Elevator on-demand maintenance frequency evaluation system based on Internet of Things

Abstract

The invention discloses an elevator on-demand maintenance frequency evaluation system based on the Internet of Things, which relates to the technical field of elevator maintenance, and comprises a data acquisition module connected with multiple types of sensors, acquiring elevator operation key parameter data, and transmitting the data to a rear end through an Internet of Things protocol after format conversion and noise filtering preprocessing; the elevator state visualization module receives the processed data, displays the real-time running state through a graphical interface, prompts and displays abnormal data in a striking mode when faults occur, and supports historical data storage, query and playback, the man-machine interaction module is provided with operation terminals inside and outside a lift car, passengers can conveniently inquire information, report repair and seek help, voice interaction is convenient for special groups, and the safety of the elevator is improved. And the intelligent early warning module collects various types of data, a prediction model is constructed through machine learning, elevator operation data can be analyzed in real time after training, abnormity can be accurately recognized, alarming is triggered, comprehensive abnormity monitoring is achieved, faults can be reduced, safety can be guaranteed, cost can be reduced, and management efficiency can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of elevator maintenance, and in particular to an on-demand elevator maintenance frequency assessment system based on the Internet of Things. Background Art

[0002] Traditional elevator maintenance is usually arranged according to a fixed time cycle. For example, according to the use of the elevator and relevant standards, the maintenance unit will formulate different maintenance plans such as biweekly maintenance, monthly maintenance, quarterly maintenance, semi-annual maintenance and annual maintenance. Biweekly maintenance mainly focuses on cleaning the inside and outside of the elevator car, and checking whether some basic safety devices such as car lighting and ventilation equipment are working properly. Monthly maintenance will add a simple check on the elevator's operating parameters, such as whether the elevator's operating speed is within the normal range, whether the opening and closing of the car door is smooth, etc. The inspection content of quarterly maintenance is more detailed, including cleaning and lubrication of the elevator traction machine, and checking whether the functions of safety components such as safety clamps and speed limiters are normal. Semi-annual and annual maintenance cover a wider range and a deeper level. The elevator's control cabinet may be fully inspected, including the aging of electrical components, whether the line connections are firm, etc., and the elevator's guide rails will be cleaned and precision checked; Traditional maintenance plans cannot obtain real-time data on the operating status of elevators, such as speed, temperature, vibration, etc., and it is difficult to detect potential faults in a timely manner. Faults are often not discovered until after they occur, reducing the safety and reliability of elevator operation. In addition, the information exchange between elevator users, maintenance units, regulatory departments and other relevant parties is not smooth, and data cannot be shared and synchronized in a timely manner, which can easily lead to information lag and incompleteness, affecting the accuracy and timeliness of decision-making. For example, if the maintenance unit does not understand the actual frequency of use and operating status of the elevator in the near future, it will lead to unreasonable maintenance plans. It is necessary to improve the existing technology. To this end, we propose an on-demand elevator maintenance frequency assessment system based on the Internet of Things to solve the above problems. Summary of the invention

[0003] The purpose of the present invention is to provide an elevator on-demand maintenance frequency assessment system based on the Internet of Things to solve the problems raised in the above background technology.

