CN1239928A - Device for managing and controlling operation of elevator - Google Patents
Device for managing and controlling operation of elevator Download PDFInfo
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- CN1239928A CN1239928A CN97180341A CN97180341A CN1239928A CN 1239928 A CN1239928 A CN 1239928A CN 97180341 A CN97180341 A CN 97180341A CN 97180341 A CN97180341 A CN 97180341A CN 1239928 A CN1239928 A CN 1239928A
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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
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
- Y02T90/10—Technologies relating to charging of electric vehicles
- Y02T90/16—Information or communication technologies improving the operation of electric vehicles
- Y02T90/167—Systems integrating technologies related to power network operation and communication or information technologies for supporting the interoperability of electric or hybrid vehicles, i.e. smartgrids as interface for battery charging of electric vehicles [EV] or hybrid vehicles [HEV]
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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
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S30/00—Systems supporting specific end-user applications in the sector of transportation
- Y04S30/10—Systems supporting the interoperability of electric or hybrid vehicles
- Y04S30/12—Remote or cooperative charging
Abstract
A device for managing and controlling the operation of elevators which is provided with a section of collecting traffic data for finding the traffic volume of the users of an elevator, a section of calculating the traffic volume based on the traffic data collected by the collecting section, a section of calculating the estimated traffic flow value of the users of the elevator moving up and down each story based on the traffic volume calculated by the traffic volume calculating section, a section of setting a control parameter for controlling the operation of the elevator based on the estimated traffic flow value calculated by the traffic flow calculating section, and a section of controlling the operation of the elevator based on the control parameter set by the setting section. In virtue of the above-specified constitution, the device is not required to store in advance the combinations of many traffic flow patterns and traffic volumes obtained from the patterns for controlling a group of elevators, but can manage and control the elevators by immediately calculating an estimated traffic flow value from traffic volume data observed so far and setting control parameters for managing and controlling the elevators corresponding to the calculated estimated traffic flow value.
Description
Technical field
The present invention relates to elevator operation management and control system.
Background technology
Fig. 7 be illustrate for example be used to estimate described in the JP-A-7-309546 with and the control purpose particularly in the annotated map of the groundwork of the flow of traffic of the prior art shaped traffic device control system of the vehicle that comprise many ladders.
In Fig. 7, label 11 expression traffic datas, it comprises such as the number of taking elevator at every layer and the quantitative information, label 13 expression explanations of opening the number of elevator in every leafing such as multilayer nerve (neural) network (control neural network) that is used for the flow of traffic 13 that basis comes from the resulting input traffic data 11 of relation that presets between volume of traffic and the traffic flow pattern by the generation of the elevator passenger of element representations such as amount, time period (timezone), direction and mobile flow of traffic and label 12 expressions.
Suppose in building, a certain building at the fixed time in the section, be multiplied by elevator at the i layer and open the elevator passenger number of elevator in the j leafing, that is, when the elevator passenger number that moves on to the j layer from the i layer is Tij, can followingly be illustrated in the flow of traffic in this time period in this time period:
Flow of traffic: T=(T12, T13 ..., Tij ...) ... (1)
So, the traffic data that can following expression generates and can observe by this flow of traffic:
Traffic data: G=(p, q) ... (2)
Wherein, p is the number that is multiplied by elevator at every layer, and q is a number of opening elevator in every leafing.
So flow of traffic is exactly the flow of this traffic, and volume of traffic is the amount that observes easily that can find according to flow of traffic.
In addition, but when the visual control result is made as E, except traffic data, also can be following expression control E as a result:
Control result: E=(r, y, m) ... (3)
Wherein, r is that distribution, y for the response time of hall call (hall call) are that number of times that (prediction miss) missed in every layer of forecast distributes and m is that number of times full when elevator (car) and when passing through every layer distributes.
Find flow of traffic T owing to be difficult to the accurate traffic data G that directly never is included in the information of the moving direction of elevator passenger in the object time section, so find flow of traffic with approximation method here.
