Elevator pit safety net system

Abstract

The invention relates to an elevator pit safety net system. A method of operating a security net system of an elevator system is provided. A method includes installing a sensor in an elevator pit of an elevator system and performing a learning phase of the sensor to verify successful installation of the sensor. The execution of the learning phase comprises: when it is known that the elevator pit has certain physical characteristics, causing a sensor to sense a physical characteristic of a portion of the elevator pit to generate a background reading, the background readings are compared to readings associated with known physical characteristics of the portion of the elevator pit and successful installation of the sensor is verified based on a result of the comparison.

Description

Background Art

[0001] The present disclosure relates to elevator systems, and in particular, to an elevator pit safety net system of an elevator system.

[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 some 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 one aspect of the disclosure, a method of operating a safety net system of an elevator system is provided. The method includes installing a sensor in an elevator pit of the elevator system and performing a learning phase of the sensor to verify successful installation of the sensor. The performance of the learning phase includes: causing the sensor to sense a physical property of a portion of the elevator pit to generate a background reading when the elevator pit is known to have certain physical properties, comparing the background reading to a reading associated with the known physical property of the portion of the elevator pit, and verifying successful installation of the sensor based on a result of the comparison.

[0004] According to additional or alternative embodiments, the method further comprises performing an operation phase of the sensor after verification of successful installation of the sensor.

[0005] According to additional or alternative embodiments, verification of successful installation of the sensor includes determining whether background readings match readings associated with known physical characteristics to a predefined degree and verifying successful installation of the sensor if the background readings match readings associated with the known physical characteristics to a predefined degree.

[0006] According to additional or alternative embodiments, the method further comprises reinstalling the sensor and repeating execution of the learning phase if the background readings do not match the readings associated with the known physical properties to a predefined degree.

[0007] According to additional or alternative embodiments, the method further comprises periodically repeating the execution of the learning phase.

[0008] According to additional or alternative embodiments, the method further comprises repeating the execution of the learning phase after an external event.

[0009] According to additional or alternative embodiments, the portion of the elevator pit comprises a plane between a pit ladder of the elevator pit and an adjacent wall of the elevator pit.

[0010] According to additional or alternative embodiments, the portion of the elevator pit comprises a plane defined along a bottom of the elevator pit.

[0011] According to an additional or alternative embodiment, the execution of the learning phase is commanded by means of a display unit communicatively coupled to the sensor, and the verification of a successful installation of the sensor comprises displaying an indication on the display unit.

[0012] According to one aspect of the disclosure, a method of operating a safety net system of an elevator system is provided. The method includes installing a sensor in an elevator pit of the elevator system and performing a learning phase of the sensor to verify successful installation of the sensor. The performance of the learning phase includes: causing the sensor to sense a physical property of a portion of the elevator pit to generate a background signal when the elevator pit is known to have certain physical properties, comparing the background signal to a signal associated with the known physical property of the portion of the elevator pit, and verifying successful installation of the sensor based on a result of the comparison.

[0013] According to additional or alternative embodiments, the method further comprises performing an operation phase of the sensor after verification of successful installation of the sensor.

[0014] According to additional or alternative embodiments, verification of successful installation of the sensor includes calculating a variance between a background signal and a signal associated with a known physical property, determining whether the variance is less than a predefined limit, and verifying successful installation of the sensor if the variance is less than the predefined limit.

[0015] According to an additional or alternative embodiment, the method further comprises reinstalling the sensor and repeating the execution of the learning phase if the variance exceeds a predefined limit.

[0016] According to additional or alternative embodiments, the method further comprises periodically repeating the execution of the learning phase.

[0017] According to additional or alternative embodiments, the method further comprises repeating the execution of the learning phase after an external event.

[0018] According to additional or alternative embodiments, the portion of the elevator pit comprises a plane defined between a pit ladder of the elevator pit and an adjacent wall of the elevator pit.

