CN114873387B - Energy-saving elevator dispatching system and method based on reinforcement learning algorithm - Google Patents
Energy-saving elevator dispatching system and method based on reinforcement learning algorithm Download PDFInfo
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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
- B66B1/02—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
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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
- B66B1/468—Call registering systems
-
- 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
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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
- B66B2201/4638—Wherein the call is registered without making physical contact with the elevator system
- B66B2201/4646—Wherein the call is registered without making physical contact with the elevator system using voice recognition
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
- Y02B50/00—Energy efficient technologies in elevators, escalators and moving walkways, e.g. energy saving or recuperation technologies
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- Engineering & Computer Science (AREA)
- Automation & Control Theory (AREA)
- Computer Networks & Wireless Communication (AREA)
- Elevator Control (AREA)
Abstract
The invention discloses an elevator energy-saving scheduling method based on reinforcement learning algorithm, which comprises the following steps of S1, acquiring a waiting person, an initial layer and a destination layer of the waiting person through face recognition, voice recognition and gesture recognition; s2, acquiring the position of the car; s3, obtaining an optimal scheduling scheme by using a reinforcement learning algorithm according to the elevator taking routes of the elevator taking personnel and the elevator waiting personnel and the positions of the cabs; and S4, driving the lift car according to the optimal dispatching scheme, and broadcasting the movement condition of the lift car to the waiting personnel and the riding personnel. The invention improves the running efficiency of the lift car.
Description
Technical Field
The invention relates to the field of elevator control, in particular to an energy-saving elevator dispatching system and method based on a reinforcement learning algorithm.
Background
Most of traditional elevator dispatching relies on users to manually add information instructions such as floors through elevator dispatching buttons, and information exchange with the users is lacking, so that obvious dispatching shortboards are faced. For example, when a plurality of cars synchronously receive an uplink request, a part of elevator groups respond to one car and the other cars do not respond, so that people flow is detained, and valuable time is wasted. The control system of part of elevators runs in the opposite way, as long as the elevator cars have no other instructions, all the up-and-down instructions of the floors are responded, one person can go downstairs, but a plurality of elevator cars respond, the electric energy is obviously wasted, and the loss of the elevators is increased.
China is the largest elevator use country in the world, the number of high-rise buildings in a plurality of cities is increased dramatically in recent years, population density is high, elevator load is large, and the defect of non-intelligent elevator scheduling is highlighted. Therefore, how to reduce energy consumption and maintenance cost by increasing information exchange between the elevator and the user is a problem to be solved at present.
Disclosure of Invention
The invention aims to provide an elevator energy-saving dispatching method based on a reinforcement learning algorithm so as to improve the running efficiency of an elevator.
In order to achieve the above purpose, the present invention provides the following technical solutions: an elevator energy-saving dispatching method based on reinforcement learning algorithm comprises the following steps,
s1, acquiring an initial layer and a target layer of a person waiting for a ladder and a person taking the ladder through face recognition, voice recognition and gesture recognition;
s2, acquiring the position of the car;
s3, obtaining an optimal scheduling scheme by using a reinforcement learning algorithm according to the elevator taking routes of the elevator taking personnel and the elevator waiting personnel and the positions of the cabs;
s4, driving the lift car according to an optimal dispatching scheme, and broadcasting the movement condition of the lift car to elevator waiting personnel and elevator riding personnel;
wherein, the S3 process is specifically as follows,
s301, initializing a neural network weight and a current state S t ；
S302, observing the state S at the time t t Calculating comprehensive cost R of waiting ladder t The calculation formula is as follows,
wherein R is w For average waiting time cost, R r For average elevator taking time cost, R c Cost for number of stops; r is R w 、R r 、R c The calculation formula of (c) is as follows,
wherein p is the number of passengers waiting for the elevator and t is the number of passengers taking the elevator i For the time of arrival of passengers, N is the number of elevators, C i The number of times of elevator stop;
s303, using the current state behavior function value Q t Select and select ladder assignment scheme u t State s t Substituting into the neural network, calculating state behavior value function minQ (s, u) forward, the calculation formula of the state behavior value function is as follows,
ΔQ t+1 (s t ，u t )＝Q t (s t ，u t )+α[R t+1 +γminQ t (s t+1 ，u)-Q t (s t ，u t )]
wherein, alpha is the learning rate of reinforcement learning, and is gradually attenuated in the learning process; gamma is a discount factor for cost, gamma e (0, 1);
s304, obtaining a new function value Q by using a reinforcement learning updating algorithm t ；
S305, utilize Q t Selecting a ladder dispatching scheme u t ；
S306, updating the weight and the state of the neural network, and repeating the steps until an optimal scheduling scheme is obtained.
