High-precision elevator door knife synchronous operation data stable monitoring system

Abstract

The invention discloses a high-precision elevator door knife synchronous operation data stable monitoring system, and relates to the technical field of elevator operation monitoring, the high-precision elevator door knife synchronous operation data stable monitoring system comprises a digital twinning construction module, a multi-source data acquisition module, a dynamic fault prediction module and a synchronous control module, the digital twinning construction module is connected with the multi-source data acquisition module through a CAN bus, and the multi-source data acquisition module is connected with the synchronous control module through a CAN bus. The dynamic fault prediction module shares collection results of the multi-source data collection module through a database, and the synchronous control module is connected to the data twinning construction module and the elevator control system through a master-slave control mechanism. By designing the digital twin module and the virtual-physical synchronization technology, the real-time data synchronization function of a virtual model and a physical system is achieved, and the problems that a traditional elevator door knife monitoring system cannot comprehensively reflect the elevator running state and the mutual influence between the elevator running state and the environment is solved; the monitoring system can adjust the operation parameters of the elevator door knife in time to adapt to environment changes, and the synchronous operation stability is improved.

Description

Technical Field

[0001] This invention relates to the field of elevator operation monitoring technology, specifically a high-precision elevator door knife synchronous operation data stability monitoring system. Background Technology

[0002] With the rapid development of intelligent buildings and automated elevator technology, the elevator door knife system, as a key component of elevator operation, is crucial to the overall performance and safety of the elevator. Traditional elevator door knife monitoring methods mostly rely on single sensor data acquisition and simple alarm mechanisms, which cannot effectively cope with various faults that occur in complex operating environments.

[0003] To address these issues, this design proposes a high-precision elevator door knife synchronous operation data stability monitoring system. Through advanced algorithms and IoT technology, it improves the operational stability, fault response speed, and adaptive adjustment capability of the elevator door knife system, ensuring the efficient and safe operation of the elevator system in complex environments.

[0004] 1. Patent document CN114920117B discloses an elevator door operator control method, device, equipment and storage medium. The above patent can improve the safety of elevator door operator operation, but the above patent cannot realize the function of real-time data synchronization between virtual model and physical system.

[0005] 2. Patent document CN105173999B discloses an elevator telescopic door knife. The above patent realizes that when the door knife tightens to the left and right, the two telescopic blade plates on the left and right move forward simultaneously to clamp the hall door ball to realize the door opening action; conversely, when the door knife opens to the left and right, the two telescopic blade plates on the left and right move backward simultaneously to retract a certain length, eliminating the safety hazard of the door knife colliding with the hall door sill or the elevator door knife hitting the door ball caused by the shaking generated when the elevator car is running at high speed. However, the above patent cannot realize the function of predicting potential failures of the elevator door knife.

[0006] 3. Patent document CN112978547B discloses an elevator door knife control system and its method. The above patent realizes that when the elevator door system closes, the car door drives the landing door to close in sync, and then the door knife is triggered to unlock, so that the landing door lock and the car door lock are released synchronously and then locked down, the safety circuit is connected, and the elevator moves normally. However, the above patent cannot realize the remote parameter adjustment function.

[0007] 4. Patent document CN104671045B discloses an elevator synchronous door knife device with a retaining component. The above patent realizes that the elevator hall door and car door open and close synchronously through the ratchet, avoiding the shaking of the elevator door during the opening and closing process. In case of failure, the car door is tightly closed when the elevator is not in the door zone, and the car door can drive the hall door to open when it is in the door zone. It also simplifies the elevator door knife device with the function of prying open the door in the door zone and preventing prying open the door in the non-door zone, and reduces mechanical wear during operation. However, the above patent cannot realize the function of real-time monitoring and correction of the door knife operation status.

[0008] In summary, the aforementioned patents cannot achieve real-time data synchronization between the virtual model and the physical system, prediction of potential elevator door knife failures, remote parameter adjustment, and real-time monitoring and correction of the door knife's operating status. This results in problems such as poor real-time performance of elevator door knife monitoring data, untimely fault prediction, inability to adapt to environmental changes, high frequency of faults, and poor operational stability.

