A method and system for predicting the remaining service life of an elevator traction system

Abstract

The present invention relates to the technical field of elevator operation and maintenance, and provides a method and system for predicting the remaining life of an elevator traction system. The method integrates links such as data acquisition, storage, preprocessing, model training verification, and result comparison. First, comprehensive elevator operation data is collected and a data storage mechanism is established to lay a solid foundation for data analysis and model training. The accuracy and generalization ability of model training are ensured through data preprocessing and set partitioning. An elevator number and data matching mechanism is introduced to achieve refined data management. The accuracy of the model is verified by comparing the first and second prediction results, which improves the prediction reliability and provides a basis for model optimization. Finally, the data features of the third prediction result are compared with the actual data to further ensure the accuracy of the prediction. This method and system provide strong support for safe operation and maintenance of elevators and preventive maintenance, and realize accurate prediction of the remaining life of the elevator traction system.

Description

Technical Field

[0001] The present invention relates to the technical field of elevator operation and maintenance, and in particular to a method and system for predicting the remaining service life of an elevator traction system. Background Art

[0002] The remaining life of an elevator traction system depends on many factors, such as equipment quality, operating environment, and maintenance. Therefore, it's difficult to give a specific remaining lifespan. Generally speaking, if an elevator traction system is well maintained and serviced, its service life can reach its designed lifespan. However, if the equipment is of poor quality, the operating environment is harsh, or maintenance is inadequate, the service life of the elevator traction system may be significantly shortened.

[0003] To ensure the proper operation of the elevator traction system and extend its service life, the following measures are recommended: Regularly inspect the elevator traction system, including the traction motor, wire rope, guide rails, and other components, to ensure they are in good working condition. Perform maintenance according to the manufacturer's recommended maintenance plan, promptly replacing worn parts to keep the equipment in good condition. Avoid improper use, such as overloading and excessive use, to reduce wear and damage to the equipment. Address faults promptly: If a fault is detected in the elevator traction system, immediately remove it from service and have it inspected by a professional to prevent further damage and potentially more serious consequences.

[0004] Existing methods for predicting the remaining life of elevator traction systems are based on eddy current testing. Using an oscillator and a detection device, eddy current testing is used to monitor the running elevator traction rope in real time. A signal conditioning module converts the monitored data into a detection signal. An analysis and evaluation module compares the collected detection signal with pre-stored database data on different levels of wear and life of the elevator traction rope to predict the life of the tested elevator traction rope.

[0005] The accuracy of existing predictions of the remaining life of elevator traction systems is highly dependent on the quality of the prediction model and the information stored in the database. If the model is not accurate enough or the database information is incomplete, the prediction results may be biased. Although eddy current testing can be used for real-time monitoring, long-term continuous monitoring can generate a large amount of data, placing higher demands on data processing and analysis capabilities. Furthermore, real-time monitoring requires continuous operation of the equipment, which may increase equipment wear and energy consumption, resulting in reduced accuracy of real-time monitoring equipment. Summary of the Invention

[0006] The present invention aims to provide a method and system for predicting the remaining life of an elevator traction system. This method addresses the problem that the accuracy of existing predictions of the remaining life of an elevator traction system is highly dependent on the quality of the prediction model and the information stored in the database. If the model is not accurate enough or the database information is not complete, the prediction results may be biased.

[0007] The present invention provides a method for predicting the remaining service life of an elevator traction system, comprising:

[0008] Acquire and store elevator traction system operating data in a database for storing elevator operation and maintenance data, the elevator traction system operating data including traction system load data, elevator operating speed, acceleration data, operating time data, fault and abnormality records, operating environment data, maintenance history data, and elevator energy consumption data;

[0009] After data preprocessing, the elevator traction system operation data is divided into a training set, a validation set, and a test set. The training set is used to train the model, the validation set is used to adjust the parameters and hyperparameters of the preset elevator traction system remaining life prediction model, and the test set is used to evaluate the final performance of the preset elevator traction system remaining life prediction model.

[0010] Establish elevator numbers for elevators in the operation and maintenance area, match the real-time collected elevator traction system operation data with the elevator numbers, and store them in the corresponding storage space in the database; retrieve the maintenance history data of the elevators in the operation and maintenance area, extract data features from the maintenance history data of the elevators in the operation and maintenance area, create a data inspection form based on the data features of the elevator maintenance history data, and collect the corresponding feature data in the data inspection form according to the preset time period;

[0011] Substituting the corresponding characteristic data in the inspection form of data collected in a preset time period into the elevator traction system remaining life prediction model to obtain a first elevator traction system remaining life prediction result, retrieving the elevator traction system operation data corresponding to the time when the elevator failure occurred, and substituting the data into the elevator traction system remaining life prediction model to obtain a second elevator traction system remaining life prediction result, comparing the first elevator traction system remaining life prediction result with the second elevator traction system remaining life prediction result, if the data comparison results are consistent, the elevator traction system remaining life prediction model and the elevator traction system data monitoring are normal, and outputting a third elevator traction system remaining life prediction result, if the data comparison results are inconsistent, optimizing the elevator traction system remaining life prediction model, and detecting the corresponding data collection interface in the elevator traction system operation data;

[0012] Extract the data features in the remaining life prediction result of the third elevator traction system to obtain the data feature information of the third prediction result, collect the corresponding data in the real-time third prediction result data, compare the remaining life prediction result of the third elevator traction system with the actual data, and check the degree of consistency between the remaining life prediction result of the third elevator traction system and the corresponding data in the collected real-time third prediction result data. If the prediction result is significantly different from the actual data, the elevator traction system remaining life prediction model needs to be adjusted and optimized.

