Patent Description:
Conventionally, this type of elevator management system moves cages between the lowest floor and the highest floor so that the distances between the plurality of cages in a gravity direction become equal. However, when some cages are delayed as many passengers get into and out of the cages, the plurality of cages cannot be operated uniformly and this may result in the occurrence of situations, for example, where the plurality of cages stop at the same floor at the same timing and waiting time at other floors become long.

So, PTL <NUM> proposes an invention for controlling an elevator management system so that cage waiting time at each floor becomes uniform in order to enhance cage operation efficiency. While this invention is premised on repetitive operation of each of the plurality of cages between the lowest floor and the highest floor, the invention is designed so that the positions and moving directions of the cages after a specified amount of time are set and the cages are operated in accordance with the set positions and moving directions. PTL <NUM> discloses an elevator control apparatus which uses a neural net to evaluate the car delay which would result from allocation of a hall call to each car. The apparatus includes an input data conversion unit for convening traffic data. including a position of the car, a direction of a movement and calls to be responded, into data that can be used as input data to the neural net. The neural net includes an input layer for taking in the input data, an output layer for outputting the estimated travel time, and an intermediate layer provided between said input and output layers and in which weighting factors are applied in combining signals from the nodes of the input layer and in distributing signals to the output nodes. The weighting factors are variable through a learning process which compares estimated and actual travel times. Different sets of weighting factors may be applied at different times of the day or under different detected conditions of passenger traffic.

PTL <NUM> discloses an elevator management system according to the preamble of claim <NUM> and an elevator management method according to the preamble of claim <NUM>.

The problem of the conventional elevator management system is that the operation of the cages may become wasteful. Therefore, the present invention aims at proposing an elevator management system and elevator management method for efficiently operating the cages.

In order to solve the above-described problem, the present invention provides an elevator management system according to claim <NUM>.

Furthermore, the present invention provides an elevator management method according to claim <NUM>.

The elevator management system and the elevator management method for operating the cages efficiently can be implemented according to the present invention.

(<NUM>) Configuration of Elevator Management System According to This Embodiment Referring to <FIG>, the reference numeral <NUM> represents an elevator management system according to this embodiment. This elevator management system <NUM> is configured by including a management server <NUM> for managing a plurality of elevators <NUM>. The management server <NUM> and the plurality of elevators <NUM> are connected via a communication path <NUM> such as an intranet.

The management server <NUM> is a management apparatus that acquires operation data of each elevator <NUM> via a receiving circuit, learns the operation status of each elevator <NUM> from the acquired operation data, and manages the operation of each elevator <NUM> by outputting management information via an output circuit. The management server <NUM> is configured by including a CPU (Central Processing Unit) <NUM>, an auxiliary storage apparatus <NUM>, and a memory <NUM>.

The CPU <NUM> is a processor (controller) that controls the operation of the entire management server <NUM>. The auxiliary storage apparatus <NUM> is composed of, for example, large-capacity nonvolatile storage devices such as hard disk drives and SSDs (Solid State Drives) and is used to store programs and data for a long period of time. Some of storage areas provided by this auxiliary storage apparatus <NUM> are used as an operation data table TB10 and a learning data table TB20 described later.

The memory <NUM> is composed of, for example, a volatile semiconductor memory, is also used as a work memory for the CPU <NUM>, and includes an operation storage module <NUM>, an operation learning module <NUM>, a route determination module <NUM>, and a route instruction module <NUM>. Incidentally, the memory <NUM> may accumulate and record the operation data as appropriate.

Each elevator <NUM> operates to lift and lower a cage <NUM> in a hoistway installed in a building between boarding places provided respectively at floor levels of, for example, a first floor to a seventh floor as illustrated in <FIG>.

This cage <NUM> is attached to one end side of a primary rope <NUM>, to the other end side of which a counterbalancing weight <NUM> is attached. Furthermore, the primary rope <NUM> is wound around a hoist <NUM>. The hoist <NUM> is a hoisting mechanism for driving the cage <NUM> to lift and lower it and is installed together with a control apparatus for controlling hoisting operation of the cage <NUM> (hereinafter referred to as an elevator control apparatus) <NUM> in a machine room provided above the hoistway.