[0009] Therefore, this application proposes a high-precision elevator door knife synchronous operation data stability monitoring system that can realize real-time data synchronization between virtual models and physical systems, prediction of potential faults of elevator door knife, remote parameter adjustment, and real-time monitoring and correction of door knife operation status. Summary of the Invention

[0010] The purpose of this invention is to provide a high-precision elevator door knife synchronous operation data stable monitoring system to solve the technical problems mentioned in the background art, which are that the virtual model and physical system cannot achieve real-time data synchronization, potential fault prediction, remote parameter adjustment, and real-time monitoring and correction of door knife operation status, resulting in poor real-time monitoring data, untimely fault prediction, inability to adapt to environmental changes, high frequency of faults, and poor operational stability of elevator door knife monitoring data.

[0011] To achieve the above objectives, the present invention provides the following technical solution: a high-precision elevator door knife synchronous operation data stability monitoring system, comprising a digital twin construction module, a multi-source data acquisition module, a dynamic fault prediction module, and a synchronous control module. The digital twin construction module and the multi-source data acquisition module are connected via a CAN bus. The dynamic fault prediction module shares the acquisition results of the multi-source data acquisition module through a database. The synchronous control module is connected to the data twin construction module and the elevator control system through a master-slave control mechanism.

[0012] The digital twin construction module is used to establish a data twin model of the elevator door knife based on real-time data and environmental parameters of the door knife's operating status, and to synchronize the virtual model with the physical system in real time. The digital twin construction module includes: a data preprocessing unit, a digital modeling unit, and a data mapping unit.

[0013] The data preprocessing unit is used to perform noise reduction, feature extraction, and time synchronization processing on the multi-source data acquired in real time. The digital modeling unit generates a virtual model of the gantry knife operation through finite element analysis, dynamic simulation, and physical law modeling. The data mapping unit is used to realize bidirectional interaction and real-time updates between the gantry knife physical system and the digital twin model.

[0014] Preferably, the multi-source data acquisition module includes: a position sensor, a speed sensor, a force feedback sensor, and an environmental perception sensor. The real-time operating data of the door knife acquired by the multi-source data acquisition module is input into the digital twin construction module to construct a virtual digital twin model.

[0015] The position sensor is used to monitor the operating displacement of the door knife in real time and calculate the displacement error. The speed sensor uses an inertial measurement unit (IMU) to acquire the operating speed of the door knife in real time. The force feedback sensor is used to monitor the torque and resistance changes of the door knife drive system. The environmental sensing sensors include temperature and humidity sensors and vibration sensors to monitor the environmental conditions of the elevator shaft.

[0016] Preferably, the dynamic fault prediction module uses a deep learning-based hybrid algorithm model to perform real-time analysis of the gantry knife operation data collected by the multi-source data acquisition module and predict potential faults.

[0017] The dynamic fault prediction module uses convolutional neural networks to extract spatial features from sensor data and long short-term memory networks to analyze the time series characteristics of gantry knife operation data. The fault prediction unit within the dynamic fault prediction module performs real-time analysis of gantry knife operation data through a trained hybrid algorithm model and outputs prediction results of potential faults.

[0018] Preferably, the synchronization control module includes: a master-slave control unit, a synchronization error compensation unit, and a parameter dynamic adjustment unit;

[0019] The master-slave control unit, i.e., the master gate knife, shares the operating data collected by the multi-source data acquisition module with the slave gate knife through wireless communication and coordinates and synchronizes their operation.

[0020] The synchronization error compensation unit adjusts the gantry drive parameters based on real-time feedback from the digital twin model to reduce synchronization errors;

[0021] The parameter dynamic adjustment unit adaptively adjusts the speed and position parameters of the gantry knife based on changes in the operating environment.

[0022] Preferably, the dynamic fault prediction module is connected to the intelligent alarm and optimization module via wireless communication, and the intelligent alarm and optimization module is connected to the elevator control system via a data signal line;

[0023] The intelligent alarm and optimization module includes: a hierarchical alarm unit and a parameter optimization unit;

[0024] The graded alarm unit generates three types of alarm information based on the severity of the fault predicted by the dynamic fault prediction module: minor warning, functional abnormality, and emergency elevator stop.