[0013] Furthermore, the elevator traction system operation data is obtained, wherein the elevator traction system operation data includes load data of the traction system, elevator operating speed, acceleration data, operating time data, fault and abnormal condition records, operating environment data, maintenance history data, and elevator energy consumption data, including;

[0014] Determine the type of elevator traction system operating data and set the synchronization frequency;

[0015] The database architecture consists of a master database and a slave database. The master database is used to handle write operations, while the slave database is used to handle read operations and data backup.

[0016] Split the load data of the traction system, elevator operating speed, acceleration data, operating time data, fault and abnormality records, operating environment data, maintenance history data, and elevator energy consumption data in the elevator traction system operation data into multiple elevator traction system data shards, and store each elevator traction system data shard on a different database node;

[0017] After each elevator traction system data shard is stored on a different database node, the data is synchronously stored in the blockchain, and a corresponding hash value and timestamp are generated for each elevator traction system data shard stored on a different database node;

[0018] If a data conflict occurs during the synchronization of the elevator traction system data, the conflicting data will be distinguished based on the hash value and timestamp corresponding to the data. If the data is the same data, only the data label and the retrieval path of the data corresponding to the label will be established.

[0019] Furthermore, after data preprocessing, the elevator traction system operation data is divided into a training set, a validation set, and a test set. The training set is used to train the model, the validation set is used to adjust the parameters and hyperparameters of the preset elevator traction system remaining life prediction model, and the test set is used to evaluate the final performance of the preset elevator traction system remaining life prediction model, including:

[0020] Performing data cleaning on the elevator traction system operation data to remove abnormal values, noise data, and duplicate data in the elevator traction system operation data to obtain the cleaned elevator traction system operation data;

[0021] Converting the cleaned elevator traction system operation data into a standard format to obtain a standardized elevator traction system operation data set;

[0022] The standardized elevator traction system operation dataset is divided into training set, validation set and test set.

[0023] Furthermore, an elevator number is established for the elevators in the operation and maintenance area, the real-time collected elevator traction system operation data is matched with the elevator number and stored in the corresponding storage space in the database, the maintenance history data of the elevators in the operation and maintenance area is retrieved, data features are extracted from the maintenance history data of the elevators in the operation and maintenance area, a data inspection form is created based on the data features of the maintenance history data of the elevators, and corresponding feature data in the data inspection form is collected according to a preset time period based on the data inspection form, including:

[0024] Using sensors or data acquisition equipment installed on the elevator traction system, the elevator traction system operation data is collected in real time, and the collected elevator traction system operation data is matched with the corresponding elevator number;

[0025] Through database query statements, retrieve the maintenance history data of the specified elevator from the database;

[0026] Perform data analysis on historical maintenance data using a Weibull distribution model to extract key data features related to the remaining life prediction of the elevator traction system, including failure interval time, number of repairs, failure type, repair time, and abnormal data;

[0027] Based on the extracted key data features, a data inspection form is created to determine the data items and collection frequency to be collected regularly. The data inspection form includes the elevator number, inspection time and key data feature value information.

[0028] Furthermore, corresponding characteristic data in the inspection form of data collected during a preset time period is substituted into the elevator traction system remaining life prediction model to obtain a first elevator traction system remaining life prediction result, elevator traction system operation data corresponding to the time when the elevator failure occurred is retrieved, and the data is substituted into the elevator traction system remaining life prediction model to obtain a second elevator traction system remaining life prediction result, the first elevator traction system remaining life prediction result is compared with the second elevator traction system remaining life prediction result, if the data comparison results are consistent, the elevator traction system remaining life prediction model and the elevator traction system data monitoring are normal, and a third elevator traction system remaining life prediction result is output, if the data comparison results are inconsistent, the elevator traction system remaining life prediction model is optimized, and the corresponding data collection interface in the elevator traction system operation data is detected, including;

[0029] Clean, convert, and standardize the corresponding feature data in the inspection form of the data collected during the preset time period. Data cleaning methods include processing missing values, outliers, and duplicate values, as well as standardizing the data;

[0030] Screening out features highly correlated with the remaining life of the elevator traction system from the pre-processed data. Features highly correlated with the remaining life of the elevator traction system include the time between failures, the number of repairs, and the wear condition of key components.

[0031] Substituting the characteristic data that is highly correlated with the remaining life of the elevator traction system into the remaining life prediction model of the elevator traction system, and processing and analyzing the data using the established remaining life prediction model of the elevator traction system;

[0032] After the elevator traction system remaining life prediction model is run, the remaining life prediction result of the first elevator traction system will be output. The remaining life prediction result of the first elevator traction system includes a specific remaining life value of the traction system;

[0033] Retrieve the traction system operation data of the elevator at the time of the failure from the elevator operation and maintenance data database, including the speed, acceleration, temperature and pressure of the elevator at the time of the failure;

[0034] The traction system operation data when the elevator fails is substituted into the same elevator traction system remaining life prediction model, and the elevator traction system remaining life prediction model outputs a remaining life prediction result of the second elevator traction system.

