Method for generating passenger data set of elevator in building

Abstract

The present disclosure relates to a passenger data set generation method of an elevator in a building, in the case that multiple passengers of a target organization take the elevator to enter the building, the time period of the passengers taking the elevator and the peak time of the time period are obtained; based on the rising time period, the falling time period, the preset peak arrival rate and the preset time in the time period, an arrival rate function is generated; according to the arrival rate function and the expected number of a single group of passengers, the arrival rate of a single group of passengers is determined; based on the arrival rate of a single group of passengers and a preset random number, the time interval between two groups of passengers is determined; based on the actual number of a single group of passengers, the time interval between two groups of passengers, the time when a single group of passengers arrives at the elevator entrance, the departure floor and the destination floor of a single group of passengers, the passenger data set of the target organization in the above time period is generated. In this way, in the peak period, the time-varying characteristics of the arrival rate function are dynamically described, and the situation of multiple people arriving at the elevator entrance at the same time is simulated, which is more in line with the actual passenger flow fluctuation law.

Description

Technical Field

[0001] This disclosure relates to the field of data analysis technology, and in particular to a method for generating passenger datasets for elevators in buildings. Background Technology

[0002] In multi-story buildings (such as office buildings) that use elevators, in order to optimize passenger capacity, waiting time and elevator energy consumption, vertical transportation design of elevators is required, which specifically involves generating elevator passenger datasets.

[0003] In related technologies, two methods are generally used to generate passenger datasets for elevators in buildings: computational methods and simulation methods. However, computational methods can only generate passenger datasets for simple scenarios; simulation methods generate passenger datasets that differ significantly from the actual situation, leading to inaccurate and incomplete simulation results.

[0004] It is evident that existing methods for generating passenger datasets for elevators in buildings have poor adaptability and accuracy, which affects elevator scheduling efficiency and increases passenger waiting time and elevator energy consumption. Summary of the Invention

[0005] To address the aforementioned technical issues, this disclosure provides a method for generating passenger datasets for elevators within buildings.

[0006] This disclosure provides a method for generating passenger datasets for elevators within buildings, including: When multiple passengers of the target institution take the elevator into the building, the first time period of the passengers taking the elevator and the first peak time of the first time period are obtained, wherein the first peak time divides the first time period into a first ascending time period and a first descending time period. Based on the first rising time period, the first falling time period, the preset peak arrival rate, the first preset time, and the second preset time, a first arrival rate function is generated. The first arrival rate of a single group of passengers is determined based on the first arrival rate function and the first expected number of a single group of passengers in the elevator, wherein the single group of passengers in the elevator consists of users of at least one institution in the building; Based on the first arrival rate of the single group of passengers and the first preset random number, the first time interval between the two groups of passengers is determined; Based on the first actual number of passengers in a single group, the first time interval between the two groups of passengers, the first time when a single group of passengers arrives at the elevator entrance, the first departure floor of a single group of passengers, and the first destination floor of a single group of passengers, a first passenger dataset of the target organization is generated within the first time interval.

[0007] The technical solution provided in this disclosure has the following advantages compared with the prior art: This disclosure discloses a method for generating passenger datasets for elevators within a building. When multiple passengers from a target organization enter the building via elevator, the method first obtains a first time period during which passengers ride the elevator and a first peak time within that time period. The first peak time divides the first time period into a first ascending time period and a first descending time period. Then, based on the first ascending time period, the first descending time period, a preset peak arrival rate, a first preset time, and a second preset time, a first arrival rate function is generated. This dynamically describes the time-varying characteristics of the arrival rate function during peak commuting hours, based on the ascending and descending time periods of passengers entering the elevator. Next, based on the first arrival rate function and the first period of a single group of passengers in the elevator... The system calculates the first arrival rate of a single group of passengers by measuring the number of passengers. Each group consists of users from at least one organization within the building. This is achieved by converting individual arrival rates into group arrival rates, simulating the simultaneous arrival of multiple passengers at the elevator entrance. Next, based on the first arrival rate of each group and a pre-set random number, the first time interval between two groups of passengers is determined. Finally, based on the actual number of passengers in each group, the first time interval between the two groups, the first arrival time of each group, the first departure floor, and the first destination floor, a first passenger dataset for the target organization within the first time interval is generated. This allows for the simulation of passenger datasets by incorporating the time intervals between adjacent floors. This approach dynamically describes the time-varying characteristics of the arrival rate function during peak hours and simulates the simultaneous arrival of multiple passengers at the elevator entrance, better reflecting actual passenger flow fluctuations. Therefore, it improves adaptability and accuracy, ultimately enhancing elevator scheduling efficiency and reducing passenger waiting time and elevator energy consumption. Attached Figure Description

[0008] The accompanying drawings, which are incorporated in and form a part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure.

[0009] To more clearly illustrate the technical solutions in the embodiments of this disclosure or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0010] Figure 1 This is a flowchart illustrating a method for generating passenger datasets for elevators within a building, as provided in an embodiment of this disclosure. Detailed Implementation

[0011] To better understand the above-mentioned objectives, features, and advantages of this disclosure, the solutions disclosed herein will be further described below. It should be noted that, unless otherwise specified, the embodiments and features described herein can be combined with each other.

[0012] Numerous specific details are set forth in the following description in order to provide a full understanding of this disclosure, but this disclosure may also be implemented in other ways different from those described herein; obviously, the embodiments in the specification are only some, and not all, of the embodiments of this disclosure.

[0013] The calculation methods used in related technologies can only handle simple situations. For example, all elevators in an elevator group are identical, and passengers go to upper floors in the same proportion on each floor.

