Blockchain-based elevator dispatch data management system
By using a blockchain-based elevator scheduling data management system, which utilizes 5G networks and neural network models to process elevator monitoring data, the problem of insufficient reliability in elevator scheduling data storage has been solved, thus ensuring the safety and data integrity of elevator operation.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- SPECIAL EQUIP SAFETY SUPERVISION INSPECTION INST OF JIANGSU PROVINCE
- Filing Date
- 2023-10-13
- Publication Date
- 2026-05-26
AI Technical Summary
The reliability of elevator scheduling data storage in existing technologies is insufficient, making it difficult to effectively protect against elevator safety hazards.
An elevator dispatching data management system based on blockchain is adopted. The monitoring data of the elevator terminal is transmitted to the dispatching server through the 5G communication network. The data processing and security judgment are performed using a neural network model. The blockchain server stores the dispatching data and feedback data to ensure the reliability and traceability of the data.
It enables reliable storage and traceability of elevator scheduling data, improves the safety and integrity of elevator operation, and ensures timely handling and prevention of elevator accidents.
Smart Images
Figure CN118145444B_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of data processing technology, and more specifically, to a blockchain-based elevator scheduling data management system. Background Technology
[0002] While elevators bring many conveniences to people's lives, they also inevitably cause a lot of trouble and damage. In particular, as elevators age, their safety hazards become more and more serious. Various elevator accidents, such as overshooting, entrapment, and falls, occur from time to time, affecting people's daily use and posing a certain threat to their lives.
[0003] In related technologies, in order to ensure the safe operation of elevators, elevator scheduling data is often uploaded to ensure the safe operation of elevators or to allow for retrospective analysis after a malfunction.
[0004] However, there has been no effective solution to address the reliability issues of data storage. Summary of the Invention
[0005] The summary section of this application is intended to provide a brief overview of the concepts, which will be described in detail in the detailed description section below. This summary section is not intended to identify key or essential features of the claimed technical solutions, nor is it intended to limit the scope of the claimed technical solutions.
[0006] Some embodiments of this application propose a blockchain-based elevator scheduling data management system to address the technical problems mentioned in the background section above.
[0007] Some embodiments of this application provide a blockchain-based elevator scheduling data management system, comprising: a plurality of elevator terminals for receiving elevator scheduling data and sending scheduling feedback data on the elevator scheduling data via a wireless communication network; a scheduling server for sending elevator scheduling data to the elevator terminals and receiving scheduling feedback data sent by the elevator terminals via a wireless communication network; and a plurality of blockchain servers capable of data interaction with the elevator terminals and / or the scheduling servers to transmit the elevator scheduling data and the scheduling feedback data; wherein the scheduling server includes: a data processing module for receiving elevator monitoring data uploaded by the elevator terminals and outputting elevator scheduling data based on the elevator monitoring data; The data processing module includes: a neural network unit that stores and runs a scheduling neural network model; the input data of the scheduling neural network model of the neural network unit includes the scheduling instructions received by the elevator terminal and the elevator monitoring data, and the output data of the scheduling neural network model of the neural network unit is the elevator scheduling data; a model uploading unit that uploads the scheduling neural network model and its input and output data at the same time to the blockchain constructed by the blockchain server; wherein, the blockchain server includes: a model storage module for receiving and storing the scheduling neural network model and its input and output data at the same time uploaded by the model uploading unit in a blockchain manner.
[0008] Furthermore, the wireless communication network includes a 5G communication network.
[0009] Furthermore, the elevator monitoring data includes: traction machine vibration data, traction machine noise data, traction machine temperature data, traction machine current data, and traction machine speed data.
[0010] Furthermore, the elevator monitoring data includes: traction wire rope vibration data and traction wire rope noise data.
[0011] Furthermore, the elevator monitoring data includes: car vibration data, car noise data, and noise posture data.
[0012] Furthermore, the scheduling neural network model is a convolutional neural network.
[0013] Furthermore, the elevator scheduling data includes: traction machine drive data.
[0014] Furthermore, the scheduling feedback data includes elevator monitoring data during the period when the elevator terminal executes the elevator scheduling data.
[0015] Furthermore, the scheduling feedback data includes whether the elevator terminal executes the scheduling instruction.
[0016] Furthermore, the data processing module includes: a safety judgment module, used to determine whether the elevator terminal can safely execute the elevator scheduling data; the safety judgment module stores and runs a safety judgment model, which is a convolutional neural network model, the input data of the safety judgment model is the elevator scheduling data and the current elevator monitoring data of the elevator terminal; the output data of the safety judgment model is whether the elevator terminal is safe after executing the elevator scheduling data.
