16results about How to "Reasonable decision" patented technology

The invention provides a micro robot motion control method and system, and the method comprises the steps: carrying out the synchronization and calibration processing of environment state information, and generating a comprehensive perception image; analyzing the comprehensive perception image to obtain a classification result, and predicting the environment state change influencing the motion track of the micro robot in a period of time in the future based on the classification result; generating a candidate path by using a fast random tree, and optimizing the candidate path in combination with an AI search algorithm to obtain a target path; constructing a three-dimensional dynamic probability graph, continuously updating the three-dimensional dynamic probability graph by using a reinforcement learning algorithm, and generating an instant decision map; and estimating the position of the micro-robot by using a Kalman filter to obtain the position state of the micro-robot, adjusting the speed and steering angle of a driving motor of the micro-robot, and generating trajectory tracking of the micro-robot. According to the technical scheme provided by the invention, the flexibility and intelligence of the micro robot are improved.

The invention relates to the technical field of police service management, discloses a police service management system and method based on an image recognition technology, and aims to improve the police service management efficiency and accuracy. The system comprises a data receiving module, a data preprocessing module, a target detection module, a behavior analysis module and an information integration module. A monitoring video stream is received, data is preprocessed to form an image data set, and then a specific target is detected by using a deep convolutional neural network and a track sequence is output. And carrying out behavior analysis on the target track by adopting a long-short-term memory network, and identifying an abnormal mode. And finally, integrating the detection result and the behavior analysis data, and generating a police service management report. According to the invention, intelligent processing of monitoring videos is realized, police service departments are assisted to respond to abnormal conditions in time, and urban public security is maintained. Through the deep learning technology, the accuracy of target detection and behavior recognition is improved, and powerful support is provided for modern police service management.

The invention discloses a real-time optimal power flow method based on a generative pre-training model (GPT) and deep reinforcement learning, relates to the technical field of intelligent regulation and control of a power grid, and utilizes technical breakthrough in the field of large language models to directly model non-quantifiable natural language stipulations as targets and constraints in an OPF problem. The method comprises the steps of establishing comprehensive performance indexes, setting contexts and problems, converting GPT agent feedback into reward signals, carrying out iterative sampling, outputting GPT agent results in a JSON format and importing the GPT agent results into a DRL agent, carrying out offline training and online application and the like. Three deep neural networks are used in the DRL process, a cost function is optimized through a primitive-dual method, a series of constraint conditions including language specification constraints are set, and the method can be applied online to solve the OPF problem in real time.

The invention provides an intelligent game role dynamic extension and cooperative combat method for a virtual battlefield, and relates to the technical field of intelligent games, the method adopts an UpDeT module to dynamically increase or reduce the number or type of agents according to the real-time battlefield condition and strategy requirements of the virtual battlefield; state features and action features corresponding to different agent types of different scenes are converted to a unified feature space through a feature mapping function; based on a self-attention mechanism of Transform, carrying out fusion processing on the feature representation of each agent to obtain a fusion feature; generating a cooperative combat decision according to the fusion feature and the communication information; therefore, the problems of fixed roles, poor expansibility and difficulty in strategy migration in the prior art are solved, the generalization ability of the intelligent game strategy is improved, and the intelligent game level is improved.

The invention provides a power load prediction method based on an enhanced channel perception attention frequency mode estimation network. Firstly, power load data is acquired; performing data preprocessing based on the obtained data; based on the preprocessed data, establishing and solving a channel perception attention network, and performing effective modeling on a dependency relationship of variables to obtain feature data of the relationship between the variables after analysis; establishing and solving a frequency mode estimation network based on characteristic data obtained by analyzing the relationship between variables, and extracting a time frequency mode necessary for representing the rhythmicity of the power load data to obtain a prediction result; and based on the prediction result, performing model training and optimization by taking reduction of a loss function as a target to obtain a final power load prediction result. By capturing the complex time dynamics of the time sequence data and the dependency relationship between the variables, the power load prediction accuracy is improved, the knowledge system of power load prediction is enriched, and the method has guiding significance for the development of an energy management system.

