CN111232773A - Elevator control method and system based on unmanned aerial vehicle monitoring and storage medium - Google Patents
Elevator control method and system based on unmanned aerial vehicle monitoring and storage medium Download PDFInfo
- Publication number
- CN111232773A CN111232773A CN201911097923.2A CN201911097923A CN111232773A CN 111232773 A CN111232773 A CN 111232773A CN 201911097923 A CN201911097923 A CN 201911097923A CN 111232773 A CN111232773 A CN 111232773A
- Authority
- CN
- China
- Prior art keywords
- elevator control
- owner
- registered owner
- time
- human body
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Withdrawn
Links
Images
Classifications
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B66—HOISTING; LIFTING; HAULING
- B66B—ELEVATORS; ESCALATORS OR MOVING WALKWAYS
- B66B1/00—Control systems of elevators in general
- B66B1/02—Control systems without regulation, i.e. without retroactive action
- B66B1/06—Control systems without regulation, i.e. without retroactive action electric
- B66B1/14—Control systems without regulation, i.e. without retroactive action electric with devices, e.g. push-buttons, for indirect control of movements
- B66B1/22—Control systems without regulation, i.e. without retroactive action electric with devices, e.g. push-buttons, for indirect control of movements with means for taking account of delayed calls
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B66—HOISTING; LIFTING; HAULING
- B66B—ELEVATORS; ESCALATORS OR MOVING WALKWAYS
- B66B1/00—Control systems of elevators in general
- B66B1/34—Details, e.g. call counting devices, data transmission from car to control system, devices giving information to the control system
- B66B1/46—Adaptations of switches or switchgear
- B66B1/468—Call registering systems
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B66—HOISTING; LIFTING; HAULING
- B66B—ELEVATORS; ESCALATORS OR MOVING WALKWAYS
- B66B5/00—Applications of checking, fault-correcting, or safety devices in elevators
- B66B5/0006—Monitoring devices or performance analysers
- B66B5/0012—Devices monitoring the users of the elevator system
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B66—HOISTING; LIFTING; HAULING
- B66B—ELEVATORS; ESCALATORS OR MOVING WALKWAYS
- B66B2201/00—Aspects of control systems of elevators
- B66B2201/40—Details of the change of control mode
- B66B2201/46—Switches or switchgear
- B66B2201/4607—Call registering systems
- B66B2201/4615—Wherein the destination is registered before boarding
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B66—HOISTING; LIFTING; HAULING
- B66B—ELEVATORS; ESCALATORS OR MOVING WALKWAYS
- B66B2201/00—Aspects of control systems of elevators
- B66B2201/40—Details of the change of control mode
- B66B2201/46—Switches or switchgear
- B66B2201/4607—Call registering systems
- B66B2201/4638—Wherein the call is registered without making physical contact with the elevator system
Landscapes
- Engineering & Computer Science (AREA)
- Automation & Control Theory (AREA)
- Computer Networks & Wireless Communication (AREA)
- Indicating And Signalling Devices For Elevators (AREA)
Abstract
The invention provides an elevator control method based on unmanned aerial vehicle monitoring, which comprises the following steps: receiving image data acquired in real time by a camera loaded on an unmanned aerial vehicle; analyzing the image data, and judging whether the registered owner is contained in the image data through a convolutional neural network; if the judgment result is that the registered owner is included, inquiring the address data of the registered owner in a preset database; obtaining the first time when the registered owner walks to the building according to the human body tracking frame output by the convolutional neural network and the building position in the registered owner address data obtained by inquiry; and judging the first time, if the first time is greater than a threshold value, delaying to send an elevator control command to the elevator control component of the floor where the owner is located, wherein the elevator control command is used for stopping the elevator to the first floor. The invention reduces the waiting time of the elevator spent by the owner returning home and improves the living quality of the owner.
Description
Technical Field
The embodiment of the invention relates to the field of elevator control, in particular to an elevator control method, an elevator control system and a storage medium based on unmanned aerial vehicle monitoring.
