117 results about "Learning rule" patented technology

The provided are a federated reinforcement learning (FRL) end-to-end autonomous driving control system and method, as well as vehicular equipment, based on complex network cognition. An FRL algorithm framework is provided, designated as FLDPPO, for dense urban traffic. This framework combines rule-based complex network cognition with end-to-end FRL through the design of a loss function. FLDPPO employs a dynamic driving guidance system to assist agents in learning rules, thereby enabling them to navigate complex urban driving environments and dense traffic scenarios. Moreover, the provided framework utilizes a multi-agent FRL architecture, whereby models are trained through parameter aggregation to safeguard vehicle-side privacy, accelerate network convergence, reduce communication consumption, and achieve a balance between sampling efficiency and high robustness of the model.

The invention discloses a building engineering automatic compliance examination method based on AI and BIM, and the method comprises the steps: obtaining a to-be-examined standard text, carrying out the natural language processing, and generating a semantic information group; by adopting a semantic mapping rule, mapping the semantic information group subjected to the information conversion algorithm and entity attributes in the IFC ontology structure, and by utilizing a logic rule generator, converting a mapped target NLP deep learning rule knowledge model into a logic rule; obtaining a to-be-examined BIM model, constructing a plurality of specific IDM views, extracting entity attributes corresponding to a specific field from the to-be-examined BIM model, mapping the entity attributes in each specific IDM view and the entity attributes in the IFC ontology structure, and converting the mapped target IFC engineering feature semantic model into a logic fact by using a preset conversion tool; and performing automatic compliance review on the logic rules and the logic facts by utilizing a rule engine through a rule reasoning algorithm and an ontology semantic reasoning algorithm to obtain a rule check report.

The invention provides an intelligent scheduling and control method and device for an integrated energy system. According to the method, power, gas and heat resource operation data are acquired, multi-scale layered modeling is performed according to a time scale and a space scale, and a power resource state space model, a gas resource flow continuity model, a heat resource heat balance model and a multi-energy coupling characteristic constraint model are established; carrying out feature extraction and dimension reduction representation by adopting a deep auto-encoder network; cooperative training of multiple groups of cognitive models is carried out through a split hierarchical federal learning framework, and a global intelligent model is obtained; constructing a neural architecture search network with a hybrid bionic learning rule, setting a hierarchical scheduling target, and generating a hierarchical intelligent scheduling strategy; and a fault-tolerant control mechanism is constructed, and error detection and correction of operation deviation are realized. According to the invention, multi-time scale collaboration, collaborative learning under multi-device group privacy protection and high-reliability fault-tolerant control are realized, and the operation efficiency and reliability of the integrated energy system are remarkably improved.

Disclosed in the present invention are an autonomous driving control system and method based on complex network cognition and end-to-end federated reinforcement learning, and a vehicle device. An autonomous driving algorithm framework (FLDPPO) based on complex network cognition and end-to-end federated reinforcement learning under urban dense traffic is provided, and by designing a loss function, a combination of rule-based complex network cognition and end-to-end federated reinforcement learning is achieved. In the FLDPPO, dynamic driving suggestions are used to guide agents to learn rules, so that the agents can cope with a complex urban driving environment and a dense traffic scenario. Moreover, the provided framework uses a multi-agent federated reinforcement learning architecture, and models are trained by means of parameter aggregation, protecting the privacy of a vehicle end while accelerating network convergence, reducing communication consumption, and achieving the balance between sample efficiency and high robustness of the models.

The invention discloses an event prediction and early warning method based on Bayesian deep learning. The event prediction and early warning method mainly comprises the following two parts: constructing a Bayesian deep learning rule model based on event characteristics, and designing a prediction and early warning model on the basis of constructing the Bayesian deep learning rule model. According to the method, the Bayesian statistical method and the deep learning model are fused, the advantages of the Bayesian statistical method and the deep learning model can be fully utilized, the uncertainty of a prediction result can be expressed in a probability distribution form by combining priori knowledge and observation data, efficient modeling and prediction are performed on complex and nonlinear time sequence data, early warning of potential events is realized, and the prediction efficiency is improved. The accuracy and reliability of event prediction and early warning are improved.

