JPH0517152B2 - - Google Patents

Description

DETAILED DESCRIPTION OF THE INVENTION [Object of the Invention] The present invention relates to an elevator that is controlled according to traffic demand, and particularly to a device that detects the characteristics of the traffic demand.

[Conventional technology]

Recently, microcomputers (hereinafter referred to as microcomputers) have been applied to various industries, and in the elevator field, they are being used as group management control devices that efficiently manage multiple elevators and unit control devices that control individual elevators. Applied. These efforts have made a significant contribution to elevator control equipment due to microcontrollers' small size, high functionality, high reliability, and low cost.

For example, in the case of group management control, it is possible to monitor individual hall calls online and select and allocate the most suitable elevator by taking into consideration the overall service status of hall calls, which greatly contributes to reducing waiting times. are doing. In addition, it has become possible to perform priority service control, such as having multiple elevators serve a hall with a large number of passengers, or having elevators with short waiting times serve executive floors, making it possible to perform fine-grained control. ing.

However, with conventional elevator group management control devices, operation is controlled using fixed, predetermined control logic (control algorithms) and parameters, so the system is not necessarily adapted to the ever-changing building environment. do not have. For example, the traffic demand at the time of building completion and the air traffic demand when there is a subsequent tenant change, business change, etc. will result in different destination traffic demands.
That is, the mode of traffic flow changes. Furthermore, within the daily traffic demand, destination traffic demand such as commuting to work, lunch, leaving work, and normal times changes significantly.

If traffic demand changes significantly in this way, efficient management and control becomes difficult, leading to a decline in service.

Conventionally, as proposed in Japanese Patent Publication No. 48-15502 and Japanese Patent Application Laid-Open No. 52-141942, traffic demand is detected and a plurality of representative points (hereinafter referred to as A method was adopted to control the elevator by determining which of the two points (referred to as the empty point) it was close to.

However, this method has the problem of not being able to adapt to cases where building management demands are not known or when new traffic demands arise due to changes in the building environment.

In addition, in many buildings, the traffic demand generally varies greatly depending on the day of the week, and a method has been proposed in which the elevator control mode is switched depending on the day of the week (Japanese Patent Laid-Open Publication No. 130457/1983).

However, it was necessary to respond individually to fluctuations in holidays and traffic demand that changed every 10 days.

Furthermore, almost the same traffic demand may occur repeatedly at schools, halls, etc. on an irregular basis, and it is difficult to understand these demands properly and, furthermore, to drive appropriately for this type of traffic. It was possible.

[Purpose of the invention]

SUMMARY OF THE INVENTION An object of the present invention is to provide an elevator traffic demand feature detection device that can automatically detect the distinctive characteristics of elevator traffic demand in accordance with the building in which the elevator is installed. .

[Features of the invention]

A feature of the present invention is that it includes means for extracting a new traffic demand mode as a control feature mode from among a large number of traffic demands detected by the elevator traffic demand detection means.

〔Example〕

An embodiment of the present invention will be described below.

The feature mode generation device for elevator traffic demand will be explained in detail with reference to specific embodiments shown in FIGS. 9 to 13. Before explaining the embodiments, the control concept of the present invention will be explained using FIGS. 1 to 8.

Figure 1 shows the traffic demand situation from 8:00 a.m. to past 2:00 p.m. on days when an elevator is installed in a building with one basement floor and 10 floors above ground, based on factors that represent the salient characteristics of traffic demand. Illustrated. However, the diagram omits the daytime period, and shows the traffic demand situation during the morning work hours and lunch hours. In the figure, curve C ₁ shows the elements of traffic volume, and time charts 9U to B1D show elevator ascent (U) and descent for each floor of the basement, 1st floor, 4th floor, and 9th floor.
The degree of congestion concentration at time (D) is shown as a digital value consisting of three values.

In order to control elevators in general, and in particular to optimally control multiple elevators installed in parallel at once, it is ideal to be able to predict in advance when each passenger will go from which floor and in which direction. However, as a practical matter, it is difficult to have thousands of users register their plans to use the elevator in advance, and it is impossible in buildings where there are many visitors or where many events are held.

Therefore, the present invention learns past traffic demand, detects the traffic demand of the building as the most effective means to enable optimal control of elevators for today's traffic demand, and calculates new traffic demand. Extract the mode as a control feature mode.

Transportation demand includes various factors, but the ones that place a particularly large burden on elevators are the traffic volume and the movement status between floors.

The function C(t) indicating traffic volume was conventionally defined by the following equation (1).