[0019] According to additional or alternative embodiments, the portion of the elevator pit comprises a plane defined along a bottom of the elevator pit.

[0020] According to an additional or alternative embodiment, the execution of the learning phase is commanded by means of a display unit communicatively coupled to the sensor, and the verification of a successful installation of the sensor comprises displaying an indication on the display unit.

[0021] According to one aspect of the disclosure, a safety net system of an elevator system is provided, and the safety net system of the elevator system includes a sensor installed in an elevator pit of the elevator system and a display unit communicatively coupled to the sensor. The display unit is operable by an operator to perform a method. The method includes performing a learning phase of the sensor to verify successful installation of the sensor. The execution of the learning phase includes: when it is known that the elevator pit has certain physical properties, causing the sensor to sense a physical property of a portion of the elevator pit to generate a background signal, comparing the background signal with a signal associated with the known physical property of the portion of the elevator pit, and verifying successful installation of the sensor based on the result of the comparison.

[0022] According to an additional or alternative embodiment, the display unit comprises an actuator actuatable by an operator to initiate the execution of the learning phase and at least one indicator activatable to indicate the completion of the verification.

[0023] According to additional or alternative embodiments, the execution of the learning phase is performed with an elevator car of the elevator system in one or more of the various positions within a hoistway of the elevator system.

[0024] Additional features and advantages are achieved through the technology of the present disclosure. Other embodiments and aspects of the disclosure are described in detail herein, and other embodiments and aspects of the disclosure are considered to be part of the claimed technical concept. In order to better understand the disclosure with advantages and features, reference is made to the description and to the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] 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:

[0026] Figure 1 is a perspective view of an elevator system according to an embodiment;

[0027] Figure 2 According to the embodiment Figure 1 A perspective view of an elevator pit of an elevator system;

[0028] Figure 3 is a side view of an elevator pit ladder with sensors for a safety net system according to an embodiment;

[0029] Figure 4 According to the embodiment, Figure 3 A front view of an elevator pit ladder with sensors;

[0030] Figure 5 According to the embodiment, Figure 3 A top view of the elevator pit ladder with sensors;

[0031] Figure 6 is a flow chart illustrating a method of operating a safety net system of an elevator system according to an embodiment;

[0032] Figure 7 is a perspective view of an elevator pit with sensors for a safety net system according to an embodiment;

[0033] Figure 8 is a top view of an elevator pit with sensors of a safety net system and additional sensors according to an embodiment;

[0034] 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;

[0035] Fig.10 is a flow chart illustrating a method of operating a safety net system of an elevator system according to an embodiment;

[0036] Fig.11 is a graphical illustration of a learning context for a safety net system according to an embodiment;

[0037] Fig.12 is a graphical illustration of a person in a learning context applied to a safety net system according to an embodiment;

[0038] Fig.13 is a graphical illustration of signal variance of sensor readings of a safety net system according to an embodiment;

[0039] Fig.14 is a flow chart illustrating a method of operating a safety net system of an elevator system according to an embodiment;

[0040] Fig.15 is a flow chart illustrating a method of operating a safety net system of an elevator system according to an embodiment; and

[0041] Fig.16 is a schematic illustration of a display unit of a safety net system according to an embodiment. DETAILED DESCRIPTION

[0042] In the elevator industry, multiple monitors and sensors are provided to monitor various parts and components of elevator systems. In particular, the key areas to be monitored are the elevator pits that 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 during some operations. Therefore, a cost-effective method for detecting people such as maintenance technicians or mechanics standing in the elevator pit or on the pit ladder of the elevator system is needed. Such a detection system needs to be easy to install and adjust and requires minimal repair and maintenance. The detection system must also have high detection performance with low false positive and false negative results. In addition, when installing the detection system, it is important that there is an appropriate verification process to ensure that the detection system is operating correctly and can be trusted to detect maintenance technicians and mechanics in dangerous locations in the elevator pit and on the pit ladder. This verification process should be simple to initiate and use and effective to provide enough data to the installer to allow them to confidently turn over the detection system.