Background
In most of the existing cells, the elevator stays on the floor to which the elevator is going for the last time after operation, and because the demand of the elevator is most unpredictable, the owner needs to wait for the elevator to fall from the high floor every time he returns home, so that the convenience is not high and certain time cost is spent.
Disclosure of Invention
In order to solve the above problems, an embodiment of the present invention provides an elevator control method based on unmanned aerial vehicle monitoring, including the following steps:
receiving image data acquired in real time by a camera loaded on an unmanned aerial vehicle;
analyzing the image data, and judging whether the registered owner is contained in the image data through a convolutional neural network;
if the judgment result is that the registered owner is included, inquiring the address data of the registered owner in a preset database;
obtaining the first time when the registered owner walks to the building according to the human body tracking frame output by the convolutional neural network and the building position in the registered owner address data obtained by inquiry;
and judging the first time, if the first time is greater than a threshold value, delaying to send an elevator control command to the elevator control component of the floor where the owner is located, wherein the elevator control command is used for stopping the elevator to the first floor, and if the first time is less than a preset value, immediately sending the elevator control command to the elevator control component of the floor where the owner is located.
Preferably, before the step of receiving image data acquired by a camera loaded on the drone in real time, the method further includes:
and carrying out handshake communication on the unmanned aerial vehicle, and if the handshake connection is successful, receiving image data acquired in real time by a camera loaded on the unmanned aerial vehicle.
Preferably, before the step of analyzing the image data and determining whether the registered owner is included in the image data by using a convolutional neural network, the method further includes:
extracting a frame image from the image data;
inputting the frame image into a Faster-RCNN convolution network to obtain a human body pixel block and a corresponding human body tracking frame body in the frame image;
and comparing the human body pixel block with standard data in a preset owner characteristic database, and further identifying the human body pixel block belonging to a registered owner.
Preferably, if the determination result indicates that the registered owner is included, the step of querying address data of the registered owner in a preset database includes:
and inquiring owner address data associated with the identity information in a preset database according to the identified identity information of the registered owner.
Preferably, the step of obtaining a first time when the registered owner walks to the building where the registered owner is located, based on the human body tracking frame output by the convolutional neural network and the building location in the registered owner address data obtained by querying, comprises:
converting coordinates of four end points of the human body tracking frame according to the coordinate position of the unmanned aerial vehicle to obtain second coordinates of the four end points in a world coordinate system;
calculating the distance d between the second coordinate of any endpoint and the building position in the registered owner address data;
dividing the distance d by a preset walking speed of the human body to obtain a first time when the registered owner walks to the building.
An embodiment of the present invention further provides an elevator control system, including:
the image module is used for receiving image data acquired by a camera loaded on the unmanned aerial vehicle in real time;
the analysis module is used for analyzing the image data and judging whether the image data contains a registered owner through a convolutional neural network;
the query module is used for querying the address data of the registered owner in a preset database if the judgment result shows that the registered owner is included;
the time calculation module is used for obtaining the first time when the registered owner walks to the building according to the human body tracking frame body output by the convolutional neural network and the building position in the registered owner address data obtained by inquiry;
and the control module is used for judging the first time, if the first time is greater than a threshold value, sending an elevator control command to the elevator control component of the floor where the owner is located in a delayed manner, wherein the elevator control command is used for stopping the elevator to the first floor, and if the first time is less than preset, immediately sending the elevator control command to the elevator control component of the floor where the owner is located.
Preferably, the image module is further configured to perform handshake communication with the unmanned aerial vehicle, and if the handshake connection is successful, receive image data acquired in real time by a camera mounted on the unmanned aerial vehicle.
Preferably, the query module further comprises:
a frame image unit for extracting a frame image from the image data;
the image detection unit is used for inputting the frame image into a Faster-RCNN convolution network to obtain a human body pixel block and a corresponding human body tracking frame body in the frame image;
and the comparison unit is used for comparing the human body pixel block with standard data in a preset owner characteristic database so as to identify the human body pixel block belonging to a registered owner.
