11results about How to "Realize intelligent maintenance" patented technology

The invention discloses an intelligent maintenance method and device for an industrial printer and a storage medium. The method comprises the following steps that S1, circuits of printer components are modularly integrated; S2, sensors of the printer components are modularly integrated; S3, feedback circuits of a nozzle circuit module, a paper feeding circuit module and an ink box circuit module are arranged; s4, setting sensor feedback signal channels of the nozzle group, the paper feeding group and the ink box group; s5, establishing a real-time monitoring and analyzing system for a feedback circuit and a feedback signal; S6, establishing an analysis model; the problem that an existing printer detection system cannot predict printer faults and fault prognosis is solved, the purpose of intelligent printer maintenance integrating maintenance and detection is achieved, and through circuit and sensor modular integration and feedback setting, the state of each component can be accurately captured in real time, and abnormity can be found in time.

The invention discloses an Internet of Things electric energy meter Bluetooth communication reliability realization method and system, and relates to the technical field of Internet of Things communication, and the method comprises the steps: an Internet of Things electric energy meter and Bluetooth communication equipment establish connection through a Bluetooth pairing process, carry out identity authentication through a secret key, initialize data transmission, carry out data transmission, and realize communication. In a data transmission process, an error correction and data recovery algorithm is utilized to reduce transmission errors, data during communication is monitored in real time, the monitored data is utilized to establish an intelligent prediction model, the communication quality is predicted, whether the quality meets requirements is evaluated according to the predicted communication quality, and a self-adaptive adjustment algorithm is utilized to adjust communication parameters. And monitoring equipment parameters and security threats in real time, and triggering a key dynamic updating mechanism to periodically update an encryption and authentication strategy according to a monitoring condition. According to the method, the problems of insufficient communication error rate, insufficient communication quality monitoring and prediction, insufficient communication parameter adjustment flexibility and low safety in the prior art can be solved.

The invention provides a data maintenance system of a converter station, and relates to the technical field of data management, and the system comprises a document reading and preprocessing module which is used for reading a document in at least one format, and distinguishing and identifying a document structure and document information; the equipment tree construction module is used for reading a converter station design drawing, identifying and extracting equipment entity information, region interval information and a logic relationship between equipment and a region, and constructing an equipment tree of the converter station based on the identified and extracted information; the equipment parameter maintenance module is used for identifying a converter station container and an equipment entity by reading a converter station document, and associating attribute parameters of the equipment entity with a pre-constructed equipment model; and the UI interface display module is used for visually displaying the equipment tree and carrying out equipment parameter maintenance and manual confirmation through manual operation. According to the scheme, the work complexity and workload of front-line personnel can be reduced, and the operation and maintenance efficiency is further improved.

The invention discloses a mechanical action fault monitoring method based on a microcontroller and a convolutional neural network, and the method specifically comprises the steps: 1) obtaining mechanical fault action data from a sensor, and sorting the data into a one-dimensional time sequence with three features; 2) preprocessing the data set, including filling a sequence to unify length, and converting a label into one-hot code; (3) a mechanical action fault classification model is constructed, the model is composed of a plurality of convolution layers, pooling layers, flattening layers and full connection layers, and finally a classification result is output through a Softmax layer; 4) training the model by using a TensorF low framework, and adopting an Adam optimizer and a classification cross entropy loss function; and 5) deploying a trained model on the microcontroller to realize real-time mechanical fault monitoring. According to the invention, the deep learning model is combined with the microcontroller, the mechanical fault action can be efficiently identified, the method is suitable for various industrial application scenes, and the fault detection capability and maintenance efficiency of mechanical equipment are remarkably improved.

The invention relates to the technical field of new energy power generation, and discloses a railway long-distance strip-shaped photovoltaic power generation control method, system and device, and the method comprises the steps: continuously installing photovoltaic panels between rails to form a photovoltaic power generation system, integrating the photovoltaic power generation system into a strip-shaped photovoltaic power station, and storing the electric power collected by the photovoltaic panels; on the premise that normal operation of a railway is guaranteed, the strip-shaped photovoltaic power stations are connected into a railway power grid system in a distributed mode or consumed in a distributed mode through cables so as to supply power to a railway power grid. A spraying facility is attached to the bottom of a train, a camera is installed in front of the train, through automatic induction and data storage of the spraying facility, spraying behaviors and abnormal data are recorded, when the train passes, whether a photovoltaic panel needs to be cleaned or is abnormal or not is judged in advance, and spraying cleaning or abnormal data feedback of the photovoltaic panel is achieved. The system comprises a photovoltaic power generation module, an electric energy network access module and a cleaning early warning module. According to the invention, utilization of green energy is promoted, and dependence on fossil fuel is reduced.

