796results about How to "Improve execution efficiency" patented technology

The invention belongs to the technical field of data security, and discloses a data management method based on a data resource security identification level, and the method achieves the automatic and intelligent decision making: achieves the data classification and the automatic identification of the security level through a machine learning and deep learning model, remarkably reduces the manual intervention, and improves the efficiency and accuracy. And dynamic real-time performance: in combination with a risk assessment model, the system can detect abnormal behaviors in real time and dynamically adjust a security policy in a data flow process, so as to ensure that the sensitive data is properly protected in different scenes. And security and compliance: a log auditing module ensures the traceability of all data operations, improves the response capability to potential risks through an anomaly detection algorithm, and provides effective compliance guarantee for data security governance. And the system can be compatible with various data sources, supports configuration and execution of various security policies, and is suitable for data security management requirements of different industries (such as finance and medical treatment).

The invention relates to the technical field of traffic management, and discloses a road obstacle clearance rescue enterprise automatic scheduling method comprising the following steps: S1, through multi-source data fusion and a dynamic convolutional neural network, generating a three-dimensional road model, and predicting accidents and congestion; s2, dynamically adjusting the priority of the rescue task by using a multi-target genetic algorithm in combination with real-time data; s3, path planning and cooperative scheduling are optimized through deep reinforcement learning and game theory algorithms; s4, dynamically correcting the path by using a vehicle-mounted sensor, and optimizing the flow and the energy consumption; s5, in an emergency, an emergency module based on reinforcement learning starts a plan and adjusts resources; and S6, after the task, analyzing an optimization strategy through collaborative learning, and carrying out self-improvement. According to the method, multi-source dynamic data fusion is carried out, a dynamic convolutional neural network is used for processing data in real time, a high-precision three-dimensional road environment model is generated, accidents and congestion are predicted, and it is ensured that a rapid and accurate scheduling decision can be made in a complex and changeable road environment.

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 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 provides an intelligent statistical analysis method and system for the implementation progress of a production project, and the method comprises the steps: carrying out the initial classification of all task lists in the production project, and constructing a directed acyclic task graph according to the initially classified task lists to represent the dependency relationship between tasks; performing data acquisition on the execution state of the task according to the initial monitoring granularity of each task, and judging whether the actual progress of each task deviates from the planned progress or not according to the acquired data; behavior data of each project role is collected, then the behavior deviation of each role is calculated based on regularization, the cooperation efficiency among the roles is calculated according to the behavior deviation of each role, and then task allocation and progress management are dynamically adjusted. Re-optimizing the task priority and resource allocation according to a dynamic adjustment result; and feeding back to a project manager after the optimization task is executed, and providing intelligent decision support. The system has higher intelligence and real-time reaction capability.

The invention discloses a block chain multi-node secure communication method for an industrial internet, and the method comprises the steps: constructing a multi-chain cooperation mechanism, and obtaining an inter-chain interaction path table; determining a task partition optimization scheme and inter-chain interaction path update data based on the inter-chain interaction path table; matching an adaptive node resource for each task according to a task partition optimization scheme; determining an encryption mechanism between a cross-chain protocol and a block chain according to the security requirement of the task; constructing a multi-level block chain node hierarchical architecture and an intra-chain and inter-chain communication architecture, and formulating a parallel communication scheduling scheme; formulating a dynamic load balancing strategy based on the node resource use condition and the task execution state; and obtaining the task execution progress and the resource use condition of each block chain node, and adjusting the task scheduling and cross-chain communication path. Through the multi-dimensional collaborative design, the execution efficiency, the resource utilization rate and the communication security of the industrial internet are remarkably improved, and the stability and the reliability in a complex multi-chain collaborative scene are ensured.

The invention provides a path planning method and system for an automatic driving trolley, and relates to the technical field of path planning, and the method comprises the steps: obtaining the data of an acoustic sensor of an underwater environment, and obtaining the contour of the underwater terrain and the position information of an obstacle; carrying out dynamic environment modeling processing; the Euclidean distance is used as a heuristic function to calculate the dynamic model of the underwater environment, and a second optimized path conforming to kinematics and dynamics constraints of the automatic driving trolley is obtained; carrying out smoothing processing on the second optimization path through a dynamic motion primitive technology; constructing a three-dimensional map for path planning and obstacle avoidance; predicting an action track of the robot in a period of time in the future; and through real-time adjustment processing of the self-adaptive control system, path planning of the automatic driving trolley is completed. According to the method, the dynamic characteristics of the underwater environment and the motion constraint of the automatic driving trolley can be comprehensively considered, the three-dimensional map containing the obstacle and topographic information can be constructed in real time, and the future motion trail of the obstacle can be predicted.

