66results about How to "Improve execution performance" patented technology

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 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 relates to the technical field of environment monitoring, in particular to a sewage monitoring method and system in a rainstorm scene based on deep learning. The method comprises the following steps: acquiring sewage quality data and meteorological data based on a water quality monitoring node, and generating an environment monitoring data sequence; performing cleaning and feature extraction on the environment monitoring data sequence to generate a water quality spatial-temporal feature matrix; inputting the water quality spatial-temporal characteristic matrix into a deep learning model, predicting a sewage quality change trend and an abnormal event, and generating a prediction result sequence; based on the prediction result sequence and historical environment monitoring data, constructing a multi-level decision tree model, and generating a multi-level regulation and control scheme; and executing the regulation and control scheme and feeding back an execution effect, and optimizing the deep learning model and the multi-stage regulation and control scheme through a federated learning updating strategy. According to the invention, sewage quality change and abnormal events can be accurately monitored and predicted under extreme weather conditions such as rainstorm, and the real-time performance and accuracy of sewage treatment are improved.

The invention relates to the technical field of control, in particular to a double-arm robot operation skill learning method and system based on big language model reasoning. The method comprises the following steps: firstly, carrying out context semantic modeling on an input natural language task instruction based on a large language model to generate a task semantic graph; in combination with a semantic entity in the task semantic graph and visual perception data collected by a robot, determining a three-dimensional space position of a target object through a multi-modal matching model, and constructing an environment semantic graph containing object nodes and spatial relation edges; generating an action sequence by using a language model according to the task semantic map and the environment semantic map, and generating a collaborative operation strategy based on the two-arm tail end state and an obstacle map; and finally, collecting feedback data of the sensor in real time when the action sequence is executed. According to the method provided by the invention, the understanding and execution capability of the two-arm robot on the unstructured natural language instruction is remarkably improved.

The invention relates to the technical field of chip compiling and mapping, in particular to a coarse-grained reconfigurable chip mapping method and device based on pre-scheduling. The method comprises the following steps: compiling an intermediate representation through the front end of a compiler according to an input program; constructing a data flow diagram structure according to the intermediate representation; carrying out pre-scheduling according to the data flow diagram and the reconfigurable chip structure information to obtain an operation time period; searching the mapping result according to a pre-allocated time period, and determining a final mapping scheme; configuration information for execution on the coarseness reconfigurable chip is generated. According to the method, the complexity of a mapping algorithm can be effectively reduced, the execution performance of the task on the coarseness reconfigurable chip is improved, heterogeneous resources on the coarseness reconfigurable chip are fully considered in the mapping process, the method is suitable for multi-node and complex-structure data flow diagram mapping in complex tasks, and the mapping efficiency is remarkably improved.

The invention discloses a computer and large model interaction system based on an artificial dictation command, and the system comprises a control unit which is used for controlling and managing the whole information collection and conversion process; the acquisition module is responsible for capturing voice signals, recognizing the voice signals and converting the voice signals into readable text data for a computer; the analysis module is used for analyzing the text data and converting the text data into a readable instruction format for a large model; the interface module is used for receiving and processing an instruction format and realizing a corresponding function; and the feedback module is used for adjusting and optimizing the accuracy of the voice recognition system and the instruction analysis module according to the feedback result of the user. According to the method, through a deep learning algorithm based on a large model technology, commands dictated by a user are accurately recognized, texts are analyzed and key information is extracted through a large model, and the texts are converted into executable instruction formats, so that seamless joint of man-machine interaction is realized.

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 embodiment of the invention provides an unmanned aerial vehicle inspection method and device for power transformation equipment. The method comprises the following steps: acquiring a virtual substation model of a target substation, wherein the virtual substation model is generated by performing three-dimensional model construction on the target substation and comprises a plurality of candidate virtual substation equipment areas and a plurality of candidate virtual visual positioning identifiers; determining at least two target virtual visual positioning identifiers from a plurality of candidate virtual visual positioning identifiers included in the virtual substation model in response to an inspection triggering operation on a target virtual power transformation equipment region in a plurality of candidate virtual power transformation equipment regions included in the virtual substation model; and generating an unmanned aerial vehicle inspection route based on the at least two target virtual visual positioning identifiers. According to the scheme, the inspection route can be automatically generated on the intelligent inspection platform according to the arrangement of the virtual power transformation equipment in the transformer substation model, and the unmanned aerial vehicle in the transformer substation is pointed to fly to inspect the power transformation equipment through the inspection route.