The elevator control apparatus <NUM> (<FIG>) is a computer apparatus for controlling the operation of the cage <NUM> and controls the hoist <NUM> to lift and lower the cage <NUM> in response to a passenger's operation (cage call information) of a call button <NUM> (<FIG>) provided at a boarding place.

The elevator <NUM> is inefficient because it is difficult for the elevator <NUM> to judge in which time slot and through which route the cage <NUM> does not have to operate; however, the elevator <NUM> is normally operated in such a manner that the cage <NUM> can be operated from the highest floor to the lowest floor. According to the present invention, the elevator management system <NUM> is equipped with a learning function in order to make the above-described judgment.

Next, the learning function mounted in the management server <NUM> of the elevator management system <NUM> will be explained. Incidentally, the learning function performs, for example, deep learning.

The learning function of the elevator management system <NUM> learns an operation tendency by predicting the operation status of the call button <NUM> and a destination floor designating button of each cage <NUM> a specified amount of time later (for example, <NUM> minutes later as a cycle for the cage <NUM> to make one run along a traveling route) after accepting the cage call information of each floor level and/or the operation of the destination floor designating button of each cage <NUM> (destination floor designating information).

<FIG> illustrates an example of the learning function. Arithmetic operation are performed by applying weighting to between neurons (circles in <FIG>) in adjacent layers (columns in <FIG>). Regarding this learning function, after an array of as many dimensions as the number of inputs to the call button <NUM> of each floor level and the destination floor designating button of each cage <NUM> is input, the management server <NUM> performs specified arithmetic operations in a plurality of hidden layers and an output layer. Incidentally, there is one input layer for a floor level where the input is performed; and there is one output layer for a floor level where the output is performed. Also, a plurality of hidden layers exist between the input layer and the output layer.

Then, as a result of the arithmetic operations, the management server <NUM> outputs an array of as many dimensions as the number of inputs to the call button <NUM> at each floor level and the destination floor designating button of each cage <NUM>. Incidentally, a specified arithmetic operation(s) in the hidden layers is, for example, an arithmetic operation(s) using activation functions such as a Sigmoid function, a hyperbolic tangent function, and a ramp function. Furthermore, a specified arithmetic operation(s) in the output layer is, for example, an arithmetic operation(s) using a Softmax function and so on.

Referring to <FIG>, when the cage <NUM> moving up is called at the <NUM>st floor and the cage <NUM> moving up and the cage <NUM> moving down are called at the <NUM>nd floor, the management server <NUM> predicts how the cage <NUM> will be called in the next cycle, according to this learning function.

In this case, the management server <NUM> predicts that the cage <NUM> moving up will be called at the <NUM>nd floor and the cage <NUM> moving down will be called at the <NUM>th floor in the next cycle.

Incidentally, referring to <FIG>, "o" represents a case where the button is pressed; and "×" represents a case where the button is not pressed. Furthermore, regarding each floor level, "↑" represents a button when calling the cage <NUM> moving up at the boarding place; "↓" represents a button when calling the cage <NUM> moving down at the boarding place; and "→" represents a button when a passenger is getting off from the cage <NUM> at the relevant floor. Incidentally, since <FIG> illustrates an example of the case where there is only one cage <NUM>, there is one row of "→" for each floor; however, there may be a plurality of cages <NUM>.

In the case of the prediction in <FIG>, the management server <NUM> predicts, by means of deep learning, that the cage <NUM> will not be called from the <NUM>th floor to the <NUM>th floor during a period of time required for the cage <NUM> to make one run along the route. The management server <NUM> issues an instruction to the elevator control apparatus <NUM> to operate the cage <NUM> along an operation route with the <NUM>th floor as a destination floor as indicated with a solid line in <FIG>. Specifically speaking, the management server <NUM> issues an instruction to the elevator control apparatus <NUM> to invert a traveling direction of the cage <NUM> after waiting time for a passenger(s) to get on and/or off the cage <NUM> at the <NUM>th floor. Incidentally, a broken line in <FIG> indicates a conventional operation route and the cage reaches to the <NUM>th floor which is the highest floor according to the conventional operation.

As means for implementing the above-described learning function, as illustrated in <FIG>, the memory <NUM> of the management server <NUM> stores the operation storage module <NUM>, the operation learning module <NUM>, the route determination module <NUM>, and the route instruction module <NUM> and the auxiliary storage apparatus <NUM> of the management server <NUM> stores the operation data table TB10 and the learning data table TB20.

