Information system and information acquisition method

Abstract

To provide an information system capable of acquiring and storing information that can be used when accurately calculating a feature quantity related to the torque of a hoist.SOLUTION: An information acquisition unit 22 acquires third information indicating the running direction of a car 11 and fourth information indicating the load inside the car 11. A storage unit 21 associates the third information and the fourth information with fifth information indicating the torque value of a hoist 15 as one set of status information and stores them. The fifth information is information indicating the torque value when the car 11 is in the middle position of a hoistway 12. As the car 11 runs at a constant speed a plurality of times, a plurality of sets of status information are stored in the storage unit 21.SELECTED DRAWING: Figure 2

Description

[Technical field]

[0001] The present disclosure relates to an information system and an information acquisition method. [Background technology]

[0002] Patent Document 1 describes an elevator control device. In the control device described in Patent Document 1, a torque current command value is acquired when the car is in the middle of the hoistway with a balanced load loaded on the car. In the control device, an unbalance current and a loss torque current are calculated based on the acquired value. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] International Publication No. 2018 / 100632 Summary of the Invention [Problem to be solved by the invention]

[0004] In the control device described in Patent Document 1, the torque current command value must be obtained when a balanced load is loaded on the car, which poses a problem in that the conditions for obtaining information required to calculate feature quantities such as loss torque current are strict.

[0005] In addition, in the control device described in Patent Document 1, the torque current command value is acquired only when a balanced load is loaded on the car, and there is a problem that the number of samples of the value is small, and it is not possible to accurately calculate the feature amount such as the loss torque current.

[0006] The present disclosure has been made to solve the problems described above. Another object of the present disclosure is to provide an information system and an information acquisition method that can acquire and store information that can be used when accurately calculating feature quantities related to the torque of a hoist. [Means for solving the problem]

[0007] The information system according to the present disclosure includes an information acquisition unit that acquires third information indicating a running direction of an elevator car and fourth information indicating a load inside the car when the elevator car is making a first run in which the car runs at a constant speed in a hoistway, and a first storage unit that associates the third information and fourth information acquired by the information acquisition unit with fifth information indicating a torque value of a hoist that drives the car as one set of status information and stores the fifth information. The fifth information is information indicating the torque value when the car is in an intermediate position in the hoistway during the first run. As the car runs at a constant speed multiple times, multiple sets of status information are stored in the first storage unit.

[0008] The information acquisition method according to the present disclosure includes an information acquisition step of acquiring third information indicating a running direction of an elevator car and fourth information indicating a load in the car when a first run in which the elevator car runs at a constant speed in a hoistway is being performed, and a first storage step of associating the third information and fourth information acquired in the information acquisition step with fifth information indicating a torque value of a hoisting machine that drives the car as one set of status information and storing them in a first storage unit. The fifth information is information indicating the torque value when the car is in an intermediate position in the hoistway during the first run. By performing the constant speed run of the car multiple times, multiple sets of status information are stored in the first storage unit. Effect of the Invention

[0009] According to the present disclosure, it is possible to acquire and store information that can be used when accurately calculating a feature quantity related to the torque of a hoist. [Brief description of the drawings]

[0010] [Figure 1] FIG. 1 is a diagram illustrating an example of an information system according to a first embodiment. [Diagram 2] FIG. 2 is a diagram for explaining functions of a monitoring device. [Diagram 3] FIG. 2 is a diagram for explaining functions of an analysis device. [Figure 4] 4 is a flowchart showing an example of the operation of the monitoring device. [Diagram 5] 4A to 4C are diagrams showing examples of first information and second information acquired by an information acquisition unit. [Figure 6] FIG. 4 is a diagram showing the relationship between the position of the cage and the torque value of the hoist. [Figure 7] FIG. 4 is a diagram showing an example of a linear regression equation generated by a regression equation generating unit. [Figure 8] FIG. 4 is a diagram for explaining a function of a regression equation generating unit. [Figure 9] 11A and 11B are diagrams illustrating examples of an ascending linear regression equation and a descending linear regression equation generated by a regression equation generating unit. [Figure 10] 13 is a flowchart showing an example of the operation of the analysis device. [Figure 11] FIG. 2 is a diagram for explaining a function of a monitoring unit. [Figure 12] FIG. 4 is a diagram showing an example of a linear regression equation generated by a regression equation generating unit. [Figure 13] FIG. 11 is a diagram showing another example of the linear regression equation generated by the regression equation generating unit. [Figure 14] FIG. 13 is a diagram illustrating an example of a coefficient of determination. [Figure 15] FIG. 13 is a diagram showing an example of a coefficient of determination based on an ascending linear regression equation and a coefficient of determination based on a descending linear regression equation. [Figure 16] FIG. 2 illustrates an example of hardware resources of a monitoring device. [Figure 17] FIG. 13 illustrates another example of hardware resources of the monitoring device. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0011] Hereinafter, a detailed description will be given with reference to the drawings. Duplicate descriptions will be appropriately simplified or omitted. In each drawing, the same reference numerals indicate the same or corresponding parts.

[0012] Embodiment 1 1 is a diagram showing an example of an information system 1 in the embodiment 1. The information system 1 shown in FIG.

[0013] The elevator apparatus 10 is applied to a facility 2. The facility 2 includes one or more buildings. The elevator apparatus 10 may be applied to a part of a building included in the facility 2. The facility 2 may be an outdoor facility or an indoor facility.

[0014] The elevator device 10 includes a car 11. The car 11 moves up and down in a hoistway 12. The hoistway 12 is a space extending up and down that is formed in a building of the facility 2. The hoistway 12 is formed so as to pass through multiple floors of the building.

[0015] The elevator device 10 further includes a counterweight 13, a main rope 14, and a hoist 15. The car 11 and the counterweight 13 are suspended in the hoistway 12 by the main rope 14. The hoist 15 includes a sheave. The main rope 14 is wound around the sheave of the hoist 15. That is, the load of the car 11 is supported by the main rope 14 wound around the sheave of the hoist 15. The load of the counterweight 13 is supported by the main rope 14 wound around the sheave of the hoist 15. The main rope 14 is, for example, a strand rope. The main rope 14 may also be a belt rope.

[0016] The hoisting machine 15 is a device for driving the car 11. As an example, the hoisting machine 15 is installed in an upper part of the hoistway 12. The hoisting machine 15 may be installed in a lower part of the hoistway 12. If there is a machine room above the hoistway 12, the hoisting machine 15 may be installed in the machine room.

[0017] The hoisting machine 15 is equipped with an electric motor. The electric motor generates a torque for rotating the sheave. When the sheave of the hoisting machine 15 rotates, the main rope 14 moves. The car 11 moves up and down the hoistway 12 depending on the direction in which the sheave rotates. The movement of the car 11 is guided by a guide rail 16. The guide rail 16 is provided in the hoistway 12 over the range of movement of the car 11. The car 11 is equipped with a guide 11a. The guide 11a is a roller or a shoe. The guide 11a faces the guide rail 16, thereby guiding the movement of the car 11.

[0018] The counterweight 13 moves up and down the hoistway 12 in a direction opposite to the direction in which the car 11 moves. The counterweight 13 is a device for reducing the difference between the load acting on a portion of the main rope 14 extending from the sheave of the hoisting machine 15 towards the car 11, and the load acting on a portion extending on the opposite side to that portion. The movement of the counterweight 13 is guided by a guide rail 17. The guide rail 17 is provided in the hoistway 12 over the movement range of the counterweight 13. The counterweight 13 has a guide 13a. The guide 13a is a roller or a shoe. The guide 13a faces the guide rail 17, thereby guiding the movement of the counterweight 13.

[0019] The elevator system 10 further includes a control device 18. As an example, the control device 18 is installed in an upper portion of the hoistway 12. The control device 18 may be installed in a lower portion of the hoistway 12. If there is a machine room above the hoistway 12, the control device 18 may be installed in the machine room.

[0020] The control device 18 controls various operations of the elevator. As an example, the control device 18 controls the hoist 15. That is, the running of the car 11 is controlled by the control device 18. The control device 18 makes the car 11 respond to calls registered by users, so that the users can ride in the car 11 and travel between floors.

[0021] As another example, the control device 18 controls devices provided in the car 11. The car 11 further includes a door 11b and an electric motor 11c. The door 11b opens and closes an entrance of the car 11. The electric motor 11c drives the door 11b. The control device 18 controls the electric motor 11c. That is, the opening and closing of the door 11b is controlled by the control device 18.

[0022] The car 11 further includes a weighing device 11d. The weighing device 11d measures the load in the car 11. Information indicating the load measured by the weighing device 11d is transmitted to the control device 18.

[0023] The control device 18 holds information related to elevator control. Hereinafter, this information will also be referred to as control information.

[0024] As an example, the control information includes information required to control devices provided in the elevator system 10. The control information may include a parameter that is causally related to transient heating or cooling. The control information may include a parameter that is causally related to both transient heating and cooling. The parameter may be continuous. The parameter may be discrete. The parameter may represent one of multiple options.

[0025] As another example, the control information may include information indicating the time, information indicating the position of the car 11, information indicating the load in the car 11, and information indicating the value of the torque output by the hoist 15. When the car 11 moves only up and down, the position of the car 11 is synonymous with the height at which the car 11 is located in the hoistway 12. The position of the car 11 may be an absolute position or a relative position. As the torque value output by the hoist 15, a current command value for the electric motor provided in the hoist 15, or a current feedback value of the electric motor may be adopted.

[0026] As another example, the control information may include information indicating whether the car 11 is traveling at a constant speed. As the information, continuous value information such as a traveling speed command value for the car 11, a speed command value for the electric motor provided in the hoist 15, or a speed feedback value of the electric motor may be adopted. If there is a specific signal by which it is possible to determine that the car 11 is traveling at a constant speed, the signal may be adopted as the information.

