496results about How to "Efficient execution" patented technology

The invention discloses an unmanned aerial vehicle scheduling method and system based on multi-level collaborative decision, and belongs to the technical field of unmanned aerial vehicle intelligent scheduling. Comprising the steps of obtaining a flight task of an unmanned aerial vehicle cluster, decomposing the flight task into a plurality of sub-tasks, and determining a sub-task allocation strategy in combination with single unmanned aerial vehicle performance and sub-task requirements; according to the sub-task allocation strategy, in combination with the feature importance of the flight influence factors, an objective function is constructed, iterative optimization is performed through a particle swarm optimization algorithm, and an optimal flight task execution scheme of the single unmanned aerial vehicle is determined; and according to the optimal flight task execution scheme, multi-unmanned aerial vehicle cooperative path planning is carried out through a path planning algorithm, and an optimal task execution path is generated. The task execution efficiency of the whole unmanned aerial vehicle system can be improved, the coping capacity of the unmanned aerial vehicle system in a dynamic complex environment is enhanced, and the problems that an existing unmanned aerial vehicle is low in response speed, low in accuracy, poor in environmental adaptability and the like in task allocation and resource scheduling are solved.

The invention discloses a distributed heterogeneous computing power reasoning task dynamic scheduling method and system. The system comprises a computing power architecture adaptation module, a task scheduling module, a model updating module, a capability service module, a dynamic scheduling module, a resource evaluation module and a state sensing module. The computing power architecture adaptation module is used for identifying and matching a local heterogeneous computing power architecture and a remote heterogeneous computing power architecture; the task scheduling module is used for selecting a proper computing power terminal from a distributed heterogeneous computing power environment; the model updating module is used for updating the AI model in real time; the capability service module is used for providing a unified AI model service calling interface; the dynamic scheduling module is used for dynamically allocating computing power resources; the resource evaluation module is used for monitoring and evaluating the state of the computing power resource; the state sensing module is used for monitoring the health state of the computing power node in real time and responding to a fault in time; the scheduling efficiency of computing power resource scheduling is improved, and stable operation of the reasoning task is guaranteed.

The invention discloses a low-orbit communication satellite load multilink cooperative transmission system, and belongs to the technical field of satellite communication. In order to solve the problems of insufficient link reliability, low utilization rate of resources in constellations, weaker emergency response and fault-tolerant capability and higher operation cost, the invention is composed of a plurality of low-orbit communication satellites, realizes global seamless coverage through reasonably deployed constellation configuration, ensures that a user can obtain stable communication service in any area, and improves the user experience. A multi-link cooperative communication mechanism is adopted, through real-time link quality evaluation and dynamic link selection, rapid switching of link faults is achieved, communication reliability is improved, a dynamic path adjustment technology is combined, a forwarding path of data in a constellation is optimized according to an actual link state, data transmission time delay is effectively reduced, and data transmission efficiency is improved. According to the time-space change of the flow demand, communication link resources are scheduled in real time, the utilization efficiency of system resources is optimized, and the communication demand of a high-priority task is guaranteed.

The invention discloses an intelligent supply chain scheduling optimization method and system based on an autonomous controllable large model, and the method comprises the steps: employing a demand prediction agent to predict a future market demand condition according to product sales data; according to supplier supply product data, adopting a supplier management agent to select an optimal supplier as a target supplier; according to the predicted future market demand condition and inventory data, an inventory optimization agent is adopted to generate an optimal inventory replenishment plan; according to the inventory level after the optimal inventory replenishment plan and the logistics transportation data, adopting a logistics scheduling agent to calculate and obtain an optimal transportation path; feedback data of all the agents are collected, performance indexes of all the agents are calculated through the information feedback agents according to the feedback data, and agent parameters are adjusted according to the performance indexes of all the agents; and the strategy output of each agent is integrated into the scheduling strategy of the supply chain. The scheduling strategy can be dynamically scheduled and updated in real time.