[0014] The relevant technology employs a simulation method to simulate the complexities of elevator use. Specifically, it uses a Poisson process to randomly generate arriving passengers. For example, during peak upward travel, assuming the moving building has 1000 passengers, if 120 people simultaneously try to board the elevator within a 5-minute window, the arrival rate is 120 / 1000 = 12%. A passenger dataset conforming to this distribution is then generated for subsequent simulation calculations. However, this simulation method differs significantly from reality, resulting in a passenger dataset that does not accurately reflect actual conditions.

[0015] To solve the above problems, the following will be combined with... Figure 1 This disclosure describes a method for generating passenger datasets for elevators within buildings, based on embodiments of the present disclosure. In these embodiments, the method for generating passenger datasets for elevators within buildings can be executed by an electronic device or a server. Optionally, the electronic device may include a mobile phone, tablet, computer, foldable screen, etc. The server may be a cloud server or a server cluster.

[0016] Figure 1 A flowchart illustrating a method for generating passenger datasets for elevators within a building, as provided in an embodiment of this disclosure, is shown.

[0017] like Figure 1 As shown, the method for generating passenger datasets for elevators within the building may include the following steps.

[0018] S110. When multiple passengers of the target institution take the elevator into the building, obtain the first time period of the passengers taking the elevator and the first peak time of the first time period.

[0019] It is understandable that for any organization, the employee arrival rate during the morning rush hour is a process of gradual influx, peak, and dissipation. Electronic devices simulate the morning rush hour of the target organization as a scenario where multiple passengers take the elevator into the building. In this scenario, the first time period of passengers taking the elevator and the first peak time of the first time period are obtained.

[0020] The first time period can be determined based on the earliest and latest start times of the target organization's employees. The first peak time refers to the peak start time for the target organization's employees.

[0021] The first peak time divides the first time period into a first rising time period and a first falling time period.

[0022] For example, if the target organization's start time is 9:00 AM, and the first 15 minutes before the start time is the first peak time (8:45 AM), then simulating 45 minutes before and after the first peak time determines the earliest and latest start times for the organization's employees. Specifically, simulating 45 minutes before the first peak time 8:45 results in 8:00 AM, and simulating 45 minutes after the first peak time 8:45 results in 9:30 AM. Therefore, the first time period is [8:00, 9:30], totaling 5400 seconds. The first peak time 8:45 divides the first time period [8:00, 9:30] into a first rising period [8:00, 8:45] and a first falling period [8:45, 9:30].

[0023] S120. Generate a first arrival rate function based on the first rising time period, the first falling time period, the preset peak arrival rate, the first preset time, and the second preset time.

[0024] Since the employee arrival rate of the target organization during the peak working hours exhibits a gradual influx-peak-dissipation process, we can use this pattern to simulate the arrival rate function during the first rising period and the first falling period, and then concatenate the arrival rate functions corresponding to the first rising period and the first falling period to generate the first arrival rate function for the first period.

[0025] In some embodiments, the specific implementation method of S120 includes, but is not limited to, the following method: generating a first rising segment arrival rate function based on a first rising time period, a preset peak arrival rate, and a first preset time; generating a first falling segment arrival rate function based on a first falling time period, a preset peak arrival rate, and a second preset time; and concatenating the first rising segment arrival rate function and the first falling segment arrival rate function according to timestamps to obtain a first arrival rate function.

[0026] The preset peak arrival rate is used to characterize the ratio of the area corresponding to the peak arrival rate to the total coverage area of ​​the arrival rate function. Optionally, the preset peak arrival rate can be 12%.

[0027] The first preset time and the second preset time can be determined based on experience. Optionally, the smaller the first preset time, the faster the arrival rate function of the first rising segment rises; the smaller the second preset time, the faster the arrival rate function of the first falling segment falls.

[0028] Alternatively, the arrival rate function for the first ascending segment can be determined by the following method:

[0029] in, This is the first peak time. yes The corresponding arrival rate, It is the first preset time. It is the earliest start time for employees of the target organization. .

[0030] Continuing with the example above, if the target organization's working hours are 9:00 AM and the first peak time is 8:45 AM, then the first time period is [8:00 AM, 9:30 AM], totaling 5400 seconds. It is 8:00. It is 9:00. =2700s, at this time, in the arrival rate function of the first rising segment The value range is [0, 2700s]. It can be 1800s.

[0031] Alternatively, the arrival rate function for the first descent segment can be determined by the following method:

[0032] in, It is the latest time for employees of the target organization to start work.

[0033] Continuing with the example above, if the target organization's working hours are 9:00 AM and the first peak time is 8:45 AM, then the first time period is [8:00 AM, 9:30 AM], totaling 5400 seconds. It is 9:30. It is 9:00. =2700s, at this time, in the arrival rate function of the first descent segment The value range is [2700s, 5400s]. It can be 750s.

[0034] Furthermore, after determining the first ascending segment arrival rate function and the first descending segment arrival rate function, the first ascending segment arrival rate function and the first descending segment arrival rate function are concatenated in chronological order to obtain the first arrival rate function.

[0035] In this way, during rush hour, the time-varying characteristics of the first arrival rate function can be dynamically described, making the first arrival rate function conform to the actual rush hour situation of gradual influx, peak, and rapid dissipation. Therefore, the first arrival rate function simulated in this way during rush hour is consistent with the actual situation.