[0027] As another example, the control information may include information indicating a learned value of the position of the elevator shaft 12 obtained by learning the positional relationship between the position where the car 11 stops at the lowest floor and the position where the car 11 stops at the top floor. The intermediate position of the elevator shaft 12 can be calculated from the learned value.

[0028] As another example, the control information may include non-continuous information representing the state of the elevator. The information may include a drive command signal for the inverter, a drive command signal for the fan, a signal related to the door 11b, and a detection signal of the landing plate. The inverter has a function of supplying a drive current to the electric motor provided in the hoisting machine 15. The fan has a function of blowing air to the air-cooling equipment. The signal related to the door 11b may include a command signal for opening the door 11b, a command signal for closing the door 11b, a signal indicating that the door 11b is fully open, and a signal indicating that the door 11b is fully closed.

[0029] As another example, the control information may include continuous or discrete numerical information. The numerical information may include information indicating the floor on which the car 11 is located and information indicating a voltage command value for the motor provided in the hoist 15. The numerical information may include information indicating the time when the operation to open the door 11b is started, the waiting time after the door 11b is fully open, and the time when the operation to close the door 11b is completed. The numerical information may include information indicating a speed command value for the door 11b, a speed feedback value for the door 11b, a current command value for the motor 11c, a current feedback value for the motor 11c, and a voltage command value for the motor 11c.

[0030] The monitoring device 20 is provided corresponding to the elevator device 10. If the information system 1 is equipped with a plurality of elevator devices, a monitoring device is provided for each elevator device. As an example, the monitoring device 20 is provided in the facility 2 to which the elevator device 10 is applied.

[0031] The monitoring device 20 has a function of acquiring information from the control device 18. The monitoring device 20 monitors the elevator device 10 based on the information acquired from the control device 18. The monitoring device 20 preferably has a function of calculating a preset feature amount based on the information acquired from the control device 18. The monitoring device 20 preferably has a function of transmitting necessary information to the analysis device 30. The functions of the monitoring device 20 will be described in detail later.

[0032] The analysis device 30 is a device for analyzing and processing information received from the monitoring device 20. The analysis device 30 is connected to the monitoring device 20 via a communication network 3. If the information system 1 is equipped with multiple monitoring devices, the analysis device 30 is connected to each monitoring device via the communication network 3. As an example, the communication network 3 is a wide area communication network such as the Internet. The communication network 3 may be a VPN (Virtual Private Network) configured on a public network such as the Internet, or another secure network.

[0033] The monitoring device 20 is, for example, an edge server and includes a microcomputer. Meanwhile, the analysis device 30 is, for example, a computer system including one or more server devices 31. The analysis device 30 may include three or more server devices 31. The analysis device 30 may have a configuration in which multiple server devices 31 having the same functions are multiplexed.

[0034] As an example, a part or all of the server devices 31 included in the analysis device 30 are installed in a location remote from the facility 2. The analysis device 30 may be installed in a country different from the country in which the facility 2 is installed. The analysis device 30 may be installed in a region different from the region in which the facility 2 is installed. When the analysis device 30 includes multiple server devices 31, some of the server devices 31 may be installed in a country different from the country in which the other server devices 31 are installed. Some of the server devices 31 may be installed in a region different from the region in which the other server devices 31 are installed. When the analysis device 30 includes multiple server devices 31, the multiple server devices 31 are connected to each other via the communication network 3.

[0035] A part or all of the functions of the analysis device 30 may be installed in an on-premise server of a maintenance company that maintains the elevator device 10. A part or all of the functions of the analysis device 30 may be installed in a cloud server. A part or all of the functions of the analysis device 30 may be realized by processing and storage resources on a cloud service. Details of the functions of the analysis device 30 will be described later.

[0036] The analysis device 30 is connected to a communication network 4. As an example, the communication network 4 is an internal communication network such as an intranet or a private network of a maintenance company of the elevator device 10. When the function of the analysis device 30 is installed in a cloud server, the communication network 4 may be a VPN configured on a public network such as the Internet.

[0037] Users who can access the communication network 4 are set in advance. The right to access the communication network 4 may be given to an individual user, or may be given based on a role assigned to the user. The right to access the communication network 4 may be given to a group to which the user belongs. FIG. 1 shows an example in which the group includes a head office 5a, a branch office 5b, a branch office 5c, a sales office 5d, and a manufacturing plant 5e of a maintenance company. The group may also include other organizations, such as a technical department of the maintenance company.

[0038] The communication network 4 is a network that can access the maintenance database 40 and the specification database 50. A part or all of the maintenance database 40 and the specification database 50 may be mounted on an on-premise server of a maintenance company of the elevator device 10. A part or all of the maintenance database 40 and the specification database 50 may be mounted on a cloud server. A part or all of the maintenance database 40 and the specification database 50 may be mounted on the analysis device 30.

[0039] The maintenance database 40 stores maintenance information for each elevator device managed by a maintenance company. The elevator device 10 is an example of an elevator device managed by a maintenance company. The maintenance information for the elevator device 10 is also stored in the maintenance database 40. As an example, the maintenance database 40 stores an identification number and maintenance information for each elevator device managed by the maintenance company in association with each other. The maintenance information includes information indicating the date and time when operation started, a history of part replacement, etc.

[0040] The specification database 50 stores specification information for each elevator device managed by the maintenance company. The specification information for the elevator device 10 is also stored in the specification database 50. As an example, the specification database 50 stores an identification number and specification information for each elevator device managed by the maintenance company in association with each other. The specification information includes information indicating the elevator control method, the model name of the hoist, the model name of the control device, the rated speed, the rated load, the lifting stroke, and the model name of the doors. The specification information may also include information indicating the height of each landing at which the car stops.

[0041] Next, functions of the information system 1 will be described in detail with reference to Fig. 2 to Fig. 11. Fig. 2 is a diagram for explaining functions of the monitoring device 20. As shown in Fig. 2, the monitoring device 20 includes a storage unit 21 (first storage unit), an information acquisition unit 22, a regression equation generation unit 23 (first regression equation generation unit), a torque calculation unit 24, a slope identification unit 25, a regression equation generation unit 26 (second regression equation generation unit), a feature calculation unit 27, and a communication unit 28 (first communication unit). Note that some of the functions of the monitoring device 20 may be included in equipment of the elevator device 10, such as the control device 18.

[0042] Fig. 3 is a diagram for explaining the functions of the analysis device 30. As shown in Fig. 3, the analysis device 30 includes a storage unit 32 (second storage unit), a communication unit 33, a communication unit 34 (second communication unit), a monitoring unit 35, and a reproduction unit 36.

[0043] First, a description will be given of the operation of the monitoring device 20. FIG.

[0044] In the monitoring device 20, the information acquiring unit 22 acquires elevator operation information from the elevator device 10 (S101). As an example, the information acquiring unit 22 acquires a part or all of the control information from the control device 18 as the operation information. The information acquiring unit 22 may acquire detailed information such as time series data as the operation information. The operation information may include information indicating the time when the information acquiring unit 22 acquired the information from the control device 18.

[0045] For example, the information acquisition unit 22 acquires information indicating the position of the car 11 from the control device 18. Hereinafter, this information is also referred to as first information. The information acquisition unit 22 acquires information indicating the torque value of the hoist 15 from the control device 18. Hereinafter, this information is also referred to as second information. As described above, the second information may be information indicating a current command value for the electric motor provided in the hoist 15, or a current feedback value of the electric motor.

[0046] Fig. 5 is a diagram showing examples of the first information and the second information acquired by the information acquisition unit 22. In the example shown in Fig. 5, the first information is information indicating the relationship between the time and the position of the car 11. The second information is information indicating the relationship between the time and the torque value of the hoist 15. The time may be an absolute time or a relative time.

[0047] In the monitoring device 20, next, a process is performed to estimate the torque value of the hoist 15 when the car 11 travels at a constant speed. Specifically, in this process, it is preferable to estimate the torque value when the car 11 is in the intermediate position of the hoistway 12. Hereinafter, this estimated value is also referred to as an intermediate position torque estimate value.

[0048] First, the reason for performing this process will be explained.

[0049] If the position of the car 11 is x and the load in the car 11 is L, the torque T(x, L) generated by the controlled object, that is, the hoist 15, is expressed by equation (1). T(x,L)=Tα(L)+Tub(L)+Tcmp(x)+Tloss …(1)

[0050] The value of torque T(x,L) changes depending on the position x and the load L. Here, Tα(L) is the torque generated due to the acceleration / deceleration of the car 11. Tub(L) is the torque generated due to the deviation between the weight of the car 11 and the weight of the counterweight 13. Hereinafter, this torque will also be referred to as unbalanced torque. The value of torque Tub(L) is determined by the load L.

[0051] Tcmp(x) is a torque generated due to the deviation between the weight of the car 11 side of the compensating rope extending downward from the car 11 and the counterweight 13 and the weight of the counterweight 13 side. The value of the torque Tcmp(x) is determined by the position x. Furthermore, regarding the torque Tcmp(x), the following equations (2) and (3) hold. Tcmp(intermediate position)=0…(2) Tcmp(top floor) = -Tcmp(bottom floor) ... (3)

[0052] Tloss is a torque that occurs due to friction between the guide 11a and the guide rail 16. Hereinafter, this torque will also be referred to as loss torque. The loss torque is a torque that acts in the direction opposite to the direction in which the car 11 travels. The value of the loss torque when the car 11 is traveling upwards and the value of the loss torque when the car 11 is traveling downwards are opposite in sign and have the same absolute value.

[0053] If the car 11 is traveling at a constant speed, the torque Tα(L) is not generated. If the car 11 is in the middle of the hoistway 12, the torque Tcmp(x) is 0. Therefore, the torque T(x, L) generated by the hoisting machine 15 when the car 11 is traveling at a constant speed and is in the middle of the hoistway 12 is expressed by equation (4). T(x,L)=Tub(L)+Tloss …(4)

[0054] As shown in equation (4), the torque T(x,L) when the car 11 traveling at a constant speed is in the middle position of the hoistway 12 is expressed as the sum of the unbalance torque Tub(L) and the loss torque Tloss. If an estimated value of the torque in the middle position can be obtained, equation (4) can be used.