The invention discloses a large model service platform for high-efficiency concurrent computing, particularly relates to the technical field of data analysis, and realizes optimal management of the large model service platform for high-efficiency concurrent computing through a task allocation module, a node health assessment module, a node division module, a dynamic adjustment module and a fault identification module. The task distribution module reasonably distributes computing resources according to task priorities and real-time monitoring data; the node health assessment module accurately assesses node health conditions through preprocessing and feature extraction; the node division module classifies the nodes according to the health assessment result; the dynamic adjustment module dynamically adjusts a resource allocation strategy by using fuzzy logic; the fault recognition module recognizes and repairs fault nodes in time by further analyzing the health conditions of the high-load nodes; the overall performance and the resource utilization rate of the system are improved, efficient and reliable execution of intelligent financial services is ensured, and the stability and the reliability of the system are enhanced.

The invention relates to the technical field of unmanned aerial vehicles, in particular to a flight mission planning and management system and method for cooperation of multiple unmanned aerial vehicles, and the method comprises the steps: decomposing a flight region of an unmanned aerial vehicle based on a flight mission of the unmanned aerial vehicle, and dividing the flight region; path planning is carried out in the flight area of the unmanned aerial vehicle, global flight path planning is carried out by adopting a genetic algorithm, and local flight path planning is carried out by adopting an A * algorithm after the global path planning; an obstacle avoidance strategy of the unmanned aerial vehicle in the flight process is constructed, and after the obstacle avoidance strategy is triggered, local flight path planning is carried out again; according to unmanned aerial vehicle flight task constraints, a priority judgment criterion of multiple unmanned aerial vehicle tasks is formulated, and unmanned aerial vehicle resources are allocated and dispatched. The intelligent level of unmanned aerial vehicle task planning can be remarkably improved, the adaptability to complex environments is enhanced, flight safety and efficient task execution are ensured, and a landing technical support is provided for actual application of the unmanned aerial vehicle.

The invention belongs to the technical field of Linux kernel schedulers, and particularly relates to a Linux kernel scheduler parameter optimization system and method.The Linux kernel scheduler parameter optimization system comprises a data collection module, a large model training and optimization module, a kernel scheduler module, a code generation and verification module, a small model training module and a small model reasoning module; the large model is responsible for periodically providing a visual angle of global optimization, generating an initial weight and a scheduler prototype of the small model, extracting a rule from long-term accumulated data, and ensuring that an optimization strategy has a global visual field. Meanwhile, the small model serves as a local adapter to process the dynamic scheduling requirement of a kernel mode in real time. The layered optimization mechanism can balance short-term adaptability and long-term performance improvement.

The invention relates to the technical field of communication networks, and discloses a service channel time slot allocation method, which comprises the following steps of: 1, acquiring network data in real time from network equipment, the network data comprising bandwidth utilization rate, delay, packet loss rate and service flow type; 2, predicting future network flow fluctuation through a long-short-term memory network model based on historical flow data and real-time network data; and step 3, dynamically adjusting the allocation of time slot resources according to the network flow fluctuation prediction result, and preferentially allocating the time slot resources for high-priority services. According to the method, the dynamic adjustment of the time slot resources is realized by combining the flow prediction of the LSTM model and the Q-learning reinforcement learning algorithm, so that the system can flexibly allocate the resources according to the actual network load condition, the resource waste is reduced, the utilization efficiency of the bandwidth is improved, and the utilization rate of the overall network resources is remarkably improved especially under the condition of high network load.

The invention relates to a multi-modal large model robot control method based on meta-learning fine tuning, and the method comprises the steps: carrying out the meta-learning pre-training of an OpenVLA model, and obtaining a pre-trained VLA model; performing task specific fine tuning to obtain a fine-tuned VLA model; and inputting the current visual data, the language cue word, the robot sensor data and the task target data graph into the fine-tuned VLA model, and outputting to obtain a decision control instruction for correspondingly controlling the working state of the robot. Compared with the prior art, the method has the advantages that meta-learning pre-training is carried out on diversified tasks, and universal features and strategies are extracted, so that the generalization ability of the model is improved; in addition, in a specific task, rapid fine tuning is performed through a small number of new samples, so that the model can adapt to new task requirements in a short time; and multi-modal information of vision, character cues and robot control is combined, so that the perception and decision-making capabilities of the model are improved, and the adaptability and operation efficiency of the robot in a complex environment are effectively improved.

