A method for detecting multiple abnormal states of an elevator drive host

Abstract

The present invention relates to the technical field of elevator operation and maintenance. The present invention provides a method for detecting multiple abnormal states of an elevator drive host. The method obtains operating data of multiple models of elevator drive hosts, uses a convolutional neural network to extract elevator drive host feature data from preprocessed data of the operating data of the multiple models of elevator drive hosts, trains an initial model of a fault identification algorithm in an initial state, receives real-time collected operating data of multiple models of elevator drive hosts, performs transfer learning on the initial model of the fault identification algorithm, optimizes the model using a gradient descent algorithm, obtains an optimized fault identification algorithm model, and substitutes the detection results of the multiple abnormal states of the elevator drive host into a preset result display system for real-time display. The method solves the problem that a set of equipment is usually only applicable to one model of elevator, takes a long time to detect, and cannot meet the needs of fast and accurate detection.

Description

Technical Field

[0001] The present invention relates to the technical field of elevator operation and maintenance, and in particular to a method for detecting multiple abnormal states of an elevator drive host. Background Art

[0002] As an indispensable means of transportation in modern urban life, elevators' safety and comfort are crucial to passengers. However, without proper maintenance and upkeep, elevators' overall performance and safety will gradually deteriorate over time, potentially leading to serious personal injury. Therefore, comprehensive elevator monitoring and maintenance are crucial.

[0003] Currently, many elevator companies have deployed a variety of sensors and small servers within elevator cars. These devices collect and analyze real-time data such as elevator vibration, speed, and height to ensure proper operation. However, these monitoring systems primarily focus on the elevator car and control system, with little attention paid to the elevator's main drive unit. As a core component of an elevator, the performance and status of the main drive unit are directly related to its overall operation and safety.

[0004] Currently, the available testing equipment for elevator drive main units is scarce and relatively limited in functionality, often only able to detect specific issues such as bearing failure or demagnetization. Furthermore, these testing devices have limited generalizability and portability, typically only applicable to a single model of elevator, which undoubtedly increases maintenance costs and difficulty. Furthermore, existing elevator drive main unit bearing fault detection technology is complex and time-consuming, failing to meet the demand for rapid and accurate testing. Summary of the Invention

[0005] The purpose of the present invention is to provide a method for detecting multiple abnormal states of an elevator drive host to solve the problem that a set of equipment is usually only applicable to one model of elevator, the detection time is long, and it cannot meet the needs of fast and accurate detection.

[0006] The present invention provides a method for detecting multiple abnormal states of an elevator drive host, including:

[0007] Acquire operation data of drive hosts of various models of elevators, wherein the operation data of the drive hosts of various models of elevators include basic operation data of elevators, elevator performance parameter data, elevator environment and safety data, data on the number of elevator users and frequency of use, and elevator technical parameters, and pre-process the operation data of the drive hosts of various models of elevators to obtain pre-processed operation data of the drive hosts of various models of elevators;

[0008] Use convolutional neural networks to extract elevator drive host feature data from preprocessed data of various elevator drive host operation data, and establish elevator operation data labels for the elevator drive host feature data;

[0009] Perform a transfer learning algorithm on one elevator model in the elevator drive host feature data, and transfer the knowledge learned on one elevator model to other elevator models;

[0010] Retrieve the data information corresponding to the elevator operation data label, train the initial model of the fault identification algorithm in the initial state, receive the real-time collected operation data of various models of elevator drive host machines to perform transfer learning of the initial model of the fault identification algorithm, and optimize the model through the gradient descent algorithm to obtain the optimized fault identification algorithm model;

[0011] Continuously receive real-time collected operating data of various models of elevator drive hosts, substitute the continuously received real-time collected operating data of various models of elevator drive hosts into the optimized fault identification algorithm model, obtain various abnormal state detection results of the elevator drive hosts, and substitute the various abnormal state detection results of the elevator drive hosts into the preset result display system for real-time display.

