Elevator full load detection method and apparatus, electronic device, and medium

Abstract

An elevator full load detection method and apparatus, an electronic device, and a medium. The method comprises: acquiring a depth image collected by a depth camera; acquiring a first number of pixels occupied by an object at an elevator car bottom in the depth image and a second number of pixels occupied by the elevator car bottom in the depth image; determining the elevator occupancy rate of an elevator on the basis of the first number and the second number; and on the basis of the elevator occupancy rate, determining whether the elevator is fully loaded. According to the elevator full load detection method, the passenger face privacy can be well protected by means of a depth image, and on the basis of the elevator occupancy rate, it is determined whether an elevator is fully loaded, so that unnecessary staying of elevators can be reduced, improving the elevator operation efficiency and the passenger riding experience, saving electric energy, and reducing mechanical abrasion of the elevators.

Description

Elevator full load detection method, device, electronic equipment and medium

[0001] This disclosure claims priority to the Chinese patent application filed with the Patent Office of China on March 8, 2024, with application number 202410268658.4 and application name “Elevator Full Load Detection Method, Device, Electronic Equipment and Medium”, and the Chinese patent application filed with the Patent Office of China on March 21, 2024, with application number 202410328218.3 and application name “Elevator Full Load Detection Method, Device, Electronic Equipment and Medium”, the entire contents of which are incorporated by reference into this disclosure. Technical Field

[0002] The embodiments of the present disclosure relate to the technical field of elevators, and more specifically, to a method for detecting an elevator full load, a device for detecting an elevator full load, an electronic device, and a computer-readable storage medium. Background Art

[0003] Traditional elevators typically use weight information to determine whether the elevator is fully loaded. When the weight reaches the approved load, the elevator is considered fully loaded; when the weight does not reach the approved load, the elevator is considered underloaded. However, in real-world scenarios, when there are many children or cargo in the elevator, even if the weight does not reach the approved load, the elevator space is insufficient to accommodate one passenger. In this case, the elevator responds to the up and down requests of other passengers, resulting in frequent stops at each floor, affecting elevator efficiency, wasting energy, and increasing mechanical wear. Summary of the Invention

[0004] One purpose of the embodiments of the present disclosure is to provide a new technical solution for elevator full load detection.

[0005] According to a first aspect of an embodiment of the present disclosure, a method for detecting a fully loaded elevator is provided, the method comprising:

[0006] Get the depth image captured by the depth camera;

[0007] Obtaining a first number of pixels occupied by an object on the elevator floor in the depth image, and a second number of pixels occupied by the elevator floor in the depth image;

[0008] determining an elevator occupancy rate of the elevator according to the first number and the second number;

[0009] Whether the elevator is fully loaded is determined according to the elevator occupancy rate.

[0010] Optionally, obtaining a second number of pixels occupied by the elevator car platform in the depth image includes:

[0011] Obtaining a first position of an upper left corner point of the elevator car platform in a world coordinate system and a second position of a lower right corner point of the elevator car platform in the world coordinate system;

[0012] Converting the first position to a third position of the upper left corner of the elevator car platform in the pixel coordinate system of the depth image, and converting the second position to a fourth position of the lower right corner of the elevator car platform in the pixel coordinate system of the depth image;

[0013] Determining the length and width of the elevator car platform in the pixel coordinate system of the depth image according to the third position and the fourth position;

[0014] According to the length and width of the elevator platform in the pixel coordinate system of the depth image, a second number of pixels occupied by the elevator platform in the depth image is obtained.

[0015] Optionally, obtaining the first number of pixels occupied by the object on the elevator floor in the depth image includes:

[0016] Acquire a three-dimensional point cloud image of the depth image;

[0017] Filtering out valid point clouds from the three-dimensional point cloud image; wherein the valid point clouds are point clouds located within the range of the elevator car bottom;

[0018] Projecting the valid point cloud onto the elevator car platform; and

[0019] The effective point cloud projected onto the elevator car platform is converted into a pixel coordinate system of the depth image to obtain a first number of pixels occupied by objects on the elevator car platform in the depth image.

[0020] Optionally, converting the valid point cloud projected onto the elevator car bed into a pixel coordinate system of the depth image to obtain a first number of pixels occupied by an object at the elevator car bed in the depth image includes:

[0021] Obtaining the number of pixels of the valid point cloud projected onto the elevator car bottom converted to the pixel coordinate system of the depth image;

[0022] According to the number of pixels, a first number of pixels occupied by the object on the elevator car bottom in the depth image is obtained.

[0023] Optionally, screening out a valid point cloud from the three-dimensional point cloud image includes:

[0024] Filtering valid point clouds from the three-dimensional point cloud image according to set constraints;

[0025] The set constraint conditions include a first constraint condition, a second constraint condition and a third constraint condition;

[0026] The first constraint condition relates to a constraint on a fifth position of any point cloud in the three-dimensional point cloud image in a first horizontal direction of the world coordinate system of the three-dimensional point cloud image;

[0027] The second constraint condition relates to a constraint on a sixth position of the arbitrary point cloud in a second horizontal direction of the world coordinate system;

[0028] The third constraint condition relates to a constraint on a seventh position of the arbitrary point cloud in the vertical direction of the world coordinate system.

[0029] Optionally, the first constraint condition includes: the fifth position of the arbitrary point cloud is located between an eighth position and a ninth position, the eighth position is determined based on the position of the upper left corner of the elevator car platform in the first horizontal direction of the world coordinate system and an elevator car platform boundary threshold, and the ninth position is determined based on the position of the lower right corner of the elevator car platform in the first horizontal direction of the world coordinate system and the elevator car platform boundary threshold;

[0030] The second constraint condition includes: the sixth position is between the tenth position and the eleventh position, the tenth position is determined based on the position of the upper left corner of the elevator car platform in the second horizontal direction of the world coordinate system and the elevator car platform boundary threshold, and the eleventh position is determined based on the position of the lower right corner of the elevator car platform in the second horizontal direction of the world coordinate system and the elevator car platform boundary threshold;

[0031] The third constraint condition includes: the seventh position is less than a set height, and the set height is determined according to the elevator car height and a ground threshold.

[0032] Optionally, determining whether the elevator is fully loaded according to the elevator occupancy rate includes:

[0033] Comparing the elevator occupancy rate with an elevator occupancy rate threshold to obtain a comparison result;

[0034] If the comparison result indicates that the elevator occupancy rate is less than or equal to the elevator occupancy rate threshold, determining that the elevator is not fully loaded;

[0035] If the comparison result indicates that the elevator occupancy rate is greater than the elevator occupancy rate threshold, it is determined that the elevator is fully loaded.

[0036] Optionally, the depth camera is arranged at the middle position of the top of the elevator car.