Preferably, the time calculation module further includes:
the conversion unit is used for converting coordinates of four end points of the human body tracking frame according to the coordinate position of the unmanned aerial vehicle to obtain second coordinates of the four end points in a world coordinate system;
the distance unit is used for solving the distance d between the second coordinate of any endpoint and the building position in the registered owner address data;
and the first time unit is used for dividing the distance d by the preset walking speed of the human body to obtain the first time when the registered owner walks to the building.
Embodiments of the present invention also provide a computer storage medium storing a computer program, where the computer program can be executed by at least one processor, and the elevator control method based on monitoring by an unmanned aerial vehicle is described above.
The elevator control method, the elevator control system and the storage medium based on unmanned aerial vehicle monitoring provided by the embodiment of the invention reduce the waiting time of the elevator spent by the owner returning home and improve the living quality of the owner.
Drawings
Fig. 1 is a flow chart of steps of an elevator control method based on unmanned aerial vehicle monitoring according to the invention;
fig. 2 is a schematic view of the program modules of an elevator control system according to the invention;
fig. 3 is a schematic diagram of a hardware structure of the computer device of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The terminology used in the embodiments of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the examples of the present invention and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
It should be understood that the term "and/or" as used herein is merely one type of association that describes an associated object, meaning that three relationships may exist, e.g., a and/or B may mean: a exists alone, A and B exist simultaneously, and B exists alone. In addition, the character "/" herein generally indicates that the former and latter related objects are in an "or" relationship.
It should be understood that although the terms first, second, etc. may be used to describe the designated key in embodiments of the present invention, the designated key should not be limited to these terms. These terms are only used to distinguish specified keywords from each other. For example, the first specified keyword may also be referred to as the second specified keyword, and similarly, the second specified keyword may also be referred to as the first specified keyword, without departing from the scope of embodiments of the present invention.
The word "if" as used herein may be interpreted as referring to "at … …" or "when … …" or "corresponding to a determination" or "in response to a detection", depending on the context. Similarly, the phrase "if determined" or "if detected (a stated condition or time)" may be interpreted as "when determined" or "in response to a determination" or "when detected (a stated condition or event)" or "in response to a detection (a stated condition or event)", depending on the context.
Referring to fig. 1, an embodiment of the present invention provides an elevator control method based on unmanned aerial vehicle monitoring, including:
step S100 receives image data acquired in real time by a camera mounted on the unmanned aerial vehicle.
Specifically, the video monitoring system is divided from the working mode, and is subject to three generations of technical innovation. The first generation is a traditional analog closed-circuit video monitoring system (CCTV), which has more limitations, can only monitor locally and is basically eliminated by time length. The second generation is an analog-digital monitoring system (DVR), which is a semi-analog-semi-digital scheme taking a digital hard Disk Video Recorder (DVR) as a core, still adopts a coaxial cable as a transmission medium to transmit video signals, belongs to a mainstream and widely adopted cheap video monitoring solution in the current market, but the DVR system still has more limitations:
(1) each camera has to be provided with a transmission cable, which leads to complicated wiring;
(2) the number of the extended cameras is limited by the DVR video recorder, and the extended cameras can be extended by 16 at most;
(3) an external server and management software are needed to realize the control of a plurality of DVRs or monitoring points;
(4) limited remote monitoring and control capabilities;
(5) the DVR recorder has a risk of disk failure, and the video data security is low.
The third generation video monitoring system is a complete IP video monitoring system (IPVS), and compared with the two schemes, the full IP video monitoring system has the advantages that a web server is built in and an Ethernet port Shenzhen WIFI module is directly provided. The camera generates JPEG, MPEG4 or h.264 data files, any authorized client can access, monitor, record and print from any location in the network, and remote access and control is really achieved. The network video monitoring system researched herein is a third-generation video monitoring system IPVS, and already uses an S3C2440 development board and an embedded Linux system as control centers, and realizes an embedded network video monitoring system which is controlled by a system on chip based on an ARM9 processor as a core and can be accessed through WIFI and WEB modes.
The third generation video monitoring system is preferentially adopted, the monitoring system is loaded on the unmanned aerial vehicle, an original fixed camera is changed into a movable camera, and the processor of the scheme of the invention pulls the monitoring video stream collected by the camera in the corresponding area to the video monitoring system.