The operation storage module <NUM> is a program having a function that acquires the operation data from the elevator control apparatus <NUM> of each elevator <NUM>, for example, every day and stores the acquired operation data in the operation data table TB10.

The operation learning module <NUM> is a program that performs learning as illustrated in <FIG> on the basis of the operation data acquired from the operation data table TB10, for example, for one year every year and changes, for example, necessary values for learning such as a weight value as illustrated in <FIG>. Furthermore, the operation learning module <NUM> records the status of the call button <NUM> at each floor level and the status of the destination floor designating button of each cage <NUM> which are calculated as a learning result (the learning result) in the learning data table TB20. Incidentally, the learning result is calculated for each combination of the status of the call button <NUM> at each arbitrary floor level and the status of the destination floor designating button of each cage <NUM>.

The route determination module <NUM> is a program that acquires the learning result from the learning data table TB20 and determines the operation route of the cage <NUM>. The route determination module <NUM> derives a route which can be omitted and along which the cage <NUM> does not have to be operated, from each learning result and determines a route which does not pass through the above-mentioned route, as a shortened route, to be the operation route of the cage <NUM>.

For example, in a case of the learning result as illustrated in an input row and an output row in <FIG>, the route determination module <NUM> recognizes that it is unnecessary to pass through the <NUM>nd floor to the <NUM>th floor regarding either the input row or the output row.

Accordingly, the route determination module <NUM> determines the shortened route, which does not pass through the 2nd floor to the 7th floor, as the operation route of the cage <NUM>. Incidentally, if there is no route which can be omitted, the route determination module <NUM> determines the normal route along which the cage <NUM> moves between the lowest floor and the highest floor, as the operation route of the cage <NUM>.

The route instruction module <NUM> is a program that corrects the operation route determined by the route determination module <NUM> according to the position, traveling direction, etc. of each cage which are given from the elevator control apparatus <NUM> of each elevator <NUM>. For example, when the call button <NUM> is pressed within the operation route where the cage <NUM> has not passed through yet during the operation of the cage <NUM>, or when something which has not occurred yet is predicted while the door for the cage <NUM> is open, the operation route of the cage <NUM> is corrected and this corrected operation route is transmitted as the management information to the elevator control apparatus <NUM> of the elevator <NUM>.

Incidentally, when the operation route of the cage <NUM> does not have to be corrected, the route instruction module <NUM> transmits the operation route determined by the route determination module <NUM>, without any change, to the elevator control apparatus <NUM> of the elevator <NUM>. Furthermore, the route instruction module <NUM> determines one or more cages <NUM> to be operated from among the plurality of cages <NUM> according to the operation route determined by the route determination module <NUM>.

The operation data table TB10 stores, as illustrated in <FIG>, the status of the call button <NUM> at each floor level (the cage call information) and the status of the destination floor designating button of each cage <NUM> (the destination floor designating information) as the operation data every <NUM> minutes (time required to make one run along the route). Incidentally, "∘", "×", "↑", "↓", and "→" in <FIG> have the same meanings as those in <FIG>.

Similarly, the learning data table TB20 stores, as illustrated in <FIG>, the status of the call button <NUM> at each arbitrary floor level and the status of the destination floor designating button of each cage <NUM> as inputs and the status of the call button <NUM> at each floor level and the status of the destination floor designating button of each cage <NUM> five minutes later (time required to make one run along the route) as outputs. Incidentally, "o", "×", "↑", "↓", and "→" in <FIG> have the same meanings as those in <FIG>.

Next, various kinds of processing executed by the above-described management server <NUM> will be explained. Incidentally, a processing subject of the various kinds of processing will be hereinafter explained as a "program"; however, it is needless to say that practically the CPU <NUM> executes the processing based on the "program.

<FIG> illustrates a processing sequence for operation data acquisition processing executed by the operation storage module <NUM>. The operation storage module <NUM> acquires the operation data from the elevator control apparatus <NUM> of each elevator <NUM> according to the processing sequence illustrated in this <FIG>.

Practically, the operation storage module <NUM> starts the operation data acquisition processing illustrated in this <FIG>, for example, at a set time every day.

