72results about How to "Stable execution" patented technology

The invention relates to the technical field of unmanned aerial vehicles, in particular to a mooring unmanned aerial vehicle safety management system which comprises a multi-source data acquisition module, a data fusion and analysis module, an intelligent risk assessment module, a control instruction generation module and an execution mechanism module. The multi-source data acquisition module acquires flight and mooring cable data of the unmanned aerial vehicle by using various sensors, and transmits the flight and mooring cable data to the data fusion and analysis module through a time division multiple access protocol with low packet loss rate and low delay; the intelligent risk assessment module judges a risk level by combining a risk assessment model based on a support vector machine and a Bayesian network algorithm according to the information, and generates prompt information if the risk level exceeds a preset range; the control instruction generation module generates instructions according to a risk prompt and a preset strategy, ranks the instructions and sends the instructions to the execution mechanism module; a flight control system in the execution mechanism module cooperates with a mooring cable electric take-up and pay-off device and the like, real-time adjustment and control over the flight attitude of the unmanned aerial vehicle and the cable state are achieved, and it is ensured that tasks are safely and stably executed in the complex environment.

The invention provides a low-altitude industry chain information intelligent cooperation and airspace management platform based on federal learning. Through cluster ad hoc network and decentralized federated learning, the method is divided into three stages of data acquisition, data preprocessing and analysis application, low-altitude scene data communication of dynamic topology and static topology is realized, and the efficient communication requirement in a low-altitude operation scene is met. According to the system, differential data transmission strategies are adopted, optimization design is carried out on a single machine, a co-task cluster, a cooperative cluster and a non-cooperative cluster, and the communication efficiency and the resource utilization rate are improved. In the data preprocessing stage, federated learning aggregation of multi-source data fusion, data anonymization and decentralization is combined, and data privacy protection and safe sharing of three parties of industry, university and research are ensured. In the analysis and application stage, low-altitude aircraft task scheduling, airspace flow control and environmental condition evaluation are realized through aircraft analysis, airspace analysis, environmental analysis and digital twinborn technologies.

The invention relates to the field of juicer communication, in particular to a background communication method and a background communication system for a juicer system. The method comprises the following steps: obtaining a fruit and vegetable image reserved in a juicer; performing self-adaptive juicing feature analysis on the reserved fruit and vegetable image, performing intelligent juicing parameter adjustment, and constructing a self-adaptive juicing strategy; identifying surrounding environment parameters of the juicer; according to the surrounding environment parameters of the juicer, a self-adaptive juicing strategy is carried out, environment trend influence intelligent adjustment is carried out, juicing control simulation is carried out, and juicing control simulation data are obtained; multi-period juicing effect evolution is carried out on the juicing control simulation data, abnormal state prediction is carried out, and juicer abnormal early warning signals and juicer abnormal fault prediction information are generated; acquiring the network access state of the current juicer; through efficient and rapid background communication, the juicer use experience and performance of a user are improved.

The invention discloses a large-depth pressure-resistant communication antenna of an unmanned underwater vehicle and the underwater vehicle, and belongs to the technical field of underwater vehicles, the antenna comprises an antenna shell, a pressure-resistant cabin body and a fin-shaped component, the pressure-resistant cabin body is a component forming a cabin body segment of the underwater vehicle, and the fin-shaped component is a fin-shaped component. The antenna shell comprises a radial supporting part fixed to the pressure-resistant cabin and a shell part fixed to the top end of the radial supporting part, the fin-shaped component is fixed to the radial supporting part, an antenna unit is installed in the shell part, and a wireless communication function unit connected with the antenna unit is arranged in the pressure-resistant cabin. The large-depth pressure-resistant communication antenna of the unmanned underwater vehicle has the advantages of being excellent in communication quality, high in pressure resistance, optimized in size, high in space utilization rate, stable and reliable for a long time and the like, and can meet operation requirements in a deep sea environment.