At first, traffic flow pattern in a large amount of buildings of the hypothesis that is prepared in advance finds the fixed control parameter by simulation again, and traffic data G that generates when each traffic flow pattern is controlled and control is E as a result.Thereby, can obtain several relations between " volume of traffic and traffic flow pattern " and " traffic flow pattern and control result ".
Then, the relation of representing " volume of traffic and traffic flow pattern " with neural network.So, prepare multilayer neural network 12 for example shown in Figure 7, and traffic data 11 be provided and provide the traffic flow pattern 13 that generates traffic data 11 to input side respectively, as allowing so-called teacher (teacher) data of neural network learning to outgoing side.
As a result, when a certain traffic data of input, traffic flow pattern of neural network 12 outputs, this pattern is to approach to generate that pattern of the traffic data of being imported in the preprepared various traffic flow pattern most.
Therefore, acquire the traffic flow pattern of sufficient amount by preparing and make neural network 12 in advance, with respect to traffic data from the relation of " volume of traffic and the traffic flow pattern " acquired so far, neural network 12 is selected and output generates the flow of traffic of any volume of traffic, perhaps at least very near the flow of traffic of this flow of traffic.
When generating identical traffic data according to a plurality of Different Traffic Flows patterns, because under the fixed control parameter, flow of traffic is not simultaneously, the result is different in control, so neural network 12 can be selected traffic flow pattern, it allows to obtain specific control result by utilizing the relation between " traffic flow pattern and control result " from the traffic flow pattern that generates identical traffic data.
In addition, owing to can set controlled variable, so that pre-prepd traffic flow pattern obtains the optimal control result with methods such as simulations in advance, so in the time can estimating flow of traffic according to traffic data, neural network 12 can be set optimization control parameter.
In this prior art, determine accuracy rate that flow of traffic is estimated depends on how many combinations that can prepare in advance between traffic flow pattern and the volume of traffic that obtains from traffic flow pattern.Yet, existing problems, that is, owing to prepare and store the traffic flow pattern of all kinds in advance and the combination of the volume of traffic that obtains from traffic flow pattern needs huge memory capacity, and it can not distribute suitable elevator by corresponding current service state effectively, so this can not put into practice.
In the technology described in the JP-B-62-36954 problem is arranged also, promptly, because though it can be analyzed which kind of flow of traffic has taken place over, which kind of flow of traffic it can not estimate to take place at that time in real time, control the elevator operation management simultaneously, so it can not corresponding current service state, distributes suitable elevator effectively.
Therefore, the object of the present invention is to provide elevator operation management and control system to solve this problem, wherein said system can be estimated also can carry out elevator operation management and control corresponding to the flow of traffic of estimating from the flow of traffic of the traffic data that observes in real time.
Summary of the invention
Device and effect (operation)
Description of drawings
Fig. 1 is the annotated map of elevator operation management of the present invention and control system.
Fig. 2 is the annotated map of elevator operation management of the present invention and control system.
Fig. 3 is the annotated map of elevator operation management of the present invention and control system.
Fig. 4 is the annotated map of elevator operation management of the present invention and control system.
Fig. 5 is the annotated map of elevator operation management of the present invention and control system.
Fig. 6 is the annotated map of elevator operation management of the present invention and control system.
Fig. 7 is the annotated map of prior art shaped traffic device control system.
Carry out optimal mode of the present invention
First embodiment
Then, utilize the description of drawings first embodiment of the present invention.
Fig. 1 is the groundwork annotated map that the flow of traffic of elevator operation management of the present invention and control system is estimated.At this, explanation utilizes the situation that a plurality of elevators are operated in group (group) management control by way of example, explains this principle.
In Fig. 1, traffic data 11 comprises quantitative information, such as, every layer of each direction (UP/DOWN), be multiplied by the number and the number of leaving elevator of elevator, and with OD (starting/destination) data representation flow of traffic 13, wherein said data are illustrated in the volume of traffic from certain one deck to the elevator passenger that moves another destination layer in the whole volume of traffic.Multilayer neural network (control neural network) 12 is estimated traffic flow data 13 according to input traffic data 11.