[0025] The parameter optimization unit is connected to the multi-source data acquisition module via a data cable. By analyzing the fault prediction results and environmental data, it automatically generates optimized operating parameters and pushes them to the elevator control system.

[0026] Preferably, the monitoring system has virtual-physical synchronization technology, which optimizes the operating status of the physical door knife system by updating the parameters in the digital twin model in real time, and ensures the stability of operation in complex environments.

[0027] Preferably, the environmental data collected by the environmental perception sensor is used by the environmental compensation unit to correct the door knife operation data;

[0028] The environmental compensation unit includes: a temperature compensation algorithm and a vibration compensation algorithm;

[0029] Temperature compensation algorithms are used to correct drift in sensor data caused by temperature changes;

[0030] Vibration compensation algorithms are used to eliminate noise caused by elevator shaft vibration in operational data.

[0031] Preferably, the monitoring system also includes a cloud-based monitoring and remote maintenance module. This module combines an edge computing unit with a cloud platform to perform remote monitoring, fault diagnosis, and parameter optimization. The cloud-based monitoring and remote maintenance module shares feedback data from the digital twin construction module, multi-source data acquisition module, dynamic fault prediction module, and intelligent alarm and optimization module through the cloud platform database.

[0032] Preferably, the cloud monitoring and remote maintenance module includes: a cloud data storage unit, a cloud analysis unit, and a remote maintenance unit;

[0033] The cloud data storage unit is used to store operational data, prediction results, and historical fault information;

[0034] The cloud-based analytics unit uses big data analytics to predict the operational status trends of the door knife system.

[0035] The remote maintenance unit allows maintenance personnel to view fault information and optimize operating parameters via a remote terminal.

[0036] Preferably, the edge computing unit is used to process multi-source data in real time at the elevator site, and reduces data transmission latency and cloud dependence through edge computing optimization algorithms, thereby improving the real-time performance and stability of the system.

[0037] Compared with the prior art, the beneficial effects of the present invention are:

[0038] 1. This invention, through the design of a digital twin module and virtual-physical synchronization technology, realizes the real-time data synchronization function between the virtual model and the physical system, solving the problem that traditional elevator door knife monitoring systems cannot fully reflect the elevator's operating status and its interaction with the environment. This enables the monitoring system to adjust the elevator door knife operating parameters in a timely manner, adapt to environmental changes, and improve the stability of synchronous operation.

[0039] 2. This invention, by designing a dynamic fault prediction module and an intelligent alarm and optimization module, realizes the function of predicting potential faults of elevator door knife, solves the problem that traditional elevator door knife monitoring technology relies on simple alarm mechanisms and lacks intelligent prediction and early warning capabilities, reduces the sudden failure rate, improves the safety and stability of the elevator system, and reduces equipment downtime and maintenance costs.

[0040] 3. This invention, by designing a cloud monitoring and remote maintenance module and an edge computing unit, realizes the function of remote parameter adjustment, solves the problem that traditional elevator door knife monitoring systems require maintenance personnel to go to the site to solve the problem, improves system maintenance efficiency and response speed, and at the same time helps in the trend analysis and optimization of long-term operating status;

[0041] 4. This invention, through the design of a multi-source data acquisition module and environmental compensation technology, realizes the real-time monitoring and correction function of the door knife's operating status, solves the problem of the impact of environmental changes in the elevator shaft on the door knife's accuracy and stability, improves the accuracy of door knife operation, ensures precise monitoring of the door knife's status, enhances the elevator's operational stability in various environments, and reduces failures caused by environmental factors. Attached Figure Description

[0042] Figure 1 This is a schematic diagram of the synchronous operation monitoring process of the present invention. Detailed Implementation

[0043] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0044] Please see Figure 1The present invention provides an embodiment of a high-precision elevator door knife synchronous operation data stability monitoring system, comprising a digital twin construction module, a multi-source data acquisition module, a dynamic fault prediction module, and a synchronous control module. The digital twin construction module and the multi-source data acquisition module are connected via a CAN bus. The dynamic fault prediction module shares the acquisition results of the multi-source data acquisition module through a database. The synchronous control module is connected to the data twin construction module and the elevator control system through a master-slave control mechanism.

