition task, acquiring configuration information of a target interface corresponding to the data acquisition task; determining an authentication mode of the target interface according to the configuration information; in response to the determination that the authentication mode is a dynamic authentication mode, determining an acquisition mode of a dynamic authentication parameter according to the configuration information; and updating the dynamic authentication parameter through the authentication interface before the dynamic authentication parameter expires in response to the determination that the acquisition mode of the dynamic authentication parameter is acquired through the authentication interface, so as to complete the data acquisition task. By applying the method and the device, the security of the target interface is ensured, and the completion efficiency of the data acquisition task is also improved.

The application discloses a target detection method and a related device, which can quickly complete a target detection task, improve the completion efficiency of the target detection task, and further improve user experience. The method of the application comprises the following steps: when target detection needs to be performed on a target image, the target image containing an object to be detected is acquired first, and then the target image is input into a target model. Next, the target model performs feature extraction on the target image, thereby obtaining first features of the target image. Then, the target model encodes the first features of the target image, thereby obtaining second features of the target image. Subsequently, the target model decodes the second features of the target image based on a preset query vector, thereby obtaining third features of the target image. Finally, the target model acquires a detection result of the target image based on the third features, and the detection result can be used to determine position information of the object and a category of the object. Thus, the target detection on the target model is completed.

The invention discloses an engineering innovation practical ability cultivation method and system based on a domestic EDA platform and AI-PBL, and belongs to the field of engineering education and intelligent teaching. A domestic EDA tool is taken as an engineering practice carrier, real engineering problems and subject competition tasks are taken as driving, and a stepped progressive project system driven by an ability matrix is constructed, so that the engineering innovation practical ability is cultivated. And systematic fusion of professional knowledge learning, engineering practice training and innovative design capability cultivation is realized. In the implementation process, an artificial intelligence technology is introduced to carry out intelligent support, design assistance, debugging analysis and engineering practice behavior data processing on the learning process, and the engineering practice ability, team cooperation ability and innovative design ability of students are comprehensively evaluated based on a multi-dimensional ability evaluation index system. According to the method, the problems of course content splitting, discontinuous ability cultivation chain and single evaluation mode in existing EDA practical teaching are solved, and the engineering practice task implementation efficiency and ability evaluation consistency are improved.

The application relates to a smart factory robot scheduling method and system, a terminal and a storage medium, and relates to the technical field of smart factories. The method comprises the following steps: receiving a task instruction from a production management system, and adding the task instruction to a task pool; obtaining running state information of robots in the smart factory; selecting at least one task instruction from the task pool, and filtering candidate robots for executing the task instruction and generating a candidate robot set according to a matching relationship between a task type of the task instruction and a robot state; calculating a task completion time of a to-be-scheduled task for the to-be-scheduled task; comparing and analyzing each candidate robot in the candidate robot set based on the task completion time, and determining a target robot for executing the to-be-scheduled task; and distributing the to-be-scheduled task to a task queue of the target robot. The application has the effect of improving the problem of low task completion efficiency caused by unreasonable robot scheduling.

The invention belongs to the technical field of sea temperature complementation, and particularly relates to a sea temperature complementation method and system based on space-time prior and a flow matching generation model. The method comprises the following steps: inputting sea surface temperature SST data, weekly mean value SST data and a mask for four continuous days of the sea surface, carrying out rough completion of space-time residual learning, introducing a lightweight 3D-CNN space-time residual learning mechanism, obtaining a rough completion graph, and explicitly modeling a space-time evolution rule of sea temperature data; then, introducing a conditional flow matching generation strategy based on optimal transmission into the repair of a total image generated by conditional flow matching, and carrying out refined reconstruction on the roughly completed image to obtain a complete sea surface temperature image; a curved and random Markov chain sampling path in a traditional denoising diffusion probability model DDPM is replaced, the problem that the reasoning efficiency is low due to dependence on hundreds of steps of random denoising is solved, and efficient reasoning under the condition of few steps is achieved.

This application relates to the field of cable testing technology, and in particular to a method, apparatus, computer device, and storage medium for missing data completion. The method includes: acquiring initial temperature data of the target cable at each sampling time within a target sampling period; determining missing temperature data in the initial temperature data, and the missing sampling time corresponding to the missing temperature data; constructing a spline interpolation function corresponding to the target sampling period based on the initial temperature data at each sampling time within the target sampling period; and completing the missing temperature data using the spline interpolation function. This application can effectively complete missing data.

The application provides a text completion method and device, electronic equipment and computer storage medium, the method is applied to the computer technical field, the method comprises the following steps: obtaining a target text input by a user, matching the target text by using a first prefix tree to obtain a first matching result; determining whether the number of recommended texts matched with the target text in the first prefix tree is less than a preset number according to the first matching result, and obtaining a second matching result according to a target classification model in the case of determining yes; merging the first matching result and the second matching result to obtain a merged result, and completing the target text according to the merged result. In this way, the efficiency and accuracy of text completion can be improved.

The present invention relates to a formation method of a hovering drone formation based on intersection trajectory replanning, comprising: S1, initialization setting; S2, all non-leader drones send their respective current positions to the leader drone; S3, the leader drone determines whether the current positions sent by all non-leader drones are received within a first preset time period; if so, an initial expected point is allocated to each drone, a corresponding initial maneuvering trajectory is generated, and the initial maneuvering trajectory is replanned to obtain a corresponding optimized trajectory; S4, the leader drone sends the optimized trajectory to the corresponding drone; S5, each drone maneuvers according to the optimized trajectory, and informs the leader drone of the completion of the maneuver after reaching the corresponding target expected point; S6, the leader drone determines whether all drones complete the maneuver within a second preset time period. The present invention can ensure that no collision occurs during maneuvering.

