Elevator pit vertical entrance safety net system

Abstract

A security net system for an elevator system including an elevator pit and a plurality of entry points to the elevator pit is provided. The security net system includes: a plurality of sensors arranged in a plurality of planes surrounding the plurality of entry points to perform sensing and generate data corresponding to sensing results; and a processor coupled to the plurality of sensors to analyze whether the data indicates that a person enters the elevator pit, and to take an action based on a result of the analysis.

Description

Technical Field

[0001] The present disclosure relates to an elevator system, and in particular, to an elevator pit vertical entrance safety net system of the elevator system. Background Art

[0002] In an elevator system, a hoistway is built into a building, and the elevator car travels up and down along the hoistway to reach the landing doors of different floors of the building. The movement of the elevator is driven by a machine, which is controlled by a controller according to instructions received from users of the elevator system. The elevator pit is the space between the lowest landing door of the hoistway and the ground at the bottom of the hoistway. The elevator pit usually includes a concrete base plate and certain mechanisms of the elevator system and is usually bounded by four walls. The elevator pit can be entered by authorized personnel (i.e., maintenance technicians) via a pit ladder. Although there are some maintenance procedures that require moving the elevator car when the mechanic is in the elevator pit, when anyone is entering the elevator pit, the elevator car should usually be removed from the elevator pit and the elevator system should be inoperative. Summary of the invention

[0003] According to an aspect of the present disclosure, there is provided a safety net system for an elevator system including an elevator pit and a plurality of entry points to the elevator pit. The safety net system includes: a plurality of sensors arranged in a plurality of planes surrounding the plurality of entry points to perform sensing and generate data corresponding to the sensing results; and a processor coupled to the plurality of sensors to analyze whether the data indicates that a person has entered the elevator pit, and take action based on the result of the analysis.

[0004] According to additional or alternative embodiments, each of the plurality of sensors is a LiDAR sensor.

[0005] According to additional or alternative embodiments, each of the plurality of sensors is a millimeter wave RADAR sensor.

[0006] According to additional or alternative embodiments, each of the plurality of sensors is an RGBD camera.

[0007] According to additional or alternative embodiments, each of the plurality of sensors is one of a LiDAR sensor, a RADAR sensor, or a camera.

[0008] According to additional or alternative embodiments, the plurality of planes surrounding the plurality of access points surround a ladder leading to the elevator pit and span a door leading to the elevator pit.

[0009] According to additional or alternative embodiments, each of the plurality of sensors is configured to generate the data as point cloud data from one or more sensing operations, and the processor is configured to analyze the point cloud data from the one or more sensing operations, various learned contexts, external switch information, car information, timing information, and car position and movement information, and determine whether the point cloud data from the one or more sensing operations indicates that the person has entered the elevator pit.

[0010] According to additional or alternative embodiments, the processor is configured to shut down elevator system operation after a delay if the data indicates that the person has entered the elevator pit, the processor is configured to shut down elevator system operation immediately if the data indicates that the person has entered the elevator pit and an emergency switch has been pressed, and the processor is further configured to execute at least a motion control response of the elevator system based on a result of the analysis.

[0011] According to an aspect of the present disclosure, an elevator system is provided, and the elevator system includes: an elevator pit, a ladder and a door for an entrance to the elevator pit, a button for operating the elevator system, and a safety net system. The safety net system includes: a plurality of sensors arranged in a plurality of planes surrounding the plurality of entry points to perform sensing and generate data corresponding to the sensing results; and a processor coupled to the plurality of sensors to analyze whether the data indicates that a person enters the elevator pit, and take action based on the result of the analysis and the corresponding state of the button.

[0012] According to additional or alternative embodiments, each of the plurality of sensors is a LiDAR sensor.

[0013] According to additional or alternative embodiments, each of the plurality of sensors is a millimeter wave RADAR sensor.

[0014] According to additional or alternative embodiments, each of the plurality of sensors is an RGBD camera.

[0015] According to additional or alternative embodiments, each of the plurality of sensors is one of a LiDAR sensor, a RADAR sensor, or a camera.

[0016] According to additional or alternative embodiments, the plurality of planes surrounding the plurality of access points surround a ladder leading to the elevator pit and span a door leading to the elevator pit.

[0017] According to additional or alternative embodiments, each of the plurality of sensors is configured to generate the data as point cloud data from one or more sensing operations, and the processor is configured to analyze the point cloud data from the one or more sensing operations, various learned contexts, external switch information, car information, timing information, and car position and movement information, and determine whether the point cloud data from the one or more sensing operations indicates that the person has entered the elevator pit.

[0018] According to additional or alternative embodiments, a first of the buttons is an emergency switch within the elevator pit in close proximity to the door, the processor is configured to shut down elevator system operation after a delay if the data indicates that the person has entered the elevator pit, the processor is configured to shut down elevator system operation immediately if the data indicates that the person has entered the elevator pit and the emergency switch is pressed, and the processor is further configured to execute at least a motion control response of the elevator system based on a result of the analysis.

[0019] According to additional or alternative embodiments, a second of the buttons is a reset button outside the elevator pit, and the processor is further configured to stop operation of the plurality of sensors and analysis by the processor after shutdown until the reset switch is pressed.

[0020] According to an aspect of the present disclosure, a method for operating a safety net system of an elevator system is provided. The method includes: sensing an object disposed along a ladder leading to an elevator pit or in an area spanning a door leading to the elevator pit; generating data corresponding to a result of the sensing; analyzing whether the data indicates that a person is standing on the ladder or entering the elevator pit through the door; and taking an action related to the operation of the elevator system based on a result of the analysis.

[0021] According to additional or alternative embodiments, taking the action includes shutting down elevator system operation after a delay if the data indicates that the person is standing on the ladder or entering the elevator pit through the door, and shutting down elevator system operation immediately if the data indicates that the person is standing on the ladder or entering the elevator pit through the door and an emergency switch is pressed.

[0022] According to additional or alternative embodiments, said taking said action further comprises stopping said sensing, said generating and said analyzing after shutting down until a reset switch is pressed.

