JP2577161B2 - How to control the operation of multiple elevator cars in a building - Google Patents

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

This invention relates to elevator control software.

[0002]

2. Description of the Related Art In order to maximize the performance of an elevator system, it is desirable to allocate a car to service a hall call. This involves a number of algorithms that determine the number of passengers in the car, determine the traffic mode of the elevator system, estimate the number of landing passengers at each landing, and calculate trade-offs between different performance parameters. Including using. These algorithms can be implemented as a plurality of fixed rules.

[0003] However, fixing rules causes problems in boundary conditions. Some of the rules that determine the system traffic mode for a day may be, for example, "time is between 7:00 AM and 9:00 AM, and <
If other conditions>, then set the system traffic mode to the rising peak. "The problem with such a rule is 6:59.
All other conditions that will cause the AM to set the system traffic mode to peak peak will already exist, but it is not possible to consider the system to be in peak traffic mode due to the fixed rules. That is not possible. Even though the input conditions, the dominant traffic pattern, will likely change gradually between 6:59 AM and 7:00 AM, the operation of the system can change abruptly depending on the traffic mode.

A similar problem exists with other car assignment evaluation algorithms. Generally, the input conditions change gradually and, in most cases, continuously, but the response to these changes, ie, the response of the system (and, ultimately, the assignment of cars to hall calls), causes the system to pass through boundary conditions. When a transition is made, it changes rapidly and discontinuously.

[0005]

SUMMARY OF THE INVENTION It is an object of the present invention to (a) estimate the number of passengers in a car, (b) assign a car to service a landing call, (c) assign a car in an elevator system. Utility (utility)
(D) determining the number of people waiting at the landing in anticipation of car service, and (e) determining the traffic mode of the elevator system.

In accordance with the present invention, a signal representing the weight of passengers in a car is generated by selecting a plurality of terms from a plurality of observed weight fuzzy sets to form a fuzzy set representing the number of passengers in the car. Used, (a) each observed weight fuzzy set corresponds to a specific passenger count, (b) each observed weight fuzzy set term (term) is (i) a basis element corresponding to passenger weight And (ii) the frequency of observation of the weight represented by the associated base element, and the degree of membership corresponding to the number of passengers represented by the set.

Further in accordance with the present invention, the assignment of cars to hall calls is performed by using a fuzzy set representing car assignment utility, the set comprising:
Base elements corresponding to each car of the elevator system;
(B) having a degree of membership corresponding to the usefulness of the assignment to hall calls that can be represented by the associated base element.

Further in accordance with the present invention, the utility of assigning each car to service a hall call is:
(A) estimating the performance of each car for each of the plurality of performance criteria; (b) normalizing the estimated performance by a value representing the importance assigned to each performance criteria; and (c).
The utility is determined by setting it equal to the maximum of the scaled performance values.

Furthermore, according to the present invention, the usefulness of assigning each car to a hall call includes: (a) estimating the performance of each car for each of a plurality of performance criteria; and (b) estimating the estimated performance. It is determined by standardizing by a value representing the importance assigned to each performance standard, and (c) selecting a minimum value from the standardized performance values. Further according to the invention, (a) when the hall call button is pressed or when the elevator service is stopped, the instantaneous passenger rate of the elevator system is calculated, (b) then the mode of the elevator system is followed, (i) the rate of increase (Ii) average these instantaneous passenger rates in one or more of the rate of descent rates, and (iii) the rate of off rates, and (c) in the time since a landing was last serviced, By multiplying by one or more of the ascending, descending, or off-rate amounts according to the mode, the number of people waiting at the landing is determined.

Further in accordance with the present invention, the rising peak start rule, rising peak end rule, falling peak start rule, and falling peak end rule are separately evaluated and combined to form a fuzzy logic set representing the traffic mode of the elevator. . This logical set has a term corresponding to the rising peak, a term corresponding to the falling peak, and a term corresponding to the off-peak, and the degree of membership of these terms depends on whether the elevator system is in the rising peak mode, the falling peak mode, respectively. And off-peak mode.

The above and other objects, features and advantages of the present invention will become apparent from the following description based on the accompanying drawings.

[0012]

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS An elevator system 20 shown in FIG.
It includes a car 22, a second car 23, a first electric motor 24 having a pulley 26, a second electric motor 25 having a pulley 27, and counterweights 28, 29. One end of the first cable 30 around the pulley 26 is connected to the first car 22, and the other end is connected to the counterweight 28. One end of the second cable 31 around the pulley 27 is connected to the second car 23, and the other end is connected to the counterweight 29.

In response to activation of one or more car call buttons 36 by a car passenger (ie, a passenger in car 22), first motor 24 causes first car 24 to move the first car to a plurality of floors of the building. Exercise between floors 32-34. In response to activation of one or more car call buttons 37 by a car passenger, the second electric motor 25 moves the second car between the plurality of floors 32-34 of the building. Also, the electric motor 24,
25 moves the cars 22,23 between floors in response to one or more landing calls. A landing call occurs when a passenger at the landing presses one or more landing call buttons 38. The landing passengers are waiting in one hallway of the floor to receive service from one of the cars 22, 23 (elevator system 20).
Is the expected user.

The electronic elevator controller 40 of the elevator system 20 includes a car call button 36, 37, a landing call button 38, a first weight sensor 42 located on the floor of the first car 22, and a second It receives an electronic input signal from a second weight sensor 43 located on the floor of the car 23. The weight sensors 42, 43 are respectively connected to the cages 22, 2,
3 generate an electronic signal that varies according to the weight of the passenger. The controller 40 supplies an output signal to the first electric motor 24 to move the first car 22 between the floors 32-34. Further, the control device 40 supplies an output signal to the second electric motor 25 to move the second car 23 between the floors 30 to 32.

The electronic hardware of controller 40 is a conventional microprocessor system, which is well known to those skilled in the art, for storing a microprocessor (not shown) and elevator control software. One or more ROMs (not shown)
Or more RAM (not shown) and motor 2
Means for supplying output signals to 4, 25 (not shown)
And car call buttons 36 and 37, hall call button 3
And means for receiving input signals from the weight sensors 42 and 43 (not shown).

The data flow diagram 50 shown in FIG. 2 illustrates the operation of elevator control software stored in ROM and executed by a microprocessor. This software causes the control device 40 to provide an output signal that commands the operation of the motors 24 and 25 in response to the input electronic signal to the control device 40. “Box” shown in FIG. 2 represents a program module (part of elevator control software), and “cylinder” represents a data element (part of elevator control data). The arrow between the box and the cylinder indicates the direction of data flow. Unlike logic flow diagrams, data flow diagrams represent temporary relationships between various modules.

The input signals shown in FIG. 2 are summarized in the following table. Signal name Description weight Passenger weight Elapsed time Elapsed time counter Departure Arrival indicating car departure from various floors Arrival Landing call indicating car arrival at various floors Calling hall call performed from various floors The weight interpretation module 52 is supplied with weight signals from the weight sensors 42 and 43 and input data from the observed weight data element 53. The weight interpretation module 52 uses the weight signal and the observed weight data element 53 to estimate the number of car passengers. Passenger estimation is weight interpretation module 5
2 to the car passenger data element 54. The details of estimating the number of car passengers using the observed weight data element 53 and the weight signals from the weight sensors 42 and 43 will be described later.