C(t) = Number of people boarding the elevator per 5 minutes in time period t / Number of people in the building served by the elevator in question x 100%... (1) This traffic volume is calculated between different elevators installed in different buildings. It is used to compare the magnitude of traffic demand between the two countries, and it is possible to roughly compare the amount of load. In other words, the normal traffic volume is around 4% to 6%, and when the traffic volume reaches 12%, it can generally be expressed as an extremely congested situation. From the perspective of elevator control, there is no need to compare with other buildings, so the number of people in the building can be treated as a mere constant. Therefore, in the present invention, the unit of traffic volume is expressed as the number of people using elevators per 5 minutes. Here, the number of passengers is expressed as the number of people in the car (the number of people boarding the car from the landing) or the number of people getting off the car (the number of people getting off the car from the car to the destination floor).

Next, the traffic volume for a predetermined period (the average traffic volume converted into one day from the traffic volume with a time constant of at least 7 days or more, preferably at least one day) is measured, and the traffic volume is calculated as shown in Figure 2. A frequency distribution map is created, and traffic volume evaluation levels α ₁ , α ₂ ,
α ₃ .

Even if the traffic volume is the same, the stress on the elevator will be more than double if all floors are evenly congested and if the number of boarding and alighting cars is concentrated only on the first and second floors. . Therefore, it is necessary to recognize this as a characteristic element in some way. However, since there is no established theory that defines the characteristics of the movement situation between floors (hereinafter abbreviated as traffic flow), the present invention uses the congestion concentration floor I(t) and the congestion concentration degree V of that floor for all floors.
I decided to express it by (t).

Due to the nature of elevator control, this congestion concentration floor I(t) and congestion concentration degree V(t) are based on the number of people in the car (symbol S), the number of people in the exit car (symbol R), and the upward direction (symbol U). In addition, by separately determining the downward direction (symbol D), the characteristics of various traffic flows associated with various traffic demands can be expressed more appropriately as characteristic modes.

From these, the following eight evaluation values are considered as characteristic elements of traffic flow.

Evaluation value indicating the distribution of passengers in the upward direction.

I ^S _oU (t) = Floor with the nth number of passengers in the ascending direction in the time period [t - Δt, t + Δt]... (2) V ^S _oU (t) = I ^S _oU (t) Number of passengers in the ascending direction car Number of passengers in the ascending direction during the time period [t-Δt, t+Δt]
...(3) Evaluation value indicating the distribution of passengers in the descending direction.

I ^S _oU (t), V ^S _oU (t), evaluation values that indicate the distribution of people in the uphill direction.

I ^R _oU (t), V ^R _oU (t) Evaluation value indicating the distribution of people in the downhill direction.

I ^R _oD (t), V ^R _oD (t) In the above, the array variable n is from 1 to the number of service floors - 1, so in the case of an elevator with 11 service floors, there will be a large number of 80 in total. It is necessary to obtain an evaluation value, store it, and process it for identification. Therefore, we decided to simplify the expression to a level that is practical for representing the characteristics of traffic flow.

First, regarding directionality, we decided to express the same floor as different floors depending on direction.

That is, the same effect can be achieved by setting data representing a directional floor as the detected crowded floor I(t).

Regarding the size of the array variable n, there is little need for a directional floor with a light load when evaluated from the stress exerted on elevator control. In other words, if several floors with heavy loads are taken as an element, it will be sufficient to improve the performance of group management. For example, when multiple elevators are managed in groups, control can be performed to give priority to other floors, or to assign multiple elevators to the same floor, allowing for optimal control according to traffic demand. becomes.

Therefore, for convenience of explanation, the values of the array variable n are assumed to be 1, 2, and 3 below. As a result, the 80 evaluation values described above can be reduced to 12. In other words, the evaluation value of each element can be obtained using equations (4) to (9) below.

I ^S _o (t) = n in time period [t-Δt, t+Δt]
Floor with the most number of passengers...(4) W ^S _i (t) = i in time period [t-Δt, t+Δt]
The number of passengers from the floor... (5) From equations (4) and (5), the congestion concentration level V ^S _o (t) of the floor I ^S _o (t) where car congestion is concentrated is as follows.

I ^R _o (t) = n in time period [t-Δt, t+Δt]
Floor with the most number of people leaving the car...(7) W ^R _i (t) = i in time period [t-Δt, t+Δt]
Number of people descending from the floor...(8) From formula (7) and formula (8), the nth floor where the elevator car is crowded is I ^R _o (t)
The congestion concentration level V ^R _o (t) is as follows.

Note that F is the number of floors with direction.

Finally, in order to further simplify this, in place of equations (6) and (9) showing the congestion concentration function V, the following
Equations (10) and (11) were adopted, and a total of eight evaluation values were used as elements expressing the characteristics of traffic flow.

V ^S (t)= _F 〓 ⁿ⁼¹ (V ^S _o (t)) ² ……(10) V ^R (t)= _F 〓 ⁿ⁼¹ (V ^R _o (t)) ² ……(11) The above equation expresses the concentration of people in the passenger car and in the alighting car.