According to the scheme, in the S1, the face recognition process is specifically that a camera is used for collecting a face image, the face image is quantized into vectors with a plurality of dimensions, and the face is determined by comparing the vectors with the quantized multidimensional vectors in the existing data set; if there is no corresponding vector in the existing dataset, the multidimensional vector is added to the existing dataset and a new dataset is formed.
According to the scheme, in the S1, the voice recognition process is specifically that the microphone is used for collecting voice, the voice is noise-reduced and then uploaded to the voice recognition cloud server, and the voice recognition cloud server converts voice information into semantic text.
According to the scheme, in the S1, the gesture recognition process is specifically that a gesture image is intercepted through a camera, and the destination floor is judged according to the number of fingers in the gesture image.
According to the above scheme, driving the car according to the optimal scheduling scheme in S4 is specifically,
s401, the control module acquires the position of the car through a sensor module arranged on the car and transmits the information of the position of the car to the upper computer;
s402, the upper computer sends an elevator operation instruction to the control module according to the optimal scheduling scheme, and the control module operates the car to operate according to the instruction;
and S403, if the dispatching scheme is changed, the upper computer sends a new elevator operation instruction to the control module.
According to the scheme, the voice and picture broadcasting is specifically performed through the loudspeaker and the display screen arranged in the elevator waiting area and the elevator car in the S4.
The elevator energy-saving dispatching system based on the reinforcement learning algorithm for realizing the elevator energy-saving dispatching method based on the reinforcement learning algorithm comprises a car identification module, a control module and a control module, wherein the car identification module comprises a camera and a microphone which are arranged in a car and is used for acquiring the number of passengers and a destination floor;
the calling identification module comprises a camera and a microphone which are arranged in the waiting area and is used for acquiring the number of people waiting for the elevator and a target layer;
the broadcasting panel is arranged in the elevator car and in the elevator waiting area and comprises a display screen and a loudspeaker, and is used for broadcasting the movement condition of the elevator car to elevator passengers and elevator waiting personnel;
the upper computer is used for receiving the number of passengers and waiting personnel and a destination floor, designating an optimal scheduling scheme according to the number of passengers and waiting personnel, sending an elevator operation instruction to the control module according to the optimal scheduling scheme, receiving the car position information from the control module and forwarding the car position information to the broadcasting panel;
the control module is used for controlling the car to move and stop according to the elevator running instruction, receiving the car position information from the sensor module and transmitting the car position information to the upper computer;
and the sensor module is used for acquiring the car position information.
A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, which processor, when executing the computer program, implements the steps of the reinforcement learning algorithm based elevator energy saving scheduling method as described above.
A readable storage medium having stored thereon a computer program which when executed by a processor implements the steps of the reinforcement learning algorithm based elevator energy saving scheduling method as described above.
The beneficial effects of the invention are as follows: the target layers of the elevator waiting personnel and the elevator taking personnel are acquired through voice recognition and gesture recognition, so that compared with the traditional buttons, frequent contact of the personnel is avoided, and further the transmission risk of partial infectious diseases is reduced. According to the running condition of the elevator, the distribution condition of the elevator waiting personnel and the target layer of the elevator riding and elevator waiting personnel, the optimal scheduling scheme of the elevator is obtained through the reinforcement learning algorithm, so that the waiting time of the elevator waiting personnel is reduced, the running efficiency of the elevator is improved, and the unnecessary round-trip running of the elevator is reduced, so that the failure rate and the loss degree of the elevator are reduced to a certain extent.
