Elevator taking demand prediction system

Abstract

The invention discloses an elevator taking demand prediction system, which comprises a recording module for recording service information of a first service provided for passengers by a first system, the first service is different from a transport service provided for the passengers by an elevator system, and the first service and the transport service are adjacent in sequence in time sequence; the duration determination module is used for determining the service duration of all or an unfinished part of the first service; the ending moment determining module is used for determining the service ending moment of the first service according to the service duration; the rule determining module is used for determining a first rule reflecting the change between a first ratio of the number of the passengers who receive the first service and can take the elevator after arriving at the elevator waiting hall to the total number of the passengers and a first time interval; and the prediction module is used for predicting elevator taking demands generated when the passengers take the elevator according to the service end time and a first rule. According to the method and the system, the elevator taking demands of large-scale passengers can be accurately predicted, so that necessary information is provided for improving the elevator allocation performance of an elevator group management system and shortening the waiting time of the passengers.

Description

Technical Field

[0001] The invention relates to the field of elevators, and in particular to an elevator demand prediction system for predicting the number of passengers waiting for elevators in an elevator lobby. Background Art

[0002] Knowing the passenger elevator demand in advance within a certain period of time in the future is of great significance for improving elevator operation efficiency and shortening passengers' waiting time.

[0003] Currently, the prediction of passengers' elevator demand usually uses historical data from the same period in the past to predict the current and future elevator demand, such as Chinese Patent Document 1 (CN200810179459.7), etc. However, this method cannot cope with the scenarios of irregular passenger flow such as adjourning meetings.

[0004] In view of the shortcomings of document 1, Chinese patent document 2 (CN201280070755.8) proposes an elevator control system comprising: an elevator management system, which obtains conference information from at least one of a calendar system and a user interface; the elevator management system generates a control command in response to the conference information; an elevator controller, which controls the destination of one or more elevator cars in response to the control command; and a position sensor for sensing the position of the user, the position sensor provides position sensor information, and one of the elevator management system and the elevator controller increases the control command in response to the position sensor information. The solution is essentially to allocate elevators (including additional allocation) according to the scheduled end time of the meeting, so as to provide transportation services for the participants as soon as possible. However, the solution simply utilizes the scheduled unchanged end time of the meeting, but usually there is always a large difference between the actual end time of the meeting and its scheduled end time (a few minutes or even more than ten minutes or more than twenty minutes), and the elevator, as a public transportation tool, cannot continue to wait at the floor where the meeting room is located for such a long time without considering other possible elevator needs.

[0005] Therefore, how to accurately predict the upcoming large-scale passenger elevator demand in order to provide necessary information for elevator group management is a technical problem that needs to be solved. Summary of the invention

[0006] The technical problem to be solved by the present invention is to provide an elevator demand prediction system for predicting the number of passengers waiting for elevators in an elevator lobby, wherein the prediction system can accurately predict the upcoming large-scale passenger elevator demand.

[0007] In order to solve the above technical problems, the present invention discloses an elevator demand prediction system, comprising:

[0008] a recording module for recording service information of a first service provided by the first system to passengers, wherein the first service is different from a transportation service provided by the elevator system to passengers, and the first service and the transportation service are sequentially adjacent in time sequence;

[0009] a duration determination module, for determining a service duration of the whole or unfinished part of the first service provided by the first system to the passenger;

[0010] an end time determination module, for determining, according to the service duration, an end time of the service provided by the first system to the passenger;

[0011] a rule determination module, determining a first rule reflecting a change between a first ratio of the number of passengers who accept the first service and will take the elevator after arriving at the elevator lobby to the total number of passengers and a first time interval, wherein the first time interval refers to a time interval between an arrival time of the passenger arriving at the elevator lobby and a service end time;

[0012] The prediction module predicts the elevator demand generated by passengers receiving the first service taking the elevator according to the service end time and the first rule.

[0013] Preferably, the service information includes the service start time when the passenger receives the first service, and the end time determination module determines the service end time of the first service according to the service start time and service duration of the first service.

[0014] Preferably, the duration determining module determines the service duration of the first service according to a pre-established correspondence between the first service and the service duration.

[0015] Preferably, the service information includes service items included in the first service, and the end time determination module first determines unfinished service items in the first service, then determines the remaining service duration based on the unfinished service items, and finally determines the service end time based on the remaining service duration.