[0037] According to a second aspect of an embodiment of the present disclosure, there is provided an elevator full load detection device, the device comprising:

[0038] A first acquisition module is used to acquire a depth image captured by a depth camera;

[0039] a second acquisition module, configured to acquire a first number of pixels occupied by an object on the elevator car floor in the depth image, and a second number of pixels occupied by the elevator car floor in the depth image;

[0040] a first determining module, configured to determine an elevator occupancy rate of the elevator according to the first number and the second number;

[0041] The second determining module is configured to determine whether the elevator is fully loaded according to the elevator occupancy rate.

[0042] According to a third aspect of an embodiment of the present disclosure, an electronic device is provided, comprising a memory and a processor, wherein the memory is used to store an executable computer program; the computer program is used to control the processor to execute the method described according to the first aspect of the embodiment of the present disclosure.

[0043] According to a fourth aspect of an embodiment of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the method described in the first aspect of the embodiment of the present disclosure is implemented.

[0044] Through the embodiments of the present disclosure, it is possible to capture a depth image of the interior of the elevator car through a depth camera, and obtain a first number of pixels occupied by objects on the elevator floor in the depth image, and a second number of pixels occupied by the elevator floor in the depth image, and determine the elevator occupancy rate of the elevator based on the first number and the second number, and then determine whether the elevator is fully loaded based on the elevator occupancy rate. The depth image can well protect the passenger's facial privacy, and detecting whether the elevator is fully loaded based on the elevator occupancy rate can reduce unnecessary stops of the elevator, improve the elevator's operating efficiency and passenger riding experience, save electricity, and reduce mechanical wear of the elevator.

[0045] Other features and advantages of the embodiments of the present disclosure will become apparent from the following detailed description of exemplary embodiments of the present disclosure with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the specification and, together with the description, serve to explain the principles of the specification.

[0047] 1 is a block diagram showing an example of a hardware configuration of an electronic device that can be used to implement an embodiment of the present disclosure;

[0048] FIG2 shows a schematic diagram of a flow chart of elevator full load detection according to an embodiment of the present disclosure;

[0049] FIG3 is a schematic diagram showing a world coordinate system according to an embodiment of the present disclosure;

[0050] FIG4 is a schematic diagram showing a pixel coordinate system according to an embodiment of the present disclosure;

[0051] FIG5 is a schematic diagram showing a depth image of a scene according to an embodiment of the present disclosure;

[0052] FIG6 is a schematic diagram showing a depth image of another scene according to an embodiment of the present disclosure;

[0053] FIG7 shows a schematic block diagram of an elevator full load detection device according to an embodiment of the present disclosure;

[0054] FIG8 shows a schematic block diagram of an electronic device according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0055] Various exemplary embodiments of the present disclosure will now be described in detail with reference to the accompanying drawings. It should be noted that unless otherwise specifically stated, the relative arrangements of components and steps, numerical expressions and numerical values ​​set forth in these embodiments do not limit the scope of the present disclosure.

[0056] The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the embodiments of the present disclosure, its application, or uses.

[0057] Technologies, methods, and equipment known to ordinary technicians in the relevant art may not be discussed in detail, but where appropriate, the technologies, methods, and equipment should be considered part of the specification.

[0058] In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not limiting. Therefore, other examples of the exemplary embodiments may have different values.

[0059] It should be noted that like reference numerals and letters refer to like items in the following figures, and therefore, once an item is defined in one figure, it need not be further discussed in subsequent figures.

[0060] <Hardware Configuration>

[0061] FIG. 1 is a block diagram illustrating a hardware configuration of an electronic device 1000 that may implement an embodiment of the present disclosure.

[0062] Electronic device 1000 can be a terminal device, a portable computer, a desktop computer, a server, a server cluster, etc. As shown in FIG1 , electronic device 1000 may include a processor 1100, a memory 1200, an interface device 1300, a communication device 1400, a display device 1500, an input device 1600, a speaker 1700, a microphone 1800, etc. Processor 1100 may be a CPU, a microprocessor MCU, etc. Memory 1200 may include, for example, ROM (read-only memory), RAM (random access memory), or a non-volatile memory such as a hard disk. Interface device 1300 may include, for example, a USB port or a headphone jack. Communication device 1400 may be capable of wired or wireless communication, specifically Wi-Fi, Bluetooth, 2G / 3G / 4G / 5G, etc. Display device 1500 may be, for example, an LCD display or a touchscreen display. Input device 1600 may include, for example, a touchscreen, a keyboard, or somatosensory input. Users can input and output voice information through speaker 1700 and microphone 1800.

[0063] The electronic device shown in FIG1 is merely illustrative and in no way implies any limitation on the embodiments of the present disclosure, its application, or use. In the embodiments of the present disclosure, the memory 1200 of the electronic device 1000 is used to store instructions, and the instructions are used to control the processor 1100 to operate to execute any one of the elevator full load detection methods provided in the embodiments of the present disclosure. It should be understood by those skilled in the art that although multiple devices are shown for the electronic device 1000 in FIG1 , the embodiments of the present disclosure may only involve some of the devices therein, for example, the electronic device 1000 only involves the processor 1100 and the storage device 1200. Technicians can design instructions based on the scheme disclosed in the embodiments of the present disclosure. How instructions control the processor to operate is well known in the art and will not be described in detail here.

[0064] The core concept of the elevator full load detection method provided by the embodiment of the present disclosure is to first calculate the elevator occupancy rate and determine whether the elevator is fully loaded based on the elevator occupancy rate. In addition, the elevator occupancy rate is calculated in a pixel coordinate system. Specifically, based on the set depth camera, the number of pixels occupied by the elevator car bottom and the number of pixels occupied by objects on the elevator car bottom are determined based on the upper left corner point and the lower right corner point of the elevator car bottom. The elevator occupancy rate can be determined by comparing the number of pixels occupied by the elevator car bottom and the number of pixels occupied by the objects on the elevator car bottom.

[0065] <Method Example>

[0066] The embodiment of the present disclosure provides a method for detecting an elevator full load, which can be implemented by an electronic device, which can be the electronic device 1000 shown in Figure 1. As shown in Figure 2, the elevator full load detection method includes steps S2100 to S2400:

[0067] Step S2100: Acquire a depth image captured by the depth camera.

[0068] As described above, in this embodiment, the number of pixels occupied by the elevator car platform and the number of pixels occupied by objects on the elevator car platform are determined based on the upper left and lower right corners of the elevator car platform using the depth camera as a reference. The elevator occupancy rate is determined by comparing the number of pixels occupied by the elevator car platform and the number of pixels occupied by objects on the elevator car platform. In other words, the principle of setting up the depth camera is to be able to determine the number of pixels occupied by the elevator car platform and the number of pixels occupied by objects on the elevator car platform. This embodiment does not specifically limit the location of the depth camera.