A car passenger data element 54 is provided as an input to a traffic module 56 to which an elapsed time signal, a departure signal, and an arrival signal are also input. The elapsed time signal represents the elapsed time (ie, a counter that is incremented at a fixed rate). Departure signal is car 2 from various floors 32-34
Represents 2,23 departures. The arrival signal indicates the arrival of the cars 22, 23 on each of the floors 32-34.

There are three mode variables used by the elevator control software. That is, a rising peak, a falling peak, and an off-peak. The magnitude of the rising peak is
It represents the extent to which passengers are boarding to go upstairs from the lobby. The magnitude of the descending peak represents the extent to which passengers are boarding to get off the lobby from the upper floor. The magnitude of the off-peak represents the extent to which passengers are boarding between non-lobby floors. The values of the rising peak, falling peak, and off-peak are calculated in the traffic module 5.
6 is stored in the traffic mode data element 58. Details of the operation of the traffic module 56 will be described later.

The elapsed time signal, the departure signal, and the arrival signal are also provided as inputs to the count estimation module 60. A hall call signal indicating that one of the hall call buttons 38 has been pressed is also provided as an input to the count estimation module 60. The count estimation module 60 is also provided with an input from the car passenger data element 54 and an input from the traffic mode data element 58. The count estimation module 60 estimates the number of landing passengers waiting at a specific landing to receive service by a car. All floors except the top floor and the bottom floor have two landings, an ascending landing and a descending landing. The top floor has only a single landing and the bottom floor has only a single landing. The count estimation module 60 outputs the estimate to the landing passenger data element 62.
Details of the operation of the count estimation module 60 will be described later.

The performance estimation module 64 is provided with landing call signals and inputs from the traffic mode data element 58 and the landing passenger data element 62. The performance estimation module 64 responds to a particular landing call by
Predict the performance of 2,23. The operation of the performance estimation module 64 will be described later. The performance data element 66 and the customer preference data element 68 provide inputs to an allocation availability calculation module 70. Assignment availability calculation module 70
Determines the usefulness of assigning each car to answer a particular hall call by predicting the performance of each car 22,23 and scaling the predicted performance based on customer preferences. Assignment availability calculation module 70
The output from is provided to an allocation availability data element 72. Details of the operation of the allocation utility calculation module 70 will be described later.

The customer preference data element 68 and the allocation utility data element 72 include the uncertainty filter module 7
4 to provide input. Uncertainty filter module 74
Assigns a particular car to a particular hall call using the assignment availability data element 72. This assignment is made only if the uncertainty associated with the assignment of one of the cars is below a predetermined threshold indicated by the customer preference data element 68. When assigning a car to a hall call, if it is allowed to wait until the last allowed moment, the uncertainty filter module 74 assigns until the uncertainty associated with the assignment usefulness data element 72 is significantly reduced. No data is supplied to data element 76. If a car assignment must be made relatively quickly after a landing call is made, the uncertainty filter module 74 supplies data to the assignment data element 76 even if the uncertainty associated with the assignment is relatively high. The operation of the uncertainty filter module 74 will be described later.

The assignment data element 76 includes the cars 22, 2
An input is provided to a motion control system module 78 which provides signals to the motors 24, 25 for moving the 3. The motion control system module 78 includes an assignment data element 76
Car 2 in response to a landing call using information from
The assigned one of 2, 23 is stopped at a particular one of floors 32-34. Many types of general elevator software motion control systems are known to those skilled in the art and are currently in use. Many of the general elevator motion control systems use the motion control system module 7
8 is suitable.

The weight interpretation module 52 uses fuzzy logic (a division of mathematics closely related to basic set theory and logic) to enable each weight sensor 4
The weight signals from 2, 43 are converted one at a time into an estimate of the number of car passengers. Fuzzy logic involves the use of sets with base elements that are only partially included in the set. For example, the classical set C can be defined as (X, Y, Z), while the fuzzy set F is {0.3 | X, 0.7
| Y, 0.1 | Z}. Here, the number before the vertical bar (|) indicates the degree of membership of the base elements X, Y, and Z. The quantity 0.3 | X is called the term of the fuzzy set. The basis elements X, Y, and Z can represent numerical or non-numeric quantities. If the base elements X, Y, and Z represent numbers, the values of the base elements or terms are simply X,
Numerical quantity represented by Y or Z. A crisp value is any value or system of values that does not use fuzzy logic. To fully understand fuzzy logic,
Published by Computer Science Press, Rockville, MD in 984. J. See Schmucker, "Fuzzy Sets, Natural Language Computation, and Risk Analysis."

The implementation details of the operation of the fuzzy system are described below, but most implementations can be automated by tools that translate high-level fuzzy logic statements into compilable computer code. One such development tool is the Togai Fuzzy C Development System, manufactured by Togai Infralogic, Inc. of Irvine, Calif., Which converts fuzzy logic statements into compilable C code.

The observed weight data element 53 shown in FIG.
Are the number of passengers in the cars 22, 23 versus the weight sensors 42, 4
This is obtained by tabulating the magnitudes of the weight signals supplied from No. 3. Ideally, this table is created for a particular building in which the elevator system 20 is installed in order to obtain data in which the passenger load and the passenger weight distribution are taken into account. However, it is possible to construct the observed weight data element 53 using a general table showing the probability and distribution of people's weight. The tabulated data is used to form a plurality of fuzzy sets, which are stored in the observed weight data element 53.
Each fuzzy set corresponds to a specific passenger count. For each set, the degree of membership of each term corresponds to the number of times a particular magnitude of weight signal has been observed, and the basis element corresponds to a particular weight. Each set can be represented as FO (N), and each element can be represented as FO (N, W), where N is a specific passenger count and W is a specific weight.

FIG. 3 is a graph 90 showing hypotheses of a fuzzy set formed by expressing the magnitude of the passenger load versus weight signal. Graph 90 includes a plurality of plots 92
, And plot 92 comprises a fuzzy set or FO describing the different values of the weight signal for a single passenger.
Corresponding to (1), plot 93 corresponds to fuzzy set FO (2) describing the different values of the weight signal for the two passengers, and so on. The relative magnitudes of plots 92-103 represent the number of times a particular magnitude of weight signal was observed,
Therefore, it indicates the degree of membership of the fuzzy set term.

FIG. 4 is a flowchart 110 illustrating the operation of the weight interpretation module 52. In a first stage 111 of the process start, a fuzzy set FW (PC) (FW is "car fuzzy weight", PC represents a specific passenger count) is initialized to have no terms. Stage 111
In the next step 112, a variable representing the hypothetical passenger count PC is initialized to one. Decision step 1 following step 112
At 13, the value of the variable PC is compared with a predetermined constant value PCMAX equal to the maximum allowable number of car passengers.

If PC is not greater than PCMAX, control proceeds from the decision step to step 114 where the fuzzy set FO stored in the observed weight data element 53 is
The terms taken from (PC) are added to the fuzzy set FW. The added term corresponds to a passenger count equal to PC and a weight equal to the magnitude of the weight signal, ie, the FO (PC, weight) term. Step 115, after step 114, comprises P
Increment the C variable.

Steps 113 to 115 are repeated until it is determined at step 113 that the PC has exceeded PCMAX, at which point control proceeds from decision step 113 to step 116 where the fuzzy set FW is normalized. . Normalization of the fuzzy set involves dividing all degrees of membership of a term by a constant value so that the sum of all degrees of membership is equal to one.