The invention discloses a JavaScript language security reinforcement method based on a virtual machine technology, and belongs to the field of JavaScript languages. The method comprises the following steps: firstly, building a test system comprising a compiler, an assembler, a virtual machine and a packer; then, a developer submits the JavaScript program or the function requirement to be tested to the test system; a test system constructs a to-be-tested program by using a Yarn environment, and a compiler compiles a JavaScript source code and analyzes and converts the JavaScript source code into an intermediate representation IR; the assembler converts the IR into byte codes, and converts the relative reference address into an absolute address by generating a character string table and a lookup table; and the generated byte code is optimized. The test system submits the optimized byte code to the virtual machine, the virtual machine runs a byte code program, and after execution is completed, the test system collects an execution result of the virtual machine and compares the execution result with an expected result; and finally generating a test report and delivering the test report to developers for confirmation. According to the method, the security of the JavaScript code is enhanced through a virtualization technology, and malicious access and analysis are prevented.

The invention provides an unmanned aerial vehicle intelligent adaptive scheduling method and system based on a large model, and the method comprises the steps: carrying out the type selection of a large model according to the specific task demands and computing resources of an unmanned aerial vehicle system, and carrying out the fine adjustment of a specific task field of the selected large model; integrating a plurality of professional models for executing different task requirements in the unmanned aerial vehicle system; for different task scenes, large model analysis judges whether a specific professional model needs to be activated or not, and the professional model is selected for task processing when needed; the large model confirms a professional model needing to be called, and automatically extracts and transmits required parameters to the professional model based on the collected environment information and task instructions; and the professional model receives the parameter information transmitted by the large model, executes the corresponding subtask, and feeds back an execution result to the large model, so that the large model performs continuous learning optimization. According to the invention, the unmanned aerial vehicle can complete the task more quickly, and the accuracy and reliability of task completion are improved.

The invention provides a software project multi-dimensional data analysis and resource scheduling automation method and system, and relates to the technical field of resource scheduling, and the method comprises the steps: collecting multi-dimensional data information, constructing a multi-dimensional data analysis model, and generating a project state evaluation index; predicting a resource demand by using a deep neural network; constructing a resource scheduling optimization model based on the prediction data, and optimizing a resource allocation scheme by using a genetic algorithm; and outputting and automatically executing the optimal resource scheduling scheme. According to the invention, intelligent management of the software project is realized, the resource utilization rate is improved, and the project delay risk is reduced.

The invention provides a campus emotion risk individual assessment method and system based on expression recognition, and the method comprises the steps: collecting monitoring image data for recognition, and obtaining recognized individual expression data and related information data; performing processing according to the individual expression data to obtain group emotion data; correcting the individual expression data according to the group emotion data to obtain individual negative emotion data; and evaluating an emotion risk target individual according to historical statistics of the individual negative emotion data. According to the method, group emotional abnormity can be accurately eliminated in a group scene, the individual emotional risk degree is objectively judged, and the accuracy of identifying the campus emotional risk target is improved.

The invention discloses a personalized voice interaction method and device, MR equipment and a storage medium, and relates to the field of man-machine interaction, and the method comprises the steps: responding to a received current voice instruction of a user, and recognizing an operation intention in the current voice instruction; querying a behavior habit tag matched with the operation intention from a tag library; resources corresponding to the behavior habit labels are called, and the operation state of the MR equipment is controlled to be switched to the equipment habit state corresponding to the behavior habit labels; the equipment habit state is generated based on a plurality of historical voice instructions of the user, historical behavior data of the user and the running state of the MR equipment within a preset time before and after each historical voice instruction is issued, and the historical behavior data at least comprises operation data of the user on the MR equipment. The technical problem that in the prior art, it is difficult to carry out voice interaction with a user in a targeted mode is solved.