[0069] In one example, the depth camera can be set in the elevator car. For example, the depth camera can be set at the middle position of the top of the elevator car. For example, the depth camera can be set within a preset horizontal distance from the middle position in the horizontal direction of the elevator car, and / or within a preset height distance from the top of the car in the height direction of the elevator car. Specifically, the depth camera is set at the middle position of the top of the elevator car to capture the depth image of the interior of the elevator car, that is, the shooting range of the depth camera includes the elevator car wall and the elevator car bottom. In other words, when the depth camera is in the middle position of the top of the elevator car, it can collect the elevator car wall and the elevator car bottom to the greatest extent. At this time, the calculation of the elevator occupancy rate is more accurate, making the elevator full load detection more accurate. For example, it can be the depth image shown in Figure 5 or the depth image shown in Figure 6. Referring to Figure 3, the height of the elevator car is 2323mm, the length of the elevator car bottom is 1850mm, the width of the elevator car bottom is 1900mm, and the depth camera is set at the middle position of the top of the elevator car, that is, the depth camera is hung at a height of 2323mm, and the shooting range includes the elevator car wall and the elevator car bottom.

[0070] It should be noted that the depth camera can be a Time of Flight (TOF) camera. Of course, the depth camera can also be other cameras, and this embodiment does not limit this. Generally, a TOF camera calculates the distance of a target object by measuring the time difference between the emission and reception of light. A TOF camera usually includes a transmitter and a receiver, the transmitter is used to emit light, and the receiver is used to receive the returned light. When the transmitter emits light and shines on the target object, part of the light will be reflected back to the receiver. By measuring the time difference between emission and reception, the TOF camera can calculate the distance of the target object.

[0071] After executing step S2100 to obtain the depth image captured by the depth camera, proceed to:

[0072] Step S2200: Obtain a first number of pixels occupied by an object on the elevator floor in the depth image, and a second number of pixels occupied by the elevator floor in the depth image.

[0073] In this embodiment, after determining the first number of pixels occupied by objects at the elevator floor in the depth image and the second number of pixels occupied by the elevator floor in the depth image, the elevator occupancy rate can be determined based on the first number and the second number, and then whether the elevator is fully loaded can be detected based on the elevator occupancy rate.

[0074] In one embodiment, step S2200 of obtaining the second number of pixels occupied by the elevator car platform in the depth image may further include the following steps S2210a to S2240a:

[0075] Step S2210a: Acquire a first position of the upper left corner of the elevator car platform in the world coordinate system, and a second position of the lower right corner of the elevator car platform in the world coordinate system.

[0076] The world coordinate system is used to position objects in the real world. With a certain point as the origin, the object's position and orientation in space are defined by three mutually perpendicular axes (X, Y, and Z). The X-axis is called the first horizontal axis of the world coordinate system, the Y-axis is called the second horizontal axis of the world coordinate system, and the Z-axis is called the vertical axis of the world coordinate system. Generally, the origin of the world coordinate system can be freely determined based on actual conditions.

[0077] In this step S2210a, the position of the depth camera is usually used as the origin of the world coordinate system to establish the X axis, Y axis and Z axis. Then, the upper left corner of the elevator car bottom is at the first position P in the world coordinate system. tofLeft =(-length / 2,-width / 2,height), the lower right corner of the elevator car is at the second position P in the world coordinate systembottomRight =(length / 2,width / 2,height).

[0078] 3, the position of the depth camera is used as the origin of the world coordinate system, and the X-axis, Y-axis and Z-axis are established. The upper left corner of the elevator car bottom is at the first position P in the world coordinate system. tofLeft Satisfy: P tofLeft =(-length / 2,-width / 2,height)=(-925,-950,2323)

[0079] Among them, the lower right corner of the elevator car bottom is at the second position P in the world coordinate system bottomRight Satisfy: P bottomRight =(length / 2,width / 2,height)=(925,950,2323)

[0080] Step S2220a: convert the first position to the third position of the upper left corner of the elevator car platform in the pixel coordinate system of the depth image, and convert the second position to the fourth position of the lower right corner of the elevator car platform in the pixel coordinate system of the depth image.

[0081] It should be noted that since the calculation of the elevator occupancy rate is performed in the pixel coordinate system, it is necessary to convert the world coordinate system and the pixel coordinate system. The conversion from the world coordinate system to the pixel coordinate system belongs to the existing technology and will not be elaborated in this embodiment.

[0082] The pixel coordinate system is a two-dimensional rectangular coordinate system that describes the position of pixels in an image in units of pixels. Referring to FIG4 , in the pixel coordinate system of the depth image 40 , the origin of the pixel coordinate system is located at the upper left corner of the depth image 40 , and each pixel has a unique coordinate (u, v), where u represents the horizontal position of the pixel (X-axis) and v represents the vertical position of the pixel (Y-axis).

[0083] In this step S2220a, the upper left corner of the elevator car bottom is located at the first position P in the world coordinate system. tofLeft Convert to the third position P of the upper left corner of the elevator car bottom in the pixel coordinate system of the depth image topLeftPixel , and the lower right corner of the elevator car bottom is at the second position P in the world coordinate system bottomRight Convert to the fourth position P of the upper left corner of the elevator car bottom in the pixel coordinate system of the depth image bottomRightPixel .

[0084] For example, the upper left corner of the elevator car shown in FIG3 is located at the first position P in the world coordinate system. tofLeftConvert to the third position P of the upper left corner of the elevator car bottom in the pixel coordinate system of the depth image topLeftPixel , the upper left corner of the elevator car bottom is at the third position P in the pixel coordinate system topLeftPixel =(P topLeftPixel .u,P topLeftPixel .v)=(205,119), where P topLeftPixel .u is the horizontal position of the upper left corner of the elevator car bottom in the pixel coordinate system of the depth image, P topLeftPixel .v is the vertical position of the upper left corner of the elevator car platform in the pixel coordinate system of the depth image.

[0085] The lower right corner of the elevator car bottom is at the fourth position P in the pixel coordinate system of the depth image. bottomRightPixel =(P bottomRightPixel .u,P bottomRightPixel .v)=(420,366), where P bottomRightPixel .u is the horizontal position of the lower right corner of the elevator car bottom in the pixel coordinate system of the depth image, P bottomRightPixel .v is the vertical position of the lower right corner of the elevator car platform in the pixel coordinate system of the depth image.

[0086] That is to say, the third position P of the upper left corner of the elevator car bottom is in the pixel coordinate system. topLeftPixel , and the fourth position P of the lower right corner of the elevator car bottom in the pixel coordinate system of the depth image bottomRightPixel 4 , the elevator car platform 30 may be determined from the depth image 40 .

