WO2020238661A1 - 一种电梯调度方法、装置、计算机设备和存储介质 - Google Patents
一种电梯调度方法、装置、计算机设备和存储介质 Download PDFInfo
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- WO2020238661A1 WO2020238661A1 PCT/CN2020/090712 CN2020090712W WO2020238661A1 WO 2020238661 A1 WO2020238661 A1 WO 2020238661A1 CN 2020090712 W CN2020090712 W CN 2020090712W WO 2020238661 A1 WO2020238661 A1 WO 2020238661A1
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- floor
- image
- elevator
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Classifications
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- 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
- B66B1/3476—Load weighing or car passenger counting devices
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- 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
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/50—Context or environment of the image
- G06V20/52—Surveillance or monitoring of activities, e.g. for recognising suspicious objects
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
- G06V40/16—Human faces, e.g. facial parts, sketches or expressions
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- 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
Definitions
- This application relates to the field of artificial intelligence technology, and in particular to an elevator dispatching method, device, computer equipment and storage medium.
- this application provides an elevator dispatching method, device, computer equipment and storage medium.
- an elevator dispatching method including:
- the waiting instruction of the floor is cleared.
- an elevator dispatching device including:
- the instruction acquisition unit is used to receive the waiting instruction of the floor
- An image acquisition unit for acquiring an image in the waiting room of the floor
- the feature acquisition unit is configured to acquire the first feature in the image in the waiting room of the floor to form the first feature vector of the image in the waiting room of the floor;
- the face recognition unit is used to input the first feature vector of the image in the waiting room of the floor to the first machine learning model, and the first machine learning model outputs whether the image in the waiting room of the floor is Results containing face information;
- the instruction clearing unit is used for clearing the waiting instruction of the floor if the image in the waiting room of the floor does not contain face information.
- a computer device including a memory and a processor, the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the processor executes the foregoing Describe the steps of the elevator dispatching method.
- a storage medium storing computer-readable instructions.
- the computer-readable instructions are executed by one or more processors, the one or more processors execute the steps of the elevator scheduling method described above. .
- the above-mentioned elevator dispatching method, device, computer equipment and storage medium optimize the operation process of the elevator, shorten the time of each operation of the elevator, and improve the operation efficiency of the elevator, so that the user will not appear in the elevator during the waiting process.
- the doors are opened by braking on floors where there is no waiting elevator, which greatly shortens the waiting time for users and improves the waiting experience for users.
- Figure 1 is an implementation environment diagram of an elevator dispatching method provided in an embodiment.
- Fig. 2 is a flowchart showing an elevator dispatching method according to an exemplary embodiment.
- Fig. 3 is a flowchart of another elevator dispatching method according to the embodiment corresponding to Fig. 2.
- Fig. 4 is a specific implementation flowchart of the first machine learning model training method shown according to the embodiment corresponding to Fig. 2 or Fig. 3.
- Fig. 5 is a flowchart of another elevator dispatching method according to the embodiment corresponding to Fig. 2.
- Fig. 6 is a flowchart of another elevator dispatching method according to the embodiment corresponding to Fig. 2.
- Fig. 7 is a specific implementation flowchart of step S110 in the elevator dispatching method according to the embodiment corresponding to Fig. 6.
- Fig. 8 is a flowchart of another elevator dispatching method according to the embodiment corresponding to Fig. 7.
- FIG. 9 is a specific implementation flowchart of the second machine learning model training method shown according to the embodiment corresponding to FIG. 8.
- Fig. 10 is a block diagram showing an elevator dispatching device according to an exemplary embodiment.
- Fig. 11 schematically shows an example block diagram of an electronic device for implementing the above-mentioned elevator dispatching method.
- Figure 12 schematically shows a computer-readable storage medium for implementing the above-mentioned elevator dispatching method.
- FIG. 1 is an implementation environment diagram of an elevator dispatching method provided in an embodiment. As shown in FIG. 1, the implementation environment includes a computer device 100, an elevator terminal 200, and an image device 300.
- the computer device 100 is an elevator dispatching device, for example, a computer, a server, and the like of an elevator dispatching center.
- the elevator terminal 200 is a terminal in the elevator and in the waiting room for the elevator to control the elevator up and down floors, such as a control board with up and down buttons or floor buttons in the waiting room or a control board with floor buttons in the elevator car.
- the image device 300 is a device arranged in an elevator to obtain a real-time image in the elevator or arranged in a waiting room to obtain a real-time image in the waiting room.
