21results about How to "Guaranteed optimality" patented technology

The invention relates to the technical field of robot control instruction analysis, in particular to a robot control instruction analysis system based on multi-algorithm fusion, which comprises a data collection module, an environment map analysis module, a global path analysis module and a moving trajectory analysis module, wherein the data collection module collects state data of a robot, surrounding environment data of the robot and a target position; the environment map analysis module determines an environment occupation grid map according to the environment data; the global path analysis module determines a global path according to an environment occupation grid map, a target position, self state data and robot motion constraint parameters; the running track analysis module determines a smooth track according to the global path and the state data of the running track analysis module and detects an environment occupation grid map, if a dynamic obstacle avoidance object exists in the environment occupation grid map, the environment occupation grid map is updated, the smooth track is modified according to the environment occupation grid map updated in real time, and the dynamic obstacle avoidance object is obtained. And the final robot moving trajectory is obtained.

The invention provides an unmanned aerial vehicle cooperative attack rapid target allocation method based on case reasoning, relates to the technical field of self-explosion unmanned aerial vehicle cluster cooperative combat decision making, and designs a rapid target allocation algorithm model with a switchable mechanism. According to the algorithm, optimization algorithm model solution of a cooperative combat task can be realized, an algorithm mechanism is converted under different combat task requirements, the solution efficiency of the algorithm is improved, the optimality of understanding is guaranteed, and the algorithm has excellent adaptability to a target allocation task of large-scale high-intensity strike. The problem that the resolving time of a traditional distribution algorithm is long and the quality of the solution is continuously reduced along with the increase of the number of weapons of both combat parties can be solved to a certain extent.

The invention discloses a distributed task scheduling method and system suitable for a space-based distributed operating system, and the method comprises the steps: monitoring and collecting network communication data of communication links between a satellite-borne computing power unit and other computing power units in real time through a distributed communication tracking system integrated in a space-based system, synchronizing to a ground control system; a distributed task scheduler deployed in a ground control system analyzes network communication data, and an optimal satellite or satellite-borne computing power unit combination is selected for a distributed task for scheduling; and carrying out rescheduling on the operated distributed task under an abnormal condition through a rescheduler deployed in a satellite-borne computing power unit. According to the method, the computing power unit combination with the best communication quality can be intelligently selected for the distributed tasks in a complex space-based system environment, efficient circulation of information interaction between the distributed tasks is ensured, the accuracy and reliability of data exchange are improved, and powerful support is provided for continuity and stability of space-based computing tasks.

The invention discloses a wrong-way meeting decision-making planning method based on game planning. The method comprises the following steps: outputting a current frame state based on perception information; updating the current self-vehicle state based on the current frame state and the self-vehicle state at the previous moment, judging whether the self-vehicle needs to enter a misplaced vehicle meeting state or not, and updating interaction obstacle information stored in the self-vehicle; judging whether the self-vehicle needs to be replanned or not, and executing the following steps according to a judgment result: if the judgment result is yes, planning an optimal coarse track of the self-vehicle through an interactive game, and then selecting a differential planarization device to access the optimal coarse track of the self-vehicle for back-end refinement; and if the judgment result is no, a differential planarization device is directly selected to be connected to the optimal self-turning coarse track for back-end refinement. The method has the beneficial effects that the self-vehicle sweeping effect and the road passing efficiency can be improved under the narrow road limit condition, and the overall optimality of the driving tracks of the self-vehicle and other vehicles in the current scene can be fully guaranteed.

The invention discloses a robot path planning control method based on map perception and construction, and relates to the technical field of mobile robots, and the method comprises the steps: collecting the information of the surrounding environment of a robot through a sensor carried on the robot; the robot performs map perception by performing preset simple mechanical motion; map construction is carried out, and the robot pose and the environment map are optimized through a Gauss-Newton method; comparing the current image with an image in a previously constructed map in real time to judge whether the robot returns to a previously visited position or not; converting the final environment map into a three-dimensional grid map; the sensor data is matched with the three-dimensional grid map, and the robot is positioned in real time; and performing global path planning by using an A * algorithm. Map perception is carried out through preset simple mechanical movement of the robot, and a safe and collision-free path is autonomously planned in a strange or complex environment.