When supposing in building, a certain building at this, at the fixed time in the section, be multiplied by elevator and open the elevator passenger number of elevator in the j leafing from the i layer, promptly, expression moves on to the OD data of the elevator passenger number of j layer from the i layer, when being made as TFij, can with the identical method of foregoing prior art example, come the following flow of traffic that is illustrated in the building, this is because it is the set of those OD data:
Flow of traffic: TF=(TF
12, TF
13..., TFij ...) ... (4)
In addition, following expression is generated and observable traffic data by this flow of traffic:
Traffic data: G4 (ON up (f1), ON dn (F1), OFF up (f1), OFF dn (f1))
ON up (f1): at the f1 layer, take upward number to elevator,
ON dn (F1): at the f1 layer, take the number of downward direction elevator,
OFF up (f1): at the f1 layer, leave upward number to elevator,
OFF dn (F1):, leave the number of downward direction elevator at the f1 layer ... (5)
Usually, though can find volume of traffic T shown in expression formula (5) from traffic flow data G, but be difficult to find accurate flow of traffic G from traffic data T on the contrary, wherein said traffic flow data G comprises the moving direction of representing elevator passenger and the information of the object time section shown in expression formula (4).
So, according to the present invention, except every day group's management and control, also from any layer move on to the past form of each traffic flow data of any layer about how many elevator passengers in the object time section by the neural network basis, find volume of traffic, and represent reflection (map) according to the volume of traffic of traffic flow data definition by neural network as every layer interlayer elevator passenger number.So, by in control group management process, utilizing the learning outcome of this neural network, utilize inverse mapping to this reflection, from traffic data, find flow of traffic G approx.
Therefore, for example make after finishing control every day that neural network is acquired the relation between flow of traffic and the volume of traffic that calculates from this flow of traffic.When input side provides traffic data, make neural network learning, and extract flow of traffic from outgoing side in this case, when a certain traffic data of input during as the run-of-the-mill of neural network, neural network can be exported and the corresponding flow of traffic of traffic data.That is, neural network can obtain to carry out as to the ability according to the inverse mapping of the mapping of traffic flow data definition volume of traffic.
Though when can special traffic during stream, operation control system is carried out group's management by setting with the corresponding controlled variable of flow of traffic, in elevator group controller control, have a plurality of controlled variable, such as the elevator number of distributing to crowded layer, set no service layer, forecast each elevator reach the time of certain layer, in the call distribution process to the weighting of each assessment index etc.
Yet, when can special traffic during stream, can be evaluated at control result under the qualification controlled variable with methods such as simulations, and can set optimum value for the controlled variable of each flow of traffic.That is, in the time can estimating flow of traffic, optimum value that can the automatic setting controlled variable.
Then, as embodiments of the invention, soluble in order to control the elevator operation management and the control system of a plurality of elevator group according to the flow of traffic of estimating by above-mentioned groundwork with Fig. 2.
Fig. 2 illustrates that group as the example of elevator operation of the present invention management and control system manages and the block diagram of control system.In Fig. 2, label (31 to 3n) expression is located at the hall call buttons at place, every layer of hall.When elevator passenger is handled at least arbitrary button of hall call buttons 31 to 3n, hall call is outputed to group management control unit 1 from the hall call buttons of handling, thereby group's management control unit 1 is implemented group's management control.
Each controller in the electric life controller 21 to 2m is handled each elevator according to the control command of group's management control unit 1, such as, move, stop and opening the door/close the door.