Then, the operation storage module <NUM> firstly acquires the operation data for one day from the elevator control apparatus <NUM> of each elevator <NUM> (S11). Subsequently, the operation storage module <NUM> stores the operation data for one day in the operation data table TB10 (S12) and terminates the operation data acquisition processing.

<FIG> illustrates a processing sequence for operation data learning processing executed by the operation learning module <NUM>. The operation learning module <NUM> learns the status of the call button <NUM> at each floor level and the status of the destination floor designating button of each cage <NUM> five minutes later (time required to make one run along the route) (the learning result) with respect to the status of the call button <NUM> at each arbitrary floor level and the status of the destination floor designating button of each cage <NUM> in accordance with the processing sequence illustrated in this <FIG> based on the operation data acquired from the operation data table TB10.

Practically, the operation learning module <NUM> starts the operation data learning processing, for example, at a set time of the year every year.

Then, the operation learning module <NUM> firstly acquires the operation data for one year from the operation data table TB10 and learns based on the acquired operation data (S15). Subsequently, the operation learning module <NUM> stores the learning result as learning data in the learning data table TB20 (S16) and terminates the operation data learning processing.

<FIG> illustrates a processing sequence for operation route determination processing executed by the route determination module <NUM>. The route determination module <NUM> determines the operation route of the cage <NUM> in accordance with the processing sequence illustrated in this <FIG>.

Practically, after the operation data learning processing terminates, the route determination module <NUM> starts the operation route determination processing illustrated in this <FIG>.

Then, the route determination module <NUM> firstly acquires the learning data from the learning data table TB20 (S21). Subsequently, the route determination module <NUM> judges whether or not there is any route which can be omitted, with respect to each learning result (S22). When a negative result is obtained in this judgment, the route determination module <NUM> transmits the normal route as the operation route to the route instruction module <NUM> and terminates the operation data learning processing.

On the other hand, when an affirmative result is obtained in the judgment of step S22 because there is a route which can be omitted, the route determination module <NUM> generates a shortened route by omitting that route (S23), transmits the shortened route to the route instruction module <NUM>, and terminates the operation data learning processing.

<FIG> illustrates a processing sequence for operation instruction processing executed by the route instruction module <NUM>. The route instruction module <NUM> designates the operation route to the cage <NUM> in accordance with the processing sequence illustrated in this <FIG>.

Practically, after receiving the passenger's operation on the call button <NUM> from the elevator control apparatus <NUM> of the elevator <NUM>, the route instruction module <NUM> starts the operation route correction processing during operation as illustrated in this <FIG>.

Then, the route instruction module <NUM> firstly determines the cage <NUM> to be operated (S25). Subsequently, the route instruction module <NUM>: transmits the operation route to the elevator control apparatus <NUM> which controls the relevant cage <NUM> (S26); and terminates the operation instruction processing. Then, the elevator control apparatus <NUM> which has received the operation route operates the cage <NUM> in accordance with this operation route.

<FIG> illustrates a processing sequence for operation route correction processing during operation, which is executed by the route instruction module <NUM>. The route instruction module <NUM> corrects the operation route of the cage <NUM> in accordance with the processing sequence illustrated in this <FIG>.

Practically, after the operation instruction processing terminates and the route instruction module <NUM> receives the passenger's operation on the call button <NUM> at a boarding place within the operation route of the cage <NUM>, whose operation is designated by this operation instruction processing, from the elevator control apparatus <NUM>, the route instruction module <NUM> starts the operation route correction processing during operation as illustrated in this <FIG>.

Then, the route instruction module <NUM> firstly acquires the position and traveling direction of the cage <NUM> from each elevator control apparatus <NUM> and judges whether there is any cage <NUM> approaching to the relevant boarding place or not (S31). When an affirmative result is obtained in this judgment because there is a cage <NUM> approaching to that boarding place, the route instruction module <NUM> terminates the operation route correction processing during operation. Since this approaching cage <NUM> stops at the relevant boarding place, the control by the management server <NUM> becomes no longer necessary.