Step S200 analyzes the image data, and determines whether the registered owner is included in the image data through a convolutional neural network.
Specifically, the fast-RCNN convolutional neural network is adopted for image detection, and the fast-RCNN convolutional neural network belongs to the prior art, so that the method is not repeated herein.
After image detection, the obtained output result is a pixel block containing human body characteristics and a human body tracking frame body at the corresponding position.
In step S300, if the result of the determination is that the registered owner is included, the address data of the registered owner in the preset database is queried.
Specifically, the technician has a database for storing address information of the owner, for example, zhang san, the address is a unit No. 606 a4, and the human body characteristics of the owner, and the image detection output result generated in the foregoing step is delivered to the human body characteristic set in the database for comparison, thereby identifying which owner the human body characteristic block detected by the image detection output result is.
Step S400 obtains the first time when the registered owner walks to the building according to the human body tracking frame output by the convolutional neural network and the building location in the registered owner address data obtained by the query.
Specifically, the distance between the position coordinates of the human body tracking frame output by the convolutional neural network and the building position coordinates in the registered owner address data is calculated, and then the distance is divided by the preset walking speed to estimate the first time when the owner walks to the building where the owner lives, wherein the position coordinates of the human body tracking frame need to be converted first, which is described in detail later.
Step S500, the first time is judged, if the first time is larger than a threshold value, an elevator control command is sent to the elevator control component of the floor where the owner is located in a delayed mode, the elevator control command is used for stopping the elevator to the first floor, and if the first time is smaller than a preset value, the elevator control command is sent to the elevator control component of the floor where the owner is located immediately.
Considering that the elevator for other residents is influenced if the elevator is prematurely descended to the first floor, the first time needs to be judged, and if the first time is less than or equal to a threshold value, an elevator control command is sent to the elevator control component of the floor where the owner is located, wherein the threshold value is the time required for the elevator to descend to the first floor.
In addition, in view of the possibility that the cell may adopt a central elevator control system or an individual elevator control system, if the central elevator control system is used, the elevator control command can be sent to the central elevator control system, and if the individual elevator control system is used, the elevator control command can be sent to the specific building elevator control system.
The processing unit or the processing system executing the solution of the invention is provided with a drive program corresponding to the elevator, so that the processing unit or the processing system sends an elevator control command to the elevator control component, and the control command can be recognized by the elevator control component.
Illustratively, Zhang III, the address is a 606 number unit of A4, the final result of the image recognition in the image recognition step is that the image contains claim III, the owner Zhang III is going to go home, the name of the owner in the recognition result is used as a query key field, address data pulling is initiated to a preset owner address database, the database returns to the 606 number unit of A4, the A4 is extracted at this time, the extracted A4 is packaged together with an elevator control command and is sent to each elevator assembly through a broadcast channel, and after each elevator assembly receives the elevator control command, only the elevator assembly of A4 corresponds to the command.
The elevator control method provided by the embodiment of the invention reduces the elevator waiting time spent by the owner returning home, and improves the living quality of the owner.
Optionally, before the step of receiving image data acquired in real time by a camera mounted on the drone in step S100, the method further includes:
and carrying out handshake communication on the unmanned aerial vehicle, and if the handshake connection is successful, receiving image data acquired in real time by a camera loaded on the unmanned aerial vehicle.
Specifically, the handshake mechanism is a common technical means in the communication field, and the present invention is not described herein again.
Optionally, before the step of analyzing the image data and determining whether the image data contains a registered owner through a convolutional neural network, the method further includes:
extracting a frame image from the image data;
inputting the frame image into a Faster-RCNN convolution network to obtain a human body pixel block and a corresponding human body tracking frame body in the frame image;
and comparing the human body pixel block with standard data in a preset owner characteristic database, and further identifying the human body pixel block belonging to a registered owner.
Specifically, frame image extraction is performed on an image video stream uploaded by the unmanned aerial vehicle by using OpenCV, so that a frame image set is obtained.