The invention relates to the technical field of ship mooring scheduling, in particular to an intelligent scheduling method and system for ship mooring. The method comprises the following steps: acquiring ship basic information and wharf facility equipment data; establishing a ship berthing rule, and obtaining a ship berthing initial plan by using the ship basic information and the ship berthing rule; obtaining an auxiliary equipment distribution plan according to the ship mooring initial plan and the wharf facility equipment data, and obtaining a ship mooring initial scheduling plan in combination with the ship mooring initial plan and the auxiliary equipment distribution plan; and constructing a ship mooring plan evaluation model, evaluating the initial ship mooring scheduling plan by using the ship mooring plan evaluation model, adjusting the initial ship mooring scheduling plan according to an evaluation result, and obtaining an optimal ship mooring scheduling plan. According to the invention, the problem of intelligent scheduling of port ship berthing is solved, intelligent scheduling of berthing and unberthing of ships is realized, and the operation efficiency and safety of a port are improved.

The invention relates to a smeltery surrounding soil heavy metal monitoring distribution and health assessment method and system. The method comprises the following steps: acquiring heavy metal pollutant emission data, regional topographic data, meteorological data and population data; obtaining heavy metal settlement data according to the heavy metal pollutant emission data, the regional topographic data and the meteorological data; arranging soil monitoring points according to the heavy metal settlement data in combination with the regional topographic data and the meteorological data; and calculating the health risk according to the population data and the heavy metal settlement data. According to the invention, the problems of too many sample distribution and inaccurate distribution of heavy metal in the surrounding soil of the smeltery in the prior art are solved, the problem that the influence of the smeltery on the health of surrounding people is not fully considered is solved, the accuracy and efficiency of monitoring distribution are improved, and according to the monitored heavy metal settlement data, the real-time monitoring of heavy metal settlement is realized. And health assessment is carried out on surrounding residents, so that prevention of diseases and control of the environmental pollution degree are facilitated.

The invention relates to the technical field of cloud manufacturing, in particular to a cloud manufacturing quality control and defect prediction method and system based on deep learning. The method comprises the following steps: acquiring production field data in a manufacturing process through data acquisition equipment, and uploading the production field data to a cloud for next analysis; extracting information features of the production field data and performing feature fusion to obtain an information data feature set; constructing a joint deep learning prediction model, and training the joint deep learning prediction model by using the information data feature set; and completing cloud manufacturing quality control and defect prediction through the trained joint deep learning prediction model. The cloud manufacturing quality control and defect prediction are completed by extracting various characteristics of the production field data and training the joint deep learning prediction model, the quality problem and the defect prediction problem in the manufacturing process are solved, the production efficiency is effectively improved, the product quality is optimized, and the intelligent and automatic upgrading of the manufacturing process is promoted.

The invention relates to the technical field of slope disaster monitoring, in particular to a high and steep slope geological disaster monitoring method and system based on multi-source data fusion. The method comprises the following steps: tracking surface deformation through PS-InSAR time sequence analysis and atmospheric phase correction to obtain surface deformation characteristics; identifying the apparent diseases of the slope, and extracting spatial and temporal distribution characteristics of the apparent diseases of the slope; analyzing spatio-temporal evolution characteristics under geologic structure constraints by utilizing slope rock mass structure characteristics and the earth surface deformation characteristics; according to underground water level dynamic and potential sliding surface weakening features and the slope apparent disease spatial and temporal distribution features, extracting disaster-causing key factor features after cooperative correction; and combining the disaster-causing key factor characteristics, the true three-dimensional geological environment background characteristics and the spatio-temporal evolution characteristics to analyze the comprehensive early warning grade and the potential instability mode of the landslide. Scientific support is provided for high and steep slope geological disaster prevention and control, and engineering construction and operation safety can be guaranteed.

The invention relates to the technical field of image recognition, in particular to a multi-goods-type material information recognition processing method and system. The method comprises the following steps: acquiring a material image in a Lab mode through image acquisition equipment; constructing a first image generation model, and combining the material image and the first image generation model to obtain a first image of the material image; in the first image, extracting texture features of the material image; on the basis of various machine classification models, the credibility of the various machine classification models is obtained by utilizing texture features of the multi-goods-type material image test samples; and according to the credibility and the material image, obtaining the joint credibility of the machine classification model for identifying the multiple kinds of materials, and completing identification processing of the information of the multiple kinds of materials based on the joint credibility. According to the method and the system, the efficiency of classifying, sorting and grading various materials in the dry bulk cargo transfer and storage process is improved by accurately identifying the information of the various kinds of materials.

