12369results about "Elevators" patented technology

The invention relates to the technical field of data processing, and discloses an elevator running state monitoring and early warning method and platform based on multi-source data fusion. The method comprises the steps that elevator operation data are collected through a multi-source sensor, an improved entropy weight method is introduced to fuse the data, a mixed deep learning model is constructed to predict the elevator state, parameter abnormity is analyzed based on association rules, graded early warning and fault diagnosis reports are generated, and intelligent monitoring and early warning of the elevator are achieved. Accurate collection, deep analysis and fault prediction of elevator operation data are achieved, and therefore scientificity and accuracy of elevator maintenance are improved.

The invention relates to the technical field of equipment management and maintenance, in particular to an elevator abnormal behavior detection system based on a multi-modal neural network, which collects visual and inertial measurement and audio data in an elevator car through a data acquisition module, and performs time-frequency domain conversion and feature vectorization on original data by using a data processing module; the multi-modal neural network model is composed of a CNN branch, an RNN branch and a voiceprint recognition network which are parallel, features are integrated through a feature fusion module by adopting a double fusion architecture, and then abnormality is judged through an abnormal behavior classifier based on a hierarchical classification strategy; the alarm and log module gives an alarm in time and records abnormal information, the model optimization module improves the model performance by applying multiple methods, and the system and an elevator control system achieve data interaction through a specific interface. According to the elevator abnormal behavior detection method, multi-modal data and an innovative algorithm are integrated, the accuracy and reliability of elevator abnormal behavior detection are remarkably improved, safe operation of an elevator is effectively guaranteed, and powerful support is provided for intelligent management of the elevator.

The invention relates to the technical field of elevator equipment state monitoring and fault diagnosis, and discloses an elevator traction machine running state real-time diagnosis method and system based on high-frequency sampling. According to the method, vibration (larger than or equal to 20 kHz), current (larger than or equal to 10 kHz), sound / sound emission, temperature and rotating speed signals of a traction machine are synchronously collected through a high-frequency multi-mode sensor array; capturing early weak fault transient characteristics; the edge computing unit completes data preprocessing, time synchronization, feature extraction and anomaly detection, and uploads key data to a cloud end through cloud-edge collaboration; the cloud end adopts a working condition self-adaptive strategy and a multi-modal fusion model to carry out deep diagnosis, and outputs fault types, positions and grades; and combining incremental learning and a degradation model to realize health quantification and residual life prediction. Through fusion of high-frequency data capture and an intelligent algorithm, the early fault detection capability, variable working condition adaptability and diagnosis real-time performance of the traction machine are improved, and a solution is provided for predictive maintenance of an elevator.

The invention provides an intelligent building elevator traffic scheduling method and system for energy-saving optimization. The method comprises the steps that real-time data of building sensing equipment and an elevator monitoring system are collected; constructing a prediction model to obtain the flow thermal distribution of future floors; designing a scheduling strategy based on multi-target reinforcement learning, wherein the scheduling strategy comprises target floor distribution and a dynamic operation path of the elevator; and formulating an execution control strategy for energy consumption optimization to realize global energy consumption minimization, and generating an optimized control instruction. Through deep fusion of flow prediction, scheduling optimization and energy consumption management, the passenger experience is remarkably improved while the adaptability, flexibility and energy efficiency of the system are improved, and an intelligent and efficient elevator scheduling solution is provided for modern high-rise buildings.

The invention discloses an elevator running state multi-source sensing internet-of-things inspection system which comprises the following steps: S1, a multi-mode sensing acquisition module for synchronously acquiring data and generating a multi-channel sensing data packet; s2, a data preprocessing module used for preprocessing the data packet; s3, the edge computing node cluster is used for executing preliminary anomaly judgment on the structured multi-modal data sequence and generating local event description information; s4, a multi-modal feature fusion module which is used for receiving local event description information from each edge computing node and generating a unified feature vector sequence; s5, a heterogeneous sensor collaborative scheduling module enables different sensors to be coordinated and consistent in triggering conditions, data frequencies and data transmission paths; and S6, the state recognition and exception labeling module is used for outputting the corresponding elevator running state label and the exception event identification code. The method has the advantages of being comprehensive in sensing dimension, low in response delay and high in anomaly recognition precision.