[0043] Therefore, as will be described below, a detection system validation process is provided and uses post-processing of captured data from a learning phase of a detection system, wherein the detection system uses a two-dimensional (2D) classification system. The validation process contemplates providing some simple metrics that can be displayed to an installer to give the installer confidence that the detection system has been successfully installed and the installation operations have been successfully performed.

[0044] 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 is configured to facilitate movement of the elevator car 103 within the elevator shaft 117 and along the guide rail 109 relative to the counterweight 105 simultaneously and in opposite directions.

[0045] 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 the movement between the elevator car 103 and the counterweight 105. The position reference system 113 can be mounted on a fixed portion at the top of the elevator shaft 117 (such as on a support or guide rail) and can be configured to provide a position signal related to the position of the elevator car 103 within the elevator shaft 117. In other embodiments, the position reference system 113 can be mounted directly to the moving components of the machine 111 or can be located 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, without 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.

[0046] As shown, the controller 115 can be located 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 elevator car 103. It is to be appreciated that the controller 115 need not be in the controller room 121, but can be in the hoistway or other locations in the elevator system. For example, the controller 115 can provide drive signals 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 position signals from the position reference system 113 or any other desired position reference device. When moving up or down along the guide rail 109 in the elevator shaft 117, the elevator car 103 can stop at one or more landings 125 as controlled by the controller 115. Although shown in the controller room 121, those skilled in the art will appreciate that the controller 115 can be located and / or configured in other locations or positions within the elevator system 101. In one embodiment, the controller 115 can be located remotely or in a distributed computing network (e.g., a cloud computing architecture). The controller 115 may be implemented using a processor-based machine such as a personal computer, a server, a distributed computing network, or the like.

[0047] The machine 111 may include a motor or similar drive mechanism. According to the disclosed embodiments, the machine 111 is configured to include an electrically driven 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 that imparts force to the tension member 107 to move the elevator car 103 within the elevator shaft 117.

[0048] 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 both, or both, elevator doors 104 integrally attached to the elevator car 103 or elevator doors 104 located on a landing 125 of the elevator system 101. The elevator doors 104 open to allow passengers to enter and exit the elevator car 103.

[0049] Continue to refer Figure 1 And also refer to Figure 2 , a bottom portion of the elevator shaft 117 of the elevator system 101 below 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 an upper portion of the elevator pit 201 to a 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 certain maintenance procedures.

[0050] Continue to refer Figure 1 and Figure 2 And also refer to Figure 3-5 , 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.

[0051] The processor 320 includes a processing unit, a memory, and an input / output (I / O) unit. 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 can be read and executed 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 can be remote from the sensor 310 or local. In the former case, the processor 320 can be operably coupled to the sensor 310 via a wired connection or via a wireless connection. In the latter case, the processor 320 can be built into the sensor 310 or provided as a component separate from the sensor 310, and the processor 320 can be operably coupled to the sensor 310 via a wired connection or via a wireless connection.

[0052] 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 further embodiments, 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 further embodiments, the sensor 310 may be provided as a plurality of sensors including a combination of one or more sensor types listed herein.

[0053] 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 consecutive or continuous 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.

[0054] 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 plane P, additional points 403 where portions of any object do not intersect plane P, and ghost points 404 where portions of foreign objects or debris (i.e., feathers or dust that drift into plane P) intersect plane P. The processor 320 analyzes each of the hit points 402, additional points 403, and ghost 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 signal matching a baseline data set taken when the elevator pit 201 was known to be empty or more generally had certain physical characteristics, and the processor 320 identifies the ghost points 404 as ghost points 404 based on their characteristic shapes or their lack of characteristic shapes and their grouping or their lack of grouping. The processor 320 then distinguishes the hit points 402 from the additional points 403 and the ghost 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 a shoe tip that intersects the plane P and therefore determines that a person is likely standing on one of the rungs 2053 of the elevator pit ladder 205. The processor 320 may then communicate at least that discovery to the controller 115 of the elevator system 101 so that the controller 115 may act, such as by preventing the elevator car 103 from entering the elevator pit 201. This may be accomplished by various processes including, but not limited to, controlling a relay to open the elevator safety chain and thereby lower the brake system and / or passing a message to the elevator electronic safety system that will open the safety chain.