The invention discloses a vehicle-mounted target tracker based on computer vision, and relates to the technical field of computer vision, and the vehicle-mounted target tracker comprises a sensor data collection module which is used for collecting real-time data from various sensors; the data fusion module is used for integrating the data from the plurality of sensors of the sensor data collection module and generating comprehensive information about the environment and the target; the data processing module is used for processing the complex data, extracting features and carrying out target identification and classification; according to the invention, by fusing the environment information and the target information, a more comprehensive and accurate context environment is provided for the system, the current scene can be understood more accurately, the authenticity of the target object can be judged more accurately, so that a more reasonable decision can be made, and the accuracy of the system is improved. And through combined use of an advanced target tracking algorithm, the precision, robustness and real-time performance of target tracking can be significantly improved.

The invention relates to the technical field of resource transmission configuration, and discloses a transmission configuration method and system for medical air transportation, and the method comprises the steps: obtaining user data and resource data through a scene modeling module, constructing a task scene model, and providing a basis for subsequent decision making; the post-sending decision-making module generates a post-sending decision-making strategy based on a result of the task scene model to obtain a post-sending scheme, and selects the post-sending scheme according to an actual situation, so that the decision-making accuracy and the resource utilization rate are improved; the simulation deduction module performs simulation deduction according to the task scene model and the selected post-delivery scheme, simulates the medical air delivery post-delivery process, and visually detects deduction data in the simulation deduction process; and after simulation deduction is finished, the decision evaluation module evaluates deduction data obtained by visual detection to optimize a post-delivery decision strategy, so that the delivery configuration system can quickly and effectively respond to a medical air delivery post-delivery scene of batch sick and wounded in wartime, and the emergency response capability of the delivery configuration system is improved.

This invention relates to the field of road construction safety monitoring technology, specifically to an intelligent identification and early warning method for road construction areas from a low-altitude perspective. This method aims to address the technical problem that existing technologies cannot accurately identify risks in construction areas when visual perception is degraded, such as in the presence of blurred images. The method includes: collecting multimodal data of the construction area using multiple sensors mounted on a drone; real-time evaluation of visible light image clarity, triggering a multimodal risk inference mode when the clarity falls below a preset threshold; extracting residual visual and non-visual features, inputting them into a risk inference model that integrates a construction safety knowledge graph and a probabilistic graphical network, and inferring scene completion results and their posterior probabilities; calculating the current comprehensive risk probability, generating early warning information and pushing it to the management terminal when the probability exceeds a threshold. This invention improves the environmental adaptability and robustness of road construction inspection and early warning systems, enabling all-weather, all-scenario construction safety management.

The invention provides an anti-intrusion alarm method and alarm system for a mine transportation track, and relates to the technical field of intrusion monitoring, and the method comprises the steps of first laying, first collection, intrusion judgment, alarm and the like. The system comprises a first layout module, a first acquisition module, an intrusion judgment module and an alarm module, the transportation track is monitored through different sensors, track intrusion events can be found and prevented in time, and then the safety of mine transportation is improved.

The application discloses a kind of brain-like reinforcement learning method and system based on layered experience playback, it is related to reinforcement learning and brain-like computing technical field.The method comprises the following steps: S1, collecting observation data and pre-processing;S2, initialize experience buffer pool and actor network, critic network and corresponding target network, parameter initialization;S3, initialize exploration noise and select action from actor network according to current state and execute, store the obtained experience sample to experience buffer pool;S4, obtain new sample from experience buffer pool, carry out short-term memory pool update;S5, use attention discrimination module to determine whether part of experience in short-term memory experience pool is transferred to long-term memory experience pool;S6, the parameters of actor network, critic network and corresponding target network are updated.The application uses the above method, improves the experience utilization rate of intelligent agent, improves the performance of reinforcement learning, and has wide application potential in many fields.