The invention discloses an elevator taking optimization system and method based on artificial intelligence, and relates to an intelligent elevator control technology combining multi-mode perception, prediction model dynamic adjustment and reinforcement learning scheduling strategies. The method comprises the steps that firstly, elevator running states, environment information and passenger behavior data are collected through multiple types of sensors, and multi-modal scene perception vectors are generated; and secondly, elevator loads and floor requirements are predicted in real time through a dynamically-adjusted prediction model, the reward function weight in reinforcement learning is adjusted in a self-adaptive mode on the basis, and accurate response and intelligent scheduling of different scenes are achieved. The system forms a perception-prediction-scheduling closed loop, significantly reduces the waiting time of passengers through multi-objective optimization, reduces the energy consumption, and improves the safety and emergency processing capability. The method is suitable for various high-rise building elevator group control systems, and has high intelligence, flexibility and wide application value.

The invention relates to the technical field of elevator risk early warning, in particular to an elevator intelligent risk early warning method and system. The method comprises the following steps: acquiring running state data of an elevator sliding part through a sensor, analyzing nonlinear characteristics of torque fluctuation, further performing friction heat energy accumulation treatment, simulating limit tolerance of a steel wire rope, and quantifying a cyclic damage expansion index of the steel wire rope according to a simulation result; and a fracture risk prefactor is identified according to the damage index, a random forest algorithm is utilized to construct an elevator intelligent risk early warning model, and finally the model is sent to a terminal for elevator intelligent risk early warning. According to the method, the elevator risk early warning technology is optimized, so that the elevator risk early warning technology is more perfect.

The invention provides an elevator safety intelligent monitoring method and system combining the Internet of Things and AI, and the method comprises the steps: obtaining a real-time sensing data set collected by a plurality of Internet of Things sensors, and carrying out the preprocessing operation, so as to generate a standard sensing data set; the pre-trained elevator state analysis model is used for carrying out feature extraction and safety state evaluation on the standard sensing data set, the real-time running state of the target elevator can be comprehensively and accurately reflected by integrating multi-source sensing data, evaluation deviation caused by single data or non-standard data is avoided, and the safety of the elevator is improved. An equipment maintenance optimization strategy is determined according to the safety state evaluation result, and a maintenance instruction set is generated to trigger maintenance operation execution, so that intelligence, automation and precision of elevator safety monitoring and maintenance are realized, the probability of elevator safety accidents is effectively reduced, the safety and reliability of elevator operation are improved, and the safety and reliability of elevator operation are improved. The manual inspection cost and the maintenance blindness are reduced, and the elevator maintenance management efficiency and quality are improved.

The invention relates to the technical field of elevator fault prediction, in particular to an elevator fault prediction method and system. The method comprises the following steps: collecting an operation vibration signal of an elevator brake through a vibration sensor, performing frequency domain conversion, performing vibration frequency disorder structure analysis, analyzing abnormal vibration frequency intensity, analyzing elevator braking force increment loss, and obtaining time sequence increment data; thirdly, incremental gradient nonlinear induction is carried out on the time sequence incremental data, and an elevator fault prediction model is constructed based on a K-nearest neighbor algorithm; the elevator fault prediction technology is optimized, so that the elevator fault prediction technology is more accurate.

The invention discloses a charging and discharging control method and device for an elevator energy storage system, a medium and a product, and relates to the field of data processing. The method comprises the steps that real-time operation data such as the position and the load capacity of an elevator car are obtained, and the instantaneous power generation power and the power change rate of a traction machine are determined accordingly; determining a power change predicted value in a future preset time period based on the parameters, and obtaining the total amount of power generation energy required to be stored by the energy storage system through zero setting of a positive value, integral operation and efficiency compensation; according to the power change rate, the state of the energy storage unit and the power grid electricity price time period, the charging and discharging priorities fed back by the super capacitor, the lithium battery and the power grid are determined; controlling the bidirectional DC / DC circuit to switch charging and discharging modes according to the priority, and adjusting the PWM duty ratio of the brake resistor; and based on the charging and discharging mode and the duty ratio, charging and discharging control over the elevator energy storage system is achieved. According to the method, the problem of inaccurate real-time matching of the dynamic load and the energy fluctuation of the current elevator energy storage system can be relieved.