[0004] In order to achieve the above-mentioned invention object, the present invention adopts the following technical scheme: The present invention provides an on-demand elevator maintenance frequency assessment system based on the Internet of Things, including a front end and a back end; The front end specifically includes: A101, data acquisition module: connects multiple types of sensors to collect elevator operation data, pre-processes data, converts formats and filters noise, ensures data availability, and transmits data to the backend using the Internet of Things protocol; A102, Elevator status visualization module: intuitively displays the real-time operation status of the elevator, with eye-catching signs and abnormal data when fault warning occurs, supports historical data query and playback, and helps analyze changes in operation performance; A103, Human-machine interaction module: Operation terminals are installed inside and outside the car for passengers to interact with maintenance personnel. The voice interaction function facilitates inquiry, reporting and seeking help, and feedback on the processing results of operation requests, forming an information interaction closed loop; A104, Identity Recognition and Rights Management Module: Use biometrics or smart cards to identify the identity of maintenance personnel, set operation permissions according to qualification and skill levels, and record the operation process; A105, Intelligent Early Warning Module: Establish a prediction model based on big data and machine learning: ) ] Early warning, monitoring of abnormal passenger behavior and elevator operating environment, and alarm in case of abnormalities; The backend specifically includes: B101, Data storage and management module: Design database architecture to store various types of elevator data, formulate storage strategies based on data characteristics and regularly back up and restore, clean and optimize data, and improve system performance and data query efficiency; B102, Data Analysis and Evaluation Module: Analyze elevator operation data and fault information in multiple dimensions, evaluate maintenance effects, and comprehensively assess elevator health status based on models; B103, maintenance plan generation module: Generate detailed maintenance task list based on multiple factors, dynamically adjust maintenance cycle according to elevator health, allocate maintenance resources and optimize and adjust plans in a timely manner; B104. Personnel management and training: manage maintenance personnel information, qualification certification and review, plan training courses and evaluate results, and optimize training work; B105. System management and maintenance module: manage user accounts and permissions, configure system parameters, monitor system operation status and logs, ensure security and stability, regularly upgrade and maintain the system, and handle sudden failures.

[0005] Preferably, the data acquisition module needs to install multiple types of sensors at various parts of the elevator, including acceleration sensors to detect changes in acceleration during elevator operation, position sensors to determine the floor position of the elevator car, load sensors to monitor the load weight in the car, and door sensors to detect the opening and closing status of the elevator door and the operation process.

[0006] Preferably, the sensors in the data acquisition module collect elevator operation data in real time. After acquiring the raw data, the data acquisition module performs preprocessing and data format conversion, and uniformly converts the raw data formats output by different sensors, such as analog signals or digital signals in specific formats, into a standard format that can be recognized and processed by the back-end system.

[0007] Preferably, the data acquisition module needs to perform noise filtering on the data. By setting a threshold range and using a filtering algorithm, noise data is identified and removed. The data transmitted to the back end has high accuracy, and the processed data is transmitted to the back end system for processing using the Internet of Things protocol.

[0008] Preferably, the elevator status visualization module needs to receive data processed and transmitted from the data acquisition module, and the data includes parameter information of the elevator operation, the current floor, running direction, speed, load in the car and the operating status of each component.

[0009] Preferably, the elevator status visualization module uses graphical interface technology based on the received data to draw a dynamic simulation diagram of the elevator car, changes in floor indicator lights, and a speed instrument panel to display the real-time operating status of the elevator, and the graphic elements are dynamically updated according to the actual operating conditions of the elevator.

[0010] Preferably, the elevator status visualization module has historical data storage and query playback functions, and stores all data during the elevator operation process in the database according to a certain time period and data format. The user can retrieve the corresponding historical data from the database by entering a specific time range and elevator number, and playback and display it on the visualization interface.

[0011] Preferably, the human-computer interaction module is configured by respectively setting operation terminals inside and outside the car, and the operation terminals are equipped with display screens and voice interaction devices. When passengers enter the elevator car, they can query information such as the operating floors and current load conditions of the elevator through the display screen interface of the operation terminal.

[0012] Preferably, when a passenger in the human-computer interaction module needs maintenance service, he / she expresses his / her needs through the voice interaction function. After the voice information is received by the terminal device, it is transmitted to the back-end system for processing and analysis. The back-end system arranges for maintenance personnel to respond. When the maintenance personnel perform maintenance work on the elevator, they input maintenance records and inspection result information through the operation terminal and interact with the back-end system for data.

[0013] Preferably, the intelligent early warning module needs to collect elevator operation data, including historical elevator operation data, passenger flow data, elevator environment data and marked passenger abnormal behavior data, for model training. During the operation of the elevator, the module receives data from the data acquisition module in real time, analyzes and judges based on the learned rules, and triggers the alarm mechanism when an abnormality is found, and sends an alarm message to the elevator maintenance personnel.

