CN110095994B - Elevator riding traffic flow generator and method for automatically generating passenger flow data based on same - Google Patents
Elevator riding traffic flow generator and method for automatically generating passenger flow data based on same Download PDFInfo
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- CN110095994B CN110095994B CN201910165472.5A CN201910165472A CN110095994B CN 110095994 B CN110095994 B CN 110095994B CN 201910165472 A CN201910165472 A CN 201910165472A CN 110095994 B CN110095994 B CN 110095994B
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- G05B17/00—Systems involving the use of models or simulators of said systems
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Abstract
The invention discloses an elevator boarding traffic flow generator and a method for automatically generating passenger flow data based on the elevator boarding traffic flow generator. The method comprises the steps that a passenger starting floor and target floor algorithm module determines a passenger starting floor according to a Monte-Carlo sampling method, and then determines a target floor of a passenger according to a starting-target matrix; the passenger arrival time module calculates the possible arrival time of each passenger according to the required passenger flow distribution curve and a passenger arrival time algorithm.
Description
Technical Field
The invention belongs to the field of elevators, and particularly relates to an elevator boarding traffic flow generator and a method for automatically generating passenger flow data based on the elevator boarding traffic flow generator.
Background
With the increasing application of elevator group control systems, the requirements for group control dispatching performance are also increasing continuously, because of the particularity of the group control system and the development of the group control algorithm, a large amount of passenger flow data is needed for testing and training, and in field use, a large amount of passenger flow data is needed for checking and testing the quality of elevator group control dispatching, and the actual passenger flow is difficult to carry out comprehensive training and perfect testing on the group control system, so that a controllable traffic flow generator input with configurable scene parameters is needed to simulate and generate passenger flow data.
At present, elevator manufacturers mainly have the following two methods for training and testing group control dispatching algorithms: (1) The practical elevator is adopted for testing, the scheme needs more elevators, and testers need to simulate passenger flow input. (2) The method has the advantages that the model simulation is carried out on an elevator body to simulate the operation scene of the actual elevator, and the stored specific passenger flow data collected on site are adopted for training, for example, the elevator simulation model described in patents CN201810011921.6 and CN200920278477.0 is the same type of elevator simulation model, so that the method has the defects that test data samples are too few and have certain regularity, and a training and dispatching algorithm is easy to fall into a local optimal solution.
Disclosure of Invention
One of the technical problems to be solved by the invention is to provide an elevator passenger flow generator capable of automatically generating passenger flow data according to scene setting aiming at the problems of the existing elevator group control test and training mode, wherein the elevator passenger flow generator adopts the passenger flow data which can be generated in real time according to scene configuration, improves the comprehensiveness and randomness of the passenger flow, and can be used as the input of an actual elevator or elevator model to test the elevator dispatching performance and train a group control dispatching algorithm.
The second technical problem to be solved by the invention is to provide a method for automatically generating passenger flow data based on the elevator passenger flow generator for generating elevator passenger flow samples, and provide passenger arrival and call signals for a traffic model simulation system or an actual elevator system.
An elevator boarding traffic flow generator as a first aspect of the invention includes:
the passenger starting floor and target floor algorithm module determines the passenger starting floor according to a Monte-Carlo sampling method and then determines the target floor of the passenger according to a starting-target matrix;
the passenger arrival time module calculates the possible arrival time of each passenger according to the required passenger flow distribution curve and a passenger arrival time algorithm;
the other passenger information module is used for collecting passenger information required by the group control system;
the passenger data storage module is used for storing passenger flow data tables generated by the parameters of the traffic flow generator, the building parameters, the algorithm module of the starting floor and the target floor of the passenger, the passenger arrival time module and the other passenger information modules together so as to generate different passenger flow data;
the inquiry module is used for inquiring the data stored by the passenger data storage module, is connected with a traffic mode learning unit interface in the group control system and provides passenger flow data for the group control system;
a human-computer interface for inputting parameters of the traffic flow generator, building parameters to the passenger origin floor and destination floor algorithm module and querying data stored by the passenger data storage module through a query module.
In a preferred embodiment of the invention, the passenger information is an estimate of the number of passengers in each car based on the weight of the passengers in each car, thereby avoiding the assignment of an already fully loaded car.