[0055] Because the processor 320 can identify and distinguish between the hit points 402 and the 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 the hit points 402 and the 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 the hit points 402 and the additional points 403 and the 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.

[0056] refer to Figure 6, a method 600 of operating a safety net system of an elevator system (such as 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.

[0057] 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 successive 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 are moving 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 collection of continuous or semi-continuous updates of a point cloud scan. In these or other cases, the analysis at block 603 and the determination at block 604 may include image processing and video processing.

[0058] Return to 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 the processor 720 is configured to analyze the data and determine whether the data indicates a person in the elevator pit 201 based on the analysis result.

[0059] 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 can be read and executed 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 can be remote from the sensor 710 or local. In the former case, the processor 720 can be operably coupled to the sensor 710 via a wired connection or via a wireless connection. In the latter case, the processor 720 can be built into the sensor 710 or provided as a component separate from the sensor 710, and the processor 720 can be operably coupled to the sensor 710 via a wired connection or via a wireless connection.

[0060] 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 yet another embodiment, 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.

[0061] In an 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 the plane P′ as a 2D plane extending away from the corner 2011 along a substantial portion of the 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 consecutive or continuous 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 in the elevator pit 201.

[0062] 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 grouping, 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 was known to be empty or more generally had certain physical characteristics, and the processor 720 identifies the false points 733 as false points 733 based on their characteristic shapes or their lack of characteristic shapes and their grouping or their lack of grouping. The processor 720 then distinguishes the hit point 731 from the additional points 732 and the false points 733 and determines that the hit point 731 indicates a portion of a person that intersects the plane P' when the hit point 731 of the point cloud data 730 is identified and distinguished and therefore determines that the person may be standing in the elevator pit 201. The processor 720 can then communicate that finding to at least the controller 115 of the elevator system 101 so that the controller 115 can take action to avoid an unsafe condition, such as by preventing the elevator car 103 from entering the elevator pit 201.

[0063] 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.

[0064] 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 results of the sensing. 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.

[0065] refer to Fig.10 , a method 1000 of operating a safety net system of an elevator system (such as 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) that improves the accuracy of the determination over time.

[0066] 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 successive 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 are moving 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 collection of continuous or semi-continuous updates of a point cloud scan. In these or other cases, the analysis at block 1003 and the determination at block 1004 may include image processing and video processing.

[0067] Although Figure 3-6 Examples and Figure 7-10 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 3-6 Examples and Figure 7-10Embodiments. 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 (millimeter wave or RGB-D cameras), two or more sensors, coverage of different planes with 2D sensors, and a range of data / image processing methods including, but not limited to, image classification, machine learning, pattern recognition, etc.

[0068] 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 being 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.

[0069] After setup, the sensor 710 learns the environmental context in the elevator pit 201 by scanning for a predefined time (e.g., for about 30 seconds) and using various elevator car positions. The processor 720 then analyzes the Fig.11 The learning profile is generated by using the statistical changes and trends in the range versus angle data plot shown in Figure 1. This results in 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 illustrated 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 The final decision by processor 720 regarding human detection is based on the number of points observed in the gray area in each scan and how many scans exceeded that trigger level.

[0070] 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.

[0071] 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 FIG. 7 , evidence of the counterweight and track is visible on the right side of the graph, and evidence of the car guide rail is visible on the left side of the graph, especially the left track is visible on the left side of the graph. When the installation of the safety net system 701 is completed, the processor 720 of the safety net system 701 can provide Fig.11 Calculation of the footprint area that can be compared with the dimensions of the elevator pit 201 as a check on the learning phase (approximately 2.65 m in this case) 2 ). Where 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.