The invention belongs to the technical field of computers, and particularly relates to a zero-code agent construction platform. The system comprises a module database, a semantic analysis and intention decomposition unit, a task flow tree generation unit and an agent construction unit, the semantic analysis and intention decomposition unit is used for performing image language analysis on a natural language instruction input by a user, decomposing the natural language instruction into a plurality of intention units with a sequence, and constructing an intention vector; the task flow tree generation unit is used for generating a plurality of corresponding task flow trees according to the intention vector; and the agent construction unit is used for analyzing all the task flow trees, finding out an optimal task flow tree, calling all target function modules in the task flow tree and completing the construction of the agent. According to the method, the precision of semantic analysis, the flexibility of task logic generation and the automation degree of agent construction are remarkably improved.

The invention provides a rapid construction and reuse method and system for an application framework, and relates to the technical field of application framework construction, and the method comprises the following steps: mapping code snippets to a high-dimensional tensor space, extracting multi-level code features, and constructing a code association graph and a deep semantic network; constructing a hierarchical Bayesian network based on the spatial-temporal feature map, executing variational reasoning and adaptive particle filtering, and generating a reconstruction code; driving a multi-level generation network by using a dual-dimension discriminator, optimizing a code structure and a dependency relationship, and generating a final optimization code through program slicing and incremental synthesis; according to the method, rapid construction and reuse of the application framework are realized, and the code quality and the execution efficiency are improved.

The embodiment of the invention provides a binary translation method, a binary translator, electronic equipment and a readable storage medium, and the method comprises the steps: calling a preset instruction if a current instruction is recognized as an indirect jump instruction; the instruction code of the preset instruction comprises a first operand and a second operand; the first operand points to the first register, and the second operand points to the second register; the target PC value is stored in the first register; the second register is used for storing a calculation result of the preset instruction; the preset instruction executes preset calculation logic to calculate a target index value corresponding to the target PC value in the hash table, and the target index value is written into a second register; the target index value is read from the second register, whether the target PC value hits the hash table or not is inquired through the target index value, and the next instruction is executed based on the inquiry result. According to the embodiment of the invention, the instruction translation number of the high-frequency instruction can be reduced, and the execution efficiency of the binary translator is improved.

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 discloses a multi-sensor fusion unmanned aerial vehicle environment monitoring and early warning system, and relates to the technical field of environment monitoring and early warning, and the system comprises the following components: an unmanned aerial vehicle platform which is used as a carrying basis of sensors and data processing equipment and has excellent flight stability and strong cruising ability; according to the invention, by introducing a task planning mechanism based on autonomous learning, the unmanned aerial vehicle can continuously learn and optimize the flight strategy when executing the monitoring task, the innovation point significantly improves the efficiency of task execution, and the unmanned aerial vehicle can perform real-time monitoring according to the actual environment feedback and historical task data. The unmanned aerial vehicle can automatically adjust the parameters of the flight path, speed and height to avoid adverse factors and select the optimal path, and meanwhile, the unmanned aerial vehicle is endowed with strong scene adaptability by the autonomous learning ability, so that the unmanned aerial vehicle can quickly respond to a complicated and changeable natural environment or a sudden emergency, and smooth completion of a task is ensured.

The invention discloses a software testing automation platform and an intelligent optimization method thereof, and particularly relates to the technical field of software testing. The test case management module simplifies definition and automatic classification of test cases through a visual interface and a natural language processing technology; secondly, a test script generation module automatically generates and optimizes a test script by utilizing a preset template library and a self-defined script function in combination with an intelligent optimization tool; the test execution module executes test tasks in parallel in a multi-server or virtual machine environment through a task scheduling algorithm, and extracts environment load and response time characteristics in real time; then, the test result analysis module comprehensively analyzes an execution result by using the data prediction model, and identifies and marks an abnormal test case; and finally, the intelligent optimization module deletes the abnormal cases and dynamically adjusts the priorities of the normal cases to ensure that the high-priority test cases are executed preferentially, so that the automation level of test management is effectively improved, and the test execution efficiency and the result accuracy are optimized.