The invention discloses an AI project management optimization system based on artificial intelligence, and relates to the technical field of project management, and the system comprises a project optimization module, a quality evaluation module, a character analysis module, a project characteristic analysis module and a personality matching module, and also comprises a completion degree division module and a reward adjustment module. The system has the advantages that the scheme can be modified through the project characteristic analysis module in combination with project directions skilled by different character traits, reasonable distribution of project tasks is realized according to the character traits of employees, the working efficiency is improved, the character traits lacked in the traits distribution scheme can be better analyzed by utilizing the project characteristic analysis module, and the project distribution efficiency is improved. According to the scheme, the most suitable employees are distributed for the project, the personality advantages of the employees are fully exerted, the personality matching module analyzes the execution time of different sub-project schemes and marks the matched employees, the employees are prevented from being repeatedly marked, and reasonable utilization of human resources is ensured.

The application relates to a session instruction distribution method and device, equipment, a storage medium and a program product. The method comprises the following steps: receiving a current instruction in a current session, the current instruction being an instruction for calling a target object, and the current instruction carrying a current session identifier corresponding to the current session; for each instance identifier in an instance identifier set, determining a target weight corresponding to the instance identifier based on the current session identifier and the instance identifier, the instance identifier representing an instance for running the target object; selecting a target instance identifier from the instance identifier set based on the target weight, and distributing the current instruction to a target instance represented by the target instance identifier for execution. The method can improve the execution effect.

The invention provides a task execution method and device and related equipment, and relates to the technical field of artificial intelligence and wireless communication, the task execution method comprises the steps that a target request is acquired, and the target request is used for requesting to execute a target task; splitting the target task to obtain a plurality of sub-tasks; obtaining target information matched with the subtask in a preset database, and packaging the subtask and the target information into a target information block; generating execution path information according to the target information block, wherein the execution path information is used for representing an operation step sequence for executing the subtask; and executing the operation step sequence based on the execution path information. Therefore, the execution accuracy of the target task can be improved, namely, the execution effect of the target task is enhanced.

The application discloses a method and system for constructing a load sharing chain of a generator group unit, and relates to the field of data processing specially used for administrative, commercial, financial, management, supervision or prediction purposes. The method comprises the following steps: calculating a comprehensive response capability value of each power plant according to operation parameters and transaction parameters, arranging the comprehensive response capability values in descending order according to the numerical values to form a response sequence, calculating the regulation capability matching degree between adjacent power plants, the difference between the regulation capability matching degrees of each adjacent response section being less than a preset matching degree threshold, calculating the output regulation range and regulation rate of each generator unit, and determining the load distribution coefficient of each response section according to the output regulation range and regulation rate; determining the regulation sequence of the units in all response sections based on the load distribution coefficient, and calculating the maximum regulation amount that can be borne by each unit, and constructing the load sharing chain according to the regulation sequence and the maximum regulation amount. By implementing the method, the efficiency of task regulation of the units of the power plant can be improved.

The invention discloses a task scheduling method and device, equipment, a computer storage medium and a program product, and relates to the technical field of big data. The method comprises the following steps: in response to a task execution request which is sent by a demand side and is used for requesting to execute a target task, calculating a first target computing power and a second target computing power; the first target computing power is the computing power consumed by the central node for executing the target task, and the second target computing power is the computing power provided by the central node for the target task; when the first target computing power and the second target computing power meet a preset computing power condition, calculating effective computing power resources of the multiple pieces of edge node equipment based on position information of the multiple pieces of edge node equipment under the center node; and based on the effective computing power resources, scheduling the edge node equipment in the multiple pieces of edge node equipment to participate in execution of the target task. According to the embodiment of the invention, the anti-pressure capability of the whole system of the central node is effectively improved, so that the execution performance and efficiency of the whole task can be improved.

The application relates to the technical field of large models, and provides a prompt vector generation method, device, equipment and medium, which extracts and embeds the multi-modal information of a target user, generates a personalized query vector carrying the personalized features of the target user, and fuses relevant information of the personalized query vector and a pre-constructed industry knowledge prompt library to generate a target prompt vector of the target user. The target prompt vector is determined in combination with the personalized features of the target user and global user common features, and the personalized and customized processing of the prompt vector is realized. The application can be applied to a financial scene, and when a downstream task such as insurance recommendation generates a personalized execution result for a user according to the target prompt vector, the personalized features of the target user and the global user common features can be simultaneously referred to, the target user's personalization is considered, and the rules or trends gradually presented in the insurance business scene are not deviated from, so that the execution effect of the downstream task is improved.