The invention relates to the field of air conditioning unit control, in particular to an industrial park elevator system optimization control system and a method thereof.The hardware part comprises a sensor unit, an executing mechanism, a field control unit, communication equipment and a server unit; the software part comprises a real-time operating system, a sensor data acquisition and fusion preprocessing module, a digital twinborn simulation platform, a model prediction control module and a cloud deep reinforcement learning module; the method comprises the steps of S1, data acquisition and primary processing, S2, data transmission and caching, S3, digital twin modeling and system simulation, S4, optimization control strategy generation and issuing, S5, online scheduling and deep reinforcement learning optimization, S6, safety monitoring and exception handling, and S7, closed-loop feedback and continuous optimization. Efficient integration of elevator modules is achieved through digital twinning and model prediction control cooperation; intelligent data processing is realized through deep reinforcement learning and adaptive data fusion; and safe and energy-saving operation is realized through fault tolerance and safety monitoring.

The invention relates to the technical field of elevator control and fault diagnosis, and discloses an elevator inverter fault pre-diagnosis and fault-tolerant control system and method based on digital twinning, and the system comprises a high-fidelity digital twinning body building module which is used for building a digital twinning body of an elevator inverter, the digital twin integrates an electric-thermal-stress coupling model, a device-level aging model, a parasitic parameter influence model and a driving circuit and protection logic model; wherein the electric-thermal-stress coupling model is used for simulating loss, junction temperature fluctuation and thermal stress circulation of a power device, and the device-level aging model is used for representing a time-varying degradation rule of device parameters. Through deep fusion of a digital twin technology, fault diagnosis and fault-tolerant control, a closed-loop system of'perception-pre-diagnosis-decision-control 'is constructed, the safety, reliability and economical efficiency of the elevator inverter are comprehensively improved, and the method has important significance in promoting transformation of the elevator industry to intelligent and unmanned maintenance.

The invention discloses a multi-elevator linkage dynamic response intelligent scheduling system and a scheduling method thereof. The scheduling method comprises the following steps: acquiring an operation rule of an elevator, constructing a historical operation database and updating the historical operation database in real time; capturing a field state in real time through a monitoring system, and generating a real-time data stream according to a time sequence; a digital twinborn model is constructed through a historical database and real-time data flow, and the elevator calling probability and the congestion risk of each floor are predicted; the elevator calling probability of each floor in the next five minutes is predicted based on a digital twinborn model, the congestion risk is calculated, a real-time demand thermodynamic diagram fusing the space-time sequence dimension is generated, and intelligent scheduling is conducted on the elevator through the thermodynamic diagram; by reducing the waiting time, the overall operation efficiency is improved, the user satisfaction is enhanced, and smoother vertical traffic experience is provided for high-rise buildings.

The invention provides a method and system for achieving elevator energy feedback data communication. The method comprises the steps that multi-modal data are collected, and fault features are extracted, encrypted and packaged; and after decryption, phase deviation of elevator operation and power grid voltage is calculated by utilizing an algorithm, a current phase is synchronously fed back, and a feedback instruction is generated. Uploading the harmonic suppression parameter to an energy center, calculating a demand, generating a region-level feedback strategy, dynamically distributing a feedback power upper limit, synchronizing the harmonic suppression parameter, and obtaining region-level feedback data. And aggregating feedback data, and setting a global optimization target, including cost minimization, feedback maximization and carbon emission reduction. And calculating feedback energy and carbon integral according to a target adjustment algorithm, automatically settling to an operator account, and adjusting the target of the next period. Through an algorithm and an optimization mechanism, the problem of energy waste of a brake resistor in a traditional feedback technology is solved, the feedback efficiency is improved, and energy waste is reduced.