FIG. 3 shows a typical traffic flow when commuting to work, which often occurs during the time period (t ₂ to _{t 3} ) shown in FIG.

a shows the degree of concentration on each floor, and the curve
fUPIN ₁ is the concentration ratio of passengers in the UP direction,
The curve fDNIN ₁ shows the concentration ratio of passengers in the DN direction. curve fUPOUT ₁ and curve
fDNOUT ₁ indicates the concentration ratio of the number of people in the elevator car in the ascending direction and the number in the descending direction respectively, and the sum of the passenger ratio in the passenger car and the passenger ratio in the passenger car is 100%, and each total number of passengers is indicated by the traffic volume. equals headcount.

Figure 3b shows the personnel ratio in the order of the floors with the highest concentration for passenger cars and unloading cars, and the curve
fVIN indicates the number of passengers in the car, and the curve fVOUT indicates the number of passengers in the car.

As described above, the traffic demand during the typical work hours [t ₂ to t ₃ ] shown in FIGS. 1 and 3 can be recognized by breaking down its characteristics into the following elements.

(1) The traffic volume is C[t ₂ ~ t _2A ] ≒ 120 people/min... (12) C [t _2A ~ _{t 3} ] ≒ 200 people/min... (13) (2) Crowding of passengers The concentration level is V ^S [t ₂ - t ₃ ] ≒ 60 ² + 30 ² ≒ 4500 ……(14) (3) The congestion concentration level of people dropping off the car is V ^R [t ₂ - _{t 3} ] ≒ 17 ² + 16 ² + 14 ² +13 ² +12 ² +11 ² +7 ² +4 ² ≒1545 ……(15) (4) The value of the array of n=1 to 3 of the car congestion concentration floor I ^S _o (t) is I ^S _o [t ₂ ~ t ₃ ] = $02, $01, $00 ...(16) (5) Array value of n = 1 to 3 of floor I ^R _o (t) where elevator car congestion is concentrated I ^R _o [t ₂ to _{t 3} ] = $07, $04, $06 ...(17) Furthermore, as for the concentration floor I(t) obtained by equations (16) and (17), the value of the congestion concentration is β as shown in Figure 2. Since a floor with less than ₁ cannot be called a concentrated floor, $00 is set in the array as the floor _Io of order n, which means that there is no concentrated floor.

In this way, the traffic demand when going to work is determined when the traffic volume is close to the maximum during the above-mentioned predetermined period (such as one day) and on a specific floor (lobby) (I ^S _{o = 1} (t) = $02). The majority of the passengers are from (V ^S (t)=
4500) and that I ^R _o (t), where the number of people moving in the DN direction is extremely small, has the characteristic that there is no $81 to $8B, which cannot be completely expressed in practical terms by the values of the five elements mentioned above. ing.

Next, we will explain how to recognize the various traffic demands that occur depending on the time of day and day of the week, extract them as distinctive feature modes for that building, and use them to control elevators.

First, the overall control principle of the elevator will be explained using the procedure shown in FIG. Note that FIG. 4 does not illustrate the operational flow of a program or a hardware circuit, but rather shows a conceptual procedure for explaining the process of detecting and learning feature modes.

First, in advance of the opening date of the new building, we set the predicted traffic demand characteristics mode in advance using an intelligent terminal, etc., in the hope that the elevator will operate smoothly after the building opens, that is, that the elevator will be optimally controlled. (Procedure)
P20). The points to be input at this time are, as shown in FIG. 14, the floors that are expected to be crowded due to boarding and alighting at the scheduled time (table No. T291), which are input in order into table No. T292. As a result, at least the most crowded floors have been determined, so the five characteristic element evaluation values described above are created as follows, assuming that the floor is most crowded between 10:00 AM and 10:30 AM.

(1) C [AM10 - AM10.30] = 135 ... (18) If the traffic volume is not specified, set 135 [persons/5 minutes], which corresponds to the intermediate level _α4 .

(2) I ^S [AM10−AM10.30]=$82, $05, $85
……(19) (3) I ^R [AM10−AM10.30]=$82, $05, $85
...(20) There was no instruction regarding crowds getting on or getting off, so I included the same floor for both.

(4) V ^S [AM10−AM10.30]=1700 ……(21) (5) V ^R [AM10−AM10.30]=1700 ……(22) Here, there is no concentration level specified, and Since three crowded floors were specified, the value corresponding to concentration level β ₂ was selected and set to 1700.

Further, since the characteristic modes such as going to work and lunch time shown in FIG. 1 are generally likely to occur, these may be set in the ROM etc. at the time of factory shipment. These are procedures that can be performed only in buildings where it is desired to have as optimal control as possible from the first day and, in principle, are unnecessary.