The invention relates to the technical field of online education, in particular to an intelligent learning scene pushing system based on a spatio-temporal sequence, which comprises a spatio-temporal data acquisition module, a spatio-temporal feature analysis module, a learning scene recommendation module, a feedback collection module, a self-adaptive optimization module and a data security protection module, the spatio-temporal data acquisition module is used for acquiring learning time and spatial data of a user in real time, the spatio-temporal feature analysis module extracts a user learning rule based on data analysis, the learning scene recommendation module pushes learning content suitable for a current environment according to an analysis result, and the adaptive optimization module dynamically adjusts a learning strategy according to user feedback. Personalized learning experience is ensured, and the data security protection module guarantees user privacy and data security through encryption and authority management; according to the invention, accurate and efficient learning pushing can be realized under different learning situations, and the method is suitable for the fields of online education, intelligent learning platforms and the like.

The invention is applied to the field of mechanical fault diagnosis signal processing, and particularly provides a pulse neural network mechanical fault diagnosis method based on STFT dimension transformation, and the method comprises the steps: collecting a one-dimensional mechanical vibration signal, carrying out the wavelet decomposition, carrying out the wavelet reconstruction of a low-frequency component and a denoised high-frequency component, and carrying out the wavelet reconstruction of the low-frequency component and the denoised high-frequency component; obtaining a denoised one-dimensional vibration signal; performing short-time Fourier transform, and converting the time-frequency two-dimensional matrix into a time-frequency two-dimensional matrix; inputting the time-frequency two-dimensional matrix into an improved HH threshold neuron model, carrying out Poisson sparse coding on the time-frequency two-dimensional matrix, and only carrying out pulse response on signal significant features; constructing a suprathreshold coding convolutional network with residual connection, inputting a sparse coding matrix, training by adopting an unsupervised learning rule based on STDP, and adaptively adjusting a network synaptic weight; and inputting to a trained above-threshold coding convolutional network, and obtaining pulse emission activity of neurons of an output layer through network forward propagation to determine a fault diagnosis result.

The invention discloses an atmospheric pollution trend analysis method and system, and relates to the technical field of atmospheric pollution trend analysis, and the method comprises the steps: collecting multi-source data in a target region, calculating the concentration change rate of all nodes by using the law of conservation of mass, updating the concentration of the nodes by using an Euler method, and inputting the updated concentration of the nodes into a pre-trained GCN model, calculating the optimized concentration and production abnormity list; modulating a propagation weight by using a Hebbian learning rule, modulating the optimized concentration by using the modulated weight, calculating a joint concentration by using a conservation type path flux method, combining the joint concentration by using a weighted linear combination, and calculating the joint concentration; and constructing a long-short-term memory network model to predict the future trend of the pollutants. The space gridding and the graph neural network are introduced, the recognition and control capability of the pollution source is improved, and the prediction precision and reliability of the analysis model are enhanced by combining the diffusion flux model and the convection flux model.

The invention provides a high-performance material visual screening system based on machine learning and an application method thereof. The system comprises a feature extraction module, a machine learning model training module, a prediction module and a feedback optimization module. The data preprocessing module is used for performing primary processing on the original data, wherein the primary processing comprises removal of invalid data and abnormal values; the feature extraction module is used for converting a time domain signal into a frequency domain signal by adopting Fourier transform, extracting a frequency domain feature, and performing data dimension reduction by adopting principal component analysis (PCA); the machine learning model training module is used for ensuring the generalization ability of the model by adopting a cross validation technology in the training process, and accelerating the convergence process of the model through a gradient descent method; the prediction module is used for predicting the material performance of the high-performance material to obtain a prediction result; and the feedback optimization module is used for automatically optimizing model parameters by adopting Bayesian optimization, analyzing a prediction result of the model and dynamically adjusting the model parameters so as to improve the prediction precision. Through the scheme of the embodiment of the invention, the law can be learned from a large amount of experimental data by a machine learning technology, and the material performance can be quickly and accurately predicted.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for enabling a user to conduct a dialogue. Implementations of the system learn when to rely on supporting evidence, obtained from an external search system via a search system interface, and are also able to generate replies for the user that align with the preferences of a previously trained response selection neural network. Implementations of the system can also use a previously trained rule violation detection neural network to generate replies that take account of previously learnt rules.