[0031] Following step 116, step 117 defuzzies the fuzzy set FW to generate a value equal to the number of car passengers. Defuzzification is the process of reducing a fuzzy logic set to crisp, or single, values. The FW can be defuzzified by using the value of the term with the highest degree of membership. FW can also be defuzzified by adding the product of the degree of membership of each item and the number of car passengers represented by each item. Step 11 following step 117
8 stores the calculated number of car passengers in the car passenger count data element 54 shown in FIG. The car passenger data element 54 includes a traffic module 56 as described below.
And used by the count estimation module 60.
The traffic module 56 and the count estimation module 60 use the number of car passengers represented as fuzzy logic sets rather than as crisp values, and the weight interpretation module 52 performs defuzzification without defuzzifying the FW.
It is also possible to store the fuzzy set FW instead of the crisp value at step 118 through the logical path 119 bypassing 17.

The code shown in the flow chart 110 of FIG. 4 means that the microprocessor of the elevator controller 40 runs in real time. However, if only crisp values of the number of passengers are desired, the code shown in flowchart 110 can be executed off-line to generate a weight interpretation table. This weight interpretation table is a table of the magnitude value of the weight signal versus the number of car passengers.
2 makes it possible to index execution times.

FIG. 5 is a flow chart 120 showing the offline structure of the weight interpretation table.
0 and accessed by the weight interpretation module 52 to provide the number of crisp car passengers based on the magnitude of the weight signal. The weight signal is an index into the table, and the number of car passengers is an entry in that index. In step 122, two variables, old PC and new PC, are initialized to zero. First stage 12
Step 124 following 2 initializes the third variable W to zero.

Step 126, after step 124, is to calculate the number of car passengers in the new PC by the method shown in flowchart 110 of FIG. 4 (but using the value of W instead of the magnitude of the weight signal). Set equal to A decision step 128 following step 126 compares the new variable PC with the old variable PC. If the variables are not equal, control passes from step 128 to step 130, where the old PC and W are entered into the weight interpretation table being created. After step 130 is step 132, in which the variable old PC is set equal to the variable new PC. The table thus created has indicia defining the value of W (similar to the magnitude of the weight signal) that changes the number of passengers that are entries in the table. During run time, the weight interpretation module 52 searches the table for two adjacent indicia, one greater than the magnitude of the weight signal and the other less than or equal to the magnitude of the weight signal. The higher entry in the indicia of the two tables is the number of car passengers written in the car passenger data element 54.

After step 132 (or if the old PC
If equal to C, then step 134 (following step 128) causes a step W to increment the variable W by a predetermined constant W increment equal to the granularity of each weight sensor 42, 43 (ie, the minimum difference between two weight measurements). increase. In a decision step 136 following the step 134, W is compared with WMAX (a predetermined constant equal to the maximum possible weight signal). If W is WMA
If not, control proceeds from step 136 to step 1
Returned to 26. If W is greater than WMAX, control is passed to step 137 where old PC and W are added as the last entry into the table. At the end of step 137, the process is complete.

The weight interpretation module 52 supplies data representing the number of passengers in the car to the car passenger data element 54. The passenger count data element 54, the elapsed time signal, the departure signal, and the arrival signal are input to the traffic module 56. The traffic module 56 determines the dominant usage pattern of the elevator system 20 and supplies the result to the traffic mode data element 58. The traffic mode of the elevator system 20 is a rising peak indicating that most traffic is going from the building lobby to the upper floor, and a descent indicating that most traffic is coming from the upper floor to the lobby. It can be described as a peak or off-peak indicating that the dominant traffic pattern cannot be identified.

The data flow diagram 150 of FIG. 6 illustrates the operation of the traffic module 56. The elapsed time signal, the departure signal, and the data from the car passenger data element 54 are input to the climb calculation module 152. The rising calculation module 152 calculates the value of the rising peak variable using the rising peak start rule (described in detail below) and stores it in the rising peak data element 154. The rise calculation module 152 also determines the value of the off-rise peak variable using an up-peak termination rule (described in detail below) and stores it in the off-rise peak data element 156.

The elapsed time signal, the arrival signal, and the data from the car passenger data element 54 are calculated by the descent calculation module 16.
2 is input. The falling calculation module 162 calculates the value of the falling peak variable using a falling peak start rule (described in detail below) and stores it in the falling peak data element 164. The descent calculation module 162 also determines the value of the off descent peak variable using a descent peak termination rule (described in detail below) and stores it in the off descent peak data element 166. Off rising peak data element 156 and off falling peak data element 166 are calculated by off calculation module 170.
Supply input to Off calculation module 170 is element 1
Combining the data from 56 and 166 (combining methods will be described later) to calculate the value of the off-peak variable,
Stored in off-peak data element 172.

The rising peak variable stored in the rising peak data element 154, the falling peak variable stored in the falling peak data element 164, and the off-peak data element 1
The off-peak variables stored in 72 are input to the mode resolver module 174. The mode resolver module 174 combines these input data and supplies the result to the traffic mode data element 58.
The result can be either a single crisp value, or a fuzzy set, depending on the nature of subsequent processes that use information from the traffic mode data element 58. The crisp value (ie, a single indication of the mode) is
It can be obtained by considering the mode to be either up-peak, down-peak, or off-peak, depending on whether the up-peak, down-peak, or off-peak variables are the largest.

The mode resolver module 174 can also provide a fuzzy set indicating the traffic mode of the elevator system 20. This set has a term corresponding to the rising peak variable, a term corresponding to the falling peak variable, and a term corresponding to the off-peak variable, and the degree of membership of each term is a rising peak variable, a falling peak variable, And off-peak variables.

The rising peak start rule used by the rising calculation module 152 has the following shape. Here, ELEVATOR-1 is the most recent car departing from the lobby, ELEVATOR-2 is the second most recently departing car, and generally ELEVATOR.
OR-i is the i-th newly started car. DPT
URE-TIME-i is defined as the time elapsed since the i-th new car departed, where i is a number from 1 to N (the number of rules). The number of rules N is set equal to the number of cars. However, N may be selected to be less than or greater than the number of cars.

As an example, the rule corresponding to the case where i is equal to 3 is as follows. For each N rules (if the same process is applied to each rule in the set of rules), the climb calculation module 152 evaluates the conditional part of the rising peak start rule and evaluates the result (final value of the rising peak variable) Set according to the value of the condition. The final value of the rising peak variable is equal to the maximum value obtained from the evaluation of each N rising peak start rules (the set of cardinal numbers N including the rule governing the start of the rising peak condition).

FIG. 7 is a flowchart for evaluating the N rising peak start rule. In a first step 182, i is initialized to 1 and in a second step 184, a variable old rising peak is initialized to 0. In the next decision step 186, the value of i is compared with the value of the number N of rules. If it is determined at decision step 186 that i is greater than N, the process is complete.
If i is not greater than N, control is transferred from decision step 186 to step 188 to calculate the value of the rising peak variable using the rising peak start rule. Following step 188 is step 190, where the variable i is incremented.

Following the step 190 is a decision step 192 where the old rising peak is compared to the rising peak. If the old rising peak is not greater than the rising peak, control passes to step 194 where the old rising peak is set equal to the rising peak. If the old rising peak is greater than the rising peak, control is passed to step 196 where the rising peak is set equal to the old rising peak. Control is returned from step 194 or step 196 to decision step 186. Stage 1
92, 194, 196 ensure that the variable rising peak and the old rising peak are always equal to the maximum of the values calculated for the rising peak in step 188.