- the computer device 100 first obtains the image in the waiting room of the floor through the imaging device 300, After the image in the waiting room, extract the first feature in the image in the waiting room of the floor to form the first feature vector of the image in the waiting room of the floor, and then combine the first feature
- the vector input machine learning model, the first machine learning model determines whether the image in the waiting room of the floor contains face information. If it does not contain the face information, it proves that there is no waiting elevator on the floor.
- the waiting instruction of the elevator terminal 200 of the floor needs to be cleared.
- the computer device 100 may be a server, a host, a server cluster, etc., but is not limited thereto.
- the imaging device 300 may be a camera, a video camera, a camera, etc., but is not limited thereto.
- the computer device 100 and the elevator terminal 200 and the image device 300 may be connected via Bluetooth, USB (Universal Serial Bus, Universal Serial Bus) or other communication connection methods, which is not limited in this application.
- an elevator dispatching method is proposed.
- the elevator dispatching method can be applied to the above-mentioned computer device 100, and specifically may include the following steps:
- Step S110 receiving a waiting instruction for a floor
- Step S120 acquiring an image in the waiting room of the floor
- the image in the waiting room of the floor is acquired when the elevator is about to decelerate to stop at the floor. Because in one case, if after initiating the floor waiting instruction, the user temporarily leaves because of some trivial matters (such as throwing garbage, going home and picking up keys, etc.), before the elevator runs to the floor, he returns In the waiting room, at this time, it cannot be simply judged that the user has given up waiting. Therefore, when judging whether the image contains facial information, the image obtained when the elevator is about to decelerate and stop at the floor shall prevail.
- acquiring the image in the waiting room of the floor may also be:
- the image in the waiting room of the floor is acquired every predetermined time.
- the predetermined time may be 0.5 seconds, 2 seconds, 7 seconds, etc., which is not limited in this application. Compared with the method in the above embodiment, the method is more conducive to making more detailed planning and scheduling for the elevator.
- Step S130 acquiring a first feature in the image in the waiting room of the floor, and composing the first feature vector of the image in the waiting room of the floor;
- the first feature includes an average RGB value, an average pixel value, etc. of the acquired image.
- the first feature vector is composed of the average RGB value, average pixel value, etc. of the acquired image, for example, it can be expressed as
- r, g, and b respectively represent the average R value, average G value, and average B value in the average RGB value of the acquired image.
- the r, g, b, and The value of p ranges from 0 to 255.
- the first feature includes an average RGB value, an average pixel value, etc. in a predetermined area of the acquired image.
- the predetermined area is a place where it is often easy to capture a human face, such as the four sides of the image or the center of the image.
- the first feature includes an average RGB value, an average pixel value, etc. in a plurality of predetermined regions of the acquired image.
- the plurality of predetermined areas are, for example, four areas of the image after being equally divided into four.
- the first feature vector can be expressed as
- a n [r n g n b n p n ]
- n 1, 2, 3, 4, A 1 to A 4 respectively represent the four regions, p n represents the average pixel value of the image of the region, and r n , g n , and b n respectively represent the region
- the average R value, average G value, and average B value in the average RGB value of the image, and the value ranges of r n , g n , b n and p n are all between 0 and 255.
- Step S140 Input the first feature vector of the image in the waiting room of the floor to a first machine learning model, and the first machine learning model outputs whether the image in the waiting room of the floor contains face information the result of;
- step S150 if the image in the waiting room of the floor does not contain face information, the waiting instruction of the floor is cleared.
- the first machine learning model determines that the image in the waiting room of the floor does not contain face information, it can be proved that no one is waiting on the floor. At that time, the computer 100 will clear the waiting instructions for the floor. If the first machine learning model determines that the image in the waiting room of the floor contains face information, it can be proved that someone in the floor is waiting for the elevator, and then the computer 100 will not clear the image of the floor. Waiting instructions, the elevator will arrive at the said floor and stop according to the program instructions, so as to allow the waiting users to board the elevator.
- the main purpose of this application is to optimize the operation process of the elevator, shorten the operation time of the elevator, and improve the operation efficiency. Therefore, in the technical solution of the present application, after receiving the waiting instruction of the floor, it starts to acquire the image in the waiting room of the floor and judges whether the image contains face information according to the image. If it contains facial information, it indicates that someone is waiting. If it does not include facial information, it indicates that no one is waiting.
- the waiting instructions of the floor can be cleared, and other tasks can be continued without stopping at the floor.
- Fig. 3 shows that in an embodiment, after step S150 in the embodiment corresponding to Fig. 2, the elevator dispatching method may further include the following steps.