The invention provides a robot visual positioning and path planning method and system, and the method comprises the steps: extracting static obstacle features and dynamic target motion features from an environment image, and constructing an environment feature map containing time-space correlation information; generating a probability distribution prediction area of the dynamic target; rasterizing and layering a space area between the current pose and the target position of the robot, and endowing grids of different levels with passage cost values; generating an initial path from the current pose to the target position, and performing smooth optimization on the initial path; in the movement process of the robot, monitoring the change of an environment characteristic spectrum, and when it is detected that the actual movement track of the dynamic target deviates from a prediction area and exceeds a deviation threshold value, adjusting a local path section; and calculating a speed and steering control instruction of the robot according to the adjusted local path section, and sending the control instruction to a robot motion control module. According to the invention, the accuracy, safety and real-time performance of robot path planning can be ensured.

The invention relates to an optimal configuration method for reusability treatment equipment based on a hybrid branch and bound method, which comprises the following steps: firstly, establishing a problem model, and defining a clear objective function to evaluate the advantages and disadvantages of a configuration scheme; thirdly, generating an initial solution set with diversity by utilizing a genetic algorithm, and providing a basis for subsequent optimization; then, performing deep optimization on the initial solution set by adopting a branch and bound method, and gradually approaching an optimal configuration scheme through iterative search and boundary pruning; on this basis, a hybrid strategy is introduced innovatively: after each iteration of the branch and bound method, part of intermediate solutions are selected as a new initial population of the genetic algorithm, the configuration of the reusability treatment equipment is further optimized, and an optimization result is fed back to the branch and bound method to form a closed-loop optimization process. According to the method, the optimization efficiency is improved, the optimality of the configuration scheme is ensured, and a new technical support is provided for efficient and accurate configuration of the reusability treatment equipment.

This application discloses a model training method, apparatus, device, storage medium, and program product, belonging to the field of machine learning technology. The method generates pseudo-samples based on a first generative model in an adversarial generative network (PGN). The predicted labels of the pseudo-samples are determined by a first discriminative model in the PGN. Based on the predicted and actual labels of the pseudo-samples, the weights of multiple loss functions of the first generative model are iteratively trained until the weights of the multiple loss functions satisfy a first convergence condition. The iterative training of the weights of the multiple loss functions ends, and a first target model is determined based on the weights of the multiple loss functions at the end of the iterative training. This method can learn the most suitable weights for each loss function during iterative training, ensuring the optimality of the weights for each loss function, thereby improving the performance of the final target model.

The invention discloses an optimal path planning method of a pulse coupling neural network based on double constraints. The method comprises the following steps: mapping a path planning environment to a DC-PCNN network; a DC-PCNN neural network model is constructed, and all neurons are initialized; activating a target neuron, and recording the current neuron as a father node; calculating an exponential decay function of the torque deviation, and multiplying the calculated value as a penalty factor by an update item of the internal activity item; calculating a gravitational function value of the flow field constraint, and using the calculated value to update a current neuron dynamic threshold value; comparing the internal activity item with a dynamic threshold; when gt; if yes, activating the neuron, and recording the current neuron as a father node; repeating the steps S4-S6 until the initial neuron is activated; and backtracking all activated nodes, and planning an optimal path. According to the method, the search efficiency is remarkably improved while the path optimality is ensured.

The invention relates to an ARIMA model-based general insurance loss ratio prediction method. The method comprises the steps of collecting historical actual loss data and historical loss data; preprocessing the collected data to obtain corresponding actual compensation time sequence data and compensation time sequence data; based on the actual compensation time sequence data and the compensation time sequence data, model parameters are determined by drawing an autocorrelation function ACF graph and a partial autocorrelation function PACF graph, and the model parameters are used for building a basic ARIMA model; on the basis of the actual compensation time sequence data and the compensation time sequence data, characteristic factors are determined through rule statistics; putting the characteristic factors into a basic ARIMA model, and verifying the model; and inputting the historical data of the compensation amount and the characteristic factor of the prediction time period into the verified ARIMA model, and outputting to obtain the compensation ratio corresponding to the prediction time period. Compared with the prior art, the method can improve the accuracy and stability of the loss ratio prediction through constructing the regional exclusive loss ratio prediction model.

The invention discloses a communication transmission method, device and equipment of a two-dimensional Polar code. On an encoder, inner code Polar coding with the code rate of 1 is adopted to balance the error probability of each outer code, so that the error probability of a total code block is reduced, a Double Gaussian Application (DGA) algorithm is proposed to optimize the Design SNR used when the outer codes are constructed, and the accuracy of reliability estimation of sub-channels of the outer codes is improved. On a decoder, a Selective Successive Cancellation (SSCL) decoder combining inner code SCL decoding and outer code SC selection path re-decoding is adopted, a relatively reliable path obtained by inner code decoding is reserved and is used as a candidate path during re-decoding after decoding failure, and the decoding reliability is improved through re-decoding of multiple re-selection paths. Meanwhile, a plurality of inner code SCL decoders decode at the same time, and additional complexity is almost not introduced under the working signal-to-noise ratio by selecting and re-decoding, so that the overall decoding time delay is reduced.