[0012] Furthermore, operating data of drive hosts of various types of elevators are obtained, wherein the operating data of drive hosts of various types of elevators include basic operating data of elevators, elevator performance parameter data, elevator environment and safety data, data on the number of elevator users and frequency of use, and elevator technical parameters. The operating data of drive hosts of various types of elevators are pre-processed to obtain pre-processed data of operating data of drive hosts of various types of elevators, including:

[0013] Data cleaning and preliminary screening are performed on the operation data of various types of elevator drive hosts to obtain the first preliminary processed elevator operation data. Outlier detection is performed on the preliminary processed elevator operation data using the interquartile range to obtain elevator operation abnormality data;

[0014] removing abnormal elevator operation data from the preliminary processed elevator operation data to obtain second preliminary processed elevator operation data;

[0015] Based on the normal operation mode and abnormal mode of the elevator, the second preliminary processing elevator operation data is identified and noise data is removed to obtain pre-processed data of operation data of elevator drive hosts of various models.

[0016] Furthermore, a convolutional neural network is used to extract elevator drive host feature data from preprocessed data of operation data of various types of elevator drive hosts, and an elevator operation data label is established for the elevator drive host feature data, including:

[0017] Using a convolutional neural network to extract elevator drive host feature data from preprocessed data of various elevator drive host operation data, the elevator drive host feature data includes operating status features, elevator performance parameter features, elevator environment and safety features, elevator usage frequency and mode features, elevator technical parameter features, elevator fault and abnormality features, and elevator image and video features;

[0018] Corresponding tags are established to extract the characteristic data of the elevator drive host, and the tags include operating status and fault type.

[0019] Furthermore, a transfer learning algorithm is applied to one elevator model in the elevator drive host feature data, and the knowledge learned on the one elevator model through the transfer learning algorithm is transferred to the remaining elevator models, including;

[0020] Identify the target elevator model for which knowledge transfer is needed, use a convolutional neural network to extract key features from the source elevator model's feature data, and train a CNN model on the source elevator model's feature data.

[0021] The trained CNN model is used as the initial model for the target elevator model. The performance of the model after transfer learning is evaluated on the feature data of the target elevator model, and the model is optimized based on the evaluation results.

[0022] The knowledge learned from one elevator model is successfully transferred to other models. At this point, the model of the target elevator model already has the knowledge and feature representation capabilities learned by the source elevator model model.

[0023] Verify the performance of the model after transfer learning in a real environment and adjust and optimize it as needed. If the model performance meets expectations, it can be deployed to the actual application system.

[0024] Furthermore, data information corresponding to the elevator operation data label is retrieved to train the initial model of the fault identification algorithm in the initial state, and real-time collected operation data of various elevator drive host models are received to perform transfer learning of the initial model of the fault identification algorithm. The model is optimized through the gradient descent algorithm to obtain the optimized fault identification algorithm model, including:

[0025] Retrieve the data information corresponding to the elevator operation data tag and select the historical operation data with the fault tag. The historical operation data with the fault tag contains various parameters of the elevator in normal operation and fault state, including speed, acceleration, current, and voltage;

[0026] Clean the selected data to remove outliers, duplicate values, and missing values;

[0027] Label the data and clearly mark the fault status and normal operation status;

[0028] Extract features related to elevator failures based on the cleaned data. Features related to elevator failures include average speed and acceleration changes over a specific time period.

[0029] Construct the initial model of fault identification algorithm based on support vector machine;

[0030] Use the labeled data set to train the initial model and adjust the parameters of the initial model of the fault identification algorithm to achieve the best initial performance of the initial model of the fault identification algorithm;

[0031] Through sensors or other data acquisition equipment, the operating data of various types of elevator drive hosts are collected in real time. According to the characteristics of the real-time collection of operating data of various types of elevator drive hosts, the initial model parameters of the fault identification algorithm are fine-tuned to adapt to different types of elevator drive hosts.