The invention discloses a spiking neural network driven robot dynamic adaptive control system, which relates to the technical field of robot control, and comprises seven modules: an environment sensing module which integrates various sensors and collects and transmits environment, attitude and interaction information; the signal preprocessing module processes data through composite filtering and feature extraction; the spiking neural network modeling module constructs a three-layer structure and performs training based on a fusion learning rule; the dynamic decision output module converts the pulse signal into a control instruction and adjusts gain; the actuating mechanism driving module drives the actuator to act; the state feedback monitoring module monitors and feeds back motion parameters and system states; and the adaptive optimization module optimizes the network and module parameters based on feedback data, and dynamically matches the environment. The control precision and the response speed of the robot in a complex environment are improved, the adaptive capacity is enhanced, the operation reliability and safety are guaranteed through multi-module cooperation, and the application scene is expanded.

The invention discloses a medical aid decision-making system based on a multi-modal large model, and relates to the technical field of medical artificial intelligence, and the system comprises a multi-modal data fusion module, a treatment scheme coding and management module, a prognosis prediction model module, a scheme simulation and deduction module, and a visual comparison module. The comprehensive state vector of a patient and the action vector of a candidate treatment scheme are jointly calculated, multiple long-term prognosis indexes after the scheme is executed are directly simulated, the model converts the treatment scheme into a computable variable by learning rules in historical treatment data, probabilistic prediction is carried out on a future result, and the prediction accuracy is improved. This enables a doctor to clearly see the risk brought by different selections and the long-term influence of treatment changes before making a decision, thereby converting the decision mode from experience-based inference to future simulation-based anticipation.

The provided are a federated reinforcement learning (FRL) end-to-end autonomous driving control system and method, as well as vehicular equipment, based on complex network cognition. An FRL algorithm framework is provided, designated as FLDPPO, for dense urban traffic. This framework combines rule-based complex network cognition with end-to-end FRL through the design of a loss function. FLDPPO employs a dynamic driving guidance system to assist agents in learning rules, thereby enabling them to navigate complex urban driving environments and dense traffic scenarios. Moreover, the provided framework utilizes a multi-agent FRL architecture, whereby models are trained through parameter aggregation to safeguard vehicle-side privacy, accelerate network convergence, reduce communication consumption, and achieve a balance between sampling efficiency and high robustness of the model.

The invention relates to an automatic driving decision planning model training method and device, and belongs to the technical field of automatic driving, and the method comprises the steps: carrying out the training of a first deep multi-agent reinforcement learning model based on the data of a first driving scene and a preset reward rule; wherein the reward rule comprises the steps that when the first deep multi-agent reinforcement learning model predicts and outputs a first decision behavior of at least one vehicle according to the data of the first driving scene, an instant reward is generated for each first decision behavior, and a local reward is generated for each first vehicle set; the first vehicle set is a set of the own vehicle and the adjacent vehicle; and based on the data of the second driving scene and a preset transfer learning rule, training the first deep multi-agent reinforcement learning model again to obtain a target deep multi-agent reinforcement learning model. The target depth multi-agent reinforcement learning model obtained through training has better generalization for different driving conditions and environments.