Here, group's management control unit 1 comprises in order to collect such as the action of each elevator and the traffic data collection part 1A of the traffic data of calling out that generates, in order to calculate the volume of traffic calculating section 1B of volume of traffic according to the traffic data of collecting, as in order to according to calculating the estimating part 1C that traffic data calculates the flow of traffic calculating section of flow of traffic estimated valve in real time, in order to produce and be used for teacher's data unit 1D of teacher's data of learning neural network by analyze moving of elevator passenger according to traffic data, produce teacher's data that part 1D produces in order to basis by traffic data, be configured to calculate the assessment function component part 1E of function of the flow of traffic estimating part 1C of flow of traffic estimated valve by learning neural network, in order to according to the flow of traffic estimated valve of estimating by flow of traffic estimating part 1C, set the controlled variable setting section 1F of the controlled variable that is used for controlling elevator group and in order to control the operation control part 1G of group's management according to presetting controlled variable.
Here, above-mentioned traffic data not only comprises in order to calculate the data of volume of traffic, also comprise in order to analyze the data of estimating flow of traffic that move of elevator passenger, such as, as the signal of calling one class of being undertaken by elevator passenger, as stop, making progress, to the elevator operation information of an inferior class, be multiplied by/leave the number of elevator, elevator information about changing such as load and object time section.
With reference to Fig. 3, the concrete operations that elevator group controller is controlled make an explanation as the operation of present embodiment especially.
Fig. 3 is the diagram of circuit that schematically illustrates group's management control.
At first, traffic data collection part 1A collects traffic data, such as, stop and moving the elevator behavior of a class, the number that is multiplied by/leaves elevator, elevator-calling, hall call and real-time called elevator (step ST10).
Then, volume of traffic calculating section 1B calculates traffic data G (step ST20) according to the traffic data of being collected by traffic data collection part 1A.By making volume of traffic calculating section 1B, can realize calculating to volume of traffic for example every just calculating in the past the number that is multiplied by/leaves elevator in 5 minutes in 1 minute.
Then, flow of traffic estimating part 1C calculates flow of traffic estimated valve (step ST30) in real time according to the traffic data that is calculated by volume of traffic calculating section 1B.Here, with reference to Fig. 4, can explain that flow of traffic is estimated operation in step S30.
The traffic data G that calculates is input to as shown in Figure 1 neural network 12 (step ST31).At this moment, will be as each element data ON up (f1) at the traffic data G as shown in the expression formula (2), ONdn (F1), the value of OFF up (f1) and OFF dn (f1) is input to each neuron (neuron) in the input layer of neural network 12.Therefore, the neuronal quantity in input layer is 4 * Z (Z is the number of plies in the building).
Here, neural network 12 is implemented known network and is calculated (step ST32) and the real-time volume of traffic estimated valve that finds by calculating of exporting.
In this case, each the neuronic output valve in the output layer of neural network 12 is made as the estimated valve of each element of the traffic flow data TF in expression formula (4).That is, by each the neuronic output valve with output layer be made as TF11, with the output valve of nervus opticus unit be made as TF12 ..., the estimated valve that can obtain traffic flow data is as the OD data.Therefore, the neuronal quantity in output layer is Z
2
Note, corresponding with each situation, can be set in the neuronal quantity in the interlayer arbitrarily.
In addition, by the building being divided into several zones, flow of traffic and volume of traffic all can be described in each zone.In this case, above-mentioned Z is a region quantity.
Now, get back to the explanation to Fig. 3, when obtaining the flow of traffic data estimator by neural network 12 in real time in step ST30, then controlled variable setting section 1F sets and the corresponding controlled variable of being estimated by neural network 12 of flow of traffic (step ST40).
Then, operation control part 1G carries out elevator group controller control (step ST50) according to the controlled variable of being set by controlled variable setting section 1F.
At will say, by repeating to proofread and correct following assessment function, can constitute in order to according to every day the group manage the traffic data that control period is realized by neural network 12, estimate this function of flow of traffic.
That is, for example, the group manages control and separate with every day, carries out off and on the correction (step ST60) by the flow of traffic assessment function of neural network 12 realizations.After finishing control every day, in the predetermined time interval perhaps for example weekly, can carry out correction to assessment function.