The method for automatically generating passenger flow data based on the elevator boarding traffic flow generator as the second aspect of the invention comprises the steps of determining the passenger starting floor by utilizing a passenger starting floor and target floor algorithm module according to a Monte-Carlo sampling method, and then determining the target floor of the passenger according to a starting-target matrix; and a method for calculating the possible arrival time of each passenger by using a passenger arrival time module according to the required passenger flow distribution curve and a passenger arrival time algorithm.
In a preferred embodiment of the invention, the parameters required in the method of determining the passenger's starting floor using the passenger starting floor and target floor algorithm module according to Monte-Carlo sampling method and then determining the passenger's target floor according to the starting-target matrix are floor height, passenger starting density vector, and percentage parameters x, y, z of up-, down-, and inter-floor passenger flows; x, y, z are related to traffic segments and are determined by empirical values in a traffic flow simulator.
In a preferred embodiment of the present invention, the method for determining the passenger starting floor by using the passenger starting floor and target floor algorithm module according to Monte-Carlo sampling method and then determining the passenger target floor according to the starting-target matrix is specifically: at the known passenger's origin density vector origin (i), the passenger's origin floor is determined by Monte Carlo sampling as follows:
(1) Calculate the sum of the starting densities of all floors:
(2) And (3) calculating the selection probability and the accumulated probability of each floor:
p i ＝origin(i)/F，
(3) For each passenger, there is [0,1]Generates a uniformly distributed random number r, if q is i-1 ＜r≤q i And selecting the i floor as a starting floor.
In a preferred embodiment of the present invention, the method for determining the passenger starting floor by using the passenger starting floor and target floor algorithm module according to Monte-Carlo sampling method and then determining the passenger target floor according to the starting-target matrix is specifically: in the case of a given passenger's origin-destination matrix od (i, j), the passenger's destination floor is determined by Monte Carlo sampling as follows:
(1) Calculating the sum of all elements in the ith row in the start-target matrix:
(2) Calculating the selection probability and the cumulative probability of each destination floor aiming at the starting floor i:
p ij ＝od(i,j)/D i ，
(3) In [0,1]Generates a uniformly distributed random number r, if q is i,j-1 ＜r≤q i,j And selecting the j floor as a target floor.
In a preferred embodiment of the present invention, in the method for calculating the possible arrival time of each passenger according to the required passenger flow distribution curve and the passenger arrival time algorithm by using the passenger arrival time module, the method comprises the following steps: the required parameters are the average passenger arrival rate, the simulation time T, and the passenger flow statistics period length T.
In a preferred embodiment of the present invention, the method for calculating the possible arrival time of each passenger by using the passenger arrival time module and the passenger arrival time algorithm according to the required passenger flow distribution curveThe method comprises the following steps: suppose the first passenger arrives at time t 0 Then the arrival time of the subsequent passenger is in turn:
t i ＝t i-1 -ln(r)/λi＝1,2,...
wherein, t 0 Refers to the start time, t, of an observation or discussion question i Is the time of arrival of the ith passenger, r is [0,1]A random number of intervals; λ is the average passenger arrival rate, related to traffic hours and floors, determined by empirical values.
In a preferred embodiment of the present invention, in the method for calculating the possible arrival time of each passenger by using the passenger arrival time algorithm according to the required passenger flow distribution curve and the passenger arrival time module, in order to determine the arrival time of a plurality of passengers, the algorithm uses a loop statement, the number of loops is the number of passengers, the number of passengers is a set value, and different values are set at different time intervals;
after the program is executed, the unit of the generated time value is second, and the time value needs to be converted so as to be convenient for a group control system to use; will arrive at time t i Dividing by 3600, and rounding down to obtain the arrival time of the passenger; secondly, subtracting the product of the rounded value and 3600 from the arrival time, dividing by 60, and rounding downwards to obtain minutes; and finally, subtracting the product of the rounded value and 3600 from the arrival time, and then taking the remainder of 60, wherein the remainder is the second value of the arrival time of the passenger.
In a preferred embodiment of the present invention, in the method for calculating the possible arrival time of each passenger according to the passenger arrival time distribution curve and the passenger arrival time algorithm by using the passenger arrival time module, the passenger arrival time distribution curve is a poisson distribution curve.