[0055] Therefore, the regression equation generating unit 23 generates a linear regression equation between the position of the car 11 and the torque value of the hoist 15 when the first run in which the car 11 runs at a constant speed in the hoistway 12 is being performed (S102). That is, the regression equation generating unit 23 generates a linear regression equation that expresses the torque value of the hoist 15 with respect to the position of the car 11 running at a constant speed, based on the first information and the second information acquired by the information acquiring unit 22.

[0056] Fig. 6 is a diagram showing the relationship between the position of the car 11 and the torque value of the hoist 15. In S102, the regression equation generation unit 23 first generates the data shown in Fig. 6 using data showing "time vs. position of the car 11" shown in the lower part of Fig. 5 and data showing "time vs. torque value" shown in the upper part of Fig. 5. That is, the regression equation generation unit 23 generates data showing the torque value of the hoist 15 relative to the position of the car 11 when the first run was performed. This generation process can be realized by associating the "position of the car 11" and the "torque value" at the same time.

[0057] In the example shown in Fig. 6, the car 11 moves from the bottom floor to the top floor. Furthermore, in section s, the car 11 travels at a constant speed. Therefore, when the regression equation generation unit 23 generates the data shown in Fig. 6, it extracts the data present in section s. The regression equation generation unit 23 generates a linear regression equation between the position of the car 11 and the torque value of the hoist 15 from the extracted data.

[0058] For example, the torque T(x, L) when the car 11 is traveling at a constant speed is expressed by the formula (5). T(x,L)=Tub(L)+Tcmp(x)+Tloss …(5)

[0059] If the load L in the car 11 is fixed, the formula (5) can be approximately expressed as a linear function of the position x, thereby obtaining the formula (6). T(x,L)=Kcmp*(x-TR / 2)+Tub(L)+Tloss …(6)

[0060] Moreover, equation (6) can be rewritten as equation (6-1). T(x,L)=Kcmp*x+Toffset_1(L) …(6-1) Here, Toffset_1(L) can be expressed by equation (6-2) and represents the intercept of the above linear regression equation. Toffset_1(L)=-Kcmp*TR / 2+Tub(L)+Tloss …(6-2)

[0061] Here, TR is the ascent / descent stroke of the car 11. That is, if the car 11 is at the bottom floor, then position x=0; if the car 11 is at an intermediate position, then position x=TR / 2; and if the car 11 is at the top floor, then position x=TR. Also, Kcmp is the coefficient of the torque component that varies depending on position x. That is, Tcmp(x)=Kcmp*(x-TR / 2).

[0062] In this way, the regression equation generating unit 23 extracts only data when the car 11 is traveling at a constant speed, and obtains the relationship between the position of the car 11 and the torque value of the hoist 15 using an approximation equation of a linear function, i.e., a linear regression equation. FIG. 7 is a diagram showing an example of the linear regression equation generated by the regression equation generating unit 23. Any method may be used for the regression equation generating unit 23 to generate the linear regression equation from the extracted data. For example, the regression equation generating unit 23 can generate the linear regression equation by using the least squares method.

[0063] Whether or not the car 11 is traveling at a constant speed can be determined using a travel speed command value for the car 11, etc.

[0064] Next, the torque calculation unit 24 calculates the torque value of the hoisting machine 15 when the car 11 is at the intermediate position of the hoistway 12 in the first run, i.e., the intermediate position torque estimation value, based on the linear regression equation generated by the regression equation generation unit 23 in S102 (S103). Specifically, the torque calculation unit 24 calculates the intermediate position torque estimation value by substituting x=TR / 2 into the linear regression equation generated by the regression equation generation unit 23. The equation for calculating the intermediate position torque estimation value can be expressed by equation (7) obtained by substituting x=TR / 2 into equation (6). T(TR / 2,L)=Tub(L)+Tloss …(7)

[0065] When the intermediate position torque estimation value is calculated by the torque calculation unit 24 in S103, information indicating the intermediate position torque estimation value is stored in the storage unit 21 (S104). Hereinafter, this information is also referred to as fifth information. In addition, in S101, the information acquisition unit 22 acquires information indicating the running direction of the car 11 when the first run is being performed from the control device 18. Hereinafter, this information is also referred to as third information. In S101, the information acquisition unit 22 acquires information indicating the load in the car 11 when the first run is being performed from the control device 18. Hereinafter, this information is also referred to as fourth information. In S104, the third information, fourth information, and fifth information are associated with each other as one set of status information and stored in the storage unit 21. Table 1 shows an example of status information stored in the storage unit 21.

[0066] [Table 1]

[0067] In this embodiment, one run is defined as a run from when the car 11 departs from the departure floor to when it arrives at the destination floor. The first run is performed in one run. When the processes shown in S101 to S103 are performed for one run, one set of status information is stored in the storage unit 21 in S104. That is, one row is added to Table 1. Multiple runs in which the car 11 runs at a constant speed through a portion of the elevator shaft 12 are performed multiple times, and multiple sets of status information are stored in the storage unit 21.

[0068] The end of one run may be detected based on a drive command signal to the inverter, etc. For a run in which the car 11 does not pass through the middle position of the elevator shaft 12, the processes shown in S101 to S103 are performed, and one set of state information may be stored in the storage unit 21 in S104.

[0069] Table 1 shows an example in which the status information includes information indicating a time (time stamp) in addition to the third information, fourth information, and fifth information. The time may be any time that is predefined. For example, the time is the time when the driving started. The time may also be the time when the driving ended.

[0070] Table 1 shows an example in which the running direction of the car 11 is represented as "up" or "down". As another example, the running direction of the car 11 may be represented by a numerical value including positive and negative values. Table 1 shows an example in which the load in the car 11 is represented as a percentage [%] of the rated load capacity of the car 11. As another example, the load in the car 11 may be represented as a mass [kg].

[0071] Table 1 shows an example in which the status information includes information indicating a coefficient of a torque component that varies depending on the position of the car 11. The coefficient is the slope of the linear regression equation generated by the regression equation generation unit 23. When the linear regression equation is generated by the regression equation generation unit 23, the slope identification unit 25 identifies the slope. The torque calculation unit 24 may calculate the torque component that varies depending on the position of the car 11 based on the slope identified by the slope identification unit 25. Furthermore, Table 1 shows an example in which the status information includes information indicating a coefficient of determination R based on the linear regression equation generated by the regression equation generation unit 23. 2 An example is shown below that includes information indicating _1. The coefficient of determination will be described later.

[0072] Next, in the monitoring device 20, a process is performed to extract a feature quantity related to the running of the car 11, more specifically, a feature quantity related to the torque of the hoist 15.

[0073] In this process, first, the regression equation generating unit 26 generates an ascent linear regression equation when the car 11 travels upward in the hoistway 12 at a constant speed (S105). In S105, the regression equation generating unit 26 generates an ascent linear regression equation between the load in the car 11 and the torque value of the hoisting machine 15 when the car 11 is in the middle position of the hoistway 12. The ascent linear regression equation is an approximation of a linear function that represents the torque value of the hoisting machine 15 with respect to the load in the car 11. The generation of the ascent linear regression equation by the regression equation generating unit 26 is performed based on a plurality of sets of state information stored in the storage unit 21.

[0074] Furthermore, the regression equation generating unit 26 generates a linear regression equation for descent when the car 11 travels downward in the hoistway 12 at a constant speed (S106). In S106, the regression equation generating unit 26 generates a linear regression equation for descent between the load in the car 11 and the torque value of the hoisting machine 15 when the car 11 is in the middle position of the hoistway 12. The linear regression equation for descent is an approximation of a linear function that represents the torque value of the hoisting machine 15 with respect to the load in the car 11. The generation of the linear regression equation for descent by the regression equation generating unit 26 is performed based on a plurality of sets of state information stored in the storage unit 21.

[0075] Fig. 8 is a diagram for explaining the function of the regression equation generating unit 26. The horizontal axis of Fig. 8 indicates the load factor in the car 11. The vertical axis of Fig. 8 indicates the intermediate position torque estimated value. Fig. 8 shows a plot of one point for one set of state information stored in the storage unit 21.

[0076] Here, the above formula (7) will be considered. Since the unbalance torque Tub(L) changes in proportion to the load L, formula (7) can be expressed as the following formula (8). T(TR / 2,L)=Kub*(L-Lbl)+Tloss …(8)

[0077] In equation (8), Lbl is the load in the car 11 when a balanced load is loaded, and Kub is the coefficient of the torque component that varies according to the load L in the car 11.

[0078] When the car 11 travels upward, the loss torque Tloss acts downward. Therefore, the torque of the hoist 15 for compensating for the loss torque Tloss is output on the positive side. On the other hand, when the car 11 travels downward, the loss torque Tloss acts upward. Therefore, the torque of the hoist 15 for compensating for the loss torque Tloss is output on the negative side. Therefore, when the car 11 travels upward, equation (8) can be expressed as equation (9). Similarly, when the car 11 travels downward, equation (8) can be expressed as equation (10). Tup_mid=Kubup*(L-Lbl)+Tloss …(9) Tdn_mid=Kubdn*(L-Lbl)-Tloss …(10)

[0079] Here, Tup_mid represents an intermediate position torque estimate value when the car 11 is traveling upward. Tdn_mid represents an intermediate position torque estimate value when the car 11 is traveling downward. Kubup is a coefficient of a torque component that varies according to the load L in the car 11 when the car 11 is traveling upward. Kubdn is a coefficient of a torque component that varies according to the load L in the car 11 when the car 11 is traveling downward.