[0032] The initial model parameters of the fault identification algorithm are optimized through the gradient descent algorithm. According to the calculated gradient, the model parameters are updated using the gradient descent algorithm. The above process is repeated until the model performance reaches the preset standard or the number of iterations reaches the upper limit, and the optimized fault identification algorithm model is obtained.

[0033] Furthermore, continuously receiving real-time collected operating data of various types of elevator drive hosts, substituting the continuously received real-time collected operating data of various types of elevator drive hosts into an optimized fault identification algorithm model, obtaining various abnormal state detection results of the elevator drive hosts, and substituting the various abnormal state detection results of the elevator drive hosts into a preset result display system for real-time display, including:

[0034] Continuously receive real-time data collected from various elevator drive host operating data, verify the received data, test the data integrity, and remove data anomalies caused by transmission errors or equipment failures;

[0035] Substitute the continuously received real-time data into the optimized fault identification algorithm model, use the optimized fault identification algorithm model to analyze and predict the input real-time data, and identify the abnormal state of the elevator drive host;

[0036] The model's prediction results are parsed into specific abnormal status information, including motor overheating, transmission system failure, and load abnormality.

[0037] Furthermore, operating data of drive hosts of various types of elevators are obtained, wherein the operating data of drive hosts of various types of elevators include basic operating data of elevators, elevator performance parameter data, elevator environment and safety data, data on the number of elevator users and frequency of use, and elevator technical parameters. The operating data of drive hosts of various types of elevators are pre-processed to obtain pre-processed data of operating data of drive hosts of various types of elevators, including:

[0038] The noise reduction is performed on the signals in the operating data of the elevator drive host of various models, and the signals in the operating data of the elevator drive host of various models are decomposed into several IMF components through CEEMDAN;

[0039] The correlation coefficients of several IMF components are screened and the wavelet phase is used for noise reduction. The characteristic frequencies are then screened to obtain the reconstructed IMF components and output the noise-reduced signal.

[0040] The beneficial effects of the present invention are as follows: The present invention provides a method for detecting multiple abnormal conditions in an elevator drive unit and a novel device for detecting abnormal conditions in an elevator drive unit. This device can monitor multiple parameters of the drive unit, such as temperature, vibration, and noise, in real time. Using advanced algorithms, it analyzes and processes this data to promptly detect and warn of potential faults. This detection device is not only comprehensive in functionality but also possesses excellent generalization and transferability, adapting to different elevator models. Furthermore, its simple operation and short detection time significantly improve the efficiency and accuracy of elevator maintenance. We believe that this novel device for detecting abnormal conditions in an elevator drive unit will provide a strong guarantee for the safe operation of elevators.

[0041] Synchronous Sampling and Multifunctional Detection: Seven-channel synchronous sampling technology enables comprehensive and accurate capture of voltage, current, and vibration signals, providing a rich and accurate data foundation for subsequent fault detection. Furthermore, the device can simultaneously detect multiple abnormal conditions, including bearing faults and demagnetization, significantly improving detection efficiency and practicality.

[0042] High-precision data acquisition and processing: The use of high-performance data acquisition cards and advanced signal processing technology ensures the accuracy of data acquisition and signal processing. This not only accurately reflects the actual operating status of the elevator drive unit, but also provides a reliable basis for subsequent fault identification and analysis.

[0043] Intelligent fault diagnosis and display: The designed multi-channel WDCNN model and domain adversarial neural network structure enable intelligent feature extraction and fault diagnosis. This not only quickly and accurately identifies the fault type, but also intuitively displays the identification results on the interface, providing great convenience for maintenance personnel.

[0044] Wide Applicability: This invention is not only applicable to specific elevator drive models, but can also adapt to different elevator models and operating conditions through domain adversarial neural networks. This gives the device broad applicability and strong migration capabilities, reducing its dependence on specific elevator models. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the technical solution of the present invention, the following is a brief introduction to the drawings required for use in the embodiments. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0046] Figure 1 A flow chart of a method for detecting multiple abnormal states of an elevator drive host is provided for an embodiment of the present invention.

