Method for Detecting and Warning the Operation Safety of a Lifting System Based on Internet of Things Sensing

Abstract

The present invention provides a method for safety detection and early warning of the operation of a lifting system based on Internet of Things perception, including the following steps: Step S1: Sensor layout installation and data configuration; Step S2: Detection signal analysis and processing; Step S3: Overspeed identification and early warning; Step S4: Abnormal stop identification and early warning; Applying this technical solution can realize the instant analysis and early warning of dangers such as overspeed out of control and abnormal stop during the operation of the lifting system, so as to achieve timely rescue and fault troubleshooting, and greatly improve the personal and property safety of various manned and cargo-carrying lifting systems.

Description

Technical Field

[0001] The present invention relates to the technical fields of Internet of Things applications and operation and maintenance monitoring of lifting systems, and particularly to a method for detecting and warning the operation safety of a lifting system based on Internet of Things perception. Background Art

[0002] Although modern elevators or relatively advanced lifting systems are designed with various safety protection devices including speed limiters, safety gears, buffers, etc., a considerable number of various operation safety accidents such as elevator falls and emergency stops still occur every year. During the accidents, due to the panic or injuries of some passengers, especially in extreme situations such as abnormal power failure in the lifting system, abnormal restart of the control subsystem, and overheating of the motor in the machine room, extreme situations such as no signal on mobile phones in the car and malfunction of the emergency call function occur, resulting in the inability to call for help in time, seriously threatening the personal safety of the occupants or the property safety of the carried goods.

[0003] How to timely and automatically detect and discover high-risk abnormal operation events of the lifting system, so as to actively report event warnings to the operation and maintenance personnel instead of passively discovering operation abnormalities, is of great importance. However, relying on the design or transformation of the existing safety protection devices of the lifting system cannot avoid the overall failure of the lifting system in some extreme situations, resulting in the failure of the protection device or the detection and warning means. Relying on the operation and maintenance personnel for manual monitoring is time-consuming, laborious, and inefficient, and both of them have great limitations. Summary of the Invention

[0004] In view of this, the purpose of the present invention is to provide a method for detecting and warning the operation safety of a lifting system based on Internet of Things perception, realizing the instant analysis and warning of dangers such as overspeed out of control and abnormal stop during the operation process of the lifting system, so as to achieve timely rescue and troubleshooting of faults, and greatly improve the personal and property safety of various manned and goods-carrying lifting systems.

[0005] To achieve the above purpose, the present invention adopts the following technical solutions: A method for detecting and warning the operation safety of a lifting system based on Internet of Things perception, comprising the following steps:

[0006] Step S1: Sensor layout installation and data configuration;

[0007] Step S2: Detection signal analysis and processing;

[0008] Step S3: Overspeed identification and warning;

[0009] Step S4: Abnormal stop identification and warning.

[0010] In a preferred embodiment: The step S1 includes the following steps:

[0011] Step S101: Plan and set detection points along the running direction of the lift system car, i.e., parallel to the lift system guide rail. The detection points are divided into up-layer detection points, down-layer detection points, top terminal station detection points, bottom terminal station detection points, and general detection point types:

[0012] Step S102: Install sensors at the planned detection points.

[0013] Step S103: Configure the system basic data in the data configuration module on the platform layer, including the lifting height of the lift system, i.e., the total running height between the bottom terminal station and the top terminal station floor, and the effective detection height of the car in the running direction.

[0014] Step S104: Configure the basic information of the sensors in the terminal management module on the platform layer, including each sensor identifier, the coordinates of the installation point relative to a fixed reference point at the bottom terminal station, the corresponding detection point type, and the relationship between the front and rear sensors.

[0015] In a preferred embodiment: Step S2 includes the following steps:

[0016] Step S201: Sensor S n Obtains the car detection signal and sends the signal data to the data service interface on the platform layer through the IoT network. The signal data includes the sensor identifier, the signal volume, and the signal generation time.