[0016] Preferably, the rule determination module first determines the first time interval based on the service end time of the first service and the historical data of passengers arriving at the elevator lobby to wait for the elevator, then counts the number of passengers corresponding to different first time intervals, and then obtains a passenger number change curve with the first time interval as the horizontal coordinate and the number of passengers as the vertical coordinate, and finally determines a function that fits the curve and uses it as the first rule.

[0017] Preferably, the service information also includes the total number of all passengers receiving the first service. The prediction module uses the total number of passengers and the first rule to predict the number of passengers corresponding to different first time intervals. Finally, based on the service end time and the first time interval and its corresponding number of passengers, the number of passengers corresponding to each time is predicted and used as the elevator demand.

[0018] Preferably, the rule determination module determines a second rule, which refers to the relationship between each second time interval and the second ratio of the number of passengers leaving the service location when the first system provides the first service to the total number of passengers, and the second time interval refers to the time interval between the departure time when the passenger leaves the service location and the end time of the service.

[0019] Preferably, the rule determination module determines a third rule, and determines a fourth rule based on the passage length between the service location and the elevator lobby when the first system provides the first service and the third rule, the third rule referring to the relationship between the speed at which passengers move from the service location to the elevator lobby and a third ratio of the corresponding number of passengers to the total number of passengers, and the fourth rule referring to the relationship between the travel time required for passengers to move from the service location to the elevator lobby and the ratio of the corresponding number of passengers to the total number of passengers.

[0020] Preferably, the rule determination module calculates the channel length and the quotient of each possible speed in the third rule respectively, and uses the calculation result as the fifth rule reflecting the travel time required for passengers to move from the service location to the elevator lobby and the corresponding number of passengers.

[0021] Preferably, the rule determination module determines the first rule according to the second rule and the fifth rule, and the steps include: step 1, enumerating all possible combinations of each second time interval in the second rule and each moving time in the fifth rule; step 2, selecting one from all combinations that have not been selected as the selected combination; step 3, determining the second ratio and the second time interval corresponding to the selected combination; step 4, determining the moving time corresponding to the selected combination, further determining the corresponding speed, and further determining the corresponding third ratio; step 5, taking the sum of the second time interval and the moving time as the first time interval; step 6, calculating the product of the determined second ratio and the third ratio, and taking it as the first ratio corresponding to the first time interval; step 7, judging whether there are still combinations that have not been selected, if so, returning to step 2, otherwise proceeding to the next step; step 8, combining all the first time intervals and their corresponding first ratios as the first rule.

[0022] Preferably, the steps of determining the second rule by the rule determination module include: step A1, analyzing the influencing factors that affect the passengers' departure from the service location; step A2, determining the mechanism and manner in which the influencing factors produce their influence; step A3, establishing a quantitative relationship between the influencing factors and the departure time when the passengers leave the service location; step A4, obtaining the actual value of the influencing factors; step A5, using the actual value of the influencing factors and the quantitative relationship to determine the departure time when the passengers leave the service location.

[0023] Preferably, the steps of determining the third rule by the rule determination module include: step B1, analyzing the influencing factors that affect the moving speed of passengers moving from the service location to the elevator lobby; step B2, determining the mechanism and manner in which the influencing factors produce their influence; step B3, establishing a quantitative relationship between the influencing factors and the passengers arriving at the elevator lobby; step B4, obtaining the actual values ​​of the influencing factors; step B5, determining the moving speed using the actual values ​​of the influencing factors and the quantitative relationship.

[0024] Preferably, the duration determination module determines the mutual influence between different service items according to the attributes of the service items, and determines the service duration of the first service according to the mutual influence between different service items, wherein the attributes refer to the characteristics of the service items themselves that can affect the duration of other service items when they are accepted by passengers.

[0025] Preferably, the first system provides the next to-be-completed item to the passenger only after one item is completed; the service information also includes the number of people waiting before the implementation of each to-be-completed item.

[0026] Preferably, the duration determination module determines the project service duration of the to-be-completed project from the time the passenger starts waiting to the time the project is completed according to the number of waiting people; the end time determination module determines the service end time when the passenger has received all project services according to the project service duration of each service project and the to-be-completed service projects of the passenger.

