Elevator shaft monitoring system based on external internet of things

Abstract

The application discloses an elevator shaft monitoring system based on an external internet of things, which comprises a laser radar installed on the top surface and the bottom surface of a car; the laser radar is fixedly installed on one end of a rotating shaft, and the other end of the rotating shaft is fixedly installed on the output end of a driving motor; two driving motors are arranged and fixedly installed on the center of the top surface and the center of the bottom surface of the car respectively. The application can collect data in the elevator shaft through the rotation of the laser radar installed on the top and bottom of the car, and establish an internet of things database; and the monitoring work of the shaft wall during the operation of the elevator can be completed through the data comparison between the laser radar and the internet of things database.

Description

Technical Field

[0001] This invention relates to the field of monitoring technology, and in particular to an elevator shaft monitoring system based on an external Internet of Things (IoT). Background Technology

[0002] Currently, elevator inspection is crucial in the field of special equipment inspection. Elevator inspection is typically conducted manually, and even when electronic dynamic inspection is used, it only employs a local area network system that sends the inspection results to a control center computer for evaluation. These methods are slow, costly, and the inspection results are not ideal. Data comparison via local area networks relies on data previously collected manually, resulting in a small sample size and limited applicability. Furthermore, the narrow data coverage may lead to poor inspection results and an inability to monitor the elevator shaft's condition in real time, potentially causing safety accidents due to insufficient monitoring.

[0003] Therefore, it is necessary to design a better detection technology to effectively monitor the condition of the elevator shaft in real time during elevator operation in order to solve the above problems. Summary of the Invention

[0004] The purpose of this invention is to provide an elevator shaft monitoring system based on an external Internet of Things (IoT) to solve the problems existing in the prior art. This system enables real-time monitoring of elevator shaft conditions, preventing potential hazards caused by insufficient monitoring, and also improves the safety of elevator operation, avoiding safety accidents caused by a lack of monitoring of shaft conditions during elevator operation.

[0005] To achieve the above objectives, the present invention provides the following solution: The present invention provides an elevator shaft monitoring system based on an external Internet of Things, comprising: a lidar installed on the top and bottom surfaces of the car; the lidar is fixedly installed at one end of a rotating shaft, and the other end of the rotating shaft is fixedly installed at the output end of a drive motor; two drive motors are provided and are respectively fixedly installed at the center of the top surface and the center of the bottom surface of the car.

[0006] The lidar on the top surface of the car is used to monitor the upper half of the left and right hoistway walls and guide rails, as well as the front hoistway wall; the lidar on the bottom surface of the car is used to monitor the lower half of the left and right hoistway walls and guide rails, as well as the rear hoistway wall.

[0007] The drive motor realizes the rotation of the lidar through the rotating shaft; the shortest distance between the lidar and the front / rear well wall is the limit; the lidar rotates within the range of -15° to 15°.

[0008] The two lidar sensors construct an Internet of Things database by dynamically scanning the elevator shaft during operation, and determine whether any abnormalities are detected in the car operation by comparing the data of the elevator shaft under normal operation with real-time monitoring data.

[0009] The duplicate data in the original data read by the lidar is cleaned up by the nearest neighbor sorting algorithm, and the deduplicated data is normalized. The Euclidean distance between each dataset is calculated to obtain the average Euclidean distance of the dataset.

[0010] The clustering results are cleaned using a genetic algorithm to eliminate useless attribute features in the dataset, thereby establishing the IoT database.

[0011] The formula for obtaining the average Euclidean distance of the dataset is as follows:

[0012] Among them, Dis(S) i ,S j ) is the data object S i and S j The Euclidean distance between them, A n The number of data objects; if the distance between each data object in the dataset and the target point is within AvgDis, then the data object is identified as a neighbor of the target point, and the number of its neighboring points is counted; the number of neighboring points of each data object in the dataset is sorted in descending order, and the first k data objects are taken as the initial cluster centers for clustering.

[0013] During the process of cleaning the results after clustering the initial cluster centers using the genetic algorithm, the initial population is a gene sequence generated from 50 01 characters, and the feature corresponding to each gene is selected as the result after clustering the initial cluster centers.

[0014] Among them, f i Let a be the fitness of gene i, N be the number of data objects in the dataset, and a be the number of data objects in the dataset. i k denoted as Σ(i) = Σ(l) = Σ(l) = Σ(l) = Σ(k) = Σ(k) = Σ(k) = Σ(l ...l) = Σ(l) = Σ(l) = Σ(

[0015] The formula used to eliminate useless attribute features in a dataset is:

[0016] Among them, f max and f min These represent the maximum and minimum fitness values ​​in the population, respectively. Regions are selected based on the fitness of individuals for crossover and mutation operations to eliminate useless attributes in the dataset. Once the maximum number of iterations is reached, a new population and the optimal result are output; otherwise, the genetic algorithm continues iterating.