[0045] The digital twin construction module is used to establish a data twin model of the elevator door knife based on real-time data and environmental parameters of the door knife's operating status, and to synchronize the virtual model with the physical system in real time. The digital twin construction module includes: a data preprocessing unit, a digital modeling unit, and a data mapping unit.

[0046] The data preprocessing unit is used to perform noise reduction, feature extraction and time synchronization processing on the multi-source data collected in real time. The digital modeling unit generates a virtual model of the gantry knife operation through finite element analysis, dynamic simulation and physical law modeling. The data mapping unit is used to realize bidirectional interaction and real-time updating between the gantry knife physical system and the digital twin model.

[0047] Furthermore, the data preprocessing unit performs noise reduction processing on the data signals acquired by the multi-source data acquisition module using the Kalman filter algorithm, extracts key features from the raw data, including the gantry knife position error, speed fluctuation range, and environmental change gradient, and adopts a unified timestamp mechanism to align the data acquired by multiple sensors.

[0048] The pre-processed multi-source data enters the data modeling unit. The digital modeling unit uses the physical structural parameters of the door knife, including material, size and connection method, to model the force situation of the door knife during operation. Based on the kinematic and dynamic equations of the door knife, it simulates the dynamic behavior of opening and closing the door, generates real-time running trajectory, and incorporates key physical laws of door knife operation, such as friction and elastic restoring force, into the model to ensure that the virtual model accurately reflects the actual operating characteristics.

[0049] After the virtual model is constructed, the data mapping unit initiates bidirectional interaction between the virtual model and the physical door knife system. The specific process is as follows: Forward mapping (physical → virtual): Real-time running data is input into the virtual model to update key parameters such as position, speed, and force feedback. The virtual model responds dynamically, reflecting the current state of the physical system and visualizing the door knife's opening and closing actions and synchronization in real time. Reverse optimization (virtual → physical): The virtual model analyzes the deviation between the running data and the target state and generates optimization suggestions, such as adjusting the door opening speed or synchronization error compensation value. The optimization parameters are transmitted back to the physical door knife system, and adjustments are implemented through the asynchronous control module, such as reducing the driving force to reduce the vibration amplitude of the door knife. The data mapping unit continuously compares the physical running data with the virtual model and corrects the parameters of the virtual model. During operation, it updates 100 times per second to ensure a high degree of consistency between the virtual model and the physical system.

[0050] Please see Figure 1 The present invention provides an embodiment of a high-precision elevator door knife synchronous operation data stability monitoring system, wherein the multi-source data acquisition module includes a position sensor, a speed sensor, a force feedback sensor and an environmental perception sensor, and the real-time operation data of the door knife collected by the multi-source data acquisition module is input into the digital twin construction module to construct a virtual digital twin model.

[0051] The position sensor is used to monitor the operating displacement of the door knife in real time and calculate the displacement error. The speed sensor uses an inertial measurement unit (IMU) to acquire the operating speed of the door knife in real time. The force feedback sensor is used to monitor the torque and resistance changes of the door knife drive system. The environmental sensing sensors include temperature and humidity sensors and vibration sensors to monitor the environmental conditions of the elevator shaft.

[0052] The environmental data collected by the environmental sensing sensor is used by the environmental compensation unit to correct the door knife operation data.

[0053] The environmental compensation unit includes: a temperature compensation algorithm and a vibration compensation algorithm;

[0054] Temperature compensation algorithms are used to correct drift in sensor data caused by temperature changes;

[0055] Vibration compensation algorithms are used to eliminate noise caused by elevator shaft vibration in operational data;

[0056] Furthermore, the position sensor monitors the operating displacement of the door knife and calculates the error between the actual displacement and the target position. The speed sensor collects the operating speed of the door knife during the opening and closing action to judge the smoothness and dynamic characteristics of the door opening and closing, such as recording the trend of acceleration changes during the opening process and analyzing whether the acceleration is too fast or too slow. The force feedback sensor monitors the torque and resistance changes of the drive system and collects the torque state of the door knife during operation, such as detecting whether the torque increases due to track blockage when opening the door. The temperature and humidity sensor collects the temperature and humidity in the elevator shaft, and the vibration sensor records the vibration amplitude of the shaft to identify whether there is external vibration interference.