A rescue response situation analysis and display method, system, medium and product, which relate to the field of situation display, and include: obtaining real-time perception data and historical rescue data of the rescue area; determining obstacle information of the rescue area based on drone image data and satellite image data; generating dynamic environmental situation of the rescue area according to obstacle type, obstacle location and sensor data; determining multiple rescue path plans based on obstacle distribution changes and weather environment changes; predicting resource allocation plans for each rescue path according to the rescue path, historical rescue path data and historical resource allocation data; associating resource allocation plans with rescue path plans for visual display. Implementing this method can improve rescue efficiency.

The present invention discloses a water quality detection method and system based on artificial intelligence, and the method includes data collection, data preprocessing, water quality pre-classification, water quality trend analysis, pollution source tracing prediction and water quality detection. The present invention relates to the field of water quality detection technology, and specifically refers to a water quality detection method and system based on artificial intelligence. The present scheme adopts an artificial intelligence water quality detection method of water quality pre-classification, trend analysis and pollution source tracing; a deep neural network method combining genetic algorithm and random forest model is used for water quality pre-classification; a deep neural network method combining genetic algorithm and random forest model is used for water quality pre-classification and water quality trend analysis; a water quality pre-classification task is used to first pre-classify the data, exclude unpolluted water sources, and then combine the causal-inspired graph neural network, and through the causal-inspired operator and the improved graph neural network, the calculation speed and calculation effect of pollution source tracing are improved.

The application relates to the technical field of unmanned planes, and discloses an unmanned plane adaptive task planning method and system fusing real-time environment perception, which has the technical scheme as follows: inputting unmanned plane cluster physical parameters and task target parameters, completing system hardware calibration, software environment and key module parameter initialization setting; based on a full-closed loop technical architecture of "perception-cognition-modeling-decision-cooperation-feedback", the following sub-steps are executed until the task termination condition is met; through the full-closed loop technical architecture, deep understanding of environment essential laws, dynamic self-evolution of planning schemes and autonomous emergence of cluster global intelligence are realized, and core technical support is provided for efficient, safe and intelligent task execution of unmanned planes in a complex dynamic environment.

This application belongs to the field of radar networking technology, specifically disclosing a method, apparatus, equipment, and medium for multi-task collaborative planning of airborne radar networking. The method includes: calling a pre-constructed multi-task collaborative planning model for airborne radar networking; the model is established with the goal of maximizing the collaborative execution efficiency of airborne radar tasks, constrained by the joint interception probability of the networked radars, the signal-to-noise ratio of the task echo, and the system detection performance; the joint interception probability of the networked radars is determined based on the interception probability when the window functions of the radiated signals of each radar simultaneously overlap in the power domain, time domain, spatial domain, and frequency domain; and solving the multi-task collaborative planning model using the detection range parameters of the current airborne radar network to determine the current task allocation results for each radar. This application can effectively improve the execution efficiency of multi-task collaborative planning of radar networking while realizing multi-task collaborative planning for multiple radars.

The present invention provides a computing device and an operation method thereof, wherein the device comprises a data module and a computing module, wherein the data module operates on data, including reading or writing data from a memory and inputting computing data to the computing module, and the computing module is used to perform relevant operations on the data according to instructions. The present invention improves the execution performance of tasks involving a large number of vector calculations, and has the advantages of concise instruction structure, flexible and efficient data operations, etc.

The invention relates to the technical field of unmanned collaboration and resource virtualization, and provides a task collaboration-oriented unmanned system virtual resource representation and organization method and device and a storage medium. The method comprises the following steps: firstly, determining physical resource virtualization granularity (platform level, equipment level and function unit level) according to unmanned collaboration styles (task level, resource level and element level); secondly, establishing a resource tree model comprising a resource base class, an attribute resource, a data table resource and a resource sub-tree based on granularity; resource storage is carried out by utilizing an organization structure constructed by a hash table, a list and a set; and finally, forming a resource pool through standardized packaging, providing management interfaces (resource registration, cancellation and state maintenance) and scheduling interfaces (demand response, resource allocation and release), and realizing resource addressing by adopting URI coding. According to the method, the problems of low task execution efficiency, insufficient resource utilization rate and complex management caused by poor coordination task and resource adaptability, non-uniform heterogeneous resource description and non-standard packaging are solved.