[0023] Additional features and advantages are achieved through the technology of the present disclosure. Other embodiments and aspects of the present disclosure are described in detail herein, and other embodiments and aspects of the present disclosure are considered to be part of the claimed technical concept. In order to better understand the present disclosure with advantages and features, reference is made to the description and to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] For a more complete understanding of the present disclosure, reference is now made to the following brief description, taken in conjunction with the accompanying drawings and detailed description, wherein like reference numerals represent like parts:

[0025] Figure 1 is a perspective view of an elevator system according to an embodiment;

[0026] Figure 2 According to the embodiment Figure 1 A perspective view of an elevator pit of an elevator system;

[0027] Figure 3 is a side view of an elevator pit ladder with sensors for a safety net system according to an embodiment;

[0028] Figure 4 According to the embodiment, Figure 3 A front view of an elevator pit ladder with sensors;

[0029] Figure 5 According to the embodiment, Figure 3 A top view of the elevator pit ladder with sensors;

[0030] Figure 6 is a flow chart illustrating a method of operating a safety net system of an elevator system according to an embodiment;

[0031] Figure 7 is a perspective view of an elevator pit with sensors for a safety net system according to an embodiment;

[0032] Figure 8 is a top view of an elevator pit with sensors of a safety net system and additional sensors according to an embodiment;

[0033] Fig. 9 is a side view of an elevator pit with non-coplanar sensors and additional sensors for a safety net system according to an embodiment;

[0034] Fig.10 is a flow chart illustrating a method of operating a safety net system of an elevator system according to an embodiment;

[0035] Fig.11 is a graphical illustration of a learned context of a safety net system according to an embodiment;

[0036] Fig.12 is a graphical illustration of a person applied to a learned context of a safety net system according to an embodiment;

[0037] Fig.13 is a graphical illustration of signal variance of sensor readings of a safety net system according to an embodiment;

[0038] Fig.14 is a flow chart illustrating a method of operating a safety net system of an elevator system according to an embodiment;

[0039] Fig.15 is a flow chart illustrating a method of operating a safety net system of an elevator system according to an embodiment;

[0040] Fig.16 is a schematic diagram of a display unit of a safety net system according to an embodiment;

[0041] Fig.17A , Fig. 17B and Fig. 17C is an image showing a sensing area of ​​an entry point of an elevator pit according to an embodiment;

[0042] Fig.18 According to the embodiment Fig.17A , Fig. 17B and Fig. 17C A top view of the elevator pit, buttons and sensing areas;

[0043] Fig.19 is a flow chart illustrating a method of operating a safety net system of an elevator system according to an embodiment; and

[0044] Fig. 20 is a schematic diagram showing a system for implementing a safety net system according to an embodiment. DETAILED DESCRIPTION

[0045] In the elevator industry, multiple monitors and sensors are provided to monitor various parts and components of an elevator system. In particular, key areas to be monitored are the elevator pit where maintenance technicians and mechanics enter to perform maintenance and repair tasks and the pit ladders that maintenance technicians and mechanics use to enter the elevator pit and stand on during some operations. Therefore, a cost-effective method of detecting a person (e.g., a maintenance technician or mechanic) standing in the elevator pit or on the pit ladder of an elevator system is needed. Such a detection system needs to be easy to install and adjust and needs to require minimal repair and maintenance. The detection system must also have high detection performance with low false positive and negative results.

[0046] As will be described below, an elevator pit vertical entrance safety net system is provided for an elevator system. The elevator pit vertical entrance safety net system provides a vertical net to span the entire entrance area in the elevator pit, wherein the vertical net is oriented parallel to the pit landing door. Positioning the elevator pit vertical entrance safety net system in this manner is at least partially based on the concept that in order to obtain the entrance to the elevator pit, the area of ​​the vertical entrance to the elevator pit must be penetrated, and due to its vertical arrangement, it is less sensitive to uncertain interference, such as moving elevator cables or ropes, small animals or debris, etc. The elevator pit vertical entrance safety net system will generally use certain detection operations: the background will be learned when the elevator car is away from the elevator pit and when he lands for the elevator pit; objects will be identified and classified to determine whether they are moving elevator cars or humans entering the elevator pit via doors or elevator pit ladders; the location of the sensed object's speed can then be used to robustly distinguish between humans and elevator equipment.

[0047] refer to Figure 1 , which is a perspective view of an elevator system 101, which includes an elevator car 103, a counterweight 105, a tension member 107, a guide rail 109, a machine 111, a position reference system 113, and a controller 115. The elevator car 103 and the counterweight 105 are connected to each other by the tension member 107. The tension member 107 can include or be configured as, for example, a rope, a steel cable, and / or a coated steel belt. The counterweight 105 is configured to balance the load of the elevator car 103 and to facilitate the movement of the elevator car 103 relative to the counterweight 105 in the elevator hoistway 117 and along the guide rail 109 simultaneously and in opposite directions.

[0048] The tension member 107 engages a machine 111, which is part of the overhead structure of the elevator system 101. The machine 111 is configured to control movement between the elevator car 103 and the counterweight 105. The position reference system 113 can be mounted on a fixed portion of the top of the elevator shaft 117, such as on a support rail or a guide rail, and can be configured to provide a position signal related to the position of the elevator car 103 in the elevator shaft 117. In other embodiments, the position reference system 113 can be directly mounted to the moving components of the machine 111, or can be positioned in other positions and / or configurations as known in the art. As known in the art, the position reference system 113 can be any device or mechanism for monitoring the position of the elevator car and / or counterweight. For example and not limitation, as will be appreciated by those skilled in the art, the position reference system 113 can be an encoder, a sensor or other system, and can include speed sensing, absolute position sensing, etc.

[0049] As shown, the controller 115 can be positioned in a controller room 121 of the elevator shaft 117 and is configured to control the operation of the elevator system 101 and in particular the operation of the elevator car 103. It is to be appreciated that the controller 115 does not need to be in the controller room 121, but can be in a lift room or other location in the elevator system. For example, the controller 115 can provide a drive signal to the machine 111 to control the acceleration, deceleration, leveling, stopping, etc. of the elevator car 103. The controller 115 can also be configured to receive a position signal from a position reference system 113 or any other desired position reference device. When moving up or down in the elevator shaft 117 along the guide rail 109, the elevator car 103 can stop at one or more landings (landing) 125 as controlled by the controller 115. Although shown in the controller room 121, it will be appreciated by those skilled in the art that the controller 115 can be positioned and / or configured in other locations or positions in the elevator system 101. In one embodiment, the controller 115 may be remotely located or located in a distributed computing network (eg, a cloud computing architecture). The controller 115 may be implemented using a processor-based machine (eg, a personal computer, a server, a distributed computing network, etc.).