[0014] Compared with the prior art, one or more of the above technical solutions have the following beneficial effects: The elevator on-demand maintenance frequency assessment system based on the Internet of Things of the present invention covers multiple functional modules. The data acquisition module is connected to multiple types of sensors to collect key parameter data of elevator operation. After format conversion and noise filtering preprocessing, it is transmitted to the back end via the Internet of Things protocol, providing rich and accurate data for maintenance assessment and improving system efficiency. The elevator status visualization module receives the processed data and displays the real-time operating status with a graphical interface. It prominently prompts and displays abnormal data in case of failure. It also supports historical data storage, query and playback, which is convenient for monitoring and performance analysis, improves monitoring efficiency and decision-making speed, and assists preventive maintenance. The human-computer interaction module is equipped with operating terminals inside and outside the car to facilitate passengers to query information, report repairs and seek help. Voice interaction is convenient for special groups, and it is also convenient for maintenance personnel to exchange data with the back end, forming an information flow closed loop, improving passenger experience and maintenance quality. The intelligent early warning module collects multiple types of data and uses machine learning to build a prediction model. After training, it can analyze elevator operation data in real time, accurately identify anomalies and trigger alarms. It has the advantages of forward-looking fault prevention, comprehensive anomaly monitoring and accurate alarm triggering, which can reduce failures, ensure safety, reduce costs and improve management efficiency. All modules work together to create an intelligent, safe and efficient elevator operation ecological environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The accompanying drawings in the specification, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0016] Figure 1 It is the front-end module architecture diagram of the present invention; Figure 2 It is the back-end module architecture diagram of the present invention; Figure 3 It is the data acquisition module architecture diagram of the present invention; Figure 4 It is a picture display module architecture diagram of the present invention; Figure 5 It is a structural diagram of the internal and external interaction module of the car of the present invention; Figure 6 It is the prediction model module architecture diagram of the present invention. DETAILED DESCRIPTION

[0017] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0018] See also Figure 1-Figure 6 The present invention provides an elevator on-demand maintenance frequency assessment system based on the Internet of Things, including a front-end data acquisition module, an elevator status visualization module, a human-computer interaction module, an identity recognition and authority management module, and an intelligent early warning module, as well as a back-end data storage and management module, a data analysis and evaluation module, a maintenance plan generation module, a personnel management training, and a system management and maintenance module. The front-end multiple modules of the elevator system and the back-end modules work together to ensure the stable operation of the elevator.

[0019] Specifically, the data acquisition module needs to establish connections with multiple types of sensors installed in various key parts of the elevator. These sensors include but are not limited to acceleration sensors used to detect changes in acceleration during elevator operation to determine the start, stop and stable operation status of the elevator, position sensors to determine the floor position of the elevator car, load sensors to monitor the load weight in the car, door sensors to detect the opening and closing status of the elevator door and the operation process, etc. The sensors collect elevator operation data in real time. After the data acquisition module obtains the raw data, the first step of preprocessing is data format conversion. The raw data formats output by different sensors, such as analog signals or digital signals in a specific format, are uniformly converted into a standard format that can be recognized and processed by the back-end system, and noise filtering is performed. During the data acquisition process, some noise data may be introduced due to the accuracy limitations of the sensors themselves, electromagnetic interference and other environmental factors. The data acquisition module identifies and removes these noise data by setting a reasonable threshold range or using filtering algorithms such as mean filtering and median filtering, ensuring that the data transmitted to the back-end has high accuracy and reliability, and is efficiently transmitted to the back-end system for further analysis and processing. By connecting multiple types of sensors, it can monitor the operation status of the elevator in an all-round way, covering the key parameters of the elevator operation, and providing a rich and accurate data basis for subsequent accurate maintenance frequency evaluation. The data format conversion and noise filtering in the data preprocessing process effectively improve the availability of the data. The unified data format eliminates the need for the back-end system to perform complex data adaptation work, and can directly analyze and process the data, improving the overall operating efficiency of the system and ensuring the reliability and authenticity of the evaluation results, so that the back-end system can adjust the maintenance plan in a timely manner according to the latest operating status of the elevator.