[0047] Figure 2 This is a schematic diagram of the overall framework of the method for detecting multiple abnormal states of the elevator drive host provided by the present invention.

[0048] Figure 3 This is a schematic diagram of the PXIe8584 board provided by the present invention.

[0049] Figure 4 This is a schematic diagram of the signal denoising process method provided by the present invention. DETAILED DESCRIPTION

[0050] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with specific embodiments of the present invention and corresponding drawings. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention. The technical solutions provided by each embodiment of the present invention are described in detail below in conjunction with the drawings.

[0051] See also Figures 1 to 4 The present invention provides a method for detecting multiple abnormal states of an elevator drive host, including:

[0052] The present invention aims to accurately detect whether the driving host has bearing failure or demagnetization by collecting the voltage, current and vibration signals of the driving host during elevator operation, and further determine the type and degree of the fault, so as to solve the problems of single fault detection function and insufficient accuracy in the existing technology.

[0053] The complete technical solution provided by the present invention

[0054] The present invention proposes a comprehensive and efficient elevator drive host abnormal state detection device, the overall structure of which is as follows: Figure 2As shown, the system primarily consists of a sensor module, an analog-to-digital conversion module, and a host computer module. The sensor module is used to obtain operating data from various elevator drive systems, some of which is retrieved through a third-party data interface. The analog-to-digital conversion module converts the electrical signals generated by elevator operation into digital signals, while the host computer module processes and analyzes the corresponding data generated by the elevator.

[0055] The sensor module consists of a voltage sensor, a current sensor, and an acceleration sensor. It can realize seven-channel synchronous sampling of the three-phase voltage, three-phase current, and vibration signals of the drive host, ensuring accurate capture of key information of the elevator permanent magnet synchronous motor during operation.

[0056] The analog-to-digital conversion module is responsible for receiving the analog voltage, current and vibration information transmitted by the sensor module and accurately converting it into digital quantities, providing accurate data for subsequent digital signal processing and analysis.

[0057] The host computer module is responsible for recording, processing, and analyzing digital information. First, it denoises the received signal to improve the signal-to-noise ratio. It then extracts characteristic information, providing a key basis for fault identification. Finally, it accurately identifies the fault type using advanced fault identification algorithms and displays the results on the interface, providing maintenance personnel with intuitive and convenient fault location services.

[0058] In terms of data acquisition, the present invention uses the high-performance PXIe8584 board, please refer to Figure 3 Its 8-channel, 14-bit, 100MS / s sampling rate ensures accurate capture of high-frequency, high-dynamic range signals. It also features up to 2GB of onboard memory to meet the needs of long-term, large-scale data acquisition.

[0059] In terms of signal processing, this invention employs advanced denoising strategies such as adaptive noise complete set empirical mode decomposition and wavelet transform to effectively remove environmental noise and ensure signal purity. Furthermore, by designing a multi-channel WDCNN model and a domain adversarial neural network structure, it enables intelligent feature extraction and fault diagnosis, significantly improving the accuracy and speed of fault identification.

[0060] In summary, this invention integrates advanced technologies and intelligent algorithms to achieve efficient and accurate detection of abnormal conditions in elevator drive units. This not only improves the safety and reliability of elevator operation but also provides maintenance personnel with a convenient and efficient fault location service.

[0061] See also Figure 1 The present invention provides a method for detecting multiple abnormal states of an elevator drive host, including:

[0062] Acquire operation data of drive hosts of various models of elevators, wherein the operation data of the drive hosts of various models of elevators include basic operation data of elevators, elevator performance parameter data, elevator environment and safety data, data on the number of elevator users and frequency of use, and elevator technical parameters, and pre-process the operation data of the drive hosts of various models of elevators to obtain pre-processed operation data of the drive hosts of various models of elevators;

[0063] Use convolutional neural networks to extract elevator drive host feature data from preprocessed data of various elevator drive host operation data, and establish elevator operation data labels for the elevator drive host feature data;