The rising peak end rule used by the rising calculation module 152 has the following form. The rise calculation module 152 processes the N rise peak end rule in the same manner as the rise peak start rule shown in FIG. 7, so the off rise peak is the maximum obtained as a result of evaluating each N rise peak end rule. Value. The rise calculation module 152 stores the value of the off rise peak variable in the off rise peak data element 156.

The falling peak start rule used by the falling calculation module 162 has the following shape. On the other hand, the descending peak end rule used by the descending calculation module 162 has the following shape.

[0047] Just like the rise calculation module 152, the fall calculation module 162 processes the N falling peak start rule and the falling peak end rule in the same manner as the processing of the rising peak start rule shown in FIG. The values obtained for the falling peak and the off falling peak are the maximum of the calculated values for the N falling peak start rule and the falling peak end rule, respectively. The descent calculation module 162 converts the values of the descent peak variable and the off descent peak variable into the falling peak data element 1
64 and off falling peak data element 166, respectively.

The off calculation module 170 calculates the variable off peak as an off rising peak (off rising peak data element 15).
6) and the maximum of the off-falling peak (from off-falling peak data element 166). The variable off-peak is provided to the off-peak data element 172 as an output of the off-calculation module 170. Rising peak start rule,
The rising peak ending rule, falling peak starting rule, and falling peak ending rule can be generally described in the following form.

If <condition> thens
et X-PEAK where X-PEAK is any of a rising peak, a falling peak, an off rising peak, or an off falling peak. In the case of a fuzzy logic conditional expression, the result variable (the condition "the
The value of the variable following n ″ is set according to the value of the condition. Therefore, in the above equation, X-PEAK is set to <condition
ion>.

The conditional parts of the rising peak start rule are connected by a logical product (AND) (DPTURE-T
Includes multiple simple expressions such as (IME-is is SHORT-PERIOD) and (iis SEVERAL-CARS). The value of the condition is the minimum of these simple expressions. To evaluate these simple expressions, use SHORT
-PERIOD, SEVERAL-CARS, and HE
AVY-LOADED needs to be quantified.

Please refer to FIG. 8, FIG. 9 and FIG. A graph 200 in FIG. 8 shows a fuzzy set for representing SHORT-PERIOD, and a graph 202 in FIG.
A fuzzy set to represent L-CARS is shown, and graph 204 of FIG. 10 shows a fuzzy set to represent HEAVY-LOADED. SEVERAL-CAR
The S-graph 202 is for an elevator system having several cars. It is difficult to show a fuzzy set graphically for a two car elevator system as in FIG.

The graph 200 shows SHORT-PERIO.
Plural squares 210 to 217 representing terms of D fuzzy set
Are superimposed. Similarly, SEVER is shown in graph 202.
A plurality of squares 2 representing terms of the AL-CARS fuzzy set
20 to 227 are superimposed, and HEAVY is displayed in the graph 204.
-LOADED A plurality of squares 230 to 242 representing the elements of the fuzzy set are superimposed.

The vertical axes of the graphs 200, 202, and 204 are squares 210 to 217, 220 to 227, and 230 to 242.
Indicates the degree of membership for the term represented by, and the horizontal axis represents the value of the base element. For example, a square 210 represents a SHORT-PERIOD fuzzy set term in which the value of the base element is 0 and the degree of membership is 1.0, and a square 213 has a value of the base element of 3 and a degree of membership of about 0. .4 represents the terms of the short-period fuzzy set.

Each simple expression for the rising peak start rule is given by SH
Evaluated using the ORT-PERIOD, SEVERAL-CARS and HEAVY-LOADED fuzzy sets. (DPTURE-TIME-i is SH
The value of (ORT-PERIOD) is such that the base element is DPTUR
The degree of membership of the SHORT-PERIOD fuzzy set term equal to E-TIME-i. For example, if the value of DPTURE-TIME-i is 5 minutes, the formula (DPTURE-TIME-i is SHORT-P
ERIOD) is SHORT-P whose base element is equal to 5 minutes
ERIOD equals the degree of membership of the fuzzy set term.

The formula (i is SEVERAL-CAR)
The value of S) is SEVERA with a basis element equal to i
L-CARS is a degree of membership of a fuzzy set term. For example, if i is equal to 3, the expression (i is S
EVERAL-CARS) is the square 2 in the graph 202.
SEV with a basis element equal to 3 as shown in 23
ERAL-CARS equals the degree of membership of the fuzzy set terms.

The formula (DPTING-ELEVATOR-i)
The evaluation of (is HEAVLY-LOADED) depends on whether the number of passengers in car i, which is the input value to the traffic module 56, is a crisp value or a fuzzy set. If the number of car passengers is a crisp value, the formula (DPTING-ELEVATOR-iisH)
EAVILY-LOADED) is HEAVILY-LOADED whose base element value is equal to the number of crisp car passengers.
Equal to the degree of membership in the terms of the fuzzy set.

If the number of passengers in the mokago i is expressed as a fuzzy set, (DPTING-ELEVATO)
R-i is HEAVILY-LOADED) formula is a fuzzy set of car passengers and HEAVILY-LOADE
It is evaluated by taking the maximum value of the degree of membership of the terms of the fuzzy set formed by an AND operation with the D fuzzy set. In general, a fuzzy set formed by an AND operation of two fuzzy sets is a fuzzy set whose degree of membership is equal to the minimum degree of membership of the corresponding term (ie, a term having the same basis element). . For example, if F1 = {0.1 | A, 0.5 |
B, 0.7 | C} and F2 = {0.3 | A0.2 | C}
Then, the AND operation of F1 and F2 is {0.1 | A,
0.2 | C}. Formula (DPTING-ELEVA)
TOR-iis HEAVILY-LOADED) is the number of car passengers fuzzy set and HEAVILY-L
The maximum degree of membership of the terms of the fuzzy set formed by an AND operation with the OADED fuzzy set.

The evaluation of the rising peak end rule, the falling peak start rule, and the falling peak end rule is similar to the evaluation of the rising peak start rule described above. The rising peak, falling peak, and off-peak variables will be between 0 and 1. The processing described with respect to the traffic module 56 can be performed at run-time or off-line, and in the case of offline processing, an indication of a possible input to the traffic module 56 and an indication of a possible output from the traffic module 56. A table is created with The creation and use of a similar table for the weight interpretation module 52 has been described with reference to FIG. Those skilled in the art will understand from the specific example of FIG.
Techniques for creating and using similar tables for 6 will be apparent.

FIG. 11 is a data flow diagram 260 illustrating the operation of the count estimation module 60 for estimating the number of landing passengers waiting at a specific landing at a specific time.
The count estimation module 60 processes the elapsed time signal, the departure signal, the arrival signal, and the landing call signal together with the data from the car passenger data element 54 and the data from the traffic mode data element 58, and outputs the output to the landing passenger data element 62. Write.

The first rate calculation module 262 of FIG.
Data from the car passenger data element 54 is received along with an elapsed time input signal, a departure input signal, and an arrival input signal. The first rate calculation module 262 estimates a rate at which a passenger arriving at a certain landing waits for the service of a certain car at the landing. The calculation by the first rate calculation module 262 is based on the number of passengers entering a car at a landing and the time since the landing was last serviced. The first rate calculation module 262 includes:
The estimated rate and information representing a particular landing are provided to a first rate data element 264.