- Step S210 receiving floor instructions in the elevator
- Step S220 acquiring an image in the elevator
- Step S230 Acquire a first feature in the image in the elevator to form a first feature vector of the image in the elevator;
- Step S240 input the first feature vector of the image in the elevator to a first machine learning model, and the first machine learning model outputs the result of whether the image in the elevator contains face information;
- Step S250 If the image in the elevator does not contain face information, clear all floor instructions in the elevator.
- obtaining the image in the elevator may be:
- the elevator executes the door closing command, it indicates that the user on the floor where the destination elevator is located has left the elevator, and the elevator is about to perform the next task. At this time, if the image in the elevator contains human face information, it indicates that the elevator There are also users who need to go to other floors. If the image in the elevator does not contain human face information, it means that there is no one in the elevator. At this time, all floor instructions in the elevator can be cleared.
- judging whether the image in the elevator contains face information can also be implemented through the first machine learning model.
- Fig. 4 shows the training method of the first machine learning model in the corresponding embodiment in Figs. 2 and 3 in an embodiment:
- Step S41 forming a first image sample set with a set containing positive samples and negative samples, wherein the positive samples are images that contain face information, and the negative samples are images that do not contain face information;
- Step S42 acquiring a first feature of each image sample in the first image sample set, and composing a first feature vector of each image sample in the first image sample set;
- Step S43 Input the first feature vector of each image sample in the first image sample set one by one into the first machine learning model for learning, and the first machine learning model outputs the judgment result of whether it contains face information, if For a positive sample output that does not contain a judgment result that conforms to the face information, or for a negative sample output that contains a judgment result that contains face information, the first machine learning model is adjusted so that the first machine learning model outputs the opposite judgment result.
- the learning method is: constantly changing the connection weight of the network under the stimulation of external input samples.
- the essence of learning is to dynamically adjust the weight of each connection. Since the expected output is known, if the output of the machine learning model does not match the expected output, the weight of each connection is automatically adjusted until the output obtained is consistent with the expected output. In this way, the first machine learning model is trained. When the first machine learning model is well trained, as long as the first feature vector extracted from the images in the waiting room of the floor is input to the first machine learning model, the first machine learning model will output the Whether the image in the waiting room on the floor contains face information.
- FIG. 5 shows that in an embodiment, after step S150 in the embodiment corresponding to Figure 2, the elevator dispatching method may further include the following steps.
- Step S310 receiving floor instructions in the elevator
- Step S320 Obtain the load weight in the elevator
- Step S330 judging whether the load weight exceeds a predetermined starting threshold
- Step S340 If the load weight does not exceed a predetermined starting threshold, all floor instructions in the elevator are cleared.
- the elevator can also confirm the load in the elevator.
- the load weight of the elevator in the empty state and the load weight in the occupant state is different.
- the load in the elevator The weight will increase, so it can be confirmed by the load in the elevator, that is, the elevator can be started only when the load of the elevator exceeds a certain weight, that is, if the load does not exceed the predetermined starting threshold, the elevator is cleared All floor instructions in the elevator; if the load exceeds the predetermined starting threshold, all floor instructions in the elevator are not cleared, and the elevator starts to run according to the floor instructions in the elevator.
- obtaining the load weight in the elevator may be obtained in real time after the elevator door is opened, or may be obtained when the elevator door is closed or before starting after the door is closed.
- the reason why it is not obtained in the starting or braking phase is because the materials in the car are in a weightless state at this time, and the load weight obtained at this time is not accurate.
- the predetermined starting threshold can be 20 kg, 25 kg, 30 kg, etc., which can be set according to specific conditions, but should not be set too high, because in the environment where the elevator is used, it is necessary to consider that school-age children use the elevator alone. For the same reason, it should not be set too low to prevent young children (with lighter weight) from entering the elevator by mistake, and the elevator recognizes a person and runs by mistake. This application does not limit it here.
- Fig. 6 shows that in an embodiment, after step S110 in the embodiment corresponding to Fig. 2, the elevator dispatching method may further include the following steps:
- Step S410 obtaining the load weight in the elevator
- Step S420 judging whether the load weight exceeds the difference between a predetermined load-bearing threshold and a predetermined load-bearing difference, where the load-bearing difference is used to indicate the value by which the elevator load will be increased;
- Step S430 If the load weight exceeds the difference between the predetermined load-bearing threshold and the predetermined load-bearing difference, the elevator is controlled to stop only according to the floor instruction in the elevator.
- this solution also sets a load-bearing difference value, where the load-bearing difference value is used to indicate the value at which the elevator load will be increased. Specifically, it is the weight that is about to enter the elevator, causing the elevator's load to increase.