[0036] S130. Determine the first arrival rate of a single group of passengers based on the first arrival rate function and the first expected number of a single group of passengers in the elevator, wherein the single group of passengers in the elevator consists of users of at least one institution in the building.

[0037] It is understandable that during rush hour, multiple people typically arrive at the elevator entrance simultaneously. Therefore, the individual arrival rate of individual passengers is converted into the group arrival rate to simulate the situation where multiple people arrive at the elevator entrance simultaneously.

[0038] Since a single group of passengers in an elevator consists of users from at least one institution within the building, the same group of passengers in the elevator may arrive at different floors.

[0039] Here, the first expected number of passengers in a single group refers to the statistical number of passengers in that single group. Optionally, the first expected number of passengers in a single group can be determined based on the actual number and probability corresponding to multiple groups of passengers.

[0040] For example, the probability of a group of passengers consisting of 1 person is 0.65, the probability of a group of passengers consisting of 2 people is 0.2, the probability of a group of passengers consisting of 3 people is 0.12, the probability of a group of passengers consisting of 4 people is 0.03, and the probability of a group of passengers consisting of 5 people is 0. Adding these 5 probabilities together, we get the first expected number of passengers per group as 1.53.

[0041] The specific implementation method of S130 includes, but is not limited to, the following method: dividing the first arrival rate function by the first expected number of passengers in a single group to obtain the first arrival rate of a single group of passengers.

[0042] For example, when the first expected number is 1.53, the first expected number of a single group of passengers is expressed as: .

[0043] S140. Based on the first arrival rate of a single group of passengers and a first preset random number, determine the first time interval between the two groups of passengers.

[0044] The first time interval can be understood as the time interval between the arrival of the two groups of passengers at the elevator entrance.

[0045] Optionally, the first time interval between the two groups of passengers can be determined by the following method:

[0046] in, The first preset random number.

[0047] S150. Based on the first actual number of passengers in a single group, the first time interval between two groups of passengers, the first time when a single group of passengers arrives at the elevator entrance, the first departure floor of a single group of passengers, and the first destination floor of a single group of passengers, generate the first passenger dataset of the target organization within the first time interval.

[0048] The arrival time of a single group of passengers at the elevator entrance and the first time interval between two groups of passengers are used to determine the arrival time of any group of passengers at the elevator entrance.

[0049] Among them, the first time a single group of passengers arrives at the elevator entrance is within the first time period.

[0050] The first passenger dataset within the first time period consists of the first time a single group of passengers arrives at the elevator entrance, the first departure floor of a single group of passengers, and the first destination floor of a single group of passengers. Furthermore, the number of sampling points in the first passenger dataset within the first time period is the first actual number of a single group of passengers.

[0051] Optionally, the dataset of the first passenger within the first time period is represented as follows:

[0052] in, For the first moment when a single group of passengers arrives at the elevator entrance, The first departure floor for a single group of passengers. The first destination floor for a single group of passengers. This represents the first actual number of passengers in a single group.

[0053] This disclosure discloses a method for generating passenger datasets for elevators within a building. When multiple passengers from a target organization enter the building via elevator, the method first obtains a first time period and a first peak time within that time period. The first peak time divides the first time period into a first ascending time period and a first descending time period. Then, based on the first ascending time period, the first descending time period, a preset peak arrival rate, a first preset time, and a second preset time, a first arrival rate function is generated. This dynamically describes the time-varying characteristics of the arrival rate function during peak commuting hours, based on the ascending and descending time periods of passenger influx into the elevator. Finally, the method is based on the first arrival rate function and the first expectation of a single group of passengers in the elevator. The system calculates the number of passengers and determines the first arrival rate of a single group of passengers. Each group consists of users from at least one organization within the building. By converting individual arrival rates into group arrival rates, the system simulates the simultaneous arrival of multiple passengers at the elevator entrance. Next, based on the first arrival rate of a single group of passengers and a first preset random number, the first time interval between two groups of passengers is determined. Finally, based on the first actual number of passengers in a single group, the first time interval between the two groups, the first arrival time of a single group at the elevator entrance, the first departure floor of a single group, and the first destination floor of a single group, a first passenger dataset for the target organization within the first time interval is generated. This allows for the simulation of passenger datasets by incorporating the time intervals between adjacent floors. In this way, during peak commuting hours, the system can dynamically describe the time-varying characteristics of the arrival rate function and simulate the simultaneous arrival of multiple passengers at the elevator entrance, more closely reflecting actual passenger flow fluctuations. Therefore, it improves adaptability and accuracy, ultimately enhancing elevator scheduling efficiency and reducing passenger waiting time and elevator energy consumption.

[0054] In another embodiment of this application, a passenger dataset during rush hour can also be simulated.

[0055] In this embodiment, while performing S110, the method also includes the following steps.

[0056] S210. When multiple passengers of the target institution take the elevator to leave the building, obtain the second time period of the passengers taking the elevator and the second peak time of the second time period, wherein the second peak time divides the second time period into a second rising time period and a second falling time period.

[0057] It is understandable that for any organization, the employee arrival rate during the evening rush hour is a process of gradual influx, peak, and dissipation. Electronic devices simulate the evening rush hour of the target organization as a scenario where multiple passengers take the elevator to leave the building. In this scenario, the second time period of passengers taking the elevator and the second peak time of the second time period are obtained.

[0058] The second time period can be determined based on the target organization's employees' off-get off work time and latest off-get off work time. The second peak time refers to the peak time for employees of the target organization to leave work.

[0059] The second peak time divides the second time period into a second rising period and a second falling period.