By making the relation between neural network 12 study flow of traffics and the volume of traffic, thereby and make neural network 12 improve the flow of traffic assessment function ability that special traffic stream assessment function ability surpasses acquisition last time, can realize correction to assessment function, wherein, calculate above-mentioned flow of traffic and volume of traffic according to the traffic flow data and the traffic data that find from the traffic data that obtains between correction assessment function of carrying out in last time and the correction assessment function of carrying out specifically.
Describe in order to proofread and correct the process of assessment function (step ST60) with reference to Fig. 5.
Fig. 5 illustrates in order to proofread and correct the diagram of circuit of flow of traffic assessment function.
Take out storage in the traffic data under the control in order to proofread and correct the data (step ST61) of assessment function from managing of among step ST10, collecting the group.
About in order to proofread and correct the traffic data of assessment function, needn't store the data of data conduct in order to proofread and correct of all collections.Can be made as one unit to about 5 minutes tentation data, and can storing predetermined data volume, for example office hours peace of for example per several data of time period, wherein occurrence characteristics traffic often between, for the usefulness of proofreading and correct assessment function.
Then, teacher's data unit 1D analyzes in order to proofread and correct assessment function to generate the traffic data (step ST62) in order to so-called teacher's data of learning neural network 12.
Here, teacher's data comprise respectively the combination from the traffic data and the traffic flow data of traffic data.Here, according to the number that is multiplied by/leaves every elevator,, come to find traffic data with the form of expression formula (5) with the method identical with the process of above-mentioned steps ST20.Form that can expression formula (4) finds traffic flow data.With reference to Fig. 6, further explain the process that finds them.
Will from it with bring into operation up or down its when reversing its route the sequence of operations of elevator be called scanning (scan).For example, suppose, in the object time section, in upwards scanning, the layer that stops is 1F (3 people are multiplied by) → 3F (2 people leave) → 4F (1 people is multiplied by) → 6F (1 people leaves) → 10F (10 people leave) with the number that is multiplied by/leaves certain elevator, as shown in Figure 6.
In this case, can two people that leave elevator at the 3F place are specific for be multiplied by the people of elevator from IF.Yet, can not be specific leave at 6F and 10F place elevator elevator passenger be multiplied by layer.
Therefore, be assigned in the same manner in the mobile combination of elevator passenger leaving the elevator number that elevator can not be specific.That is, in this case, two people 1F → 6F (0.5 people) that following distribution can not be specific, 4F → 6F (0.5 people), 1F → 10F (0.5 people) and 4F → 10F (0.5 people).
Then, these data are all changed in each zone.In example at Fig. 6, IF is made as the first area, 2F to 6F is made as second area and 7F to 10F was made as for the 3rd when zone, following row expression formula (6) expression traffic flow data is as OD (initial/destination) data:
TF12=2.5 (1F → 3F (2 people) and 1F → 6F (0.5 people))
TF13=0.5 (1F → 10F (0.5 people))
TF22=0.54F → 6F (0.5 people))
TF23=0.5 (4F → 10F (0.5 people)) ... (6)
By every elevator and every scanning calculating and integration said process, can find traffic flow data, wherein reflected mobile message about each elevator passenger in the object time section.
So, make neural network 12 study regulate neural network 12 (step ST63) as teacher's data by traffic data and data flow data that the traffic data with each storage obtains.
Known so-called passback broadcasting method is used as learning neural network 12.
Then, detect the estimation accuracy rate of flow of traffic.As the index of estimating accuracy rate, adopted the summation of the error of each corresponding element square, wherein element is meant the teacher's data that adopted and the flow of traffic estimated valve (step ST64) that is calculated according to the traffic data of teacher's data by neural network 12.
That is, amount to error E that the following expression formula (7) about all teacher's data finds respectively and total value is made as the index of estimating accuracy rate.Can consider that total value is more little, estimate that accuracy rate is good more.E=∑ (TFij-TFij)
2Each element value TFij=of the traffic flow data of TFij=teacher's data is according to each element value of the volume of traffic estimated valve of the traffic data calculating of teacher's data
…(7)
Then, assessment function component part 1E by the total value of the error E that finds of utilization expression formula (7) with in the process of proofreading and correct the assessment function of carrying out last time, by the total value E that uses the error that expression formula (7) finds compare (step ST65).