The invention discloses a self-adaptive control method for lead clothes disinfection, and relates to the technical field of intelligent control. The method comprises the following steps: constructing a lead clothes management library, obtaining a lead clothes demand condition, obtaining lead clothes use parameters and material parameters, obtaining a self-adaptive control instruction through a self-adaptive disinfection control model, monitoring the lead clothes disinfection process in real time, adaptively adjusting disinfection parameters, and optimizing the self-adaptive disinfection control model by using a disinfection historical record. According to the method, the self-adaptive disinfection control model is used for matching the first batch of recommended disinfection modes according to the lead garment material parameters, the demand index and the bacterial pollution index are calculated, the optimal second batch of disinfection modes are dynamically selected from the recommended disinfection modes, and the disinfection parameters are adjusted by using real-time monitoring data.

The invention discloses a three-dimensional model generation method and device based on a porous structure and electronic equipment, and the method specifically comprises the steps: constructing a cell element model corresponding to cell element model data according to the obtained cell element model data and cell element size data; and then, according to the constructed cell element model and the obtained porous array data, constructing a target three-dimensional model with a porous structure corresponding to the cell element model. By means of the method, the porous three-dimensional model with the complex topological structure can be efficiently constructed through the implicit modeling technology, and resource waste and redundant time consumption caused by manual design and construction of the complex topological structure are avoided to a great extent; and the execution efficiency of subsequent tasks executed based on the porous three-dimensional model is improved to a certain extent.

The invention discloses a garbage truck real-time monitoring and remote scheduling method based on the Internet of Things, and the method comprises the following steps: S1, collecting the real-time data of the operation state of a garbage truck, and carrying out the preprocessing of the real-time data; s2, constructing a task prediction model based on an improved adaptive graph neural network, and generating a preliminary task distribution matrix and a path planning scheme; s3, optimizing the preliminary task allocation matrix and the path planning scheme by adopting an improved PPO algorithm; s4, the optimized task allocation matrix and the path planning scheme are sent to a garbage truck vehicle-mounted terminal, and the task completion rate and the path deviation value are calculated; s5, the operation state of the garbage truck is monitored in real time, and the monitoring result and the operation state data are transmitted to the cloud Internet of Things platform; and S6, dynamically updating the scheduling instruction. According to the method, the Internet of Things, the adaptive graph neural network and the improved PPO algorithm are fused, intelligent scheduling and real-time optimization of the garbage truck are realized, and the method has the advantages of high efficiency, stability and low-cost operation.

The invention discloses an unmanned ship multi-path optimization method for large-scale dynamic search and rescue tasks, and belongs to the technical field of unmanned ship path planning. According to the method, a multi-track optimization framework combined with a deep reinforcement learning algorithm is introduced, so that the efficiency and accuracy of path planning are effectively improved. Through a multi-trajectory sampling technology, the problem of trajectory data sampling deviation in a traditional method is solved, and the robustness and adaptability of an algorithm in a dynamic rescue environment are enhanced. Meanwhile, an encoder-decoder structure is designed, path exploration of the unmanned ship is optimized by utilizing reinforcement learning, and the exploration capability of the model to a complex solution space is greatly improved. According to the method, the optimal searching path can be quickly found, and the unmanned ship is prevented from falling into a local optimal solution. According to the invention, the unmanned ship can intelligently adapt to a large-scale emergency rescue scene, the search time is greatly shortened, the task success rate is improved, and key technical support is provided for the application of the unmanned ship in emergency rescue.

The invention provides an automatic test system and method capable of being arranged and executed in batches, and the system comprises a test case arrangement plan table which is used for customizing the starting time, the precondition and the dependency relationship of each test item, whether execution is terminated or not when the test item fails, grouping execution, result archiving, product dependency and user-defined labels; the default configuration management module is used for parameterizing execution and supporting simple configuration to realize visual operation of the test script; the software framework supports an object-oriented language and provides multi-process processing to improve execution efficiency; and the distributed structure is used for realizing concurrent parallel execution across a plurality of processors or machines and supporting synchronous import and concurrent asynchronous execution of a plurality of test items. Through the support of multi-process processing and a distributed structure, the advantages of a multi-core processor and computing resources of cross-network nodes can be fully utilized, multiple test items are executed in parallel, the execution efficiency of automatic testing is greatly improved, and the overall testing period is shortened.

The invention relates to the technical field of computing power optimization, and provides a computer and cloud computing power optimization method based on multi-task collaboration. The method comprises the following steps: receiving Q to-be-executed tasks of a local cloud collaborative network; performing priority constraint on the Q to-be-executed tasks according to the multivariate priority evaluation factors, and establishing a task chain; collecting callable computing resource information of a local cloud collaborative network, and establishing a resource space; based on the resource space, performing task chain resource allocation by utilizing a computing resource scheduling optimization algorithm to obtain a scheduling strategy chain; performing cloud computing power balance optimization according to the scheduling strategy chain to obtain an optimized chain; and executing the task chain according to the optimization chain based on the cloud collaborative network. The technical problems of unbalanced scheduling and low resource utilization rate between the cloud and the local computing resources are solved, and the technical effects of improving the cooperative work efficiency and the resource utilization rate of the cloud and the local computing resources through priority constraint and computing resource scheduling optimization are achieved.

