33results about How to "Ability to learn independently" patented technology

The invention relates to a feedback fuzzy inference-based unmanned plane real-time route planning method. The method comprises the following steps of: 1, updating a mode of the unmanned plane route; 2, establishing a fuzzy rule base; 3, acquiring a fuzzy antecedent; 4, designing a feedback fuzzy inference method; 5, defuzzifying; 6, determining other parameters; 7, determining the next position of the unmanned plane; and 8, completing the design. Compared with the conventional unmanned plane real-time route planning method, the method ensures that the unmanned plane has primary independent studying capability by applying the feedback fuzzy inference method so as to improve the route planning effect. The real-time route planning method designed according to the method has the advantages of short route, smooth path, global astringency and easy implementation, and is a more practical and reliable unmanned plane real-time route planning method for engineering application.

The invention discloses an indoor intelligent energy saving system based on Internet of Things and machine learning. The indoor intelligent energy saving system based on the Internet of Things disclosed by the invention comprises a control input parameter module, a main control unit of the Internet of things, a terminal node set of the Internet of things, a terminal device set, an intelligent energy saving system module and an effect feedback module. The input parameter module comprises a variety of control input parameters, comprising, but not limited to, device control events, human sensingsensors, ambient light sensing sensors, area information, planned schedules, user preferences, scene information, environmental sensors, and the like. The indoor intelligent energy saving system disclosed by the invention has the beneficial effects that: 1. energy conservation, user experience and work performance can be comprehensively optimized by the cooperative use of the Internet of Things and an intelligent algorithm; and 2. multiple input parameters are comprehensively input, so that the decision making is more comprehensive and more scientific.

The invention provides an unmanned control method, comprising the following steps of: S1, entering the unmanned mode, collecting real-time vehicle environment information with T for a cycle; S2, matching the collected real-time vehicle environment information with the existing vehicle environment information in the local database, and executing the corresponding automatic driving mode when the matching is successful, and proceeding to step S3 when the matching fails; S3, exiting the unmanned mode, recording the manual driving mode until the collected real-time vehicle environment information is successfully matched with the existing vehicle environment information in the local database; and S4, storing the failed real-time vehicle environment information and the manual driving mode in thenext T cycle in the local database as a new driving strategy. According to the unmanned driving system and control method thereof, the unmanned system is an artificial intelligence unmanned system that enriches the driving strategy in the database and has the ability to learn independently.

The invention discloses a method for conducting central air conditioner energy-saving regulation and control with an AI expert system. Through use of an AI learning module and a computer cloud servermodule, automatic adjustment is conducted, the autonomous learning capacity is achieved, and autonomous adjustment is conducted on a main area central air conditioner module. The method for conductingcentral air conditioner energy-saving regulation and control with the AI expert system is characterized in that the main area central air conditioner module, the computer cloud server module, an AI learning module and a time module are involved; when a time signal and a time reference signal are identical, the computer cloud server module transmits collected electric energy parameters to the AI learning module, and the computer cloud server module resets the time module and enables the time module to conduct timing again at the same time; when the main area central air conditioner module cannormally work according to a regulation signal, a feedback signal is output; and after the computer cloud server module receives the feedback signal of the main area central air conditioner module, the AI learning module stores the regulation signal. The method for conducting central air conditioner energy-saving regulation and control with the AI expert system improves the safety and reliabilityof a system and can prevent and completely eradicate generation and spreading of faults influencing normal operation of the system.

The invention discloses an English learning management robot, which is a humanoid educational robot. The robot can interact with learners during teaching, and has a complete mechanism to stimulate the learners. If the learners do well in learning, the learners will be encouraged or prizes will be offered to the learners, thus increasing the learning interest of the learners. Through adoption of a regular learning unit, a regular learning function is provided to enable the learners to learn specified contents at the specified time, thus helping the learners to develop good learning habits. The robot communicates with the learners through voice recognition, voice processing and the like, and pure English communication better helps to improve the ability of the learners to speak English. An automatic learning ability is provided, the learners put books in a word scan identifying place, and the robot learns and understands contents of the books, so the robot can teach wider contents and is suitable for a wider range of population.

The invention discloses an autonomous obstacle avoidance method based on deep learning and a binocular stereoscopic vision technology. Environment video information is collected in real time through a binocular camera, an obstacle three-dimensional detection algorithm is constructed based on the target detection in deep learning in combination with the binocular stereoscopic vision technology, and a robot performs autonomous obstacle avoidance according to an obtained obstacle avoidance steering decision, the three-dimensional coordinates of an obstacle and the obstacle avoidance angle. According to the method, deep learning target detection serves as a core, depth information of traditional machine vision is fused, the defect that only two-dimensional coordinate information can be obtained through a single deep learning algorithm is overcome, and complete three-dimensional coordinates are obtained. According to the invention, end-to-end learning in an obstacle avoidance scene is realized, complicated three-dimensional reconstruction and path planning problems are avoided, and the system cost is saved.