The invention provides an innovative deep concealed ore deposit detection method, which comprehensively utilizes geology, geophysics, geochemistry and remote sensing technologies, and realizes effective integration of multi-source geological data by developing an advanced data fusion algorithm. According to the method, data collected by geological survey, wide-area electromagnetic method exploration, hydrocarbon-mercury superposition halo analysis and aviation hyperspectral remote sensing are subjected to standardized preprocessing, so that the consistency and accuracy of the data are ensured; and then, deep analysis is carried out on the fused data by utilizing machine learning and statistical analysis technologies so as to identify and predict mineralization modes and abnormal regions. Finally, a three-dimensional geologic model is constructed, the form and distribution of an ore deposit are visually displayed, and a scientific basis is provided for geological exploration. The method is characterized in that a multi-source data fusion technology and mineralization mode identification automation and precision are realized, so that the efficiency and the accuracy of deep hidden ore deposit detection are remarkably 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 terminal data automatic backup and recovery method and system, and relates to the technical field of data backup and recovery, and the method comprises the steps: dynamically adjusting the priority, and generating a personalized backup strategy; capturing data change in real time and executing incremental backup; and encrypting the backup data for version recovery. According to the terminal data automatic backup and recovery method provided by the invention, accurate analysis and priority division are performed on the terminal data through the classification model, so that the backup plan can be dynamically adjusted according to the importance of the data in the backup process. Data change is captured in real time, incremental backup is executed, and the real-time performance and efficiency of backup are effectively improved. An incremental backup algorithm is adopted, only a file change part is recorded, and a change area of a data block is quickly positioned by utilizing hash value comparison, so that the requirement of a storage space is reduced, and meanwhile, the transmission overhead of backup data is reduced. According to the method, better effects are achieved in the aspects of backup real-time performance, backup accuracy, backup pertinence and backup efficiency.

The invention relates to the technical field of production scheduling of manufacturing workshops, in particular to a distributed intelligent production scheduling decision-making system based on cloud computing. The system is composed of a cloud computing platform, a data acquisition module, a data processing module, an intelligent production scheduling engine and a distributed execution control module. The problems of performance bottleneck, data acquisition limitation, lack of flexibility and adaptability of a production scheduling scheme and the like in a traditional production scheduling system are solved, intelligent distribution and scheduling of production tasks can be realized through fusion application of advanced technologies such as cloud computing, big data and artificial intelligence, the production efficiency and the product quality are improved, and the production cost is reduced. And greater economic benefits and social benefits are created for enterprises.

The invention discloses a large-model-driven Web task automatic execution method and system, and relates to the field of artificial intelligence. According to the method, Web page content is perceived and analyzed through a large model, a state-action graph is constructed and maintained, a task sequence library is stored and managed, and semantic matching and automatic task execution are carried out. According to the method, the problems of high manual intervention requirement and low task processing efficiency in traditional Web task automation are solved, and the technical effects of remarkably improving the automation level, enhancing the intelligent decision-making capability, perfecting a knowledge memory mechanism and efficiently executing are achieved.

The invention discloses a big language model evaluation system and method, and belongs to the technical field of big language model evaluation. The system comprises a data preparation module, a basic performance evaluation module, an efficiency evaluation module, a robustness and multi-mode evaluation module, a distributed task scheduling module and a feedback optimization module. Through the system, the technical effect of multi-dimensionally, efficiently and automatically evaluating the large language model is achieved.