[0050] The machine 111 may include a motor or similar drive mechanism. According to an embodiment of the present disclosure, the machine 111 is configured to include an electric drive motor. The power supply for the motor may be any power source, including an electrical grid, which is supplied to the motor in combination with other components. The machine 111 may include a traction sheave, which transmits force to the tension member 107 to move the elevator car 103 within the elevator shaft 117.

[0051] The elevator system 101 also includes one or more elevator doors 104. The elevator doors 104 may be integrally attached to the elevator car 103 or the elevator doors 104 may be located on a landing 125 of the elevator system 101, or both. The embodiments disclosed herein may be applicable to elevator doors 104 integrally attached to the elevator car 103 or to elevator doors 104 located on a landing 125 of the elevator system 101, or both. The elevator doors 104 open to allow passengers to enter and exit the elevator car 103.

[0052] Continue to refer Figure 1 And also refer to Figure 2, the bottom portion of the elevator shaft 117 of the elevator system 101, which is lower than the lowest landing in the landing 125, is provided as an elevator pit 201. The elevator pit 201 may include a base 202, four surrounding elevator pit walls 203, a base portion 204, and an elevator pit ladder 205, wherein the base portion 204 may include or be provided as a plate and one or more components 2041 provided for supporting the elevator car 103. The elevator pit ladder 205 extends from the upper portion of the elevator pit 201 to the lower portion of the elevator pit 201 and allows a maintenance technician or mechanic (hereinafter referred to as a "mechanic") to enter the elevator pit 201. The elevator pit ladder 205 is adjacent to one of the elevator pit walls 203 and includes vertical members 2051, 2052 and a crosspiece 2053 extending between the vertical members 2051, 2052. When a mechanic is inside the elevator pit 201 or standing on the elevator pit ladder 205 (i.e., standing on one of the rungs 2053 of the elevator pit ladder 205), the elevator car 103 should generally be removed from the elevator pit 201, and the elevator car 103 should generally be prevented from entering the elevator pit 201 except in the case of certain maintenance procedures.

[0053] Continue to refer Figure 1 and Figure 2 And also refer to Figure 3-Figure 5 , a safety net system 301 is provided to reliably identify whether a mechanic or another person is standing on or supported on an elevator pit ladder 205 in an elevator pit 201, so that appropriate actions can be taken to ensure safety. The safety net system 301 includes a sensor 310 and a processor 320. The sensor 310 is arranged in a plane P defined between the elevator pit ladder 205 and one of the elevator pit walls 203. The sensor 310 is configured to perform sensing to sense an object disposed along the plane P and generate data corresponding to the result of the sensing. The processor 320 is operably coupled to the sensor 310 and is configured to analyze the data and determine whether the data indicates a person standing on the ladder based on the analysis result.

[0054] The processor 320 includes a processing unit, a memory, and an input / output (I / O) unit, through which the processor 320 communicates with the sensor 310 and at least with the controller 115 (see Figure 1) communication. The memory has executable instructions stored thereon, which are readable and executable by the processing unit. When the processing unit reads and executes the executable instructions, the executable instructions cause the processor to operate as described herein. According to an embodiment, the executable instructions may include a machine learning algorithm that improves certain operations of the processing unit over time. The processor 320 may be remote from the sensor 310 or local. In the former case, the processor 320 may be operably coupled to the sensor 310 via a wired connection or via a wireless connection. In the latter case, the processor 320 may be built into the sensor 310, or provided as a component separate from the sensor 310 and the processor 320 may be operably coupled to the sensor 310 via a wired connection or via a wireless connection.

[0055] According to an embodiment, the sensor 310 may include or be provided as one or more of a light detection and ranging or laser imaging, detection and ranging (LiDAR) sensor, a radio detection and ranging (RADAR) sensor and / or a camera. According to another embodiment, the sensor 310 may be provided as one or more of a 2D LiDAR sensor, a millimeter wave RADAR sensor and / or a red, green, blue, depth (RGBD) camera. According to still other embodiments, the sensor 310 may be provided as a plurality of sensors including a combination of one or more sensor types listed herein.

[0056] In the exemplary case where the sensor 310 is a 2D LiDAR sensor, the sensor 310 is configured to sense the entire length L1 of the elevator pit ladder 205 (see Figure 2 ), where plane P may be approximately 50-100 mm behind the elevator pit ladder 205 and between the elevator pit ladder 205 and one of the elevator pit walls 203. In these or other cases, the sensor 310 is configured to generate the point cloud data 401 (see FIG. 4 ) using a single scan for image processing, multiple scans for image processing, and / or multiple continuous or sustained scans for video processing. Figure 4 ) data, and the processor 320 is configured to analyze the point cloud data 401 and determine whether the point cloud data 401 indicates a person standing on the elevator pit ladder 205.

[0057] That is, where the elevator pit ladder 205 includes rungs 2053, the object being sensed or detected may be the toe of a person standing on one of the rungs 2053, and the point cloud data 401 may include hit points 402 where different portions of the toe intersect the plane P, additional points 403 where portions of any object do not intersect the plane P, and phantom points 404 where portions of foreign objects or debris (i.e., feathers or dust that drift into the plane P) intersect the plane P. The processor 320 analyzes each of the hit points 402, additional points 403, and phantom points 404. The processor 320 identifies the hit points 402 as hit points 402 based on their characteristic shapes and their grouping, the processor 320 identifies the additional points 403 as additional points 403 based on their signals matching a baseline data set taken when the elevator pit 201 is known to be empty or more generally has certain physical characteristics, and the processor 320 identifies the false points 404 as false points 404 based on their characteristic shapes or the lack of their characteristic shapes and their grouping or the lack of their grouping. The processor 320 then distinguishes the hit points 402 from the additional points 403 and the false points 404 and determines that when the hit points 402 of the point cloud data 401 are identified and distinguished, the hit points 402 indicate that the toe of the shoe intersects the plane P and therefore determines that a person may be standing on one of the rungs 2053 of the elevator pit ladder 205. The processor 320 may then communicate this finding to at least the controller 115 of the elevator system 101 so that the controller 115 may take action, such as by preventing the elevator car 103 from entering the elevator pit 201 .

[0058] Because the processor 320 can identify and distinguish between hit points 402 and additional points 403, the incidence of false negative determinations of the safety net system 301 is reduced. Similarly, because the processor 320 can identify and distinguish between hit points 402 and false points 404, the incidence of false positive determinations of the safety net system 301 is also reduced. When the executable instructions stored on the memory unit of the processor 320 include a machine learning algorithm, the ability of the processor 320 to identify and distinguish between hit points 402 and additional points 403 and false points 404 can be improved over time, and the incidence of false negative determinations and false positive determinations of the safety net system 301 can be continuously reduced over time in a corresponding manner.