- the load weight in the elevator does not reach the load-bearing threshold
- the load-bearing weight does not exceed the difference between the predetermined load-bearing threshold and the predetermined load-bearing difference
- the load-bearing weight does not exceed the difference between the predetermined load-bearing threshold and the predetermined load-bearing difference, it indicates that the load in the elevator is surplus, and the remaining load-bearing capacity of the elevator is sufficient to load At least one person, that is, at least one person entering the elevator increases the load of the elevator, and the elevator will not be overloaded.
- the load threshold of the elevator is equal to If the difference in the load-bearing weight in the elevator is less than the load-bearing difference, it indicates that the load in the elevator is large, and the remaining load-bearing capacity of the elevator is small. If it is less than the value that will increase the load-bearing value of the elevator, then only follow the floor instructions in the elevator Stop, this can shorten the elevator running time, improve the efficiency of elevator operation, reduce the user's waiting and boarding time, and optimize the user's experience.
- the predetermined load-bearing threshold can be 180 kg, 190 kg, 200 kg, etc., which can be specifically set according to the specifications and usage of the elevator. It is the maximum load value that the car can bear during the operation of the elevator. The application is not limited here.
- the predetermined load-bearing difference can be 40 kg, 50 kg, 55 kg, etc., and should be the weight of a normal adult, which can be specifically set according to the elevator usage, which is not limited in this application.
- step S110 may include the following steps:
- Step S111 acquiring images in the waiting room of the floor from different directions, where the different directions are two intersecting directions;
- Step S112 Synthesize the two images in different directions into a stereo image in the waiting room of the floor.
- Fig. 8 shows that in an embodiment, after step S110 in the embodiment corresponding to Fig. 7 and before step S420, the elevator dispatching method may further include the following steps.
- Step S530 If the first machine learning model outputs the result that the image in the waiting room on the floor contains face information, locate all human figures in the stereo image in the waiting room on the floor;
- Step S540 Acquire the second feature in each portrait in the stereo image in the waiting room of the floor, respectively, to form a second feature vector of the face;
- the second feature includes the height and measurements of the portrait.
- Step S550 input the second feature vector of the human face to a second machine learning model, and the second machine learning model outputs the weight value of the human portrait;
- Step S560 Determine the predetermined weight-bearing difference according to the weight value of the human portrait.
- the smallest value among the weight values of the all human figures may be used as the predetermined load-bearing difference value, so that when the elevator stops at the floor, the waiting The one with the lightest weight among the elevator users can take the elevator to leave, which improves the capacity of the elevator.
- the maximum value among the weight values of the all human figures may be used as the predetermined weight-bearing difference, because in daily life, when an elevator stops at the floor
- the waiting user with the lightest weight may not be the first to enter the car.
- the waiting user with the lightest weight will have There is a high probability of giving up entering the car, so time is wasted. Therefore, in this embodiment, the maximum value among the weight values of the all human figures is used as the predetermined weight bearing difference.
- FIG. 9 shows the training method of the second machine learning model in the embodiment corresponding to FIG. 8 in an embodiment:
- Step S51 Obtain a second image sample set of stereo images in the waiting room of the floor including the portrait, and each stereo image sample in the second image sample set has a weight label attached in advance;
- Step S52 acquiring a second feature of each stereo image sample in the second image sample set, and composing a second feature vector of each image sample in the first image sample set;
- Step S53 Input the second feature vector of each stereo image sample in the second image sample set into the second machine learning model one by one, and the second machine learning model outputs the judged weight, which is compared with the weight label, such as If they are not consistent, adjust the second machine learning model to make the weight output by the machine learning model consistent with the label.
- the weight of the portrait is known. Use the known result as the desired output to train the machine learning model.
- the learning method is: constantly changing the connection weight of the network under the stimulation of the external input sample.
- the essence of learning is to dynamically adjust the weight of each connection. Since the expected output is known, if the output of the machine learning model does not match the expected output, the weight of each connection is automatically adjusted until the output obtained is consistent with the expected output. In this way, the second machine learning model is trained. When the second machine learning model is trained well enough, as long as the second feature vector extracted from the images in the waiting room of the floor is input to the second machine learning model, the second machine learning model will output the portrait Weight.
- an elevator dispatching device may be integrated into the aforementioned computer equipment 100, and may specifically include an instruction acquisition unit 110, an image acquisition unit 120, and a feature acquisition unit. 130.
- the face recognition unit 140 and the instruction clearing unit 150 may be provided.