[0060] For example, if the target organization's closing time is 18:00, and the 20 minutes following the closing time is the second peak time, i.e., the second peak time is 18:20, then simulating one hour forward from the closing time to determine the latest closing time for the target organization's employees is 19:00, then the second time period is [18:00, 19:00], totaling 3600 seconds. Among them, the second peak time of 18:20 divides the second time period [18:00, 19:00] into a second rising period [18:00, 18:20] and a second falling period [18:20, 19:00].

[0061] S220. Based on the second rising time period, the second falling time period, the preset peak arrival rate, the third preset time, and the fourth preset time, generate the second arrival rate function.

[0062] Since the employee arrival rate of the target organization during the evening rush hour follows a gradual influx-peak-dissipation process, we can simulate the arrival rate function in the second rising period and the second falling period respectively based on this pattern. Then, we can concatenate the arrival rate functions corresponding to the second rising period and the second falling period to generate the second arrival rate function for the second period.

[0063] In some embodiments, the specific implementation method of S220 includes, but is not limited to, the following method: generating a second rising segment arrival rate function based on a second rising time period, a preset peak arrival rate, and a third preset time; generating a second falling segment arrival rate function based on a second falling time period, a preset peak arrival rate, and a fourth preset time; and concatenating the second rising segment arrival rate function and the second falling segment arrival rate function according to the timestamp to obtain the second arrival rate function.

[0064] The preset peak arrival rate is used to characterize the ratio of the area corresponding to the peak arrival rate to the total coverage area of ​​the arrival rate function. Optionally, the preset peak arrival rate can be 12%.

[0065] The first preset time and the second preset time can be determined based on experience. Optionally, the smaller the first preset time, the faster the arrival rate function of the first rising segment rises; the smaller the second preset time, the faster the arrival rate function of the first falling segment falls.

[0066] Alternatively, the arrival rate function of the second rising segment can be determined by the following method:

[0067] in, This is the second peak time. yes The corresponding arrival rate, It is the third preset time. It is the earliest time for employees of the target organization to leave work. .

[0068] Continuing with the example above, if the target organization's closing time is 18:00 and the second peak time is 18:20, then the second time period is [18:00, 19:00], totaling 3600 seconds. =0, The arrival rate function for the second rising segment is 1200s. The value range is [0, 3600s]. It can be 750s.

[0069] Alternatively, the arrival rate function for the second descent segment can be determined by the following method:

[0070] in, It is the latest time for employees of the target organization to leave work.

[0071] Continuing with the example above, if the target organization's closing time is 18:00 and the second peak time is 18:20, then the second time period is [18:00, 19:00], totaling 3600 seconds. =1200s, It is 3600. It can be 3600s.

[0072] Furthermore, after determining the second ascending segment arrival rate function and the second descending segment arrival rate function, the second ascending segment arrival rate function and the second descending segment arrival rate function are concatenated in chronological order to obtain the second arrival rate function.

[0073] In this way, during the evening rush hour, the time-varying characteristics of the second arrival rate function can be dynamically described, making the second arrival rate function conform to the actual rush hour situation of gradual influx-peak-rapid dissipation. Therefore, the second arrival rate function simulated in this way during the evening rush hour is consistent with the actual situation.

[0074] S230. Determine the second arrival rate of a single group of passengers based on the second arrival rate function and the second expected number of a single group of passengers in the elevator, wherein the single group of passengers in the elevator consists of users of at least one institution in the building.

[0075] It is understandable that during rush hour, multiple people typically arrive at the elevator entrance simultaneously. Therefore, the individual arrival rate of individual passengers is converted into the group arrival rate to simulate the situation where multiple people arrive at the elevator entrance simultaneously.

[0076] Since each group of passengers in an elevator consists of users from at least one institution within the building, the same group of passengers in an elevator may ride from different floors.

[0077] Here, the second expected number of passengers in each group refers to the statistical number of passengers in each group. Optionally, the second expected number of passengers in each group can be determined based on the actual number and probability corresponding to each of the multiple groups of passengers.

[0078] For example, the probability of a group of 1 passenger is 0.71, the probability of a group of 2 passengers is 0.22, the probability of a group of 3 passengers is 0.04, the probability of a group of 4 passengers is 0.02, and the probability of a group of 5 passengers is 0. Adding these 5 probabilities together, we get the first expected number of passengers in each group as 1.36.

[0079] The specific implementation method of S230 includes, but is not limited to, the following method: dividing the second arrival rate function by the second expected number of passengers in each group to obtain the second arrival rate of each group of passengers.

[0080] For example, when the first expected number is 1.36, the first expected number of passengers per group is expressed as: .

[0081] S240. Based on the second arrival rate of a single group of passengers and a second preset random number, determine the second time interval between the two groups of passengers.

[0082] The second time interval can be understood as the time interval between the arrival of the two groups of passengers at the elevator entrance.

[0083] Optionally, the second time interval between the two groups of passengers can be determined by the following method:

[0084] in, This is the second preset random number.

[0085] S250. Based on the second actual number of a single group of passengers, the second time interval between the two groups of passengers, the second time when a single group of passengers arrives at the elevator entrance, the second departure floor of a single group of passengers, and the second destination floor of a single group of passengers, generate a second passenger dataset for the target organization within the second time interval.

[0086] The arrival time of a single group of passengers at the elevator entrance and the second time interval between two groups of passengers are used to determine the arrival time of any group of passengers at the elevator entrance.

[0087] Among them, the second time when a single group of passengers arrives at the elevator entrance is within the second time period.