[0059] refer to Figure 6, a method 600 of operating a safety net system of an elevator system (e.g., the safety net system 301 of the elevator system 101 described above) is provided. The method 600 includes: sensing an object disposed along a plane defined between a ladder in an elevator pit and an elevator pit wall (block 601), generating data corresponding to the result of the sensing (block 602), analyzing the data (block 603), and determining whether the data indicates a person standing on the ladder based on the result of the analysis (block 604). As described above, the object may be the tip of a shoe of a person standing on a rung of the ladder, and the determination of block 604 may include executing a machine learning algorithm (block 6041) that improves the accuracy of the determination over time.

[0060] While the image processing described above relates to a single frame of points in a single scan of a point cloud, the processor 320 may also process continuous scans to help classify points as hit points 402 versus additional points 403 or false points 404 by determining how persistent the points are and whether they move together as one would expect in a valid hit point associated with a mechanic. As such, the generation of data at block 602 may include generating data for multiple scans of a point cloud, where the term "data" may relate to a continuously or semi-continuously updated set of point cloud scans. In these or other cases, the analysis at block 603 and the determination at block 604 may include image processing and video processing.

[0061] Reference Figure 1 and Figure 2 And also refer to Figure 7 , a safety net system 701 is provided to reliably identify whether a mechanic or another person is standing in the elevator pit 201 so that appropriate actions can be taken to ensure safety. The safety net system 701 includes a sensor 710 and a processor 720. The sensor 710 is arranged in a plane P' defined along the bottom of the elevator pit 201. The sensor 710 is configured to perform sensing to sense an object set along the plane P' and generate data corresponding to the result of the sensing. The processor 720 is operably coupled to the sensor 710 and is configured to analyze the data and determine whether the data indicates a person in the elevator pit 201 based on the analysis result.

[0062] The processor 720 includes a processing unit, a memory, and an input / output (I / O) unit, and the processor 720 communicates with the sensor 710 and at least with the controller 115 (see Figure 1) communication. The memory has executable instructions stored thereon, which are readable and executable by the processing unit. When the processing unit reads and executes the executable instructions, the executable instructions cause the processor to operate as described herein. According to an embodiment, the executable instructions may include a machine learning algorithm that improves certain operations of the processing unit over time. The processor 720 may be remote from the sensor 710 or local. In the former case, the processor 720 may be operably coupled to the sensor 710 via a wired connection or via a wireless connection. In the latter case, the processor 720 may be built into the sensor 710, or provided as a component separate from the sensor 710 and the processor 720 may be operably coupled to the sensor 710 via a wired connection or via a wireless connection.

[0063] According to an embodiment, the sensor 710 may include or be provided as one or more of a light detection and ranging or laser imaging, detection and ranging (LiDAR) sensor, a radio detection and ranging (RADAR) sensor and / or a camera. According to another embodiment, the sensor 710 may be provided as one or more of a 2D LiDAR sensor, a millimeter wave RADAR sensor and / or a red, green, blue, depth (RGBD) camera. According to still other embodiments, the sensor 710 may be provided as a plurality of sensors including a combination of one or more sensor types listed herein. A description of the plurality of sensors will be provided below.

[0064] In the exemplary case where the sensor 710 is a 2D LiDAR sensor, the sensor 710 is disposed in a corner 2011 of the elevator pit 201 and is configured to sense a plane P′ that is a 2D plane extending away from the corner 2011 along a substantial portion of an area of ​​the bottom of the elevator pit 201. The plane P′ may be approximately 18-24″ above the base 202. In these or other cases, the sensor 710 is configured to generate data as the point cloud data 730 using a single scan for image processing, multiple scans for image processing, and / or multiple continuous or sustained scans for video processing, and the processor 720 is configured to analyze the point cloud data 730 and determine whether the point cloud data 730 indicates a person standing in the elevator pit 201.

[0065] That is, the object being sensed or detected may be a person in the elevator pit 201, and the point cloud data 730 may include hit points 731 where different portions of the person intersect with plane P', additional points 732 where portions of the person or other object do not intersect with plane P', and false points 733 where portions of foreign objects or debris (i.e., feathers or dust that drift into plane P') intersect with plane P'. The processor 720 analyzes each of the hit points 731, additional points 732, and false points 733. The processor 720 identifies the hit points 731 as hit points 731 based on their characteristic shapes and their groupings, the processor 720 identifies the additional points 732 as additional points 732 based on their signals matching a baseline data set taken when the elevator pit 201 is known to be empty or more generally has certain physical characteristics, and the processor 720 identifies the false points 733 as false points 733 based on their characteristic shapes or the lack of their characteristic shapes and their groupings or the lack of their groupings. The processor 720 then distinguishes the hit point 731 from the additional point 732 and the false point 733, and determines that the hit point 731 indicates that a portion of the person intersects the plane P' when the hit point 731 of the point cloud data 730 is identified and distinguished, and thus determines that the person may be standing in the elevator pit 201. The processor 720 can then communicate this finding to at least the controller 115 of the elevator system 101 so that the controller 115 can take action, such as by preventing the elevator car 103 from entering the elevator pit 201, to avoid an unsafe condition.

[0066] Because the processor 720 can identify and distinguish between the hit point 731 and the additional point 732, the incidence of false negative determinations of the safety net system 701 is reduced. Similarly, because the processor 720 can identify and distinguish between the hit point 731 and the false point 733, the incidence of false positive determinations of the safety net system 701 is also reduced. When the executable instructions stored on the memory unit of the processor 720 include a machine learning algorithm, the ability of the processor 720 to identify and distinguish between the hit point 731 and the additional point 732 and the false point 733 can be improved over time, and the incidence of false negative determinations and false positive determinations of the safety net system 701 can be continuously reduced over time in a corresponding manner.

[0067] refer to Figure 8 and Fig. 9 And according to an embodiment, one or more additional sensors 801 may be arranged in plane P' and configured to perform sensing to sense an object and generate additional data corresponding to the sensing result. In these or other cases, as Figure 8As shown in FIG. 1 , the sensor 710 may be disposed in a corner 2011 of the elevator pit 201, and the one or more additional sensors 801 may be disposed in one or more other corners 2012 of the elevator pit 201 and may be oriented laterally relative to the sensor 710. The processor 720 will be operably coupled to the sensor 710 and the one or more additional sensors 801, and will be configured to analyze the data generated by the sensor 710 and the additional data generated by the one or more additional sensors 801 and determine whether the data and the additional data indicate a person in the elevator pit 201 based on the analysis results. Fig. 9 As shown in , at least one of the one or more additional sensors 801 is arranged in a unique plane P" and is non-coplanar with respect to the sensor 710.