This invention proposes an artificial intelligence-based vehicle control method and system, comprising: acquiring raw data of the vehicle's surrounding environment to construct a digital twin model of the vehicle's surrounding environment; identifying the current driving intention type based on environmental information, historical vehicle driving data, and driver operation behavior characteristics, and calculating a multi-dimensional risk situation index by combining the spatiotemporal correlation between obstacle movement trajectories and the vehicle's current state; performing simulation based on the multi-dimensional risk situation index to generate an adaptive control strategy sequence and convert it into control commands; monitoring the vehicle's actual response parameters after the control commands are executed, and calculating the deviation between the predicted state and the vehicle's actual state; when the deviation exceeds a preset threshold, fine-tuning the deep neural network and reinforcement learning model using the actual response parameters; and storing the optimized model parameters and synchronizing them to other vehicle nodes in the vehicle network. This invention achieves accurate environmental perception and intelligent, flexible generation of control strategies.

The present invention discloses a plug-in hybrid vehicle energy management method based on improved multi-objective DDPG, comprising the following steps: establishing an energy management system model of a plug-in hybrid vehicle, including a vehicle longitudinal dynamics model, an engine fuel consumption model, a battery equivalent circuit model, a battery life model and a drive / generator model; obtaining the state information of the plug-in hybrid vehicle in actual driving and inputting it into the energy management system model; using the energy management system model as an intelligent body of IMDDPG, configuring the reward function of IMDDPG according to the cumulative fuel consumption of the engine, the offset degree of the battery SOC and the change of the battery health status, using IMDDPG to perform multi-objective optimization on the energy management system model to obtain a trained reinforcement learning model; inputting the initial state information and driving conditions into the reinforcement learning model to obtain an energy management strategy during driving. The present invention comprehensively considers fuel economy and battery life, and improves the optimality and universality of the strategy while ensuring real-time performance.

The invention discloses a Hungary algorithm and genetic algorithm combined CR-NOMA resource allocation scheme, which relates to a wireless communication technology, and comprises the following steps: decomposing a constructed optimization problem P0 into a secondary user channel matching problem P1 and a secondary user power allocation problem P2; solving a secondary user channel matching problem P1 by using a Hungary algorithm, designing a secondary user pairing clustering strategy to obtain an optimal channel matching matrix, and solving a secondary user power distribution problem P2 by using an adaptive genetic algorithm to obtain an optimal power distribution scheme; and iteratively executing the steps until the adaptive genetic algorithm reaches the maximum number of iterations, and outputting the optimal channel matching scheme and the optimal power allocation scheme of the secondary users. According to the method, an optimization problem P0 is decomposed into a secondary user channel matching problem P1 and a secondary user power distribution problem P2, so that the problem solving complexity is reduced; and solving a secondary user power distribution problem P2 by using a self-adaptive genetic algorithm, and improving the global search capability by dynamically adjusting the crossover and mutation probability.

This invention belongs to the field of automatic control technology, specifically relating to an autonomous coverage detection and obstacle avoidance control device for a shield tunnel inspection robot. It includes: an inspection sensor assembly, an odometer assembly, a computing and processing assembly, a memory, a motion control assembly, a communication assembly, and a power supply assembly. The inspection sensor assembly includes a vision sensor and a radar sensor. The computing and processing assembly is communicatively connected to the inspection sensor assembly, the odometer assembly, the motion control assembly, the communication assembly, and the power supply assembly. The memory stores instructions that can be executed by the computing and processing assembly. This invention fully utilizes prior knowledge of the shield tunnel structure to improve positioning accuracy, employs a mail carrier coverage algorithm to ensure the integrity of the detection coverage and path optimization, introduces a control obstacle function to achieve real-time obstacle avoidance under safety constraints, and ensures the continuity and reliability of the inspection task in a dynamic environment through the tight coupling of the three components.

The present invention relates to the technical field of design parameter processing, and in particular to a structural parameter optimization design method based on hardware. The method comprises the following steps: obtaining hardware material structure information data and performing structural design parameter conversion processing and three-dimensional simulation modeling processing to generate a three-dimensional simulation space model of the hardware structure; performing structural performance simulation testing and performance optimization demand analysis on the three-dimensional simulation space model of the hardware structure to obtain performance optimization demand data of the hardware structure simulation model; determining the structural optimization target and constraint conditions of the three-dimensional simulation space model of the hardware structure to obtain the optimization target of the hardware structure model and the constraint conditions of the hardware structure model; performing structural parameter optimization design on the hardware material structure design parameter set to obtain an optimized combination of hardware material structure design parameters. The present invention can realize an efficient optimization design process for hardware structure parameters.