[0057] Environmental data collected by environmental sensing sensors is transmitted to the environmental compensation unit. Real-time temperature data collected by temperature and humidity sensors is input into the temperature compensation algorithm. The temperature compensation algorithm corrects the drift value of the sensor caused by temperature changes by looking up the temperature drift compensation table. Vibration sensors monitor the vibration amplitude of the wellbore and extract the frequency characteristics of the vibration signal through fast Fourier transform. The vibration compensation algorithm uses adaptive filtering technology to classify the frequency components in the signal and eliminate the high-frequency noise caused by wellbore vibration.

[0058] The data transmission channel digital twin construction module after environmental compensation ensures that the data accuracy of the input model is not affected by external interference; the environmental compensation unit corrects the impact of environmental interference on the data, ensuring that the data input to the digital twin model has high accuracy and reliability.

[0059] Please see Figure 1 The present invention provides an embodiment of a high-precision elevator door knife synchronous operation data stability monitoring system, wherein the dynamic fault prediction module uses a deep learning-based hybrid algorithm model to perform real-time analysis on the door knife operation data collected by the multi-source data acquisition module and predict potential faults.

[0060] The dynamic fault prediction module uses a convolutional neural network to extract spatial features from sensor data and a long short-term memory network to analyze the time series characteristics of the gantry knife operation data. The fault prediction unit in the dynamic fault prediction module analyzes the gantry knife operation data in real time through a trained hybrid algorithm model and outputs the prediction results of potential faults.

[0061] The dynamic fault prediction module is connected to the intelligent alarm and optimization module via wireless communication, and the intelligent alarm and optimization module is connected to the elevator control system via a data signal line.

[0062] The intelligent alarm and optimization module includes: a hierarchical alarm unit and a parameter optimization unit;

[0063] The graded alarm unit generates three types of alarm information based on the severity of the fault predicted by the dynamic fault prediction module: minor warning, functional abnormality, and emergency elevator stop.

[0064] The parameter optimization unit is connected to the multi-source data acquisition module via a data cable. By analyzing the fault prediction results and environmental data, it automatically generates optimized operating parameters and pushes them to the elevator control system.

[0065] Furthermore, the dynamic fault prediction module receives real-time operating data of the door knife from the multi-source data acquisition module, including displacement, speed, force feedback, and environmental parameters. After preprocessing and environmental compensation, this data is input into a deep learning model for fault prediction. The convolutional neural network processes the sensor data and extracts key spatial features, including abnormal displacement trends and areas with excessive driving force. For example, in the displacement sensor data, it identifies whether there is a mechanical jamming area in the door knife. In the force feedback data, it identifies possible abnormal stress in the positioning drive system. The long short-term memory network models the time series of the door knife operating data and analyzes the dynamic patterns of data changes over time, such as identifying the gradual decrease in the door knife switching speed and detecting repeated small-amplitude speed fluctuations.

[0066] The fault prediction unit adopts a hybrid deep learning model that combines convolutional neural networks and long short-term memory networks to perform comprehensive analysis of multi-source data in real time. During model training, a large amount of historical door knife data is used, including normal operation and fault cases, to form a high-precision identification capability for typical fault modes. The prediction results output include: potential fault type (such as door knife jamming and drive overload), fault severity score (0~100 points) and time window of probability of fault occurrence. The dynamic fault prediction module sends the fault prediction results to the intelligent alarm and optimization module in real time through a wireless communication interface.

[0067] The graded alarm unit analyzes the fault severity score and type output by the dynamic fault prediction module and generates the following three alarm messages: minor warning (0~30 points), reminding maintenance personnel to pay attention to possible minor abnormalities, such as slightly high door knife friction; functional abnormality (31~70 points), the system reminds the maintenance team to check the door knife operation status as soon as possible, and records the abnormal information for subsequent analysis; emergency stop (71~100 points), directly sending an emergency stop signal to the elevator control system to prevent serious faults that may cause equipment damage or passenger danger.