The second rate calculation module 266 receives an elapsed time input signal, a departure input signal, an arrival input signal, and a hall call input signal. The second rate calculation module 266 also
Estimate the rate at which a landing passenger arriving at a landing waits for one of the cars serving that landing. The second rate calculation module 266 supplies the estimated rate and information representing the specific landing to the second rate data element 268. The calculation by the second rate calculation module 266 (detailed below) calculates the time that elapses between when one car serves a particular landing and when the landing passenger pushes the landing call button for that landing later. based on.

The rate averaging module 270 uses the data from the rate data elements 264, 268 along with the data from the traffic mode data element to calculate the rate of increase and store it in the rate of increase data element 272 to calculate the rate of decrease. Then, the off rate is calculated and stored in the off rate data element 276. The climb rate data element 272 includes information representing the climb passenger rate (ie, the rate at which landing passengers arrive at the lobby and wait for a car to go to another floor). The descent rate data element 274 includes information representing the rate at which landing passengers arrive at another floor and wait for a car to go to the lobby. The off-rate data element 276 includes information indicating the rate at which the landing passengers arrive and wait for the car traveling between the non-lobby floors.

The first and second rate calculation modules 262, 2
Each time a new rate value is calculated by 66 and placed in the first and second rate data elements 264, 268, respectively, the rate averaging module 270 causes the elevator system 20
, The rising rate data element 272, the falling rate data element 274, and the off rate data element 276 are updated in accordance with the current mode. If the mode is a crisp value (i.e., a single value representing either rising, falling, or off), the rate averaging module 270 converts the data from the first and second rate data elements 264, 268 to the rising rate data element, respectively. 272, the falling rate data element 274, or the appropriate one of the off rate data elements 276.

On the other hand, if the traffic mode of the elevator system 20 is expressed as a fuzzy set representing the spread of the system being in ascending mode, descending mode and off mode, the first and second rate data elements 2
64, 268 are the rate-of-increase data elements 27 in proportion to the degree of membership of the traffic mode fuzzy set terms stored in the traffic mode data element 58.
2, applied to the falling rate data element 274 or the off rate data element 276.

The traffic mode data element 58, the up rate data element 272, the down rate data element 274, and the off rate data element 276 are provided as inputs to the rate conversion module 278. The rate conversion module 278 uses these input data and the arrival, departure, and elapsed time signals to estimate the number of landing passengers waiting for car service at a particular landing. The number of landing passengers at a certain landing (depending on the traffic mode of the system,
(From one or more of rise rate data element 272, fall rate data element 274, and off rate data element 276)
It is determined by multiplying the rate by the amount of time that has elapsed since the last landing for the service. The number of passengers is provided to the landing passenger data element 62 as a fuzzy set or crisp value, depending on the requirements of the subsequent process. Details of the operation of the rate conversion module 278 will be described later.

The first rate calculating module 262 calculates an instantaneous passenger rate INSTRATE1 when a car stops on the floor in order to respond to a certain hall call. Before the landing passenger gets into the car, the passenger in the car shall get off the car. Thus, the number of passengers in the car is determined by subtracting the number of minimum car passengers from the number of final car passengers (ie, the number of passengers in the car when the elevator door closes). First rate data element 26
The instantaneous passenger rate INSTRATE1 supplied to 4 is equal to the number of passengers in the car divided by the elapsed time since the particular landing was last serviced.

If the number of passengers supplied from the car passenger data element 54 is a crisp value, the above-described subtraction and division are performed as they are. However, if the number of car passengers is represented as a fuzzy set, the fuzzy set describing the number of passengers when the weight is minimum is derived from the fuzzy set describing the number of passengers in the car when the elevator door closes. Will be deducted. This subtraction subtracts all combinations of base elements,
This is accomplished by taking the minimum degree of membership of the terms thus subtracted. Terms with the same basis element are combined into a single term with a degree of membership equal to the maximum degree of membership of the combined terms.

For example, if the fuzzy set F1 is ｛u | A, v
| B, w | C}, and the fuzzy set F2 is {x |
D, y | E, z | F}. The fuzzy set formed by subtracting F2 from F1 is be equivalent to.

Terms with identical basis elements (for example, if (AD) equals (CE)) are combined by taking the maximum degree of membership of these terms. For example, max (min (u, x), min (w, y)). As an additional step to subtract the passenger count fuzzy set, eliminate any terms in the set that have basis elements less than 0 (because less than 0 passengers are in the car It is not possible).
The first rate calculation module 262 includes INSTRATE1,
That is, a fuzzy set indicating the arrival rate of the landing passengers is determined. The resulting passenger rate fuzzy set is provided to a first rate data element 264 along with information representing a particular landing.

When the landing call button is pressed, the second rate calculation module 266 determines that the landing passenger arrival rate INSTRAT
Determine E2. An INSTRATE2 fuzzy set is created using the elapsed time (T) between the last service for a particular landing and pressing the landing call button. This INSTRATE2 fuzzy set has a value of 1 / T, 2
/ T, 3 / T,. . . It has 10 / T basis elements, and each term has a degree of membership defined by the following equation:

Degree of membership = RTe^-RT  Where e is the natural logarithm and R is the fuzzy set correspondence
It is the base element of the attached term. Second rate calculation module
Rate fuzzy set INSTR generated by H.266
In ATE2, the arrival of the landing passenger follows the Poisson distribution
And The number of landing passengers increased by only one at a time
Absent. INSTRATE2 represents that particular landing
Along with the information, the second rate calculation module 266 sends the second rate data.
Data element 268.

The first rate calculation module 262 updates the value of INSTRATE1 stored in the first rate data element 264 in response to the car servicing the hall call. The second rate calculation module 266 responds to the landing passenger pressing a landing call by a second rate data element 268.
Is updated in INSTRATE2 stored in. I
The fuzzy sets representing NSTRATE1 and INSTRATE2 are ascending rate data element 272 and descending rate data element 2
74, used to update the fuzzy set stored in the off-rate data element 276.

Before using the values stored in the ascent rate data element 272, descent rate data element 274, and off rate data element 276 to update the value for the overall system rate, the lower floor is raised To ensure that higher floors are more likely to make a descending hall call.
Adjust the values of SRATE1 and INSTRATE2. When a new INSTRATE1 or INSTRATE2 is calculated in response to a car call or landing call during a descent, INSTRATE1 and INSTRATE
The E2 fuzzy set is divided by (i-1) / (F-1), where F is the total number of floors in the building and i is the specific floor serviced by the system. Similarly,
When a new calculation is made in response to a climbing car serving a call, INSTRATE1 and I
The base element of NSTRATE2 fuzzy set is (F-1)
/ (F-1).