The invention discloses a flight management equipment route intelligent generation method, and belongs to the technical field of flight control, and the method comprises the following steps: 1, a basic route is generated through flight management equipment, and the basis conditions for generating the basic route at least comprise a takeoff airport, a landing airport, a flight direction, a civil aviation route and a navigation database; step 2, optimizing the basic route by using a deep learning network model: obtaining meteorological information and air traffic control information corresponding to the basic route according to historical route generation experience and correlation learning rules in combination with a network, optimizing and adjusting the route, and generating a flight route meeting safety requirements; and 3, completing three-dimensional route three-dimensional obstacle avoidance by using global high-precision terrain elevation data. According to the invention, the efficiency and accuracy of route input and the efficiency and safety of flight management are improved.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for enabling a user to make a conversation. Implementations of the system learn when depends on support evidence obtained from an external search system via a search system interface, and can also generate replies for the user that conform to preferences of a previously trained response selection neural network. Implementations of the system may also use a previously trained violation detection neural network to generate replies that take into account previously learned rules.

A rule training system and methods are provided that are configured to generate machine learning rules for fraud detection based on an automated feature selection by a rule creation system. The system includes a processor and a computer readable medium operably coupled thereto, the computer readable medium comprising a plurality of instructions stored in association therewith that are accessible to, and executable by, the processor, to perform rule creation operations which include accessing a training dataset, performing a data preprocessing and a data sampling on the training dataset, obtaining a plurality of features from the processed and sampled dataset using a feature engineering operation, selecting a subset of features from the plurality of features using simulated annealing operations, generating the plurality of detection rules using the subset of features, iteratively selecting from the plurality of detection rules, and evaluating a rule performance of each rule in those selected.

The invention discloses a system security access control method based on deep learning and dynamic risk awareness, which comprises the following steps of: generating a behavior state vector by collecting user identity, operation, equipment state and environment information, constructing an access behavior map, and establishing an access behavior map through a Hebbian learning rule; the method comprises the following steps: dynamically updating connection strength between behaviors in combination with historical behavior data, after receiving an access request, selecting an association path from a map, calculating an activation potential value, selecting an optimal path as an access decision basis by applying a Winner-Take-All selection mechanism, executing an access control operation, generating a risk mark and a behavior evolution mark, and establishing an access control strategy; the method is used for adjusting a selection threshold and an atlas weight. And finally, compressing and coding the related information, and recording the information in an access log. According to the method, intelligent judgment on the access behavior is realized by using deep learning and a dynamic risk perception mechanism.

The invention relates to a battery life and safety collaborative prediction method and system based on an improved pulse neural network algorithm, and belongs to the technical field of electric tool battery management. The method is based on a four-level SNN architecture, an input layer receives voltage, current, temperature, stress and change rate parameters of a battery, and neural pulse conversion of the parameters is realized through rate coding and time coding; the synaptic layer simulates a physical hysteresis effect between parameters by using a double-index model, and the hidden layer passes through Adaptive Iamp; the F neurons integrate historical memory and nonlinear coupling, and the output layer generates health status, residual life and risk index. The synaptic weight is optimized through a supervised STDP learning rule, the contribution degree of each parameter is quantified, a membrane potential linear accumulation mechanism of the life dimension and an abnormal pulse triggering mechanism of the safety dimension are established, and a joint early warning decision is realized. The system is deployed in a battery management system, the battery state can be monitored in real time, and accurate decision support is provided for battery full life cycle management.

A method, system, and apparatus, including a computer program encoded on a computer storage medium, for enabling a user to conduct a dialogue. System implementations can learn when to rely on supporting evidence obtained from an external search system via a search system interface and generate answers for the user that are consistent with the preferences of a previously trained response selection neural network. System implementations can also use a previously trained rule violation detection neural network to generate answers that take into account previously learned rules.