[0080] Equation (9) represents the linear regression equation at the time of ascent. Equation (10) represents the linear regression equation at the time of descent. FIG. 9 is a diagram showing an example of the linear regression equation at the time of ascent and the linear regression equation at the time of descent generated by the regression equation generating unit 26. As shown in FIG. 9, the slope of the linear regression equation at the time of ascent, i.e., the coefficient Kubup, and the slope of the linear regression equation at the time of descent, i.e., the coefficient Kubdn, show almost the same value. The regression equation generating unit 26 may generate the linear regression equation at the time of ascent and the linear regression equation at the time of descent in any manner. For example, the regression equation generating unit 26 may generate the linear regression equation at the time of ascent and the linear regression equation at the time of descent by using the least squares method.

[0081] The linear regression equation at the time of ascent and the linear regression equation at the time of descent can be generated if a necessary number of sets of state information for generating the approximation equation are stored in the storage unit 21. For example, the linear regression equation at the time of ascent and the linear regression equation at the time of descent may be generated based on one day's worth of state information. The linear regression equation at the time of ascent and the linear regression equation at the time of descent may be generated based on one week's or one month's worth of state information. The linear regression equation at the time of ascent and the linear regression equation at the time of descent may be generated every time a certain number of sets of state information are stored in the storage unit 21, or may be generated periodically. When the information system 1 is provided with a plurality of elevator devices, the conditions for generating the linear regression equation at the time of ascent and the linear regression equation at the time of descent can be arbitrarily set for each elevator device.

[0082] Next, the feature amount calculation unit 27 calculates feature amounts related to the torque of the hoist 15 based on the ascending linear regression equation and the descending linear regression equation generated by the regression equation generation unit 26 (S107). An example in which the feature amount calculation unit 27 calculates the loss torque value, the unbalanced torque value, and the car balanced load estimated value as feature amounts will be described in detail below. The feature amount calculation unit 27 may calculate only the loss torque value, only the unbalanced torque value, or only the car balanced load estimated value as feature amounts. The feature amount calculation unit 27 may calculate other feature amounts. The feature amounts calculated by the feature amount calculation unit 27 are set in advance.

[0083] The feature amount calculation function will be specifically described below.

[0084] By transforming equation (9), equations (11) and (12) can be obtained. Tup_mid=Kubup*L+Toffset_up_mid …(11) Toffset_up_mid=Tloss-Kubup*Lbl …(12) Here, Toffset_up_mid represents the intercept of the linear regression equation during ascent.

[0085] By transforming equation (10), equations (13) and (14) can be obtained. Tdn_mid=Kubdn*L+Toffset_dn_mid …(13) Toffset_dn_mid=-Tloss-Kubdn*Lbl …(14) Here, Toffset_dn_mid represents the intercept of the descending linear regression equation.

[0086] Regarding formulas 12 and 14, since Tloss>0, formula (15) holds. Toffset_up_mid>Toffset_dn_mid …(15)

[0087] Equation (16) can be obtained by subtracting equation (12) from equation (14). (Toffset_up_mid-Toffset_dn_mid) / 2 =Tloss-(Kubup-Kubdn)*Lbl / 2 …(16)

[0088] Equation (17) can be obtained by adding equation (12) and equation (14). (Toffset_up_mid+Toffset_dn_mid) / 2 =-Lbl*(Kubup+Kubdn) / 2 …(17) Here, −Lbl represents the load in the car 11 when no load is loaded in the car 11.

[0089] Furthermore, with respect to equation (17), equation (18) is defined. Kub_ave=(Kubup+Kubdn) / 2 …(18) Here, Kub_ave is the average value of the coefficient of the torque component that varies according to the load L in the car 11. Specifically, Kub_ave indicates the average value of the coefficient during constant speed ascent and the coefficient during constant speed descent.

[0090] By transforming equation (16), equation (19) can be obtained. Tloss=(Toffset_up_mid-Toffset_dn_mid) / 2 +(Kubup-Kubdn)*Lbl / 2 …(19) If the coefficient Kubup during constant speed ascent and the coefficient Kubdn during constant speed descent are approximately the same, then equation (19) can be approximated as equation (20). Tloss ≒(Toffset_up_mid-Toffset_dn_mid) / 2…(20)

[0091] Equation (19) means that the slope and intercept of the linear regression equation when increasing and the slope and intercept of the linear regression equation when decreasing are necessary to calculate the loss torque Tloss. That is, the feature calculation unit 27 can calculate the value of the loss torque Tloss using information indicating the slope and intercept of the linear regression equation when increasing and information indicating the slope and intercept of the linear regression equation when decreasing.

[0092] Equation (20) means that the intercept of the linear regression equation when increasing and the intercept of the linear regression equation when decreasing are necessary for the approximate calculation of the loss torque Tloss. That is, the feature amount calculation unit 27 can approximately calculate the value of the loss torque Tloss using information indicating the intercept of the linear regression equation when increasing and information indicating the intercept of the linear regression equation when decreasing. When calculating the loss torque Tloss based on Equation (20), the feature amount calculation unit 27 does not need to use information indicating the slope of the linear regression equation when increasing and information indicating the slope of the linear regression equation when decreasing. Note that even when calculating the torque Tloss based on Equation (20), it is possible to obtain calculation accuracy sufficient for practical use.

[0093] Moreover, by modifying equation (17), equation (21) can be obtained. (Toffset_up_mid+Toffset_dn_mid) / 2 =-Lbl*Kub_ave …(21)

[0094] Equation (21) means that the intercept of the linear regression equation during ascent and the intercept of the linear regression equation during descent are necessary to calculate the unbalance torque when there is no load inside the car 11. That is, the feature calculation unit 27 can calculate the value of the unbalance torque by using information indicating the intercept of the linear regression equation during ascent and information indicating the intercept of the linear regression equation during descent.

[0095] Next, using equations (11) and (13), the condition of the load L under which the absolute value of the intermediate position torque estimated value when the car 11 travels upward is equal to the absolute value of the intermediate position torque estimated value when the car 11 travels downward is considered. That is, consider the load condition L' under which Tup_mid+Tdn_mid=0.

[0096] By adding together equation (11) and equation (13) and rearranging them under the above conditions, equation (22) can be obtained. L´=-(Toffset_up_mid+Toffset_dn_mid) / (Kubup+Kubdn) …(22)

[0097] Ideally, L' coincides with the load Lbl in the car 11 when a balanced load is loaded. For this reason, L' is also called the car balanced load estimate. If a change occurs in the balance state between the car 11 and the counterweight 13, the car balanced load estimate L' will no longer coincide with the load Lbl. In addition, if the measurement error of the weighing device 11d, i.e., the output deviation, becomes large, the car balanced load estimate L' will no longer coincide with the load Lbl. For this reason, the value of the car balanced load estimate L' can be used to confirm the change in the balance state or to confirm the output deviation of the weighing device 11d.

[0098] Equation (22) means that the slope and intercept of the linear regression equation when ascending and the slope and intercept of the linear regression equation when descending are necessary to calculate the car balanced load estimated value L'. That is, the feature calculation unit 27 can calculate the car balanced load estimated value L' by using information indicating the slope and intercept of the linear regression equation when ascending and information indicating the slope and intercept of the linear regression equation when descending.

[0099] Next, in the monitoring device 20, a process for transmitting the calculated feature amount to the analysis device 30 is performed.

[0100] In this process, first, the communication unit 28 determines whether or not the communication load on the communication network 3 is high (S108). The criterion for determining that the load is high in S108 is set in advance. If the communication unit 28 determines that the communication load on the communication network 3 is not high (No in S108), it transmits information indicating the feature calculated by the feature calculation unit 27 in S107 to the analysis device 30 (S109). If the communication unit 28 determines that the communication load on the communication network 3 is high (Yes in S108), it does not transmit information indicating the feature to the analysis device 30.

[0101] In S108, it may be determined whether the processing load of the analysis device 30 is high. A criterion for determining that the processing load of the analysis device 30 is high is set in advance. In such a case, the communication unit 28 may inquire about the state of the processing load of the analysis device 30. The communication unit 28 determines whether the processing load of the analysis device 30 is high based on a response from the analysis device 30. If the communication unit 28 determines that the processing load of the analysis device 30 is not high (No in S108), it transmits information indicating the feature calculated by the feature calculation unit 27 in S107 to the analysis device 30 (S109). If the communication unit 28 determines that the processing load of the analysis device 30 is high (Yes in S108), it does not transmit information indicating the feature to the analysis device 30.

[0102] The communication unit 28 may transmit information during times when the elevator device 10 is less used, such as at night. The communication unit 28 may transmit information based on schedule management by the analysis device 30. In this example, it is possible to prevent the processing load from concentrating on the analysis device 30. The communication unit 28 may transmit information by receiving a regular or irregular polling trigger from the analysis device 30. After the communication unit 28 starts transmitting information, if it is detected that the communication load on the communication network 3 has increased, the communication unit 28 may temporarily stop transmitting information. In this example, the communication load may be monitored by the monitoring device 20 or the analysis device 30.

[0103] Next, a description will be given of the operation of the analysis device 30. FIG 10 is a flowchart showing an example of the operation of the analysis device 30.

[0104] In the analysis device 30, the communication unit 33 determines whether or not information indicating the feature amount has been received from the monitoring device 20 via the communication network 3 (S201). When the communication unit 33 receives the information indicating the feature amount transmitted by the communication unit 28 in S109, the result of S201 is determined as Yes.

[0105] If the determination in S201 is Yes, the information indicating the feature amount received by the communication unit 33 in S201, i.e., the information indicating the feature amount calculated by the feature amount calculation unit 27, is stored in the storage unit 32 as monitored information in association with other information (S202). Table 2 shows an example of the monitored information stored in the storage unit 32.

[0106] [Table 2]

[0107] Table 2 shows a suitable example in which the monitored information is stored in the storage unit 32 in chronological order. In the example shown in Table 2, one piece of monitored information is stored in the storage unit 32 every month. That is, one row is added to Table 2 every month. The frequency at which new monitored information is added to the storage unit 32 is not limited to the frequency shown in Table 2. Note that, since the amount of monitored information is not large, the storage unit 32 may store the monitored information for the entire operation period for each elevator device.