[0088] The second passenger dataset within the second time period consists of the second time when a single group of passengers arrives at the elevator entrance, the second departure floor of a single group of passengers, and the second destination floor of a single group of passengers. Furthermore, the number of sampling points in the second passenger dataset within the second time period is the second actual number of a single group of passengers.

[0089] Optionally, the second passenger dataset within the second time period is represented as follows:

[0090] in, The second time a single group of passengers arrives at the elevator entrance. The second departure floor is for single groups of passengers. The second destination floor for a single group of passengers. This represents the second actual number of passengers in a single group.

[0091] Using the above method, when multiple passengers from a target organization leave the building via elevator, firstly, the second time period of passenger elevator use and the first peak time of the second time period are obtained. The second peak time divides the second time period into a second ascending time period and a second descending time period. Then, based on the second ascending time period, the second descending time period, a preset peak arrival rate, a third preset time, and a fourth preset time, a second arrival rate function is generated. This dynamically describes the time-varying characteristics of the arrival rate function during the evening rush hour, based on the ascending and descending time periods of passenger influx into the elevator. Next, based on the second arrival rate function and the second expected number of passengers in a single group in the elevator, the number of passengers in a single group is determined. The second arrival rate is used to simulate the simultaneous arrival of multiple passengers at the elevator entrance by converting individual arrival rates into group arrival rates. Next, based on the second arrival rate of a single passenger group and a second preset random number, a second time interval between two passenger groups is determined. Finally, based on the second actual number of passengers in a single group, the second time interval between the two groups, the second arrival time of a single passenger group at the elevator entrance, the second departure floor of the single passenger group, and the second destination floor of the single passenger group, a second passenger dataset for the target institution within the second time interval is generated to simulate passenger datasets by combining the time intervals of adjacent floors. This allows for dynamic description of the time-varying characteristics of the arrival rate function during rush hour and simulation of simultaneous arrival of multiple passengers at the elevator entrance, more closely reflecting actual passenger flow fluctuations. Therefore, it improves adaptability and accuracy, ultimately enhancing elevator scheduling efficiency and reducing passenger waiting time and elevator energy consumption.

[0092] In yet another embodiment of this application, a dataset of passengers entering and exiting a single institution on different floors can also be simulated.

[0093] In this embodiment, while performing S210, the method also includes the following steps.

[0094] S310. When passengers of the target facility travel from the third departure floor to the third destination floor of the building, obtain the number of passengers on the third departure floor and the number of passengers on the third destination floor.

[0095] It is understandable that different departments of the same organization may be located on different floors. On weekdays, there may be situations where employees of a department on one floor go to a department on another floor to handle business. Electronic devices simulate the situation of employees of the target organization entering and leaving different floors as if passengers of the target organization were going from the third departure floor to the third destination floor of the building. In this scenario, the number of passengers on the departure floor and the number of passengers on the arrival floor are obtained.

[0096] The number of passengers on the departure floor refers to the number of employees who take the elevator from the departure floor to the destination floor. For example, if the floor where Department 1 is located is the departure floor and the floor where Department 2 is located is the destination floor, then the number of passengers on the departure floor is the number of employees who take the elevator from Department 1 to Department 2.

[0097] The number of passengers on the destination floor refers to the number of employees who take the elevator from the destination floor to the departure floor. For example, if the departure floor is the floor where Department 1 is located and the destination floor is the floor where Department 2 is located, then the number of passengers on the destination floor is the number of employees who take the elevator from Department 2 to Department 1.

[0098] S320. Generate the number of round-trip passengers to the target facility based on the number of passengers on the third departure floor and the number of passengers on the third destination floor.

[0099] The specific implementation methods of S320 include, but are not limited to, the following: summing the number of passengers on the third departure floor and the number of passengers on the third destination floor to obtain the total number of passengers; and calculating twice the total number of passengers as the number of round trips to the target institution.

[0100] Understandably, after employees of the target organization complete their business from one department on one floor to another, the total number of passengers is calculated by adding the number of passengers on the third departure floor to the number of passengers on the third destination floor. This total number is then doubled and used as the number of round-trip passengers for the target organization.

[0101] S330. Based on the number of passenger round trips to the target institution, the third time when a single group of passengers arrives at the elevator entrance, the third departure floor of a single group of passengers, and the third destination floor of a single group of passengers, generate the third passenger dataset for the target institution.

[0102] Among them, the third time when a single group of passengers arrives at the elevator entrance is within the time period that is the union of the first and second time periods.

[0103] The target organization's third passenger dataset consists of the third time a single group of passengers arrives at the elevator entrance, the third departure floor of a single group of passengers, and the third destination floor of a single group of passengers. Furthermore, the number of sampling points in the target organization's third passenger dataset is the number of round trips by passengers of the target organization.

[0104] Optionally, the target agency's third passenger dataset is represented as:

[0105] in, The third time a single group of passengers arrives at the elevator entrance. The third departure floor is for single groups of passengers. The third destination floor for a single group of passengers. The number of passenger round trips to the target institution.

[0106] Using the above method, when passengers from the target organization travel from the third departure floor to the third destination floor, the system first obtains the number of passengers on the third departure floor and the number of passengers on the third destination floor. Then, based on these numbers, it generates the passenger round-trip volume for the target organization to accommodate the employee travel patterns. Next, based on the passenger round-trip volume, the third arrival time of a single group of passengers at the elevator, the third departure floor of a single group of passengers, and the third destination floor of a single group of passengers, it generates the third passenger dataset for the target organization. This allows for the simulation of two departments within the same organization located on different floors, more closely reflecting actual passenger flow fluctuations. Therefore, it improves adaptability and accuracy, ultimately enhancing elevator scheduling efficiency and reducing passenger waiting time and elevator energy consumption.