[0017] The beneficial effects of this application are: it provides a blockchain-based elevator scheduling data management system that can effectively manage scheduling and ensure the reliability and traceability of data storage. Attached Figure Description
[0018] The accompanying drawings, which form part of this application, are used to provide a further understanding of the application and to make other features, objects, and advantages of the application more apparent. The illustrative embodiments and descriptions of this application are used to explain the application and do not constitute an undue limitation of the application.
[0019] Furthermore, throughout the accompanying drawings, the same or similar reference numerals denote the same or similar elements. It should be understood that the drawings are schematic, and the elements are not necessarily drawn to scale.
[0020] In the attached diagram:
[0021] Figure 1 This is a schematic diagram of the system architecture of a blockchain-based elevator scheduling data management system according to an embodiment of this application;
[0022] Figure 2 This is a schematic diagram of the architecture of the scheduling server in a blockchain-based elevator scheduling data management system according to an embodiment of this application;
[0023] Figure 3 This is a schematic diagram of the architecture of a blockchain server in a blockchain-based elevator scheduling data management system according to an embodiment of this application;
[0024] Figure 4 This is a schematic diagram of the communication architecture of a blockchain-based elevator scheduling data management system according to an embodiment of this application. Detailed Implementation
[0025] Embodiments of this disclosure will now be described in more detail with reference to the accompanying drawings. While some embodiments of this disclosure are shown in the drawings, it should be understood that this disclosure can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of this disclosure. It should be understood that the accompanying drawings and embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of protection of this disclosure.
[0026] It should also be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings. Unless otherwise specified, the embodiments and features described in this disclosure can be combined with each other.
[0027] It should be noted that the concepts of "first" and "second" mentioned in this disclosure are used only to distinguish different devices, modules or units, and are not used to limit the order of functions performed by these devices, modules or units or their interdependencies.
[0028] It should be noted that the terms "a" and "a plurality of" used in this disclosure are illustrative rather than restrictive, and those skilled in the art should understand that, unless otherwise expressly indicated in the context, they should be understood as "one or more".
[0029] The names of messages or information exchanged between multiple devices in the embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of such messages or information.
[0030] This disclosure will now be described in detail with reference to the accompanying drawings and embodiments.
[0031] Reference Figures 1 to 4 As shown, the blockchain-based elevator dispatching data management system of this application includes: several elevator terminals, a dispatching server, and several blockchain servers.
[0032] Among them, several elevator terminals are used to receive elevator scheduling data and send scheduling feedback data to the elevator scheduling data through a wireless communication network; the scheduling server is used to send elevator scheduling data to the elevator terminals and receive scheduling feedback data sent by the elevator terminals through a wireless communication network; several blockchain servers can interact with the elevator terminals and / or the scheduling server to exchange elevator scheduling data and scheduling feedback data.
[0033] The scheduling server includes a data processing module. This module receives elevator monitoring data uploaded by the elevator terminals and outputs elevator scheduling data based on this data. The data processing module includes a neural network unit and a model uploading unit. The neural network unit stores and runs a scheduling neural network model; the input data to the scheduling neural network model includes scheduling instructions received from the elevator terminals and elevator monitoring data; the output data of the scheduling neural network model is the elevator scheduling data. The model uploading unit uploads the scheduling neural network model, its input data, and its output data at the same time to the blockchain constructed by the blockchain server.
[0034] The blockchain server includes a model storage module. This module receives and stores, in a blockchain format, the scheduling neural network model uploaded by the model uploading unit at the same time, along with its input and output data.
[0035] Specifically, wireless communication networks include 5G communication networks.
[0036] Specifically, elevator monitoring data includes: traction machine vibration data, traction machine noise data, traction machine temperature data, traction machine current data, and traction machine speed data.
[0037] Specifically, elevator monitoring data includes: traction wire rope vibration data and traction wire rope noise data.
[0038] Specifically, elevator monitoring data includes: car vibration data, car noise data, and noise posture data.
[0039] Specifically, the scheduling neural network model is a convolutional neural network.
[0040] Specifically, elevator scheduling data includes traction machine drive data.
[0041] Specifically, the dispatch feedback data includes elevator monitoring data during the period when the elevator terminal executes elevator dispatch data.
[0042] Specifically, the dispatch feedback data includes whether the elevator terminal executes the dispatch instructions.
[0043] Specifically, the data processing module includes a safety judgment module. This module determines whether the elevator terminal can safely execute elevator scheduling data. It stores and runs a safety judgment model, which is a convolutional neural network model. The input data for this model are the elevator scheduling data and the current elevator monitoring data from the elevator terminal. The output data of the model indicates whether the elevator terminal is safe after executing the elevator scheduling data.
[0044] This not only stores and retrieves elevator scheduling data, but also stores the neural network models that generate them, thus ensuring subsequent analysis needs.