[0108] In the example shown in Table 2, the monitored information includes, in addition to information indicating the above-mentioned feature quantities, information indicating the number of sets (number of samples) of status information used to calculate the feature quantities and information indicating the period during which the status information was acquired. The monitored information also includes elevator information indicating the number of times the elevator device 10 was started during the period, the cumulative number of starts, the cumulative travel distance, and the cumulative start time, and environmental information indicating the average temperature and average humidity during the period. Furthermore, Table 2 shows an example in which the monitored information includes information indicating a coefficient of determination based on an ascent linear regression equation generated by the regression equation generating unit 26 and information indicating a coefficient of determination based on a descent linear regression equation. The coefficient of determination will be described later.

[0109] The elevator information and the environmental information may be acquired from the maintenance database 40 and the specification database 50. In such a case, the communication unit 34 receives the necessary elevator information and environmental information from the maintenance database 40 and the specification database 50 via the communication network 4. In S202, the information indicating the feature amount received by the communication unit 33 and the elevator information, the environmental information, etc. received by the communication unit 34 are stored in the storage unit 32 in association with each other.

[0110] The communication unit 34 may receive other maintenance information from the maintenance database 40. The communication unit 34 may receive other specification information from the specification database 50. The monitored information may include other maintenance information and other specification information received by the communication unit 34. In such a case, the monitored information may be stored in the storage unit 32 after being classified according to specific elevator specifications or specific maintenance histories.

[0111] The monitoring unit 35 monitors changes in the feature amounts calculated by the feature amount calculation unit 27 based on the monitored information stored in the storage unit 32 (S203). As an example, the monitoring unit 35 monitors changes over time or seasonal changes in the feature amounts stored in the storage unit 32.

[0112] Monitoring by the monitoring unit 35 may be performed for each elevator apparatus, or for each similar elevator apparatus. For example, elevator apparatuses of the same model may be considered to be similar elevator apparatuses. As another example, elevator apparatuses with the same rated load, elevator apparatuses with the same rated speed, elevator apparatuses with the same model of hoisting machine, and elevator apparatuses with the same model of control device may each be considered to be similar elevator apparatuses. When monitoring by the monitoring unit 35 is performed for each similar elevator apparatus, comparison of average components or comparison of fluctuation range components, etc. may be performed.

[0113] Fig. 11 is a diagram for explaining the function of the monitoring unit 35. The vertical axis of Fig. 11 indicates the value of loss torque as an example of the feature calculated by the feature calculation unit 27. As shown in Fig. 11, the value of loss torque varies depending on the season. The monitoring unit 35 may determine whether or not the value of loss torque stored in the storage unit 32 is within an allowable range. The allowable range for determining whether the value of loss torque is appropriate, i.e., the upper limit and lower limit, is preferably set in advance for each elevator device.

[0114] The monitoring results by the monitoring unit 35 may be reflected in a maintenance plan for the elevator device 10. The monitoring results include, for example, information indicating whether or not the value of the loss torque is within an allowable range. In a simple example, the monitoring results by the monitoring unit 35 are stored in the storage unit 32. In this case, for example, a maintenance worker for the elevator device 10 can view the monitoring results stored in the storage unit 32 from a maintenance terminal via the communication network 4, and thereby reflect the monitoring results in the maintenance plan.

[0115] As another example, information for reflecting the monitoring results by the monitoring unit 35 in the maintenance plan for the elevator device 10 may be transmitted from the communication unit 34 to the maintenance database 40. In such a case, the monitoring results by the monitoring unit 35 are written into the maintenance information of the elevator device 10 stored in the maintenance database 40.

[0116] For example, in the elevator device 10, lubricating oil is used to reduce friction between the guide 11a and the guide rail 16. If the lubricating oil runs short, it is possible that the value of the loss torque will fall outside the allowable range. A maintenance worker who has confirmed such an event can replenish the lubricating oil before it runs out.

[0117] Also, a function similar to the monitoring function by the monitoring unit 35, for example, a function for determining whether or not the value of the loss torque is within an allowable range, may be used to determine when recovery is performed after the occurrence of an earthquake. For example, in the elevator device 10, when an earthquake occurs, a controlled operation is performed to stop the car 11 at the nearest floor and rescue users. In addition, in the elevator device 10, a diagnostic operation is performed after the controlled operation, and if no abnormality is detected in the diagnostic operation, an automatic recovery is performed under certain conditions. At this time, as one of the conditions for recovery, it may be determined whether or not the value of the loss torque calculated by the feature calculation unit 27 is within an allowable range.

[0118] In addition, various judgments regarding maintenance may be made based on the monitoring results by the monitoring unit 35. Hereinafter, such judgments are also referred to as maintenance judgments. The maintenance judgments include an inspection cycle judgment, an inspection content change judgment, a maintenance cycle judgment, a maintenance content change judgment, and a device replacement judgment for the elevator device 10.

[0119] The maintenance judgment may be performed by the analysis device 30. In such a case, the analysis device 30 performs the maintenance judgment based on a preset criterion. The analysis device 30 may perform the maintenance judgment by relatively comparing the monitoring results of a plurality of elevator devices. As another example, the maintenance judgment may be performed by a user who can access the analysis device 30 via the communication network 4. The user may also be a maintenance worker for the elevator device 10. By performing such a maintenance judgment, it becomes easier to organize matters that should be addressed in advance, such as confirmation of the inspection cycle, inspection contents, maintenance cycle, and maintenance contents, arrangement of replacement equipment, and scheduling of replacement work.

[0120] Furthermore, the monitoring results by the monitoring unit 35 may be reflected in a renewal plan for the elevator device 10. For example, the current state of the elevator device 10 can be understood from the monitoring results stored in the storage unit 32, and the appropriateness of the timing for renewal can be determined. Furthermore, when the elevator device 10 is actually renewed, the monitoring results by the monitoring unit 35 can be used to determine which devices require replacement and to select the devices to be applied.

[0121] In the example shown in this embodiment, in the monitoring device 20, the information acquisition unit 22 acquires the first information and the second information, and the regression equation generation unit 23 generates a linear regression equation. By using this linear regression equation, it is possible to accurately calculate the intermediate position torque estimation value. By using the calculated intermediate position torque estimation value, it is possible to calculate various feature quantities related to the torque of the hoisting machine 15. In order to generate the linear regression equation, the first information and the second information when the car 11 is traveling at a constant speed are required, but the car 11 frequently travels at the rated speed during normal operation. In addition, there is no condition on the load in the car 11 when acquiring the first information and the second information. Therefore, in the example shown in this embodiment, it is possible to relax the conditions for acquiring information that can be used when calculating the feature quantities related to the torque of the hoisting machine 15.

[0122] Since the information can be acquired during normal operation, it is possible to acquire the information periodically and continuously. Also, it is not necessary to run the car 11 just to acquire the information.

[0123] In the example shown in this embodiment, the torque value of the hoisting machine 15, i.e., the intermediate position torque estimate value, is calculated based on the linear regression equation generated by the regression equation generating unit 23. For this reason, even if a value caused by a disturbance is superimposed on the torque value of the hoisting machine 15 at the timing when the car 11 actually passes through the intermediate position of the hoistway 12, for example, the influence of the value caused by this disturbance can be suppressed. That is, with the example shown in this embodiment, the intermediate position torque estimate value can be calculated with high accuracy.

[0124] In the example shown in the present embodiment, the fifth information indicating the intermediate position torque estimate calculated by the torque calculation unit 24 is associated with the third information and the fourth information and is stored in the storage unit 21 as one set of state information. By storing a plurality of sets of state information in the storage unit 21, it becomes possible for the regression equation generation unit 26 to generate the linear regression equation during ascent and the linear regression equation during descent. In addition, when the regression equation generation unit 26 generates the linear regression equation during ascent and the linear regression equation during descent, it becomes possible for the feature calculation unit 27 to calculate the feature. Therefore, by storing a plurality of sets of state information in the storage unit 21, it becomes possible to acquire and store information that can be used when accurately calculating the feature associated with the torque of the hoist. In addition, it is also possible to provide the information.

[0125] Note that this embodiment shows a suitable example in which information indicating the intermediate position torque estimate value calculated by the torque calculation unit 24 is used as the fifth information. Information indicating the torque value of the hoist 15 obtained by another method may also be adopted as the fifth information. In such a case, the fifth information is information indicating the torque value when the car 11 traveling at a constant speed is in the intermediate position of the hoistway 12. Even in such an example, by storing multiple sets of state information in the storage unit 32, it becomes possible to acquire and store information that can be used when accurately calculating a feature amount related to the torque of the hoist.

[0126] In the example shown in the present embodiment, a linear regression equation for an upward trend and a linear regression equation for a downward trend are generated using a plurality of sets of state information. By generating two linear regression equations, various feature quantities can be calculated using information indicating the slopes and information indicating the intercepts of these equations.

[0127] In the present embodiment, a preferred example has been described in which the feature amount calculated by the feature amount calculation unit 27 is used to monitor the elevator device 10. The feature amount may be used in other ways.

[0128] As an example, the feature calculated by the feature calculation unit 27 may be used in the control device 18 of the elevator device 10. In this case, for example, the communication unit 28 transmits information indicating the feature calculated by the feature calculation unit 27 to the control device 18. The control device 18 uses the information as a control parameter for controlling the hoisting machine 15. For example, the information may include a value of loss torque, a value of a torque component that varies depending on the position of the car 11, and a value of unbalanced torque. The information is reflected in a feedforward term of a speed controller provided in the control device 18. This makes it possible to improve the speed tracking ability of the hoisting machine 15. The torque component that varies depending on the position of the car 11 will be described later.