[0107] In another embodiment of this application, inter-floor passenger datasets between different agencies can also be simulated.

[0108] In some embodiments, the method further includes the following steps while performing S310.

[0109] S410, when passengers of the first institution go from the floor where the first institution is located to the floor where the second institution is located, obtain the first peak time period and the fourth peak time period when passengers of the first institution take the elevator, wherein the first probability of passengers of the first institution going from the floor where the first institution is located to the floor where the second institution is located within multiple natural days is greater than a first preset value.

[0110] Understandably, for buildings with restaurants, the restaurant itself is considered an institution, and the passenger flow between these institutions and other institutions is closely related to time and floor. For example, lunchtime and dinnertime create two peak periods. Electronic devices simulate lunchtime and dinnertime as a scenario where passengers from the first institution move from the floor of the first institution to the floor of the second institution. In this scenario, the first and fourth peak periods for passengers from the first institution taking the elevator are obtained.

[0111] The fourth peak time refers to the peak time for lunch or dinner for employees of the first institution.

[0112] The first peak time period is used to characterize the ratio of the coverage area corresponding to the peak duration to the total coverage area. Optionally, the first peak time period can be 99% of the Gaussian area.

[0113] The floor where the second institution is located can be a floor with a restaurant.

[0114] The first preset value can be used to assess whether the floors visited by employees of the first organization are floors with restaurants. Optionally, the first preset value can be determined based on experience. For example, the first preset value is 95%.

[0115] S420. Generate the fourth arrival rate function based on the first peak time period and the fourth peak time period.

[0116] In this embodiment, the electronic device can simulate the fourth arrival rate function according to the Gaussian function based on the first peak time period and the fourth peak time period.

[0117] Alternatively, the fourth arrival rate function can be expressed as follows:

[0118] in, This is the fourth peak time. The first peak period / 6, 6 It can cover 99% of the Gaussian area.

[0119] S430. Determine the fourth arrival rate of a single group of passengers based on the fourth arrival rate function and the fourth expected number of a single group of passengers in the elevator, wherein the single group of passengers in the elevator consists of users of at least one institution in the building.

[0120] In this embodiment, the number of people in groups of diners varies under different circumstances. The fourth arrival rate function is divided by the fourth expected number of passengers in a single group in the elevator and then added together to obtain the fourth arrival rate of a single group of passengers.

[0121] Optionally, the expected number of people in each group are respectively , … The fourth arrival rate for a single group of passengers can be determined using the following method:

[0122] S440. Based on the fourth arrival rate of a single group of passengers and the fourth preset random number, determine the fourth time interval between the two groups of passengers.

[0123] The fourth time interval can be understood as the time interval between the arrival of the two groups of passengers at the elevator entrance.

[0124] Optionally, the fourth time interval between the two groups of passengers can be determined by the following method:

[0125] in, This is the fourth preset random number.

[0126] S450, Based on the fourth actual number of a single group of passengers, the fourth time interval between two groups of passengers, the fourth time when a single group of passengers arrives at the elevator entrance, the fourth departure floor of a single group of passengers, and the fourth destination floor of a single group of passengers, the first agency generates a fourth passenger dataset for the fourth time interval.

[0127] Among them, the fourth time when a single group of passengers arrives at the elevator entrance and the fourth time interval between two groups of passengers are used to determine the time when any group of passengers arrives at the elevator entrance.

[0128] Among them, the fourth time when a single group of passengers arrives at the elevator entrance falls within the first time period.

[0129] The fourth passenger dataset within the fourth time period consists of the fourth time when a single group of passengers arrives at the elevator entrance, the fourth departure floor of a single group of passengers, and the fourth destination floor of a single group of passengers. Furthermore, the number of sampling points in the fourth passenger dataset within the fourth time period is the fourth actual number of a single group of passengers.

[0130] Optionally, the fourth passenger dataset within the fourth time period is represented as follows:

[0131] in, The fourth time a single group of passengers arrives at the elevator entrance. The fourth departure floor is for single groups of passengers. The fourth destination floor for a single group of passengers. This represents the fourth actual number of passengers in a single group.

[0132] Using the above method, when passengers from the first institution move from the floor of the first institution to the floor of the second institution, firstly, the first peak time period and the fourth peak time period for passengers from the first institution to take the elevator are obtained, wherein the probability of passengers from the first institution moving from the floor of the first institution to the floor of the second institution within multiple natural days is greater than a first preset value; then, based on the first ascending time period, the first descending time period, the preset peak arrival rate, the first preset time, and the second preset time, a first arrival rate function is generated, which dynamically describes the time-varying characteristics of the arrival rate function according to the ascending and descending time periods of passengers entering the elevator during the morning rush hour; then, based on the first peak time period and the fourth peak time, a fourth arrival rate function is generated, which realizes the calculation of the arrival rate function according to the peak time and peak period of passengers entering the elevator during lunch and dinner. The system dynamically describes the time-varying characteristics of the arrival rate function. Next, based on the fourth arrival rate function and the fourth expected number of passengers in a single group in the elevator, it determines the fourth arrival rate of a single group of passengers. Each group of passengers consists of users from at least one institution within the building. Thus, by converting individual arrival rates into group arrival rates, it simulates the simultaneous arrival of multiple passengers at the elevator entrance. Then, based on the fourth arrival rate of a single group of passengers and a fourth preset random number, it determines the fourth time interval between two groups of passengers. Finally, based on the fourth actual number of passengers in a single group, the fourth time interval between two groups of passengers, the fourth time of arrival at the elevator entrance for a single group of passengers, the fourth departure floor for a single group of passengers, and the fourth destination floor for a single group of passengers, it generates a fourth passenger dataset for the first institution within the fourth time interval, thereby simulating the passenger dataset by combining the time intervals of adjacent floors. In this way, during lunch and dinner times, the system can dynamically describe the time-varying characteristics of the arrival rate function and simulate the simultaneous arrival of multiple passengers at the elevator entrance, more closely reflecting actual passenger flow fluctuations. Therefore, it improves adaptability and accuracy, ultimately enhancing elevator scheduling efficiency and reducing passenger waiting time and elevator energy consumption.