[0064] Perform a transfer learning algorithm on one elevator model in the elevator drive host feature data, and transfer the knowledge learned on one elevator model to other elevator models;

[0065] Retrieve the data information corresponding to the elevator operation data label, train the initial model of the fault identification algorithm in the initial state, receive the real-time collected operation data of various models of elevator drive host machines to perform transfer learning of the initial model of the fault identification algorithm, and optimize the model through the gradient descent algorithm to obtain the optimized fault identification algorithm model;

[0066] Continuously receive real-time collected operating data of various models of elevator drive hosts, substitute the continuously received real-time collected operating data of various models of elevator drive hosts into the optimized fault identification algorithm model, obtain various abnormal state detection results of the elevator drive hosts, and substitute the various abnormal state detection results of the elevator drive hosts into the preset result display system for real-time display.

[0067] Specifically, the operation data of the drive host of multiple models of elevators are obtained, wherein the operation data of the drive host of multiple models of elevators include basic operation data of elevators, elevator performance parameter data, elevator environment and safety data, data on the number of elevator users and frequency of use, and elevator technical parameters; and the operation data of the drive host of multiple models of elevators are pre-processed to obtain pre-processed operation data of the drive host of multiple models of elevators, including:

[0068] Data cleaning and preliminary screening are performed on the operation data of various types of elevator drive hosts to obtain the first preliminary processed elevator operation data. Outlier detection is performed on the preliminary processed elevator operation data using the interquartile range to obtain elevator operation abnormality data;

[0069] removing the abnormal elevator operation data from the preliminary processed elevator operation data to obtain second preliminary processed elevator operation data;

[0070] Based on the normal operation mode and abnormal mode of the elevator, the second preliminary processing elevator operation data is identified and noise data is removed to obtain pre-processed data of operation data of elevator drive hosts of various models.

[0071] Specifically, a convolutional neural network is used to extract elevator drive host feature data from the preprocessed data of various elevator drive host operation data, and an elevator operation data label is established for the elevator drive host feature data, including:

[0072] Using a convolutional neural network to extract elevator drive host feature data from preprocessed data of various elevator drive host operation data, the elevator drive host feature data includes operating status features, elevator performance parameter features, elevator environment and safety features, elevator usage frequency and mode features, elevator technical parameter features, elevator fault and abnormality features, and elevator image and video features;

[0073] Corresponding tags are established to extract the characteristic data of the elevator drive host, and the tags include operating status and fault type.

[0074] Specifically, a transfer learning algorithm is used on one elevator model in the elevator drive host feature data, and the knowledge learned on one elevator model through the transfer learning algorithm is transferred to the remaining elevator models, including;

[0075] Identify the target elevator model for which knowledge transfer is needed, use a convolutional neural network to extract key features from the source elevator model's feature data, and train a CNN model on the source elevator model's feature data.

[0076] The trained CNN model is used as the initial model for the target elevator model. The performance of the model after transfer learning is evaluated on the feature data of the target elevator model, and the model is optimized based on the evaluation results.

[0077] The knowledge learned from one elevator model is successfully transferred to other models. At this point, the model of the target elevator model already has the knowledge and feature representation capabilities learned by the source elevator model model.

[0078] Verify the performance of the model after transfer learning in a real environment and adjust and optimize it as needed. If the model performance meets expectations, it can be deployed to the actual application system.

[0079] Specifically, the data information corresponding to the elevator operation data label is retrieved to train the initial model of the fault identification algorithm in the initial state. The real-time collected operation data of the elevator drive host of various models are received to perform transfer learning of the initial model of the fault identification algorithm. The model is optimized through the gradient descent algorithm to obtain the optimized fault identification algorithm model, including:

[0080] Retrieve the data information corresponding to the elevator operation data tag and select the historical operation data with the fault tag. The historical operation data with the fault tag contains various parameters of the elevator in normal operation and fault state, including speed, acceleration, current, and voltage;

[0081] Clean the selected data to remove outliers, duplicate values, and missing values;

[0082] Label the data and clearly mark the fault status and normal operation status;

[0083] Extract features related to elevator failures based on the cleaned data. Features related to elevator failures include average speed and acceleration changes over a specific time period.