The invention discloses a task scheduling method and device, a medium and equipment. And obtaining task description information of the calculation task. And inputting the task description information into a pre-trained first prediction model to obtain first execution time of the calculation task output by the first prediction model. And determining a resource scheduling strategy of the calculation task according to the task description information and the first execution time. And executing the calculation task according to the resource scheduling strategy and the task description information. Determining resource utilization information in the execution process of the calculation task, and determining second execution time of the calculation task according to the resource utilization information. According to the second execution time, a resource scheduling strategy of the calculation task is adjusted, and the calculation task continues to be executed according to the adjusted resource scheduling strategy. By dynamically adjusting the resource scheduling strategy of the calculation task, the utilization efficiency of calculation resources and the execution efficiency of the calculation task are improved.

The invention relates to a task processing method and device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: acquiring data processing tasks from an edge processing node, and acquiring task demand information corresponding to each data processing task; acquiring node state information of each processing node in the computing power network; according to the node state information and task demand information carried by the data processing task, determining a target processing node by taking resource utilization rate maximization of each processing node in the computing power network as a target; determining a target transmission path and a target transmission protocol by taking maximization of transmission bandwidth and minimization of transmission delay as targets according to data size information and time delay requirements carried by the data processing task, and a network state and a transmission protocol of a target processing node; and transmitting the data processing task to the target processing node through the target transmission path and the target transmission protocol. By adopting the method, the task processing efficiency can be improved.

The invention discloses a function calling method, a related method, a related device, equipment and a storage medium, and the function calling method comprises the following steps: obtaining a historical calling record, and obtaining a preset function matched with a current demand description as a current function; wherein the current demand description is a user demand description currently processed by the agent, and the historical call record comprises a historical function finally called by the agent processing historical demand description; obtaining function similarity between the current function and each historical function; and obtaining a finally called target function for processing the current demand description based on a historical function selected by the function similarity. According to the scheme, the function call for the historical demand can be referred to when the new demand is processed, and the learning ability and the execution efficiency of the intelligent agent can be improved.

The invention relates to the technical field of intelligent management, in particular to a computer intelligent service management system and method based on network big data, and the system comprises a service state monitoring module, an operation state modeling module, a service optimization module, an execution scheme adjustment module and a service task distribution module. According to the invention, through data dynamic analysis of the resource consumption proportion, the task processing duration and the completion time, reliable support is provided for task allocation, the resource occupation relationship between tasks is disclosed, the rationality and efficiency improvement of resource allocation are ensured, the task allocation deviation and resource allocation are optimized, and the resource use is dynamically balanced; the service execution efficiency and accuracy are improved, resource waste and task delay are reduced by analyzing task delay and resource utilization changes, identifying execution bottlenecks and adjusting structures, the execution sequence is dynamically optimized in combination with task completion time and a resource allocation proportion, and the system operation efficiency and response capability are improved. And meanwhile, resource utilization, task scheduling and service quality are optimized.

The invention provides an intelligent bee colony type quality optimization management method and system for multi-source tag data, and relates to the technical field of data tags, and the method comprises the steps: extracting a comprehensive feature vector of a tag through a feature extraction module, carrying out the processing through a deep feature enhancement network, generating a similarity and dependency feature matrix, and constructing a tag quality evaluation model; mapping the label data into bee colony network nodes, performing three-level division, and constructing a node optimization association network; and performing optimization based on the collaborative optimization rule set until the quality improvement value is smaller than a preset threshold value. The label data quality can be effectively improved, the data processing cost is reduced, and the optimization efficiency is improved.

The invention discloses a transfer learning-based cross-data center storage and calculation cooperative training acceleration method. The method comprises the following steps of S1, deploying an initial version of a deep learning model in a plurality of heterogeneous data centers; s2, performing initial training on the deep learning model in the first data center to generate a first model parameter set; s3, generating a local deep learning model parameter set; s4, establishing a dynamic weight adjustment mechanism of the heterogeneous data center; s5, distributing different computing resources to each heterogeneous data center according to the participation weight; s6, scheduling storage resources and computing resources in real time according to resource use conditions and task requirements of the heterogeneous data centers; s7, performing multi-task parallel processing; s8, monitoring the resource utilization rate of each heterogeneous data center through a dynamic resource scheduling strategy; and S9, generating a final global optimization model parameter set, and applying the global optimization model parameter set to subsequent task deployment of each heterogeneous data center. According to the method, the collaborative training efficiency of the cross-heterogeneous data center is optimized.