The embodiments of the present application provide a method, device, electronic device and computer-readable medium for executing SQL instructions, which relate to the field of databases. The method includes: converting the original binary tree structure of the logical connection operator corresponding to the received first SQL instruction to obtain the dense connection operator corresponding to the first SQL instruction and the multi-tree structure of the dense connection operator; grouping the data tables connected by the dense connection operator to obtain the grouping results of the data tables; based on the grouping results, converting the multi-tree structure of the dense connection operator corresponding to the first SQL instruction to obtain the standard binary tree structure of the logical connection operator corresponding to the first SQL instruction, wherein the standard binary tree structure is used to determine the logical connection operator that can be pushed down to the node execution of the distributed database. Through the embodiments of the present application, the execution efficiency of the first SQL instruction can be significantly improved.

The invention belongs to the technical field of unmanned aerial vehicle flight control, and discloses an unmanned aerial vehicle rapid composite control method and system based on fuzzy asymptotic. In the aspect of steady-state precision, the method breaks through the inherent limitation that traditional finite time control can only achieve bounded neighborhood convergence, and the reliability and execution capacity of the system in a high-precision task are remarkably improved. In the aspect of transient response, a nonlinear feedback mechanism in a fractional power form is introduced, so that the system can obtain a super-linear convergence rate in an initial stage or when encountering large-deviation disturbance. In terms of robustness and realizability, a structure combining low-pass filtering and dynamic error compensation is adopted in the method, and implementation difficulty caused by high-order differential calculation is effectively avoided; meanwhile, parameter perturbation, unmodeled dynamics, external wind disturbance and the like are estimated and compensated in combination with an adaptive fuzzy system, and the adaptive capacity and anti-interference performance of the system in a strong-coupling and nonlinear environment are remarkably enhanced.

The decision flow execution method involved in the present application includes: when detecting that there are elements in the decision flow execution stack, obtaining the type information of the current node at the top of the decision flow execution stack; judging the type of the current node according to the type information; in response to the current node being a branch node, sequentially pushing the starting nodes corresponding to each branch into the decision flow execution stack; in response to the current node being an access node, obtaining the access condition information corresponding to the access node, judging whether the target execution object meets the access condition information, and if so, pushing the child node of the access node into the decision flow execution stack; and in response to the current node being a decision node, obtaining the execution rule of the decision node, executing the corresponding decision according to the execution rule, and after the decision node is popped from the decision flow execution stack, pushing the child node corresponding to the decision node into the decision flow execution stack. The use of this method can improve the efficiency of decision flow execution.

The application discloses a personalized recommendation method for serving cultural and creative products, and relates to the technical field of personalized recommendation, which comprises the following steps: based on a cultural and creative exhibition area map connection structure and a tourist visit transfer sequence, a node visit continuation probability is counted and a continuation probability peak interval is determined, an access continuation span is calculated, and a first recommendation trigger buffer time limit is obtained; the time limit is subjected to scale reconstruction and interval disturbance to generate a multi-granularity second buffer time limit; an engineering mapping is established based on a time interval and a control action corresponding relationship, the second buffer time limit is converted into a recommendation candidate reservation span and a detection period as a control target; a buffer time limit score is output; the score is scanned along the buffer time limit distribution and an optimal interval is identified, and the optimal second buffer time limit is screened from the optimal interval and applied to cultural and creative product recommendation trigger and candidate management, so that the problem that a low recommendation effect is caused by an unreasonable location sensing cultural and creative product recommendation trigger time is solved.

The invention discloses a multi-unmanned aerial vehicle adaptive target surrounding control method, and relates to the technical field of multi-unmanned aerial vehicle control, and the method comprises the steps: obtaining a kinetic model and a communication topology of an unmanned aerial vehicle; according to the dynamic model, based on a formation transformation operator, determining a formation pattern of the unmanned aerial vehicle; keeping the formation pattern of the unmanned aerial vehicles unchanged, adjusting the motion direction of the virtual unmanned aerial vehicles according to the motion state of the target until the distance between the target and the virtual unmanned aerial vehicles is smaller than a preset distance threshold, and obtaining a surrounding path; constructing a dual-channel event trigger function based on a dynamic event trigger mechanism according to the kinetic model; according to the triggering time, the communication topology and the surrounding path, constructing an unmanned aerial vehicle formation distributed dynamic event triggering target surrounding control law; according to the unmanned aerial vehicle formation distributed dynamic event, triggering a target surrounding control law to surround the target; according to the invention, the trigger threshold can be adjusted in the dynamic change process, and the optimal balance between the control performance and the communication efficiency is realized.