The invention discloses an intelligent fire safety monitoring system which comprises a sensing layer, a network layer, a platform layer and an application layer. The sensing layer comprises a sensor module and a camera module; the network layer comprises a data transmission module; the platform layer comprises a data processing module, a data storage module and a threshold adjustment module; the application layer comprises a monitoring interface module, an alarm system module, an emergency response module, a remote monitoring and commanding module and a man-machine interaction module; the data processing module receives data from the data transmission module of the network layer and transmits the processed data to the data storage module and the application layer, and the threshold adjustment module receives feedback data from the application layer; according to the invention, the self-adaptive algorithm is introduced, and the self-adaptive algorithm unit can learn rules in historical data and automatically adjust the fire early warning threshold according to the current environmental parameters and the change of the time period, so that the accuracy and reliability of fire monitoring are improved, and the change condition of the fire risk is reflected more accurately.

The invention discloses a method and system for improving image classification performance by using a spiking neural network, and the method comprises the steps: focusing on a neuron activation mode and synaptic plasticity in an unsupervised handwritten numeral recognition research process based on a triple pulse time-dependent plasticity learning rule, and after the fluctuation of accuracy is observed, carrying out the recognition of an unsupervised handwritten numeral recognition model, and carrying out the recognition of the unsupervised handwritten numeral recognition model. According to the method, key influence factors such as weight initialization, STDP randomness and hyper-parameter sensitivity are deeply analyzed; according to the invention, a common SNN structure is optimized, a pulse Inception module is realized, and the SNN is trained through a triple STDP; according to the method, the simulated annealing algorithm and the STDP are innovatively combined to be used for neuron parameter optimization, an optimal hyper-parameter combination is established through SA, after STDP training, the performance is remarkably improved under the condition of a limited data set, and particularly, the high efficiency of one-time perceptual classification of hundreds of digits is shown.

The invention relates to the technical field of data processing, and particularly discloses a financial information management system and method based on big data analysis. The system comprises a multi-source heterogeneous data acquisition module, a data semantic understanding and mapping module, a dynamic rule engine module, a real-time stream processing module and a financial decision support module, realizes data semantic unification through deep learning and a knowledge graph, and dynamically adapts to business change by means of a self-learning rule engine. And real-time calculation and anomaly detection are guaranteed based on a distributed stream processing framework, and finally the intelligence level and the operation efficiency of financial decision making are improved through visualization and predictive analysis.

An iterative learning fault-tolerant consistency control method for a multi-flexible-mechanical-arm system comprises the following steps that S1, a directed communication topological structure is constructed, flexible mechanical arms serve as followers, and a virtual leader is established; a mathematical model of the multi-flexible mechanical arm system is established based on the Hamiltonian principle; s2, constructing a finite time observer for estimating the angular displacement and the angular velocity of the virtual leader for each follower of the multi-flexible mechanical arm system; s3, designing an iterative learning rule, a parameter adaptive rule and a control signal of the multi-flexible mechanical arm system based on the virtual leader state estimated by the finite time observer in combination with an actuator fault model and an event triggering rule based on sampling data; according to the control method, fault-tolerant consistency control design based on a sampling data event triggering mechanism is carried out in combination with an estimated value of a finite time observer, so that communication resources are saved, and meanwhile, consistency control of the system is realized.

The invention discloses a low-altitude dynamic target unmanned aerial vehicle track traceability identification method based on machine learning. The method comprises the following steps: step 1, constructing a known track state sequence set; 2, constructing an identity track neurograph; 3, updating a synaptic connection weight by adopting an improved Hebbian learning rule, and generating an identity track memory map through a synaptic enhancement and attenuation mechanism; 4, constructing a target trajectory neural map, executing an improved Hebbian learning rule to update the synaptic connection weight, and obtaining a target trajectory memory map; 5, constructing a candidate identity set; 6, calculating a resonance identification score; and 7, identifying the candidate identity track memory map with the highest resonance identification score as a target traceability, and outputting a mapping result of a corresponding identity tag and a synaptic path. According to the invention, improved Hebbian learning rules and atlas resonance identification are fused, and low-altitude dynamic target unmanned aerial vehicle track traceability identification is realized.