Specifically, for a video stream uploaded by the unmanned aerial vehicle, frame image extraction is performed by using opencv to obtain a frame image set, and exemplary codes are as follows:
optionally, in step S300, if the determination result indicates that the registered owner is included, the step of querying the address data of the registered owner in the preset database includes:
and inquiring owner address data associated with the identity information in a preset database according to the identified identity information of the registered owner.
Optionally, the step S400 of obtaining the first time when the registered owner walks to the building according to the human body tracking frame output by the convolutional neural network and the building location in the registered owner address data obtained by querying includes:
step S410, converting coordinates of four end points of the human body tracking frame according to the coordinate position of the unmanned aerial vehicle to obtain second coordinates of the four end points in a world coordinate system;
step S420, calculating the distance d between the second coordinate of any endpoint and the building position in the registered owner address data;
step S430 divides the distance d by a preset human walking speed to obtain a first time when the registered owner walks to the building.
Specifically, the coordinates of the human tracking frame are converted into coordinate data under a world coordinate system through the steps, the distance d is obtained from any end point and the coordinates of the building position where the owner lives, and the first time when the registered owner walks to the building is further obtained.
In addition, referring to fig. 2, an embodiment of the present invention further provides an elevator control system, including:
the image module 100 is used for receiving image data acquired by a camera loaded on the unmanned aerial vehicle in real time;
the analysis module 200 is used for analyzing the image data, and judging whether the image data contains a registered owner through a convolutional neural network;
the query module 300 is configured to query address data of a registered owner in a preset database if the determination result indicates that the registered owner is included;
a time calculation module 400, configured to obtain a first time when the registered owner walks to the building according to the human body tracking frame output by the convolutional neural network and the building location in the registered owner address data obtained by querying;
and the control module 500 is used for judging the first time, if the first time is greater than a threshold value, sending an elevator control command to the elevator control component of the floor where the owner is located in a delayed manner, wherein the elevator control command is used for stopping the elevator to the first floor, and if the first time is less than a preset value, immediately sending the elevator control command to the elevator control component of the floor where the owner is located.
Optionally, the image module 100 is further configured to perform handshake communication with the unmanned aerial vehicle, and if the handshake connection is successful, receive image data acquired in real time by a camera mounted on the unmanned aerial vehicle.
Optionally, the query module 300 further includes:
a frame image unit 310 for extracting a frame image from the image data;
the image detection unit 320 is configured to input the frame image into a fast-RCNN convolutional network to obtain a human body pixel block and a corresponding human body tracking frame in the frame image;
the comparing unit 330 is configured to compare the human body pixel block with standard data in a preset owner feature database, and further identify a human body pixel block belonging to a registered owner.
Optionally, the time calculation module 400 further includes:
the conversion unit 410 is used for converting coordinates of four end points of the human body tracking frame according to the coordinate position of the unmanned aerial vehicle to obtain second coordinates of the four end points in a world coordinate system;
a distance unit 420, configured to calculate a distance d between the second coordinate of any one endpoint and the building location in the registered owner address data;
a first time unit 430, configured to divide the distance d by a preset walking speed of the human body to obtain a first time when the registered owner walks to the building.
Please refer to fig. 3, which is a schematic diagram of a hardware architecture of a computer device according to an embodiment of the present invention. In the present embodiment, the computer device 2 is a device capable of automatically performing numerical calculation and/or information processing in accordance with a preset or stored instruction. The computer device 2 may be a personal computer, a tablet computer, a mobile phone, a smartphone, or a rack server, a blade server, a tower server, or a cabinet server (including an independent server or a server cluster composed of a plurality of servers), and the like, and is configured to provide a virtual client. As shown, the computer device 2 includes at least, but is not limited to, a memory 21, a processor 22, a network interface 23, and an elevator control system 20, communicatively connected to each other by a system bus, wherein:
in this embodiment, the memory 21 includes at least one type of computer-readable storage medium including a flash memory, a hard disk, a multimedia card, a card-type memory (e.g., SD or DX memory, etc.), a Random Access Memory (RAM), a Static Random Access Memory (SRAM), a Read Only Memory (ROM), an Electrically Erasable Programmable Read Only Memory (EEPROM), a Programmable Read Only Memory (PROM), a magnetic memory, a magnetic disk, an optical disk, and the like. In some embodiments, the storage 21 may be an internal storage unit of the computer device 2, such as a hard disk or a memory of the computer device 2. In other embodiments, the memory 21 may also be an external storage device of the computer device 2, such as a plug-in hard disk, a Smart Media Card (SMC), a secure digital (secure digital) SD card, a flash card (FlashCard) or the like provided on the computer device 20, and of course, the memory 21 may also include both an internal storage unit and an external storage device of the computer device 2. In this embodiment, the memory 21 is used for storing an operating system and various kinds of application software installed in the computer device 2, such as program codes of the elevator control system 20. Further, the memory 21 may also be used to temporarily store various types of data that have been output or are to be output.