The invention discloses a novel script development method based on a JVM (Java Virtual Machine), which comprises the following steps of: S1, setting a script permission list and a script forbidding list in a system configuration file, setting an initial variable and configuration information of a script, and monitoring the change of the configuration file by using an observer mode; s2, collecting external input data by utilizing a response type programming library, performing conversion processing by taking the external input data as an event, and adding the external input data into a script running context; s3, analyzing script contents into marks and nodes, and constructing an abstract syntax tree; s4, checking whether the variables of the abstract syntax tree accord with the permission and prohibition lists in the configuration file or not; s5, traversing the abstract syntax tree, and executing node logic of the abstract syntax tree; and S6, collecting execution results of all nodes, and returning the results to a caller. According to the method, configuration changes are monitored through an observer mode, syntax analysis and security check are carried out in combination with an abstract syntax tree, and the functions of script online development and real-time adjustment are achieved.

The invention discloses a tower crane installation path planning method and system, a terminal and a medium, and the method comprises the following steps: collecting and preprocessing three-dimensional point cloud data and visual information in a tower crane installation area, fusing the point cloud data with image data, and generating a fused three-dimensional point cloud image which comprises distance and color information; a deep learning model is adopted to carry out obstacle detection on the fused three-dimensional point cloud image; an RRT algorithm is adopted, and a safety path is planned according to the obstacle detection result and the requirement of the hoisting task; and controlling the movement of the hoisting equipment according to the obtained safe path, and executing ascending, descending, horizontal moving and rotating actions of a lifting hook according to a path planning result. The method has the advantages that the safety and the efficiency in the hoisting operation of the power transmission tower are improved, and the quality and the reliability of power transmission line construction are improved.

The invention discloses an intelligent obstacle avoidance and path planning method and system for a medical auxiliary robot, and the method comprises the steps: obtaining multi-modal data from the medical auxiliary robot, and constructing a three-dimensional environment perception model according to the multi-modal data; identifying according to the three-dimensional environment perception model to obtain a character type and an equipment condition, and performing intelligent obstacle avoidance according to the character type, the equipment condition and a preset obstacle avoidance priority rule; performing crowd behavior prediction according to the multi-modal data and a preset social force model, and performing intelligent obstacle avoidance according to a crowd behavior prediction result; and calculating an optimal path according to the three-dimensional environment perception model, and performing path planning according to the optimal path. The method can improve the execution efficiency and flexibility of the medical auxiliary robot in a complex hospital environment, and can be widely applied to the technical field of path planning.

The invention aims to provide an efficient memory allocation and recovery method for an Android device, and relates to the technical field of memory processing, the method comprises the following steps: step 1: obtaining running data of the Android device, and calculating a memory state entropy based on the running data; calculating a memory fragment risk degree in combination with the priority matrix and the operation data; 2, calculating a memory allocation dynamic threshold according to the memory fragmentation risk degree; calculating a self-adaptive memory compression rate; step 3, using a self-adaptive memory compression rate to compress memory data which is not used by the Android device at present; the page number of the activity process is lower than the set minimum page threshold value; 4, if the memory fragmentation risk degree exceeds a set fragmentation threshold value, the Android device executes memory fragmentation; and averagely distributing the residual memory capacity to all the active processes. According to the invention, accurate allocation and efficient recovery of memory resources are realized.

The invention discloses a quantum state amplitude coding method and device, equipment and a medium. The method comprises the following steps: carrying out normalization processing on a real vector to be coded; setting a Hamiltonian, wherein the Hamiltonian is a negative value of an outer product of the normalized real vector to be coded and the Hamiltonian; constructing a variable component sub-line according to the normalized real vector to be coded; substituting a preset parameter value as a training parameter into the variable component sub-line so as to output a training quantum state evolved by the variable component sub-line; measuring an expected value of the training quantum state, which is evolved by the variable component sub-line, about the Hamiltonian; training the training parameters of the variable component sub-lines through a classical optimizer; in response to convergence of the training parameter or the expected value to a set threshold value, determining the training quantum state evolved by the variable component sub-line as a target quantum state; wherein the normalized real vector to be coded is mapped to the amplitude of the target quantum state. According to the invention, the execution efficiency of amplitude coding can be further improved.