[0035] Furthermore, the present invention provides a method for predicting the remaining life of an elevator traction system, further comprising:

[0036] The data storage space used to store the elevator traction system operation data is tested for remaining data storage space at a preset time period. If the remaining data storage space in the test result is less than 1T data storage space, an insufficient data storage space warning message will be generated, and a communication connection will be established with a third-party cloud storage space interface. If the data storage space is full, the data will be automatically stored in the third-party cloud storage space.

[0037] Before storing the data in a third-party cloud storage space, the data to be stored will be encrypted by privacy computing. After the data is encrypted by privacy computing, it will be stored in the third-party cloud storage space.

[0038] Real-time detection of local storage space. If the capacity of this storage space increases, the data stored in the third-party cloud storage space will be retrieved and stored in the local storage space, and the data stored in the third-party cloud storage space will be deleted simultaneously.

[0039] In a second aspect, the present invention provides an elevator traction system remaining life prediction system, comprising: a server side, an elevator equipment side, a background control side, and an operator equipment side;

[0040] The server side establishes a communication connection with the elevator equipment side, the background control side and the operator equipment side, and the server side is used to process the data fed back by the elevator equipment side, the background control side and the operator equipment, and transmit the data processing results to the background control side and the operator equipment side;

[0041] The server side includes a data acquisition unit, a model building unit, a data inspection unit, an elevator traction system life prediction unit and a prediction model optimization unit;

[0042] Data acquisition unit: acquires elevator traction system operation data and stores it in a database for storing elevator operation and maintenance data, wherein the elevator traction system operation data includes traction system load data, elevator operating speed, acceleration data, operating time data, fault and abnormality records, operating environment data, maintenance history data, and elevator energy consumption data;

[0043] Model building unit: After data preprocessing, the elevator traction system operation data is divided into a training set, a validation set, and a test set. The training set is used to train the model, the validation set is used to adjust the parameters and hyperparameters of the preset elevator traction system remaining life prediction model, and the test set is used to evaluate the final performance of the preset elevator traction system remaining life prediction model;

[0044] Data inspection unit: establishes elevator numbers for elevators in the operation and maintenance area, matches the real-time collected elevator traction system operation data with the elevator number, and stores it in the corresponding storage space in the database; retrieves the maintenance history data of the elevators in the operation and maintenance area, extracts data features from the maintenance history data of the elevators in the operation and maintenance area, creates a data inspection form based on the data features of the elevator maintenance history data, and collects the corresponding feature data in the data inspection form according to the preset time period;

[0045] Elevator traction system life prediction unit: substitutes the corresponding characteristic data in the inspection form of data collected in a preset time period into the elevator traction system remaining life prediction model to obtain a first elevator traction system remaining life prediction result, retrieves the elevator traction system operation data corresponding to the time when the elevator failure occurs, and substitutes the data into the elevator traction system remaining life prediction model to obtain a second elevator traction system remaining life prediction result, compares the first elevator traction system remaining life prediction result with the second elevator traction system remaining life prediction result, if the data comparison results are consistent, the elevator traction system remaining life prediction model and the elevator traction system data monitoring are normal, and outputs a third elevator traction system remaining life prediction result, if the data comparison results are inconsistent, optimizes the elevator traction system remaining life prediction model, and detects the corresponding data collection interface in the elevator traction system operation data;

[0046] Prediction model optimization unit: extract the data features in the prediction result of the remaining life of the third elevator traction system, obtain the data feature information of the third prediction result, collect the corresponding data in the real-time third prediction result data, compare the prediction result of the remaining life of the third elevator traction system with the actual data, check the degree of consistency between the prediction result of the remaining life of the third elevator traction system and the corresponding data in the collected real-time third prediction result data. If the prediction result is significantly different from the actual data, the elevator traction system remaining life prediction model needs to be adjusted and optimized.

[0047] The beneficial effects of the present invention are as follows: The present invention provides a method and system for predicting the remaining life of an elevator traction system. This method integrates multiple steps, including data acquisition, storage, preprocessing, model training and verification, and result comparison, to form a complete and systematic prediction process. First, by comprehensively acquiring various operating data of the elevator traction system and establishing an effective data storage mechanism, a solid foundation is provided for subsequent data analysis and model training. Data preprocessing and set partitioning ensure the accuracy and generalization capabilities of model training. Furthermore, this application introduces a mechanism for matching elevator numbers with data, enabling refined data management.

[0048] During the model application phase, by substituting characteristic data, the remaining life prediction results for the first and second elevator traction systems were obtained. The accuracy of the model was verified by comparing these two sets of results. This dual verification mechanism not only improved the reliability of the predictions but also provided a basis for model optimization. Finally, by extracting the data features of the third prediction result and comparing it with the actual data, the accuracy of the prediction was further ensured.

[0049] This application achieves accurate prediction of the remaining life of the elevator traction system through the comprehensive use of technical means such as data analysis, machine learning and real-time monitoring, providing strong technical support for the safe operation and preventive maintenance of elevators. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] 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.

[0051] Figure 1 A flow chart of a method for predicting the remaining life of an elevator traction system is provided for an embodiment of the present invention. DETAILED DESCRIPTION

[0052] 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 part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts 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.