[0017] Step S202: The signal analysis and processing module loads or reads the configuration data and processes the current signal data in real time. The processed signal data includes the signal volume of the signal, the signal generation time, the corresponding sensor identifier, the predecessor sensor identifier, the successor sensor identifier, the corresponding detection point coordinates, and the detection point type.

[0018] Step S203: Creates or updates the time series data of the most recent period of Sensor S n to obtain the signal change characteristics of Sensor Sn, i.e., to obtain the running state of the car relative to Sensor Sn.

[0019] Step S204: The signal analysis and processing module creates or updates a sliding window for all the sensor time series data of the most recent period, and performs real-time analysis and aggregation on the signal change characteristics of multiple sensors within the same period, and finally obtains the complete state information corresponding to the current signal of the car, including the estimated coordinate value of the current position, the running direction, and the state type. The state type includes "arrival", "departure", "stop", and "running" of the car.

[0020] Step S205: If the recognized state type of the current signal is "arrival" or "departure" of the car, then generate corresponding messages, including Sensor S nThe identification, signal generation time, running direction, and status type are published to the topic of the message queue.

[0021] In a preferred embodiment: The step S3 includes the following steps:

[0022] Step S301: The overspeed identification module subscribes to and receives the messages in the message queue in real time, and parses to obtain the current signal generation time t n , the identification of the corresponding sensor S n , the car running direction, and the status type;

[0023] Step S302: According to the status type in the message content at time t n , read the corresponding forward message of a specific type: If the status type in the message content at time t n is "arrival", then its forward message is the nearest "departure" type message before time t n . If the status type in the message content at time t n is "departure", then its forward message is the nearest "arrival" type message before time t n ;

[0024] Step S303: Parse the corresponding forward message to obtain its signal generation time t m and the identification of the corresponding sensor S m , where t n > t m ;

[0025] Step S304: Read the coordinate values of sensors S m , S n and the effective height h of the car, and obtain the displacement Δy m from t n to t n , and calculate the average running speed of the car in this interval accordingly:

[0026] Step S305: Compare the average running speed v n of this interval with the rated speed v 额 of the lifting system. If v n > v 额 , then report overspeed warning information of different levels according to the size of the overspeed percentage;

[0027] Step S306: When the car is overspeed descending and the distance from the bottom end station is less than a certain threshold, report a warning of a bottoming accident; when the car is overspeed ascending and the distance from the top end station is less than a certain threshold, report a warning of a topping accident.

[0028] In a preferred embodiment: The step S4 includes the following steps:

[0029] Step S401: The abnormal stop recognition module subscribes to and receives the messages in the message queue in real time, and parses to obtain the current signal generation time t n , the identifier of the corresponding sensor S n , the car running direction, the status type, etc.;

[0030] Step S402: If the status type in the message content at time t n is of the "departure" type, or the status type at time t n is of the "arrival" type but the corresponding sensor S n type is not a landing or end station leveling detection point, then obtain the corresponding successor sensor S k , and read the coordinate values of the current sensor S n and the corresponding backward sensor S k , as well as the effective height h of the car;

[0031] Step S403: Calculate the displacement Δy n of the car from the "departure" or "arrival" at the S k detection point, "arrival" or "departure" at the S n detection point, and the expected running time

[0032] Step S404: The abnormal stop recognition module monitors the signal change of the sensor S k . If the signal of the car arriving or departing from the sensor S n is not received after exceeding a certain threshold of the expected running time Δt k , it is determined that an abnormal stop event occurs at a certain position between the sensors S n , S k , and an alarm message of the corresponding level is reported;

[0033] Step S405: When an abnormal stop event of the car occurs, if an overspeed alarm is obtained from the overspeed recognition module for the car at the point S n sensor, an emergency stop accident warning is reported.

[0034] In a preferred embodiment: The signal quantity related to the car operation is obtained through the installed and distributed sensors, and the detection signal is sent to the service module at the platform layer through the IoT network. The service module calculates and analyzes to determine the car motion state, and finally realizes the detection and warning of the high-risk events such as elevator falling and emergency stop during the operation of various lifting systems.