Due to the adoption of the technical scheme, the invention adopts the passenger flow data which can be generated in real time according to the scene configuration, thereby improving the comprehensiveness and randomness of the passenger flow.
Drawings
Fig. 1 is a schematic diagram of the relationship between the traffic stream generator and other unit modules of the group control system according to the present invention.
Fig. 2 is a schematic diagram of a traffic flow generator according to the present invention.
Fig. 3 is a schematic program diagram of the method for automatically generating passenger flow data based on the traffic flow generator according to the present invention.
Detailed Description
The invention is described in detail below with reference to the drawings and the detailed description.
Referring to fig. 1, the traffic stream generator of the present invention simulates call signals of a passenger hall and a car and transmits a passenger sequence, a starting floor sequence, a target floor sequence and a time sequence as input signals to a traffic pattern learning unit of a group control system in real time.
The traffic mode learning unit learns current traffic flow data in real time through learning historical traffic flow data by means of a neural network and the like, modifies current traffic mode parameters (such as passenger arrival rate lambda, percentages of passenger flows in an ascending mode, a descending mode and an interlayer traffic flow) in real time, and stores the current traffic mode parameters into a mode library.
When the dispatching is needed, the group control dispatching unit sends an instruction to the prediction unit, the prediction unit reads the current mode parameters from the mode library, and the passenger flow probability distribution of the next moment (usually 5 minutes in the future) is calculated according to the current mode parameters; such as the probability distribution of calls occurring on each floor and the probability distribution of calls going to the destination floor.
And when the group control dispatching unit needs to dispatch the elevator, reading the current mode from the mode library, and adopting a proper dispatching strategy according to the prediction result of the prediction unit.
Referring to fig. 2, the traffic flow generator proposed by the present invention is part of an elevator operation model, for which simulated signals of passenger arrival and call are provided. The system comprises a passenger starting floor and target floor algorithm module, a passenger arrival time module, other passenger information modules, a passenger data storage module and a query module.
Referring to fig. 2 in combination, the algorithm employed by the passenger arrival time module and the passenger starting floor and target floor algorithm module in the traffic flow generator of the present invention is as follows:
1. passenger arrival time module:
determining the required flatness according to the required passenger flow distribution curve (i.e. Poisson distribution curve, required to be flat)Mean passenger arrival rate λ), the possible arrival time of each passenger is calculated. The required parameters are the average passenger arrival rate, the simulation time T, and the passenger flow statistics period length T. When the building type is an office building, the passenger flow condition in a fixed time interval is relatively stable, the arrival number of passengers in unit time is generally taken within an interval, and therefore according to different operation time intervals, the design is applied to various typical time intervals (up peak, down peak, working time, before lunch, after lunch, during lunch, off time, out-of-work time and the like). Suppose that the time of arrival of the first passenger is t 0 Then the arrival time of the subsequent passenger is in turn:
t i ＝t i-1 -ln(r)/λi＝1,2,...
wherein, t 0 Refers to the start time, t, of an observation or discussion question i Is the time of arrival of the ith passenger, r is [0,1]Random number of intervals. λ is the arrival density of passengers, related to traffic hours and floors, determined by empirical values.
To determine the arrival times of multiple passengers, the algorithm uses a loop statement, the number of loops being the number of passengers (for a set value, with different values set for different time periods).
After the program is executed, the unit of the generated time value is second, and the time value needs to be converted so as to be convenient for the elevator system to use. Will arrive at time t i Divide by 3600 and get the whole downwards, that is the passenger arrives at the hour. And secondly, subtracting the product of the rounded value and 3600 from the arrival time, dividing the product by 60, and rounding down to obtain the minutes. And finally, subtracting the product of the rounded value and 3600 from the arrival time, and then taking the remainder of 60, wherein the remainder is the second value of the arrival time of the passenger.