[0017] The present invention discloses the following technical effects: by installing rotatable lidars on the upper and lower parts of the elevator car, data is collected from the elevator shaft by the rotation of the lidars, and an Internet of Things (IoT) database is established; by comparing the data between the lidars and the IoT database, the monitoring of the shaft walls during elevator operation is completed. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 Arrange the main view for the lidar;

[0020] Figure 2 Left view showing the layout of the lidar;

[0021] Figure 3 This is a schematic diagram of the workflow of the present invention;

[0022] The components include: 1. LiDAR; 2. Car; 3. Front shaft wall; 4. Rear shaft wall; 5. Left shaft wall; 6. Right shaft wall; 7. Rotating shaft; 8. Rotating motor; and 9. Elevator door. Detailed Implementation

[0023] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0024] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0025] The present invention provides an elevator shaft monitoring system based on an external Internet of Things, including: a lidar 1 installed on the top and bottom surfaces of the car 2; the lidar 1 is fixedly installed on one end of a rotating shaft 7, and the other end of the rotating shaft 7 is fixedly installed on the output end of a drive motor 8; there are two drive motors 8, which are respectively fixedly installed at the center of the top surface and the center of the bottom surface of the car 2.

[0026] The lidar 1 on the top surface of the car 2 is used to monitor the upper half of the left shaft wall 5 and the right shaft wall 6, the guide rails, and the front shaft wall 3; the lidar 1 on the bottom surface of the car 2 is used to monitor the lower half of the left shaft wall 5 and the right shaft wall 6, the guide rails, and the rear shaft wall 4.

[0027] The drive motor 8 rotates the lidar 1 via the rotating shaft 7; the lidar 1 rotates within a range of -15° to -15°, with the shortest distance between the lidar 1 and the front shaft wall 3 / rear shaft wall 4 as the limit.

[0028] Two lidar sensors 1 construct an IoT database by dynamically scanning the elevator shaft during operation, and determine whether any abnormalities are detected in the operation of the car 2 by comparing the data of the elevator shaft under normal operation with real-time monitoring data.

[0029] The duplicate data in the original data read by LiDAR 1 is cleaned up by the nearest neighbor sorting algorithm, and the deduplicated data is normalized. The Euclidean distance between each data set in the dataset is calculated to obtain the average Euclidean distance of the dataset.

[0030] The results of clustering are cleaned using genetic algorithms to eliminate useless attribute features in the dataset, thus enabling the establishment of an IoT database.

[0031] The formula for calculating the average Euclidean distance of the dataset is:

[0032] Among them, Dis(S) i ,S j ) is the data object S i and S j The Euclidean distance between them, A n The number of data objects; if the distance between each data object in the dataset and the target point is within AvgDis, then the data object is considered a neighbor of the target point, and the number of its neighbors is counted; the number of neighbors of each data object in the dataset is sorted in descending order, and the first k data objects are taken as the initial cluster centers for clustering.

[0033] During the process of cleaning the results after clustering the initial cluster centers using a genetic algorithm, the initial population is a gene sequence generated from 50 01 characters. The feature corresponding to each gene is selected as the result after clustering the initial cluster centers.

[0034] Among them, f i Let a be the fitness of gene i, N be the number of data objects in the dataset, and a be the number of data objects in the dataset. i k denoted as Σ(i) = Σ(l) = Σ(l) = Σ(l) = Σ(k) = Σ(k) = Σ(k) = Σ(l ...l) = Σ(l) = Σ(l) = Σ(

[0035] The formula used to eliminate useless attribute features in a dataset is:

[0036] Among them, f max and f min These represent the maximum and minimum fitness values ​​in the population, respectively. Regions are selected based on the fitness of individuals for crossover and mutation operations to eliminate useless attributes in the dataset. Once the maximum number of iterations is reached, a new population and the optimal result are output; otherwise, the genetic algorithm continues iterating.

[0037] Furthermore, by employing this algorithm, clustering time can be saved and clustering results optimized during the construction of IoT databases.

[0038] In one embodiment of the present invention, the construction of the Internet of Things (IoT) database is achieved by reading image information obtained after noise reduction and image stitching by LiDAR, extracting and storing key information, and repeatedly collecting structural information of key components in various parts of the elevator shaft during elevator operation. This key information is then stored to construct the IoT database. When the detection work begins, LiDAR collects image information, stitches the LiDAR feedback information, compares the current image data with the data information in the IoT system, and determines whether there are straightness problems in the surrounding shaft walls and the guide rails on both sides under the current elevator operating conditions, whether there are foreign objects trapped in the current landing door, whether the sealing rubber is damaged, and whether the current leveling device is installed in the correct position and whether there is any skew or damage.