The application relates to the technical field of robots, and discloses a robot repositioning method and a robot, the robot repositioning method comprising the following steps: determining a current floor where the robot is located according to a test signal emitted by an elevator car at a stop floor; obtaining an environment map corresponding to the current floor; and determining the position of the robot on the current floor based on the environment map. The current floor is determined through the test signal emitted by the elevator car, the environment map corresponding to the current floor is obtained, and repositioning is completed through the environment map, so that the problem of repositioning failure of the robot when the robot is squeezed out of the elevator car is solved, and the positioning efficiency of the robot is improved.

The invention discloses a multi-purpose optimized elevator intelligent scheduling method and system, and the method comprises the steps: achieving the prediction of an elevator passenger flow direction mode through the construction of a flow direction transfer tensor and an improved density peak clustering algorithm, and adjusting a clustering result when a passenger behavior suddenly changes; a direction inertia factor is introduced, a direction switching decision rule and a local direction stability adjusting mechanism are combined, and the elevator running direction is dynamically optimized; passenger priority factors are defined, comprehensive priority scores of passengers are calculated, a passenger gradient optimization scheduling model is constructed based on the comprehensive priority scores, and the priorities of the passengers are dynamically adjusted; a multi-elevator non-cooperative game model is constructed, and task allocation among multiple elevators is realized by defining an elevator revenue function and a strategy space and utilizing Nash equilibrium solution. According to the method, the scheduling strategy can be adaptively adjusted in a complex traffic mode, so that the response speed is higher, the resource utilization rate is better, and the passenger waiting time is shorter.

The invention relates to an elevator fault real-time monitoring and early warning method, system and terminal, and belongs to the technical field of elevator monitoring. The monitoring and early warning method comprises the steps that multi-source sensing data of an elevator are collected, and timestamp synchronization is conducted on the multi-source sensing data; calculating a comprehensive risk value of the elevator based on the multi-source sensing data after timestamp synchronization; judging whether the comprehensive risk value is greater than a risk threshold value or not; if yes, Granger causal test is operated, an abnormal causal event is searched for from a pre-constructed causal graph, and relevant data corresponding to the abnormal causal event is packaged and sent to the elevator digital twinborn body; fault acceleration simulation is carried out in the digital twinborn body, and an abnormal root is positioned; and a health assessment report of the elevator is generated according to the simulation process data and the abnormal root. The method has the beneficial effects that the actual running state of the elevator is truly reflected, so that the running safety and reliability of the elevator are improved.

The invention discloses an in-use elevator brake safety performance detection method, relates to the technical field of special equipment elevator detection, and is used for solving the technical problems of related test methods and risk category research and judgment in brake evaluation in accordance with GB / T-42615 Safety Evaluation Standards for In-use Elevator. The in-use elevator brake safety performance detection method comprises the following steps of brake response time measurement, brake synchronism measurement and action voltage measurement of an in-use elevator brake. Wherein the measurement of the braking response time is divided into two working conditions, one working condition is that the elevator ascends to the upper part of the stroke without load to stop, and the other working condition is that the car is loaded with 1.25 times of rated load to downwards run to the lower part of the stroke to stop; it is suggested that no-load ascending braking is carried out firstly, and then a 1.25-time rated load descending braking test is carried out.

The invention discloses an electromechanical fusion intelligent operation and maintenance elevator integrated control system which comprises a multi-mode sensing fusion system, an edge computing control center and a cloud intelligent decision platform. The multi-mode sensing system collects elevator physical state data in real time through a high-precision electromagnetic field sensor, a vibration sensing network and an infrared-optical fiber fusion temperature measurement module. The edge computing center adopts an FPGA accelerated signal processor to realize multi-source data feature fusion, and the multi-source data feature fusion is uploaded to the cloud platform through an encrypted data link; and the cloud digital twin engine constructs a multi-body dynamics and electromagnetic field coupling model, generates an optimization control instruction in combination with a deep learning algorithm, and feeds back the optimization control instruction to the edge layer for execution. According to the system, a sensing-edge-cloud three-level collaborative architecture is innovatively provided, millisecond-level sensing, minute-level decision making and microsecond-level control closed loop of the elevator running state are achieved, and the fault early warning accuracy and the operation and maintenance response efficiency are further improved.