[0084] Construct the initial model of fault identification algorithm based on support vector machine;

[0085] Use the labeled data set to train the initial model and adjust the parameters of the initial model of the fault identification algorithm to achieve the best initial performance of the initial model of the fault identification algorithm;

[0086] Through sensors or other data acquisition equipment, the operating data of various types of elevator drive hosts are collected in real time. According to the characteristics of the real-time collection of operating data of various types of elevator drive hosts, the initial model parameters of the fault identification algorithm are fine-tuned to adapt to different types of elevator drive hosts.

[0087] The initial model parameters of the fault identification algorithm are optimized through the gradient descent algorithm. According to the calculated gradient, the model parameters are updated using the gradient descent algorithm. The above process is repeated until the model performance reaches the preset standard or the number of iterations reaches the upper limit, and the optimized fault identification algorithm model is obtained.

[0088] Specifically, the system continuously receives real-time collected operating data of various types of elevator drive hosts, substitutes the continuously received real-time collected operating data of various types of elevator drive hosts into an optimized fault identification algorithm model, obtains various abnormal state detection results of the elevator drive hosts, substitutes the various abnormal state detection results of the elevator drive hosts into a preset result display system for real-time display, including:

[0089] Continuously receive real-time data collected from various elevator drive host operating data, verify the received data, test the data integrity, and remove data anomalies caused by transmission errors or equipment failures;

[0090] Substitute the continuously received real-time data into the optimized fault identification algorithm model, use the optimized fault identification algorithm model to analyze and predict the input real-time data, and identify the abnormal state of the elevator drive host;

[0091] The model's prediction results are parsed into specific abnormal status information, including motor overheating, transmission system failure, and load abnormality.

[0092] Specifically, the operation data of the drive host of multiple models of elevators are obtained, wherein the operation data of the drive host of multiple models of elevators include basic operation data of elevators, elevator performance parameter data, elevator environment and safety data, data on the number of elevator users and frequency of use, and elevator technical parameters; and the operation data of the drive host of multiple models of elevators are pre-processed to obtain pre-processed operation data of the drive host of multiple models of elevators, including:

[0093] The noise reduction is performed on the signals in the operating data of the elevator drive host of various models, and the signals in the operating data of the elevator drive host of various models are decomposed into several IMF components through CEEMDAN;

[0094] The correlation coefficients of several IMF components are screened and the wavelet phase is used for noise reduction. The characteristic frequencies are then screened to obtain the reconstructed IMF components and output the noise-reduced signal.

[0095] As can be seen from the above embodiments, the present invention provides a method for detecting multiple abnormal states of an elevator drive host, which obtains operating data of multiple models of elevator drive hosts, pre-processes the operating data of the multiple models of elevator drive hosts, obtains pre-processed data of the operating data of the multiple models of elevator drive hosts, uses a convolutional neural network to extract elevator drive host feature data from the pre-processed data of the operating data of the multiple models of elevator drive hosts, establishes elevator operating data labels for the elevator drive host feature data, performs a transfer learning algorithm on one elevator model in the elevator drive host feature data, transfers the knowledge learned on the one elevator model through the transfer learning algorithm to the remaining elevator models, retrieves data information corresponding to the elevator operating data labels, trains an initial model of a fault recognition algorithm in an initial state, receives operating data of multiple models of elevator drive hosts collected in real time, performs transfer learning on the initial model of the fault recognition algorithm, optimizes the model using a gradient descent algorithm, obtains an optimized fault recognition algorithm model, continuously receives operating data of multiple models of elevator drive hosts collected in real time, substitutes the continuously received operating data of multiple models of elevator drive hosts into the optimized fault recognition algorithm model, obtains detection results of multiple abnormal states of the elevator drive host, and substitutes the detection results of multiple abnormal states of the elevator drive host into a preset result display system for real-time display. It solves the problem that a set of equipment can only be applied to one type of elevator, the detection time is long, and it cannot meet the needs of fast and accurate detection.