2. The starting floor and target floor algorithm module:
here the passenger starting floor is determined according to the Monte-Carlo sampling method and then the passenger target floor is determined according to the starting-target matrix. The required parameters are floor height, passenger starting density vector, and percentage parameters x, y, z for up, down, and inter-floor passenger flow. x, y, z are related to traffic segments, determined in the traffic simulator from empirical values:
at the known passenger's origin density vector origin (i), the passenger's origin floor is determined by Monte Carlo sampling as follows:
(1) Calculate the sum of the starting densities of all floors:
(2) And (3) calculating the selection probability and the accumulated probability of each floor:
p i ＝origin(i)/F，
(3) For each passenger, at [0,1]Generates a uniformly distributed random number r, if q is i-1 ＜r≤q i And selecting the i floor as a starting floor.
In the case of a given passenger's origin-destination matrix od (i, j), the passenger's destination floor is determined by Monte Carlo sampling as follows:
(1) Calculating the sum of all elements in the ith row in the start-target matrix:
(2) Calculating the selection probability and the cumulative probability of each destination floor aiming at the starting floor i:
p ij ＝od(i,j)/D i ，
(3) In [0,1]Generates a uniformly distributed random number r, if q is i,j-1 ＜r≤q i,j And selecting the j floor as a target floor.
3. Other passenger information generation module:
for gathering passenger information that may be needed by other group control algorithms, such as passenger weight, can be used to estimate the number of passengers in a car to avoid assigning an already fully loaded car.
4. The passenger data storage module is used for storing passenger flow data tables generated by the parameters of the traffic flow generator, the building parameters, the algorithm module of the starting floor and the target floor of the passenger, the passenger arrival time module and the other passenger information modules so as to generate different passenger flow data;
5. the inquiry module is used for inquiring the data stored by the passenger data storage module, is connected with a traffic mode learning unit interface in the group control system and provides passenger flow data for the group control system;
6. and the human-computer interaction interface is used for inputting the parameters of the traffic flow generator and the building parameters to the passenger starting floor and target floor algorithm module and inquiring the data stored in the passenger data storage module through the inquiry module.
The design of the traffic flow generator algorithm is independent of the function of the elevator operation logic, is mainly used for the generation of passenger flow call data of a group control elevator, and is used as an input data transmitter of a boarding call and an arrival call of an actual group control elevator or a group control simulation elevator model.
The passenger flow generated by the traffic flow generator can be directly input into the traffic mode learning unit of the group control system through a specific protocol interface, and can also be accessed into the elevator system through a specific communication interface, and then the group control system can input the passenger flow data into the traffic mode learning unit through exchanging data with the elevator.
Claims (8)
1. An elevator hall traffic flow generator, comprising:
the passenger starting floor and target floor algorithm module determines the passenger starting floor according to a Monte-Carlo sampling method and then determines the target floor of the passenger according to a starting-target matrix;
the passenger arrival time module calculates the possible arrival time of each passenger according to the required passenger flow distribution curve and a passenger arrival time algorithm; the method comprises the following steps:
according to whatCalculating the possible arrival time of each passenger according to the passenger flow distribution curve; the required parameters comprise average passenger arrival rate, simulation time T and passenger flow statistics time interval length T; suppose the first passenger arrives at time t 0 Then the arrival time of the subsequent passenger is in turn:
t i ＝t i-1 -ln(r)/λ i＝1,2,...