The rate averaging module 270 is activated when INSTRATE1 is updated in response to a car servicing the lobby, or INSTRATE2 is updated in response to the lobby landing call button being pressed. The fuzzy set stored in the rate data element 272 is updated. The new rising rate fuzzy set is calculated by (0.2 × UM × INSTRATE) + (1.0−
(0.2 × UM)) × {fuzzy set of old rise rate} INSTRATE in the above equation is INSTRATE1 or I
NSTRATE2. The UM (elevation membership) corresponds to the elevating traffic rate (typically the number of passengers per minute entering the lobby to go to another floor besides the lobby) (traffic mode data element 58).
Is the degree of membership of the traffic mode fuzzy set term. UM is between 0 and 1. If UM = 0.2, the update is 0.04 and the new IN
STRATE + 0.96 old INSTRATE, if UM = 0.8, the update is 0.16 and the new I
NSTRATE + 0.84 old INSTRATE.
The use of UM in the above equation causes the climb rate fuzzy set to be affected by INSTRATE only to the extent that the elevator system is currently in climb mode. The above multiplication affects the degree of membership of the fuzzy set terms. The addition is performed using well-known standard techniques for fuzzy set addition.

The rate averaging module 270 may be configured to update INSTRATE1 in response to a car descending, or INSTRATE2 in response to the descending hall call button being depressed. Update the descent rate fuzzy set stored in the rate data element 274 to store the new value. The new descent rate fuzzy set is calculated by:

(0.2 × DM × INSTRATE) +
(1.0- (0.2 × DM)) × {False descent rate fuzzy set} INSTRATE in the above equation is INSTRATE1 or I
NSTRATE2. DM (Descending Membership) is the degree of membership in the traffic mode fuzzy set term corresponding to the descending traffic rate (usually the number of passengers per minute gathering on floors other than the lobby to go to the lobby). DM ranges from 0 to 1. The rate averaging module 270 includes INSTRATE1 or IN
When SRATE2 is updated, the off-rate data element 27
Update the off-rate fuzzy set stored in 6 and store the new value. The new value of the off-rate fuzzy set is calculated by:

(0.2 × OP × INSTRATE) +
(1.0− (0.2 × OP)) × {fuzzy set of old off rate} INSTRATE in the above equation is INSTRATE1 or I
NSTRATE2. OP (off peak)
Is the degree of membership of the traffic mode fuzzy set term corresponding to the off-peak traffic rate.
OP ranges from 0 to 1. Rate conversion module 278
Generates a fuzzy set representing the number of landing passengers waiting for a car at a specific landing at a specific time. First, a total rate fuzzy set is created by combining the fuzzy set from the rising rate data element 272, the fuzzy set from the falling rate data element 274, and the fuzzy set from the off rate data element 276. These sets are combined by reducing the degree of membership of each term of the set by the relative degree of membership of the corresponding term of the traffic mode fuzzy set. That is, the degree of membership of the fuzzy set is calculated as UM / (UM +
DM + OP), the degree of membership of the descent fuzzy set is reduced by DM / (UM + DM + OP), and the degree of membership of the off-rate fuzzy set is OP
Reduce by (/ UM + DM + OP). After reducing the degree of membership, the three sets are added to each other, and then the resulting base element value is divided by 3 to determine the total rate fuzzy set.

The elapsed time signal and the departure signal are used to determine the amount of time since a particular landing was last serviced. A fuzzy set representing the number of landing passengers waiting at a specific landing is obtained by multiplying the value of the base element of the total rate fuzzy set by the amount of elapsed time. The determined fuzzy set is provided by the count estimation module 60 to the landing passenger data element 62.

The processing described with respect to the count estimating module 60 can be performed at run time or off-line, in the case of off-line processing an indication of possible inputs to the count estimating module 60 and A table is created with entries representing possible outputs. The creation and use of a similar table for the weight interpretation module 52 is shown in FIG. 5 and described. One skilled in the art will readily create and use a similar table for the count estimation module 60 with reference to the specific example of FIG.

There are a number of indicators that are measures of the performance of an elevator system, such as average waiting time, waiting threshold, and average service time. The average waiting time is the average time between a hall call and service to that hall call. The wait threshold is the average number of people waiting longer than a predetermined amount of time. The average service time is the average time from when a landing passenger presses the landing call button to when he arrives at the destination floor. Average waiting time,
The details of calculating the wait threshold and the average service time are known. These elevator performance indicators can be calculated using either crisp values or fuzzy sets.

The hall call signal is transmitted to the performance estimation module 64
Supplied as input to. The performance estimation module 64 responds to a particular landing call by using the traffic mode data element 58 and the landing passenger data element 62 to form a plurality of performance fuzzy sets. Each performance fuzzy set corresponds to a performance indicator for a particular elevator. Each term in each set represents an estimate of a particular performance indicator corresponding to serving that landing with a particular car. Performance estimation module 64 stores these fuzzy sets in performance data element 66.

The graph 290 of FIG.
297, where the height of each bar represents the reciprocal of the estimated average latency for a particular car. A high bar indicates a short average waiting time. The graph 290 can represent a performance fuzzy set by having each base element of the set indicate a particular car and the degree of membership of each base element indicating the height of each bar 292-297. Similar fuzzy sets can be formed for any other elevator performance indicator, either directly measured or derived from direct measurement. The particular label chosen and the method of calculation will depend on various functional factors known to those skilled in the art.

The flow chart 300 shown in FIG. 13 shows the steps for constructing a plurality of performance fuzzy sets (eg, of latency), which are shown in flow chart 30.
It is indicated by P at 0. P (I, C) is the C-th term (car number equal to C) of the I-th performance fuzzy set
Is shown. In a first step 302, P is initialized to include no terms and fuzzy sets. Next stage 3
At 04, an index variable I that indexes all performance fuzzy sets is initialized to one. In decision step 306 following step 304, I is compared to a predetermined constant IMAX equal to the number of performance indicators.

If step 306 determines that I is not greater than IMAX, control is passed from step 306 to step 307, where the index variable C to index through the terms of the performance fuzzy set (and thus for each car) is one. Is initialized to
In the next decision step 308, C is compared with the number of cars CMAX in the system. If C is not greater than CMAX, control transfers from step 308 to step 309 where car C is assigned to service a particular hall call at a particular hall.

In step 310 following step 309,
P (I, I) equal to the C-th term of the I-th fuzzy set
C) is determined. At step 310, car C is assigned to a particular hall call and the performance of the system is calculated using formulas and calculation methods appropriate to the I-th performance indicator. The value calculated in step 310 is the degree of membership of the Cth term in the Ith performance fuzzy set.

After step 310 is step 311 in which the index variable C is incremented. After step 311, control is returned to decision step 308. If decision step 308 is C is CMAX
If so, indicating that the system performance for the I-th performance indicator has been calculated for all cars, control is transferred to step 312 where the index variable I representing a particular performance criterion is incremented. . Control is returned from step 312 to step 306, where I is compared to IMAX. If I is determined to be greater than IMAX, then all performance fuzzy sets have been calculated and the process is complete.

The customer preference graph 320 of FIG. 14 has a plurality of bars 322-328, each bar 322-328 corresponding to a particular elevator performance indicator, and the height of each bar indicating the importance of the performance indicator to the customer. Represents For example, the height of the bar 323 representing the average service time is higher than the height of the bar 322 representing the average latency, so that the average latency is used to optimize performance or to reduce the average service time. If you choose to use it to optimize performance,
This indicates that the customer prefers to use average service hours.

The graph 320 shows that each base element of the fuzzy set corresponds to a predetermined elevator performance sign, and the height of each bar 322 to 328 representing the relative importance of the performance sign of each elevator to the customer is represented by the fuzzy set. Corresponding to the degree of membership of each term, it is possible to represent a customer's favorite fuzzy set. The customer preference fuzzy set can be constructed by the elevator manufacturer or entered by the customer using various input means familiar to those skilled in the art. The customer preference fuzzy set is stored in the customer preference data element 68.