The invention discloses an AI model training method, an AI model using method, computer equipment and a storage medium, and the method comprises the steps: obtaining a strategy library, and initializing a running environment according to a scene in the strategy library; based on a unified AI model, multiple agents are controlled to play chess in the operation environment to obtain a training sample, and the training sample comprises a scene analysis feature, an instant state feature, a task instruction feature and a role feature; and carrying out model training on the AI model according to the training sample until the model converges, and obtaining the trained AI model. According to the method, task allocation is optimized on the basis of a unified AI model in combination with environment information and role information, conflicts and resource waste among multiple agents are reduced, meanwhile, a strategy library is added into AI model training, the AI model can learn rules and experience and make better decisions under different conditions, and therefore the team cooperation effect among the multiple agents is improved.

This invention belongs to the field of unmanned aerial vehicle (UAV) control technology, and particularly relates to a feedforward control method for multi-rotor UAVs based on a dynamic cascaded spiking neural network (RCN). The method includes: S1: constructing a network model of the RCN based on pulse signals; S2: constructing a cost function of the RCN based on the network model and pulse errors; S3: solving for the weights of the RCN; S4: setting a preset similarity threshold and determining the learning rules for the dynamic cascaded structure based on the preset similarity threshold; S5: obtaining the output signal of the RCN according to the weights and the learning rules of the dynamic cascaded structure, and implementing feedforward control of the multi-rotor UAV based on the output signal. This invention improves the adaptability and robustness of multi-rotor UAVs in complex flight environments.

Embodiments provide systems and methods for learning extraction, transformation, and loading mappings by example. Multiple features can be extracted from a source schema and a target schema. An example ETL mapping can be provided to a machine learning algorithm, the example ETL mapping including definitions for extracting data from a source table and loading the extracted data into a target table. Using the machine learning algorithm and based on the source schema, the target schema, and the extracted features, one or more ETL rules can be predicted that define logic for extracting data from the source schema and loading the extracted data into the target schema. Additional ETL mappings can be generated based on the predicted ETL rules, the additional ETL mappings providing additional definitions for extracting data from the source schema and loading the extracted data into the target schema.

A spiral electric radiant tube comprises a spiral electric heating wire, a spiral magnetic column, a magnetic disk and a radiant tube sleeve. A cavity structure is arranged in the radiant tube sleeve, a plurality of spiral magnetic columns are arranged in the cavity structure, a plurality of magnetic discs are arranged on the spiral magnetic columns in a sleeving mode, the spiral magnetic columns are fixedly installed in the radiant tube sleeve through the magnetic discs, and the spiral electric heating wire is evenly wound in the axial direction of the spiral magnetic columns. According to the method, the spiral electric radiant tube is accurately controlled through a model combining a single neuron model and a PID control model, a Delta learning rule is adopted, and the function relationship between the weighting coefficient and the input, output and output deviation of the neuron is considered; when the Delta learning rule is adopted, the connection weight can be adjusted according to the difference between the actual output and the expected output of the neuron, and the weight coefficient can be adjusted online according to the learning capability and the self-adaptability of the neural network, so that the method has a more stable tracking characteristic and better robustness, and has higher self-adaptability and higher control precision.

The present invention discloses a fully parallel vector outer product calculation operation method based on electrochemical random access memory, including constructing a memory array with electrochemical random access memory and its gate-select transistor as basic units, and a corresponding random pulse update method. Among them, the conductivity change of the electrochemical random access memory is highly linearly related to the number of stimulation pulses. When no stimulation pulse is applied, the device channel conductance remains unchanged, showing non-volatile characteristics. Through random pulse programming, the device's conductance update and online training can be performed in situ, the vector outer product calculation is completed in parallel, and the calculation result is stored in the device's conductance, and subsequent network in-memory calculation acceleration is performed, avoiding additional data handling, and effectively reducing operation complexity and device programming delay. In addition, this solution can be extended to the network training paradigm related to the Hebbian learning rule to accelerate the training process of related algorithms.