- the instruction acquisition unit 110 is used to receive a waiting instruction for a floor
- the image acquisition unit 120 is used to acquire the image in the waiting room of the floor;
- the feature acquisition unit 130 is configured to acquire the first feature in the image in the waiting room of the floor, and compose the first feature vector of the image in the waiting room of the floor;
- the face recognition unit 140 is configured to input the first feature vector of the image in the waiting room on the floor into a first machine learning model, and the first machine learning model outputs the image in the waiting room on the floor Whether it contains the result of face information;
- the instruction clearing unit 150 is configured to clear the waiting instruction of the floor if the image in the waiting room of the floor does not contain face information.
- modules or units of the device for action execution are mentioned in the above detailed description, this division is not mandatory.
- the features and functions of two or more modules or units described above may be embodied in one module or unit.
- the features and functions of a module or unit described above can be further divided into multiple modules or units to be embodied.
- the exemplary embodiments described herein can be implemented by software, or can be implemented by combining software with necessary hardware. Therefore, the technical solution according to the embodiments of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (can be a CD-ROM, U disk, mobile hard disk, etc.) or on the network , Including several instructions to make a computing device (which may be a personal computer, a server, a mobile terminal, or a network device, etc.) execute the method according to the embodiment of the present disclosure.
- a computing device which may be a personal computer, a server, a mobile terminal, or a network device, etc.
- an electronic device capable of implementing the above method.
- the electronic device 500 according to this embodiment of the present application will be described below with reference to FIG. 11.
- the electronic device 500 shown in FIG. 11 is only an example, and should not bring any limitation to the function and scope of use of the embodiments of the present application.
- the electronic device 500 is represented in the form of a general-purpose computing device.
- the components of the electronic device 500 may include, but are not limited to: the aforementioned at least one processing unit 510, the aforementioned at least one storage unit 520, and a bus 530 connecting different system components (including the storage unit 520 and the processing unit 510).
- the storage unit stores program code, and the program code can be executed by the processing unit 510, so that the processing unit 510 executes the various exemplary methods described in the “exemplary method” section of this specification.
- the processing unit 510 may execute step S110 as shown in FIG.
- step S120 obtain an image in a waiting room of the floor
- step S130 obtain The first feature in the image in the waiting room constitutes the first feature vector of the image in the waiting room of the floor
- step S140 the first feature vector of the image in the waiting room of the floor is input to The first machine learning model, the first machine learning model outputs the result of whether the image in the waiting room on the floor contains face information
- step S150 if the image in the waiting room on the floor does not contain a person Face information, clear the waiting instructions of the floor.
- the storage unit 520 may include a readable medium in the form of a volatile storage unit, such as a random access storage unit (RAM) 5201 and/or a cache storage unit 5202, and may further include a read-only storage unit (ROM) 5203.
- RAM random access storage unit
- ROM read-only storage unit
- the storage unit 520 may also include a program/utility tool 5204 having a set (at least one) program module 5205.
- program module 5205 includes but is not limited to: an operating system, one or more application programs, other program modules, and program data, Each of these examples or some combination may include the implementation of a network environment.
- the bus 530 may represent one or more of several types of bus structures, including a storage unit bus or a storage unit controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local area using any bus structure among multiple bus structures. bus.
- the electronic device 500 may also communicate with one or more external devices 700 (such as keyboards, pointing devices, Bluetooth devices, etc.), and may also communicate with one or more devices that enable a user to interact with the electronic device 500, and/or communicate with Any device (such as a router, modem, etc.) that enables the electronic device 500 to communicate with one or more other computing devices. This communication can be performed through an input/output (I/O) interface 550.
- the electronic device 500 may also communicate with one or more networks (for example, a local area network (LAN), a wide area network (WAN), and/or a public network, such as the Internet) through the network adapter 560.
- networks for example, a local area network (LAN), a wide area network (WAN), and/or a public network, such as the Internet
- the network adapter 560 communicates with other modules of the electronic device 500 through the bus 530.
- other hardware and/or software modules can be used in conjunction with the electronic device 500, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives And data backup storage system, etc.
- the exemplary embodiments described herein can be implemented by software, or can be implemented by combining software with necessary hardware. Therefore, the technical solution according to the embodiments of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (can be a CD-ROM, U disk, mobile hard disk, etc.) or on the network , Including several instructions to make a computing device (which may be a personal computer, a server, a terminal device, or a network device, etc.) execute the method according to the embodiments of the present disclosure.
- a computing device which may be a personal computer, a server, a terminal device, or a network device, etc.
- a computer-readable storage medium is also provided.
- the storage medium may be non-volatile or volatile, and a storage medium capable of implementing the above method of this specification is stored thereon.