[0087] Step S2230a: Determine the length and width of the elevator car platform in the pixel coordinate system of the depth image according to the third position and the fourth position.

[0088] In this step S2230a, the length of the elevator car platform in the pixel coordinate system of the depth image, lengthPixel, satisfies: lengthPixel=P bottomRightPixel .uP topLeftPixel .u

[0089] In this step S2230a, the width of the elevator car platform in the pixel coordinate system of the depth image satisfies: widthPixel=P bottomRightPixel .vP topLeftPixel .v

[0090] 3 and 4, the length of the elevator car platform in the pixel coordinate system of the depth image can be calculated as lengthPixel=P bottomRightPixel .uP tppLeftPixel.u=420-205=215, and the width of the elevator car platform in the pixel coordinate system of the depth image widthPixel=P bottomRightPixel .vP topLeftPixel .v=366-119=247.

[0091] Step S2240a: Obtain a second number of pixels occupied by the elevator platform in the depth image according to the length and width of the elevator platform in the pixel coordinate system of the depth image.

[0092] In this step S2240a, the second number of pixels occupied by the elevator car platform in the depth image is lengthPixel×widthPixel, where lengthPixel is the length of the elevator car platform in the pixel coordinate system of the depth image, and widthPixel is the width of the elevator car platform in the pixel coordinate system of the depth image.

[0093] According to the above steps S2210a to S2240a, since the range of the depth camera usually includes the elevator car wall and the elevator car floor, and the detection of the elevator occupancy rate only requires the information of the elevator car floor, therefore, through steps S2210a to S2240a, the elevator car floor area can be cropped from the depth image, and then the elevator occupancy rate can be detected based on the cropped elevator car floor area.

[0094] In one embodiment, step S2200 of obtaining the first number of pixels occupied by the object on the elevator floor in the depth image may further include the following steps S2210b to S2240b:

[0095] Step S2210b: Acquire a three-dimensional point cloud image of the depth image.

[0096] The three-dimensional point cloud image can be understood as a three-dimensional point cloud image in a world coordinate system.

[0097] Step S2220b: Filter out valid point clouds from the three-dimensional point cloud image.

[0098] The effective point cloud is a point cloud located within the elevator car bottom range.

[0099] Optionally, screening out valid point clouds from the three-dimensional point cloud image in step S2220b may further include: screening out valid point clouds from the three-dimensional point cloud image according to set constraints;

[0100] Among them, the first constraint condition involves the constraint on the fifth position of any point cloud in the three-dimensional point cloud image in the first horizontal direction of the world coordinate system of the three-dimensional point cloud image; the second constraint condition involves the constraint on the sixth position of the arbitrary point cloud in the second horizontal direction of the world coordinate system; the third constraint condition involves the constraint on the seventh position of the arbitrary point cloud in the vertical direction of the world coordinate system.

[0101] In one example, the first constraint condition includes: the fifth position of the arbitrary point cloud is determined by the position of the upper left corner of the elevator car platform in the first horizontal direction of the world coordinate system and the position of the lower right corner of the elevator car platform in the first horizontal direction of the world coordinate system. That is, P topLeft .x<pPoints.x<P bottomRight .x, where pPoints represents any point cloud in the 3D point cloud image, pPoints.x represents the position of any point cloud in the first horizontal direction (X axis) of the world coordinate system, and P topLeft .x represents the position of the upper left corner of the elevator car bottom in the first horizontal direction of the world coordinate system, P bottomRight .x represents the position of the lower right corner of the elevator car platform in the first horizontal direction of the world coordinate system.

[0102] The second constraint condition includes: the sixth position of the arbitrary point cloud is determined by the position of the upper left corner of the elevator car platform in the second horizontal direction of the world coordinate system and the position of the lower right corner of the elevator car platform in the second horizontal direction of the world coordinate system. That is, P topLeft .y<pPoints.y<P bottomRight .y, where pPoints.y represents the position of any point cloud in the second horizontal direction (Y axis) of the world coordinate system, P topLeft .y represents the position of the upper left corner of the elevator car bottom in the second horizontal direction of the world coordinate system, P bottomRight .y represents the position of the lower right corner of the elevator car platform in the second horizontal direction of the world coordinate system.

[0103] The third constraint condition includes: the seventh position of the arbitrary point cloud is less than the height of the elevator car, that is, pPoints.z<height, where pPoints.z represents the vertical position (Z axis) of the arbitrary point cloud in the world coordinate system, and height represents the height of the elevator car.

[0104] Combined with Figure 3, the point cloud that satisfies the following first constraint, second constraint, and third constraint conditions is a valid point cloud: -925<pPoints.x<925 -950<pPoints.y<950 pPoints.z<2323

[0105] Taking the point cloud pPoints1(960, 900, 1733), point cloud pPoints2(900, 900, 2200) and point cloud pPoints3(900, 900, 1328) in the three-dimensional point cloud image as examples, since the point cloud pPoints2(900, 900, 2200) and the point cloud pPoints3(900, 900, 1328) meet the above set constraints, the point cloud pPoints2(900, 900, 2200) and the point cloud pPoints3(900, 900, 1328) are valid point clouds.

[0106] In another example, the first constraint condition includes: the fifth position of the arbitrary point cloud is between the eighth position and the ninth position, the eighth position is determined according to the position of the upper left corner of the elevator car platform in the first horizontal direction of the world coordinate system and the elevator car platform boundary threshold, and the ninth position is determined according to the position of the upper left corner of the elevator car platform in the first horizontal direction of the world coordinate system and the elevator car platform boundary threshold. That is, (P topLeft .x-detectThreshold)<pPoints.x<(P bottomRight .x+detectThreshold), where pPoints represents any point cloud in the 3D point cloud image, pPoints.x represents the position of any point cloud in the first horizontal direction (X axis) of the world coordinate system, and P topLeft .x represents the position of the upper left corner of the elevator car bottom in the first horizontal direction of the world coordinate system, P bottomRight .x represents the position of the lower right corner of the elevator car platform in the first horizontal direction of the world coordinate system, and detectThreshold represents the elevator car platform boundary threshold.

[0107] The second constraint condition includes: the sixth position is between the tenth position and the eleventh position, the tenth position is determined based on the position of the upper left corner of the elevator car platform in the second horizontal direction of the world coordinate system and the elevator car platform boundary threshold, and the eleventh position is determined based on the position of the lower right corner of the elevator car platform in the second horizontal direction of the world coordinate system and the elevator car platform boundary threshold. That is, (P topLeft .y-detectThreshold)<pPoints.y<(P bottomRight .y+detectThreshold), where pPoints.y represents the position of any point cloud in the second horizontal direction (Y axis) of the world coordinate system, P topLeft .y represents the position of the upper left corner of the elevator car bottom in the second horizontal direction of the world coordinate system, P bottomRight.y represents the position of the lower right corner of the elevator car platform in the second horizontal direction of the world coordinate system.