[0035] Compared with the prior art, the present invention has the following beneficial effects:

[0036] (1) The present invention can effectively and reliably detect high-risk events such as elevator falls and emergency stops during the operation of various lifting systems and give warnings in a timely manner. It can greatly improve the personal safety of passengers and the safety of goods and property in various lifting systems, and safeguard the vital interests of the people.

[0037] (2) The principle and design of the present invention are simple. Its construction, installation, and operation are independent of the existing lifting systems. It can be applied to new lifting system projects and is also suitable for the transformation of existing lifting systems. It has a wide range of applications and is easy to promote. The design of the entire operation detection and warning method is flexible and simple. Its installation and operation are independent of the existing lifting systems, avoiding increasing the complexity of the installation or transformation of the lifting systems.

[0038] (3) The implementation networking of the present invention is flexible, the deployment methods are rich, and there are very few restrictions on the specific types of sensors. Various types of proximity sensors can be selected according to the system environment and project budget, with the characteristics of low cost and high reliability.

[0039] (4) The present invention can greatly reduce various costs for the daily operation and maintenance of traditional elevators and other lifting systems, bringing advantages such as low cost, high accuracy, and fast response. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 is the schematic diagram of the method of the preferred embodiment of the present invention;

[0041] Figure 2 is the detection point diagram of the preferred embodiment of the present invention;

[0042] Figure 3 is the system structure diagram of the preferred embodiment of the present invention;

[0043] Figure 4 is the method flow chart of the preferred embodiment of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0044] The present invention will be further described below with reference to the drawings and embodiments.

[0045] It should be noted that the following detailed description is illustrative and is intended to provide further explanation of the present application. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present application belongs.

[0046] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present application; as used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should also be understood that when the terms "comprise" and / or "include" are used in this specification, they specify the presence of features, steps, operations, devices, components and / or combinations thereof.

[0047] A method for safety detection and early warning of the operation of a lifting system based on Internet of Things perception, referring to Figures 1 to 4 , step S1: Sensor layout installation and data configuration;

[0048] Step S101: Along the running direction of the car of the lifting system (i.e., the direction parallel to the guide rail of the lifting system), detect points are planned and set at reasonable positions. The detection points are divided into types such as landing up detection points, landing down detection points, top terminal station detection points, bottom terminal station detection points, and general detection points;

[0049] Step S102: Install sensors at the planned detection points.

[0050] Step S103: Configure the system basic data in the data configuration module of the platform layer, including the lifting height of the lifting system (i.e., the total running height between the bottom terminal station and the top terminal station floor) and the effective detection height of the car in the running direction.

[0051] Step S104: Configure the basic information of the sensors in the terminal management module of the platform layer, including each sensor identifier, the coordinates of the installation point relative to a fixed reference point of the bottom terminal station, the corresponding detection point type, and the relationship between the front and rear sensors.

[0052] Step S2: Detection signal analysis and processing;

[0053] Step S201: Sensor S n Obtains the car detection signal and sends the signal data to the data service interface of the platform layer through the IoT network. The signal data includes the sensor identifier, the signal quantity, and the signal occurrence time;

[0054] Step S202: The signal analysis and processing module loads or reads the configuration data and processes the current signal data in real time. The processed signal data includes the signal quantity of the signal, the signal occurrence time, the corresponding sensor identifier, the predecessor sensor identifier, the successor sensor identifier, the corresponding detection point coordinates, and the detection point type;

[0055] Step S203: Creates or updates the time series data of the most recent period of sensor S n and obtains sensor S nThe signal change characteristics, that is, obtaining relative to the sensor S n The car running state;

[0056] Step S203: The signal analysis and processing module creates or updates a sliding window for all the sensor time series data in the most recent period, and performs real-time analysis and aggregation on the signal change characteristics of multiple sensors in the same period, and finally obtains the complete state information corresponding to the current signal of the car, including the estimated coordinate value of the current position, the running direction, the state type, etc., where the state type includes "arrival", "departure", "stop", "running", etc. of the car;

[0057] Step S205: If the recognized state type of the current signal is "arrival" or "departure" of the car, generate corresponding messages (including the identifier of the sensor S n , the signal generation time, the running direction, the state type, etc.), and publish them to the topic of the message queue.