The performance data element 66 and the customer preference data element 68 are provided as inputs to an allocation availability calculation module 70. Assignment availability calculation module 7
0 determines the usefulness of assigning each car to service a certain hall call, and the assigned utility fuzzy set with the base element corresponding to each car (the degree of membership of each base element determines the associated car (Corresponding to the utility of assigning to a particular hall call) to the Assignment Utility data element 72.

[0091] The flow chart 340 of FIG. Symbol AU
Represents the assigned utility fuzzy set, and the symbol SP represents a plurality of reduced performance fuzzy sets. The symbol CP represents a customer's favorite fuzzy set. In the first step 342, A
The U and SP fuzzy sets are initialized to empty. Stage 34
In step 344 following 2, the index variable I to index into the performance fuzzy set and the reduced performance fuzzy set is 1
Is initialized to In a test step 346 following step 344, I is a predetermined constant IM equal to the number of performance fuzzy sets (ie, the number of performance indicators as in FIG. 14).
AX.

If I is not greater than IMAX at step 346, control passes from step 346 to step 348, at which step the variable C for indexing through all cars of the elevator system is set to one. You.
In a test step 350 following step 348, CMA is a predetermined constant equal to the number of cars in the elevator system.
Compared with X. If C is not greater than CMAX at step 350, control passes from step 350 to step 352.

In step 352, the degree of membership of the C-th term in the I-th reduced performance fuzzy set is determined by the degree of membership of the C-th term in the I-th performance fuzzy set and the customer preference fuzzy. I of set CP (I)
Set equal to the product of the degree of membership of the second term. If CP (I) is close to 1, indicating that the I-th performance indicator is important to the customer, the degree of membership of the C-th term in the I-th reduced performance fuzzy set is Will be approximately equal to the Cth term in the Ith performance fuzzy set. On the other hand, if CP (I) is zero or close to zero, indicating that the I th performance indicator is not important to the customer, the value of the C th term in the I th performance fuzzy set Irrespective of, the Cth term in the Ith reduced performance fuzzy set will be equal to or close to zero.

At step 354 following step 352, the variable C is incremented. After step 354, control passes to test step 35.
It is returned to 0 and C is compared to CMAX. If stage 35
If 0 indicates that C is greater than CMAX and all terms in the I-th reduced performance fuzzy set have been calculated, control passes from step 350 to step 356, and indexing through the performance indicator at step 356. The variable I used to attach is incremented. After step 356, control is returned to test step 346, where I is the number of performance indicators I
Compared to MAX. When all of the car-by-car factors are reduced according to the preferences of the building operator in step 352, the maximum degree of suitability is determined for each car.

If in test phase 346 I is IMAX
If so, control passes from step 346 to step 358 where index variable C is initialized to one. In test 360 following step 358, C is the number of performance indicators CMA.
Compare with X. If C is not greater than CMAX at step 360, control transfers to step 362, where the degree of membership AU of the Cth term of the allocation availability fuzzy set is AU
(C) is the first reduced performance fuzzy set SP
Set equal to the Cth term in (I, C) (first, car 1). In step 364 following step 362,
Variable I to index the reduced performance fuzzy set
Is set to 2 so that the terms of the second reduced performance fuzzy set for this car are compared with the terms of the first reduced performance fuzzy set for this car (S
P (1,2) vs. SP (2,2)).

In a test step 366 after step 364, I is compared to the number of cars IMAX in the system. If I is not greater than IMAX, control passes to step 368, where the Cth (car = C) term of the assigned utility fuzzy set is the Ith (first, second) reduced performance It is set equal to the larger of the leading values of the Cth term of the fuzzy set and the Cth term of the allocation utility fuzzy set. Step 368 ensures that the Cth term of the assigned utility fuzzy set is always equal to the maximum value among the Cth terms of all reduced performance fuzzy sets.

In the step 370 following the step 368, the variable I is incremented. After step 370, control returns to test step 366. If I is greater than IMAX at step 366, control proceeds from step 366 to step 372, where the car index variable C is incremented. Control after step 372 returns to test step 360, where C is the CMA
If it is larger than X, the process is completed. The resulting assigned utility fuzzy set is output to assigned utility data element 72.

The processing for the performance estimation module 64 and the allocation availability calculation module 70 described above is as follows.
Can be done at runtime or offline. For off-line processing, a table having indicia representing possible inputs to the performance estimation module 64 and the allocation utility calculation module 70 and entries representing possible outputs from the performance estimation module 64 and the allocation utility calculation module 70 is provided. Created. FIG. 5 illustrates and illustrates the creation and use of a similar table for the weight interpretation module 52. Those skilled in the art will readily create and use similar tables for the performance estimation module 64 and the allocation availability calculation module 70 from the specific example of FIG.

The allocation utility data element 72 is provided as an input to the uncertainty filter module 74. The uncertainty filter module 74 determines the final car assignment (crisp value) by selecting the assignment utility fuzzy set term with the highest degree of membership.
To determine. The uncertainty filter module 74 determines whether the assignment uncertainty is less than or equal to a predetermined value stored in the customer preference data element 68 and input to the uncertainty filter module 74, to the assignment data element 76. Supply quota. The uncertainty in the assignment determines the degree of membership of the term with the highest degree of membership,
Assigned Utility Defined as the quotient divided by the sum of the degrees of membership of all terms in the fuzzy set. Customers who prefer relatively early car assignments for landing calls will specify a high degree of uncertainty, and customers who do not care about early assignments will specify a low degree of uncertainty.

[0099] Alternatively, the uncertainty filter module 74 may provide the assignment to the assignment data element 76 after a certain predetermined amount of time stored in the customer preference data element 68. The value of the assignment would be the car represented by the basis element of the assignment utility fuzzy set with the highest degree of membership. As a third alternative, the uncertainty filter module 74 can adjust the uncertainty threshold as a function of the time elapsed since the landing call button was pressed. The threshold is increased as the elapsed time increases. The function of threshold versus time can be linear or non-linear depending on the requirements of the particular system.

The processing for the uncertainty filter module 74 described above can be performed at runtime or off-line. For off-line processing, a table is created having indicia representing possible inputs to uncertainty filter module 74 and entries representing possible outputs from uncertainty filter module 74. FIG. 5 illustrates and illustrates the creation and use of a similar table for the weight interpretation module 52. One skilled in the art will readily be able to create and use a similar table for the uncertainty filter module 74 from the specific example of FIG.

The invention described above is applicable to elevator systems having any number of cars, any number of floor landings, any maximum capacity, any maximum speed, or any other particular set of physical characteristics. is there. Similarly, the present invention provides a drive, counterweight, cabling,
It can be implemented independently of the physical design of the elevator system, including the door mechanism, hall call and car call signaling.

While the present invention has been described with reference to an elevator system having a single lobby floor on the lowest floor of a building, the present invention provides that the elevator system has one or more lobby floors, or none. This can be achieved regardless of whether the lobby floor is on the lowest floor of the building. Further, the present invention can be implemented independent of the process used to perform other elevator dispatch functions, the particular electronic hardware used to implement the present invention, or the design of the load weighing device. . Portions of the process described above are described in U.S. Pat.
94, 162, "Emotional Actuator Failure Detection Using Directional Thresholds", which can be implemented using electronic hardware equivalent to the hardware / software described. Instead of reading data from and writing data to data elements, the hardware will communicate by sending and receiving electronic signals.