Next, automatic setting of feature modes for elevator control, that is, extraction and setting of new feature modes (procedures)
P30) will be explained. The detailed procedure of this part is shown in FIG. For example, one day's worth of traffic demand or one week's worth of traffic demand is detected (step P31), and the five characteristic elements described above, namely traffic volume and traffic flow characteristic elements, are calculated (step P32) and stored (step P33). ), at this time, for example, if traffic demand is detected every 7.5 minutes, 1
There were 192 groups for the day and 1344 groups for the week.
It takes approximately
10KB of non-volatile memory is required. Furthermore, learning calculations for feature mode extraction also take time, requiring high-speed calculation hardware. Furthermore, it is impossible to express the characteristics when the number of users is small. Therefore, if the detection is performed at a longer cycle when the traffic volume is small and at a shorter cycle when the traffic volume is large, the number of sets to be stored will be reduced accordingly. For example, if the characteristics are detected when a certain number of people are in traffic, there will be about 48 groups per day. However, for a predetermined period of time, e.g.
If 30 minutes or more have passed, feature detection will be performed at that point. Note that when this method is used, time data indicating the time width must also be stored in pairs with the feature data. The above procedure is repeated for a predetermined period of time, for example, one day (procedure P34), and one day's worth of analyzed traffic demand consisting of dozens or hundreds of sets is stored. Next, a feature mode extraction function for evaluating whether or not to set a new feature mode is determined.

First of all, the already determined feature element values are reevaluated into rough numerical values. First, the traffic levels α ₆ to _{α 1} shown in FIG. 2 are determined (step P35).

Next, using this traffic volume level, each set of traffic volume is converted into a traffic volume level function CV(t).

The number of traffic volume values obtained in Figure 2 a is 7 ($06
~$00), but it may be even smaller, for example, 4.

In addition, we created a frequency distribution of congestion concentration, and Figure 2b
A curve V ₁ is created, and distribution levels consisting of levels β ₁ to _{β 4} are provided. As a result, the congestion concentration function value V(t) of each set is converted into a congestion concentration level function VV(t) (step P37). Through the above steps, an array is created that expresses each set of traffic demand detected for a predetermined period using a coarse function (procedure
P37).

Furthermore, for example, using a feature mode extraction function PS _(n) expressed by the following formula, calculations are performed for all sets of detected traffic elements to extract feature modes (step P38). m is the number given to the feature mode.

PS _(n) = T _(n) {k ₁ (CV _(n) ) + k ₂ (VV ^S _(
n) +VV ^R _(n) }...(23) Note that feature mode numbers m may be added in the order in which they are detected. And the coefficient T(m) is for each feature mode m
The number of times or cumulative time that the same feature mode or similar mode is detected for This formula extracts characteristic modes from both the size of traffic volume, the number of times it has been detected, and the size of cumulative time, and is convenient for optimizing elevator control.

In the event that the number of feature modes exceeds a predetermined number, the feature mode extracted first and with a small T _(n) is included in the other closest feature modes.

In addition, when recognizing the same feature mode, it may be determined whether the first congestion concentration floor I ^S ₁ and I ^R ₁ match, or if all of I ^S ₁ to I ^S ₃ match. You can also judge by whether it is present (Step P38).

Multiple feature mode extraction function values obtained as above
Compare PS _(n) with each other and among novel feature modes.
Select the set with the largest PS _(n) or the top set,
Temporarily register as a new elevator control feature mode (Step P39). At this time, if there are no feature modes set in advance or if there are only a few feature modes, we will temporarily register more, or we will always temporarily register the control feature modes until the maximum number of control feature modes can be set. Make sure that the feature mode settings are completed. Note that the element values of the feature mode to be temporarily registered are not rough function values such as the congested floor concentration level function VV(t) and the traffic volume level function CV(t), but are based on the original functions V(t) and C(t). ), then subsequent identification will be accurate.

As described above, it was possible to automatically extract the characteristic forms (modes) that existed in the traffic demand unique to the elevator, but the present invention is not limited to this, and the present invention is not limited to this. It is also possible to configure a feature mode to be set.

A feature mode priority function φP _(n) that determines the priority of the newly automatically extracted and temporarily registered feature mode and the already registered feature mode,
By modifying the feature element function value of the registered feature mode, a new feature mode is generated, i.e., feature mode generation control (procedure) in which the extracted and temporarily registered new feature mode is permanently registered in the memory (table).
P40) will be explained using FIG. If there is a limit to the number of feature modes that can be registered due to memory capacity, this method determines whether or not to register based on their importance, deletes those with lower importance, and registers those with higher importance. It is to be registered.