[0027] Preferably, the end time determination module uses the actual project end time of the service project provided by the first system as the service end time of the first service.

[0028] Beneficial technical effects

[0029] The elevator demand prediction system of the present invention can accurately predict the upcoming large-scale elevator demand of passengers, thereby providing necessary information for improving the elevator dispatching performance of the elevator group management system and shortening the waiting time of passengers. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 It is a schematic diagram of the structure of the elevator demand prediction system of the present invention. DETAILED DESCRIPTION

[0031] The present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0032] Example 1

[0033] like Figure 1 As shown, this embodiment provides an elevator demand prediction system, including:

[0034] a recording module for recording service information of a first service provided by the first system to passengers, wherein the first service is different from a transportation service provided by the elevator system to passengers, and the first service and the transportation service are sequentially adjacent in time sequence;

[0035] a duration determination module, for determining a service duration of the whole or unfinished part of the first service provided by the first system to the passenger;

[0036] an end time determination module, for determining, according to the service duration, an end time of the service provided by the first system to the passenger;

[0037] a rule determination module, determining a first rule reflecting a change between a first ratio of the number of passengers who accept the first service and will take the elevator after arriving at the elevator lobby to the total number of passengers and a first time interval, wherein the first time interval refers to a time interval between an arrival time of the passenger arriving at the elevator lobby and a service end time;

[0038] The prediction module predicts the elevator demand generated by passengers receiving the first service taking the elevator according to the service end time and the first rule.

[0039] It should be noted that the first service is adjacent to the elevator transportation service in terms of time sequence, and the first service comes first and the elevator transportation service comes later.

[0040] Example 2

[0041] In this embodiment, based on the first embodiment, the service information includes the service start time when the passenger receives the first service, and the end time determination module determines the service end time of the first service according to the service start time and service duration of the first service.

[0042] In this embodiment, the movie viewing service provided by the cinema is described in detail as the first service, wherein the cinema is the first system and the movie viewing service is the first service. The audience watches the movie first and leaves the theater by taking the elevator later, and the two are adjacent in time sequence.

[0043] The duration determination module determines the service duration according to the movie name and the playing time provided by the theater. When there are multiple movies, a correspondence between the movie name and the playing time can be established in advance, and then the obtained movie name is used to determine the service duration of the movie according to the correspondence. That is, the duration determination module determines the service duration of the first service according to the pre-established correspondence between the first service and the service duration.

[0044] After the audience finishes the first service provided by the cinema, they come out of the screening room and go to the elevator lobby to take the elevator to leave, thus forming a relatively concentrated peak in the demand for elevators. In fact, not all audiences come to the elevator lobby at the same time after the first service ends, nor does the peak in the demand for elevators appear immediately after the first service ends, because some audiences may leave early or wait for some time after the elevator ends before leaving.

[0045] Therefore, the rule determination module is also used to determine a second rule, wherein the second rule refers to the relationship between each second time interval and the corresponding second ratio of the number of passengers leaving the service location when the first system provides the first service to the total number of passengers, and the second time interval refers to the time interval between the departure time when the passenger leaves the service location and the end time of the service.

[0046] The rule determination module determines a first rule reflecting the change between a first ratio of the number of passengers who receive the first service and will take the elevator after arriving at the elevator lobby to the total number of passengers and a first time interval, with the following examples being given.

[0047] Method 1: Determine based on historical data of passengers waiting in the elevator lobby

[0048] The rule determination module first determines the first time interval based on the service end time of the first service and the historical data of passengers arriving at the elevator lobby to wait for the elevator, then counts the number of passengers corresponding to different first time intervals, and then obtains the passenger number change curve with the first time interval as the horizontal axis and the number of passengers as the vertical axis, and finally determines the function that fits the curve and uses it as the first rule.

[0049] The service information also includes the total number of all passengers receiving the first service. The prediction module uses the total number of passengers and the first rule to predict the number of passengers corresponding to different first time intervals. Finally, based on the service end time and the first time interval and its corresponding number of passengers, the number of passengers corresponding to each moment is predicted (the moment corresponding to the time interval can be obtained by moving the corresponding first time interval based on the service end time) and used as the elevator demand.