Drawings
Fig. 1 is a schematic diagram of an elevator energy-saving dispatching system based on a reinforcement learning algorithm according to an embodiment of the present invention;
fig. 2 is a flowchart of an elevator energy-saving scheduling method based on a reinforcement learning algorithm according to an embodiment of the present invention;
FIG. 3 is a flowchart of a reinforcement learning algorithm according to an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present disclosure more apparent, the technical solutions of the embodiments of the present disclosure will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present disclosure. It will be apparent that the described embodiments are some, but not all, of the embodiments of the present disclosure. All other embodiments, which can be made by one of ordinary skill in the art without the need for inventive faculty, are within the scope of the present disclosure, based on the described embodiments of the present disclosure.
Referring to fig. 2, an elevator energy-saving scheduling method based on reinforcement learning algorithm includes the steps of,
s1, acquiring an initial layer and a target layer of a person waiting for a ladder and a person taking the ladder through face recognition, voice recognition and gesture recognition;
s2, acquiring the position of the car;
s3, obtaining an optimal scheduling scheme by using a reinforcement learning algorithm according to the elevator taking routes of the elevator taking personnel and the elevator waiting personnel and the positions of the cabs;
s4, driving the lift car according to an optimal dispatching scheme, and broadcasting the movement condition of the lift car to elevator waiting personnel and elevator riding personnel;
wherein, the S3 process is specifically as follows,
s301, initializing a neural network weight and a current state S t ；
S302, observing the state S at the time t t Calculating comprehensive cost R of waiting ladder t The calculation formula is as follows,
wherein R is w For average waiting time cost, R r For average elevator taking time cost, R c Cost for number of stops; r is R w 、R r 、R c The calculation formula of (c) is as follows,
wherein p is the number of passengers waiting for the elevator and t is the number of passengers taking the elevator i For the time of arrival of passengers, N is the number of elevators, C i The number of times of elevator stop;
s303, using the current state behavior function value Q t Select and select ladder assignment scheme u t State s t Substituting into the neural network, calculating state behavior value function minQ (s, u) forward, the calculation formula of the state behavior value function is as follows,
ΔQ t+1 (s t ，u t )＝Q t (s t ，u t )+α[R t+1 +γminQ t (s t+1 ，u)-Q t (s t ，u t )]
wherein, alpha is the learning rate of reinforcement learning, and is gradually attenuated in the learning process; gamma is a discount factor for cost, gamma e (0, 1);
s304, obtaining a new function value Q by using a reinforcement learning updating algorithm t ；
S305, utilize Q t Selecting a ladder dispatching scheme u t ；
S306, updating the weight and the state of the neural network, and repeating the steps until an optimal scheduling scheme is obtained.
Further, in the step S1, the face recognition process is specifically that a camera is used for collecting a face image, the face image is quantized into vectors with a plurality of dimensions, and the face is determined by comparing the vectors with the quantized multidimensional vectors in the existing data set; if there is no corresponding vector in the existing dataset, the multidimensional vector is added to the existing dataset and a new dataset is formed.
Further, in the step S1, the voice is collected through a microphone, the voice is uploaded to a voice recognition cloud server after noise reduction processing, and the voice recognition cloud server converts voice information into semantic text.
Further, in the step S1, the gesture image is intercepted by the camera, and the destination floor is determined according to the number of fingers in the gesture image.
Further, driving the car according to the optimal scheduling scheme in S4 is specifically,
s401, the control module acquires the position of the car through a sensor module arranged on the car and transmits the information of the position of the car to the upper computer;
s402, the upper computer sends an elevator operation instruction to the control module according to the optimal scheduling scheme, and the control module operates the car to operate according to the instruction;
and S403, if the dispatching scheme is changed, the upper computer sends a new elevator operation instruction to the control module.
Further, the broadcasting in S4 specifically performs voice and picture broadcasting through a speaker and a display screen disposed in the elevator waiting area and inside the car.