Note that the element functions of the registered feature mode include the already mentioned detected traffic demand feature element function values C _n ,
In addition to I _n and V _n , a periodic function TP _n for learning features that can be repeated regularly and a time function TH _n for learning features that occur at regular times on a daily basis are added.

First, the empty points created by the multidimensional vectors expressed by the N1 set of feature element functions created in step P33.
For each P _n , the following procedure is used to learn which of the empty points P _n formed by the multidimensional vectors expressed by M1 sets of registered feature mode element functions is closest.

Note that weighting between each element is performed using constants k ₃ to _{k 5} .

The vector of the empty point P _o created by the vector of the detected feature element function of the traffic demand is expressed by equation (25).

K= _def k ₃ 0 k ₄ k ₄ k ₅ k ₅ 0 ......(24) P _o = KC _o I ^S _o I ^R _o V ^S _o V ^R _o ......(25) The empty point P _n is also described in the same way possible, a scalar between two points
P^ _on is obtained using equation (26) (step P41). In order to explain the principle, only the first floor is evaluated for the crowded floors, and the formula is simplified, but in reality, the second and third crowded floors were also included in the evaluation with lighter weights. It's better. You can also calculate the floor number difference (difference value = I ^S _o − I ^S _n ), but if they match, it will be “0”,
If there is a mismatch, "1" may be given.

The registered feature mode number m having the closest empty point P _n is determined by equation (26) and is stored as the closest registered feature mode (step P42).

m (P _o ) = MIN (P^ _o1 , P^ _o2 , ...P^ _oM1 ) ... (27) The above procedure is executed for n = 1 to n = N1, and the characteristic elements of traffic demand are detected one after another. m(P _o ) is stored in correspondence with the function (step P42).

Next, for example, the integrated value of the number of times or time of selection for each registered feature mode is determined and used as the evaluation function φT _n of the registered feature mode (step P44).

Next, the feature mode extraction priority function φP (m) is (28)
All registered feature modes are obtained from the formula.

φP _(n) = (1-k ₆ ) φP _n +k ₆ ×φT _n ... (28) In addition, φP _n on the right side of equation (28) is the value up to the previous processing, and the left side is the right-hand side calculation from the current processing. This means that it will be updated based on the results.

The feature mode having the smallest value or a plurality of lower feature modes is removed from registration (step P45). Therefore,
The feature mode that was temporarily registered earlier cannot take a large value, so it is likely to be removed.

The finally determined registered feature mode P _n (number
M2) is learned and set by exponential smoothing similar to equation (28) (procedure
P46).

For example, the traffic function C _n or the traffic level function
CV _n is the element value of the traffic demand determined to be close to characteristic mode m in the current predetermined period (one day or one week), or the weighted average of the element values of multiple traffic demands determined to be characteristic mode m. A learning (exponent smoothing process) calculation similar to that of equation (28) is performed using the obtained value and the element value of the registered feature mode to obtain a traffic volume element function value. Regarding the crowded floors, please refer to the second section.
Third function values, including past data, are selected in descending order of frequency of occurrence.

Also, the time-related functions TP _(n) and TH _(n) are (28)
Find it by exponential smoothing as in the formula (Step P47).

As for the period, the time from the previous detection to the current detection is learned for each feature mode P _n . If you learn about multiple cycles that occur frequently,
Features that repeat at different intervals can also be learned. Further, by individually learning a plurality of times that occur in one day and recording them as a time function TH _(n) , more accurate predictive control becomes possible.

Based on the feature modes extracted in the above manner, learned and set to generate (mainly registered) the functions of the elements, the traffic information for each feature mode is stored and learned in step P50 in Figure 4, and the data is Calculate the optimal control parameters for each feature mode and select and store the optimal control program based on the procedure.
P60).

Calculation of control parameters, selection of control programs, etc. should be performed by the actual computer if it has the capacity, or by an elevator or building monitoring computer or a large computer at the central maintenance center. .

Next, the characteristics of the current traffic demand are analyzed using a method similar to steps P31 and P32, and processing for identifying registered feature modes is performed using a method similar to steps P41 and P42 (step P70).

However, here I will provide a supplementary explanation of a slightly different example of improvement. In other words, in the former case, we did not include the time element, but in actual operation, especially in group management controlled elevators, the control delay from switching the control algorithm or control parameters to actually functioning is equivalent to the elevator's one-round time. average
It is considered to be several minutes rather than 120 seconds, and it has the property of becoming stable after 10 minutes.