[0068] refer to Fig.10 , a method 1000 of operating a safety net system of an elevator system (e.g., the safety net system 701 of the elevator system 101 described above) is provided. The method 1000 includes: sensing an object disposed along a plane defined along the bottom of an elevator pit in at least one direction along the plane (block 1001), generating data corresponding to the result of the sensing (block 1002), analyzing the data (block 1003), and determining whether the data indicates a person standing in the elevator pit based on the result of the analysis (block 1004). As described above, the determination of block 1004 may include executing a machine learning algorithm (block 10041), which improves the accuracy of the determination over time.

[0069] While the image processing described above relates to a single frame of points in a single scan of a point cloud, the processor 720 may also process continuous scans to help classify points as hit points 731 versus additional points 732 or false points 733 by determining how persistent the points are and whether they move together as one would expect in a valid hit point associated with a mechanic. As such, the generation of data at block 1002 may include generating data for multiple scans of a point cloud, where the term "data" may relate to a continuously or semi-continuously updated set of point cloud scans. In these or other cases, the analysis at block 1003 and the determination at block 1004 may include image processing and video processing.

[0070] Although Figure 3-Figure 6 Examples and Figure 7-Figure 10 The embodiments of the present invention are described above as being separate from each other, but it is to be understood that this is not required and can be combined in various combinations. Figure 3-Figure 6 Examples and Figure 7-Figure 10Embodiments. For example, sensor 310 may be provided as a single 2D LiDAR sensor with a field of view that captures the front area of ​​the elevator pit through which the mechanic must pass to enter the elevator pit, and sensor 710 may be provided as a set of two 2D LiDAR sensors in opposite corners of the pit area with a field of view that captures most or all of the area where the mechanic may be standing in the elevator pit. Additional sensing in these or other cases may include three-dimensional (3D) sensing, alternating sensing (mmWave or RGB-D cameras), two or more sensors, coverage of different plans with a range of 2D sensors, and data / image processing methods (including but not limited to image classification, machine learning, pattern recognition, etc.).

[0071] refer to Fig.11 and Fig.12 , the method of operation of sensor 310 and sensor 710 may be a 2D classification method. This 2D classification method will be described in the context of sensor 710. This is done for the sake of clarity and brevity, and it is to be understood that the 2D classification method is also applicable to sensor 310.

[0072] After setup, the sensor 710 learns the environmental context in the elevator pit 201 by scanning and using various elevator car positions within a predefined time (e.g., within about 30 seconds). The processor 720 then analyzes the Fig.11 The statistical changes and trends of the range and angle data shown in the figure are used to generate learning profiles. This leads to Fig.11 After the learning phase, the sensor 710 scans the elevator pit 201 at an update rate (e.g., approximately 10 scans / second). The processor 720 then generates the survey area shown in the gray area in FIG. Fig.11 The updated data is shown as points in the graph compared with the background. Any point inside the gray area is considered as Fig.12 A potential indicator of a human is shown in . The final decision on human detection made by processor 720 is based on the number of points observed in the gray area in each scan and how many scans exceeded the trigger level.

[0073] The 2D classification method may be re-executed periodically or in response to external events. Periodic re-execution allows for changes in the elevator system 201 over time to be accounted for (i.e., degradation or damage of components, changes in components, etc.). Re-execution in response to external events may be performed as needed, such as when the sensor 710 is bumped or moved and needs to be recalibrated.

[0074] Continue to refer Fig.11, provides a typical environmental background of the elevator pit 201 from the learning phase of the safety net system 701. Figure 1 In the graph, evidence of the counterweight and rails are visible on the right side of the graph, and evidence of the car guide rails, especially the left rail, is visible on the left side of the graph. When installation of the safety net system 701 is complete, the processor 720 of the safety net system 701 can provide a comparison with the dimensions of the elevator pit 201 as a check on the learning phase. Fig.11 Calculation of the footprint area (in this case approximately 2.65m 2 ). In the event that the comparison indicates that the footprint area is close to the size of the elevator pit 201, the learning phase may be considered successful. Any subsequent deviations from the footprint area picked up by the safety net system 701 during the operating phase may be identified as a potential person standing in the elevator pit 201. Further processing by the processor 720 may be performed to confirm that the deviation is caused by a person, which may then be detected by the processor 720 and Figure 1 The controller 115 takes appropriate action.

[0075] refer to Fig.13 , the normal variance of the range detection at each angle of the sensor 710 can be established during the learning phase and can also be used to verify successful installation. In this case, during the operation phase Fig.13 Excessive changes in the signal of will indicate that the sensor 710 is faulty or that the elevator pit 201 is not empty. As described above, further processing by the processor 720 can be performed to confirm the deviation caused by humans, so the processor 720 and Figure 1 The controller 115 takes appropriate action.

[0076] The variance of multiple collected point clouds for the learning phase (e.g., at one vertical car position) can generate a range of acceptance criteria. Examples include: the magnitude of the average change across all angles in the field of view, the worst case magnitude change observed at any angle within the field of view, a drift or change in the point cloud range value at any angle that is trending during the scanned learning phase of the observed range value, or a change in a point cloud feature that can be tracked to a rotational change of the sensor 710 during the learning phase.

[0077] As used herein, the term "variance" can be a discriminator for successful learning, where there can be two types of data metrics useful for determining whether the learning phase was successful. These include the difference or error between the learned results and the desired predetermined idea, such as the area of ​​the annotated items / objects or learned background in the sensor's field of view, and the observed changes in the collected data as seen in consecutive scans that are not linked to any predetermined idea that was desired.

[0078] Reference will now be made to the features described in detail above and which will not be described again below. Fig.11 (and Fig.12 )and Fig.13 The operation methods associated with the curve graph.