A connector assembly includes a connector body and a suspension member. The suspension member extends along a center axis and comprises a plurality of tension members encased in a jacket. The suspension member has one end received within the connector body, and this one end has an end face extending at an angle relative to the center axis with ends of the plurality of tension members being exposed. A plurality of contact members are supported within the connector body, and each contact member contacts an exposed end face of at least one tension member to establish electrically conductive connections with the plurality of tension members.

The invention relates to the technical field of intelligent elevator control, in particular to an intelligent elevator control method and system based on the Internet of Things, and the method comprises the following steps: S1, monitoring human body three-dimensional coordinate data of an elevator waiting area of each floor in real time, and collecting multi-modal sensing data in an elevator car; s2, generating a dynamic people flow distribution thermodynamic diagram of each floor, and constructing a time-space weight matrix; s3, generating an elevator dynamic scheduling strategy; s4, the abnormal state type of the lift car is judged by combining the gas component spectrum and the vibration spectrum; s5, a grading alarm instruction is sent to the central controller through the LoRaWAN protocol; and S6, automatically correcting the opening and closing speed curve of the portal crane. According to the method, efficient operation and safety guarantee of the elevator system in a complex people flow environment are achieved by fusing multi-source sensing, dynamic scheduling and an abnormal response mechanism.

The invention provides an elevator multi-caliper disc brake diagnosis method, system and device, a medium and a product, and relates to the field of fault diagnosis of elevator multi-caliper disc brakes. The diagnosis method comprises the following steps: acquiring a state transition matrix among preset states in a Markov jump system state transition model; first operation data of the multi-caliper disc brake are obtained and preprocessed; according to the preprocessed first operation data, a current braking system state estimation value and a corresponding operation data range are obtained; second operation data of the multi-caliper disc brake are collected in real time and preprocessed; and when the preprocessed second operation data exceed the operation data range, the fault type of the multi-caliper disc brake is determined according to the current braking system state estimation value, the Markov jump system state transition model and the state transition matrix. According to the invention, fault diagnosis of the multi-caliper disc brake of the elevator can be realized, and support is provided for daily overhaul and maintenance of an elevator brake system.

The invention discloses an elevator energy-saving control system based on a conversion module, which relates to the field of elevator control and comprises a battery module, the conversion module and an energy-saving control module, the battery module is connected with at least one elevator; the AC side of the conversion module is connected with commercial power, and the DC side of the conversion module is connected with the battery module; the energy-saving control module is connected with the battery module, and the energy-saving control module is in communication connection with the battery module and the conversion module. The stable operation of the elevator is guaranteed, the electricity utilization economical efficiency of the elevator is effectively improved, and the electricity utilization cost is reduced.

The invention discloses an intelligent elevator host bearing fault diagnosis system based on AI and fault physical fusion, and belongs to the technical field of elevator diagnosis. Comprising an edge end which is connected with a cloud end; when fault diagnosis is carried out, the edge end firstly collects a high-frequency signal through a sensor, sequentially executes signal preprocessing, feature extraction, anomaly detection and data compression, and finally sends compressed feature data to the cloud end; after the cloud end receives and verifies the integrity of the data, the feature data is stored in a time series database, deep analysis is executed through a data analysis engine, a lightweight model is optimized based on historical data, an analysis result is displayed through a remote monitoring platform, and an alarm notification is sent when abnormity is detected; meanwhile, the cloud end issues the optimized lightweight model to the edge end to form a continuously optimized closed loop. Through the feature extraction and data compression technology of the edge end, the data volume needing to be transmitted is greatly reduced, and the network bandwidth requirement and the transmission delay are remarkably reduced.

The invention comprises a method and a system for conveying a mobile robot in an elevator involving the monitoring of elevator operating components and of persons surrounding the elevator and operating the elevator by selecting a floor to which the mobile robot is to move. In one aspect an inertial sensor is used to learn or determine the number of floors. The robot may also have methods implemented to assess the floor space inside the elevator or to position itself in front of the elevator in such a way that it can, for example, monitor the operation of elevator operating components without being perceived as an obstacle from the elevator users' perspective.