Referring to fig. 1, an energy-saving elevator dispatching system based on reinforcement learning algorithm for implementing the energy-saving elevator dispatching method based on reinforcement learning algorithm described above comprises a car identification module, a control module and a control module, wherein the car identification module comprises a camera and a microphone which are arranged in a car and is used for acquiring the number of passengers and a destination floor;
the calling identification module comprises a camera and a microphone which are arranged in the waiting area and is used for acquiring the number of people waiting for the elevator and a target layer;
the broadcasting panel is arranged in the elevator car and in the elevator waiting area and comprises a display screen and a loudspeaker, and is used for broadcasting the movement condition of the elevator car to elevator passengers and elevator waiting personnel;
the upper computer is used for receiving the number of passengers and waiting personnel and a destination floor, designating an optimal scheduling scheme according to the number of passengers and waiting personnel, sending an elevator operation instruction to the control module according to the optimal scheduling scheme, receiving the car position information from the control module and forwarding the car position information to the broadcasting panel;
the control module is used for controlling the car to move and stop according to the elevator running instruction, receiving the car position information from the sensor module and transmitting the car position information to the upper computer;
and the sensor module is used for acquiring the car position information.
A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, which processor, when executing the computer program, implements the steps of the reinforcement learning algorithm based elevator energy saving scheduling method as described above.
A readable storage medium having stored thereon a computer program which when executed by a processor implements the steps of the reinforcement learning algorithm based elevator energy saving scheduling method as described above.
Examples of scheduling using the above methods and systems are as follows:
the initial floors of the elevator 1 and the elevator 2 are in the 4 floors, 9 people appear in the 4 floors at the moment, the destination floors are all 1 floor, 9 people appear in the 3 floors, and the destination floors are also 1 floor. Then 6 people appear in the floor 2, and the destination floor is also the floor 1.
The traditional elevator is prepared by the following steps: elevators 1 and 2 travel to floor 3 after loading passengers on floor 4, to floor 2 after loading passengers on floor 3, and finally to floor 1 after loading passengers on floor 2.
The intelligent elevator comprises the following allocation flow: after the calling system confirms the waiting conditions of the 3 th floor and the 4 th floor, the data are uploaded to an upper computer, the upper computer calculates the comprehensive waiting cost to obtain a state behavior value function, and a scheduling scheme is provided. And then the calling system uploads the elevator waiting condition of the building 2 to an upper computer, the upper computer updates the weight, and the comprehensive elevator waiting cost and state behavior value function is recalculated, so that the following scheduling scheme is finally obtained: the elevator 1 goes to the floor 2 after loading the passengers in the floor 4, the elevator 2 goes directly to the floor 3 to load the passengers, then goes to the floor 2, and the two elevators go to the floor 1 after loading the passengers in the floor 2 together.
The foregoing description is only illustrative of the present invention and is not intended to limit the scope of the invention, and all equivalent structures or equivalent processes or direct or indirect application in other related technical fields are included in the scope of the present invention.
Claims (9)
1. An elevator energy-saving scheduling method based on a reinforcement learning algorithm is characterized by comprising the following steps of: comprises the steps of,
s1, acquiring an initial layer and a target layer of a person waiting for a ladder and a person taking the ladder through face recognition, voice recognition and gesture recognition;
s2, acquiring the position of the car;
s3, obtaining an optimal scheduling scheme by using a reinforcement learning algorithm according to the elevator taking routes of the elevator taking personnel and the elevator waiting personnel and the positions of the cabs;
s4, driving the lift car according to an optimal dispatching scheme, and broadcasting the movement condition of the lift car to elevator waiting personnel and elevator riding personnel;
wherein, the S3 process is specifically as follows,
s301, initializing a neural network weight and a current state S t ；
S302, observing the state S at the time t t Calculating comprehensive cost R of waiting ladder t The calculation formula is as follows,
wherein R is w For average waiting time cost, R r For average elevator taking time cost, R c Cost for number of stops; r is R w 、R r 、R c The calculation formula of (c) is as follows,
wherein p is the number of passengers waiting for the elevator and t is the number of passengers taking the elevator i For the time of arrival of passengers, N is the number of elevators, C i The number of times of elevator stop;
s303, using the current state behavior function value Q t Select and select ladder assignment scheme u t State s t Substituting into the neural network, calculating state behavior value function minQ (s, u) forward, the calculation formula of the state behavior value function is as follows,
ΔQ t+1 (s t ，u t )＝Q t (s t ，u t )+α[R t+1 +γminQ t (s t+1 ，u)-Q t (s t ，u t )]
wherein, alpha is the learning rate of reinforcement learning, and is gradually attenuated in the learning process; gamma is a discount factor for cost, gamma e (0, 1);
s304, obtaining a new function value Q by using a reinforcement learning updating algorithm t ；
S305, utilize Q t Selecting a ladder dispatching scheme u t ；
S306, updating the weight and the state of the neural network, and repeating the steps until an optimal scheduling scheme is obtained.