[0108] The third constraint condition includes: the seventh position is less than a set height, and the set height is determined by the elevator car height and the ground threshold. That is, pPoints.z < (height - groundThreshold), where pPoints.z represents the vertical position (Z axis) of the arbitrary point cloud in the world coordinate system, height represents the elevator car height, and groundThreshold represents the ground threshold.

[0109] The elevator floor boundary threshold detectThreshold and ground threshold groundThreshold are used to tolerate point cloud noise. They are set based on the elevator car height and the actual elevator installation scenario. They can be positive or negative values, as described in detail below.

[0110] It's important to note that the above introduction to TOF cameras explains their measurement principle: they calculate the distance to an object by measuring the time difference between light emission and reception. A TOF camera's transmitter emits light, and its receiver receives the returning light. When the transmitter emits light and it strikes an object, some of it is reflected back to the receiver. By measuring the time difference between emission and reception, the TOF camera can calculate the distance to the object.

[0111] However, in addition to light reflected or emitted by the target object, light from non-target objects entering the optical system creates interfering light called ambient stray light. This light can come from sunlight, lamplight, reflected light, and other sources. Without stray light interference, the depth image should clearly and accurately display the target object's shape and position. However, when stray light is present, it interferes with signal reception, reducing the ratio of useful signal to noise and affecting the accuracy of the target object's depth data.

[0112] One of the effects of ambient stray light on elevator scenes is jitter at the elevator car bottom boundary. Due to varying environments, the elevator car bottom boundary may expand. Therefore, the elevator car bottom boundary threshold (detectThreshold) is set to mitigate the impact of boundary noise. The detectThreshold value can be adjusted based on the actual environment. Similarly, elevator car bottom planar data also exhibits jitter, with a lot of noise generated by stray light interference. Therefore, the ground threshold (groundThreshold) is set to mitigate this noise.

[0113] Combined with Figure 3, set groundThreshold = 300, detectThreshold = 30, and the point cloud that meets the following first constraint, second constraint, and third constraint is a valid point cloud: (-925-30)<pPoints.x<(925+30) (-950-30)<pPoints.y<(950+30) pPoints.z<(2323-300)

[0114] Taking the point cloud pPoints1(960, 900, 1733), point cloud pPoints2(900, 900, 2200), point cloud pPoints3(900, 900, 1328) and point cloud pPoints4(900, 900, 1854) in the three-dimensional point cloud image, the point cloud pPoints3(900, 900, 1328) and point cloud pPoints4(900, 900, 1854) meet the above set constraints. Therefore, the point cloud pPoints3(900, 900, 1328) and the point cloud pPoints4(900, 900, 1854) are valid point clouds.

[0115] Step S2230b: projecting the valid point cloud onto the elevator car platform.

[0116] In step S2230b, point clouds with different coordinates in the vertical direction (Z axis) of the world coordinate system need to be projected onto the same point cloud to project the valid point cloud onto the elevator car platform. Specifically, for example, pPointsValid.z = height can be set to project the valid point cloud onto the elevator car platform, where pPointsValid is the valid point cloud obtained after screening in step S2220b. Points with the same pPointsValid.x and pPointsValid.y coordinates but different pPointsValid.z coordinates will be projected onto the same point.

[0117] Exemplarily, setting pPointsValid.z=2323 can project the valid point cloud to the elevator car platform, for example, the valid point cloud pPoints3 (900, 900, 1328) and the point cloud pPoints4 (900, 900, 1854), the coordinates after projection are the same valid point cloud (900, 900, 2323).

[0118] Step S2240b: converting the effective point cloud projected onto the elevator car platform into the pixel coordinate system of the depth image, and obtaining the first number of pixels occupied by the object on the elevator car platform in the depth image.

[0119] Optionally, this step S2240b converts the valid point cloud projected onto the elevator car bed into the pixel coordinate system of the depth image, and obtaining the first number of pixels occupied by the object at the elevator car bed in the depth image can further include: obtaining the number of pixels of the valid point cloud projected onto the elevator car bed converted into the pixel coordinate system of the depth image; and obtaining the first number of pixels occupied by the object at the elevator car bed in the depth image based on the number of pixels.

[0120] Specifically, after converting the valid point cloud projected to the elevator car bottom from the world coordinate system to the pixel coordinate system, the number of pixels corresponding to the valid point cloud projected to the elevator car bottom is counted as the first number of pixels occupied by the object of the elevator car bottom in the depth image.

[0121] Referring to Figure 4, the black graph is used to represent the pixels converted to the pixel coordinate system after the portrait point cloud is projected onto the valid point cloud of the elevator car bottom. After the valid point cloud projected onto the elevator car bottom is converted from the world coordinate system to the pixel coordinate system, the pixels corresponding to the valid point cloud projected onto the elevator car bottom are set to 1, and the number of 1s is counted as countP. CountP is used as the first number of pixels occupied by the object on the elevator car bottom in the depth image.

[0122] According to the above steps S2210b to S2240b, the first number of pixels occupied by the object at the elevator floor in the depth image can be determined, and then the elevator occupancy rate of the elevator can be determined based on the first number.

[0123] After executing step S2200 to obtain the first number of pixels occupied by the object on the elevator floor in the depth image and the second number of pixels occupied by the elevator floor in the depth image, proceed to:

[0124] Step S2300: Determine the elevator occupancy rate of the elevator based on the first number and the second number.

[0125] The elevator occupancy rate can be understood as the percentage of the area occupied by objects on the elevator floor. The elevator occupancy rate satisfies:

[0126] Wherein, lengthPixel is the length of the elevator platform in the pixel coordinate system of the depth image, widthPixel is the width of the elevator platform in the pixel coordinate system of the depth image, lengthPixel×widthPixel is the second number of pixels occupied by the elevator platform in the depth image, and countP is the first number of pixels occupied by the object in the elevator platform in the depth image.

[0127] For example, countP=25788, lengthPixel=215, widthPixel=247, and the final elevator occupancy rate is

[0128] After executing step S2300 to determine the elevator occupancy rate of the elevator according to the first number and the second number, proceed to:

[0129] Step S2400: Determine whether the elevator is fully loaded according to the elevator occupancy rate.