The invention provides an ROS-based flight control method in an unmanned aerial vehicle denial environment, and belongs to the technical field of unmanned aerial vehicle flight control. An unmanned aerial vehicle system for realizing the control method comprises an airborne computer main control module, a flight control module, a power module, a camera module, an optical flow module, a communication module and an upper computer module; the upper computer module is connected with the airborne computer main control module through the communication module, the flight control module, the camera module and the optical flow module are all connected with the airborne computer main control module, the flight control module is connected with the power module, and the camera module comprises a tracking camera module and a monocular camera module. Tasks such as autonomous takeoff, fixed-height hovering, fixed-point flying and autonomous landing can be achieved under the condition of GPS denial, accurate control is achieved, due to the ROS distributed design thinking, functions of the unmanned aerial vehicle become easy to achieve, and corresponding functions which do not need to be set can be started through one key only by packaging used modules and then writing the modules in a launch program.

The embodiment of the invention discloses a breakpoint continuous migration method and device for heterogeneous database distributed migration and a medium, belongs to the technical field of heterogeneous database migration, and solves the problems that after heterogeneous database migration is abnormally terminated, the whole migration process is usually restarted, resources are calculated, and the total migration time is prolonged. Comprising the steps that a system service node carries out persistence on migration execution information, and a migration service node carries out persistence on execution task information; in response to abnormal interruption of the system service node and / or the migration service node, after the system service node and / or the migration service node are / is recovered, the system service node determines a completed execution task and an uncompleted execution task based on the persistent migration execution information; starting a breakpoint continuous migration process, and redistributing the determined uncompleted execution task to a migration service node by the system service node; and the migration service node checks whether an uncompleted execution task is completed or not based on the execution task information, and returns an execution result to the system service node.

The invention relates to the technical field of photoacoustic imaging, in particular to a three-dimensional photoacoustic imaging method and system based on a two-dimensional outline of an imaging part. The method comprises the following steps: extracting a two-dimensional contour of an imaging part, and planning a two-dimensional moving path according to the two-dimensional contour; according to the two-dimensional moving path, the position coordinates of the movement of the mechanical arm are obtained; adjusting the position of the photoacoustic imaging probe based on the position coordinates, and adjusting the posture of the photoacoustic imaging probe according to the mechanical information of the photoacoustic imaging probe; and completing the three-dimensional photoacoustic imaging of the target area by using the photoacoustic imaging probe with the updated attitude. According to the invention, full-coverage conformal three-dimensional photoacoustic imaging of the outline of the imaging part is realized through the low-cost two-dimensional camera, and the problems that in the three-dimensional photoacoustic imaging technology based on photoacoustic tomography, the luminous flux distribution of imaging planes in different target areas is inconsistent due to the fixed orientation of a probe, an imaging blind area exists, and the imaging range is limited are solved.

The invention relates to the technical field of rock mechanics and intelligent detection, in particular to a rock strength and failure mode prediction method and system. The method comprises the following steps: acquiring data of a rock sample to be detected to obtain a multi-source feature set; constructing a feature coefficient prediction model, and inputting the multi-source feature set into the feature coefficient prediction model to obtain a feature coefficient; screening the multi-source feature set based on the feature coefficient to obtain an optimal feature subset; constructing a rock strength and failure mode prediction model, and optimizing hyper-parameters of the rock strength and failure mode prediction model by adopting an automatic hyper-parameter optimization framework; and inputting the optimal feature subset into the optimized rock strength and failure mode prediction model to obtain a rock strength prediction value and a failure mode. The problem that rock strength and failure mode prediction precision is not accurate enough and cannot be predicted at the same time in the prior art is solved, and the prediction precision is greatly improved through multi-source data collaborative prediction.