[0068] The parameter optimization unit receives fault prediction results and environmental data, analyzes possible operation optimization strategies, such as adjusting the driving force parameters to reduce abnormal pressure in door knife jamming prediction; and optimizing the door opening and closing speed to reduce the load on mechanical components when the temperature environment is harsh. The parameter optimization unit pushes the optimized parameters to the elevator control system through the data line to adjust the operating status of the elevator door knife in real time.

[0069] It enables real-time monitoring, accurate prediction, and intelligent response of the gantry knife's operating status, providing a reliable guarantee for the safe operation of the elevator system.

[0070] Please see Figure 1 The present invention provides an embodiment of a high-precision elevator door knife synchronous operation data stability monitoring system. The monitoring system has virtual-physical synchronization technology, which optimizes the operation status of the physical door knife system by updating the parameters in the digital twin model in real time and ensures the operation stability in complex environments.

[0071] The synchronization control module includes: a master-slave control unit, a synchronization error compensation unit, and a parameter dynamic adjustment unit;

[0072] The master-slave control unit, i.e., the master gate knife, shares the operating data collected by the multi-source data acquisition module with the slave gate knife through wireless communication and coordinates and synchronizes their operation.

[0073] The synchronization error compensation unit adjusts the gantry drive parameters based on real-time feedback from the digital twin model to reduce synchronization errors;

[0074] The parameter dynamic adjustment unit adaptively adjusts the speed and position parameters of the gantry blade based on changes in the operating environment;

[0075] Furthermore, the main gate knife collects its own displacement, velocity, force feedback, and environmental data, and transmits them to the slave gate knife via wireless communication. The slave gate knife receives the operating data of the main gate knife in real time as a target reference for synchronous control. Based on the operating data of the main gate knife, the master and slave control units calculate the target position and velocity parameters of the slave gate knife. The synchronous control command is executed through the drive system of the slave gate knife to ensure that the operating trajectories of the two gate knives are consistent.

[0076] The synchronization error compensation unit receives real-time feedback from the digital twin model and compares the position and speed errors of the master and slave gate tools. If the error exceeds the set threshold, such as the position error exceeding 2mm or the speed error exceeding 0.01m / s, the error compensation mechanism is triggered. The synchronization error compensation unit reduces the synchronization error by adjusting the operating parameters of the slave gate tool drive system. The error compensation adjustment includes increasing or decreasing the slave gate tool drive torque and fine-tuning the operating speed.

[0077] The parameter dynamic adjustment unit acquires real-time environmental parameters, including temperature, humidity, and vibration, through environmental sensors. When environmental parameters change significantly, such as a temperature increase exceeding 10°C or a vibration amplitude exceeding 0.5g, a dynamic adjustment mechanism is triggered. Based on environmental data and predictions from a digital twin model, the operating parameters of the master and slave door cutters, including speed curves and target positions, are dynamically adjusted. The adjustment algorithm automatically generates optimization schemes using a deep learning model combined with historical operating data. For example, when the temperature inside the elevator shaft rises to 45°C, the digital twin model predicts that the door cutter drive performance may decrease. The parameter dynamic adjustment unit reduces the operating speed of the master and slave door cutters from 0.6 m / s to 0.5 m / s and adjusts the drive torque to reduce the system load.

[0078] Through the master-slave control unit and the synchronization error compensation unit, the synchronization accuracy of the master and slave door knife reaches ±1mm, and the consistency of the running trajectory is significantly improved. The synchronization error compensation and environmental adaptation adjustment mechanism effectively reduces the safety hazards caused by running errors and environmental influences. The virtual-physical synchronization technology and its synchronization control module provide comprehensive protection for the efficient and safe operation of the elevator door knife system, and are particularly suitable for complex environments and high-precision scenarios.

[0079] Please see Figure 1 The present invention provides an embodiment of a high-precision elevator door knife synchronous operation data stability monitoring system. The monitoring system is further designed with a cloud monitoring and remote maintenance module. The cloud monitoring and remote maintenance module combines an edge computing unit and a cloud platform to perform remote monitoring, fault diagnosis and parameter optimization. The cloud monitoring and remote maintenance module shares feedback data from the digital twin construction module, multi-source data acquisition module, dynamic fault prediction module and intelligent alarm and optimization module through the cloud platform database.