[0133] In some embodiments, while performing S410, the method further includes the following steps.

[0134] S510. When passengers of the third institution go from the floor where the third institution is located to the floor where the fourth institution is located, the fifth actual number of passengers in a single group is determined based on the total number of users of the building, wherein the second probability that passengers of the third institution go from the floor where the third institution is located to the floor where the fourth institution is located within multiple natural days is less than the second preset value.

[0135] It's understandable that for two organizations with no business dealings, their round-trip passenger flow would be constant at 0, which doesn't reflect reality because random events like going to the wrong floor or property maintenance can occur. Electronic devices simulate the interaction between two organizations with no business dealings as a scenario where passengers from a third organization move from the floor of the third organization to the floor of the fourth organization. In this scenario, the actual number of passengers in a single group is determined based on the total number of users in the building.

[0136] Among them, the third and fourth institutions refer to two institutions that have no business dealings with each other.

[0137] Optionally, the second preset value can be determined based on experience. For example, the second preset value is 5%.

[0138] The specific implementation method of S510 includes, but is not limited to, the following method: multiply the total number of building users by a preset ratio to obtain the fifth actual number of a single group of passengers.

[0139] Optionally, the preset ratio can be determined based on experience. For example, the preset ratio is 0.5%.

[0140] S520. Based on the fifth actual number of passengers in a single group, the fifth time when the single group of passengers arrives at the elevator, the fifth departure floor of the single group of passengers, and the fifth destination floor of the single group of passengers, generate the fifth passenger dataset of the third agency.

[0141] Among them, the fifth time when a single group of passengers arrives at the elevator entrance falls within the first time period.

[0142] The fifth passenger dataset within the fifth time period consists of the fifth time when a single group of passengers arrives at the elevator, the fifth departure floor of a single group of passengers, and the fifth destination floor of a single group of passengers. Furthermore, the number of sampling points in the fifth passenger dataset within the fifth time period is the fifth actual number of a single group of passengers.

[0143] Optionally, the fifth passenger dataset within the fifth time period is represented as follows:

[0144] in, The fifth time a single group of passengers arrives at the elevator entrance. The fifth departure floor is for single groups of passengers. The fifth destination floor for single groups of passengers. This is the fifth actual number of passengers in a single group.

[0145] Using the above method, when passengers from the third institution travel from the floor of the third institution to the floor of the fourth institution, firstly, based on the total number of building occupants, the fifth actual number of passengers in a single group is determined. The second probability that passengers from the third institution travel from the floor of the third institution to the floor of the fourth institution within multiple natural days is less than a second preset value. Then, based on the fifth actual number of passengers in a single group, the fifth time the single group arrives at the elevator, the fifth departure floor of the single group, and the fifth destination floor of the single group, a fifth passenger dataset for the third institution is generated. In this way, for two institutions with no business dealings, inter-institutional passenger flow can be generated according to the total number of building occupants, thus closely reflecting actual passenger flow fluctuations. Therefore, adaptability and accuracy are improved, ultimately enhancing elevator scheduling efficiency and reducing passenger waiting time and elevator energy consumption.

[0146] Furthermore, after generating the aforementioned datasets, at least two of the datasets can be aggregated. Therefore, the method further includes: calculating the union of at least two passenger datasets from the first, second, third, fourth, and fifth passenger datasets.

[0147] By simulating passenger datasets from different scenarios using the above method, the passenger datasets from multiple scenarios are aggregated to obtain a global passenger dataset within the building. This achieves alignment with the passenger flow fluctuation patterns of the entire building, ultimately improving the elevator scheduling efficiency of the entire building and reducing passenger waiting time and elevator performance.

[0148] Based on the above description of the implementation methods, those skilled in the art can clearly understand that this disclosure can be implemented using software and necessary general-purpose hardware, and of course, it can also be implemented using hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this disclosure, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk, or optical disk, etc., including several instructions to cause a computer cloud platform (which may be a personal computer, server, or network cloud platform, etc.) to execute the passenger dataset generation method for elevators in buildings provided in the various embodiments of this disclosure.

[0149] Note that the above description is merely a preferred embodiment and the technical principles employed in this disclosure. Those skilled in the art will understand that this disclosure is not limited to the specific embodiments described herein, and various obvious changes, readjustments, and substitutions can be made without departing from the scope of protection of this disclosure. Therefore, although this disclosure has been described in detail through the above embodiments, it is not limited to the above embodiments. Many other equivalent embodiments may be included without departing from the concept of this disclosure, and the scope of this disclosure is determined by the scope of the appended claims.