The invention discloses a safety protection system and method for power edge computing, and the system comprises a physical infrastructure safety protection module, a data plane gateway safety protection module, and a management safety protection module. By interface equipment and a micro-processor of the physical infrastructure safety protection module, identity registration and authentication identification are carried out on access equipment, automatic binding of interfaces of electrical equipment is carried out, real-time monitoring and sensitive data protection are carried out on attack behaviors through a data processor of the data plane gateway security protection module, and security protection of a data link is achieved. Password verification and log recording are carried out on platform operation and the like through a core processor of the management security protection module, so that the purpose of data security protection in the power edge computing system is achieved, and the data security and reliability of the whole power edge computing system are improved.

The invention provides an intelligent gateway for air conditioning operation and maintenance, which is connected with external equipment. The intelligent gateway comprises an air conditioner and heating operation information interface module, an artificial intelligent analysis and processing module, a fault location collection and identification module, a wireless communication module, an abnormalalarm display module, a maintenance end module, a camera collection module and a data storage module. The invention further provides an air conditioning operation and maintenance detection method, which has the capability of independent study, the adjustment and repair of a central air conditioner are assisted, the failure of the air conditioner is prewarned in time, and the failure of the air conditioner is solved once by maintenance personnel with the help of the air conditioning operation and maintenance detection method.

The invention relates to an outbound robot distribution method, which comprises the following steps that: 1) when a customer calls, the customer selects to enter different incoming call skill groups through a routing module; 2) after entering the call-in skill group, dependent variables are predicted through principal component analysis and multiple regression analysis methods in sequence, i.e., the time required by the robot to dial the task load; and 3) based on the prediction result in the step 2), autonomous machine learning is carried out on the prediction result by using a method of combining a time sequence model and BP neural network weighting, an optimized learning rule is obtained, and then robot seats are distributed. Compared with the prior art, the method has the advantages that the labor cost is reduced, the prediction precision is improved, and the neural network autonomous learning ability is achieved.

The technical field of data analysis of the present invention, in particular, relates to an energy data analysis system and method based on big data. The system includes: a data acquisition unit, including: various types of acquisition units deployed on site, and the acquisition unit is configured to collect raw energy data; a data storage unit: divides and classifies the collected raw energy data Storage, remove data redundancy; data preprocessing unit: check the integrity of data, remove noise and singular values, use data compression and transformation methods to reduce the number of effective variables or data samples to be considered according to the goal of the task, or Find the invariant identity of the data, and finally standardize the data. Through data segmentation and classification of energy data, it effectively reduces the redundancy of energy data and improves the efficiency of subsequent data processing; at the same time, it uses a deep learning model to conduct multi-scale analysis of energy data, improving the accuracy of data analysis results sex.

The invention discloses a power distribution switch welding current diagnosis system. The power distribution switch welding current diagnosis system comprises a comprehensive core management module, a data real-time acquisition module, a real-time curve drawing driving module, a real-time welding current analysis module and a current grading strategy management module; the comprehensive core management module is connected with the data real-time acquisition module, the real-time curve drawing driving module, the real-time welding current analysis module and the current grading strategy management module; the current grading strategy management module is connected with an instantaneous current waveform curve automatic screenshot module; and the comprehensive core management module is used for controlling the data real-time acquisition module, the real-time curve drawing driving module, the real-time welding current analysis module and the current grading strategy management module. According to the design, the intelligent degree is improved, and the automation degree is also improved.

The invention provides a steam generator water supply system fault diagnosis method based on multi-source information reinforcement learning. The method comprises the steps of carrying out the state monitoring of a steam generator, a water supply pump and a water supply valve in a steam generator water supply system, and obtaining a plurality of pieces of state information; clustering the associated state information based on the information source type to form a feature information cluster; establishing a feature matrix based on feature information cluster integration, and extracting a state feature vector from the feature matrix; and inputting the state feature vector into a data analysis model system to diagnose the fault type, and evaluating, regulating and controlling the state parameter of the corresponding component. Aiming at multi-source information fault diagnosis of a steam generator water supply system, technologies of signal processing, mutual information association, nonlinear dimension reduction, deep reinforcement learning and the like are adopted, so that the problems of feature extraction, feature fusion, feature integration, feature learning and the like of multi-source information are solved; and a new technical scheme is provided for autonomous learning and state recognition and diagnosis of multi-source information of the steam generator water supply system.

E The invention discloses an Edge AI-based voice posture sensing electric toothbrush system, which comprises an MCU provided with an edge calculation training model, and comprises a voice acquisition mechanism, an attitude sensing mechanism, a voice output mechanism, a motor control mechanism, a networking mechanism and an indicator light mechanism which are simultaneously connected with the MCU. The MCU is also connected with a crystal oscillator for providing an accurate clock beat and a Flash storage chip. The edge calculation training model is provided with a decision model based on edge calculation training and a voice recognition model and a posture perception recognition model which are connected with the decision model at the same time, the voice recognition model is connected with an input voice processing module, and the posture perception recognition model is connected with a posture data processing module. The decision-making model is connected with a networking processing module, a state indicating lamp processing module, an indicating voice output processing module and a posture adjusting data output processing module at the same time.