The network interface 23 may comprise a wireless network interface or a limited network interface, and the network interface 23 is typically used for establishing a communication connection between the computer device 2 and other electronic apparatuses. For example, the network interface 23 is used to connect the computer device 2 with an external terminal necklace, establish a data transmission channel and a communication connection between the computer device 2 and an external interrupt, and the like via a network. The network may be a wireless or wired network such as an Intranet (Intranet), the Internet (Internet), a global system for mobile communications (GSM), Wideband Code Division Multiple Access (WCDMA), a 4G network, a 5G network, Bluetooth (Bluetooth), or Wi-Fi.
In this embodiment, the elevator control system 20 stored in the memory 21 can also be divided into one or more program modules, which are stored in the memory 21 and executed by one or more processors (in this embodiment, the processor 22) to accomplish the present invention.
In addition, the present embodiment also provides a computer-readable storage medium, such as a flash memory, a hard disk, a multimedia card, a card-type memory (e.g., SD or DX memory, etc.), a Random Access Memory (RAM), a Static Random Access Memory (SRAM), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), a programmable read-only memory (PROM), a magnetic memory, a magnetic disk, an optical disk, a server, an App application store, etc., on which a computer program is stored, which when executed by a processor implements a corresponding function. The computer-readable storage medium of the present embodiment is used to store an elevator control system 20, which when executed by a processor implements the elevator control method of the present invention.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner.
The above description is only a preferred embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (10)
1. An elevator control method based on unmanned aerial vehicle monitoring is characterized by comprising the following steps:
receiving image data acquired in real time by a camera loaded on an unmanned aerial vehicle;
analyzing the image data, and judging whether the registered owner is contained in the image data through a convolutional neural network;
if the judgment result is that the registered owner is included, inquiring the address data of the registered owner in a preset database;
obtaining the first time when the registered owner walks to the building according to the human body tracking frame output by the convolutional neural network and the building position in the registered owner address data obtained by inquiry;
and judging the first time, if the first time is greater than a threshold value, delaying to send an elevator control command to the elevator control component of the floor where the owner is located, wherein the elevator control command is used for stopping the elevator to the first floor, and if the first time is less than a preset value, immediately sending the elevator control command to the elevator control component of the floor where the owner is located.
2. The elevator control method based on unmanned aerial vehicle monitoring of claim 1, wherein before the step of receiving image data collected in real time by a camera mounted on the unmanned aerial vehicle, the method further comprises:
and carrying out handshake communication on the unmanned aerial vehicle, and if the handshake connection is successful, receiving image data acquired in real time by a camera loaded on the unmanned aerial vehicle.
3. The elevator control method based on unmanned aerial vehicle monitoring of claim 1, wherein before the step of analyzing the image data therein and determining whether the image data contains a registered owner through a convolutional neural network, the method further comprises:
extracting a frame image from the image data;
inputting the frame image into a Faster-RCNN convolution network to obtain a human body pixel block and a corresponding human body tracking frame body in the frame image;
and comparing the human body pixel block with standard data in a preset owner characteristic database, and further identifying the human body pixel block belonging to a registered owner.