[0045] As a more specific solution, refer to Figure 4 As shown, more specifically, an elevator generally includes a traction machine, traction steel cables, and a car. The traction machine is the elevator's power unit, also known as the elevator main unit. Its function is to deliver and transmit power to make the elevator run. It consists of a motor, brake, coupling, gearbox, traction sheave, frame, guide wheels, and an auxiliary handwheel. The guide wheels are generally mounted on the frame or the load-bearing beam below the frame. The handwheel is sometimes fixed to the motor shaft, and sometimes it is normally hung on a nearby wall and then slipped onto the motor shaft when in use. The traction steel cables connect the traction machine and the car to transmit tension, thereby raising or lowering the car.
[0046] To monitor the safety of elevator operation, relevant parameters of the traction machine, traction cables, and car can be tested. Generally speaking, such as... Figure 4 As shown, vibration sensors and noise sensors can be used to detect the vibration and noise of the traction machine, traction wire rope, and car. Simultaneously, temperature sensors can be used to detect the temperature of the traction machine to determine if it is overheating. Furthermore, the current and speed required for the traction machine's own control are also included as detection items.
[0047] In addition, to better monitor the car, one or more gyroscopes can be used to detect the car's attitude, thereby obtaining data that reflects the car's attitude.
[0048] As can be seen from the above, these sensors are installed at different locations throughout the elevator. As a preferred embodiment, the elevator terminal of this application includes several Internet of Things (IoT) communication modules (not shown in the figure), which can perform short-range communication to aggregate the data uploaded by each sensor, such as... Figure 4 As shown, the IoT communication module can use a Smartmesh network module for communication networking, and one Smartmesh network module can be set up for each of the traction machine, traction wire rope and car.
[0049] In addition, the elevator terminal is equipped with a central remote wireless communication module (not shown in the figure) that aggregates the data collected by the various IoT communication modules and sends it to the dispatch server (cloud platform). For example... Figure 4 As shown, the remote wireless communication module can be a 5G module.
[0050] Specifically, the elevator terminal configured for each elevator is equivalent to the elevator's "black box".
[0051] The elevator terminal includes sensors for acquiring the status of key components, a sensor networking module, a 5G communication module, etc. Figure 4 The upper-layer service applications mainly refer to the scheduling server (cloud platform) on the computer side. Sensors primarily include vibration sensors for monitoring vibration, noise sensors for picking up noise signals, temperature sensors, and attitude sensors composed of a three-axis accelerometer and a three-axis gyroscope. These sensors need to be independently installed in appropriate locations on key components. Additionally, current and speed signals are acquired using the traction machine's built-in current and speed sensors. These sensors are then networked to form a local gateway, sending signals to a 5G module. Each elevator device forms a blockchain, which is then uploaded to the Ethernet via the 5G module. The scheduling server (cloud platform) retrieves information from the Ethernet and performs interactive processing.
[0052] More specifically, measuring points should be placed at appropriate locations on key components of the elevator, such as the traction machine, traction rope, and car.
[0053] Various sensors in the elevator terminal are used to collect vibration, noise, temperature, current, and speed signals from the traction machine. Specifically, they extract and analyze characteristic quantities from vibration, noise, and temperature signals to enable health monitoring, fault prediction, and emergency rescue. Based on current and speed signals and relevant traction machine parameters, they calculate the traction machine's output torque, thereby estimating the brake's braking torque and enabling brake health monitoring, fault prediction, and emergency rescue. They also collect and analyze vibration and noise signals from the traction ropes to monitor the state of the traction ropes and sheaves, and to predict faults and provide emergency rescue. Finally, they collect and analyze the car's attitude signals and external noise signals to monitor the car's state, predict faults, and provide emergency rescue.
[0054] The sensor networking method in the elevator shaft: Sensors at various measuring points within each elevator are networked using the Smartmesh protocol to form a local gateway. This local gateway connects to a 5G module, transmitting sensor data to the dispatch server (cloud platform) via the 5G network.
[0055] As a specific solution, the elevator monitoring data in this application includes: traction machine vibration data, traction machine noise data, traction machine temperature data, traction machine current data and traction machine speed data, traction wire rope vibration data, traction wire rope noise data, car vibration data, car noise data and car posture data.
[0056] Specifically, the car attitude data includes two sets of data: X-axis tilt angle and Y-axis tilt angle. These are the tilt angles of the car in two directions on the horizontal plane.
[0057] Among them, such as Figure 2As shown, the scheduling server (cloud platform) includes a signal processing module, a fault prediction module, and a maintenance management module. The signal processing module primarily processes, extracts, and analyzes characteristic quantities from transmitted sensor signals to achieve health monitoring. The fault prediction module focuses on predicting the faults and remaining lifespan of relevant components or equipment based on big data analysis and evaluation. The maintenance management module uses the fault prediction results to construct the resource types required for predictive and on-demand maintenance, including personnel, spare parts, tools, and time, achieving closed-loop control of Plan, Do, Check, Act (PDCA).