The invention relates to the technical field of power supply, and provides an aircraft power supply system health state evaluation method based on dynamic weight, comprising the following steps: S1, determining the grade of an aircraft power supply system; s2, initializing parameters; s3, a subjective weight determination model and an objective weight determination model are established; s4, obtaining the weight of each index in the finished product parameter level; s5, calculating the health index of the index; s6, judging whether circulation is ended or not; s7, obtaining subjective and objective weights of the layer indexes; s8, obtaining subjective and objective weight difference and a health state change rate; s9, obtaining a subjective and objective weight ratio; s10, obtaining fuzzy subjective and objective weights; s11, fuzzy weighting is adopted to obtain a weight; s12, carrying out normalization on the weight values; s13, returning to the step S5; and S14, obtaining the health level of the power supply system. According to the method, the model and the fuzzy rule table are determined through the subjective and objective weight ratio, and nonlinear self-adaptive fusion is achieved.

Embodiments of the present application provide a subway maintenance planning data processing method, device, medium and electronic equipment. The method comprises: acquiring decision reference data associated with train maintenance planning in the whole subway network; based on the decision reference data, determining train maintenance planning data for each date within a future period through a pre-constructed mixed integer programming model, the train maintenance planning data including the date for each train to perform a balanced maintenance work package, the date for performing a pantograph work package, and the date for performing a car washing work package. The technical solution of the embodiments of the present application can improve the accuracy of subway maintenance planning data processing.

The utility model discloses building monitoring equipment convenient to maintain, which relates to the technical field of building monitoring and comprises a mounting assembly and a mounting top plate, a sliding assembly is fixed at the bottom of the mounting top plate, a fixing part is fixed on the surface of the mounting top plate, a connecting part is movably connected to the inner wall of the bottom of the sliding assembly, and the connecting part is fixedly connected with the mounting top plate. An external electric part is arranged on one side of the mounting top plate, and the auxiliary assembly is arranged on the top of the connecting part. The beneficial effects of the utility model are that through the cooperation of the quick-release locking mechanism, the synchronous power supply linkage system and the self-adaptive dustproof heat dissipation device, the maintenance efficiency and the operation safety of the equipment are significantly improved, the problems of tedious disassembly, delayed power supply and insufficient dust prevention are effectively solved, the safety of electrical operation is ensured, and the maintenance cost is reduced. And meanwhile, intelligent maintenance of power-off quick release is realized, and the reliability and maintenance convenience of the monitoring system are greatly improved.

The present invention proposes a data processing method for an intelligent gateway protocol, including: collecting data transmission data, building an adaptive deep learning model, and dynamically optimizing the protocol conversion strategy; building a load prediction model, optimizing the protocol conversion strategy and dynamically allocating network resources; building an edge computing model and training the edge computing model, and using the edge computing capability of the edge computing model to locally process data and make decisions; building a collaborative control model and a feedback mechanism, and using the collaborative control model to perform real-time collaborative control of intelligent gateway devices; using blockchain technology to encrypt and verify the data transmission process; designing the optimal configuration scheme of intelligent agent technology, and simultaneously monitoring the status of intelligent gateway devices in real time, predicting and pre-maintaining potential failures of intelligent gateway devices. The present invention improves data transmission efficiency, system performance and security by introducing adaptive deep learning, edge computing, reinforcement learning, blockchain and intelligent agent technologies.

This invention discloses a machine needle lifecycle management system based on big data and artificial intelligence, specifically relating to the field of machine needle management. It includes data collection, analysis, and optimization at each stage, such as needle receiving, loading, use, maintenance, replacement, and disposal. Through intelligent data analysis and prediction, the system can monitor the health status and operation of machine needles in real time, accurately predict remaining lifespan and potential risks, greatly improving management efficiency. Unlike traditional manual management methods, this system integrates various types of data through a big data platform and automatically generates maintenance strategies using artificial intelligence algorithms, achieving data-driven precision maintenance and risk warning. Furthermore, through quality traceability and risk scoring modules, the system can effectively identify potential problems and handle them in a real-time closed-loop manner, ensuring production quality. Overall, this technology provides a more efficient, transparent, and intelligent solution for machine needle management.