The invention discloses an information input system, comprising an operation screen which is composed of a plurality of single bodies and is used for receiving an input operation which is implemented by an input person to implement information input, and the single bodies are minimum units capable of identifying the input operation; the detection part is used for detecting the operated condition of the single body and outputting a detection result, and the detection result comprises the position information of the single body and a detection signal representing the operated condition of the single body; a processing unit for processing the detection result of the detection unit and outputting an operation point of an input operation by an input person; and a determination unit for determining input information corresponding to the operation point. The information input system can accurately register and input information for passengers at low cost.

The invention discloses an elevator control system with an electricity-saving quantity calculation function, which relates to the field of elevator control and comprises an energy-saving control module, a first electric energy sampling module, a second electric energy sampling module, a third electric energy sampling module, a fourth electric energy sampling module, a fifth electric energy sampling module, a battery module, a conversion module and an auxiliary power supply module, the mains supply and the battery module are used for supplying power to the auxiliary power supply module, the auxiliary power supply module supplies power to the energy-saving control module and the conversion module, the alternating-current side of the conversion module is connected with the second electric energy sampling module connected with the mains supply, and the direct-current side of the conversion module is connected with the battery module; the third electric energy sampling module is arranged between the joint of the conversion module and the battery module and the battery module, the fifth electric energy sampling module is arranged between the joint of the conversion module and the battery module and each elevator, and the fourth electric energy sampling module is arranged between each elevator and the mains supply; the first electric energy sampling module is arranged between the auxiliary power supply module and the commercial power. According to the invention, the saved total electric quantity can be calculated.

The invention discloses an elevator fault prediction method and system based on the big data technology, and the method comprises the steps: collecting and preprocessing elevator multi-source data, and generating a standardized data set; performing kernel function mapping and kernel principal component analysis dimensionality reduction to obtain a dimensionality-reduced feature sequence; the sequence is sent into a gating Transform in a segmented mode, and a time sequence modeling result is output; identifying a fault type based on a time sequence modeling result and predicting a future fault probability; the risk threshold is compared, the fault risk is judged, and early warning information is generated; and sending a judgment result and early warning information to an operation and maintenance end to assist in maintenance decision making. According to the method, efficient prediction and intelligent early warning of elevator faults are achieved by fusing kernel principal component analysis and gating Transform, and the operation and maintenance response efficiency and the equipment operation safety are improved.

The invention provides an elevator steel wire rope detection method and device, equipment and a storage medium, and belongs to the technical field of automatic monitoring. The method comprises the steps that electromagnetic signal data in a steel wire rope and vibration signal data of the end of the steel wire rope are obtained; performing feature extraction on the electromagnetic signal data and the vibration signal data to obtain electromagnetic noise features and vibration noise features; performing cross fusion on the electromagnetic noise feature and the vibration noise feature to obtain a first joint noise feature; performing denoising processing on the electromagnetic signal data and the vibration signal data based on the first joint noise feature to obtain denoised electromagnetic signal data and denoised vibration signal data; and performing fault prediction based on the denoised electromagnetic signal data and the denoised vibration signal data to obtain fault information of the steel wire rope. According to the elevator steel wire rope detection method and device, the equipment and the storage medium, the elevator steel wire rope fault detection precision can be improved.

The invention discloses an elevator operation fault early warning method and system, relates to the technical field of intelligent elevator control, and aims to improve the operation safety and fault response capability of an elevator in a potential abnormal state. By constructing a load critical drift test working condition, a sudden non-vertical stress test working condition and a weak power supply fluctuation interference test working condition, abnormal behaviors which are difficult to appear but have potential safety hazards in the normal operation process of an elevator are induced; the method comprises the following steps: acquiring operation data under various working conditions, respectively calculating a drift overload response coefficient, a guide rail non-vertical interference coefficient and a power supply induced instability coefficient, respectively comparing the coefficients with preset thresholds, and judging whether operation faults such as brake response mismatching, asynchronous action of safety tongs and door lock control interference exist or not; and if the judgment result is abnormal, a targeted strategy is automatically generated and early warning is given out. According to the method, active recognition and classified response of elevator faults can be achieved, and the maintenance efficiency and the operation reliability are effectively improved.