[0050] This method ignores all factors that may affect the audience's arrival at the elevator lobby after watching the movie, and uses the historical data of the service end time of the movie and the actual arrival of the moviegoers at the elevator lobby to establish a model that characterizes the service end time of the movie and the change of the number of moviegoers arriving at the elevator lobby over time. Then, using this model, with the service end time of the movie as the input of the model, the change of the number of moviegoers arriving at the elevator lobby over time can be obtained, thereby determining the first rule. In this method, it is necessary to obtain the number of audiences (from the theater's ticketing system), and at the same time, it is required that the screening hall for which the historical data is obtained is the same as the screening hall currently required to be predicted, or the distance between the two and the elevator lobby is equal.

[0051] This method does not need to distinguish whether the passengers leave the service location immediately for the elevator lobby at the end of the service (in fact, the number of passengers leaving must be a time distribution function centered on the end of the service time). It only generally determines the correspondence between the time interval between the arrival time of the passengers at the elevator lobby and the end of the service time and the number of arriving passengers, and then makes predictions based on the correspondence.

[0052] Method 2: Determine based on the distance between the service location of the first service and the elevator lobby, the number of passengers, and the speed of passengers moving

[0053] The rule determination module determines a third rule, and determines a fourth rule based on the passage length between the service location and the elevator lobby when the first system provides the first service and the third rule, wherein the third rule refers to the relationship between the speed at which passengers move from the service location to the elevator lobby and a third ratio of the corresponding number of passengers to the total number of passengers, and the fourth rule refers to the relationship between the travel time required for passengers to move from the service location to the elevator lobby and the ratio of the corresponding number of passengers to the total number of passengers.

[0054] The rule determination module calculates the quotient of the channel length and each possible speed in the third rule, and uses the calculation result as the fifth rule reflecting the travel time required for passengers to move from the service location to the elevator lobby and the corresponding number of passengers. Finally, the rule determination module determines the first rule based on the second rule and the fifth rule.

[0055] In the first system of the cinema, the screening room is used as a service location, and the length of the passage between it and the elevator lobby (the length of the passage between different screening rooms and the elevator lobby is different), as well as the number of spectators corresponding to the elevator lobby, can be obtained from the first system. The third rule, the speed of passengers moving from the service location to the elevator lobby, can be obtained by modeling using historical data, or by other means to obtain the distribution law of the audience's moving speed (usually a normal distribution with the average speed of a person as the center point, and the distribution law of the moving speed is usually fixed without considering the influencing factors).

[0056] The method for the rule determination module to determine the first rule according to the second rule and the fifth rule includes: step 1, enumerating all possible combinations of each second time interval in the second rule and each moving time in the fifth rule; step 2, selecting one from all the combinations that have not been selected as the selected combination; step 3, determining the second ratio and the second time interval corresponding to the selected combination; step 4, determining the moving time corresponding to the selected combination, further determining the corresponding speed, and further determining the corresponding third ratio; step 5, taking the sum of the second time interval and the moving time as the first time interval; step 6, calculating the product of the determined second ratio and the third ratio, and taking it as the first ratio corresponding to the first time interval; step 7, judging whether there are still combinations that have not been selected, if so, returning to step 2, otherwise proceeding to the next step; step 8, combining all the first time intervals and their corresponding first ratios as the first rule.

[0057] Method 3: Based on Method 2, this method further considers the impact of various factors on the third law and the second law to obtain a higher prediction accuracy than Method 2. Method 2 actually assumes that the distribution of audience movement speed is predetermined and constant. Such an assumption is usually different from the actual situation, which leads to the accuracy of the final prediction result.

[0058] The steps of determining the third rule by the rule determination module include: step B1, analyzing the influencing factors that affect the moving speed of passengers moving from the service location to the elevator lobby; step B2, determining the mechanism and method of the influencing factors; step B3, establishing a quantitative relationship between the influencing factors and the passengers arriving at the elevator lobby; step B4, obtaining the actual value of the influencing factors; step B5, determining the moving speed using the actual value of the influencing factors and the quantitative relationship.

[0059] The factors that affect the speed of passengers moving from the service location to the elevator lobby include the audience's age, gender, whether there are multiple people in the same group, the mood after watching the movie, the congestion at the exit and on the path, etc. The emotional factors after watching the movie are mainly the film attributes (the film attributes further include: the film theme (such as: comedy or tragedy, ethical film, romance film, action film, science fiction film, suspense film, war film, horror film, cartoon, etc.), the playback format (normal, 3D, 4D, etc.). The factors that affect the congestion at the exit and on the path are mainly the building characteristics (distribution and number of entrances and exits of the screening hall, maximum evacuation capacity, seat distribution, paths between each exit and the elevator lobby, etc.).