On the other hand, when a negative result is obtained in the judgment of step S31 because there is no cage <NUM> approaching, the route instruction module <NUM> selects a cage <NUM> closest to the boarding place (S32). Subsequently, the route instruction module <NUM>: transmits an instruction to the elevator control apparatus <NUM>, which controls the selected cage <NUM>, to invert the traveling direction of the relevant cage <NUM> (S33); and terminates the operation route correction processing during operation.

<FIG> illustrates a processing sequence for operation route correction processing executed by the route instruction module <NUM> while the door is open. The route instruction module <NUM> corrects the operation route of the cage <NUM> in accordance with the processing sequence illustrated in this <FIG>.

Practically, after the operation instruction processing terminates and the route instruction module <NUM> receives a button pressing operation, which has not occurred yet with respect to the cage <NUM> (and which should have occurred according to the prediction based on the learning result), from the elevator control apparatus <NUM> while the door of the cage <NUM> which has been designated to operate according to this operation instruction processing is opened (hereinafter referred to as door-opened time), the route instruction module <NUM> starts the operation route correction processing while the door is open as illustrated in this <FIG>.

Then, the route instruction module <NUM> firstly judges whether or not time elapsed from the time when the button pressing operation should have occurred to this door-opened time is equal to or less than a specified value (S41). When the specified amount of time has passed and the route instruction module <NUM> judges that an error in the prediction based on the learning result cannot be corrected, and when a negative result is thereby obtained in this judgment, the route instruction module <NUM> terminates the operation route correction processing while the door is open.

On the other hand, when the specified amount of time has not passed and the route instruction module <NUM> judges that the error in the prediction based on the learning result can be corrected, and when an affirmative result is thereby obtained in the judgment of step S41, the route instruction module <NUM>: transmits an instruction to the elevator control apparatus <NUM>, which controls this cage <NUM>, to extend the time to open the door (S42); and then terminates the operation route correction processing while the door is open.

With the elevator management system <NUM> according to this embodiment as described above, the management server <NUM> issues the instruction to each elevator <NUM> to operate by omitting any route which can be omitted, by predicting, based on the learning data, how each elevator will operate in the next cycle.

Therefore, this elevator management system <NUM> makes it possible to apply the operation according to the status of use to the cage(s) <NUM> without any alterations or the like of programs and the cage(s) <NUM> can be operated efficiently.

Incidentally, the aforementioned embodiment has described the case where the elevator management system <NUM> to which the present invention is applied is configured as illustrated in <FIG>; however, the present invention is not limited to this example and a wide variety of other configurations can be applied as the configurations of these elevator management systems.

For example, as illustrated in <FIG>, an elevator management system <NUM> may be configured to connect the management server <NUM> with each elevator <NUM> via a communication network <NUM> such as the Internet. In this case, the management server <NUM> is a cloud server or a server apparatus installed at a data center. The management server <NUM> is connected to the elevators <NUM> via the communication network <NUM>, communication equipment <NUM>, <NUM> such as a switching hub and a router, and a communication path <NUM> such as an intranet.

In the case of the configuration as illustrated in <FIG>, an inside space of the hoistway of the elevators <NUM> or a machine room is assumed as a place to install the management server <NUM>, but it is sometimes difficult to install large-capacity data storage devices capable of saving the operation data for one year and the server apparatus for implementing the deep learning at such a place. However, the elevator management system <NUM> can apply the present invention even in such a case by employing the configuration as illustrated in <FIG>.

Furthermore, the elevator management system <NUM> is installed at, for example, another building operated with the same working hours or business hours by being connected to the outside via, for example, the Internet and can acquire the operation data of the elevators <NUM> which operate in similar manners. Accordingly, the elevator management system <NUM> can acquire many pieces of operation data for learning and enhance the accuracy of learning.

Furthermore, as the elevator management system <NUM> is connected to the outside via, for example, the Internet and uses weather data and operation information of public transportation facilities as information for learning, it can enhance the accuracy of learning.

Furthermore, regarding an elevator management system <NUM> as illustrated in <FIG>, a cloud server <NUM> for calculating learning data may be connected to a management server <NUM>, which is a server apparatus, and each elevator <NUM> via the communication network <NUM> and communication equipment <NUM>, <NUM> such as a switching hub and a router. Incidentally, the cloud server <NUM> is composed of a cloud server, a data center, and so on.