[0079] refer to Fig.14 , a method 1400 of operating a safety net system of an elevator system (e.g., the safety net system 301 and the safety net system 701 described above) is provided. The method 1400 includes installing a sensor in an elevator pit of the elevator system (block 1401) and performing a learning phase of the sensor to verify successful installation of the sensor (block 1402). The execution of the learning phase of block 1402 includes: causing the sensor to sense physical properties of a portion of the elevator pit to generate background readings when the elevator pit is known to have certain physical properties (block 1403), comparing the background readings with readings associated with the known physical properties of the portion of the elevator pit (block 1404), and verifying successful installation of the sensor based on the results of the comparison (block 1405). The execution of the learning phase of block 1402 may include the concept of learning the background in the elevator pit for various vertical positions of the elevator car that cause various elevator components, such as counterweights, travel cables, compensating ropes, tie-down compensators, etc., to move into or out of the field of view of the sensor. The portion of the elevator pit may include or be provided as one or more of: a plane between a pit ladder of the elevator pit and an adjacent wall of the elevator pit and a plane defined along the bottom of the elevator pit. The method 1400 may also include: performing an operation phase of the sensor after verifying successful installation of the sensor (block 1406); periodically repeating execution of a learning phase (block 1407), particularly to the extent that physical properties of the elevator pit are known to change (i.e., due to the elevator car occupying different vertical positions as described above) and / or change over time (i.e., due to degradation and / or addition or removal of elevator components or supporting mechanical elements); and repeating execution of the learning phase after an external event such as the sensor being bumped or moved (block 1408).

[0080] According to an embodiment, execution of the learning phase of block 1402 may be commanded via a display unit communicatively coupled to the sensor, and verification of successful installation of the sensor of block 1405 may include displaying an indication on the display unit.

[0081] The verification of successful installation of the sensor of block 1405 includes determining whether the background reading matches the reading associated with the known physical characteristic to a predefined degree (block 14051), and verifying the successful installation of the sensor if the background reading matches the reading associated with the known physical characteristic to a predefined degree (block 14052). In the case where the known physical characteristic is the area of ​​the portion of the elevator pit, the predefined degree may be a relatively small percentage (i.e., less than about 1-5%) difference between the background reading and the area of ​​the portion of the elevator pit. Fig.14 As shown in , method 1400 may include reinstalling the sensor as in block 1401 and repeating execution of the learning phase of block 1402 if the background readings do not match the readings associated with the known physical properties to a predefined degree.

[0082] refer to Fig.15 , a method 1500 of operating a safety net system of an elevator system (e.g., the safety net system 301 and the safety net system 701 described above) is provided. The method 1500 includes installing a sensor in an elevator pit of the elevator system (block 1501) and performing a learning phase of the sensor to verify successful installation of the sensor (block 1502). The execution of the learning phase of block 1502 includes: causing the sensor to sense physical properties of a portion of the elevator pit to generate a background signal when the elevator pit is known to have certain physical properties (block 1503), comparing the background signal with a signal associated with the known physical properties of the portion of the elevator pit (block 1504), and verifying successful installation of the sensor based on the results of the comparison (block 1505). The execution of the learning phase of block 1502 may include the concept of learning the background in the elevator pit for various vertical positions of the elevator car that cause various elevator components, such as counterweights, travel cables, compensating ropes, tie-down compensators, etc., to move into or out of the field of view of the sensor. The portion of the elevator pit may include or be provided as one or more of: a plane between a pit ladder of the elevator pit and an adjacent wall of the elevator pit and a plane defined along the bottom of the elevator pit. The method 1500 may also include: performing an operation phase of the sensor after verifying successful installation of the sensor (block 1506); periodically repeating execution of a learning phase (block 1507), particularly to the extent that physical properties of the elevator pit are known to change (i.e., due to the elevator car occupying different vertical positions as described above) and / or change over time (i.e., due to degradation and / or addition or removal of elevator components or supporting mechanical elements); and repeating execution of the learning phase after an external event such as the sensor being bumped or moved (block 1508).

[0083] According to an embodiment, execution of the learning phase of block 1502 may be commanded via a display unit communicatively coupled to the sensor, and verification of successful installation of the sensor of block 1505 may include displaying an indication on the display unit. Verification of successful installation of the sensor of block 1505 includes calculating the variance between a background signal and a signal associated with a known physical property (block 15051), determining whether the variance is less than a predefined limit (block 15052), and verifying successful installation of the sensor if the variance is less than the predefined limit (block 15053). The predefined limit may be some relatively small percentage of the variance (i.e., approximately 1-5%). As shown in FIG. Fig.15 As shown in FIG. 15 , method 1500 may include reinstalling the sensor as in block 1501 and repeating execution of the learning phase of block 1502 if the background signal does not match the signal associated with the known physical characteristic to a predefined degree.

[0084] refer to Fig.16 , a display unit 1600 of a safety net system of an elevator system (e.g., the safety net system 301 and the safety net system 701 described above) is provided. The display unit 1600 is communicatively coupled to a sensor (i.e., sensor 310 or sensor 710) and may be provided locally or remotely. In the former case, the display unit 1600 may be connected to the sensor by wire or wirelessly and may include a processor (i.e., processor 320 or processor 720). In the latter case, the display unit 1600 may be a handheld device or may be a virtual machine of an application running on a computing device. In any case, the display unit 1600 is operable by an operator to perform, for example, Fig.14 Method 1400 or Fig.15 Method 1500 or the like. Fig.16 As shown in FIG. 1 , the display unit 1600 includes an actuator 1601 such as a button or a switch and at least one indicator 1602. The actuator 1601 is actuatable by the operator to initiate the execution of the learning phase described above. The at least one indicator 1601 can be activated to indicate the completion of the verification. The at least one indicator 1601 may include a plurality of indicators that sequentially indicate the progress of the learning phase described above, so that the operator can be made aware of the type of problem in the event that a problem occurs in one of the operations.

[0085] refer to Fig.17A , Fig. 17B and Fig. 17C as well as Fig.18, an elevator system 1701 is provided and includes an elevator pit 1710, a ladder 1711 for providing a vertical passage to the elevator pit 1710, a door 1712 for providing an additional passage to the elevator pit 1710, buttons for operating the elevator system (including but not limited to a pit emergency switch (PES) 1720, a disable switch 1721, and a reset switch 1722), and a safety net system 1730. The safety net system 1730 includes a plurality of sensors 1731 (only one sensor is shown for clarity) and a processor 1733. The plurality of sensors 1731 are arranged in a plurality of planes to perform sensing in the plurality of planes and generate data corresponding to the sensing results, the plurality of planes surrounding a plurality of entry points of the elevator pit 1710 (i.e., the ladder 1711 and the area across the door 1712). The processor 1733 is generally similar to the above-described processor. The processor 1733 is coupled to the plurality of sensors 1731. The processor 1733 is also coupled to the PES switch 1720, the disable switch 1721, and the reset switch 1722. The processor 1733 is configured to analyze whether the data indicates a person who is entering or has entered the elevator pit 1710 (i.e., a person standing on the ladder 1711, a person standing in an area across the door 1712, or a person reaching the elevator pit 1710 through the door 1712). The processor 1733 can also be configured to take actions related to the operation of the elevator system based on the analysis results and in some cases based on the operating states of the PES switch 1720, the disable switch 1721, and the reset switch 1722.