The invention provides a multi-target reordering method and device, electronic equipment and a storage medium, and can be applied to the technical field of artificial intelligence and digital marketing. The method comprises: in response to an access request for a target page, obtaining a pre-recommendation result about the target page, the pre-recommendation result comprising a plurality of material features of a plurality of to-be-displayed objects, the plurality of material features being respectively related to a plurality of indexes; determining respective current weight coefficients of the plurality of indexes at the current moment; processing the respective current weight coefficients of the plurality of indexes by using a super network to obtain dynamic network parameters; performing parameter adjustment on the initial model based on the dynamic network parameters to obtain a reordering model; and inputting the plurality of material features of the plurality of to-be-displayed objects into the reordering model to obtain a reordering result.

The invention discloses an alternating current servo system control method for controlling the stability of an obstacle breaking vehicle body, and the method comprises the steps: constructing an unscented Kalman filter (UKF) load observer based on the UKF, and estimating a load torque value and the rotor angular speed of a permanent magnet synchronous motor in real time through the UKF load observer; feeding back a rotor angular velocity output based on a UKF load observer to an extended state observer (ESO), introducing a load torque value as a feedforward compensation amount into an NLSEF control law, introducing a Newton-Raphson optimization algorithm (NRBO), and performing global optimization on parameters of an active disturbance rejection controller (ADRC) by taking an improved ITAE index as a fitness function; compared with the prior art, the method can improve the tracking precision and the anti-interference performance of the barrier-breaking vehicle under the complex dynamic working condition, and guarantees the stability of the barrier-breaking vehicle body in the advancing process.

This application discloses a method, system, device, equipment, and medium for controlling the facial expressions of a bionic robot, relating to the field of simulated robot control. The method includes: constructing a virtual model of the bionic robot, setting several virtual attachment points, acquiring a continuous facial expression sequence, extracting several target facial expression moments and corresponding target facial expression states from the continuous facial expression sequence; setting the target facial expression state at each target facial expression moment as the target to be imitated, using the displacement parameters of each servo motor as optimization variables, and constructing an objective function based on the geometric error between the actual position of the virtual attachment point in the virtual model and the target position under the target facial expression state; iteratively sampling and simulating in the parameter space that satisfies the servo motor constraints, calculating the objective function values ​​corresponding to the displacement parameters, until the optimal servo motor parameter set at the corresponding target facial expression moment is obtained, and combining it into a continuous control sequence; converting the continuous control sequence into facial expression control commands, and deploying them to the servo motors of the bionic robot for execution. This achieves optimization of the bionic head's facial expressions.

The invention provides a natural gas P2 heavy load vehicle-oriented EMS adaptive mixed domain dynamic planning method, and relates to the field of new energy. The invention provides a self-adaptive hybrid domain dynamic planning method. Through systematic layering, the state and action space is greatly reduced on the premise of maintaining the calculation precision. In order to solve the problem that a large-capacity battery is neglected in discretization calculation due to small single-step SOC change, a coarse and fine combined adaptive SOC grid is constructed to dynamically adjust a calculation grid. Three-dimensional fitting is carried out on multiple gears of a motor and an engine based on a physical model so as to establish an optimal gear shifting model, and then the current optimal gear is rapidly locked according to the accelerator opening degree and the vehicle speed. And the optimal motor-engine torque combination is quickly screened in a mode of combining gradient descent and global search. By adopting the AHD-DP method disclosed by the invention, excellent robustness, optimality and generalization ability are shown.

The application discloses a robot autonomous navigation method and device fusing a patrol strategy, comprising the following steps: matching and screening from a pre-constructed feature vector library through a preset patrol rule, and extracting key nodes; performing cluster analysis on the extracted key nodes, and removing redundant nodes with too close distances to obtain topological nodes; analyzing the spatial connectivity among the topological nodes, establishing edge association among the nodes, and generating a topological graph; calculating the shortest comprehensive cost path from a start matching node to an end matching node in the topological graph to obtain a global node sequence; performing interpolation processing on the global node sequence, and generating a continuous and smooth trajectory curve as a navigation path of the robot. The application realizes environment space dimension reduction through construction of a topological graph, and fuses a preset patrol rule to perform node extraction and path planning, so that the search efficiency is significantly improved, and the calculation amount is reduced while the optimal path is ensured.