The invention relates to the technical field of material carrying, in particular to a grab bucket grabbing point calculation method and system based on terrain distribution. The method comprises the following steps that point cloud data of materials in a cabin are obtained through radar equipment, and the point cloud data are divided into a plurality of grabbing areas according to the structure of the cabin; carrying out grid processing on the point cloud data in the grabbing area to obtain grid point cloud data; obtaining a grid value of a grid standard point according to the grid point cloud data; and constructing a target grabbing point screening model, and screening a target grabbing point in the grid point cloud data by combining the target grabbing point screening model, the grid value and the grab bucket parameter. According to the method, the point cloud data of the material terrain distribution are obtained through the radar equipment, and then the point cloud data are subjected to data analysis to obtain the optimal grabbing point, so that the problem of improving the material grabbing efficiency is solved.

The invention provides a high-precision and stability implementation method for PLCOPEN motion control, and the method comprises the following steps: S1, separating a user control logic task and a motor control service task to different threads for execution in a process of loading a PLC application program; s2, providing a motor motion control service on a motor control task by taking the function block as a unit; and S3, the user control logic executes the motion control function block. According to the PLCOPEN motion control high precision and stability realization method provided by the invention, the user control logic task and the motor control service task are separated to different threads for execution, so that the influence of the user logic execution time change on the motor control instruction issuing time can be avoided; through the separation, the motor control service task can be executed more stably and accurately, the control precision deviation caused by the instruction time problem is reduced, it is ensured that the motor motion can be carried out strictly according to expectation, and the control precision of the whole system is effectively improved.

The invention relates to the technical field of natural language processing, in particular to a multi-label classification data enhancement method and system based on a large language model. The method comprises the following steps: obtaining an original text data set, and obtaining a double-weighted label relation network according to the original text data set; performing tail driving sampling in the double-weighted label relation network, and generating a creative label of the original text data set according to a sampling result; generating a creative label text by using a large language model and the creative label; and combining the creative label texts to obtain multi-label classification enhanced data. According to the method, tail driving sampling is carried out after the label relation network is established by utilizing existing labels, label combinations are designed and innovated in consideration of label matching and style consistency, the number of instances of rare labels is increased, the problem of long-tail distribution is effectively solved, meanwhile, consistency and correlation with original data can be kept, and the method is suitable for large-scale popularization and application. And the generalization ability of the large language model in practical application is improved.

The present application relates to the technical field of industrial unmanned aerial vehicle application and intelligent scheduling, and particularly discloses an unmanned aerial vehicle highway inspection task intelligent planning management system, which comprises a task input module, an intelligent planning engine, a task execution management module, a data hub and a feedback optimization module; the task input module receives and checks and analyzes inspection task requirements and highway basic information; the intelligent planning engine decomposes task priorities based on highway multi-dimensional attributes, dynamically allocates unmanned aerial vehicle resources and generates an optimal path meeting multiple constraints; the task execution management module issues adaptive instructions, monitors unmanned aerial vehicle states and processes inspection data; the data hub uniformly stores whole-process data; and the feedback optimization module optimizes a decision model based on historical data. The present application improves inspection planning accuracy and resource utilization, guarantees data quality, realizes self-evolution and ensures efficient and reliable highway inspection.

The invention discloses a carbon electrode process control method and system, and the method comprises the steps: carrying out the secondary index division of multi-source data indexes of a carbon raw material, and constructing a comparable data space of the indexes of the raw material; establishing a comprehensive index system based on the comparable data space, and constructing a two-stage linear weight coupling structure based on the comprehensive index system to form an index fusion weight vector; based on the index fusion weight vector, constructing a multi-path evaluation decision model, and performing weighted calculation on each index in the comparable data space according to the index fusion weight to generate a corresponding sorting result; based on the sorting result, establishing a standard reaching probability prediction model, and correcting the sorting result according to the standard reaching probability; based on the corrected sorting result, unification processing of the process window is achieved, and a process parameter map is constructed; and carrying out process execution optimization and process monitoring on the carbon electrode production process based on the process parameter atlas. According to the method, the stability and reusability of the carbon electrode process decision are improved.