[0129] As an example of using the feature amount in the control device 18, the value of the loss torque transmitted from the communication unit 28 may be used in the control device 18 to calculate the moment of inertia component, which is one of the control parameters. This can improve the calculation accuracy of the moment of inertia component, and can improve the speed tracking ability of the hoisting machine 15. The value of the loss torque may also be used in the control device 18 to determine the acceleration and maximum speed, which are control parameters. In other words, if the value of the loss torque is small, the acceleration and maximum speed can be set to larger values. This can expand the driving range of the hoisting machine 15.

[0130] Furthermore, as an example of using the feature quantity in the control device 18, the car balanced load estimation value transmitted from the communication unit 28 may be used for correcting the output value of the weighing device 11d in the control device 18. This makes it possible to reduce the start-up shock after the brake is released, and improve the riding comfort of the car.

[0131] As another example, the feature amounts calculated by the feature amount calculation unit 27 may be used to reproduce the traveling phenomenon of the car 11 in cyberspace. The reproduction unit 36 ​​has a function of reproducing the traveling phenomenon of the car 11 in cyberspace by using the feature amounts calculated by the feature amount calculation unit 27. As an example, the reproduction unit 36 ​​may have a function similar to the function of the "reproduction unit 27" disclosed in International Application No. PCT / JP2023 / 013958.

[0132] For example, there is a high possibility that the loss torque value will differ between the value calculated on paper and the value actually measured. This is because the loss torque value varies depending on the season and the maintenance state. Therefore, the reproduction unit 36 ​​reproduces the running phenomenon of the car 11 using the loss torque value calculated by the feature amount calculation unit 27, thereby improving the accuracy of the reproduction.

[0133] If the value of the unbalanced torque when there is no load inside the car 11 is known, it is possible to know the unbalanced torque when the car 11 is traveling more accurately. Furthermore, if information on the current of the electric motor of the hoist 15 is used as the information on the torque output by the hoist 15, it is possible to know the relationship between the torque value and the current value. The relationship between the torque and current of the hoist 15 includes a variable factor that varies depending on the temperature. Therefore, the reproduction unit 36 ​​reproduces the traveling phenomenon of the car 11 using the relationship between the unbalanced torque value and the current value calculated by the feature calculation unit 27, thereby improving the accuracy of the reproduction.

[0134] Furthermore, when the estimated car balanced load value is known, the balance state with the counterweight 13 or the output deviation state of the weighing device 11d can be known. In a case where a reproduction method is adopted in which the supply current to the hoist 15 is determined based on the output value of the weighing device 11d when reproducing a traveling phenomenon, when the estimated car balanced load value is known, it becomes possible to correct the increase or decrease in the supply current based on the value. Therefore, the reproduction unit 36 ​​reproduces the traveling phenomenon of the car 11 using the estimated car balanced load value calculated by the feature calculation unit 27, thereby improving the accuracy of the reproduction.

[0135] The information indicating the reproduction result by the reproduction unit 36 ​​and the information indicating the analysis result obtained from the reproduction result may be transmitted from the communication unit 34 to the maintenance database 40. In this case, the reproduction result by the reproduction unit 36 ​​and the analysis result obtained from the reproduction result are written into the maintenance information of the elevator device 10 stored in the maintenance database 40.

[0136] In the example shown in the present embodiment, the analysis device 30 may grant specific authority to a user who can access the communication network 4. The authority includes, for example, a viewing access authority to the information stored in the storage unit 32 and an update access authority to the surveillance model used by the monitoring unit 35. The authority may include a viewing access authority and an access authority to the reproduction result and the analysis result related to the reproduction unit 36. The granting of the authority by the analysis device 30 may be set for each user, each group, or each role. The analysis device 30 may cancel the granting of the authority or change the type of the authority to be granted depending on the situation. For example, the analysis device 30 may grant a user at the manufacturing plant 5e a viewing access authority to the information stored in the storage unit 32 and an update access authority to the surveillance model. The analysis device 30 may grant a user at the sales office 5d only a viewing access authority to the information stored in the storage unit 32.

[0137] In the present embodiment, a preferred example has been described in which the feature amount calculation unit 27 is provided in the monitoring device 20. That is, in the example shown in the present embodiment, the feature amount is calculated in the monitoring device 20. As another example, the feature amount may be calculated in the analysis device 30.

[0138] In such a case, the communication unit 28 of the monitoring device 20 transmits information required for calculating the feature amount to the analysis device 30 via the communication network 3. The analysis device 30 also includes a calculation unit having a function similar to that of the feature amount calculation unit 27. When the communication unit 33 receives the information transmitted from the communication unit 28, the calculation unit calculates the feature amount based on the information received by the communication unit 33. In S202, information indicating the feature amount calculated by the calculation unit is stored in the storage unit 32 as monitored information in association with other information.

[0139] In this example, when the feature amount is utilized in the control device 18 of the elevator device 10, information indicating the feature amount calculated by the calculation unit of the analysis device 30 is transmitted from the communication unit 33 to the elevator device 10 via the communication network 3. At this time, the information indicating the feature amount may be transmitted to the elevator device 10 via the monitoring device 20.

[0140] The above-mentioned example is a suitable example in which the feature quantity related to the torque of the hoist 15 can be calculated in the information system 1. However, it is not essential that the information system 1 has a function for calculating the feature quantity. The information system 1 only needs to relax the conditions for acquiring information that can be used to calculate the feature quantity, and does not need to have a function for calculating the feature quantity. For example, the feature quantity may be calculated in another system or device.

[0141] In the above, an example has been described in detail in which the linear regression equation during ascent and the linear regression equation during descent are generated by acquiring the first information to the fifth information, and the linear regression equation during ascent and the linear regression equation during descent are used to calculate feature quantities such as loss torque. As another example, a linear regression equation as shown in Fig. 7 may be generated by acquiring the first information and the second information, and other feature quantities related to the torque of the hoist 15 may be calculated using the linear regression equation.

[0142] As described above, when acquiring the first information to the fifth information, there is no condition on the load in the car 11 during traveling, and each piece of information can be acquired during normal operation, for example. Therefore, in both the above-mentioned example and the example described later, it is possible to relax the acquisition conditions for information required for calculating the feature amount related to the torque of the hoisting machine 15.

[0143] For example, in the example shown in the present embodiment, the slope of the linear regression equation generated by the regression equation generating unit 23 is identified by the slope identifying unit 25. Since it is possible to calculate a torque component (e.g., a fluctuation rate) of the torque value of the hoisting machine 15 that fluctuates depending on the position of the car 11 based on the slope, the slope can be regarded as one of the important characteristic quantities.

[0144] As an example, a torque component that varies depending on the position of the car 11 is calculated based on the inclination identified by the inclination identification unit 25. By monitoring the change in the calculated torque component by the monitoring unit 35, it is also possible to determine whether an obstacle is present in the elevator shaft 12. This determination can also be reflected in the maintenance plan.

[0145] Moreover, the calculated torque component is correlated with the mass component of the main rope 14 alone. That is, when the mass of the main rope 14 alone changes, the calculated torque component, i.e., the slope of the linear regression equation generated by the regression equation generating unit 23, changes. For this reason, the slope may be stored in the storage unit 32 as monitored information, and the slope may be one of the monitoring targets of the monitoring unit 35. The monitoring unit 35 may determine whether the value of the slope is within a preset allowable range. Moreover, the monitoring unit 35 may monitor the change in the slope over time, and the monitoring result may be reflected in the maintenance plan for the elevator device 10, more specifically, in the maintenance plan for the main rope 14.

[0146] As another example, the calculated torque component may be used in the control device 18 of the elevator device 10. In this case, for example, the communication unit 28 transmits information indicating the inclination identified by the inclination identification unit 25 to the control device 18. The control device 18 uses the information as a control parameter for controlling the hoisting machine 15. For example, a torque component that varies depending on the position of the car 11 is calculated based on the inclination identified by the inclination identification unit 25. The calculated torque component is reflected in a feedforward term of a speed controller provided in the control device 18. This makes it possible to improve the speed tracking ability of the hoisting machine 15.

[0147] As another example, the calculated torque component may be used to reproduce the running of the car 11 in cyberspace. In such a case, the reproduction unit 36 ​​reproduces the running phenomenon of the car 11 in cyberspace using the calculated torque component. This makes it possible to improve the accuracy of estimating the current value of the hoist 15, and to improve the accuracy of reproducing the running phenomenon of the car 11.

[0148] Furthermore, the feature amount calculation unit 27 may calculate a value related to a residual based on the linear regression equation generated by the regression equation generation unit 23, as a feature amount related to the torque of the hoist 15. That is, the feature amount calculation unit 27 calculates a value related to a residual between the torque value (observed value) of the hoist 15 acquired from the control device 18 by the information acquisition unit 22 and the value obtained from the linear regression equation (predicted value), as a feature amount.

[0149] As an example, the value for the residual is the coefficient of determination R 2 The coefficient of determination R based on the linear regression equation generated by the regression equation generation unit 23 is 2 _1(L) is expressed by equation (23). R 2 _1(L)=1-residual sum of squares / total sum of squares …(23) Here, the residual sum of squares is the sum of the squares of the residual between the observed value and the predicted value. The total sum of squares is the sum of the squares of the difference between the observed value and the average value of all the observed values. As another example, the feature calculation unit 27 may calculate the root mean squared error (RMSE), the mean squared error (MSE), the mean absolute error (MAE), or the like as a value related to the residual.

[0150] Coefficient of determination R 2 is 1 minus the ratio of the residual variation to the total variation, i.e., the coefficient of determination R 2 The minimum value of _1(L) is 0 and the maximum value is 1. The closer to 1, the higher the accuracy of the linear regression equation generated by the regression equation generating unit 23.

[0151] FIG. 12 is a diagram showing an example of a linear regression equation generated by the regression equation generating unit 23, and corresponds to FIG. 7. FIG. 12 shows an example in which the vibration of the car 11 during constant speed travel has increased compared to the example shown in FIG. 7. When the vibration of the car 11 increases, the coefficient of determination R 2 _1(L) becomes smaller. Therefore, the coefficient of determination R calculated from the example shown in Figure 12 2 _1(L) is the coefficient of determination R calculated from the example shown in Figure 7 2 Less than _1(L).