So, when estimating that accuracy rate improves (YES in step ST65), under the login of neural network that assessment function component part IE will regulate in step S63 (step ST67), otherwise (No in step S765) then gets back to last neural network and logins when precision fails to improve.
Therefore, the foregoing description does not need to prepare in advance and store a large amount of traffic flow patterns and the combination of the volume of traffic that obtains according to traffic flow pattern, immediately from the traffic data that is observed so far calculate the flow of traffic estimated valve and by set for the controlled variable of calculating the corresponding group's management of flow of traffic estimated valve control, can carry out elevator group controller control.
In addition, because the input data do not comprise any estimated valve, and be the volume of traffic that can observe immediately, so can and more accurately estimate flow of traffic with the calculating of pinpoint accuracy ground.In addition, thereby owing to so arrange present embodiment to be created in relation between volume of traffic and the flow of traffic by neural network, and constitute and proofread and correct assessment function by the analysis result that makes the neural network learning traffic data, so it does not need by storing mass data in advance both relation to be interrelated with a large amount of logics, and can be reduced to both are connected and calculate required program and storage area.
In addition, because according to once being adjusted to based on accuracy rate preceding based on traffic flow data and actual traffic amount data between interval of this time regulating of accuracy rate, can keep the estimation accuracy rate of the flow of traffic estimated valve estimated by the flow of traffic estimating part fine, so present embodiment allows elevator operation management and control system to meet the mobile variation of the elevator passenger that whenever hits the building, wherein above-mentioned variation depends on building and time period.
In addition, learn as teacher's data with the index of the estimation accuracy rate of calculating flow of traffic estimating part, can worsen the estimation accuracy rate so needn't worry the assessment function component part by adopting the unstable state traffic flow data.
It is because it comes to estimate and corresponding flow of traffic of time period at every predetermined amount of time by utilization teacher data that the present invention allows neural network to obtain adjusting, thereby its allows the corresponding flow of traffic of more accurately estimating with the time period, no matter rather than use the calculating section of what time period all as one man estimating flow of traffic.
In addition, in whole volume of traffic, the flow of traffic calculating section calculates the volume of traffic of flow of traffic estimated valve as the elevator passenger that moves between destination layer, so inerrably expressed moving of elevator passenger in the building.
In addition, the present invention is not only very effective at elevator operation management aspect of control, and distributes to a plurality of elevators mutually by calling out, and allows the elevator operation management of complexity is used for so-called group's management control to the control of execution optimum operation.
Commercial Application:
As mentioned above, can suitably use elevator operation management of the present invention and control system.
Claims (7)
1. an elevator operation is managed and control system, it is characterized in that, comprising:
Traffic data collection partly is used to collect the traffic data of the volume of traffic of trying to achieve elevator passenger;
The volume of traffic calculating section is used for calculating according to the traffic data of partly being collected by described traffic data collection the volume of traffic calculating section of described volume of traffic;
The flow of traffic calculating section is used for the flow of traffic estimated valve according to the described volume of traffic calculating mobile described elevator passenger between each layer that is calculated by described volume of traffic calculating section;
The controlled variable setting section is used for setting the controlled variable that is used to control described elevator operation according to the described flow of traffic estimated valve that is calculated by described flow of traffic calculating section; With
The operation control part is used for controlling according to the described controlled variable of being set by described controlled variable setting section the described operation of described elevator.
2. elevator operation management as claimed in claim 1 and control system, it is characterized in that, form described flow of traffic calculating section by neural network, wherein set the described volume of traffic of described elevator passenger, and set the described flow of traffic of described elevator passenger at described outgoing side at the input side of described neural network.