[0020] In the elevator visualization module, the data processed and transmitted from the data acquisition module are received. These data contain various real-time parameter information of the elevator operation, such as the current floor, running direction, speed, load in the car, and the operating status of each key component. Based on the received data, the elevator status visualization module uses graphical interface technology, such as drawing dynamic simulation diagrams of the elevator car, changes in floor indicator lights, speed dashboards and other intuitive elements to clearly display the real-time operating status of the elevator. Under normal operation, these graphic elements are dynamically updated according to the actual operation of the elevator, so that maintenance personnel or monitoring personnel can know the current working status of the elevator at a glance. The module also has historical data storage and query playback functions. When it is necessary to analyze the trend of changes in the operating performance of the elevator, users can retrieve the corresponding historical data from the database by entering specific time ranges, elevator numbers and other query conditions, and play them back on the visualization interface. The playback process can simulate the changes in the operating status of the elevator in a historical time period, helping maintenance personnel and technicians to deeply analyze the performance of the elevator in the past period of time, find potential problems and hidden faults, and present the complex operating status information of the elevator to maintenance personnel and monitoring personnel in a concise and easy-to-understand form through intuitive graphical display. In the routine monitoring of daily elevator operation or in response to sudden failures, relevant personnel can quickly obtain key information, make timely responses and decisions. At the first time of an elevator failure, relevant personnel can be reminded in a striking manner and detailed abnormal data can be provided, which helps to shorten the time interval between fault discovery and handling, quickly rush to the site for maintenance, reduce elevator downtime, improve the reliability and safety of elevator operation, reduce the inconvenience and potential safety risks caused to users by elevator failures, formulate targeted maintenance plans and component replacement strategies in advance, achieve preventive maintenance, extend the service life of the elevator, and reduce overall maintenance costs.

[0021] At the same time, human-computer interaction is carried out by setting up operation terminals inside and outside the car, and the operation terminals are equipped with hardware facilities such as display screens and voice interaction devices. When passengers enter the elevator car, they can query information through the display screen interface of the operation terminal. If passengers encounter an emergency or need maintenance services, they can directly express their needs through the voice interaction function. After the voice information is received by the terminal device, it will be transmitted to the back-end system for processing and analysis. The back-end system queries relevant data or arranges maintenance personnel to respond based on the content of the voice command, and then feeds back the processing results or related information to the passengers through the voice function of the operation terminal, informing them that the request has been received, the processing progress, and the estimated completion time, etc. For maintenance personnel, when performing maintenance work on the elevator, they can also input maintenance records, inspection results and other information through the operation terminal, interact with the back-end system, and receive the elevator's historical fault data, recent abnormal operation reports and other information pushed by the back-end system, so as to better carry out maintenance work and timely discover potential problems. It provides passengers with a simple, intuitive and efficient interaction method, greatly improving the passenger experience and sense of security during the use of the elevator, and reducing the elevator use troubles and safety hazards caused by information asymmetry or communication barriers. For maintenance personnel, the operation terminals inside and outside the car break the spatial limitations of information acquisition, enabling them to obtain comprehensive information about the elevator in a timely manner, forming an efficient maintenance information flow closed loop, improving the overall efficiency and quality of maintenance work, and reducing the risk of maintenance errors or delays caused by poor information transmission. This information interaction closed loop can ensure that all types of information in the elevator operation process can be processed and utilized in a timely and effective manner, which helps to create a more intelligent, safe and efficient elevator operation ecological environment.