The present application relates to the field of earthquake monitoring and early warning, in particular to a dynamic evaluation and early warning method and system for active fault earthquake risk, which constructs and optimizes an active fault earthquake detection system according to historical information of an active fault monitoring area; a three-dimensional equidistant grid model of the active fault is established, and information collection points in the active fault earthquake detection system are screened according to an error analysis model of the collection points to determine target monitoring points of the system; information of the active fault is monitored through the detection system and the target monitoring points to obtain active fault earthquake monitoring data; the relative displacement of the active fault is predicted based on the monitoring data and the three-dimensional equidistant grid model, and the evaluation and early warning of the active fault earthquake are realized in combination with the relative displacement and the active fault earthquake monitoring data. The present application can monitor the active fault in real time and analyze the movement of the active fault, realize the evaluation and early warning of the active fault earthquake, and accurately predict the possibility and influence range of the earthquake.

The present application relates to the technical field of automation control, in particular to a steel ring plate control method and system based on reinforcement learning and error compensation. The method comprises the following steps: acquiring a bidirectional positioning error of a mobile carrier associated with a target moving component; based on the bidirectional positioning error, accurately aligning a displacement sensor with a detection area of the target moving component, and generating a mechanism compensation result; acquiring original detection data collected by the displacement sensor after accurate alignment, preprocessing the original detection data, and fusing the mechanism compensation result with the preprocessed laser data to obtain a first fusion vector; based on the first fusion vector, performing optimization processing through a reinforcement learning model to output a first action vector representing an optimized motion state of the target moving component, wherein a reward function of the reinforcement learning model is constructed based on a matching degree between an actual motion state of the target moving component and the first action vector; and based on the first action vector, generating a follow-up control instruction for an actuator.

The present disclosure belongs to the technical field of video quality evaluation, and particularly relates to a video quality evaluation method, system, device and medium based on multi-modal parallel analysis, which comprises the following steps: receiving a course video to be audited, loading user preset audit parameter configuration; unpacking the course video to be audited to obtain an initial video coding stream and an initial audio coding stream, simultaneously performing audio track integrity checking, terminating the audio analysis process based on the checking result, only performing video analysis, or simultaneously performing video and audio analysis audit processes; pre-processing the initial video coding stream and the initial audio coding stream; based on an asynchronous parallel architecture, performing multiple audit tasks on the standard image frame sequence and the standard audio data in parallel to obtain multiple output results; converging the multiple output results, performing cross-modal space-time alignment and fusion deduplication processing to obtain a processing result; and generating a structured audit report according to the processing result. The present application improves the efficiency and accuracy of video quality evaluation.

The present invention discloses a clock synchronization method, device, mobile terminal and storage medium, the method comprising: obtaining a clock synchronization beacon; calculating a first clock synchronization error according to the clock synchronization beacon; wherein the first clock synchronization error is an error generated when performing the nth clock synchronization, and n is a positive integer; according to the first clock synchronization error and the synchronization period adaptive adjustment parameter, in combination with a first formula, calculating a first clock synchronization period; wherein the first clock synchronization period is a period when performing the n+1th clock synchronization; performing the n+1th clock synchronization according to the first clock synchronization period. The embodiment of the present invention can realize the adaptive adjustment of the clock synchronization period according to the clock synchronization error, thereby improving the accuracy of clock synchronization.

This invention discloses a method, equipment, and storage medium for active heave compensation to prevent swaying in a folding boom crane, relating to the field of marine engineering technology. The method includes: calculating the horizontal target position of the lifting point before initiating heave compensation operation; predicting heave, roll, and pitch prediction information within the target time frame after initiating heave compensation operation; constructing and calculating the target rotation angle of the main boom and the folding boom based on a system of two nonlinear equations; calculating the target displacement required for active compensation in the vertical direction by the hydraulic winch based on a vertical spatial position transformation model; using the target rotation angle of the main boom, the target rotation angle of the folding boom, and the target displacement as tracking targets, calculating the control signals for the hydraulic motor / pump, the main boom hydraulic cylinder, and the folding boom hydraulic cylinder; and driving the main boom, the folding boom, and the winch to operate based on the control signals. This invention can effectively counteract the interference of roll and pitch motion on the horizontal position of the lifting point, suppressing the swaying tendency of the load at its source.