- Program product In some possible implementation manners, various aspects of the present application can also be implemented in the form of a program product, which includes program code. When the program product runs on a terminal device, the program code is used to enable the The terminal device executes the steps according to various exemplary embodiments of the present application described in the above-mentioned "Exemplary Method" section of this specification.
- a program product 600 for implementing the above method according to an embodiment of the present application is described. It can adopt a portable compact disk read-only memory (CD-ROM) and include program code, and can be installed in a terminal device, For example, running on a personal computer.
- CD-ROM compact disk read-only memory
- the program product of this application is not limited to this.
- the readable storage medium can be any tangible medium that contains or stores a program, and the program can be used by or combined with an instruction execution system, device, or device.
- the program product can use any combination of one or more readable media.
- the readable medium may be a readable signal medium or a readable storage medium.
- the readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or a combination of any of the above. More specific examples (non-exhaustive list) of readable storage media include: electrical connections with one or more wires, portable disks, hard disks, random access memory (RAM), read only memory (ROM), erasable Type programmable read only memory (EPROM or flash memory), optical fiber, portable compact disk read only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination of the above.
- the computer-readable signal medium may include a data signal propagated in baseband or as a part of a carrier wave, and readable program code is carried therein. This propagated data signal can take many forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the foregoing.
- the readable signal medium may also be any readable medium other than a readable storage medium, and the readable medium may send, propagate, or transmit a program for use by or in combination with the instruction execution system, apparatus, or device.
- the program code contained on the readable medium can be transmitted by any suitable medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination of the foregoing.
- the program code used to perform the operations of this application can be written in any combination of one or more programming languages.