[0022] Furthermore, in the intelligent early warning module, a large amount of data related to elevator operation is collected, including historical elevator operation data such as speed, load, operation time, fault records, etc., passenger flow data such as the number of people entering and leaving in different time periods, floor distribution, etc., elevator environment data such as temperature, humidity, noise, etc., and marked passenger abnormal behavior data such as violent destruction of elevator facilities, jumping in the car, etc., using these data to build a prediction model through machine learning algorithms; ) ] y is the prediction result, 0 means normal, 1 means abnormal, N is the number of training samples, is the true label of the i-th sample, normal or abnormal, is the result predicted by the above model for the i-th sample, and by continuously adjusting the value, Minimize, so as to obtain a trained model for subsequent abnormal warning judgment. When the prediction result y meets the abnormal judgment condition, y>5, an alarm is triggered; During the model training process, the model parameters are constantly adjusted so that it can accurately identify the association between various data patterns and abnormal features. Let the model learn patterns that may indicate faults, such as abnormal speed fluctuations and sudden changes in load; for abnormal passenger behavior, learn the data features such as actions and sounds corresponding to different abnormal behaviors. After the model training is completed, during the actual operation of the elevator, the intelligent early warning module receives various data from the data acquisition module in real time, including real-time elevator operation parameters, surveillance video data in the car, and environmental sensor data. These data are input into the trained prediction model, and the model analyzes and judges based on the learned rules. Once the data features are found to match the abnormal pattern, such as the elevator speed suddenly exceeds the normal range and is accompanied by abnormal noise, the passenger is detected in the car violently hitting the elevator wall, the ambient temperature is too high and the current of the elevator electrical system fluctuates, etc., the alarm mechanism is immediately triggered and an alarm message is sent to relevant personnel. The prediction model based on big data and machine learning can detect potential faults in the elevator in advance, instead of the traditional response after the fault occurs. Through in-depth analysis of a large amount of historical data and real-time data, it can predict the impending failure based on some subtle abnormal data change trends before the fault symptoms are obvious, providing maintenance personnel with enough time to perform preventive maintenance, thereby effectively reducing the number of sudden elevator failures, reducing the risks of people being trapped and equipment damage caused by elevator failures, and improving the reliability and safety of elevator operation. It can not only monitor the abnormal operation status of the elevator itself, but also monitor the abnormal behavior of passengers and the elevator operation environment. This helps to timely discover some elevator safety hazards caused by human factors, such as the deliberate destruction of passengers, which may damage the key components of the elevator and affect the normal operation of the elevator; at the same time, environmental factors such as harsh temperature and humidity conditions may accelerate the aging and damage of elevator components. Through comprehensive monitoring and early warning of these aspects, measures can be taken in advance to intervene, ensure that the elevator operates in a suitable environment, extend the service life of the elevator, and reduce operating costs. The system's multiple modules work together, and the cooperation of the front end and the back end jointly reduces the sudden failure of the elevator, ensures safety, extends the life of the elevator and reduces costs. Each module jointly builds an intelligent, safe and efficient elevator operation and maintenance environment.

[0023] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. An elevator on-demand maintenance frequency assessment system based on the Internet of Things, characterized in that: Including front-end and back-end; The front end specifically includes: A101, data acquisition module: connect multiple types of sensors to collect elevator operation data, pre-process data, convert format and filter noise, ensure data availability, and transmit data to the back end using the Internet of Things protocol; A102, Elevator status visualization module: intuitively displays the real-time operation status of the elevator, with eye-catching signs and abnormal data when fault warning occurs, supports historical data query and playback, and helps analyze changes in operation performance; A103, Human-machine interaction module: Operation terminals are installed inside and outside the car for passengers to interact with maintenance personnel. The voice interaction function facilitates inquiry, reporting and seeking help, and feedback on the processing results of operation requests, forming an information interaction closed loop; A104, Identity Recognition and Rights Management Module: Use biometrics or smart cards to identify the identity of maintenance personnel, set operation permissions according to qualification and skill levels, and record the operation process; A105, Intelligent Early Warning Module: Establish a prediction model based on big data and machine learning: ) ] Early warning, monitoring of abnormal passenger behavior and elevator operating environment, and alarm in case of abnormalities; The backend specifically includes: B101, Data storage and management module: Design database architecture to store various types of elevator data, formulate storage strategies based on data characteristics and regularly back up and restore, clean and optimize data, and improve system performance and data query efficiency; B102, Data Analysis and Evaluation Module: Analyze elevator operation data and fault information in multiple dimensions, evaluate maintenance effects, and comprehensively assess elevator health status based on models; B103, maintenance plan generation module: Generate detailed maintenance task list based on multiple factors, dynamically adjust maintenance cycle according to elevator health, allocate maintenance resources and optimize and adjust plans in a timely manner; B104. Personnel management and training: manage maintenance personnel information, qualification certification and review, plan training courses and evaluate results, and optimize training work; B105. System management and maintenance module: manage user accounts and permissions, configure system parameters, monitor system operation status and logs, ensure security and stability, regularly upgrade and maintain the system, and handle sudden failures.