The invention discloses a method and a device for determining an inspection path of a coal mine robot and electronic equipment. The method comprises the steps that in the process that the coal mine robot executes an inspection task in a coal mine environment, the network signal intensity received by the coal mine robot is acquired; when the signal intensity comparison result is that the network signal intensity is greater than the preset signal intensity threshold value, controlling the coal mine robot to determine a real-time inspection path by adopting a network communication positioning mode; under the condition that the signal intensity comparison result is that the network signal intensity is smaller than or equal to the preset signal intensity threshold value, controlling the coal mine robot to adopt a preset self-positioning mode to determine an offline inspection path; a target inspection path is generated based on the real-time inspection path and / or the offline inspection path, and the target inspection path is used for guiding the coal mine robot to complete the inspection task. According to the invention, the technical problem of low efficiency of determining the inspection path of the coal mine robot in the prior art is solved.

The present application provides a business allocation method, device, storage medium and equipment for a clinical trial project, and relates to the field of clinical trial research technology. The business allocation method for the clinical trial project includes: determining the project label data of the clinical trial project according to the trial project business to be allocated of the clinical trial project; determining N selected business execution agencies based on the project label data and M candidate business execution agencies; and allocating the trial project business to be allocated based on the N selected business execution agencies. The present application considers the two dimensions of subjects and business execution agencies to reasonably allocate project business to business execution agencies. The allocation method is more scientific and also improves the efficiency of project completion.

This invention provides an indoor unit control method, device, and air conditioner. The method includes: determining a target rotation speed based on the current coil temperature; controlling the indoor unit to adjust its rotation speed based on the target rotation speed, so that the indoor unit can continuously operate at the adjusted rotation speed for a first preset time; if it is determined that the next coil temperature is in a first range, controlling the indoor unit to reduce the fan speed based on the adjusted rotation speed; if it is determined that the next coil temperature is in a second range, controlling the indoor unit to increase the fan speed based on the adjusted rotation speed. The indoor unit control method, device, and air conditioner provided by this invention analyze the system pressure value based on the coil temperature and adjust the indoor unit's fan speed accordingly, so that the coil temperature under high-temperature self-cleaning mode is always maintained within the normal temperature range, ensuring that the outlet air temperature is stable at 56°C, improving the efficiency of high-temperature self-cleaning, and avoiding shutdown caused by sudden increases or decreases in system pressure due to unilaterally maintaining the outlet air temperature by adjusting the frequency.

The present application belongs to the technical field of machine vision, and relates to product surface defect detection, and is a kind of all-around defect detection method and system for valve oil seal industrial defects. The method comprises the following steps: collecting a top view image of the valve oil seal, obtaining lip radius and lower spring seat radius data from the front end interface, and positioning and detecting the lip rubber head and the lower spring seat; collecting a side view image of the valve oil seal, obtaining metal skeleton height and lip height data from the front end interface, and positioning and detecting the metal skeleton, lip bottom edge and compression spring; collecting a bottom view image of the valve oil seal, positioning and detecting the lower spring seat; and integrating the obtained detection information to determine qualified products and unqualified products. The present application has the ability to detect different types of valve oil seals, the detection system has good compatibility, the algorithm has good robustness, and meets the quality inspection requirements.

The present invention discloses a tree point cloud completion method based on deep learning. The method consists of two stages: missing point cloud prediction and completion result optimization. In the prediction stage, we use an end-to-end neural network to predict the missing part by giving an incomplete tree point cloud. In this stage, only the point cloud of the missing part is predicted, and the input point cloud is kept unchanged. However, the predicted result is not well integrated with the input point cloud. To this end, we use a point cloud optimization network to further process the predicted results and the original input to optimize the overall distribution. Our results show that for the completion of incomplete tree point clouds, the combination of the prediction network and the optimization network has achieved better results. It has the characteristics of high completion efficiency, realistic morphology and wide application range. In order to avoid the high storage cost and loss of geometric information caused by voxelization, the present invention constructs and runs a network on the original point cloud, which can effectively repair the incomplete tree point cloud.

The application provides a multi-dimensional risk information visualization method and system based on cognitive load optimization, and relates to the technical field of data visualization. It includes: social risk multi-dimensional data attribute analysis and classification; calculating the perception efficiency coefficient of each channel; fusing scene prior, data-driven and user-defined three-source signal to calculate information importance weight, and combining dimension complexity to construct cognitive load function; under the constraint of channel mutual exclusion, the optimal mapping configuration of data attribute to visual channel is solved with the goal of minimizing total cognitive load; the current task type of the user is identified, the view importance weight is calculated, and after stability control processing, the screen space is dynamically optimized and allocated through a nonlinear allocation algorithm; finally, a multi-view linkage interface is rendered and presented. Through the perception efficiency driven visual encoding optimization and task driven layout dynamic adaptation, the cognitive load of the management personnel is significantly reduced, and the risk judgment efficiency and system usability are improved.

The invention discloses a reinforcement learning and Bayesian optimization-based student autonomous learning ability improvement method. The method comprises the following steps of S1, collecting a student behavior data set in a student learning process; s2, forming a learning feature set of student individuals; s3, establishing a learning path optimization model based on a hierarchical reinforcement learning algorithm; s4, key parameters in the high-layer reinforcement learning model and the low-layer reinforcement learning model are adjusted through Bayesian optimization; s5, a reward mechanism based on a reinforcement learning algorithm; s6, updating the learning behavior model of the student at the end of each learning cycle; and S7, forming a cross-stage task connection mechanism based on the long-term learning target of the high-level reinforcement learning model and the short-term learning task of the low-level reinforcement learning model, and automatically formulating a learning strategy after each learning stage for the student. According to the invention, the self-adaptability of the system and the learning efficiency of students are obviously improved.