- the programming languages include object-oriented programming languages—such as Java, C++, etc., as well as conventional procedural Programming language-such as "C" language or similar programming language.
- the program code can be executed entirely on the user's computing device, partly on the user's device, executed as an independent software package, partly on the user's computing device and partly executed on the remote computing device, or entirely on the remote computing device or server Executed on.
- the remote computing device can be connected to a user computing device through any kind of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computing device (for example, using Internet service providers) Business to connect via the Internet).
- LAN local area network
- WAN wide area network
- Internet service providers Internet service providers
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Abstract
Description
Claims (20)
- 一种电梯调度方法,其中,所述方法包括:接收一楼层的候梯指令;获取所述楼层的候梯间内的图像;获取所述楼层的候梯间内的图像中的第一特征,组成所述楼层的候梯间内的图像的第一特征向量;将所述楼层的候梯间内的图像的第一特征向量输入到第一机器学习模型,所述第一机器学习模型输出所述楼层的候梯间内的图像是否包含人脸信息的结果;若所述楼层的候梯间内的图像中不包含人脸信息,清除所述楼层的候梯指令。
- 如权利要求1所述的方法,其中,在清除所述楼层的候梯指令之后,所述方法还包括:接收电梯内的楼层指令;获取所述电梯内的图像;获取所述电梯内的图像中的第一特征,组成所述电梯内的图像的第一特征向量;将所述电梯内的图像的第一特征向量输入到第一机器学习模型,所述第一机器学习模型输出所述电梯内的图像是否包含人脸信息的结果;若所述电梯内的图像中不包含人脸信息,清除所述电梯内的所有楼层指令。
- 如权利要求1或2所述的方法,其中,所述第一机器学习模型如下训练出:用包含正样本和负样本的集合构成第一图像样本集,其中,所述正样本为包含人脸信息的图像,所述负样本为不包含人脸信息的图像;获取所述第一图像样本集中的每一个图像样本的第一特征,组成所述第一图像样本集中的每一个图像样本的第一特征向量;将所述第一图像样本集中的每一个图像样本的第一特征向量逐一输入第一机器学习模型中进行学习,所述第一机器学习模型输出是否包含人脸信息的判断结果,如果对于正样本输出不包含符合人脸信息的判断结果,或对于负样本输出包含人脸信息的判断结果,调整第一机器学习模型,使第一机器学习模型输出相反判断结果。
- 如权利要求1所述的方法,其中,在清除所述楼层的候梯指令之后,所述方法还包括:接收电梯内的楼层指令;获取所述电梯内的载重重量;判断所述载重重量是否超过预定启动阈值;若所述载重重量未超过预定启动阈值,清除所述电梯内的所有楼层指令。
- 如权利要求1所述的方法,其中,在接收楼层的候梯指令之后,所述方法还包括:获取所述电梯内的载重重量;判断所述载重重量是否超过预定承重阈值与预定承重差值之差,其中,所述承重差值用于指示将增加电梯载重的值;若所述载重重量超过预定承重阈值与预定承重差值之差,控制所述电梯仅按照电梯内的楼层指令停靠。
- 如权利要求5所述的方法,其中,获取所述楼层的候梯间内的图像,具体包括:从不同方向获取所述楼层的候梯间内的图像,所述不同方向不互相平行;将所述两个不同方向的图像合成为所述楼层的候梯间内的立体图像。
- 如权利要求6所述的方法,其中,在判断所述载重重量是否超过预定承重阈值与预定承重差值之差之前,将所述楼层的候梯间内的图像的第一特征向量输入到第一机器学习模型,所述第一机器学习模型输出所述楼层的候梯间内的图像是否包含人脸信息的结果之后,还可以包括:如果所述第一机器学习模型输出所述楼层的候梯间内的图像包含人脸信息的结果,定位所述楼层的候梯间内的立体图像中的所有人像;分别获取所述楼层的候梯间内的立体图像中的每一张人像中的第二特征,分别组成所述人脸的第二特征向量;将所述人脸的第二特征向量输入到第二机器学习模型,所述第二机器学习模型输出所述人像的体重值;根据所述所有人像的体重值确定所述预定承重差值;其中,所述第二机器学习模型如下训练出:获取包括人像的楼层的候梯间内的立体图像的第二图像样本集合,所述第二图像样本集合中的每个立体图像样本事先贴有体重标签;获取所述第二图像样本集中的每一个立体图像样本的第二特征,组成所述第一图像样本集中的每一个图像样本的第二特征向量;将所述第二图像样本集中的每一个立体图像样本的第二特征向量逐一输入第二机器学习模型,第二机器学习模型输出判定的体重,与贴有的体重标签比对,如不一致,则调整所述第二机器学习模型,使所述机器学习模型输出的体重与标签一致。
- 一种电梯调度装置,其中,所述装置包括:指令获取单元,用于接收楼层的候梯指令;图像获取单元,用于获取所述楼层的候梯间内的图像;特征获取单元,用于获取所述楼层的候梯间内的图像中的第一特征,组成所述楼层的候梯间内的图像的第一特征向量;人脸识别单元,用于将所述楼层的候梯间内的图像的第一特征向量输入到第一机器学习模型,所述第一机器学习模型输出所述楼层的候梯间内的图像是否包含人脸信息的结果;指令清除单元,用于若所述楼层的候梯间内的图像中不包含人脸信息,清除所述楼层的候梯指令。