The invention provides an intelligent route planning system for fan unmanned aerial vehicle inspection. The system comprises a data acquisition module, a route planning module, a dynamic adjustment module and an analysis optimization module. The data acquisition module is used for acquiring fan operation state information and environment information in real time; the route planning module is used for generating an inspection route of the unmanned aerial vehicle according to preset task requirements and fan distribution; the dynamic adjustment module is used for dynamically optimizing the flight route of the unmanned aerial vehicle in the unmanned aerial vehicle inspection process; the analysis optimization module is used for optimizing and analyzing a follow-up task planning route of the unmanned aerial vehicle; according to the invention, through real-time data acquisition, dynamic path adjustment and intelligent optimization, high efficiency, self-adaptability and accuracy of the unmanned aerial vehicle routing inspection route are realized, and the routing inspection efficiency and task quality are significantly improved.

The invention provides an HIVE grouping operation performance optimization and scheduling method based on key column partitioning, which relates to the technical field of data processing, and comprises the following steps: preprocessing data by constructing a key column distribution histogram and determining a key column partitioning strategy; optimizing data partitions based on resource evaluation and a dynamic programming algorithm; and calculating an optimal data transmission path by adopting a minimum spanning tree algorithm, and dynamically adjusting an allocation scheme. According to the method, HIVE grouping operation performance can be improved, dynamic balance of calculation loads is achieved, the problem of data skew is effectively solved, and large-scale data processing efficiency is improved.

The invention discloses a hierarchical collaborative deep reinforcement learning decision-making method for multi-agent path planning, and belongs to the technical field of multi-agent path planning. The method comprises the following steps: constructing a PPO-based hierarchical collaborative decision-making model through a bottom-layer task allocation decision-making module and an upper-layer behavior decision-making module taking PPO as a framework; taking environment RGB image data, a task request and observation information as input of a layered collaborative decision model based on PPO; wherein the observation information comprises bottom layer observation information and upper layer observation information; and the optimal control strategy of the multi-agent system is obtained by continuously training the PPO-based hierarchical collaborative decision model. The method has the advantages that task resource allocation is efficient, the system state is stable, the execution time is shortened, and the robustness of the system is enhanced.

The invention discloses an unmanned aerial vehicle traffic jam and personnel gathering detection method based on AI identification, and the method comprises the following steps: S1, obtaining a real-time video and three-dimensional space data of a target region, and constructing a data set; s2, preliminarily processing the data set, and extracting key features; s3, the extracted key features are transmitted to a cloud server in real time through a 5G network, and an AI model is constructed and optimized; s4, performing multi-target detection and classification on the received key features to generate a multi-target detection result; s5, performing global scheduling optimization by adopting an intelligent scheduling algorithm of fuzzy logic control and particle swarm optimization; s6, identifying possible equipment faults in advance by using a graph neural network and an adaptive lifting algorithm; s7, monitoring the flight state and the recognition result of the unmanned aerial vehicle in real time; and S8, displaying the identification result and the analysis report through a visual interface. According to the invention, AI identification and intelligent scheduling are combined, unmanned aerial vehicle path planning and maintenance are optimized, and the method has the advantages of high efficiency, safety and high adaptability.

The invention relates to the technical field of computers, in particular to a mobile application large model risk automatic assessment method based on improved BERT, and the method comprises the following steps: S1, a test preparation stage; s2, realizing an automatic question answering whole process of the large model of the mobile application version based on an interactive automatic question asking method for simulating click by integrated group control equipment; s3, realizing batch acquisition of test result text data and picture information based on a dynamic and static data acquisition engine of the integrated group control equipment; s4, based on the risk study and judgment model of the improved BERT, performing safety risk judgment and classification on the test result, and performing evaluation and scoring according to a safety risk evaluation rule; and S5, uniformly and properly storing the assessment result and the test result screenshots. The method improves the accuracy and reliability of an output result discrimination algorithm, and establishes a comprehensive security risk assessment system.