3. elevator operation management as claimed in claim 2 and control system, it is characterized in that, also comprise teacher's data unit and assessment function component part, wherein said teacher's data unit is used for producing teacher's data that described neural network is used for learning according to the described traffic data of partly being collected by described traffic data collection, and described assessment function component part is used for calculating the described flow of traffic estimated valve of described flow of traffic calculating section by coming for described neural network learning according to the described teacher's data that produced by described teacher's data unit.
4. elevator operation management as claimed in claim 3 and control system, it is characterized in that, described assessment function component part is set the index of a value as described estimation accuracy rate according to two squares error of each corresponding element of traffic flow data, and wherein said two squares are respectively teacher's data of being adopted with corresponding according to the described flow of traffic estimated valve that the described traffic data of described teacher's data calculates by described flow of traffic calculating section.
5. as claim 3 or management of 4 described elevator operation and control system, it is characterized in that described teacher's data unit is at the fixed time in the section, according to partly produced described teacher's data by described traffic data collection.
6. as described elevator operation management of claim 1 to 5 and control system, it is characterized in that in whole volume of traffic, described flow of traffic calculating section calculates the described volume of traffic of described flow of traffic estimated valve as the described elevator passenger that moves between destination layer.
7. as described elevator operation management of claim 1 to 6 and control system, it is characterized in that described operation control part is implemented implementation and operation control as group's management control.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CNB971803412A CN1187251C (en) | 1997-10-07 | 1997-10-07 | Device for managing and controlling operation of elevator |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CNB971803412A CN1187251C (en) | 1997-10-07 | 1997-10-07 | Device for managing and controlling operation of elevator |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN1239928A true CN1239928A (en) | 1999-12-29 |
| CN1187251C CN1187251C (en) | 2005-02-02 |
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ID=5177709
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| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CNB971803412A Expired - Lifetime CN1187251C (en) | 1997-10-07 | 1997-10-07 | Device for managing and controlling operation of elevator |
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| CN (1) | CN1187251C (en) |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN1750389B (en) * | 2004-09-15 | 2010-08-25 | 株式会社三丰 | Control parameter setting method for control circuit in measurement control system and measuring instrument |
| CN101837910A (en) * | 2009-03-19 | 2010-09-22 | 株式会社东芝 | Elevator cluster management system and method thereof |
| CN102050362A (en) * | 2009-11-10 | 2011-05-11 | 东芝电梯株式会社 | Elevator group management control device and elevator group management control method |
| CN103771199A (en) * | 2012-10-24 | 2014-05-07 | 通用电梯（中国）有限公司 | Elevator allocation calling system |
| CN103889872A (en) * | 2011-08-31 | 2014-06-25 | 通力股份公司 | Elevator system |
| CN104044965A (en) * | 2013-03-15 | 2014-09-17 | 株式会社日立制作所 | Elevator capable of logging in approximate number of passengers in advance |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP4980642B2 (en) * | 2006-04-12 | 2012-07-18 | 株式会社日立製作所 | Elevator group management control method and system |
-
1997
- 1997-10-07 CN CNB971803412A patent/CN1187251C/en not_active Expired - Lifetime
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1750389B (en) * | 2004-09-15 | 2010-08-25 | 株式会社三丰 | Control parameter setting method for control circuit in measurement control system and measuring instrument |
| CN101837910A (en) * | 2009-03-19 | 2010-09-22 | 株式会社东芝 | Elevator cluster management system and method thereof |
| CN102050362A (en) * | 2009-11-10 | 2011-05-11 | 东芝电梯株式会社 | Elevator group management control device and elevator group management control method |
| CN103889872A (en) * | 2011-08-31 | 2014-06-25 | 通力股份公司 | Elevator system |
| CN103889872B (en) * | 2011-08-31 | 2016-01-20 | 通力股份公司 | Elevator device |
| CN103771199A (en) * | 2012-10-24 | 2014-05-07 | 通用电梯（中国）有限公司 | Elevator allocation calling system |
| CN104044965A (en) * | 2013-03-15 | 2014-09-17 | 株式会社日立制作所 | Elevator capable of logging in approximate number of passengers in advance |
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| CN1187251C (en) | 2005-02-02 |
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