[0080] The cloud monitoring and remote maintenance module includes: a cloud data storage unit, a cloud analysis unit, and a remote maintenance unit;

[0081] The cloud data storage unit is used to store operational data, prediction results, and historical fault information;

[0082] The cloud-based analytics unit uses big data analytics to predict the operational status trends of the door knife system.

[0083] The remote maintenance unit allows maintenance personnel to view fault information and optimize operating parameters via a remote terminal;

[0084] The edge computing unit is used to process multi-source data in real time at the elevator site, and reduces data transmission latency and cloud dependence through edge computing optimization algorithms, thereby improving the real-time performance and stability of the system.

[0085] Furthermore, the edge computing unit is deployed at the elevator site to receive the door knife operation data collected by the multi-source data acquisition module in real time. The edge computing optimization algorithm is used to preprocess the data, including data noise reduction, feature extraction and data compression. The edge computing unit performs rapid analysis and optimization of the preliminary diagnosis results of the dynamic fault prediction module, generates a preliminary fault prediction report locally, and uploads the data to the cloud platform. The cloud data storage unit receives and stores the real-time uploaded operation data, dynamic fault prediction results and historical operation records. Based on big data analysis technology, the cloud analysis unit performs trend prediction and system optimization on the stored data.

[0086] Maintenance personnel can access the cloud platform via remote terminals to view fault information and operating status in real time. They can also submit optimization plans, modify door knife drive parameters and operating speed curves, and adjust master-slave door knife synchronization control strategies. The cloud monitoring and remote maintenance module effectively improves the intelligence level of the elevator door knife monitoring system and provides strong support for the efficient operation of the system.

[0087] The working principle is as follows: First, the multi-source data acquisition module detects the operating status of the door knife in real time and transmits the collected data to the digital twin construction module to generate a virtual digital twin model. This model is synchronized with the physical system in real time and the operating parameters of the door knife are dynamically updated.

[0088] Then, the dynamic fault prediction module in the system uses deep learning algorithms to analyze sensor data through convolutional neural networks and long short-term memory networks, identify potential fault modes, output fault prediction information, and perform hierarchical alarms through the intelligent alarm and optimization module to notify maintenance personnel. The parameter optimization unit adjusts the operating parameters to mitigate or avoid potential faults.

[0089] Finally, by combining the cloud platform and edge computing units, the system can remotely monitor the elevator's operating status and perform data storage, analysis, and trend prediction. Maintenance personnel can access cloud data through remote terminals to perform fault diagnosis and parameter optimization, thereby improving the system's maintenance efficiency. The cloud analysis unit uses big data technology to analyze the historical data of the elevator door knife system, predict future operating status, and optimize operating strategies.

[0090] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

Claims

1. A high-precision elevator door blade synchronous operation data stability monitoring system, comprising a digital twin construction module, a multi-source data acquisition module, a dynamic fault prediction module and a synchronous control module, characterized in that: The digital twin construction module is connected with the multi-source data acquisition module through a CAN bus, the dynamic fault prediction module shares the acquisition results of the multi-source data acquisition module through a database, and the synchronous control module is connected to the data twin construction module and the elevator control system through a master-slave control mechanism; The digital twin construction module is used for establishing a data twin model of the elevator door based on real-time data of a door blade operation state and environmental parameters, and performing real-time synchronization between a virtual model and a physical system. The data preprocessing unit is used for performing noise reduction, feature extraction and time synchronization processing on the real-time collected multi-source data.

2. The high-precision elevator door blade synchronous operation data stability monitoring system according to claim 1, characterized in that: The digital modeling unit generates a virtual model of the door blade operation through finite element analysis, dynamic simulation and physical law modeling. The data mapping unit is used for realizing bidirectional interaction and real-time updating between the door blade physical system and the digital twin model.

3. The high-precision elevator door blade synchronous operation data stability monitoring system according to claim 1, characterized in that: The multi-source data acquisition module includes a position sensor, a speed sensor, a force feedback sensor and an environmental perception sensor. The position sensor is used for monitoring the running displacement of the door blade in real time and calculating displacement error.