The invention relates to the field of robot collaborative optimization, in particular to an industrial robot collaborative optimization system. Comprising the following steps: creating a task distribution matrix, optimizing the task distribution matrix by adopting a multi-objective optimization strategy, setting constraint conditions, and introducing a self-adaptive feedback adjustment mechanism to obtain an optimized task distribution matrix; and performing path optimization based on the optimized task allocation matrix. The problems that a fixed or simple scheduling algorithm is adopted for existing task allocation, and overload of part of industrial robots is likely to be caused; a dynamic feedback adjustment mechanism for resource consumption and state of the industrial robot is lacked, and optimization of task allocation cannot be performed according to real-time load and resource state of the industrial robot; the technical problems that path planning is prone to falling into a local optimal solution, and an industrial robot may frequently select a fixed path when executing a task, resulting in path resource competition and overall efficiency reduction are solved.

The invention discloses an underwater robot intelligent control method and system based on deep learning, and relates to the technical field of underwater obstacle avoidance of underwater robotics.The method comprises the steps that a deep learning calibration model is established through a deep learning algorithm, and the avoidance rate index BrsL in the underwater robot avoidance process is constructed; the ocean current speed and direction of the area where the underwater robot is located and the real-time vertical descending depth, the underwater temperature value and the submerging seawater pressure value of the propeller in the obstacle avoiding process are monitored and monitored, an ocean current influence index YL and a relative strength index Xd are calculated and evaluated, and the ocean current influence index YL and the relative strength index Xd are evaluated; if a multi-stage calibration control strategy of the first calibration instruction and the second calibration instruction is triggered, the power of the propeller and the posture of the underwater robot are adjusted, and timely adjustment is carried out according to the change of the vertical descending depth so as to adapt to the ocean current environment, and it is guaranteed that the underwater robot stably moves in the obstacle avoidance process.

The invention provides a behavior instruction generation method and device, equipment, a robot, a medium and a product, and belongs to the technical field of robots, and the method comprises the steps: processing the environment perception information of the robot to generate the environment description information of the environment where the robot is located; robot task planning is carried out based on the environment description information and the natural language command, and a first atomic instruction combination of the robot is obtained; wherein the first atomic instruction combination comprises a plurality of atomic instructions; matching the plurality of atomic instructions in the first atomic instruction combination with an atomic instruction library of the robot, and performing task logic verification on the plurality of atomic instructions to obtain a processing result; and under the condition that the processing result indicates that at least one atomic instruction belongs to the atomic instruction library and the at least one atomic instruction passes task logic verification, taking the at least one atomic instruction as a behavior instruction of the robot. According to the invention, the flexibility and reliability of understanding and executing the behavior instruction by the intelligent system can be improved.

The invention discloses a logistics remote control method and system based on virtual reality, relates to the technical field of logistics transportation management, and reduces the possibility of human errors through automatic encasement scheme generation and path planning. The system automatically calculates and generates a boxing scheme according to goods characteristics and loading constraints, and the risk of manual operation errors is avoided. And if the efficiency evaluation index is higher than 0.8, the system confirms that the current scheme is the optimal scheme. The trajectory planning module combines the anchoring position of the target ship and the container yard position, generates a plurality of paths by using a dynamic path planning method, and calculates a trajectory coefficient and transportation time. The path planning method based on real-time data can flexibly cope with sudden changes in the transportation process, the transportation time is shortened, and it is ensured that tasks are completed in time. The dynamic path planning module can adjust the path and the position of a hoisting point in real time, and collision or other potential safety hazards possibly occurring in the operation process are avoided.

The invention relates to the technical field of data processing, and discloses a method and system for generating a chart through question and answer dialogues and a storage medium. The method comprises the following steps: carrying out semantic understanding on questions of a user, and extracting keywords and context information; then generating a normalized DSL script based on the semantic understanding result and the DSL specification; analyzing the DSL script to obtain a chart configuration task sequence and a dependency relationship; and finally, carrying out parallel scheduling and resource allocation on the task sequence to generate a visual chart meeting user analysis requirements. According to the task features and the system resource conditions, the execution strategy is dynamically optimized, and the efficiency and quality of chart generation are improved.