[0096] Those skilled in the art will clearly understand that the techniques in the embodiments of the present invention can be implemented using software plus a necessary general-purpose hardware platform. Based on this understanding, the technical solutions in the embodiments of the present invention, or the portion that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a storage medium such as ROM / RAM, a magnetic disk, or an optical disk, and includes a number of instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute the methods described in various embodiments of the present invention or certain portions of the embodiments.

[0097] The above-described embodiments of the present invention do not limit the protection scope of the present invention.

Claims

1. A method for detecting various abnormal states of an elevator drive host, characterized in that: include; Acquire operation data of drive hosts of various models of elevators, wherein the operation data of the drive hosts of various models of elevators include basic operation data of elevators, elevator performance parameter data, elevator environment and safety data, data on the number of elevator users and frequency of use, and elevator technical parameters, and pre-process the operation data of the drive hosts of various models of elevators to obtain pre-processed operation data of the drive hosts of various models of elevators; Use convolutional neural networks to extract elevator drive host feature data from preprocessed data of various elevator drive host operation data, and establish elevator operation data labels for the elevator drive host feature data; Perform a transfer learning algorithm on one elevator model in the elevator drive host feature data, and transfer the knowledge learned on one elevator model to other elevator models; Retrieve the data information corresponding to the elevator operation data label, train the initial model of the fault identification algorithm in the initial state, receive the real-time collected operation data of various models of elevator drive host machines to perform transfer learning of the initial model of the fault identification algorithm, and optimize the model through the gradient descent algorithm to obtain the optimized fault identification algorithm model; Continuously receive real-time collected operating data of various models of elevator drive hosts, substitute the continuously received real-time collected operating data of various models of elevator drive hosts into the optimized fault identification algorithm model, obtain various abnormal state detection results of the elevator drive hosts, and substitute the various abnormal state detection results of the elevator drive hosts into the preset result display system for real-time display.

2. The method according to claim 1, wherein S101: Acquire operation data of drive hosts of various models of elevators, wherein the operation data of the drive hosts of various models of elevators include basic operation data of elevators, elevator performance parameter data, elevator environment and safety data, data on the number of elevator users and frequency of use, and elevator technical parameters; and pre-process the operation data of the drive hosts of various models of elevators to obtain pre-processed operation data of the drive hosts of various models of elevators, including: Data cleaning and preliminary screening are performed on the operation data of various types of elevator drive hosts to obtain the first preliminary processed elevator operation data. Outlier detection is performed on the preliminary processed elevator operation data using the interquartile range to obtain elevator operation abnormality data; removing the abnormal elevator operation data from the preliminary processed elevator operation data to obtain second preliminary processed elevator operation data; Based on the normal operation mode and abnormal mode of the elevator, the second preliminary processing elevator operation data is identified and noise data is removed to obtain pre-processed data of operation data of elevator drive hosts of various models.

3. The method according to claim 1, wherein S102: using a convolutional neural network to extract elevator drive host feature data from pre-processed data of operation data of various types of elevator drive hosts, and establishing elevator operation data labels for the elevator drive host feature data, including: Using a convolutional neural network to extract elevator drive host feature data from preprocessed data of various elevator drive host operation data, the elevator drive host feature data includes operating status features, elevator performance parameter features, elevator environment and safety features, elevator usage frequency and mode features, elevator technical parameter features, elevator fault and abnormality features, and elevator image and video features; Corresponding tags are established to extract the characteristic data of the elevator drive host, and the tags include operating status and fault type.