4. The elevator control method based on unmanned aerial vehicle monitoring of claim 3, wherein if the result of the determination is that the registered owner is included, the step of querying address data of the registered owner in a preset database comprises:
and inquiring owner address data associated with the identity information in a preset database according to the identified identity information of the registered owner.
5. The unmanned aerial vehicle monitoring-based elevator control method according to claim 1, wherein the step of obtaining the first time when the registered owner walks to the building where the registered owner is located, based on the human body tracking frame outputted from the convolutional neural network and the building location in the registered owner address data obtained by querying, comprises:
converting coordinates of four end points of the human body tracking frame according to the coordinate position of the unmanned aerial vehicle to obtain second coordinates of the four end points in a world coordinate system;
calculating the distance d between the second coordinate of any endpoint and the building position in the registered owner address data;
dividing the distance d by a preset walking speed of the human body to obtain a first time when the registered owner walks to the building.
6. An elevator control system, comprising:
the image module is used for receiving image data acquired by a camera loaded on the unmanned aerial vehicle in real time;
the analysis module is used for analyzing the image data and judging whether the image data contains a registered owner through a convolutional neural network;
the query module is used for querying the address data of the registered owner in a preset database if the judgment result shows that the registered owner is included;
the time calculation module is used for obtaining the first time when the registered owner walks to the building according to the human body tracking frame body output by the convolutional neural network and the building position in the registered owner address data obtained by inquiry;
and the control module is used for judging the first time, if the first time is greater than a threshold value, sending an elevator control command to the elevator control component of the floor where the owner is located in a delayed manner, wherein the elevator control command is used for stopping the elevator to the first floor, and if the first time is less than preset, immediately sending the elevator control command to the elevator control component of the floor where the owner is located.
7. The elevator control system of claim 6, wherein the image module is further configured to communicate with the drone in a handshake manner, and if the handshake connection is successful, receive image data acquired by a camera mounted on the drone in real time.
8. The elevator control system of claim 6, wherein the query module further comprises:
a frame image unit for extracting a frame image from the image data;
the image detection unit is used for inputting the frame image into a Faster-RCNN convolution network to obtain a human body pixel block and a corresponding human body tracking frame body in the frame image;
and the comparison unit is used for comparing the human body pixel block with standard data in a preset owner characteristic database so as to identify the human body pixel block belonging to a registered owner.
9. The unmanned aerial vehicle monitoring-based elevator control method of claim 6, wherein the time calculation module further comprises:
the conversion unit is used for converting coordinates of four end points of the human body tracking frame according to the coordinate position of the unmanned aerial vehicle to obtain second coordinates of the four end points in a world coordinate system;
the distance unit is used for solving the distance d between the second coordinate of any endpoint and the building position in the registered owner address data;
and the first time unit is used for dividing the distance d by the preset walking speed of the human body to obtain the first time when the registered owner walks to the building.
10. A computer storage medium, characterized in that the computer storage medium stores a computer program executable by at least one processor to perform a method of elevator control based on drone monitoring according to claims 1 to 5.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911097923.2A CN111232773A (en) | 2019-11-12 | 2019-11-12 | Elevator control method and system based on unmanned aerial vehicle monitoring and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911097923.2A CN111232773A (en) | 2019-11-12 | 2019-11-12 | Elevator control method and system based on unmanned aerial vehicle monitoring and storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN111232773A true CN111232773A (en) | 2020-06-05 |
Family
ID=70869332
