CN-116199058-B - Elevator well monitoring system based on external internet of things

Abstract

The invention discloses an elevator hoistway monitoring system based on external Internet of things, which comprises laser radars arranged on the top surface and the bottom surface of a car, wherein the laser radars are fixedly arranged at one end of a rotating shaft, the other end of the rotating shaft is fixedly arranged at the output end of a driving motor, and the driving motor is provided with two driving motors which are respectively and fixedly arranged at the center of the top surface and the center of the bottom surface of the car. According to the elevator hoistway wall monitoring system, the rotatable laser radars are arranged up and down on the elevator car, data acquisition is carried out in the elevator hoistway through rotation of the laser radars, the internet of things database is built, and the monitoring work of the hoistway wall in the elevator operation process is completed through data comparison between the laser radars and the internet of things database.

Inventors

• ZHOU CHENG
• TIAN SHENGYU
• XIA YU
• CAO ZHENGQIANG
• LIU JING
• TIAN YING
• GU JIAYING
• LIU JUNMING
• XIE FEI
• FENG HUI
• WANG HAIJIAN
• ZHOU FENG

Assignees

• 青岛市特种设备检验研究院
• 山东科技大学

Dates

Publication Date

20260508

Application Date

20230220

Claims (4)

1. 1. The elevator hoistway monitoring system based on the external Internet of things is characterized by comprising a laser radar (1) arranged on the top surface and the bottom surface of a lift car (2), wherein the laser radar (1) is fixedly arranged at one end of a rotating shaft (7), the other end of the rotating shaft (7) is fixedly arranged at the output end of a driving motor (8), and the driving motor (8) is provided with two driving motors which are respectively fixedly arranged at the center of the top surface and the center of the bottom surface of the lift car (2); the two laser radars (1) are used for constructing an Internet of things database through dynamic scanning in the operation process of the elevator shaft, and judging whether the operation of the lift car (2) is abnormal or not through data comparison real-time monitoring data of the elevator shaft under normal operation; Cleaning repeated data in the original data read by the laser radar (1) through a proximity sorting algorithm, normalizing the data after de-duplication, calculating Euclidean distances among all data sets in the data sets, and obtaining the average Euclidean distance of the data sets; Cleaning the clustered results through a genetic algorithm, eliminating useless attribute characteristics in a data set, and establishing the database of the Internet of things; The formula for obtaining the average Euclidean distance of the data set is Wherein Dis (S i ,S j ) is the Euclidean distance between data objects S i and S j , A n is the number of data objects, and each data object in the data set is considered to be the adjacent point of the target point and the number of the adjacent points is counted if the distance between the data object and the target point is within AvgDis; In the process of cleaning the clustered results of the initial cluster centers through the genetic algorithm, the initial population is a gene sequence generated by 50 characters 01, and the characteristics corresponding to each gene are selected as the clustered results of the initial cluster centers; wherein f i is the fitness of the gene i, N is the number of data objects in the dataset, a i k is the number of the gene i which is divided into errors in the clustering result, l is the number of individuals in the population, and k is the number of clusters.
2. 2. The elevator hoistway monitoring system based on the external internet of things according to claim 1, wherein the laser radar (1) on the top surface of the elevator car (2) is used for monitoring upper half-section hoistway walls and guide rails of a left hoistway wall (5) and a right hoistway wall (6) and a front hoistway wall (3), and the laser radar (1) on the bottom surface of the elevator car (2) is used for monitoring lower half-section hoistway walls and guide rails of the left hoistway wall (5) and the right hoistway wall (6) and a rear hoistway wall (4).
3. 3. The elevator hoistway monitoring system based on the external internet of things according to claim 1, wherein the driving motor (8) is used for realizing rotation of the laser radar (1) through the rotating shaft (7), and the shortest distance between the laser radar (1) and the front hoistway wall (3)/the rear hoistway wall (4) is defined as 0 limit, and the laser radar (1) -15 DEG rotation is realized in a range.
4. 4. The elevator hoistway monitoring system based on the external Internet of things of claim 1, wherein the formula for eliminating useless attribute features in the data set is as follows Wherein f max and f min respectively represent the maximum and minimum values of fitness in the population, selecting regions according to the fitness of individuals to perform crossover operation and mutation operation, eliminating useless attribute features in the data set, outputting a new population and an optimal result if the maximum iteration number is reached, otherwise, continuing iteration by using a genetic algorithm.