[0086] According to an embodiment, the plurality of sensors 1731 may be arranged in a first two-dimensional (2D) vertical or horizontal plane along the length of the ladder 1711, and in a second 2D vertical or horizontal plane including an area across the door 1712. Alternatively, it will be appreciated that the plurality of sensors 1731 may be oriented in other ways and / or additional sensors may be provided in a plurality of different orientations. In any case, each of the plurality of sensors 1731 is configured to generate data as point cloud data from one or more sensing operations, respectively, and the processor 1733 is configured to analyze the point cloud data from the one or more sensing operations and determine whether the point cloud data from the one or more sensing operations indicates a person who is entering or has entered the elevator pit 1710.

[0087] According to an embodiment and as described above, the plurality of sensors 1731 may each include or be provided as one or more of a light detection and ranging or laser imaging, detection and ranging (LiDAR) sensor, a radio detection and ranging (RADAR) sensor and / or a camera. According to further embodiments, each of the plurality of sensors 1731 may be provided as one or more of a 2D LiDAR sensor, a millimeter wave RADAR sensor and / or a red, green, blue, depth (RGBD) camera. According to still further embodiments, each of the plurality of sensors 1731 may be provided as a plurality of sensors including a combination of one or more sensor types listed herein.

[0088] refer to Fig.18 And as described above, each of the multiple sensors 1731 is configured to generate data as point cloud data 1801 from one or more sensing operations, and the processor 1733 is configured to analyze the point cloud data 1801 from one or more sensing operations, and determine whether the point cloud data 1801 from one or more sensing operations indicates that a person has entered the elevator pit 1710.

[0089] According to an embodiment, PES 1720 may be very close to door 1712, such as Fig.18 , where a person is able to pass through door 1712 to press PES 1720, a disable switch 1721 may be further disposed in the elevator pit 1710, and a reset switch 1722 may be disposed outside the elevator pit 1710. In these or other cases, the processor 1733 may be configured to shut down the elevator system operation after a delay (e.g., approximately 4 seconds or less) if the data indicates that a person has entered the elevator pit 1710. Alternatively, in these or other cases, the processor 1733 may be configured to shut down the elevator system operation immediately if the data indicates that a person has entered the elevator pit and PES 1720 has been pressed. In addition, the processor 1733 may be configured to stop the operation of the plurality of sensors 1731 and the analysis performed by the processor 1733 after shutting down until the reset switch 1722 is pressed.

[0090] refer to Fig.19 , a method for operating a safety net system of an elevator system as described above is provided. Fig.19 As shown in , the method includes sensing an object set along a ladder leading to an elevator pit or in an area spanning a door leading to the elevator pit (box 1901), generating data corresponding to the sensing result (box 1902), analyzing whether the data indicates that a person is standing on the ladder or entering the elevator pit through the door (box 1903), and taking any one of a plurality of actions related to elevator system operation based on the analyzed result (box 1904).

[0091] According to an embodiment, taking the action of box 1904 may include shutting down the elevator system operation after a delay if the data indicates that a person is standing on a ladder or entering the elevator pit through a door (box 19041), and shutting down the elevator system operation immediately if the data indicates that a person is standing on a ladder or entering the elevator pit through a door and an emergency switch is pressed (box 19042). In addition, taking the action of box 1904 may also include stopping sensing, generating, and analyzing after shutting down until a reset switch is pressed (box 19043).

[0092] refer to Fig. 20 , describes a system that implements a safety net system. Fig. 20 As shown in FIG, the system includes a range sensor for scanning and collecting data in an elevator pit (box 2001), data processing for learning multiple and various sensed contexts in the elevator pit (box 2002), data processing for identifying and classifying objects of interest (box 2003), an intelligent response coordinator (box 2004) that receives input from switches, classification algorithms, clock timers, and elevator car movement and position information to determine the best response, clock timers required to allow some maintenance functions (box 2006), safety switches required to optimize system response (box 2005), components for monitoring car position and movement (box 2007), connected and controllable relays for opening the elevator safety chain and stopping movement (box 2008), to Figure 1 The elevator controller 115 is connected to change the control mode (box 2009) and security alarm (box 2010) based on the sensed information.

[0093] According to an embodiment, taking the action of box 2008 may include shutting down the elevator system operation if the data indicates that a person is standing on a ladder or entering the elevator pit through a door. Taking the action of box 2008 may also or alternatively include immediately shutting down the elevator system operation if the data indicates that a person is standing on a ladder or entering the elevator pit through a door. Alternatively, taking the action of box 2009 may include changing the movement of the elevator car, changing its destination, or commanding a controlled stop under motor control. The third action is that the security system may activate an alarm (box 2010).

[0094] Boxes 2001-2007 cover the sensing and data processing steps and components used to make the above decisions. A range sensor (e.g. Fig.17A , Fig. 17B and Fig. 17CA sensor 1731) or multiple range sensors scans an area of ​​interest (box 2001), thereby providing point cloud data. This data is initially analyzed for multiple positions of the elevator car (box 2002) to capture all potential objects that may appear in the sensor's field of view, thereby generating a series of learned backgrounds. In operation, the range sensor 1731 or multiple sensors collects its point cloud data and sends it to a processing unit that identifies and classifies objects of interest (box 2003). To this end, points in the point cloud that deviate from the learned background by some distance margin in their position are classified as "points of interest". These points are then further processed with a clustering algorithm to identify "objects of interest". If these objects of interest persist for a predefined number of scans, they are classified as caused by humans.