[0053] See also Figure 1 The present invention provides a method for predicting the remaining life of an elevator traction system, comprising:

[0054] In step S101, the elevator traction system operation data is obtained and stored in a database for storing elevator operation and maintenance data, wherein the elevator traction system operation data includes load data of the traction system, elevator operating speed, acceleration data, operating time data, fault and abnormal condition records, operating environment data, maintenance history data, and elevator energy consumption data;

[0055] In step S101, the system comprehensively collects operational data from the elevator's traction system. This data includes, but is not limited to, key information such as load, operating speed, acceleration, and operating time. It also records faults and abnormalities, operating environment data (such as temperature and humidity), maintenance history, and elevator energy consumption data. This data is stored in a dedicated elevator operation and maintenance database for subsequent analysis and processing.

[0056] Comprehensive data collection provides a rich information foundation for subsequent data analysis and model training. Database storage makes data management and retrieval more efficient and facilitates long-term storage and analysis.

[0057] In step S102, after data preprocessing, the elevator traction system operation data is divided into a training set, a validation set, and a test set. The training set is used to train the model, the validation set is used to adjust the parameters and hyperparameters of the preset elevator traction system remaining life prediction model, and the test set is used to evaluate the final performance of the preset elevator traction system remaining life prediction model.

[0058] The collected raw data undergoes preprocessing steps such as cleaning, transformation, and standardization to eliminate outliers, missing values, and duplicate values. The preprocessed dataset is then divided into training, validation, and test sets. The training set is used to train the predictive model, the validation set is used to adjust the model's parameters and hyperparameters, and the test set is used to evaluate the model's final performance.

[0059] Data preprocessing improves data quality and consistency, making model training more accurate. Proper data segmentation helps objectively evaluate the generalization ability of the model and prevent overfitting.

[0060] In step S103, an elevator number is established for each elevator in the operation and maintenance area, and the real-time collected elevator traction system operation data is matched with the elevator number and stored in the corresponding storage space in the database. The maintenance history data of the elevators in the operation and maintenance area is retrieved, and data features are extracted from the maintenance history data of the elevators in the operation and maintenance area. Based on the data features of the maintenance history data of the elevators, a data inspection form is created, and the corresponding feature data in the data inspection form is collected according to a preset time period according to the data inspection form.

[0061] In step S103, each elevator is assigned a unique number and the real-time data collected is matched to this number to ensure data accuracy and traceability. Simultaneously, the system retrieves historical maintenance data and extracts key data features. Based on these features, a data inspection form is created, which will be used to guide subsequent data collection.

[0062] Elevator numbering ensures accurate data matching, improving the efficiency and accuracy of data management. Data feature extraction helps identify key information in maintenance history, laying the foundation for subsequent prediction work.

[0063] In step S104, the corresponding characteristic data in the inspection form of data collected during the preset time period is substituted into the elevator traction system remaining life prediction model to obtain a first elevator traction system remaining life prediction result, the elevator traction system operation data corresponding to the time when the elevator failure occurred is retrieved, and the data is substituted into the elevator traction system remaining life prediction model to obtain a second elevator traction system remaining life prediction result, and the first elevator traction system remaining life prediction result is compared with the second elevator traction system remaining life prediction result. If the data comparison results are consistent, the elevator traction system remaining life prediction model and the elevator traction system data monitoring are normal, and a third elevator traction system remaining life prediction result is output. If the data comparison results are inconsistent, the elevator traction system remaining life prediction model is optimized, and the corresponding data collection interface in the elevator traction system operation data is detected.

[0064] Based on a preset time period, the system collects the corresponding characteristic data from the data inspection form and inserts this data into a trained elevator traction system remaining life prediction model to generate a first prediction result. Simultaneously, when an elevator malfunction occurs, the system retrieves the operating data at the time of the malfunction and inserts it into the model for a second prediction. The two prediction results are then compared to verify the model's accuracy.

[0065] By comparing the two prediction results, potential problems with the model or data acquisition interface can be discovered in a timely manner. This dual verification mechanism improves the reliability of the prediction and provides a strong guarantee for the safe operation and maintenance of elevators.

[0066] In step S105, the data features in the remaining life prediction result of the third elevator traction system are extracted to obtain the data feature information of the third prediction result, the corresponding data in the real-time third prediction result data is collected, the remaining life prediction result of the third elevator traction system is compared with the actual data, and the degree of consistency between the remaining life prediction result of the third elevator traction system and the corresponding data in the collected real-time third prediction result data is checked. If the prediction result is significantly different from the actual data, the elevator traction system remaining life prediction model needs to be adjusted and optimized.

[0067] In step S105, the data features in the final prediction result are extracted and compared with the real-time collected data. If it is found that the prediction result is significantly different from the actual data, the prediction model needs to be adjusted and optimized.

[0068] By comparing the model with actual data, the prediction model can be continuously verified and optimized to improve its accuracy. Continuous optimization of the model helps to more accurately predict the remaining life of the elevator traction system, providing decision support for preventive maintenance.

[0069] Specifically, the elevator traction system operation data is obtained, wherein the elevator traction system operation data includes load data of the traction system, elevator operating speed, acceleration data, operating time data, fault and abnormal condition records, operating environment data, maintenance history data, and elevator energy consumption data, including;

[0070] Determine the type of elevator traction system operating data and set the synchronization frequency;

[0071] The database architecture consists of a master database and a slave database. The master database is used to handle write operations, while the slave database is used to handle read operations and data backup.