4. The method according to claim 1, wherein S103: performing a transfer learning algorithm on one elevator model in the elevator drive host feature data, and transferring the knowledge learned on the one elevator model through the transfer learning algorithm to the remaining elevator models, including: Identify the target elevator model for which knowledge transfer is needed, use a convolutional neural network to extract key features from the source elevator model's feature data, and train a CNN model on the source elevator model's feature data. The trained CNN model is used as the initial model for the target elevator model. The performance of the model after transfer learning is evaluated on the feature data of the target elevator model, and the model is optimized based on the evaluation results. The knowledge learned from one elevator model is successfully transferred to other models. At this point, the model of the target elevator model already has the knowledge and feature representation capabilities learned by the source elevator model. Verify the performance of the model after transfer learning in a real environment and adjust and optimize it as needed. If the model performance meets expectations, it can be deployed to the actual application system.

5. The method according to claim 1, wherein S104: Retrieving data information corresponding to elevator operation data labels, training an initial model of a fault identification algorithm in an initial state, receiving real-time collected operation data of various elevator drive host models to perform transfer learning on the initial model of the fault identification algorithm, and optimizing the model using a gradient descent algorithm to obtain an optimized fault identification algorithm model, including: Retrieve the data information corresponding to the elevator operation data tag and select the historical operation data with the fault tag. The historical operation data with the fault tag contains various parameters of the elevator in normal operation and fault state, including speed, acceleration, current, and voltage; Clean the selected data to remove outliers, duplicate values, and missing values; Label the data and clearly mark the fault status and normal operation status; Extract features related to elevator failures based on the cleaned data. Features related to elevator failures include average speed and acceleration changes over a specific time period. Construct the initial model of fault identification algorithm based on support vector machine; Use the labeled data set to train the initial model and adjust the parameters of the initial model of the fault identification algorithm to achieve the best initial performance of the initial model of the fault identification algorithm; Through sensors or other data acquisition equipment, the operating data of various types of elevator drive hosts are collected in real time. According to the characteristics of the real-time collection of operating data of various types of elevator drive hosts, the initial model parameters of the fault identification algorithm are fine-tuned to adapt to different types of elevator drive hosts. The initial model parameters of the fault identification algorithm are optimized through the gradient descent algorithm. According to the calculated gradient, the model parameters are updated using the gradient descent algorithm. The above process is repeated until the model performance reaches the preset standard or the number of iterations reaches the upper limit, and the optimized fault identification algorithm model is obtained.

6. The method according to claim 1, wherein S105: continuously receiving real-time collected operating data of various types of elevator drive hosts, substituting the continuously received real-time collected operating data of various types of elevator drive hosts into an optimized fault identification algorithm model, obtaining various abnormal state detection results of the elevator drive hosts, substituting the various abnormal state detection results of the elevator drive hosts into a preset result display system for real-time display, including: Continuously receive real-time data collected from various elevator drive host operating data, verify the received data, test the data integrity, and remove data anomalies caused by transmission errors or equipment failures; Substitute the continuously received real-time data into the optimized fault identification algorithm model, use the optimized fault identification algorithm model to analyze and predict the input real-time data, and identify the abnormal state of the elevator drive host; The model's prediction results are parsed into specific abnormal status information, including motor overheating, transmission system failure, and load abnormality.

7. The method according to claim 1, wherein Acquiring operation data of drive hosts of various models of elevators, wherein the operation data of the drive hosts of various models of elevators include basic operation data of elevators, elevator performance parameter data, elevator environment and safety data, data on the number of elevator users and frequency of use, and elevator technical parameters; and preprocessing the operation data of the drive hosts of various models of elevators to obtain preprocessed operation data of the drive hosts of various models of elevators, including: The noise reduction is performed on the signals in the operating data of the elevator drive host of various models, and the signals in the operating data of the elevator drive host of various models are decomposed into several IMF components through CEEMDAN; The correlation coefficients of several IMF components are screened and the wavelet phase is used for noise reduction. The characteristic frequencies are then screened to obtain the reconstructed IMF components and output the noise-reduced signal.

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