The application discloses a kind of based on artificial intelligence's consumer goods harmful substance intelligent screening method, it is related to artificial intelligence technical field, it is difficult to effectively identify and analyze the technical problem that a large number of natural language, commercial name or commonly known as etc. non-standardized description exists in ingredient list, it is also difficult to accurately convert these fuzzy text information into the standardized terminology that can be accurately matched by regulation database;Including the following steps: the fuzzy ingredient list containing natural language or commercial name description is preprocessed by multi-task text regularization model;Then, utilize embedding semantic similarity model and the multi-evidence fusion presumption unit of improved attention mechanism, fuzzy component is intelligently analyzed and matched, and standard substance identification and confidence are output;Finally, through multi-agent risk decision framework combines real-time regulation database, dynamic compliance screening and risk assessment are carried out;The application improves the intelligent level of consumer goods safety compliance management.

The invention relates to a mine water disaster grouting treatment effect evaluation method and system. The method comprises the steps that indexes for mine water disaster grouting treatment effect evaluation are obtained; determining weight values of the indexes for evaluating the grouting treatment effect of the mine water disasters by adopting a superior sequence diagram-CRITIC combined weighting method; and based on the indexes and the weight values of the indexes for evaluating the grouting treatment effect of the mine water disasters, utilizing a grouting effect evaluation model to obtain a result for evaluating the grouting treatment effect of the mine water disasters. According to the method, the problems of unicity of index weighting, one-sidedness of single index evaluation, weak universality and the like in the prior art are solved, theoretical guidance is provided for enhancing the stability of coal and rock masses and reducing coal loss, and reliable technical support is provided for safe and efficient production of coal mines.

The application provides a bus side knife switch operation risk assessment method and device, and relates to the technical field of electric power engineering. The method comprises the following steps: if the knife switch in the power system is a bus side knife switch, the wiring type of the power system is obtained; then, according to the wiring type, the operation type corresponding to the wiring type is determined; then, according to the wiring type and the operation type, the simulation fault equipment of the power system is determined; finally, according to the simulation fault equipment, the power grid accident risk level of the power system is determined. Through the method, the accuracy of the risk assessment of the bus side knife switch operation can be improved.

The disclosed vehicle autonomous car-following control method for uneven road surface extracts car-following events from unstructured road driving data, constructs a multi-modal state space containing vehicle kinematic state and road geometric and physical characteristics, and a two-dimensional action space of continuous acceleration and steering coordination; designs a road surface adaptive reward function; uses a double experience replay buffer and target network soft update for training, and deploys the obtained road surface perception strategy model to an unstructured road automatic driving vehicle to output continuous acceleration and steering control instructions. The invention embeds road geometric and physical properties into the reinforcement learning state space by introducing a road feature encoder and designs a road surface adaptive reward weight, which alleviates the model mismatch problem of traditional car-following methods on low adhesion and high undulating uneven road surfaces, and improves the car-following efficiency and comfort under the premise of zero collision.

The application relates to a time window graph model-based timing circuit critical node positioning method and equipment, which comprises the following steps: 1) analyzing a circuit netlist, initializing a criticality calculation matrix according to a circuit node type, wherein the criticality calculation matrix comprises a single fault calculation matrix and a multiple fault calculation matrix; 2) based on a time window graph model, aggregating circuit node states through circuit simulation simulation to obtain circuit node slices; 3) calculating the criticality of all nodes in the slices to a target node based on probability and the criticality calculation matrix; and 4) obtaining critical nodes of the timing circuit according to node criticality sorting. Compared with the prior art, the application has the advantages of small time and space overhead, and the result obtained based on the method has greater reliability improvement for reinforcement of the timing circuit.

The invention relates to the technical field of equipment monitoring, in particular to a laboratory equipment operation monitoring system which comprises a server side and a user side. The server comprises an arrangement module, an association module, a data acquisition module, a risk judgment module, a risk identification module and an association module; wherein the risk identification module is used for acquiring sensor data of other sensors and operation data of other devices in a preset range according to identified sensor data with risks and operation data corresponding to abnormal devices; inputting the data of the sensor with the risk, the operation data corresponding to the equipment with the abnormity, the data of the sensor of other sensors in a preset range and the operation data of other equipment into a preset risk identification model as input data, and outputting corresponding risk abnormity reason data; and the association module is used for associating the output risk abnormity reason data with the corresponding sensor or equipment and sending the data to the corresponding user side.