[0058] Step S3: Overspeed identification and warning;

[0059] Step S301: The overspeed identification module subscribes to and receives the messages in the message queue in real time, and parses to obtain the current signal generation time t n , the identifier corresponding to the sensor S n , the running direction of the car, the state type, etc.;

[0060] Step S302: According to the state type in the message content at time t n , read the corresponding forward message of the specific type: If the state type in the message content at time t n = "arrival", then its forward message is the nearest "departure" type message before time t n , if the state type in the message content at time t n = "departure", then its forward message is the nearest "arrival" type message before time t n ;

[0061] Step S303: Parse the corresponding forward message to obtain its signal generation time t m and the corresponding sensor S m identifier, where t n > t m ;

[0062] Step S304: Read the coordinate values of the sensors S m , S n and the effective height h of the car in the configuration data, obtain the displacement Δy m from t n to t n , and calculate the average running speed of the car in this interval accordingly:

[0063]

[0064] Step S305: Compare the average running speed v of this interval n with the rated speed v of the lifting system 额 , if v n > v 额 , then report overspeed warning information of different levels according to the size of the overspeed percentage;

[0065] Step S306: Further, when the car is overspeed descending and the distance from the bottom terminal station is less than a certain threshold, report a warning of bottoming accident; when the car is overspeed ascending and the distance from the top terminal station is less than a certain threshold, report a warning of overshooting accident.

[0066] Step S4: Abnormal stop identification and warning;

[0067] Step S401: The abnormal stop identification module subscribes to and receives the messages in the message queue in real time, and parses to obtain the current signal generation time t n , the identification of the corresponding sensor S n , the running direction of the car, the status type, etc.;

[0068] Step S402: If the status type in the message content at time t n is of the "departure" type, or the status type at time t n is of the "arrival" type but the type of the corresponding sensor S n is not a floor station or a terminal station leveling detection point, then obtain the corresponding successor sensor S k , and read the coordinate values of the current sensor S n and the corresponding backward sensor S k and the effective height h of the car;

[0069] Step S403: Calculate the displacement Δy n of the car from the "departure" (or "arrival") S k detection point to the "arrival" (or "departure") S n detection point and the expected running time

[0070] Step S404: The abnormal stop identification module monitors the signal change of the sensor S k . If the signal of the car arriving (or departing) from the sensor S n is not received after exceeding a certain threshold of the expected running time Δt k , it is determined that an abnormal stop event has occurred at a certain position between the sensors S n , S k , and the corresponding level of warning information is reported;

[0071] Step S405: Further, when an abnormal stop event of the car occurs, if it is obtained from the overspeed identification module that the car is at point S n and the sensor has an overspeed warning, an emergency stop accident warning is reported.

[0072] In this embodiment, specific sensor types can be selected according to the environment of the lifting system and the detection accuracy requirements, including proximity sensors of inductive, capacitive, optoelectronic, ultrasonic and other types.

[0073] In this embodiment, the number of sensor layout points can be appropriately adjusted according to the engineering cost budget and the detection accuracy requirements.

[0074] In this embodiment, flexible networking can be realized at the network layer. Each sensor can be directly connected to the platform layer through a 3G / 4G / 5G link, or can communicate with the platform layer through an Internet of Things gateway. Even the two networking modes can be used in combination according to the network signal status of different points of the sensor.

[0075] In this embodiment, the platform service module can be flexibly deployed according to the number scale and location distribution of the lifting systems to be monitored. The service module can be deployed in a remote central computer room or in a proximal edge computing unit.

[0076] Preferably, in this embodiment, in order to improve the accuracy of detecting and identifying the operation of the car, the sensor layout at the landing can adopt a dual-sensor layout to respectively detect the leveling action when the car arrives at the landing or the end station during upward and downward travel.