[0060] The second rule may also be affected by a variety of factors, and the steps for the rule determination module to determine the second rule include: step A1, analyzing the influencing factors that affect the passengers' departure from the service location; step A2, determining the mechanism and manner in which the influencing factors produce their influence; step A3, establishing a quantitative relationship between the influencing factors and the departure time when the passengers leave the service location; step A4, obtaining the actual value of the influencing factors; step A5, using the actual value of the influencing factors and the quantitative relationship to determine the departure time when the passengers leave the service location.

[0061] Factors that influence passengers' departure from the service location include exit congestion, the excitement of the ending of the movie, and the age and gender of the audience.

[0062] Example 3

[0063] This embodiment is based on the first embodiment and describes in detail the catering service provided by a restaurant on a certain floor of an office building as the first service, wherein the restaurant serves as the first system and the catering service serves as the first service.

[0064] The dining format of the restaurant is, for example, that diners freely select dishes that suit their taste from a series of displayed dishes and place them on their plates. After selecting dishes, they present the selected dishes to the cashier, who then charges a one-time fee based on the selected dishes. The charging system can obtain the diners' selection results (selected dish names or dish codes) and charging time information.

[0065] Obviously, in this embodiment, the time when different diners start dining (ignoring the time interval between the diners paying the bill and finding a seat to start dining) is scattered and random, and because of the different dishes selected, the duration of the diners' meals will also be different. These two points are the essential differences between this embodiment and Embodiment 2.

[0066] In order to be able to predict the demand for taking the elevator when diners finish their meals and go to the elevator lobby, we need to count the actual arrival time of each diner at the elevator lobby after finishing their meals, so as to make statistics based on the arrival time, thereby obtaining the change of the number of passengers in the elevator lobby with time, and thereby obtaining the demand for taking the elevator. In order to obtain the actual arrival time of the diners at the elevator lobby after finishing their meals, it is necessary to determine the dining time and dining duration of the diners. Considering that the dining start time of the diners is known, that is, the service start time, the focus of this embodiment is on how to estimate the dining duration of the diners, that is, the service duration.

[0067] Since the end time of the catering service as the first service is very different from that of the movie watching service as the first service in Example 2, the third rule and the fourth rule in Example 2 have little impact on the prediction of this embodiment, so the focus of this embodiment is on the service duration. The duration determination module determines the service duration of the first service according to the pre-established correspondence between the first service and the service duration.

[0068] For a given restaurant, the average dining duration of diners can be simply obtained using historical data, and the average dining duration and the dining time of diners can be used to determine the actual arrival time of diners at the elevator lobby after finishing their meals, so as to perform statistics based on the arrival time. In order to improve the accuracy of the prediction results, we analyze the possible factors that affect the dining duration, and the specific analysis steps are similar to method 3 in embodiment 2.

[0069] For a given restaurant, factors that may affect the duration of a meal include: the type and quantity of dishes selected (e.g., steamed eggs take much less time to eat than crayfish; three dishes usually take less time to eat than four dishes), dining time (e.g., lunch or dinner. The beginning and end of lunch are usually different. The same diners may eat at different speeds at different times. Also, the composition (age, occupation, etc.) of diners at different dining times is usually different).

[0070] In engineering practice, the duration determination module determines the mutual influence between different service items according to the attributes of the service items, and determines the service duration of the first service according to the mutual influence between different service items, wherein the attributes refer to the characteristics of the service items themselves that can affect the duration of other service items when they are accepted by passengers. For example, a consumption duration can be pre-set for each dish based on experience, and then the total dining duration (primary dining duration) can be calculated based on the actual dishes selected by the diners, and then the primary dining duration is corrected according to the different effects (such as extension or shortening) of influencing factors (such as: the influence of the combination of different dishes on the consumption time and other influencing factors) to obtain the corrected dining duration, and finally the corrected dining duration is used to determine the actual arrival time of the diners at the elevator lobby after finishing their meals.