[0072] Split the load data of the traction system, elevator operating speed, acceleration data, operating time data, fault and abnormality records, operating environment data, maintenance history data, and elevator energy consumption data in the elevator traction system operation data into multiple elevator traction system data shards, and store each elevator traction system data shard on a different database node;

[0073] After each elevator traction system data shard is stored on a different database node, the data is synchronously stored in the blockchain, and a corresponding hash value and timestamp are generated for each elevator traction system data shard stored on a different database node;

[0074] If a data conflict occurs during the synchronization of the elevator traction system data, the conflicting data will be distinguished based on the hash value and timestamp corresponding to the data. If the data is the same data, only the data label and the retrieval path of the data corresponding to the label will be established.

[0075] This step solves the problem that although eddy current testing can be used for real-time monitoring, long-term continuous monitoring may generate a large amount of data, which places higher demands on data processing and analysis capabilities.

[0076] Specifically, after data preprocessing, the elevator traction system operation data is divided into a training set, a validation set, and a test set. The training set is used to train the model, the validation set is used to adjust the parameters and hyperparameters of the preset elevator traction system remaining life prediction model, and the test set is used to evaluate the final performance of the preset elevator traction system remaining life prediction model, including;

[0077] Performing data cleaning on the elevator traction system operation data to remove abnormal values, noise data, and duplicate data in the elevator traction system operation data to obtain the cleaned elevator traction system operation data;

[0078] Converting the cleaned elevator traction system operation data into a standard format to obtain a standardized elevator traction system operation data set;

[0079] The standardized elevator traction system operation dataset is divided into training set, validation set and test set.

[0080] Specifically, an elevator number is established for the elevators in the operation and maintenance area, the real-time collected elevator traction system operation data is matched with the elevator number and stored in the corresponding storage space in the database, the maintenance history data of the elevators in the operation and maintenance area is retrieved, and data features are extracted from the maintenance history data of the elevators in the operation and maintenance area. According to the data features of the maintenance history data of the elevators, a data inspection form is created, and the corresponding feature data in the data inspection form is collected according to a preset time period according to the data inspection form, including:

[0081] Using sensors or data acquisition equipment installed on the elevator traction system, the elevator traction system operation data is collected in real time, and the collected elevator traction system operation data is matched with the corresponding elevator number;

[0082] Through database query statements, retrieve the maintenance history data of the specified elevator from the database;

[0083] Perform data analysis on historical maintenance data using a Weibull distribution model to extract key data features related to the remaining life prediction of the elevator traction system, including failure interval time, number of repairs, failure type, repair time, and abnormal data;

[0084] Based on the extracted key data features, a data inspection form is created to determine the data items and collection frequency to be collected regularly. The data inspection form includes the elevator number, inspection time and key data feature value information.

[0085] Specifically, the corresponding characteristic data in the inspection form of data collected in a preset time period is substituted into the elevator traction system remaining life prediction model to obtain a first elevator traction system remaining life prediction result, the elevator traction system operation data corresponding to the time when the elevator failure occurs is retrieved, and the data is substituted into the elevator traction system remaining life prediction model to obtain a second elevator traction system remaining life prediction result, the first elevator traction system remaining life prediction result is compared with the second elevator traction system remaining life prediction result, if the data comparison results are consistent, the elevator traction system remaining life prediction model and the elevator traction system data monitoring are normal, and the third elevator traction system remaining life prediction result is output, if the data comparison results are inconsistent, the elevator traction system remaining life prediction model is optimized, and the corresponding data collection interface in the elevator traction system operation data is detected, including;

[0086] Clean, convert, and standardize the corresponding feature data in the inspection form of the data collected during the preset time period. Data cleaning methods include processing missing values, outliers, and duplicate values, as well as standardizing the data;

[0087] Screening out features highly correlated with the remaining life of the elevator traction system from the pre-processed data. Features highly correlated with the remaining life of the elevator traction system include the time between failures, the number of repairs, and the wear condition of key components.

[0088] Substituting the characteristic data that is highly correlated with the remaining life of the elevator traction system into the remaining life prediction model of the elevator traction system, and processing and analyzing the data using the established remaining life prediction model of the elevator traction system;

[0089] After the elevator traction system remaining life prediction model is run, the remaining life prediction result of the first elevator traction system will be output. The remaining life prediction result of the first elevator traction system includes a specific remaining life value of the traction system;

[0090] Retrieve the traction system operation data of the elevator at the time of the failure from the elevator operation and maintenance data database, including the speed, acceleration, temperature and pressure of the elevator at the time of the failure;

[0091] The traction system operation data when the elevator fails is substituted into the same elevator traction system remaining life prediction model, and the elevator traction system remaining life prediction model outputs a remaining life prediction result of the second elevator traction system.

[0092] Specifically, the present invention provides a method for predicting the remaining life of an elevator traction system, further comprising:

[0093] The data storage space used to store the elevator traction system operation data is tested for remaining data storage space at a preset time period. If the remaining data storage space in the test result is less than 1T data storage space, an insufficient data storage space warning message will be generated, and a communication connection will be established with a third-party cloud storage space interface. If the data storage space is full, the data will be automatically stored in the third-party cloud storage space.