wherein, t 0 Refers to the start time, t, of an observation or discussion question i Is the time of arrival of the ith passenger, and r is [0,1 [ ]]A random number of the interval, λ being the arrival density of the passenger, related to the traffic hours and floors, determined by empirical values;
in order to determine the arrival time of a plurality of passengers, the algorithm adopts a loop statement, and the loop times are the number of the passengers; after the program is executed, the unit of the generated time value is second, and the time value needs to be converted so as to be convenient for the elevator system to use, specifically, the arrival time t i Dividing by 3600, and rounding down to obtain the arrival hour of the passenger; secondly, subtracting the product of the rounded value and 3600 from the arrival time, dividing by 60, and rounding downwards to obtain minutes; finally, subtracting the product of the rounded value and 3600 from the arrival time, and then taking a remainder for 60, wherein the remainder is the second value of the arrival time of the passenger;
the other passenger information module is used for collecting passenger information required by the group control system;
the passenger data storage module is used for storing passenger flow data tables generated by the parameters of the traffic flow generator, the building parameters, the algorithm module of the starting floor and the target floor of the passenger, the passenger arrival time module and the other passenger information modules together so as to generate different passenger flow data;
the inquiry module is used for inquiring the data stored by the passenger data storage module, is connected with a traffic mode learning unit interface in the group control system and provides passenger flow data for the group control system;
a human-computer interaction interface, which is used for inputting the parameters of the traffic flow generator and the building parameters to the passenger starting floor and target floor algorithm module and inquiring the data stored in the passenger data storage module through an inquiry module;
the traffic flow generator simulates calling signals of a passenger station and a car, and sends a passenger sequence, an initial layer sequence, a target layer sequence and a time sequence as input signals to a traffic mode learning unit of the group control system in real time;
the traffic mode learning unit learns the historical passenger flow data through a neural network, learns the current traffic flow data in real time, modifies the current traffic mode parameters in real time and stores the current traffic mode parameters into a mode library;
when the dispatching is needed, the group control dispatching unit sends an instruction to the prediction unit, and the prediction unit reads the current mode parameters from the mode library and calculates the passenger flow probability distribution at the next moment according to the current mode parameters;
and when the group control dispatching unit needs to dispatch the elevator, reading the current mode from the mode library, and adopting a proper dispatching strategy according to the prediction result of the prediction unit.
2. The elevator cab traffic flow generator of claim 1 wherein the passenger information is an estimate of the number of passengers in each car based on the weight of passengers in each car to avoid assigning cars that are already fully loaded.
3. The method for automatic generation of passenger flow data by an elevator hall traffic flow generator according to claim 1, characterized by comprising a method for determining a passenger starting floor according to a Monte-Carlo sampling method by using a passenger starting floor and target floor algorithm module, and then determining a passenger target floor according to a starting-target matrix; and a method for calculating the possible arrival time of each passenger by using a passenger arrival time module according to the required passenger flow distribution curve and a passenger arrival time algorithm.
4. The method of claim 3, wherein the parameters required in the method of determining the passenger's starting floor using the passenger's starting floor and target floor algorithm module according to Monte-Carlo sampling method and then determining the passenger's target floor according to the starting-target matrix are floor height, passenger starting density vector, and percentage parameters x, y, z of up, down, and inter-floor passenger flows; x, y, z are related to traffic segments and are determined by empirical values in a traffic flow simulator.
5. Method according to claim 4, characterized in that the method for determining the passenger's starting floor by means of the passenger's starting floor and target floor algorithm module according to the Monte-Carlo sampling method and then determining the passenger's target floor according to the starting-target matrix is embodied in that: at the known passenger's origin density vector origin (i), the passenger's origin floor is determined by Monte Carlo sampling as follows:
(1) Calculate the sum of the starting densities of all floors:
(2) And (3) calculating the selection probability and the accumulated probability of each floor:
p i ＝origin(i)/F，
(3) For each passenger, at [0,1]Generates a uniformly distributed random number r, if q is i-1 ＜r≤q i And selecting the i floor as a starting floor.
6. Method according to claim 4, characterized in that the method for determining the passenger's starting floor by means of the passenger's starting floor and target floor algorithm module according to the Monte-Carlo sampling method and then determining the passenger's target floor according to the starting-target matrix is embodied in that: in the case of a given passenger's origin-destination matrix od (i, j), the passenger's destination floor is determined by Monte Carlo sampling as follows:
(1) Calculating the sum of all elements in the ith row in the start-target matrix:
(2) Calculating the selection probability and the cumulative probability of each destination floor aiming at the starting floor i:
p ij ＝od(i,j)/D i ，
(3) In [0,1]Generates a uniformly distributed random number r, if q is i,j-1 ＜r≤q i,j And selecting the j floor as a target floor.
7. The method of claim 3, wherein in the method for calculating the possible arrival time of each passenger using the passenger arrival time module based on the desired passenger flow profile, the passenger arrival time algorithm: the required parameters are the average passenger arrival rate, the simulation time T, and the passenger flow statistics period length T.
8. The method of claim 3, wherein said passenger arrival time algorithm calculates the likely arrival time of each passenger based on a desired passenger flow profile, said passenger flow profile being a poisson profile.
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