4. The high-precision elevator door blade synchronous operation data stability monitoring system according to claim 1, characterized in that: The speed sensor is used for acquiring the running speed of the door blade in real time by using an inertial measurement unit (IMU). The force feedback sensor is used for monitoring the torque and resistance changes of the door blade driving system. The environmental perception sensor includes a temperature and humidity sensor and a vibration sensor, and is used for monitoring the environmental conditions of the elevator shaft. The dynamic fault prediction module adopts a hybrid algorithm model based on deep learning to perform real-time analysis on the door blade operation data collected by the multi-source data acquisition module and predict potential faults.

5. The high-precision elevator door blade synchronous operation data stability monitoring system according to claim 3, characterized in that: The dynamic fault prediction module extracts spatial features in sensor data by using a convolutional neural network, analyzes time sequence characteristics of the door blade operation data by using a long short-term memory network, and a fault prediction unit in the dynamic fault prediction module performs real-time analysis on the door blade operation data by using the trained hybrid algorithm model and outputs a prediction result of potential faults. The synchronous control module includes a master-slave control unit, a synchronization error compensation unit and a parameter dynamic adjustment unit. The master-slave control unit shares the operation data collected by the multi-source data acquisition module with the slave door blade through wireless communication and coordinates synchronous operation. The synchronization error compensation unit adjusts the door blade driving parameters according to real-time feedback of the digital twin model to reduce synchronization error. The parameter dynamic adjustment unit adaptively adjusts the door blade speed and position parameters based on changes in the operating environment. The dynamic fault prediction module is connected to the intelligent alarm and optimization module through wireless communication, and the intelligent alarm and optimization module is connected to the elevator control system through a data signal line. The intelligent alarm and optimization module includes a hierarchical alarm unit and a parameter optimization unit. The hierarchical alarm unit generates three kinds of alarm information of slight warning, function anomaly and emergency stop according to the fault severity predicted by the dynamic fault prediction module. The parameter optimization unit is connected to the multi-source data acquisition module through a data line, and automatically generates optimized operation parameters and pushes them to the elevator control system through analysis of the fault prediction results and environmental data.

6. The high-precision elevator door blade synchronous operation data stability monitoring system according to claim 1, characterized in that: The monitoring system has a virtual-physical synchronization technology, which optimizes the operation state of the physical door blade system by updating the parameters in the digital twin model in real time, and ensures the stability of operation in complex environments.

7. The high-precision elevator door blade synchronous operation data stability monitoring system according to claim 2, characterized in that: The environmental data collected by the environmental perception sensor is used to correct the door blade operation data through the environmental compensation unit; The environmental compensation unit includes a temperature compensation algorithm and a vibration compensation algorithm; The temperature compensation algorithm is used to correct the drift caused by temperature changes in the sensor data; The vibration compensation algorithm is used to eliminate the noise in the operation data caused by elevator shaft vibration.

8. The high-precision elevator door blade synchronous operation data stability monitoring system according to claim 1, characterized in that: The monitoring system also has a cloud monitoring and remote maintenance module, which combines edge computing units and cloud platforms for remote monitoring, fault diagnosis and parameter optimization. The cloud monitoring and remote maintenance module shares feedback data from the digital twin construction module, multi-source data acquisition module, dynamic fault prediction module and intelligent alarm and optimization module through the cloud platform database.

9. The high-precision elevator door blade synchronous operation data stability monitoring system according to claim 8, characterized in that: The cloud monitoring and remote maintenance module includes a cloud data storage unit, a cloud analysis unit and a remote maintenance unit; The cloud data storage unit is used to store operation data, prediction results and historical fault information; The cloud analysis unit predicts the trend of the door blade system's operation state based on big data analysis; The remote maintenance unit allows maintenance personnel to view fault information and optimize operation parameters through a remote terminal.

10. The high-precision elevator door blade synchronous operation data stability monitoring system according to claim 8, characterized in that: The edge computing unit is used to process multi-source data in real time at the elevator site, and reduces data transmission delay and cloud dependence through edge computing optimization algorithms, improving the real-time performance and stability of the system.

Images