[0094] Before storing the data in a third-party cloud storage space, the data to be stored will be encrypted by privacy computing. After the data is encrypted by privacy computing, it will be stored in the third-party cloud storage space.

[0095] Real-time detection of local storage space. If the capacity of this storage space increases, the data stored in the third-party cloud storage space will be retrieved and stored in the local storage space, and the data stored in the third-party cloud storage space will be deleted simultaneously.

[0096] This step solves the problem that real-time monitoring also means that the device needs to run continuously, which may increase device wear and energy consumption, resulting in reduced monitoring accuracy of real-time monitoring equipment, by detecting local storage space and establishing a communication connection with cloud storage space.

[0097] In a second aspect, the present invention provides an elevator traction system remaining life prediction system, comprising: a server side, an elevator equipment side, a background control side, and an operator equipment side;

[0098] The server side establishes a communication connection with the elevator equipment side, the background control side and the operator equipment side, and the server side is used to process the data fed back by the elevator equipment side, the background control side and the operator equipment, and transmit the data processing results to the background control side and the operator equipment side;

[0099] The server side includes a data acquisition unit, a model building unit, a data inspection unit, an elevator traction system life prediction unit and a prediction model optimization unit;

[0100] Data acquisition unit: acquires elevator traction system operation data and stores it in a database for storing elevator operation and maintenance data, wherein the elevator traction system operation data includes traction system load data, elevator operating speed, acceleration data, operating time data, fault and abnormality records, operating environment data, maintenance history data, and elevator energy consumption data;

[0101] Model building unit: After data preprocessing, the elevator traction system operation data is divided into a training set, a validation set, and a test set. The training set is used to train the model, the validation set is used to adjust the parameters and hyperparameters of the preset elevator traction system remaining life prediction model, and the test set is used to evaluate the final performance of the preset elevator traction system remaining life prediction model;

[0102] Data inspection unit: establishes elevator numbers for elevators in the operation and maintenance area, matches the real-time collected elevator traction system operation data with the elevator number, and stores it in the corresponding storage space in the database; retrieves the maintenance history data of the elevators in the operation and maintenance area, extracts data features from the maintenance history data of the elevators in the operation and maintenance area, creates a data inspection form based on the data features of the elevator maintenance history data, and collects the corresponding feature data in the data inspection form according to the preset time period;

[0103] Elevator traction system life prediction unit: substitutes the corresponding characteristic data in the inspection form of data collected in a preset time period into the elevator traction system remaining life prediction model to obtain a first elevator traction system remaining life prediction result, retrieves the elevator traction system operation data corresponding to the time when the elevator failure occurs, and substitutes the data into the elevator traction system remaining life prediction model to obtain a second elevator traction system remaining life prediction result, compares the first elevator traction system remaining life prediction result with the second elevator traction system remaining life prediction result, if the data comparison results are consistent, the elevator traction system remaining life prediction model and the elevator traction system data monitoring are normal, and outputs a third elevator traction system remaining life prediction result, if the data comparison results are inconsistent, optimizes the elevator traction system remaining life prediction model, and detects the corresponding data collection interface in the elevator traction system operation data;

[0104] Prediction model optimization unit: extract the data features in the prediction result of the remaining life of the third elevator traction system, obtain the data feature information of the third prediction result, collect the corresponding data in the real-time third prediction result data, compare the prediction result of the remaining life of the third elevator traction system with the actual data, check the degree of consistency between the prediction result of the remaining life of the third elevator traction system and the corresponding data in the collected real-time third prediction result data. If the prediction result is significantly different from the actual data, the elevator traction system remaining life prediction model needs to be adjusted and optimized.

[0105] As can be seen from the above embodiments, the present invention provides a method and system for predicting the remaining life of an elevator traction system. The technical solution of this application first comprehensively collects various operating data of the elevator traction system, including load, speed, acceleration, operating time, etc., and stores this data in a dedicated elevator operation and maintenance database. The collected data is cleaned and preprocessed to eliminate outliers and noise. The data is then divided into training, validation, and test sets, which are used for model training, parameter adjustment, and performance evaluation, respectively. A unique number is established for each elevator, and the real-time collected data is matched with the elevator number and stored in the corresponding database space. Key features are extracted from the maintenance history data, and data inspection forms are created based on these features. These feature data are then collected according to a preset time period. The collected feature data is substituted into the trained elevator traction system remaining life prediction model to obtain a first prediction result. When an elevator failure occurs, the operating data at the time of the failure is retrieved and substituted into the model for a second prediction. Compare the first and second prediction results. If they match, the model and data monitoring are functioning properly, and the final prediction result is output. If they do not, optimize the model and check the data acquisition interface. Extract data features from the final prediction result and compare them with real-time data to verify the accuracy of the prediction. If the prediction result differs significantly from the actual data, adjust and optimize the prediction model.

[0106] This technical solution allows users to effectively predict the remaining life of elevator traction systems, promptly identify potential safety hazards, and provide strong support for elevator operation, maintenance, and management. This solution addresses the problem that the accuracy of existing predictions of the remaining life of elevator traction systems is highly dependent on the quality of the prediction model and the information stored in the database. If the model is not accurate enough or the database information is incomplete, the prediction results may be biased.

[0107] 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.

[0108] The above-described embodiments of the present invention do not limit the protection scope of the present invention.