As a result of employing the configuration illustrated in <FIG>, processing mainly focused on the operation data learning processing which requires transfer of the operation data with heavy load and learning processing can be executed by the cloud server <NUM> with high performance; and processing mainly focused on the operation instruction processing which requires frequent communication with the elevators <NUM> and for which any delay in the communication would be fatal can be executed by the management server <NUM>.

Accordingly, the elevator management system <NUM> can reduce any influence caused by the delay in the communication and can be installed also in a relatively limited installment space. Incidentally, the acquisition of the learning data by a learning data acquisition module <NUM> and the operation instruction to the relevant elevator <NUM> can be implemented promptly by installing the management server <NUM> in a DMZ (demilitarized zone).

Furthermore, regarding an elevator management system <NUM> as illustrated in <FIG>, communication via the communication equipment <NUM> (<FIG>) such as the switching hub and the router becomes no longer necessary by providing a management server <NUM>, which is a server apparatus, with a communication module <NUM>. Therefore, the present invention can be applied even in a case where the switching hub, the router, and so on cannot be used due to the environment where the switching hub, the router, and so on are not installed, or due to some security reason. Incidentally, the configuration in <FIG> can return to the conventional operation of the elevators <NUM> simply by removing the management server <NUM>.

Furthermore, when it is difficult to download the learning data via the communication network, an auxiliary storage apparatus <NUM> such as an SD card in which learning data TB30 is recorded may be connected to an elevator management system <NUM> as illustrated in <FIG>. The auxiliary storage apparatus <NUM> updates the learning data TB <NUM> when a customer engineer who periodically performs maintenance and inspection of the elevators <NUM> performs carrying maintenance or performs inspection. The learning data TB30 is created by copying the learning data TB20. Incidentally, the configuration in <FIG> can return to the conventional operation of the elevators <NUM> simply by removing a management server <NUM> which is a server apparatus.

Furthermore, the aforementioned embodiment has described the case where the deep learning is used as a learning means; however, the present invention is not limited to this example and a statistic means such as regression analysis may be used and machine learning other than the deep learning may be used.

Furthermore, the aforementioned embodiment has described the case where the pressed state of the call button <NUM> at each floor level and the destination floor designating button of each cage <NUM> after one run along the route is predicted based only on the pressed state of the call button <NUM> at each floor level and the destination floor designating button of each cage <NUM>; however, the present invention is not limited to this example and season information such as spring, summer, fall, and winter, year information such as a year when the Olympics will be held or a leap year, time slot information such as morning, noon, and night, and so on may be reflected.

Claim 1:
An elevator management system (<NUM>, <NUM>, <NUM>, <NUM>, <NUM>) for managing a plurality of elevators (<NUM>) each equipped with a control apparatus (<NUM>) for operating a cage (<NUM>) across a plurality of floors,
the elevator management system (<NUM>, <NUM>, <NUM>, <NUM>, <NUM>) comprising a management apparatus (<NUM>) for managing the control apparatuses (<NUM>), wherein the management apparatus (<NUM>) includes:
a receiving circuit configured to receive destination floor designating information and cage call information, the destination floor designating information being a status of a destination floor designating button of each cage (<NUM>) and the cage call information being a status of a call button (<NUM>) at each floor;
an operation data table (TB10) configured to store the cage call information and the destination floor designating information as operation data every time a cage makes one run along a travelling route;
a memory (<NUM>, <NUM>, <NUM>, <NUM>, <NUM>) configured to accumulate and record the information received by the receiving circuit;
a controller (<NUM>, <NUM>, <NUM>, <NUM>) configured to learn an operation tendency of the cages (<NUM>) based on the information recorded in the memory (<NUM>, <NUM>, <NUM>, <NUM>, <NUM>); and
an output circuit configured to output management information to each control apparatus (<NUM>);
characterized in that the controller (<NUM>, <NUM>, <NUM>, <NUM>):
is configured to predict the destination floor designating information and the cage call information a specified amount of time later from the information received by the receiving circuit on the basis of a result of the learning, the specified amount of time being the time needed by a cage to make one run along the travelling route; and
to form the management information on the basis of a result of the prediction of the specified amount of time period later so as to limit a range of operation floors of the cages (<NUM>); and
in that the control apparatuses (<NUM>) are configured to control operation of the cages (<NUM>) on the basis of the management information.