[0058] The fault prediction module of the scheduling server (cloud platform) is specifically used to store and run at least one neural network model so that the neural network model can output prediction results data for determining elevator safety based on the elevator monitoring data or its derived data input to the neural network model.
[0059] According to the above scheme, the elevator monitoring data in this application is a sequence of numbers, which can be viewed as a matrix with only one row. At this point, a neural network model can be trained on the scheduling server (cloud platform), using the elevator monitoring data as output data and the elevator status corresponding to the monitoring data as output data. This allows the neural network model to be trained to predict elevator status. The elevator status can be set to three types: "Danger," "Under Investigation," and "Normal," and assigned values of -1, 0, and 1 respectively; thus, the training of the neural network model can be achieved.
[0060] The neural network model of the scheduling server (cloud platform) in this application can be a convolutional neural network, a fully connected neural network, a generative adversarial network, or, of course, a recurrent neural network or a long short-term memory network. As a preferred embodiment, the neural network model used in the scheduling server (cloud platform) of this application is a convolutional neural network, which can be used for both classification and prediction.
[0061] Historical data on similar elevators can be collected, and the corresponding elevator monitoring data and elevator status values can be input into the aforementioned neural network model. This neural network model then possesses prediction or classification capabilities, allowing it to analyze and output the elevator's next operating state or its current state. Alternatively, two neural network models can be set up, one for prediction and the other for classification.
[0062] The neural network model that uses convolutional neural networks and corresponding matrix data for prediction and classification is a common technique known to those skilled in the art, and is not the focus of this application, so it will not be elaborated here.
[0063] The above description is merely a selection of preferred embodiments of this disclosure and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of the invention involved in the embodiments of this disclosure is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-described inventive concept. For example, technical solutions formed by substituting the above-described features with (but not limited to) technical features with similar functions disclosed in the embodiments of this disclosure.
Claims
1. A blockchain-based elevator dispatching data management system, comprising: Several elevator terminals are used to receive elevator dispatch data and send dispatch feedback data on the elevator dispatch data through a wireless communication network. The scheduling server is used to send elevator scheduling data to the elevator terminal via a wireless communication network and to receive scheduling feedback data sent by the elevator terminal. Several blockchain servers can interact with the elevator terminal and / or the scheduling server to store the elevator scheduling data and the scheduling feedback data; Its features are: The scheduling server includes: The data processing module is used to receive elevator monitoring data uploaded by the elevator terminal and output elevator scheduling data based on the elevator monitoring data; The data processing module includes: A neural network unit stores and runs a scheduled neural network model; The input data of the scheduling neural network model of the neural network unit includes the scheduling instructions received by the elevator terminal and the elevator monitoring data, and the output data of the scheduling neural network model of the neural network unit is the elevator scheduling data; The model uploading unit uploads the scheduling neural network model and its input and output data at the same time to the blockchain constructed by the blockchain server; The blockchain server includes: The model storage module is used to receive and store the scheduling neural network model and its input and output data uploaded by the model upload unit at the same time in a blockchain manner.
2. The elevator dispatching data management system based on blockchain according to claim 1, characterized in that: The wireless communication network includes a 5G communication network.
3. The elevator dispatching data management system based on blockchain according to claim 1, characterized in that: The elevator monitoring data includes: traction machine vibration data, traction machine noise data, traction machine temperature data, traction machine current data, and traction machine speed data.
4. The elevator dispatching data management system based on blockchain according to claim 1, characterized in that: The elevator monitoring data includes: traction wire rope vibration data and traction wire rope noise data.
5. The elevator dispatching data management system based on blockchain according to claim 1, characterized in that: The elevator monitoring data includes: car vibration data, car noise data, and noise posture data.
6. The elevator dispatching data management system based on blockchain according to claim 1, characterized in that: The scheduling neural network model is a convolutional neural network.
7. The elevator dispatching data management system based on blockchain according to claim 1, characterized in that: The elevator scheduling data includes: traction machine drive data.
8. The elevator dispatching data management system based on blockchain according to claim 1, characterized in that: The scheduling feedback data includes elevator monitoring data during the period when the elevator terminal executes the elevator scheduling data.
9. The elevator dispatching data management system based on blockchain according to claim 1, characterized in that: The scheduling feedback data includes whether the elevator terminal executes the scheduling instruction.
10. The blockchain-based elevator scheduling data management system according to any one of claims 1 to 9, characterized in that: The data processing module includes: A safety judgment unit is used to determine whether the elevator terminal can safely execute the elevator scheduling data; The safety judgment unit stores and runs a safety judgment model, which is a convolutional neural network model. The input data of the safety judgment model are the elevator scheduling data and the current elevator monitoring data of the elevator terminal. The output data of the safety judgment model is whether the elevator terminal is safe after executing the elevator scheduling data.