- 一种计算机设备,包括存储器和处理器,所述存储器中存储有计算机可读指令,所述计算机可读指令被所述处理器执行时,使得所述处理器执行如下步骤:接收一楼层的候梯指令;获取所述楼层的候梯间内的图像;获取所述楼层的候梯间内的图像中的第一特征,组成所述楼层的候梯间内的图像的第一特征向量;将所述楼层的候梯间内的图像的第一特征向量输入到第一机器学习模型,所述第一机器学习模型输出所述楼层的候梯间内的图像是否包含人脸信息的结果;若所述楼层的候梯间内的图像中不包含人脸信息,清除所述楼层的候梯指令。
- 如权利要求9所述的计算机设备,其中,所述计算机可读指令被所述处理器执行时,使得所述处理器执行所述在清除所述楼层的候梯指令的步骤之后,还用于执行如下步骤:接收电梯内的楼层指令;获取所述电梯内的图像;获取所述电梯内的图像中的第一特征,组成所述电梯内的图像的第一特征向量;将所述电梯内的图像的第一特征向量输入到第一机器学习模型,所述第一机器学习模型输出所述电梯内的图像是否包含人脸信息的结果;若所述电梯内的图像中不包含人脸信息,清除所述电梯内的所有楼层指令。
- 如权利要求9或10所述的计算机设备,其中,所述计算机可读指令被所述处理器执行时,使得所述处理器执行所述接收一楼层的候梯指令的步骤之前,还用于执行如下步骤:用包含正样本和负样本的集合构成第一图像样本集,其中,所述正样本为包含人脸信息的图像,所述负样本为不包含人脸信息的图像;获取所述第一图像样本集中的每一个图像样本的第一特征,组成所述第一图像样本集中的每一个图像样本的第一特征向量;将所述第一图像样本集中的每一个图像样本的第一特征向量逐一输入第一机器学习模型中进行学习,所述第一机器学习模型输出是否包含人脸信息的判断结果,如果对于正样本输出不包含符合人脸信息的判断结果,或对于负样本输出包含人脸信息的判断结果,调整第一机器学习模型,使第一机器学习模型输出相反判断结果。
- 如权利要求9所述的计算机设备,其中,所述计算机可读指令被所述处理器执行时,使得所述处理器执行所述在清除所述楼层的候梯指令的步骤之后,还用于执行如下步骤:接收电梯内的楼层指令;获取所述电梯内的载重重量;判断所述载重重量是否超过预定启动阈值;若所述载重重量未超过预定启动阈值,清除所述电梯内的所有楼层指令。
- 如权利要求9所述的计算机设备,其中,所述计算机可读指令被所述处理器执行时,使得所述处理器执行所述在接收楼层的候梯指令的步骤之后,还用于执行如下步骤:获取所述电梯内的载重重量;判断所述载重重量是否超过预定承重阈值与预定承重差值之差,其中,所述承重差值用于指示将增加电梯载重的值;若所述载重重量超过预定承重阈值与预定承重差值之差,控制所述电梯仅按照电梯内的楼层指令停靠。
- 如权利要求9所述的计算机设备,其中,所述计算机可读指令被所述处理器执行时,使得所述处理器执行所述获取所述楼层的候梯间内的图像的步骤,包括:从不同方向获取所述楼层的候梯间内的图像,所述不同方向不互相平行;将所述两个不同方向的图像合成为所述楼层的候梯间内的立体图像。
- 一种存储有计算机可读指令的存储介质,所述计算机可读指令被一个或多个处理器执行时,使得一个或多个处理器执行如下的步骤:接收一楼层的候梯指令;获取所述楼层的候梯间内的图像;获取所述楼层的候梯间内的图像中的第一特征,组成所述楼层的候梯间内的图像的第一特征向量;将所述楼层的候梯间内的图像的第一特征向量输入到第一机器学习模型,所述第一机器学习模型输出所述楼层的候梯间内的图像是否包含人脸信息的结果;若所述楼层的候梯间内的图像中不包含人脸信息,清除所述楼层的候梯指令。
- 如权利要求15所述的存储介质,其中,所述计算机可读指令被一个或多个处理器执 行时,使得一个或多个处理器执行所述在清除所述楼层的候梯指令的步骤之后,还用于执行如下步骤:接收电梯内的楼层指令;获取所述电梯内的图像;获取所述电梯内的图像中的第一特征,组成所述电梯内的图像的第一特征向量;将所述电梯内的图像的第一特征向量输入到第一机器学习模型,所述第一机器学习模型输出所述电梯内的图像是否包含人脸信息的结果;若所述电梯内的图像中不包含人脸信息,清除所述电梯内的所有楼层指令。
- 如权利要求15或16所述的存储介质,其中,所述计算机可读指令被一个或多个处理器执行时,使得一个或多个处理器执行所述接收一楼层的候梯指令的步骤之前,还用于执行如下步骤:用包含正样本和负样本的集合构成第一图像样本集,其中,所述正样本为包含人脸信息的图像,所述负样本为不包含人脸信息的图像;获取所述第一图像样本集中的每一个图像样本的第一特征,组成所述第一图像样本集中的每一个图像样本的第一特征向量;将所述第一图像样本集中的每一个图像样本的第一特征向量逐一输入第一机器学习模型中进行学习,所述第一机器学习模型输出是否包含人脸信息的判断结果,如果对于正样本输出不包含符合人脸信息的判断结果,或对于负样本输出包含人脸信息的判断结果,调整第一机器学习模型,使第一机器学习模型输出相反判断结果。
- 如权利要求15所述的存储介质,其中,所述计算机可读指令被一个或多个处理器执行时,使得一个或多个处理器执行所述在清除所述楼层的候梯指令的步骤之后,还用于执行如下步骤:接收电梯内的楼层指令;获取所述电梯内的载重重量;判断所述载重重量是否超过预定启动阈值;若所述载重重量未超过预定启动阈值,清除所述电梯内的所有楼层指令。
- 如权利要求15所述的存储介质,其中,所述计算机可读指令被一个或多个处理器执行时,使得一个或多个处理器执行所述在接收楼层的候梯指令的步骤之后,还用于执行如下步骤:获取所述电梯内的载重重量;判断所述载重重量是否超过预定承重阈值与预定承重差值之差,其中,所述承重差值用于指示将增加电梯载重的值;若所述载重重量超过预定承重阈值与预定承重差值之差,控制所述电梯仅按照电梯内的楼层指令停靠。
- 如权利要求15所述的存储介质,其中,所述计算机可读指令被一个或多个处理器执行时,使得一个或多个处理器执行所述获取所述楼层的候梯间内的图像的步骤,包括:从不同方向获取所述楼层的候梯间内的图像,所述不同方向不互相平行;将所述两个不同方向的图像合成为所述楼层的候梯间内的立体图像。
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