[0077] Preferably, in this embodiment, in order to quickly find the predecessor or successor sensor of a specified sensor, when the platform service loads or reads configuration data, the forward and backward relationships of the sensors can be expressed in the data structure of a doubly linked list:

[0078] Preferably, in this embodiment, in order to avoid repeatedly loading and reading the relevant configuration table and ensure the low latency of the platform layer service, the above-mentioned doubly linked list can be loaded in memory or cache, loaded once and used multiple times.

[0079] Preferably, in this embodiment, a global timing data object is created for the detection signal of each sensor and is continuously updated and maintained within the platform service cycle;

[0080] Preferably, in this embodiment, a global sliding window object is created for the timing data of all sensors and is continuously updated and maintained within the platform service cycle;

[0081] Preferably, in this embodiment, in order to ensure the high performance, fault tolerance, and message transmission correctness of the platform service, a streaming computing framework, including Strom, Spark, Flink, etc., can be applied during the real-time analysis of signal data by the signal analysis and processing module.

[0082] The schematic diagram of the method is as Figure 1 shown, where a) - e) are the process diagrams of the car passing through a certain sensor S n detection points, f) is the relationship between the change of the sensor signal volume and the car position and movement state during this process, and g) is the relationship between the time series of the changes of multiple sensor signals and the movement direction of the car. The schematic diagram includes the following contents:

[0083] (1) When the car is approaching S n , but still moving or docking outside the detection range of S n , S n is in the standby state, and the output signal volume is continuously low level ( Figure 1 -a); at the moment when the car reaches the S n point, S n is in the active state, and the output signal volume changes from low level to high level ( Figure 1 -b); when the car is moving or docking within the detection range of S n , S n is in the active state, and the output signal volume is continuously high level ( Figure 1 -c); at the moment when the car leaves the S n point, S n is in the standby state, and the output signal volume changes from high level to low level ( Figure 1 -d); when the car completely leaves S n , and is moving or docking outside the detection range of S n , S n is in the standby state, and the output signal volume is continuously low level ( Figure 1 -e).

[0084] (2) The characteristics and change trends of the signal volume of a single sensor can be analyzed to obtain information about the car position and movement state.

[0085] (3) The characteristics and change trends presented by the superposition of continuous multiple sensor signal volumes can be analyzed to obtain information about the car's operation in time and space, including its operation direction (such as in the Figure 1 time window shown in -f, the car runs sequentially along S n-1 →S n →S n+1 →S n interval), etc.

[0086] (4) By reasonably setting the number and positions of the detection points, complete signal data of the car's operation cycle on its system guide rails can be obtained.

[0087] The detection point layout diagram, as Figure 2 shown, includes the following contents:

[0088] (1) The detection points are set along the running direction of the car of the lifting system (i.e., in the direction parallel to the guide rails of the lifting system).

[0089] (2) Two leveling detection points are set at the landing stations, respectively used to detect whether the car sill and the landing door sill reach the same plane when the car travels up and down to this landing station. Among them, the downward leveling detection point is flush with the landing door sill, and the upward leveling detection point at the same landing station is located above the landing door sill, and the distance from the landing door sill is equal to or slightly less than the effective detection height of the car.

[0090] (3) One leveling detection point is set at the terminal stations. The leveling detection point at the bottom terminal station is flush with the terminal landing door sill, and the leveling detection point at the top terminal station is located above the landing door sill, and the distance from the landing door sill is equal to or slightly less than the effective detection height of the car.

[0091] (4) If necessary, one risk detection point can be added at each of the two terminal stations for rapid detection of high-risk events such as the car overshooting the top or crashing to the bottom. Among them, the risk detection point at the bottom terminal station is located above the landing door sill, and the distance from the landing door sill is equal to or slightly less than the effective detection height of the car, and the risk detection point at the top terminal station is flush with the terminal landing door sill.