Description

Elevator well monitoring system based on external internet of things Technical Field The invention relates to the technical field of monitoring, in particular to an elevator hoistway monitoring system based on external internet of things. Background At present, in the detection field of special equipment, the detection work of an elevator is very important, the current elevator detection usually adopts a manual detection method, an electronic dynamic detection mode is also adopted, a detection device is only used, detection results are sent to a local area network system for judging by a control center computer, the speed of the method is low, the cost is high, the detection effect is not ideal, data comparison is carried out through the local area network, data are only collected by former staff manually, data samples are fewer, the application range is small, the detection result is poor due to narrow data culvert surface, real-time monitoring of the state of a hoistway in the elevator operation process is not carried out, and the possibility of safety accidents caused by insufficient monitoring exists. Therefore, it is necessary to design a better detection technology to effectively monitor the situation of the hoistway in real time during the operation of the elevator, so as to solve the above-mentioned problems. Disclosure of Invention The invention aims to provide an elevator shaft monitoring system based on external Internet of things, so as to solve the problems in the prior art, realize real-time monitoring of the condition of an elevator shaft, prevent hidden danger caused by insufficient monitoring, improve the safety of the elevator in the operation process and avoid safety accidents caused by lack of monitoring of the condition of the elevator shaft in the operation process. The elevator hoistway monitoring system based on the external Internet of things comprises a laser radar installed on the top surface and the bottom surface of a car, wherein the laser radar is fixedly installed at one end of a rotating shaft, the other end of the rotating shaft is fixedly installed at the output end of a driving motor, and the driving motor is provided with two driving motors which are respectively and fixedly installed at the center of the top surface and the center of the bottom surface of the car. The laser radar on the top surface of the lift car is used for monitoring the upper half section of the left well wall and the right well wall, the guide rail and the front well wall, and the laser radar on the bottom surface of the lift car is used for monitoring the lower half section of the left well wall and the right well wall, the guide rail and the rear well wall. The driving motor realizes the rotation of the laser radar through the rotating shaft, and the laser radar rotates within the interval of-15 degrees to 15 degrees with the shortest distance between the laser radar and the front shaft wall/the rear shaft wall being 0 limit. And the two laser radars are used for constructing an Internet of things database through dynamic scanning in the operation process of the elevator shaft, and judging whether the operation of the lift car is abnormal or not through data comparison real-time monitoring data of the elevator shaft under normal operation. Cleaning repeated data in the original data read by the laser radar through a proximity ordering algorithm, normalizing the data after de-duplication, calculating Euclidean distances among all data sets in the data sets, and obtaining the average Euclidean distance of the data sets; and cleaning the clustered results through a genetic algorithm, eliminating useless attribute characteristics in the data set, and establishing the database of the Internet of things. The formula for obtaining the average Euclidean distance of the data set is The Dis (S i,Sj) is the Euclidean distance between the data objects S i and S j, A n is the number of data objects, the distance between each data object in the data set and the target point is within AvgDis, the data object is considered as the adjacent point of the target point, the number of the adjacent points is counted, the number of the adjacent points of each data object in the data set is arranged in a descending order, and the first k data objects are taken as initial clustering centers for clustering. In the process of cleaning the clustered results of the initial cluster centers through the genetic algorithm, the initial population is a gene sequence generated by 50 characters 01, and the characteristics corresponding to each gene are selected as the clustered results of the initial cluster centers; wherein f i is the fitness of the gene i, N is the number of data objects in the dataset, a ik is the number of the gene i which is divided into errors in the clustering result, l is the number of individuals in the population, and k is the number of clusters. The formula for eliminating useless attribute features in the