Claims

1. A method for predicting the remaining life of an elevator traction system, characterized in that: include; Acquire and store elevator traction system operating data in a database for storing elevator operation and maintenance data, the elevator traction system operating data including traction system load data, elevator operating speed, acceleration data, operating time data, fault and abnormality records, operating environment data, maintenance history data, and elevator energy consumption data; After data preprocessing, the elevator traction system operation data is divided into a training set, a validation set, and a test set. The training set is used to train the model, the validation set is used to adjust the parameters and hyperparameters of the preset elevator traction system remaining life prediction model, and the test set is used to evaluate the final performance of the preset elevator traction system remaining life prediction model. Establish elevator numbers for elevators in the operation and maintenance area, match the real-time collected elevator traction system operation data with the elevator numbers, and store them in the corresponding storage space in the database; retrieve the maintenance history data of the elevators in the operation and maintenance area, extract data features from the maintenance history data of the elevators in the operation and maintenance area, create a data inspection form based on the data features of the elevator maintenance history data, and collect the corresponding feature data in the data inspection form according to the preset time period; Substituting the corresponding characteristic data in the inspection form of data collected in a preset time period into the elevator traction system remaining life prediction model to obtain a first elevator traction system remaining life prediction result, retrieving the elevator traction system operation data corresponding to the time when the elevator failure occurred, and substituting the data into the elevator traction system remaining life prediction model to obtain a second elevator traction system remaining life prediction result, comparing the first elevator traction system remaining life prediction result with the second elevator traction system remaining life prediction result, if the data comparison results are consistent, the elevator traction system remaining life prediction model and the elevator traction system data monitoring are normal, and outputting a third elevator traction system remaining life prediction result, if the data comparison results are inconsistent, optimizing the elevator traction system remaining life prediction model, and detecting the corresponding data collection interface in the elevator traction system operation data; Extract the data features in the remaining life prediction result of the third elevator traction system to obtain the data feature information of the third prediction result, collect the corresponding data in the real-time third prediction result data, compare the remaining life prediction result of the third elevator traction system with the actual data, and check the degree of consistency between the remaining life prediction result of the third elevator traction system and the corresponding data in the collected real-time third prediction result data. If the prediction result is significantly different from the actual data, the elevator traction system remaining life prediction model needs to be adjusted and optimized.

2. The method according to claim 1, wherein Acquiring elevator traction system operation data, including traction system load data, elevator operating speed, acceleration data, operating time data, fault and abnormality records, operating environment data, maintenance history data, and elevator energy consumption data, including; Determine the type of elevator traction system operating data and set the synchronization frequency; The database architecture consists of a master database and a slave database. The master database is used to handle write operations, while the slave database is used to handle read operations and data backup. Split the load data of the traction system, elevator operating speed, acceleration data, operating time data, fault and abnormality records, operating environment data, maintenance history data, and elevator energy consumption data in the elevator traction system operation data into multiple elevator traction system data shards, and store each elevator traction system data shard on a different database node; After each elevator traction system data shard is stored on a different database node, the data is synchronously stored in the blockchain, and a corresponding hash value and timestamp are generated for each elevator traction system data shard stored on a different database node; If a data conflict occurs during the synchronization of the elevator traction system data, the conflicting data will be distinguished based on the hash value and timestamp corresponding to the data. If the data is the same data, only the data label and the retrieval path of the data corresponding to the label will be established.

3. The method according to claim 1, wherein After data preprocessing, the elevator traction system operation data is divided into a training set, a validation set, and a test set. The training set is used to train the model, the validation set is used to adjust the parameters and hyperparameters of the preset elevator traction system remaining life prediction model, and the test set is used to evaluate the final performance of the preset elevator traction system remaining life prediction model, including: Performing data cleaning on the elevator traction system operation data to remove abnormal values, noise data, and duplicate data in the elevator traction system operation data to obtain the cleaned elevator traction system operation data; Converting the cleaned elevator traction system operation data into a standard format to obtain a standardized elevator traction system operation data set; The standardized elevator traction system operation dataset is divided into training set, validation set and test set.

4. The method according to claim 1, wherein Establishing elevator numbers for elevators in the operation and maintenance area, matching the real-time collected elevator traction system operation data with the elevator numbers and storing them in the corresponding storage space in the database, retrieving the maintenance history data of the elevators in the operation and maintenance area, extracting data features from the maintenance history data of the elevators in the operation and maintenance area, creating a data inspection form based on the data features of the maintenance history data of the elevators, and collecting corresponding feature data in the data inspection form according to a preset time period based on the data inspection form, including; Using sensors or data acquisition equipment installed on the elevator traction system, the elevator traction system operation data is collected in real time, and the collected elevator traction system operation data is matched with the corresponding elevator number; Through database query statements, retrieve the maintenance history data of the specified elevator from the database; Perform data analysis on historical maintenance data using a Weibull distribution model to extract key data features related to the remaining life prediction of the elevator traction system, including failure interval time, number of repairs, failure type, repair time, and abnormal data; Based on the extracted key data features, a data inspection form is created to determine the data items and collection frequency to be collected regularly. The data inspection form includes the elevator number, inspection time and key data feature value information.