[0130] In this embodiment, step S2400 of determining whether the elevator is fully loaded based on the elevator occupancy rate may further include: comparing the elevator occupancy rate with the elevator occupancy rate threshold to obtain a comparison result; when the comparison result indicates that the elevator occupancy rate is less than or equal to the elevator occupancy rate threshold, determining that the elevator is not fully loaded; when the comparison result indicates that the elevator occupancy rate is greater than the elevator occupancy rate threshold, determining that the elevator is fully loaded.

[0131] Among them, the elevator occupancy rate threshold occupancyThreshold can be set according to the actual application scenario. When the elevator occupancy rate is less than or equal to the elevator occupancy rate threshold, it is determined that the elevator is not fully loaded. When the elevator occupancy rate is greater than the elevator occupancy rate threshold, it is determined that the elevator is fully loaded.

[0132] For example, occupancyThreshold is set to 95%. Referring to Figure 5, the elevator occupancy rate is 48.56%. Since 48.56% is less than 95%, the elevator is judged to be not fully loaded. Referring to Figure 6, if the elevator occupancy rate is 81.89%, since 81.89% is less than 95%, the elevator is also judged to be not fully loaded.

[0133] Through the embodiments of the present disclosure, it is possible to capture a depth image of the interior of the elevator car through a depth camera, and obtain a first number of pixels occupied by objects on the elevator floor in the depth image, and a second number of pixels occupied by the elevator floor in the depth image, and determine the elevator occupancy rate of the elevator based on the first number and the second number, and then determine whether the elevator is fully loaded based on the elevator occupancy rate. The depth image can well protect the passenger's facial privacy, and detecting whether the elevator is fully loaded based on the elevator occupancy rate can reduce unnecessary stops of the elevator, improve the elevator's operating efficiency and passenger riding experience, save electricity, and reduce mechanical wear of the elevator.

[0134] <Example>

[0135] Next, an example of an elevator full load detection method is shown. In this example, the elevator full load detection method includes:

[0136] In step S301, the elevator height is 2323 mm, the elevator car bottom length is 1850 mm, the elevator car bottom width is 1900 mm, and a depth camera is set in the middle position of the top of the elevator car to collect a depth image.

[0137] Taking the position of the depth camera as the origin of the world coordinate system, the position P of the upper left corner of the elevator car bottom in the world coordinate system can be obtained tofLeft =(-length / 2,-width / 2,height)=(-1850 / 2,-1900 / 2,2323)=(-925,-950,2323), the position of the lower right corner of the elevator car in the world coordinate system is P bottomRight =(length / 2,width / 2,height)=(925,950,2323). Convert the position of the upper left corner of the elevator car bottom in the world coordinate system to the position of the upper left corner of the elevator car bottom in the pixel coordinate system of the depth image P topLeftPixel =(P topLeftPixel .u,P topLeftPixel .v)=(205,119), and convert the position of the lower right corner of the elevator car bottom in the world coordinate system to the position P of the lower right corner of the elevator car bottom in the pixel coordinate system of the depth image bottomRightPixel =(P bottomRightPixel .u,P bottomRightPixel .v)=(420,366).

[0138] The length of the elevator car platform in the pixel coordinate system of the depth image is lengthPixel = P bottomRightPixel .uP topLeftPixel .u=420-205=215, widthPixel=P of the elevator car platform in the pixel coordinate system of the depth image bottomRightPixel .vP topLeftPixel .v=366-119=247.

[0139] Step S302 : converting the depth image into a three-dimensional point cloud image, and screening out valid point clouds from the three-dimensional point cloud image.

[0140] For example, groundThreshold = 300, detectThreshold = 30, and a point cloud that satisfies the following first, second, and third constraints is considered a valid point cloud: (-925-30) < pPoints.x < (925+30) (-950-30) < pPoints.y < (950+30) pPoints.z < (2323-300)

[0141] Taking the point cloud pPoints1(960, 900, 1733), point cloud pPoints2(900, 900, 2200), point cloud pPoints3(900, 900, 1328) and point cloud pPoints4(900, 900, 1854) in the three-dimensional point cloud image, the point cloud pPoints3(900, 900, 1328) and point cloud pPoints4(900, 900, 1854) meet the above set constraints. Therefore, the point cloud pPoints3(900, 900, 1328) and the point cloud pPoints4(900, 900, 1854) are valid point clouds.

[0142] Step S303: Project the valid point cloud onto the elevator car platform.

[0143] Exemplarily, setting pPointsValid.z=2323 can project the valid point cloud to the elevator car platform, for example, the valid point cloud pPoints3 (900, 900, 1328) and the point cloud pPoints4 (900, 900, 1854), the coordinates after projection are the same valid point cloud (900, 900, 2323).

[0144] Step S304: determining the elevator occupancy rate of the elevator.

[0145] First, the valid point cloud projected to the elevator car bottom is converted from the world coordinate system to the pixel coordinate system of the depth image, and the first number countP of pixels occupied by objects on the elevator car bottom in the depth image is obtained.

[0146] Then, the elevator occupancy satisfies:

[0147] Wherein, lengthPixel is the length of the elevator platform in the pixel coordinate system of the depth image, widthPixel is the width of the elevator platform in the pixel coordinate system of the depth image, lengthPixel×widthPixel is the second number of pixels occupied by the elevator platform in the depth image, and countP is the first number of pixels occupied by the object in the elevator platform in the depth image.

[0148] For example, countP=25788, lengthPixel=215, widthPixel=247, and the final elevator occupancy rate is

[0149] Step S305: Determine whether the elevator is fully loaded according to the elevator occupancy rate.

[0150] For example, the elevator occupancy threshold occupancyThreshold is set to 95%. When the elevator occupancy rate obtained in step S304 is 48.56%, since 48.56% is less than 95%, it is determined that the elevator is not fully loaded.

[0151] According to this example, the passenger's facial privacy can be well protected through depth images, and whether the elevator is fully loaded can be detected based on the elevator occupancy rate, which can reduce unnecessary stops of the elevator, improve the elevator operation efficiency and passenger riding experience, save electricity, and reduce elevator mechanical wear.

[0152] <Device Example>

[0153] FIG7 is a schematic diagram of the structure of an elevator full load detection device according to an embodiment. As shown in FIG7 , the elevator full load detection device 700 includes a first acquisition module 710 , a second acquisition module 720 , a first determination module 730 , and a second determination module 740 .

[0154] A first acquisition module 710 is configured to acquire a depth image captured by a depth camera;

[0155] A second acquisition module 720 is configured to acquire a first number of pixels occupied by an object on the elevator floor in the depth image, and a second number of pixels occupied by the elevator floor in the depth image;

[0156] A first determining module 730 is configured to determine an elevator occupancy rate of the elevator based on the first number and the second number;

[0157] The second determining module 740 is configured to determine whether the elevator is fully loaded according to the elevator occupancy rate.