Claims

1. A method for generating a passenger dataset for an elevator in a building, characterized in that, include: When multiple passengers of the target institution take the elevator into the building, the first time period of the passengers taking the elevator and the first peak time of the first time period are obtained, wherein the first peak time divides the first time period into a first ascending time period and a first descending time period. Based on the first rising time period, the first falling time period, the preset peak arrival rate, the first preset time, and the second preset time, a first arrival rate function is generated. The first arrival rate of a single group of passengers is determined based on the first arrival rate function and the first expected number of a single group of passengers in the elevator, wherein the single group of passengers in the elevator consists of users of at least one institution in the building; Based on the first arrival rate of the single group of passengers and the first preset random number, the first time interval between the two groups of passengers is determined; Based on the first actual number of passengers in a single group, the first time interval between the two groups of passengers, the first time when a single group of passengers arrives at the elevator entrance, the first departure floor of a single group of passengers, and the first destination floor of a single group of passengers, a first passenger dataset of the target organization is generated within the first time interval. The step of generating a first arrival rate function based on the first rising time period, the first falling time period, a preset peak arrival rate, a first preset time, and a second preset time includes: Based on the first rising time period, the preset peak arrival rate, and the first preset time, a first rising segment arrival rate function is generated. Based on the first descent time period, the preset peak arrival rate, and the second preset time, a first descent segment arrival rate function is generated. The first arrival function is obtained by concatenating the first rising segment arrival rate function and the first falling segment arrival rate function according to the timestamp; Also includes: In the case of multiple passengers leaving the building by elevator in the target institution, a second time period of passenger elevator ride and a second peak time of the second time period are obtained, wherein the second peak time divides the second time period into a second rising time period and a second falling time period; A second arrival rate function is generated based on the second rising time period, the second falling time period, the preset peak arrival rate, the third preset time, and the fourth preset time. The second arrival rate of a single group of passengers is determined based on the second arrival rate function and the second expected number of a single group of passengers in the elevator, wherein the single group of passengers in the elevator consists of users of at least one institution in the building; Based on the second arrival rate of the single group of passengers and the second preset random number, the second time interval between the two groups of passengers is determined; Based on the second actual number of passengers in a single group, the second time interval between the two groups of passengers, the second time when a single group of passengers arrives at the elevator entrance, the second departure floor of a single group of passengers, and the second destination floor of a single group of passengers, a second passenger dataset for the target organization is generated within the second time interval.

2. The method according to claim 1, characterized in that, The step of determining the first arrival rate of a single group of passengers based on the first arrival rate function and the first expected number of passengers in a single group in the elevator includes: The first arrival rate function is divided by the first expected number of passengers in a single group to obtain the first arrival rate of the single group of passengers.

3. The method according to claim 1, characterized in that, Also includes: If passengers of the target institution travel from the third departure floor to the third destination floor of the building, obtain the number of passengers on the third departure floor and the number of passengers on the third destination floor. The number of passengers traveling to and from the target facility is generated based on the number of passengers on the third departure floor and the number of passengers on the third destination floor. Based on the number of passenger round trips to the target institution, the third time when a single group of passengers arrives at the elevator entrance, the third departure floor of a single group of passengers, and the third destination floor of a single group of passengers, a third passenger dataset for the target institution is generated.

4. The method according to claim 3, characterized in that, The step of generating the passenger round-trip number for the target facility based on the number of passengers on the third departure floor and the number of passengers on the third destination floor includes: The total number of passengers is obtained by summing the number of passengers on the third departure floor and the number of passengers on the third destination floor. Calculate twice the total number of passengers as the number of passenger round trips to the target institution.

5. The method according to claim 3, characterized in that, Also includes: When passengers of the first institution go from the floor where the first institution is located to the floor where the second institution is located, the first peak time period and the fourth peak time period for passengers of the first institution to take the elevator are obtained. Among them, the first probability that passengers of the first institution go from the floor where the first institution is located to the floor where the second institution is located within multiple natural days is greater than a first preset value. A fourth arrival rate function is generated based on the first peak time period and the fourth peak time period; The fourth arrival rate of a single group of passengers is determined based on the fourth arrival rate function and the fourth expected number of a single group of passengers in the elevator, wherein the single group of passengers in the elevator consists of users of at least one institution in the building. Based on the fourth arrival rate and the fourth preset random number of the single group of passengers, the fourth time interval between the two groups of passengers is determined; Based on the fourth actual number of a single group of passengers, the fourth time interval between the two groups of passengers, the fourth time when a single group of passengers arrives at the elevator entrance, the fourth departure floor of a single group of passengers, and the fourth destination floor of a single group of passengers, the first agency generates a fourth passenger dataset for the fourth time interval.

6. The method according to claim 5, characterized in that, Also includes: In the case where passengers of the third institution go from the floor where the third institution is located to the floor where the fourth institution is located, the fifth actual number of passengers in a single group is determined based on the total number of users of the building, wherein the second probability that passengers of the third institution go from the floor where the third institution is located to the floor where the fourth institution is located within multiple natural days is less than the second preset value. Based on the fifth actual number of passengers in a single group, the fifth time the single group of passengers arrives at the elevator entrance, the fifth departure floor of the single group of passengers, and the fifth destination floor of the single group of passengers, the fifth passenger dataset of the third agency is generated.

7. The method according to claim 6, characterized in that, The determination of the fifth actual number of passengers in a single group based on the total number of building occupants includes: Multiply the total number of building users by the preset ratio to obtain the fifth actual number of passengers in a single group.

8. The method according to claim 6, characterized in that, Also includes: Calculate the union of at least two passenger datasets from the first passenger dataset, the second passenger dataset, the third passenger dataset, the fourth passenger dataset, and the fifth passenger dataset.

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