[0072] 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 the normal variance of the range detection at each angle of the sensor 710 can also be used to verify successful installation. In this case, during the operation phase Fig.13 Excessive changes in the signal would indicate that either the sensor 710 is malfunctioning or the elevator pit 201 is unclear. As described above, further processing by the processor 720 may be performed to confirm deviations caused by humans, which may then be determined by the processor 720 and Figure 1 The controller 115 takes appropriate action.

[0073] 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 values ​​at any angle that is trending over the learning phase of the scan of observed range values, or a change in a point cloud feature that can be tracked to a rotational change of the sensor 710 during the learning phase.

[0074] As used herein, the term "variance" can be a discriminator for successful learning, where there can be two types of data metrics used to determine whether a learning phase has been successful. These include the difference or error between the learning results and the desired predetermined idea, such as the area of ​​the learning background or annotated items / objects in the sensor's field of view, and the observed changes in the collected data as seen in successive scans that are not linked to any predetermined idea that was expected.

[0075] 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.

[0076] refer to Fig.14 , a method 1400 of operating a safety net system of an elevator system (such as 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 a background reading when the elevator pit is known to have certain physical properties (block 1403), comparing the background reading to a reading 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 learning the concept of background in the elevator pit at various vertical locations of the elevator car that cause various elevator components such as counterweights, traveling cables, compensating ropes, tie-down compensators, etc. to move into or out of the sensor's field of view. 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).

[0077] According to an embodiment, execution of the learning phase of block 1402 may be commanded by means of 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.

[0078] 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 successful installation of the sensor if the background reading matches the reading associated with the known physical characteristic to a predefined degree (block 14052). 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 FIG. 14 , 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.

[0079] refer to Fig.15, a method 1500 of operating a safety net system of an elevator system (such as 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 to 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 learning the concept of background in the elevator pit at various vertical locations of the elevator car for causing various elevator components such as counterweights, traveling cables, compensating ropes, tie-down compensators, etc. to move into or out of the sensor's field of view. 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).

[0080] According to an embodiment, the execution of the learning phase of box 1502 can be commanded by means of a display unit that is communicatively coupled to the sensor, and the verification of the successful installation of the sensor of box 1505 can include displaying an indication on the display unit. The verification of the successful installation of the sensor of box 1505 includes: calculating the variance between the background signal and the signal associated with the known physical property (box 15051), determining whether the variance is less than a predefined limit (box 15052), and verifying the successful installation of the sensor if the variance is less than the predefined limit (box 15053). The predefined limit can 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.

[0081] refer to Fig.16, a display unit 1600 of a safety net system of an elevator system (such as 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 the display unit 1600 can be provided locally or remotely. In the former case, the display unit 1600 can be connected to the sensor by wire or wirelessly and can include a processor (i.e., processor 320 or processor 720). In the latter case, the display unit 1600 can be a handheld device or can be a virtual machine of an application running on a computing device. In any case, the display unit 1600 can be operated by an operator to perform operations such as Fig.14 Method 1400 or Fig.15 Method 1500. 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 can be actuated by an operator to initiate the execution of the learning phase described above. The at least one indicator 1601 is activatable 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 in the event of a problem with one of the operations, the operator can be made aware of the type of problem.

[0082] A technical effect and benefit of the present disclosure is to provide a detection system for an elevator system in which a set of processed signals available from sensors can be used and displayed to an installer to ensure that the detection system is properly installed and calibrated.

[0083] 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 defined as being 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 disclosure. The embodiments are selected and described in order to best explain the principles and practical applications of the 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.

[0084] 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 claims which follow. These claims should be construed to maintain the proper protection for the disclosure first described.