[0092] (5) Ordinary detection points can be set at any position of the landing stations, terminal stations, and non-landing stations. If higher accuracy requirements for the calculation of the running state are needed, the setting of ordinary detection points can be increased. If a non-landing station has a large running span, ordinary detection points can be appropriately set at the non-landing station.

[0093] (6) In combination with the schematic diagram of the method, the partial relationships between the car running state and the signal volume characteristics of the landing detection points are described as follows: When the car descends to a certain landing and the car sill and the landing door sill are on the same plane at the moment, the sensor output signal volume at the downward leveling detection point changes from low level to high level; when the car ascends to a certain landing and the car sill and the landing door sill are on the same plane at the moment, the sensor output signal volume at the upward leveling detection point changes from low level to high level; when the car normally stops at this landing, the sensor output signal volumes at both the upward and downward leveling detection points are high levels; further, when the car starts to descend and leave from the normal stop state, the signal volume output by the sensor at the upward leveling detection point changes from high level to low level, and at the same time, the signal volume output by the sensor at the downward leveling detection point remains high level until the moment when the car completely leaves this landing, the signal volume output by the sensor at the downward leveling detection point changes from high level to low level.

[0094] (7) In combination with the schematic diagram of the method, the partial relationships between the car running state and the signal volume characteristics of the terminal landing detection points are described as follows: When the car ascends or descends to the terminal landing and the car sill and the landing door sill are on the same plane at the moment, the sensor output signal volume at the terminal landing leveling detection point changes from low level to high level; when the car normally stops at the terminal landing, the sensor output signal volume at the leveling detection point remains high level; further, when a bottoming or overshooting event may occur to the car, the sensor output signal volume at the terminal landing leveling detection point is high level, and at the same time, the sensor output signal volume at the risk detection point changes from high level to low level.

[0095] The system structure diagram, as Figure 3 shown, includes the following content:

[0096] (1) The system implemented in this embodiment is divided into four layers: the perception layer, the network layer, the platform layer, and the application layer. Among them, the perception layer and the platform layer are the cores of the entire system.

[0097] (2) The perception layer mainly completes the collection and reporting of the car running signals of the lifting system.

[0098] (3) The network layer mainly serves as an information channel to transmit the data obtained by the perception layer to the platform layer. It can support both wired access forms such as Ethernet, serial communication, and USB, and wireless access forms such as Wifi, 2G / 3G / 4G / 5G, and NB-IoT. It can support both local area network networking methods and wide area network networking methods.

[0099] (4) Platform layer, which is mainly responsible for the configuration management, monitoring, operation and maintenance of sensor devices, receiving the car operation signals reported by sensors and analyzing and processing them. It consists of functional modules such as data configuration, terminal management, detection signal analysis and processing, overspeed out-of-control identification, and abnormal stop identification. It can be deployed either in the remote central computer room or cloud computing center, or in the proximal edge computing unit.

[0100] (5) Application layer, mainly based on the results of signal analysis, processing and identification in the platform layer, and according to the type and risk level of alarms or early warnings, starts corresponding applications such as alarm push and alarm confirmation. It can even interface with the emergency department to start corresponding emergency plans for rapid implementation of personnel rescue.

[0101] The method flow chart, as Figure 4 shown, this flow chart mainly describes the processing and analysis of detection signals and the identification of abnormal risks, and does not include the preconditions such as detection point planning and setting, sensor layout and installation, configuration of system basic data and sensor basic information in the whole method. The method flow chart includes the following contents:

[0102] a) Detection signal analysis and processing

[0103] (1) The sensor reports the car detection signal;

[0104] (2) The detection signal analysis and processing parses and processes the currently received sensor detection signal, creates or updates the time-series data object of the current sensor, and obtains the signal change characteristics of sensor S n That is, the car operation state relative to sensor S n is obtained;

[0105] (3) The signal analysis and processing module creates or updates a sliding window for all the sensor time-series data in the most recent period, and performs real-time analysis and aggregation on the signal change characteristics of multiple sensors within the same period, and finally obtains the complete state information corresponding to the current signal of the car;

[0106] (4) Generate corresponding messages (including sensor Sn identification, signal generation time, running direction, state type, etc.), and publish them to the topic of the message queue.