The invention provides a big language model training method and device, electronic equipment and a storage medium, and relates to the technical field of artificial intelligence. The training method of the large language model comprises the following steps: acquiring an instruction response data pair; wherein the instruction response data pair is composed of an instruction and a response generated by utilizing a large language model according to the instruction, and the response comprises a text of a preset field; extracting constraint information contained in the response from the response contained in the instruction response data pair; generating a training data set according to the response, constraint information contained in the response and an instruction corresponding to the response; and using the training data set to continuously train the pre-trained large language model to obtain a target large language model, and using the target large language model to generate a response according to the instruction. According to the method, the large language model capable of accurately executing the instruction with the complex constraint can be efficiently trained, so that the utilization rate of computing resources is effectively improved.

The invention discloses a distributed container simulation method and device, equipment, a storage medium and a product, and relates to the technical field of data processing.The distributed container simulation method comprises the steps that available host machines are determined based on the computing resource occupation amount of all the host machines; the available host machines are host machines whose computing resource occupation amount does not reach an upper limit; determining a host machine load index of the available host machine; determining a load balancing index of the hardware platform based on the host machine load index; according to the computing resource consumption of the to-be-simulated container, determining a target host machine which enables the load balancing index to be the lowest in the available host machines; and simulating and running the to-be-simulated container through the target host machine. According to the method, dynamic allocation of host machine resources is realized in the unified deployment stage before simulation is started, and the container deployment condition can be dynamically adjusted according to different requirements and real-time container load conditions in the simulation stage, so that a platform can ensure efficient execution of simulation tasks while maximizing the resource utilization rate of the container host machine.

The invention relates to a resource configuration method and device, computer equipment and a computer storage medium, and the method comprises the steps: firstly generating multiple batches of raw information processing tasks based on pre-obtained biological information original data; performing configuration analysis processing on the first batch of the raw signal processing tasks to obtain target resource configuration information of the raw signal processing tasks; and according to the target resource configuration information, performing resource configuration processing on the remaining batches of the raw signal processing tasks to obtain resource configuration results corresponding to the raw signal processing tasks. According to the method, the target resource configuration information is determined, various types of data collected in the first batch of task processing process can be fully utilized, and resource configuration processing is carried out on the remaining batches of signal processing tasks according to the target resource configuration information, so that the data processing process can be carried out under a unified and optimized resource configuration framework; and it is ensured that all the raw signal processing tasks can be efficiently executed in a proper resource environment.

The invention relates to the technical field of water conservancy data monitoring and early warning, and discloses a water conservancy monitoring data dynamic early warning method and system, electronic equipment and a storage medium, and the method comprises the steps: collecting water level data and rainfall data of a reservoir or a river channel in real time, and calculating a sliding window difference value of the water level and the rainfall; setting a basic early warning threshold value of the water level according to the sliding window difference value; according to a flood season and a non-flood season, combining historical data and water level data and rainfall data which are acquired in real time, and dynamically adjusting an early warning threshold value by adopting a regression algorithm model based on a support vector machine; and performing fuzzification processing on the water level data and the rainfall data which are acquired in real time and the adjusted water level height early warning threshold, making a decision according to a fuzzy rule, and judging the type of released early warning. By introducing the decision algorithm based on the regression algorithm model and the fuzzy logic, the dynamic adjustment of the early warning threshold and the intelligence of the early warning decision are realized, the accuracy and adaptability of the early warning system are improved, and the false alarm and missing alarm conditions are reduced.

The invention provides a distributed task scheduling method, device and system, electronic equipment and a storage medium. The distributed task scheduling method comprises the following steps: acquiring the use condition of real-time resources of each edge node collected by a plurality of terminals and task requests of the plurality of terminals; evaluating the use condition of the real-time resource of each edge node to obtain a smooth data sequence of the state of each edge node; predicting a real-time resource use condition and a task request of each edge node to obtain a time sequence task demand of each edge node at a future time point; according to the smooth data sequence and time sequence task requirements of the state of each edge node and the obtained historical task execution efficiency, tasks are distributed to each edge node in a self-adaptive mode; when the task demand of each edge node changes or the edge nodes have resource shortage, the task allocation queues are dynamically adjusted based on the corresponding task priorities and the load conditions of the corresponding edge nodes, load balancing is achieved, and the resource utilization rate is increased.