Therefore, formula (26) or (27) for feature recognition
It is preferable to take temporal continuity into consideration in equations, etc. For example, in equation (27), only the term of the scalar quantity P^ _tn of the feature mode m that was selected last time and is currently controlling the elevator is set to (P^ _tn ) k ₇ , and the coefficient k ₇ is assumed to take a value smaller than 1. Then, the previously selected and current elevator control feature mode can be selected more easily. Second, it is also effective to provide similar means for characteristic modes that are repeated at the same time every day and to detect them early.

For example, if one of the characteristics is extracted and learned that the peak of work attendance occurs around 8:15 every day on weekdays, and 0.8 and 15 are stored as the time element function TH _(n) , For example, by switching only the relevant term in equation (27) to equation (29) and identifying the feature mode (step P70), it is possible to adapt to work attendance based on the attendance data learned in the past days (steps P50 and P60). It can be a driving mode.

P^ _tn ′=(P^ _tn )・(1−k ₈ k ₉ −｜t−TH _n
｜/k ₉・｜t−TH _n ｜<k ₉ ｜)……(29) Similarly to equation (28), equation (29) has the meaning that the left side P^ _tn is updated by the result of the right side operation. . Also, ||
The value of t-TH _n |<k ₉ | takes "1" when the conditional expression in | | is satisfied, and takes "0" when it does not hold.

For example, if k ₉ is 15 minutes, then if the current time t falls within ±15 minutes of the time indicated by the values 0, 8, and 15 of the element function TH _(n) indicating the predicted time learned up to the past day, then P^ _tn When the value of ' becomes smaller than the scalar quantity P^ _tn and the times match, the value of 1- _k8 becomes easier to select.

The characteristic mode identification (step P70) as described above indicates that the characteristics of the current traffic demand are approximately approximated to the registered characteristic mode P _n (the value obtained by equation (29) or (26) is smaller than the predetermined value). The operation of the elevator is controlled using the control data (control parameters and control program created in steps P50 and P60) determined by the characteristic mode P _n (step P75).

Due to changes in the building environment and layout within the building,
If the above values (values obtained using equations (29) and (26)) exceed the predetermined value due to sudden changes in traffic demand, etc., not only the first characteristic mode P _n but also relatively close Identify multiple feature modes, interpolate and use the control data for each feature mode, or add up the traffic information with weights according to the degree of proximity, execute step P60, and use the obtained parameters, etc. Control the elevator (step P75).

However, if there is an event that will take place only on that day, please specify the time and content of the event in advance by floor and type of control.
Input from a switch or KEY board, etc., and analyze the input contents to determine which floor and what kind of control will be performed (assignment of 2 priority services, extension of door opening time, non-stop, code registration permission, etc.) ), and if it is determined that the predetermined time period has arrived, the procedure
Priority is given to the operation mode and control parameters determined by P75, and at least part of the operation is commanded according to the event reservation contents (step 80). Through these steps,
The elevator operation is controlled using the obtained operating method (algorithm) and its control constants (parameters) that are predicted to always provide optimal control (P90).

Also, use the KEY board, etc., to set schedules that are expected to be subject to sudden changes in traffic demand due to building layout changes or exhibitions held for one month, etc., and set the characteristic element function values in the same way as explained in step P20. Temporarily set it as a new feature mode so that the learning speed until it is registered as a control feature mode is accelerated (Step P95).

It is assumed that the above description has provided an understanding of the mode of use of the present invention shown in FIG. 4, but a supplementary explanation regarding the overall matter will be provided below.

(1) After step 95 is completed, generally root P92−
2 returns to the feature mode settings, but in buildings where adaptive learning control cannot be implemented due to factors other than aiming for optimal control of elevators, such as ease of use and morale, it may be possible to use route P95-1. For example, by using a schedule setting device It is better to proceed to route P95-2 only when instructed.

An even better method would be to always proceed to route P95-2, but restrict active operations in steps P40 and P60, turn the device in steps P80 or P90 into an intelligent terminal, and send the learning results to a CRT, etc. for human judgment. Displayed in an easy-to-use format,
This means that the elevator manager performs active learning control such as checking or partially correcting the learned content and registering it.

(2) Figure 4 shows the learning control procedure.
Actual operations include, for example, traffic demand collection and detection included in step P30, feature mode identification in step P70, and elevator operation control in steps P75 and 90 are executed in parallel.

In particular, elevator operation control is generally required to operate immediately at all times, and it is naturally necessary to configure the system so that it operates in parallel with other procedures. For example, in the case of computer control, programs that require a large amount of processing, such as steps P40, P60, and P75, can be assigned to tasks lower than the task in step P90, so that the elevator control program can be executed preferentially, and learning control can be performed in free time. This can be achieved by having a configuration that executes it.

Alternatively, the learning control section may be used as a separate computer to perform parallel processing.

Next, the effects of implementing the principles of the present invention will be explained in the seventh section.
This will be explained with reference to FIG.