[0158] Optionally, the second acquisition module 720 is specifically configured to acquire a first position of the upper left corner of the elevator car platform in the world coordinate system and a second position of the lower right corner of the elevator car platform in the world coordinate system; convert the first position into a third position of the upper left corner of the elevator car platform in the pixel coordinate system of the depth image, and convert the second position into a fourth position of the lower right corner of the elevator car platform in the pixel coordinate system of the depth image; determine the length and width of the elevator car platform in the pixel coordinate system of the depth image based on the third and fourth positions; and obtain a second number of pixels occupied by the elevator car platform in the depth image based on the length and width of the elevator car platform in the pixel coordinate system of the depth image.

[0159] Optionally, the second acquisition module 720 is specifically used to obtain a three-dimensional point cloud image of the depth image; filter out valid point clouds from the three-dimensional point cloud image; wherein the valid point clouds are point clouds located within the range of the elevator car platform; project the valid point clouds to the elevator car platform; and convert the valid point clouds projected to the elevator car platform into the pixel coordinate system of the depth image to obtain the first number of pixels occupied by objects on the elevator car platform in the depth image.

[0160] Optionally, the second acquisition module 720 is specifically used to obtain the number of pixels of the effective point cloud projected onto the elevator car bottom converted to the pixel coordinate system of the depth image; based on the number of pixels, obtain the first number of pixels occupied by the object on the elevator car bottom in the depth image.

[0161] Optionally, the second acquisition module 720 is specifically configured to filter out valid point clouds from the three-dimensional point cloud image according to set constraints;

[0162] The set constraint conditions include a first constraint condition, a second constraint condition and a third constraint condition;

[0163] The first constraint condition relates to a constraint on a fifth position of any point cloud in the three-dimensional point cloud image in a first horizontal direction of the world coordinate system of the three-dimensional point cloud image;

[0164] The second constraint condition relates to a constraint on a sixth position of the arbitrary point cloud in a second horizontal direction of the world coordinate system;

[0165] The third constraint condition relates to a constraint on a seventh position of the arbitrary point cloud in the vertical direction of the world coordinate system.

[0166] Optionally, the first constraint condition includes: the fifth position of the arbitrary point cloud is located between an eighth position and a ninth position, the eighth position is determined according to the position of the upper left corner of the elevator car platform in the first horizontal direction of the world coordinate system and an elevator car platform boundary threshold, and the ninth position is determined according to the position of the lower right corner of the elevator car platform in the first horizontal direction of the world coordinate system and the elevator car platform boundary threshold;

[0167] The second constraint condition includes: the sixth position is between the tenth position and the eleventh position, the tenth position is determined based on the position of the upper left corner of the elevator car platform in the second horizontal direction of the world coordinate system and the elevator car platform boundary threshold, and the eleventh position is determined based on the position of the lower right corner of the elevator car platform in the second horizontal direction of the world coordinate system and the elevator car platform boundary threshold;

[0168] The third constraint condition includes: the seventh position is less than a set height, and the set height is determined according to the elevator car height and a ground threshold.

[0169] Optionally, the second determination module 730 is specifically used to compare the elevator occupancy rate and the elevator occupancy rate threshold to obtain a comparison result; when the comparison result indicates that the elevator occupancy rate is less than or equal to the elevator occupancy rate threshold, it is determined that the elevator is not fully loaded; when the comparison result indicates that the elevator occupancy rate is greater than the elevator occupancy rate threshold, it is determined that the elevator is fully loaded.

[0170] Optionally, the depth camera is arranged at the middle position of the top of the elevator car.

[0171] Through the embodiments of the present disclosure, it is possible to capture a depth image of the interior of the elevator car through a depth camera, and obtain a first number of pixels occupied by objects on the elevator floor in the depth image, and a second number of pixels occupied by the elevator floor in the depth image, and determine the elevator occupancy rate of the elevator based on the first number and the second number, and then determine whether the elevator is fully loaded based on the elevator occupancy rate. The depth image can well protect the passenger's facial privacy, and detecting whether the elevator is fully loaded based on the elevator occupancy rate can reduce unnecessary stops of the elevator, improve the elevator's operating efficiency and passenger riding experience, save electricity, and reduce mechanical wear of the elevator.

[0172] <Electronic Equipment Example>

[0173] In this embodiment, an electronic device 110 is further provided, as shown in FIG8 , including a memory 1120 and a processor 1110 .

[0174] The memory 1120 is used to store an executable computer program; the computer program is used to control the processor 1110 to execute any one of the tourist destination display methods provided in this embodiment.

[0175] In this embodiment, the electronic device 110 may be the electronic device 1000 shown in FIG. 1 , or may be a device with other hardware structures, which is not limited here.

[0176] In another embodiment, the electronic device 110 may include the above-mentioned travel destination display device 900 .

[0177] In one embodiment, each module of the above elevator full load detection device 700 can be implemented by the processor 1110 running a computer program stored in the memory 1120.

[0178] <Computer-readable storage medium>

[0179] In this embodiment, a computer-readable storage medium is further provided, on which a computer program is stored. When the computer program is executed by a processor, the elevator full load detection method as described in any embodiment of the present disclosure is implemented.

[0180] It should be noted that all actions of acquiring signals, information or data in the embodiments of the present disclosure are performed in compliance with the corresponding data protection laws and policies of the country where they are located and with the authorization given by the corresponding device / account owner.

[0181] The embodiments of the present disclosure may be systems, methods, and / or computer program products. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for causing a processor to implement various aspects of the embodiments of the present disclosure.

[0182] A computer-readable storage medium can be a tangible device that can hold and store instructions for use by an instruction execution device. A computer-readable storage medium can be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, such as a punch card or a raised structure in a groove on which instructions are stored, and any suitable combination thereof. As used herein, a computer-readable storage medium is not to be construed as a transient signal per se, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., a light pulse through a fiber optic cable), or an electrical signal transmitted through an electrical wire.

[0183] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions to be stored in the computer-readable storage medium in each computing / processing device.

[0184] The computer program instructions for performing the operation of the disclosed embodiments can be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, and conventional procedural programming languages ​​such as "C" language or similar programming languages. Computer-readable program instructions can be executed entirely on a user's computer, partially on a user's computer, executed as an independent software package, partially on a user's computer, partially on a remote computer, or completely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer via any type of network including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (such as using an Internet service provider to connect via the Internet). In certain embodiments, by utilizing the state information of computer-readable program instructions to personalize an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), the electronic circuit can execute computer-readable program instructions, thereby realizing the various aspects of the disclosed embodiments.

[0185] Various aspects of the embodiments of the present disclosure are described herein with reference to flowcharts and / or block diagrams of methods, apparatus (systems), and computer program products according to the embodiments of the present disclosure. It should be understood that each block of the flowcharts and / or block diagrams, and combinations of blocks in the flowcharts and / or block diagrams, can be implemented by computer-readable program instructions.