[0095] The major decisions in this safety system are made in the Intelligent System Response Coordinator (box 2004). In addition to the object of interest inputs, this component also reads switches (box 2005), clock timers (box 2006), and information about car position and movement (box 2007). When PES1720 is used to shut down the system for repairs, the switch inputs allow the safety system to adapt to existing maintenance procedures. Reset switch 1722 allows the system to be restarted after a triggering event. Disable switch 1721 allows the system to be deactivated in those rare circumstances when maintenance actions must be performed in the pit area. When a mechanic may arrive to press PES1720 (see Figure 3 A clock timer is used (block 2006) when a human-caused object of interest is detected in the area of ​​1801 of FIG. 1802. In this case, after the detection, the system will start a timer, and if the PES 1720 is engaged within a predefined amount of time, the system will be deactivated to allow normal pit entry. An additional sensory input that can be used by the intelligent system response coordinator (block 2004) is car position and movement (block 2007). In this case, the system's optimized response to the detected human can be selected between the three indicated actions (blocks 2008, 2009, and 2010) based on car movement and position.

[0096] The technical effects and benefits of the present disclosure are to provide a safety net system for an elevator system, which uses a low-cost sensor (such as a LiDAR sensor) to scan the elevator pit entrance area using both a horizontal scanning plane and a vertical scanning plane to provide sufficient coverage, and is coupled with a relatively low-complexity detection algorithm to provide a robust pit entrance detection system.

[0097] The corresponding structures, materials, actions and equivalents of all parts or steps plus function elements in the claims below are intended to include any structure, material or action for performing a function in combination with other claimed elements as specifically claimed. The description of the present disclosure has been presented for the purpose of illustration and description, but is not intended to be exhaustive or limited to the technical concepts of the disclosed form. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the present disclosure. The embodiments are selected and described in order to best explain the principles and practical applications of the present disclosure and to enable other persons of ordinary skill in the art to understand the disclosure of various embodiments with various modifications such as those suitable for the intended specific use.

[0098] While the preferred embodiment of the disclosure has been described, it will be understood that those skilled in the art, both now and in the future, may make various improvements and enhancements which fall within the scope of the following claims. These claims should be construed to maintain the proper protection for the disclosure first described.

Claims

1. A safety net system for an elevator system including an elevator pit and a plurality of access points to the elevator pit, the safety net system comprising: a plurality of sensors arranged in a plurality of planes surrounding the plurality of entry points to perform sensing and generate data corresponding to the sensing results; as well as A processor is coupled to the plurality of sensors to analyze the data to see if a person has entered the elevator pit and to take action based on a result of the analysis.

2. The safety net system according to claim 1, wherein: Each of the plurality of sensors is a LiDAR sensor.

3. The safety net system according to claim 1, wherein: Each of the plurality of sensors is a millimeter wave RADAR sensor.

4. The safety net system according to claim 1, wherein: Each of the plurality of sensors is an RGBD camera.

5. The safety net system according to claim 1, wherein: Each of the plurality of sensors is one of a LiDAR sensor, a RADAR sensor, or a camera.

6. The safety net system according to claim 1, wherein: The plurality of planes surrounding the plurality of entry points surround a ladder leading to the elevator pit and span a door leading to the elevator pit.

7. The safety net system according to claim 1, wherein: Each of the plurality of sensors is configured to generate the data as point cloud data from one or more sensing operations, and The processor is configured to analyze the point cloud data from the one or more sensing operations, various learned contexts, external switch information, car information, timing information, and car position and movement information, and determine whether the point cloud data from the one or more sensing operations indicates that the person has entered the elevator pit.

8. The safety net system of claim 1, wherein: the processor being configured to shut down elevator system operation after a delay if the data indicates that the person has entered the elevator pit, The processor is configured to immediately shut down elevator system operation if the data indicates that the person has entered the elevator pit and an emergency switch has been pressed, and The processor is further configured to execute at least a motion control response of the elevator system based on a result of the analyzing.

9. An elevator system, comprising: Elevator pit; a ladder and door for access to said elevator pit; buttons for operating the elevator system; as well as A safety net system, the safety net system comprising: a plurality of sensors arranged in a plurality of planes surrounding the plurality of entry points to perform sensing and generate data corresponding to the sensing results; as well as A processor is coupled to the plurality of sensors to analyze whether the data indicates a person entering the elevator pit and to take action based on a result of the analysis and a corresponding state of the button.

10. The elevator system according to claim 9, wherein: Each of the plurality of sensors is a LiDAR sensor.

11. The elevator system according to claim 9, wherein: Each of the plurality of sensors is a millimeter wave RADAR sensor.

12. The elevator system according to claim 9, wherein: Each of the plurality of sensors is an RGBD camera.

13. The elevator system according to claim 9, wherein: Each of the plurality of sensors is one of a LiDAR sensor, a RADAR sensor, or a camera.

14. The elevator system according to claim 9, wherein: The plurality of planes surrounding the plurality of entry points surround a ladder leading to the elevator pit and span a door leading to the elevator pit.

15. The elevator system of claim 9, wherein: Each of the plurality of sensors is configured to generate the data as point cloud data from one or more sensing operations, and The processor is configured to analyze the point cloud data from the one or more sensing operations, various learned contexts, external switch information, car information, timing information, and car position and movement information, and determine whether the point cloud data from the one or more sensing operations indicates that the person has entered the elevator pit.

16. The elevator system of claim 9, wherein: A first of the buttons is an emergency switch in the elevator pit very close to the door, the processor being configured to shut down elevator system operation after a delay if the data indicates that the person has entered the elevator pit, the processor being configured to immediately shut down elevator system operation if the data indicates that the person has entered the elevator pit and the emergency switch has been pressed, and The processor is further configured to execute at least a motion control response of the elevator system based on a result of the analyzing.

17. The elevator system of claim 16, wherein: A second of the buttons is a reset button outside the elevator pit, and The processor is also configured to cease operation of the plurality of sensors and analysis by the processor after shutdown until the reset switch is pressed.

18. A method of operating a safety net system of an elevator system, the method comprising: sensing an object disposed along a ladder leading to an elevator pit or in an area spanning a door leading to the elevator pit; generating data corresponding to the sensing result; analyzing whether the data indicates a person standing on the ladder or entering the elevator pit through the door; as well as Actions related to elevator system operation are taken based on results of the analysis.

19. The method according to claim 18, wherein: The taking of the actions comprises: shutting down elevator system operation after a delay if the data indicates that the person is standing on the ladder or entering the elevator pit through the door, and In the event that the data indicates that the person is standing on the ladder or entering the elevator pit through the door and an emergency switch is pressed, elevator system operation is immediately shut down.

20. The method according to claim 19, wherein: The taking the action further comprises stopping the sensing, the generating and the analyzing after shutting down until a reset switch is pressed.

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