This invention relates to the field of roadbed engineering construction scheduling technology, specifically to an intelligent scheduling method and system for roadbed filling and excavation operations based on multi-source data. The method includes the following steps: A multi-feature fusion network based on an attention mechanism and implicitly constrained by the scheduling decision space obtains global situational awareness features based on the semantic segmentation layer, road network state matrix, and dynamic safety risk index distribution map; a spatiotemporal graph convolutional network is used to model the dynamically changing vehicle-road network system, and combined with mechanical efficiency profiles and vehicle predicted trajectories, a transportation topology efficiency map representing the overall capacity and bottlenecks of the current transportation network is obtained; engineering rules are introduced as a reward function, and scheduling decisions are based on multi-agent collaborative reinforcement learning, combining engineering scheduling units, global situational awareness features, and the transportation topology efficiency map to generate optimal scheduling instructions. This invention achieves precise, dynamic, and collaborative scheduling of roadbed filling and excavation operations, effectively improving scheduling efficiency.

This invention discloses a trolley with an electric-assisted wheel, comprising a body; a handlebar assembly mounted on one side of the body; and an electric-assisted wheel assembly mounted on the bottom of the body. The electric-assisted wheel assembly includes a fixed base, a swing arm, an electric wheel, an electric actuator, and a controller. The fixed base is fixed to the body, the swing arm is rotatably connected to the fixed base via a first pivot, and the electric wheel is mounted on the movable end of the swing arm. A hook-shaped plate is fixed to the swing arm at an angle, and the output rod of the electric actuator is connected to the hook plate. The electric actuator is configured to drive the hook plate to rotate the swing arm around the first pivot, thereby switching the electric wheel between a ground-contact state and a ground-lift state. The controller is electrically connected to the electric actuator and the electric wheel, and is used to receive control signals from the handlebar assembly and, in response to these signals, control the lifting and rotation of the electric wheel. This invention allows for switching between ground-contact and ground-lift electric wheels, enabling the trolley to be both manually pushed and electrically assisted, saving effort and providing convenience.

This invention relates to the field of construction management technology, specifically to a method and system for dynamic coordination of construction resources. The method includes the following steps: analyzing construction progress characteristics based on process completion data, dynamically decomposing material demand sequences to obtain final demand characteristics; calculating the status indicators of the construction resources based on their inventory status; integrating the construction progress characteristics, the final demand characteristics, and the resource-material-process correlation characteristics to obtain a comprehensive resource-process matching matrix; constructing a comprehensive risk assessment matrix using construction risk coefficients; and combining the status indicators, the comprehensive resource-process matching matrix, and the comprehensive risk assessment matrix to complete the dynamic coordination of construction resources based on a multi-objective genetic algorithm. This invention solves the technical problems of inaccurate progress prediction, mismatch between material demand and processes, incomplete resource assessment, and delayed risk response in existing construction resource coordination methods, achieving accurate, dynamic, and efficient coordination of construction resources.

The present application provides a ladle scheduling method and device based on a heat plan, equipment and storage medium, the method comprises the following steps: obtaining the heat plan and the ladle information; formulating the corresponding ladle plan according to the heat plan; determining the planned start time and the planned end time of the ladle plan based on the time information corresponding to the multiple process stages of the heat plan; establishing a ladle matching model according to the ladle plan and the ladle information; taking the highest ladle utilization rate and the least number of matched new ladles as the optimization target; solving the ladle matching model to obtain the matching result of the ladle and the ladle plan; and matching the heat plan and the ladle based on the matching result and the corresponding relationship between the ladle plan and the heat plan, so as to schedule the ladle based on the heat plan. The method solves the technical problems of low efficiency and easy errors in manual ladle scheduling.

The application discloses a robot grabbing method and device, a computing device and a computer readable storage medium. The method comprises the following steps: obtaining joint state space information of a robot; inputting the joint state space information into a pre-trained reinforcement learning collaborative decision network; outputting chassis control instructions and mechanical arm grabbing instructions in parallel by the reinforcement learning collaborative decision network; controlling the movement of the mobile chassis according to the chassis control instructions, and controlling the mechanical arm to perform a grabbing action according to the mechanical arm grabbing instructions, so that the mobile chassis and the mechanical arm are controlled in parallel. In this way, the grabbing efficiency of the robot in a dynamic environment can be improved.