[0152] Fig. 13 is a diagram showing another example of the linear regression equation generated by the regression equation generating unit 23, and corresponds to Fig. 7. Compared with the example shown in Fig. 7, Fig. 13 shows an example in which a large vibration suddenly occurs when the car 11 is traveling at a constant speed. The coefficient of determination R calculated from the example shown in Fig. 13 2 _1(L) is the coefficient of determination R calculated from the example shown in Figure 7 2 _1(L). On the other hand, the coefficient of determination R calculated from the example shown in Figure 13 2 _1(L) is the coefficient of determination R calculated from the example shown in Figure 12 2 _Greater than 1(L).

[0153] In this way, when the vertical vibration of the car 11 increases, the coefficient of determination R 2 The value of _1(L) becomes smaller. Therefore, the coefficient of determination R 2 _1(L) is stored in the storage unit 32 as monitored information, and the coefficient of determination R 2 _1(L) may be one of the monitoring targets of the monitoring unit 35. 2 14 shows an example of the coefficient of determination R_1(L). FIG. 14 shows an example of the value when normal vibration occurs in the car 11, that is, when no abnormal vibration occurs, the value when the vibration increases, and the value when a sudden vibration is superimposed. The monitoring unit 35 is 2 It may be determined whether the value of _1(L) falls below a preset lower limit value. 2 The change over time of _1(L) may be monitored, and the monitoring results may be reflected in the maintenance plan for the elevator device 10.

[0154] The coefficient of determination R 2 Regarding the above, the feature calculation unit 27 may calculate a value related to a residual based on the ascending linear regression equation generated by the regression equation generation unit 26 as a feature related to the torque of the hoist 15. That is, the feature calculation unit 27 calculates a value related to a residual between the intermediate position torque estimated value (observed value) calculated by the torque calculation unit 24 and the value obtained from the ascending linear regression equation (predicted value) as a feature. Note that the value related to the residual based on the ascending linear regression equation can be calculated even if the descending linear regression equation is not generated by the regression equation generation unit 26.

[0155] As an example, the value for the residual is the coefficient of determination R 2 The coefficient of determination R based on the ascending linear regression equation generated by the regression equation generating unit 26 is 2 _2_up is expressed by equation (24) similarly to equation (23). R 2 _2_up=1-residual sum of squares / total sum of squares …(24) As another example, the feature amount calculation section 27 may calculate RMSE, MSE, MAE, or the like as a value related to the residual.

[0156] An example of the ascending linear regression equation is shown in Fig. 9. The horizontal axis of Fig. 9 indicates the load factor in the car 11, and the vertical axis indicates the intermediate position torque estimate value calculated by the torque calculation unit 24. Therefore, the coefficient of determination R 2 When the value of _2_up becomes smaller, there is a possibility that a change has occurred in the correspondence relationship between the load factor in the car 11 and the intermediate position torque estimation value. Note that the horizontal axis of FIG. 9 indicates values ​​based on the actual measurement values ​​of the weighing device 11d, for example. For this reason, the coefficient of determination R 2 When the value of _2_up becomes small, it is more reasonable to determine that the output of the weighing device 11d has become unstable, rather than determining that the intermediate position torque estimate value calculated using the linear regression equation has become unstable.

[0157] Coefficient of determination R 2 The _2_up is also stored in the storage unit 32 as monitored information, and the coefficient of determination R 2 _2_up may be one of the targets monitored by the monitoring unit 35. For example, the monitoring unit 35 may monitor the coefficient of determination R 2 It may be determined whether the value of _2_up falls below a preset lower limit value. 2 The change over time of _2_up may be monitored, and the monitoring result may be reflected in the maintenance plan for the elevator device 10. In this example, the coefficient of determination R 2 This does not deny the possibility of determining that the intermediate position torque estimate value has become unstable when the value of _2_up has become small.

[0158] The feature amount calculation unit 27 may calculate a value related to the residual based on the linear regression equation during descent generated by the regression equation generation unit 26, instead of a value related to the residual based on the linear regression equation during ascent, as a feature amount related to the torque of the hoist 15. The feature amount calculation unit 27 may calculate a value related to the residual based on the linear regression equation during descent, together with a value related to the residual based on the linear regression equation during ascent. FIG. 15 shows the coefficient of determination R 2 _2_up and the coefficient of determination R based on the linear regression equation when declining 22 is a diagram showing an example of the feature amount _2_dn. In this example, the feature amount calculation unit 27 calculates a value related to the residual between the intermediate position torque estimated value (observed value) calculated by the torque calculation unit 24 and the value (predicted value) obtained from the linear regression equation during descent as the feature amount. Note that the value related to the residual based on the linear regression equation during descent can be calculated even if the linear regression equation during ascent has not been generated by the regression equation generation unit 26.

[0159] As an example, the value for the residual is the coefficient of determination R 2 The coefficient of determination R based on the descending linear regression equation generated by the regression equation generating unit 26 is 2 _2_dn is expressed by equation (25), similar to equation (24). R 2 _2_dn=1-residual sum of squares / total sum of squares …(25) As another example, the feature amount calculation section 27 may calculate RMSE, MSE, MAE, or the like as a value related to the residual.

[0160] 16 is a diagram illustrating an example of hardware resources of the monitoring device 20. The monitoring device 20 includes, as hardware resources, a processing circuit 60 including a processor 61 and a memory 62. The processing circuit 60 may include a plurality of processors 61. The processing circuit 60 may include a plurality of memories 62.

[0161] In this embodiment, each unit indicated by the reference numerals 21 to 28 indicates a function possessed by the monitoring device 20. The function of the storage unit 21 is realized by a memory 62. The function of each unit indicated by the reference numerals 22 to 28 can be realized by software written as a program, firmware, or a combination of software and firmware. The program is stored in the memory 62. The monitoring device 20 realizes the function of each unit indicated by the reference numerals 22 to 28 by executing the program stored in the memory 62 by a processor 61 (computer).

[0162] The processor 61 is also called a CPU (Central Processing Unit), central processing unit, processing unit, arithmetic unit, microprocessor, microcomputer, or DSP. The memory 62 may be a semiconductor memory, a magnetic disk, a flexible disk, an optical disk, a compact disk, a mini disk, or a DVD. Examples of semiconductor memories that can be used include RAM, ROM, flash memory, EPROM, and EEPROM.

[0163] Fig. 17 is a diagram showing another example of hardware resources of the monitoring device 20. In the example shown in Fig. 17, the monitoring device 20 includes a processing circuit 60 including a processor 61, a memory 62, and dedicated hardware 63. Fig. 17 shows an example in which some of the functions of the monitoring device 20 are realized by the dedicated hardware 63. All of the functions of the monitoring device 20 may be realized by the dedicated hardware 63. As the dedicated hardware 63, a single circuit, a composite circuit, a programmed processor, a parallel programmed processor, an ASIC, an FPGA, or a combination of these can be adopted.

[0164] The hardware resources of the analysis device 30 are similar to those shown in FIG. 16 or FIG. 17. The analysis device 30 includes a processing circuit including a processor and a memory as the hardware resources. The processing circuit may include multiple processors. The processing circuit may include multiple memories. The function of the storage unit 32 is realized by the memory. The analysis device 30 realizes the functions of each unit indicated by the reference numerals 33 to 36 by executing a program stored in the memory by a processor (computer). The analysis device 30 may include a processing circuit including a processor, a memory, and dedicated hardware as the hardware resources. As for the analysis device 30, some or all of the functions of the device may be realized by dedicated hardware.

[0165] Below, examples of aspects that may be included in the present disclosure are set forth as appendices.