[0071] Example 4

[0072] This embodiment is based on the first embodiment and describes in detail the physical examination service provided by the physical examination center as the first service, wherein the physical examination center serves as the first system and the physical examination service serves as the first service.

[0073] The difference between the physical examination service and the service in the previous embodiment is that the service information includes the service items included in the first service (i.e., multiple physical examination items), and the end time determination module first determines the service items that have not been completed in the first service, and then determines the remaining service duration based on the service items that have not been completed, and finally determines the service end time based on the remaining service duration.

[0074] The first system provides the next item to be completed to the passenger only after completing one item; therefore, the service information also includes the number of people waiting before the implementation of each item to be completed.

[0075] This example considers the physical examination center of a hospital. The biggest feature of this center is that in order to shorten the waiting time of each physical examination personnel as much as possible, the next physical examination item to be performed will be dynamically arranged for each physical examination personnel according to the duration of the physical examination item and the number of people waiting in real time. It is precisely because of this physical examination operation system design that we can obtain the information of the physical examination items that have not been completed for each physical examination personnel (including the examination duration and waiting time of the item), so it is easy to obtain the ID of the physical examination personnel currently undergoing the physical examination, the number of people waiting for each physical examination item, the examination duration of each physical examination item, etc. For a specific physical examination personnel, we can obtain the physical examination item ID that the physical examination personnel has not completed, the total waiting time of the physical examination item ID that has not been completed, the total examination duration of the physical examination item ID that has not been completed, and the expected physical examination completion time (this time can also be corrected according to the actual examination start time of the physical examination item).

[0076] Therefore, the duration determination module determines the service duration of the to-be-completed project from the time the passenger starts waiting to the time the project is completed according to the number of waiting people; the end time determination module determines the service end time when the passenger receives all project services according to the project service duration of each service project and the to-be-completed service projects of the passenger.

[0077] That is, the expected end time of the physical examination for each examinee can be obtained. By using the expected end time information of each examinee, after appropriate statistics, the elevator demand formed by the examinees taking the elevator to leave the physical examination center after the physical examination can be obtained. In this way, the change over time of the number of examinees who have completed the physical examination and are waiting in the elevator lobby can be predicted.

[0078] The biggest difference between this embodiment and the aforementioned example is that the waiting passengers have multiple items to be completed, and whether each item has been implemented and the actual implementation time can be obtained. The implementation time can be used to estimate the end time when the waiting passengers complete all the items to be implemented.

[0079] The present invention has been described in detail above through specific implementation modes and embodiments, but these do not constitute limitations of the present invention. Without departing from the principles of the present invention, those skilled in the art may also make many variations and improvements, which should also be regarded as the protection scope of the present invention.

Claims

1. A system for predicting elevator demand, characterized in that: include: a recording module for recording service information of a first service provided by the first system to passengers, wherein the first service is different from a transportation service provided by the elevator system to passengers, and the first service and the transportation service are sequentially adjacent in time sequence; a duration determination module, for determining a service duration of the whole or unfinished part of the first service provided by the first system to the passenger; an end time determination module, for determining, according to the service duration, an end time of the service provided by the first system to the passenger; a rule determination module, determining a first rule reflecting a change between a first ratio of the number of passengers who accept the first service and will take the elevator after arriving at the elevator lobby to the total number of passengers and a first time interval, wherein the first time interval refers to a time interval between an arrival time of the passenger arriving at the elevator lobby and a service end time; The prediction module predicts the elevator demand generated by passengers receiving the first service taking the elevator according to the service end time and the first rule.

2. The elevator demand prediction system according to claim 1, characterized in that: The service information includes a service start time when the passenger receives the first service, and the end time determination module determines a service end time of the first service according to the service start time and service duration of the first service.

3. The elevator demand prediction system according to claim 2, characterized in that: The duration determination module determines the service duration of the first service according to a pre-established correspondence between the first service and the service duration.

4. The elevator demand prediction system according to claim 1, characterized in that: The service information includes service items included in the first service. The end time determination module first determines unfinished service items in the first service, then determines the remaining service duration according to the unfinished service items, and finally determines the service end time according to the remaining service duration.