[0186] These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, thereby producing a machine, so that when these instructions are executed by the processor of the computer or other programmable data processing device, a device is generated that implements the functions / actions specified in one or more blocks in the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium, where these instructions cause the computer, programmable data processing device, and / or other device to operate in a specific manner. Thus, the computer-readable medium storing the instructions comprises an article of manufacture that includes instructions for implementing various aspects of the functions / actions specified in one or more blocks in the flowchart and / or block diagram.

[0187] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device so that a series of operational steps are performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to implement the functions / actions specified in one or more blocks in the flowchart and / or block diagram.

[0188] The flowcharts and block diagrams in the accompanying drawings show the possible architecture, functions and operations of the systems, methods and computer program products according to multiple embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, program segment or part of an instruction, and a part of the module, program segment or instruction contains one or more executable instructions for implementing the specified logical function. In some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of boxes in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system that performs the specified function or action, or can be implemented by a combination of dedicated hardware and computer instructions. It is well known to those skilled in the art that implementation by hardware, implementation by software, and implementation by a combination of software and hardware are all equivalent.

[0189] The embodiments of the present disclosure have been described above. The above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terms used herein are selected to best explain the principles of the embodiments, their practical applications, or technical improvements in the marketplace, or to enable other persons skilled in the art to understand the embodiments disclosed herein. The scope of the embodiments of the present disclosure is defined by the appended claims.

Claims

1. A method for detecting full load of an elevator, characterized in that: The method comprises: Get the depth image captured by the depth camera; Obtaining a first number of pixels occupied by an object on the elevator floor in the depth image, and a second number of pixels occupied by the elevator floor in the depth image; determining an elevator occupancy rate of the elevator according to the first number and the second number; Whether the elevator is fully loaded is determined according to the elevator occupancy rate.

2. The method according to claim 1, characterized in that Obtaining a second number of pixels occupied by the elevator car platform in the depth image, including: Obtaining a first position of an upper left corner point of the elevator car platform in a world coordinate system and a second position of a lower right corner point of the elevator car platform in the world coordinate system; Converting the first position to a third position of the upper left corner of the elevator car platform in the pixel coordinate system of the depth image, and converting the second position to a fourth position of the lower right corner of the elevator car platform in the pixel coordinate system of the depth image; Determining the length and width of the elevator car platform in the pixel coordinate system of the depth image according to the third position and the fourth position; According to the length and width of the elevator platform in the pixel coordinate system of the depth image, a second number of pixels occupied by the elevator platform in the depth image is obtained.

3. The method according to claim 1 or 2, characterized in that Obtaining the first number of pixels occupied by the object on the elevator floor in the depth image, including: Acquire a three-dimensional point cloud image of the depth image; Filtering out valid point clouds from the three-dimensional point cloud image; wherein the valid point clouds are point clouds located within the range of the elevator car bottom; Projecting the valid point cloud onto the elevator car platform; and The effective point cloud projected onto the elevator car platform is converted into a pixel coordinate system of the depth image to obtain a first number of pixels occupied by objects on the elevator car platform in the depth image.

4. The method according to claim 3, characterized in that The converting the valid point cloud projected onto the elevator car bed into the pixel coordinate system of the depth image to obtain a first number of pixels occupied by an object at the elevator car bed in the depth image includes: Obtaining the number of pixels of the valid point cloud projected onto the elevator car bottom converted to the pixel coordinate system of the depth image; According to the number of pixels, a first number of pixels occupied by the object on the elevator car bottom in the depth image is obtained.

5. The method according to claim 3 or 4, characterized in that The step of screening out a valid point cloud from the three-dimensional point cloud image comprises: Filtering valid point clouds from the three-dimensional point cloud image according to set constraints; The set constraint conditions include a first constraint condition, a second constraint condition and a third constraint condition; The first constraint condition relates to a constraint on a fifth position of any point cloud in the three-dimensional point cloud image in a first horizontal direction of the world coordinate system of the three-dimensional point cloud image; The second constraint condition relates to a constraint on a sixth position of the arbitrary point cloud in a second horizontal direction of the world coordinate system; The third constraint condition relates to a constraint on a seventh position of the arbitrary point cloud in the vertical direction of the world coordinate system.

6. The method according to claim 5, characterized in that The first constraint condition includes: the fifth position of the arbitrary point cloud is between the eighth position and the ninth position, the eighth position is determined based on the position of the upper left corner of the elevator car platform in the first horizontal direction of the world coordinate system and the elevator car platform boundary threshold, and the ninth position is determined based on the position of the lower right corner of the elevator car platform in the first horizontal direction of the world coordinate system and the elevator car platform boundary threshold; The second constraint condition includes: the sixth position is between the tenth position and the eleventh position, the tenth position is determined based on the position of the upper left corner of the elevator car platform in the second horizontal direction of the world coordinate system and the elevator car platform boundary threshold, and the eleventh position is determined based on the position of the lower right corner of the elevator car platform in the second horizontal direction of the world coordinate system and the elevator car platform boundary threshold; The third constraint condition includes: the seventh position is less than a set height, and the set height is determined according to the elevator car height and a ground threshold.

7. The method according to any one of claims 1 to 6, characterized in that The determining whether the elevator is fully loaded according to the elevator occupancy rate includes: Comparing the elevator occupancy rate with an elevator occupancy rate threshold to obtain a comparison result; If the comparison result indicates that the elevator occupancy rate is less than or equal to the elevator occupancy rate threshold, determining that the elevator is not fully loaded; If the comparison result indicates that the elevator occupancy rate is greater than the elevator occupancy rate threshold, it is determined that the elevator is fully loaded.

8. The method according to any one of claims 1 to 7, characterized in that The depth camera is arranged at the middle position of the top of the elevator car.

9. An elevator full load detection device, characterized in that: The device comprises: A first acquisition module is used to acquire a depth image captured by a depth camera; a second acquisition module, configured to acquire a first number of pixels occupied by an object on the elevator car floor in the depth image, and a second number of pixels occupied by the elevator car floor in the depth image; a first determining module, configured to determine an elevator occupancy rate of the elevator according to the first number and the second number; The second determining module is configured to determine whether the elevator is fully loaded according to the elevator occupancy rate.

10. An electronic device, characterized in that: The system comprises a memory and a processor, wherein the memory is used to store an executable computer program; and the computer program is used to control the processor to execute the method according to any one of claims 1 to 8.

11. A computer-readable storage medium, characterized in that A computer program is stored thereon, which implements the method according to any one of claims 1 to 8 when executed by a processor.

Images