[0166] [Appendix 1] an information acquisition unit that acquires first information indicating a position of an elevator car and second information indicating a torque value of a hoist that drives the car; a first regression equation generating unit that generates a linear regression equation between a position of the car and a torque value of the hoist when a first run in which the car runs at a constant speed in a hoistway is being performed, based on the first information and the second information acquired by the information acquiring unit; An information system equipped with [Appendix 2] 2. The information system according to claim 1, further comprising a torque calculation unit that calculates a torque value of the hoist when the car is at an intermediate position of the hoistway during the first run, based on the linear regression equation generated by the first regression equation generation unit. [Appendix 3] 2. The information system according to claim 1, further comprising a slope specification unit that specifies a slope of the linear regression equation generated by the first regression equation generation unit. [Appendix 4] Further comprising a first storage unit, the information acquisition unit acquires third information indicating a traveling direction of the car when the first traveling is being performed and fourth information indicating a load in the car; The information system described in Appendix 2, wherein the third information and the fourth information acquired by the information acquisition unit and fifth information indicating a torque value calculated by the torque calculation unit are associated with each other as a set of status information and stored in the first storage unit. [Appendix 5] 5. The information system according to claim 4, wherein a plurality of sets of state information are stored in the first storage unit by causing the car to travel at a constant speed a plurality of times. [Appendix 6] an information acquisition unit that acquires third information indicating a traveling direction of the elevator car when the elevator car is performing a first run in which the elevator car travels in a hoistway at a constant speed and fourth information indicating a load in the elevator car; a first storage unit that stores the third information and the fourth information acquired by the information acquisition unit and fifth information indicating a torque value of a hoist that drives the car in association with each other as one set of status information; Equipped with The fifth information is information indicating a torque value when the car is in an intermediate position of the hoistway during the first run, An information system in which a plurality of sets of state information are stored in the first storage unit by causing the car to travel at a constant speed a plurality of times. [Appendix 7] 7. The information system according to claim 5 or 6, further comprising a second regression equation generating unit that generates, based on a plurality of sets of state information stored in the first storage unit, a linear regression equation during ascent between the load in the car when the car ascends at a constant speed and a torque value of the hoist when the car is in an intermediate position of the hoistway, and generates a linear regression equation during descent between the load in the car when the car descends at a constant speed and the torque value of the hoist when the car is in an intermediate position of the hoistway. [Appendix 8] The information system according to claim 7, further comprising a feature calculation unit that calculates a feature related to a torque of the hoisting machine based on the ascent linear regression equation and the descent linear regression equation generated by the second regression equation generation unit. [Appendix 9] The information system according to claim 8, wherein the feature calculation unit calculates a value of a loss torque as the feature using information indicating an intercept of the linear regression equation during the increase and information indicating an intercept of the linear regression equation during the decrease. [Appendix 10] 10. The information system according to claim 9, wherein the feature calculation unit calculates the feature without using information indicating a slope of the ascending linear regression equation and information indicating a slope of the descending linear regression equation. [Appendix 11] 11. The information system of claim 8, wherein the feature calculation unit calculates a value of an unbalance torque as the feature using information indicating an intercept of the linear regression equation during ascent and information indicating an intercept of the linear regression equation during descent. [Appendix 12] The information system according to any one of Appendix 8 to Appendix 11, wherein the feature calculation unit calculates an in-car balanced load estimate as the feature by using information indicating a slope and an intercept of the linear regression equation during ascent and information indicating a slope and an intercept of the linear regression equation during descent. [Appendix 13] 13. The information system according to any one of appendix 8 to appendix 12, further comprising a reproduction unit that reproduces a traveling phenomenon of the car in cyberspace by using the feature calculated by the feature calculation unit. [Appendix 14] 14. The information system according to any one of Supplementary Note 8 to Supplementary Note 13, further comprising a first communication unit configured to transmit information indicating the feature calculated by the feature calculation unit to a control device that controls the hoisting machine so that the information indicating the feature calculated by the feature calculation unit is used as a control parameter in the control device. [Appendix 15] 15. The information system according to any one of claims 8 to 14, further comprising a second storage unit configured to store information indicating the feature amount calculated by the feature amount calculation unit in chronological order. [Appendix 16] 16. The information system according to claim 15, further comprising a monitoring unit that monitors changes in the feature amount stored in the second storage unit. [Appendix 17] 17. The information system according to claim 16, further comprising a second communication unit that transmits information for reflecting a monitoring result by the monitoring unit in a maintenance plan for an elevator system including the car. [Appendix 18] The information system according to claim 1, further comprising a feature calculation unit that calculates a value relating to a residual based on the linear regression equation generated by the first regression equation generation unit as a feature related to a torque of the hoist. [Appendix 19] a second regression equation generating unit that generates a linear regression equation during ascent between a load in the car when the car ascends at a constant speed and a torque value of the hoist when the car is at an intermediate position of the hoistway, based on a plurality of sets of state information stored in the first storage unit; a feature amount calculation unit that calculates a value related to a residual based on the ascending linear regression equation generated by the second regression equation generation unit as a feature amount related to a torque of the hoist; 7. The information system according to claim 5 or 6, further comprising: [Appendix 20] a second regression equation generating unit that generates a linear regression equation during descent between a load in the car when the car descends at a constant speed and a torque value of the hoist when the car is at an intermediate position of the hoistway, based on a plurality of sets of state information stored in the first storage unit; a feature amount calculation unit that calculates a value related to a residual based on the descending linear regression equation generated by the second regression equation generation unit as a feature amount related to a torque of the hoist; 19. The information system of claim 5, 6, or 19, further comprising: [Appendix 21] an information acquisition step of acquiring first information indicating a position of an elevator car and second information indicating a torque value of a hoist that drives the car; a first regression equation generating step of generating a linear regression equation between a position of the car and a torque value of the hoist when a first run in which the car runs at a constant speed in a hoistway is being performed, based on the first information and the second information acquired in the information acquiring step; The information acquisition method includes: [Appendix 22] an information acquisition step of acquiring third information indicating a traveling direction of the elevator car when the elevator car is performing a first run in which the elevator car runs at a constant speed in the elevator shaft and fourth information indicating a load in the elevator car; a first storage step of associating the third information and the fourth information acquired in the information acquisition step with fifth information indicating a torque value of a hoist that drives the car as one set of status information and storing the information in a first storage unit; Equipped with The fifth information is information indicating a torque value when the car is in an intermediate position of the hoistway during the first run, An information acquisition method in which a plurality of sets of status information are stored in the first storage unit by causing the car to travel at a constant speed a plurality of times.

[0167] The matters described in Supplements 2-5 and 7-20 may be added directly or indirectly to Supplement 21, just as they are added directly or indirectly to Supplement 1. The matters described in Supplements 7-17 and 19-20 may be added directly or indirectly to Supplement 21, just as they are added directly or indirectly to Supplement 6. [Explanation of symbols]

[0168] REFERENCE SIGNS LIST 1 information system, 2 facilities, 3-4 communication network, 5a head office, 5b branch office, 5c branch office, 5d sales office, 5e factory, 10 elevator device, 11 car, 11a guide, 11b door, 11c motor, 11d scale device, 12 hoistway, 13 counterweight, 13a guide, 14 main rope, 15 hoist, 16-17 guide rail, 18 control device, 20 monitoring device, 21 storage unit, 22 information acquisition unit, 23 regression equation generation unit, 24 torque calculation unit, 25 inclination identification unit, 26 regression equation generation unit, 27 feature amount calculation unit, 28 communication unit, 30 analysis device, 31 server device, 32 storage unit, 33 communication unit, 34 communication unit, 35 monitoring unit, 36 reproduction unit, 40 maintenance database, 50 specification database, 60 processing circuit, 61 processor, 62 memory, 63 dedicated hardware

Claims

1. an information acquisition unit that acquires third information indicating a traveling direction of the elevator car when the elevator car is performing a first run in which the elevator car travels at a constant speed in the elevator shaft and fourth information indicating a load in the elevator car; a first storage unit that associates the third information and the fourth information acquired by the information acquisition unit with fifth information indicating a torque value of a hoist that drives the car as one set of status information and stores the information; Equipped with The fifth information is information indicating a torque value when the car is in an intermediate position of the hoistway during the first travel, An information system in which a plurality of sets of state information are stored in the first storage unit by causing the car to travel at a constant speed a plurality of times.

2. 2. The information system according to claim 1, further comprising a second regression equation generating unit that generates, based on a plurality of sets of state information stored in the first storage unit, a linear regression equation during ascent between the load in the car when the car ascends at a constant speed and a torque value of the hoist when the car is in an intermediate position of the hoistway, and generates a linear regression equation during descent between the load in the car when the car descends at a constant speed and a torque value of the hoist when the car is in an intermediate position of the hoistway.

3. The information system according to claim 2, further comprising a feature calculation unit that calculates a feature related to a torque of the hoist based on the ascending linear regression equation and the descending linear regression equation generated by the second regression equation generation unit.

4. The information system according to claim 3 , wherein the feature calculation unit calculates a value of a loss torque as the feature using information indicating an intercept of the linear regression equation during the increase and information indicating an intercept of the linear regression equation during the decrease.

5. The information system according to claim 4 , wherein the feature amount calculation unit calculates the feature amount without using information indicating a slope of the ascending linear regression equation and information indicating a slope of the descending linear regression equation.

6. 6. The information system according to claim 3, wherein the feature calculation unit calculates a value of an unbalance torque as the feature by using information indicating an intercept of the linear regression equation during an increase and information indicating an intercept of the linear regression equation during a decrease.

7. The information system according to any one of claims 3 to 5, wherein the feature calculation unit calculates an in-car balanced load estimate as the feature using information indicating a slope and an intercept of the linear regression equation during ascent and information indicating a slope and an intercept of the linear regression equation during descent.

8. 6. The information system according to claim 3, further comprising a reproduction unit that reproduces a traveling phenomenon of the car in a cyberspace by using the feature calculated by the feature calculation unit.

9. The information system according to any one of claims 3 to 5, further comprising a first communication unit that transmits information indicating the feature calculated by the feature calculation unit to a control device that controls the hoist, so that the information indicating the feature calculated by the feature calculation unit is used as a control parameter in the control device.

10. The information system according to claim 3 , further comprising a second storage unit configured to store, in chronological order, information indicating the feature amounts calculated by the feature amount calculation unit.

11. The information system according to claim 10 , further comprising a monitoring unit that monitors changes in the feature amount stored in the second storage unit.

12. 12. The information system according to claim 11, further comprising a second communication unit configured to transmit information for reflecting a monitoring result by the monitoring unit in a maintenance plan for an elevator system including the car.

13. a second regression equation generating unit that generates a linear regression equation during ascent between a load in the car when the car ascends at a constant speed and a torque value of the hoist when the car is at an intermediate position of the hoistway, based on a plurality of sets of state information stored in the first storage unit; a feature amount calculation unit that calculates a value related to a residual based on the ascending linear regression equation generated by the second regression equation generation unit as a feature amount related to a torque of the hoist; The information system of claim 1 further comprising:

14. a second regression equation generating unit that generates a linear regression equation during descent between a load in the car when the car descends at a constant speed and a torque value of the hoist when the car is in an intermediate position of the hoistway, based on a plurality of sets of state information stored in the first storage unit; a feature amount calculation unit that calculates a value related to a residual based on the descending linear regression equation generated by the second regression equation generation unit as a feature amount related to a torque of the hoist; The information system according to claim 1 or claim 13, further comprising:

15. an information acquisition step of acquiring third information indicating a traveling direction of the elevator car when the elevator car is performing a first run in which the elevator car runs at a constant speed in the elevator shaft and fourth information indicating a load in the elevator car; a first storage step of associating the third information and the fourth information acquired in the information acquisition step with fifth information indicating a torque value of a hoist that drives the car as one set of status information and storing the information in a first storage unit; Equipped with The fifth information is information indicating a torque value when the car is in an intermediate position of the hoistway during the first travel, An information acquisition method in which a plurality of sets of status information are stored in the first storage unit by causing the car to travel at a constant speed a plurality of times.

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