Although only the execution time operations of the traffic module 56, the count estimation module 60, the performance estimation module 64, the allocation availability calculation module 70, and the uncertainty filter module 74 have been described, these modules 56, 60, 64, 70, 74 can be run off-line to generate a look-up table containing all possible inputs and available outputs. For the offline generation and use of the look-up table, FIG. 5 illustrates the weight interpretation module 52 and has been described in connection therewith.

Many modules that use fuzzy values as input can be adapted to use crisp values using techniques well known to those skilled in the art. The performance estimation module 64 and the customer preference data element 68 can be adapted for use with any type of elevator performance criteria. The invention can be implemented independent of the mechanism used to set or change customer preferences.

Although a specific embodiment of the present invention has been described above, it is understood that those skilled in the art can make various modifications, omissions, and additions without departing from the spirit and scope of the present invention. Should.

[Brief description of the drawings]

FIG. 1 is a perspective view showing an outline of an elevator system.

FIG. 2 is a data flow diagram showing the operation of elevator control software.

FIG. 3 is a graph showing experimentally observed elevator weight load data.

FIG. 4 is a flowchart showing the operation of the weight interpretation software module.

FIG. 5 is a flowchart showing offline creation of a weight interpretation table.

FIG. 6 is a flowchart showing the operation of the traffic module.

FIG. 7 is a flowchart showing the operation of the climb calculation module.

FIG. 8 is a graph showing a short-period fuzzy logical set.

FIG. 9 is a graph showing a SEVERAL-CARS fuzzy logical set.

FIG. 10 is a graph showing a HEAVY-LOADED fuzzy logic set.

FIG. 11 is a data flow diagram showing the operation of the count estimation module.

FIG. 12 is a bar graph showing an average latency performance fuzzy set.

FIG. 13 is a data flow chart showing the operation of the performance estimation module.

FIG. 14 is a bar graph showing a customer's favorite fuzzy set.

FIG. 15 is a data flow diagram illustrating the operation of the assignment utility calculation module.

[Explanation of symbols]

 Reference Signs List 20 elevator system 22, 23 car 24, 25 electric motor 26, 27 pulley 28, 29 counterweight 30, 31 cable 32 to 34 floor 36, 37 car call button 38 landing call button 40 electronic elevator control device 42, 43 weight sensor 52 Weight Interpretation Module 53 Observed Weight Data Element 54 Car Passenger Data Element 56 Traffic Module 58 Traffic Mode Data Element 60 Count Estimation Module 62 Landing Passenger Data Element 64 Performance Estimation Module 66 Performance Data Element 68 Customer Preference Data Element 70 Assignment Utility Calculation Module 72 Assignment Utility Data Element 74 Uncertainty Filter Module 76 Assignment Data Element 78 Motion Control System Module 152 Lift Calculation Module 154 Lift Peak Data element 156 Off rising peak data element 162 Down calculation module 164 Down peak data element 166 Off falling peak data element 170 Off calculation module 172 Off peak data element 174 Mode resolver module 262, 266 Rate calculation module 264, 268 Rate data element 270 Rate average Module 272 Rise rate data element 274 Descent rate data element 276 Off rate data element 278 Rate conversion module

 ──────────────────────────────────────────────────続 き Continued on the front page (31) Priority number 07/693181 (32) Priority date April 29, 1991 (33) Priority country United States (US) (72) Inventor Paul T. Wasser Jr. United States of America Connecticut 06074 South Windsor Maskel Road 67 (56) References JP-A-1-203187 (JP, A) JP-A-3-102082 (JP, A) JP-A-4-49182 (JP, A)

Claims (8)

(57) [Claims] 1. A relationship between the number of passengers of a plurality of elevator cars in a building and a weight signal of the elevator cars is obtained by experimentally and empirically measuring in advance, a base element of each term is set to a specific weight, and each base is specified. Multiple sets of fuzzy sets FO (N) = one set for each zero or maximum number of passengers in a particular car, where the degree of membership of the element is the frequency at which its weight appears during measurement.
{M ₁ / W ₁ , M ₂ / W ₂ ,... M _j / W _j } are created, and the fuzzy set FO (N) is accessed by using the current weight signal from each elevator car as an index, The number of passengers of some accessible fuzzy sets FO (N) is used as a base element of each term, and the degree of membership of the specific fuzzy set FO (N) that can be accessed is used as the degree of membership of the base elements. One fuzzy set FW
(PC), estimating the number of passengers of each elevator car from the fuzzy set FW (PC), and controlling the operation of the elevator car based on the estimation of the number of passengers. To control the operation of the elevator car in a car. 2. Estimating the number of passengers of each elevator car from the fuzzy set FW (PC) comprises:
The method of controlling the operation of a plurality of elevator cars in a building according to claim 1, including the step of determining the number of car passengers from the (PC) as a single crisp value. 3. The method for controlling the operation of a plurality of elevator cars in a building according to claim 2, wherein the base element of the term having the highest degree of membership is a crisp value. 4. Normalizing the degree of membership of each term of the fuzzy set FW (PC) and adding the product of the normalized degree of membership and the number of car passengers represented by each term. The method for controlling the operation of a plurality of elevator cars in a building according to claim 2, wherein the crisp value is obtained by the following. 5. The step of estimating the number of passengers of each elevator car from the fuzzy set FW (PC) comprises:
2. The method of controlling the operation of a plurality of elevator cars in a building according to claim 1, comprising the step of normalizing the degree of membership of each term of the (PC) and expressing the number of car passengers as a fuzzy set. 6. A relationship between the number of passengers of a plurality of elevator cars in a building and a weight signal of the elevator cars is obtained by experimentally and empirically measuring in advance. Multiple sets of fuzzy sets FO (N) = one set for each zero or maximum number of passengers in a particular car, where the degree of membership of the element is the frequency at which its weight appears during measurement.
{M ₁ / W ₁ , M ₂ / W ₂ ,... M _j / W _j } is created and stored, and a weight of a given value close to zero is set, and this is used as an index to obtain the fuzzy Access the set FO (N),
The number of passengers of some accessible fuzzy sets FO (N) is used as the base element of each term, and the degree of membership of the specific fuzzy set FO (N) that is accessible is used as the degree of membership of the base element. One fuzzy set F
W (PC) is created, the number of passengers of each elevator car is obtained as a single crisp value from the fuzzy set, and the given value is sequentially increased by a predetermined increment to a weight corresponding to the maximum number of passengers. The above process is repeated to create a weight-to-passenger table, and in response to the weight signal from the weight sensor of each elevator car, the elevator car based on the passenger number estimated from the weight-to-passenger table. Controlling the operation of a plurality of elevator cars in a building, characterized by controlling the operation of a plurality of elevator cars. 7. The method for controlling operation of a plurality of elevator cars in a building according to claim 6, wherein a base element of a term having the highest degree of membership is a crisp value. 8. Normalizing the degree of membership of each term of the fuzzy set FW (PC) and adding the product of the normalized degree of membership and the number of car passengers represented by each term. 7. The method for controlling operation of a plurality of elevator cars in a building according to claim 6, wherein the crisp value is obtained by the following.