2. The reinforcement learning algorithm-based elevator energy-saving scheduling method according to claim 1, characterized in that: the face recognition process in S1 specifically comprises the steps of collecting a face image through a camera, quantizing the face image into vectors with a plurality of dimensions, and determining the face through comparing the vectors with the quantized multidimensional vectors in the existing data set; if there is no corresponding vector in the existing dataset, the multidimensional vector is added to the existing dataset and a new dataset is formed.
3. The reinforcement learning algorithm-based elevator energy-saving scheduling method according to claim 1, characterized in that: in the S1, the voice recognition process is specifically that voice is collected through a microphone, the voice is uploaded to a voice recognition cloud server after noise reduction treatment, and the voice recognition cloud server converts voice information into semantic text.
4. The reinforcement learning algorithm-based elevator energy-saving scheduling method according to claim 1, characterized in that: the gesture recognition process in the S1 specifically includes that a gesture image is intercepted through a camera, and a destination floor is judged according to the number of fingers in the gesture image.
5. The reinforcement learning algorithm-based elevator energy-saving scheduling method according to claim 1, characterized in that: driving the car according to the optimal scheduling scheme in S4 is specifically,
s401, the control module acquires the position of the car through a sensor module arranged on the car and transmits the information of the position of the car to the upper computer;
s402, the upper computer sends an elevator operation instruction to the control module according to the optimal scheduling scheme, and the control module operates the car to operate according to the instruction;
and S403, if the dispatching scheme is changed, the upper computer sends a new elevator operation instruction to the control module.
6. The reinforcement learning algorithm-based elevator energy-saving scheduling method according to claim 1, characterized in that: and S4, broadcasting voice and picture broadcasting is specifically performed through a loudspeaker and a display screen arranged in the elevator waiting area and the inside of the elevator car.
7. An energy-saving elevator dispatching system based on reinforcement learning algorithm for implementing the energy-saving elevator dispatching method based on reinforcement learning algorithm as set forth in any one of claims 1-6, characterized in that: comprising the steps of (a) a step of,
the car identification module comprises a camera and a microphone which are arranged in the car and is used for acquiring the number of passengers and a target floor;
the calling identification module comprises a camera and a microphone which are arranged in the waiting area and is used for acquiring the number of people waiting for the elevator and a target layer;
the broadcasting panel is arranged in the elevator car and in the elevator waiting area and comprises a display screen and a loudspeaker, and is used for broadcasting the movement condition of the elevator car to elevator passengers and elevator waiting personnel;
the upper computer is used for receiving the number of passengers and waiting personnel and a destination floor, designating an optimal scheduling scheme according to the number of passengers and waiting personnel, sending an elevator operation instruction to the control module according to the optimal scheduling scheme, receiving the car position information from the control module and forwarding the car position information to the broadcasting panel;
the control module is used for controlling the car to move and stop according to the elevator running instruction, receiving the car position information from the sensor module and transmitting the car position information to the upper computer;
and the sensor module is used for acquiring the car position information.
8. A computer device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized by: the processor, when executing the computer program, implements the steps of the reinforcement learning algorithm-based elevator energy-saving scheduling method of any one of claims 1-6.
9. A readable storage medium having stored thereon a computer program, characterized by: the computer program when executed by a processor implements the steps of the reinforcement learning algorithm-based elevator energy saving scheduling method of any one of claims 1-6.
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