[0107] b) Overspeed identification

[0108] (5) The overspeed identification module subscribes to and receives the messages in the message queue in real time, and parses to obtain the current signal generation time t n , the identification of the corresponding sensor S n , the running direction of the car, the state type, etc.;

[0109] (6) According to t nRead and parse the forward message of the corresponding specific type from the status type in the message content at the moment to obtain its signal generation time t m and the corresponding sensor S m identifier;

[0110] (7) Calculate the running distance and time of the car from t m to t n and finally obtain the average running speed of this interval;

[0111] (8) Compare the average running speed of this interval with the rated speed of the lifting system to determine whether it is overspeed;

[0112] (9) Further analyze whether there is a risk of bottoming out or overshooting according to the overspeed percentage and the current position.

[0113] c) Abnormal stop identification

[0114] (10) The abnormal stop identification module subscribes to and receives the messages in the message queue in real time, and parses to obtain the current signal generation time t n 、the identifier of the corresponding sensor S n 、the running direction of the car, the status type, etc.;

[0115] (11) Obtain the corresponding subsequent sensor S k , and read the coordinate values of the current sensor S n and the corresponding backward sensor S k as well as the effective height h of the car;

[0116] (12) Calculate the expected running time of the car from the "departure" (or "arrival") of S n detection point to the "arrival" (or "departure") of S k detection point;

[0117] (13) The normal stop identification module monitors the signal change of the sensor S k . If the car arrival (or departure) signal sent by the sensor S k is not received after exceeding a certain threshold of the expected running time, it is determined that an abnormal stop event has occurred at a certain position between the sensors S n 、S k , and report the alarm information of the corresponding level;

[0118] (14) Further, when an abnormal stop event of the car occurs, if an overspeed alarm is obtained from the overspeed identification module for the car at the point S n sensor, report an emergency stop accident warning.

[0119] d) Alarm

[0120] (15) Send warning messages of different levels according to the recognition result and property determination.

[0121] The above are only the preferred embodiments of the present invention, and all equivalent changes and modifications made according to the scope of the patent application of the present invention shall fall within the scope of the present invention.

Claims

1. Method for detecting and warning the operation safety of a lifting system based on Internet of Things perception, characterized in that: It includes the following steps: Step S1: Sensor layout installation and data configuration; Step S2: Detection signal analysis and processing; Step S3: Overspeed identification and warning; Step S4: Abnormal stop identification and warning; The said Step S2 includes the following steps: Step S201: Sensor S n Obtain the car detection signal and send the signal data to the data service interface of the platform layer through the IoT network. The signal data includes the sensor identifier, the signal volume, and the signal generation time; Step S202: The signal analysis and processing module loads or reads the configuration data, and processes the current signal data in real time. The processed signal data includes the signal volume of the signal, the signal occurrence time, the corresponding sensor identifier, the predecessor sensor identifier, the successor sensor identifier, the corresponding detection point coordinates, and the detection point type; Step S203: Create or update the time series data of the sensor S in the most recent period by applying the processed signal data, and obtain the signal change characteristics of the sensor Sn, that is, obtain the car running state relative to the sensor Sn; n ​ Step S204: The signal analysis and processing module creates or updates a sliding window for all sensor time series data in the most recent period, and performs real-time analysis and aggregation on the signal change characteristics of multiple sensors within the same period, and finally obtains the complete status information corresponding to the current signal of the car, including the predicted coordinate value of the current position, the running direction, and the status type. The status type includes "arrival", "departure", "stop", and "running" of the car; Step S205: If the recognized status type of the current signal is "arrival" or "departure" of the car, generate a corresponding message, including the sensor S n identification, signal generation time, running direction, status type, and publish it to the topic of the message queue.