5. The method according to claim 1, wherein Substituting the corresponding characteristic data in the inspection form of data collected during a preset time period into the elevator traction system remaining life prediction model to obtain a first elevator traction system remaining life prediction result, retrieving the elevator traction system operation data corresponding to the time when the elevator failure occurred, and substituting the data into the elevator traction system remaining life prediction model to obtain a second elevator traction system remaining life prediction result, comparing the first elevator traction system remaining life prediction result with the second elevator traction system remaining life prediction result, if the data comparison results are consistent, the elevator traction system remaining life prediction model and the elevator traction system data monitoring are normal, and outputting a third elevator traction system remaining life prediction result, if the data comparison results are inconsistent, optimizing the elevator traction system remaining life prediction model, and detecting the corresponding data collection interface in the elevator traction system operation data, including; Clean, convert, and standardize the corresponding feature data in the inspection form of the data collected during the preset time period. Data cleaning methods include processing missing values, outliers, and duplicate values, as well as standardizing the data; Screening out features highly correlated with the remaining life of the elevator traction system from the preprocessed data. Features highly correlated with the remaining life of the elevator traction system include the time between failures, the number of repairs, and the wear condition of key components. Substituting the screened-out feature data highly correlated with the remaining life of the elevator traction system into the remaining life prediction model of the elevator traction system, and processing and analyzing the data using the established remaining life prediction model of the elevator traction system; After the elevator traction system remaining life prediction model is run, the remaining life prediction result of the first elevator traction system will be output. The remaining life prediction result of the first elevator traction system includes a specific remaining life value of the traction system; Retrieve the traction system operation data of the elevator at the time of the failure from the elevator operation and maintenance data database, including the speed, acceleration, temperature and pressure of the elevator at the time of the failure; The traction system operation data when the elevator fails is substituted into the same elevator traction system remaining life prediction model, and the elevator traction system remaining life prediction model outputs a remaining life prediction result of the second elevator traction system.

6. The method according to claim 2, wherein Also includes; The data storage space used to store the elevator traction system operation data is tested for remaining data storage space at a preset time period. If the remaining data storage space in the test result is less than 1T data storage space, an insufficient data storage space warning message will be generated, and a communication connection will be established with a third-party cloud storage space interface. If the data storage space is full, the data will be automatically stored in the third-party cloud storage space. Before storing the data in a third-party cloud storage space, the data to be stored will be encrypted by privacy computing. After the data is encrypted by privacy computing, it will be stored in the third-party cloud storage space. Real-time detection of local storage space. If the capacity of this storage space increases, the data stored in the third-party cloud storage space will be retrieved and stored in the local storage space, and the data stored in the third-party cloud storage space will be deleted simultaneously.

7. An elevator traction system remaining life prediction system, characterized in that: Including; server side, elevator equipment side, background control side and operator equipment side; The server side establishes a communication connection with the elevator equipment side, the background control side and the operator equipment side, and the server side is used to process the data fed back by the elevator equipment side, the background control side and the operator equipment, and transmit the data processing results to the background control side and the operator equipment side; The server side includes a data acquisition unit, a model building unit, a data inspection unit, an elevator traction system life prediction unit and a prediction model optimization unit; Data acquisition unit: acquires elevator traction system operation data and stores it in a database for storing elevator operation and maintenance data, wherein the elevator traction system operation data includes traction system load data, elevator operating speed, acceleration data, operating time data, fault and abnormality records, operating environment data, maintenance history data, and elevator energy consumption data; Model building unit: After data preprocessing, the elevator traction system operation data is divided into a training set, a validation set, and a test set. The training set is used to train the model, the validation set is used to adjust the parameters and hyperparameters of the preset elevator traction system remaining life prediction model, and the test set is used to evaluate the final performance of the preset elevator traction system remaining life prediction model; Data inspection unit: establishes elevator numbers for elevators in the operation and maintenance area, matches the real-time collected elevator traction system operation data with the elevator number, and stores it in the corresponding storage space in the database; retrieves the maintenance history data of the elevators in the operation and maintenance area, extracts data features from the maintenance history data of the elevators in the operation and maintenance area, creates a data inspection form based on the data features of the elevator maintenance history data, and collects the corresponding feature data in the data inspection form according to the preset time period; Elevator traction system life prediction unit: substitutes the corresponding characteristic data in the inspection form of data collected in a preset time period into the elevator traction system remaining life prediction model to obtain a first elevator traction system remaining life prediction result, retrieves the elevator traction system operation data corresponding to the time when the elevator failure occurs, and substitutes the data into the elevator traction system remaining life prediction model to obtain a second elevator traction system remaining life prediction result, compares the first elevator traction system remaining life prediction result with the second elevator traction system remaining life prediction result, if the data comparison results are consistent, the elevator traction system remaining life prediction model and the elevator traction system data monitoring are normal, and outputs a third elevator traction system remaining life prediction result, if the data comparison results are inconsistent, optimizes the elevator traction system remaining life prediction model, and detects the corresponding data collection interface in the elevator traction system operation data; Prediction model optimization unit: extract the data features in the prediction result of the remaining life of the third elevator traction system, obtain the data feature information of the third prediction result, collect the corresponding data in the real-time third prediction result data, compare the prediction result of the remaining life of the third elevator traction system with the actual data, check the degree of consistency between the prediction result of the remaining life of the third elevator traction system and the corresponding data in the collected real-time third prediction result data. If the prediction result is significantly different from the actual data, the elevator traction system remaining life prediction model needs to be adjusted and optimized.

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