[0039] In one embodiment of the present invention, although there is a certain height difference during the monitoring process of the upper and lower lidars 1, which is the height of the elevator car 2, the elevator allows the front shaft wall 3, including the leveling device and landing door on the same side as the front shaft wall 3, and the rear shaft wall 4 to be monitored by lidar 1 installed above the elevator car 2 and lidar 1 installed below the elevator car 2, respectively, and they are independent of each other. Therefore, the front shaft wall 3, the leveling device and landing door on the same side as the front shaft wall 3, and the rear shaft wall 4 are not affected by the height difference of the elevator operation; the left shaft wall 5 and the right shaft wall 6, as well as the guides installed on the two shaft walls, are also affected. The system requires image synthesis. When the elevator car 2 is moving upwards, the trajectory of the left shaft wall 5 and the right shaft wall 6 fed back by the LiDAR 1 located below the elevator car 2 is synthesized. When the elevator car 2 is moving downwards, the trajectory of the left shaft wall 5 and the right shaft wall 6 fed back by the LiDAR 1 located above the elevator car 2 is synthesized. Although there is some error, the current error is acceptable because the length of the elevator shaft is much greater than the height of the car 2. This saves the cost of arranging LiDAR 1 around the elevator car 2, reduces monitoring costs, and effectively achieves real-time monitoring of the shaft conditions during elevator operation.

[0040] In the description of this invention, it should be understood that the terms "longitudinal", "lateral", "up", "down", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, and are only for the convenience of describing this invention, and are not intended to indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of this invention.

[0041] The embodiments described above are merely preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Various modifications and improvements made by those skilled in the art to the technical solutions of the present invention without departing from the spirit of the present invention should fall within the protection scope defined by the claims of the present invention.

Claims

1. An elevator shaft monitoring system based on an external Internet of Things (IoT), characterized in that, include: The laser radar (1) is installed on the top and bottom surfaces of the car (2); the laser radar (1) is fixedly installed on one end of the rotating shaft (7), and the other end of the rotating shaft (7) is fixedly installed on the output end of the drive motor (8); there are two drive motors (8) and they are respectively fixedly installed at the center of the top surface and the center of the bottom surface of the car (2); The two laser radars (1) construct an Internet of Things database by dynamically scanning the elevator shaft during operation, and determine whether the elevator car (2) is abnormal by comparing the data of the elevator shaft under normal operation with the real-time monitoring data. The duplicate data in the original data read by the lidar (1) is cleaned up by the nearest neighbor sorting algorithm, and the deduplicated data is normalized. The Euclidean distance between each dataset in the dataset is calculated, and the average Euclidean distance of the dataset is obtained. The clustering results are cleaned using a genetic algorithm to eliminate useless attribute features in the dataset, thereby establishing the IoT database. The formula for obtaining the average Euclidean distance of the dataset is as follows: Among them, Dis(S) i ,S j ) is the data object S i and S j The Euclidean distance between them, A n The number of data objects; if the distance between each data object in the dataset and the target point is within AvgDis, then the data object is identified as a neighbor of the target point, and the number of its neighboring points is counted; the number of neighboring points of each data object in the dataset is sorted in descending order, and the first k data objects are taken as the initial cluster centers for clustering; During the process of cleaning the results after clustering the initial cluster centers using the genetic algorithm, the initial population is a gene sequence generated from 50 01 characters, and the feature corresponding to each gene is selected as the result after clustering the initial cluster centers. Among them, f i Let a be the fitness of gene i, N be the number of data objects in the dataset, and a be the number of data objects in the dataset. i k denoted as Σ(i) = Σ(l) = Σ(l) = Σ(l) = Σ(k) = Σ(k) = Σ(k) = Σ(l ...l) = Σ(l) = Σ(l) = Σ( 2. The elevator shaft monitoring system based on an external Internet of Things as described in claim 1, characterized in that: The lidar (1) on the top surface of the car (2) is used to monitor the upper half of the left shaft wall (5) and the guide rail of the right shaft wall (6) and the front shaft wall (3); the lidar (1) on the bottom surface of the car (2) is used to monitor the lower half of the left shaft wall (5) and the guide rail of the right shaft wall (6) and the rear shaft wall (4).

3. The elevator shaft monitoring system based on an external Internet of Things as described in claim 1, characterized in that: The drive motor (8) realizes the rotation of the lidar (1) through the rotating shaft (7); the shortest distance between the lidar (1) and the front shaft wall (3) / rear shaft wall (4) is the limit; the lidar (1) rotates within the range of -15°-15°.

4. The elevator shaft monitoring system based on an external Internet of Things as described in claim 1, characterized in that: The formula used to eliminate useless attribute features in a dataset is: Among them, f max and f min These represent the maximum and minimum fitness values ​​in the population, respectively. Regions are selected based on the fitness of individuals for crossover and mutation operations to eliminate useless attributes in the dataset. Once the maximum number of iterations is reached, a new population and the optimal result are output; otherwise, the genetic algorithm continues iterating.

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