2. Method for detecting and warning the operation safety of a lifting system based on Internet of Things perception according to claim 1, characterized in that: The said Step S1 includes the following steps: Step S101: Along the running direction of the car of the lifting system, that is, parallel to the guide rail of the lifting system, detection points are planned and set. The detection points are divided into floor up leveling detection points, floor down leveling detection points, top terminal floor leveling detection points, bottom terminal floor detection points, and ordinary detection point types; Step S102: Install sensors at the planned detection points; Step S103: Configure the system basic data in the data configuration module of the platform layer, including the lifting height of the lifting system, that is, the total running height between the bottom terminal floor and the top terminal floor, and the effective detection height of the car in the running direction; Step S104: Configure the basic information of the sensors in the terminal management module of the platform layer, including each sensor identifier, the coordinates of the installation point relative to a fixed reference point on the bottom terminal floor, the corresponding detection point type, and the relationship between the front and rear sensors.

3. Method for detecting and warning the operation safety of a lifting system based on Internet of Things perception according to claim 1, characterized in that: The said Step S3 includes the following steps: Step S301: The overspeed identification module subscribes to and receives the messages in the message queue in real time, and parses to obtain the current signal generation time t n , the identifier of the corresponding sensor S n , the car running direction, and the status type; Step S302: According to the status type in the message content at time t n Read the corresponding specific type of forward message: If the status type in the message content at time t n is "arrival", then its forward message is the nearest "departure" type message before time t n . If the status type in the message content at time t n is "departure", then its forward message is the nearest "arrival" type message before time t n . Step S303: Parse the corresponding forward message to obtain its signal generation time t m and the corresponding sensor S m identifier, where t n > t m ; Step S304: Read the coordinate values of sensors S m and S n and the effective car height h to obtain the displacement from t m to t n . Then calculate the average running speed of the car in the interval from t to t m to t n : ; Step S305: Compare t m to t n average running speed v in the interval n with the rated speed v of the lifting system 额 , if , then report different levels of overspeed warning information according to the magnitude of the overspeed percentage; Step S306: When the car travels at an overspeed and the distance from the bottom terminal floor is less than a certain threshold, report a warning of a bottoming accident; when the car travels at an overspeed and the distance from the top terminal floor is less than a certain threshold, report a warning of a topping accident.

4. Method for detecting and warning the operation safety of a lifting system based on Internet of Things perception according to claim 1, characterized in that: The said Step S4 includes the following steps: Step S401: The abnormal stop recognition module subscribes to and receives the messages in the message queue in real time, and parses to obtain the current signal generation time t n , the identifier of the corresponding sensor S n , the car running direction, the status type, etc.; Step S402: If the status type in the message content at time t n is of the "departure" type, or the status type at time t n is of the "arrival" type but the corresponding sensor S n is not a landing or terminal landing leveling detection point, then obtain the corresponding successor sensor S k , and read the coordinate values of the current sensor S n and the corresponding backward sensor S k as well as the effective height h of the car; Step S403: Calculate the displacement of the car from the "departure" or "arrival" at S n detection point, "arrival" or "departure" at S k detection point and the expected running time ; Step S404: The abnormal stop recognition module monitors the signal changes of sensor S k If, after exceeding the expected running time a certain threshold value, the car arrival or departure signal sent by sensor S k is not received yet, it is determined that an abnormal stop event has occurred at a certain position between sensor S n and S k , and an alarm message of the corresponding level is reported; Step S405: When an abnormal stop event of the car occurs, if an overspeed warning is obtained from the overspeed recognition module for the car at point S n from the sensor, an emergency stop accident warning is reported.

5. Method for detecting and warning the operation safety of a lifting system based on Internet of Things perception according to claim 1, characterized in that: Obtain the signal volume related to the running of the car through the installed sensors, and send the detection signal to the service module of the platform layer through the IoT network. The service module calculates and analyzes to determine the running state of the car, and finally realizes the detection and warning of high-risk events such as elevator falling and emergency stop during the running of various lifting systems.

Images