Figure 7 shows the traffic volume curve of the traffic demand for only visitors in the case of a building with many visitors during the day, such as a broadcasting center.
C ₃ is illustrated. The learning process for those who do not have a schedule reservation in advance will be explained on the first, second and nth days.
The dates are shown. The symbol PLW indicates the magnitude of the time function TH _o for the characteristic mode P _o of visitor traffic demand, which is zero on the first day and gradually expands.
Accordingly, it has been shown that the time period extracted as a feature mode is selected earlier in accordance with the traffic volume of visitors.

The symbol PLK indicates the detection status of the feature element function for extracting and setting the visitor's feature mode.

Figure 8 shows the movement of traffic flow between floors, including visitors. In other words, the number of passengers getting off at the top and bottom of the 4th floor and the bottom of the 1st floor is expressed as the number of people getting off the car.
fUPOUT ₁₈ and fDNOUT ₁₈ are shown, and a feature element function corresponding to this is learned.

Next, a first specific embodiment of the present invention will be explained with reference to the circuit diagrams shown in FIGS. 9 to 11 and the stored data shown in FIGS. 12 and 13.

The elevator operation control system 110 (executes step 90) is composed of a hall call registration circuit 111 to an elevator driving device 115, and devices for realizing each of these control devices or circuits 111 to 117 may be of known technology (for example, Tokukai Akira
52-140149) discloses a car weight detection device and a means for detecting the number of passengers getting on and off the car. The difference from conventional methods is that the information on these circuits is detailed and
For example, it is included in the door opening/closing control device 114. Open,
Even the operating signals of the close button and the photoelectric device are input to the traffic demand detection circuit 130 newly provided according to the present invention to detect traffic demand, and the characteristic mode of the traffic demand is learned and adapted to the identified result. It has a means for switching the control algorithm and control constants (parameters) based on the signal from the elevator operation control mode selection circuit 170 (step P75) that is finally fed back as a signal.

Traffic information detection circuit 130 whose details are shown in FIG.
The traffic demand signal D130 detected by the traffic demand characteristic mode identification circuit 150 (details are shown in FIG. 11)
The information learning circuit 160 for each feature mode stores data for each mode according to the identified feature mode that is input to and outputs from the traffic demand storage circuit D16.
1A and service status (number of elevators in operation, hall call duration, door opening time, noise level in the building,
Accumulation and learning of data collected by the memory circuit D161B (mistakes, mischief, car refusal, power supply voltage, temperature, etc.) and the time zone memory circuit D161C that memorizes the detected time and cycle (Step P50)
and memorize the results. Other circuits include a time signal generation circuit 140, which is used to control the operation of each circuit. There is also a reservation/setting circuit 190 related to the control of steps P20, P80, and P95.
, time storage circuit 191 , and control target registration circuit 19
3, the necessary data is stored. In addition, an energy saving degree command circuit 181 and a service degree command circuit 182
There is also a target value setting circuit 180 which is composed of an environmental degree command circuit 183 and an input device 184 which serves as an input means to these, and mainly includes a control form selection circuit 1.
70 and reflected on the elevator control.

Extraction of characteristic modes of traffic demand, which is particularly relevant to the present invention, will be explained with reference to FIGS. 10 and 11.

Input signal line L from elevator control system 110
111 to L117 collect data regarding elevator operation and traffic demand, and data is accumulated in circuit D131.

The start time at this time is stored in the circuit D131T. Such traffic information measurement is carried out by the circuit 131, and the current traffic demand is calculated by the circuit 133 every predetermined period (several minutes). That is, circuit 13
It is obtained by dividing the data of 1 by the measurement elapsed time obtained by finding the difference between the time stored in the circuit D131T and the current time.

This current value is smoothed by an arithmetic circuit 134 having a time constant of about several tens of minutes, and outputted as the traffic demand for the current time period through a signal line D134.

This signal is mainly used in feature identification by the circuit 157 (step P70) to stabilize the identification.

The circuit 132 receives an output signal D152 of the traffic demand element value calculation request circuit 152 shown in FIG. 11, which operates depending on the size of the data LD131 and the passage of time.
The contents of the circuits D131 and D131T and the current time are sampled and held.

Then, the data in circuit D131 is cleared,
It resets the current time to circuit D131T and performs data collection for the next traffic demand detection (including other information sampling).

Signal D containing the traffic demand detected in this way
Reference numeral 132 is input data to the traffic demand feature extraction section (step P30) made up of circuits 151 to 156 in FIG.

First, the circuit 151 calculates a function of the characteristic elements of the traffic demand using the signal D152-2, which is output a little later than the signal D152, every time a new traffic demand is detected, and stores this in the circuit 153 (step P32 , equivalent to P33).