Claims

1. A method for operating a safety net system of an elevator system, the method comprising: installing a sensor in an elevator pit of the elevator system; as well as performing a learning phase of the sensor to verify successful installation of the sensor, the performing of the learning phase comprising: causing the sensor to sense a physical characteristic of a portion of the elevator pit to generate a background reading when the elevator pit is known to have certain physical characteristics; comparing the background readings to readings associated with known physical characteristics of the portion of the elevator pit; and The successful installation of the sensor is verified based on a result of the comparing. 2 . The method of claim 1 , further comprising performing an operation phase of the sensor after the verification of the successful installation of the sensor.

3. The method according to claim 1, wherein: The verification of the successful installation of the sensor comprises: determining whether the background reading matches the reading associated with the known physical characteristic to a predefined degree; and The successful installation of the sensor is verified if the background reading matches the reading associated with the known physical characteristic to the predefined degree.

4. The method of claim 3, further comprising reinstalling the sensor and repeating the execution of the learning phase if the background reading does not match the reading associated with the known physical characteristic to the predefined degree. The method of claim 1 , further comprising periodically repeating the execution of the learning phase. The method of claim 1 , further comprising repeating said performing of said learning phase after an external event.

7. The method according to claim 1, wherein: The portion of the elevator pit includes at least one of a plane between a pit ladder of the elevator pit and an adjacent wall of the elevator pit and a plane defined along a bottom of the elevator pit.

8. The method according to claim 1, wherein: commanding said execution of said learning phase by means of a display unit communicatively coupled to said sensor, and The verification of the successful installation of the sensor includes displaying an indication on the display unit.

9. A method of operating a safety net system of an elevator system, the method comprising: installing a sensor in an elevator pit of the elevator system; as well as performing a learning phase of the sensor to verify successful installation of the sensor, the performing of the learning phase comprising: causing the sensor to sense a physical property of a portion of the elevator pit to generate a background signal when the elevator pit is known to have certain physical properties; comparing the background signal to a signal associated with a known physical characteristic of the portion of the elevator pit; and The successful installation of the sensor is verified based on a result of the comparing.

10. The method of claim 9, further comprising performing an operation phase of the sensor after the verification of the successful installation of the sensor.

11. The method according to claim 9, wherein: The verification of the successful installation of the sensor comprises: calculating a variance between the background signal and the signal associated with the known physical property; determining whether the variance is less than a predefined limit; and The successful installation of the sensor is verified if the variance is less than the predefined limit.

12. The method of claim 11, further comprising reinstalling the sensor and repeating the execution of the learning phase if the variance exceeds the predefined limit.

13. The method of claim 9, further comprising periodically repeating said performing of said learning phase.

14. The method of claim 9, further comprising repeating said performing of said learning phase after an external event.

15. The method according to claim 9, wherein: The portion of the elevator pit includes a plane defined between a pit ladder of the elevator pit and an adjacent wall of the elevator pit.

16. The method according to claim 9, wherein: The portion of the elevator pit includes a plane defined along a bottom of the elevator pit.

17. The method of claim 9, wherein: commanding said execution of said learning phase by means of a display unit communicatively coupled to said sensor, and The verification of the successful installation of the sensor includes displaying an indication on the display unit.

18. A safety net system for an elevator system, the safety net system comprising: a sensor installed in an elevator pit of said elevator system; as well as a display unit communicatively coupled to the sensor, the display unit operable by an operator to perform a method comprising: performing a learning phase of the sensor to verify successful installation of the sensor, the performing of the learning phase comprising: causing the sensor to sense a physical property of a portion of the elevator pit to generate a background signal when the elevator pit is known to have certain physical properties; comparing the background signal to a signal associated with a known physical characteristic of the portion of the elevator pit; and The successful installation of the sensor is verified based on a result of the comparing.

19. The safety net system according to claim 18, wherein: The display unit comprises: an actuator actuatable by said operator to initiate said performance of said learning phase; and At least one indicator activatable to indicate completion of said verifying.

20. The safety net system of claim 18, wherein: The performing of the learning phase is performed with an elevator car of the elevator system in one or more of various positions within a hoistway of the elevator system.

Images