2. According to the Internet of Things-based elevator on-demand maintenance frequency assessment system according to claim 1, it is characterized in that: The data acquisition module needs to install multiple types of sensors in various parts of the elevator. The sensors include acceleration sensors to detect changes in acceleration during elevator operation, position sensors to determine the floor position of the elevator car, load sensors to monitor the load weight in the car, and door sensors to detect the opening and closing status of the elevator door and the operation process.

3. According to the Internet of Things-based elevator on-demand maintenance frequency assessment system of claim 1, it is characterized in that: The sensors in the data acquisition module collect elevator operation data in real time. After obtaining the raw data, the data acquisition module performs preprocessing and data format conversion, and converts the raw data formats output by different sensors, such as analog signals or digital signals in specific formats, into a standard format that can be recognized and processed by the back-end system.

4. The elevator on-demand maintenance frequency assessment system based on the Internet of Things according to claim 1 is characterized in that: The data acquisition module needs to perform noise filtering on the data. By setting the threshold range and using the filtering algorithm, the noise data is identified and removed. The data transmitted to the back end has high accuracy, and the processed data is transmitted to the back end system for processing using the Internet of Things protocol.

5. The elevator on-demand maintenance frequency assessment system based on the Internet of Things according to claim 1 is characterized in that: The elevator status visualization module needs to receive data processed and transmitted from the data acquisition module. The data includes parameter information of elevator operation, current floor, running direction, speed, load in the car and the operating status of each component.

6. The elevator on-demand maintenance frequency assessment system based on the Internet of Things according to claim 1 is characterized in that: The elevator status visualization module uses graphical interface technology based on the received data to draw a dynamic simulation diagram of the elevator car, changes in floor indicator lights, and a speed instrument panel to display the real-time operating status of the elevator. The graphic elements are dynamically updated according to the actual operating conditions of the elevator.

7. The elevator on-demand maintenance frequency assessment system based on the Internet of Things according to claim 1 is characterized in that: The elevator status visualization module has the functions of historical data storage and query playback. All data during the elevator operation process is stored in the database according to a certain time period and data format. Users can retrieve the corresponding historical data from the database by entering a specific time range and elevator number, and playback and display it on the visualization interface.

8. The elevator on-demand maintenance frequency assessment system based on the Internet of Things according to claim 1 is characterized in that: The human-computer interaction module is configured by setting operation terminals inside and outside the elevator car respectively. The operation terminals are equipped with display screens and voice interaction devices. When passengers enter the elevator car, they can query information such as the elevator's operating floors and current load conditions through the display screen interface of the operation terminal.

9. The elevator on-demand maintenance frequency assessment system based on the Internet of Things according to claim 1 is characterized in that: When a passenger in the human-computer interaction module needs maintenance service, he / she expresses his / her needs through the voice interaction function. After the voice information is received by the terminal device, it is transmitted to the back-end system for processing and analysis. The back-end system arranges maintenance personnel to respond. When the maintenance personnel perform maintenance work on the elevator, they input maintenance records and inspection result information through the operation terminal and interact with the back-end system for data.

10. The elevator on-demand maintenance frequency assessment system based on the Internet of Things according to claim 1, characterized in that: The intelligent early warning module needs to collect elevator operation data, including historical elevator operation data, passenger flow data, elevator environment data and marked passenger abnormal behavior data, for model training. During the elevator operation process, the module receives data from the data acquisition module in real time, analyzes and judges based on the learned rules, and triggers the alarm mechanism when an abnormality is found, and sends an alarm message to the elevator maintenance personnel.

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