Next, the circuit 154 executes control to create a traffic volume level to create an evaluation function (step P35) and to find a function to evaluate the degree of concentration of traffic flow (step P36). This control is executed when the current traffic volume (data signal D134) is low and a predetermined period of time, for example, one day has passed. At this time, the circuit 159 is provided for the purpose of creating the curve C2 shown in FIG. ₂ in advance before the elapse of a predetermined period and speeding up the control of the circuit 154.

Note that each time traffic demand is detected, the memory circuit 153
Data as shown in FIG. 12 is stored in the memory. That is, the nth one-time storage data D153n
This figure shows a case where the memory is composed of 13 pieces, which are sequentially stored in a band-shaped or functionally ring-shaped memory circuit.

A supplementary explanation will be provided regarding the passenger class identification element functions TM _1o and TM _2o , which were not described in the principle section.

Even for the same transportation demand, its characteristics change depending on the customer group and time of day.

For example, in the morning, the robot moves quickly, but at night, it moves slowly, and you may end up missing the elevator that came first. Let this state be the environment function TM _io .

In addition, there are many children, so the average load per person is light, and there are many prank calls, wheelchair calls, etc.
The customer demographic function TM _2o is used to represent the occurrence rate of VIP calls. These passenger class identification functions also become part of the stress on elevator control, so they are added as characteristic elements.

Feature extraction is performed in the circuit 155 from the data stored as described above (steps P37 to P38), and the extracted feature mode is stored in the stored data D159 shown in FIG.
is stored as data D156 of M1 sets of feature modes (total of 9 here) (procedure
P39). Furthermore, feature mode data based on event schedules and schedule settings in steps P80 and P95 are separately stored as D158.

It is assumed that feature modes based on schedule settings and initialized data are not erased by automatic learning.

Note that the process of identifying the characteristics of the current traffic demand by comparing it with the registered characteristic mode (step P70) is carried out by the circuit 130, which is the means for executing steps P31 and P32, and the means for executing steps P41 and P42, as described above. It can be similarly executed using the circuit 150 (circuits 156 and 157). The control feature mode, which is the output signal of the circuit 150, becomes an input to the control mode selection circuit 170, and a control mode signal adapted to the mode is input to the elevator operation circuit 116.

Although one embodiment of the present invention has been described above based on FIG. 9, the present invention is not limited to this.
For example, the circuits 151 to 156 and circuit 159 shown in FIG. 11 can make the section for setting and generating feature modes independent of the elevator control device.

For example, in the case of a device in which the elevator control circuit is controlled by a digital computer, the part that sets and generates the feature mode described above may be processed by a separate digital computer, or a device installed in the building management computer or elevator control room. , it is also possible to adopt a configuration in which the processing is controlled by a computer.

In addition, using the portable elevator maintenance device disclosed in Japanese Patent Application Laid-open No. 55-70684, this device can be connected only for a necessary period to capture changes in elevator traffic demand, set new feature modes, and It is also possible to perform generation by learning the feature mode that exists.

〔Effect of the invention〕

As explained above, according to the present invention, it is possible to accurately extract a new characteristic mode of traffic demand that an elevator has in accordance with the building in which the elevator is installed.

[Brief explanation of the drawing]

FIGS. 1 to 8 are diagrams for explaining the principle of an embodiment of the present invention, and FIGS. 9 to 11 are configuration diagrams of an embodiment of the traffic demand characteristic detection device according to the present invention.
2 and 13 are diagrams explaining stored data,
FIG. 14 is a diagram illustrating data for temporarily setting characteristic elements of traffic demand. 110... Elevator control system, 130... Traffic information detection circuit, 140... Time signal generation circuit,
150...traffic demand characteristic mode identification circuit, 15
7... Traffic demand feature identification circuit, 160... Information learning circuit for each feature mode, 170... Control form selection circuit, 151... Traffic demand feature element value calculation circuit, 155... Setting of feature mode for elevator control Circuit, 156... Elevator control feature mode generation circuit.

Claims (1)

[Claims] 1. A traffic demand detection means, a means for comparing the output of the traffic demand detection means with a set or registered traffic demand mode based on a predetermined evaluation value, and determining which traffic demand mode it belongs to; An elevator control comprising: means for selecting control parameters responsive to a traffic demand mode and applied to the traffic demand; and means for controlling a plurality of elevators serving multiple floors in accordance with the selected control parameters. An elevator traffic demand characteristic detection device for use in an elevator traffic demand detection device, wherein the determination means calculates a predetermined evaluation function including the number of passengers and the number of people alighting at the time of ascending and descending for each of a plurality of floors detected by the transportation demand detection means. An elevator traffic demand characteristic detecting device comprising means for extracting a new traffic demand characteristic mode other than the set or registered traffic demand mode based on the value.