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201911097923.2A Withdrawn CN111232773A (en) | 2019-11-12 | 2019-11-12 | Elevator control method and system based on unmanned aerial vehicle monitoring and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111232773A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113104710A (en) * | 2021-04-09 | 2021-07-13 | 山西工程职业学院 | Intelligent maintenance equipment for head sheave of mine hoist |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107600411A (en) * | 2017-08-16 | 2018-01-19 | 广州唯品会研究院有限公司 | Express delivery allocator and device based on unmanned plane |
| CN107796402A (en) * | 2017-10-24 | 2018-03-13 | 汤庆佳 | Robot and intelligent the door body shared system and control method of unmanned plane integration and cooperation |
| CN109132742A (en) * | 2017-06-15 | 2019-01-04 | 株式会社日立大厦系统 | Elevator user guides system |
| CN109195895A (en) * | 2016-05-31 | 2019-01-11 | 因温特奥股份公司 | remote elevator monitoring and detection |
| CN109502437A (en) * | 2017-09-15 | 2019-03-22 | 株式会社日立制作所 | The building management system device supporting robot and linking with it |
| US20190325548A1 (en) * | 2018-04-18 | 2019-10-24 | Carrier Corporation | Unmanned aerial vehicle people detection and evacuation systems |
-
2019
- 2019-11-12 CN CN201911097923.2A patent/CN111232773A/en not_active Withdrawn
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109195895A (en) * | 2016-05-31 | 2019-01-11 | 因温特奥股份公司 | remote elevator monitoring and detection |
| CN109132742A (en) * | 2017-06-15 | 2019-01-04 | 株式会社日立大厦系统 | Elevator user guides system |
| CN107600411A (en) * | 2017-08-16 | 2018-01-19 | 广州唯品会研究院有限公司 | Express delivery allocator and device based on unmanned plane |
| CN109502437A (en) * | 2017-09-15 | 2019-03-22 | 株式会社日立制作所 | The building management system device supporting robot and linking with it |
| CN107796402A (en) * | 2017-10-24 | 2018-03-13 | 汤庆佳 | Robot and intelligent the door body shared system and control method of unmanned plane integration and cooperation |
| US20190325548A1 (en) * | 2018-04-18 | 2019-10-24 | Carrier Corporation | Unmanned aerial vehicle people detection and evacuation systems |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113104710A (en) * | 2021-04-09 | 2021-07-13 | 山西工程职业学院 | Intelligent maintenance equipment for head sheave of mine hoist |
| CN113104710B (en) * | 2021-04-09 | 2022-06-21 | 山西工程职业学院 | Intelligent maintenance equipment for head sheave of mine hoist |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US8938092B2 (en) | Image processing system, image capture apparatus, image processing apparatus, control method therefor, and program | |
| US20140072188A1 (en) | Fingerprint identification and verification system and method thereof | |
| US10999503B2 (en) | Pre-roll image capture implemented by a power limited security device | |
| CN110848897B (en) | Intelligent air conditioner adjusting method and computer readable storage medium | |
| CN110705356B (en) | Function control method and related equipment | |
| WO2015090099A1 (en) | Algorithm configuration method and system | |
| CN111191507A (en) | Safety early warning analysis method and system for smart community | |
| KR20180103191A (en) | Method and system for extracting characteristic information | |
| CN110415403B (en) | Control method of intelligent lockset system based on edge calculation | |
| CN108401247B (en) | Method for controlling Bluetooth device, electronic device and storage medium | |
| CN110807886A (en) | Community security early warning method and system | |
| CN111176967A (en) | Query method and device for remote out-of-band SSD log information and BMC | |
| CN110697517A (en) | Elevator control method, system and storage medium based on cell | |
| CN111232773A (en) | Elevator control method and system based on unmanned aerial vehicle monitoring and storage medium | |
| CN111783910A (en) | Building project management method, electronic equipment and related products | |
| CN111966647A (en) | Cloud storage method and device for small files, server and storage medium | |
| CN104902033B (en) | Log in address recording method and device | |
| CN110980454A (en) | Automatic control method and device for elevator in intelligent community and readable storage medium | |
| CN201523394U (en) | Monitoring system based on network technology | |
| CN111199182A (en) | Lost object method, system and storage medium based on intelligent community | |
| CN113516006A (en) | Efficient information processing method and system for intelligent robot | |
| CN112039936B (en) | Data transmission method, first data processing equipment and monitoring system | |
| CN112016380A (en) | Wild animal monitoring method and system | |
| US20230164431A1 (en) | Image capturing apparatus, processing system, and control method | |
| CN111368711A (en) | Method and device for adjusting lifting of display screen |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| WW01 | Invention patent application withdrawn after publication | ||
| WW01 | Invention patent application withdrawn after publication |
Application publication date: 20200605 |