5. The elevator demand prediction system according to claim 3, characterized in that: The rule determination module first determines the first time interval based on the service end time of the first service and the historical data of passengers arriving at the elevator lobby to wait for the elevator, then counts the number of passengers corresponding to different first time intervals, and then obtains a passenger number change curve with the first time interval as the horizontal axis and the number of passengers as the vertical axis, and finally determines a function that fits the curve and uses it as the first rule.

6. The elevator demand prediction system according to claim 5, characterized in that: The service information also includes the total number of all passengers receiving the first service. The prediction module uses the total number of passengers and the first rule to predict the number of passengers corresponding to different first time intervals. Finally, based on the service end time and the first time interval and its corresponding number of passengers, the number of passengers corresponding to each time is predicted and used as the elevator demand.

7. The elevator demand prediction system according to claim 2 or 4, characterized in that: The rule determination module determines a second rule, which refers to the relationship between each second time interval and the second ratio of the number of passengers leaving the service location when the first system provides the first service to the total number of passengers, and the second time interval refers to the time interval between the departure time when the passenger leaves the service location and the end time of the service.

8. The elevator demand prediction system according to claim 7, characterized in that: The rule determination module determines a third rule, and determines a fourth rule based on the passage length between the service location and the elevator lobby when the first system provides the first service and the third rule, wherein the third rule refers to the relationship between a speed at which passengers move from the service location to the elevator lobby and a third ratio of the corresponding number of passengers to the total number of passengers, and the fourth rule refers to the relationship between a moving time required for passengers to move from the service location to the elevator lobby and the ratio of the corresponding number of passengers to the total number of passengers.

9. The elevator demand prediction system according to claim 8, characterized in that: The rule determination module calculates the channel length and the quotient of each possible speed in the third rule respectively, and uses the calculation result as the fifth rule reflecting the travel time required for passengers to move from the service location to the elevator lobby and the corresponding number of passengers.

10. The elevator demand prediction system according to claim 9, characterized in that: The rule determination module determines the first rule according to the second rule and the fifth rule, and the steps include: Step 1, enumerate all possible combinations of each second time interval in the second rule and each moving time in the fifth rule; Step 2, select one of all the combinations that have not been selected as the selected combination; Step 3, determining a second ratio and a second time interval corresponding to the selected combination; Step 4, determining the movement time corresponding to the selected combination, further determining the corresponding speed, and further determining the corresponding third ratio; Step 5: taking the sum of the second time interval and the moving time as the first time interval; Step 6: Calculate the product of the determined second ratio and the third ratio, and use the product as the first ratio corresponding to the first time interval; Step 7: Determine whether there are any combinations that have not been selected. If yes, return to step 2; otherwise, proceed to the next step. Step 8: Combining all first time intervals and their corresponding first ratios as a first rule.

11. The elevator demand prediction system according to claim 7, characterized in that: The step of determining the second rule by the rule determination module comprises: Step A1, analyzing the factors that affect passengers leaving the service location; Step A2: determine the mechanism and mode of action of the influencing factors; Step A3: establishing a quantitative relationship between the influencing factors and the departure time when the passenger leaves the service location; Step A4: Obtaining actual values ​​of influencing factors; Step A5: Determine the departure time when the passenger leaves the service location by using the actual value and the quantitative relationship of the influencing factors.

12. The elevator demand prediction system according to claim 8, characterized in that: The step of determining the third rule by the rule determination module comprises: Step B1, analyzing factors affecting the moving speed of passengers from the service location to the elevator lobby; Step B2, determine the mechanism and mode of action of the influencing factors; Step B3, establishing a quantitative relationship between the influencing factors and the passengers arriving at the elevator lobby; Step B4, obtaining the actual value of the influencing factor; Step B5: Determine the moving speed using the actual values ​​and quantitative relationships of the influencing factors.

13. The elevator demand prediction system according to claim 4, characterized in that: The duration determination module determines the mutual influence between different service items according to the attributes of the service items, and determines the service duration of the first service according to the mutual influence between different service items, wherein the attributes refer to the characteristics of the service items themselves that can affect the duration of other service items when they are accepted by passengers.

14. The elevator demand prediction system according to claim 4, characterized in that: The first system provides the next item to be completed to the passenger only after completing one item; the service information also includes the number of people waiting before the implementation of each item to be completed.

15. The elevator demand prediction system according to claim 14, characterized in that: The duration determination module determines the service duration of the to-be-completed project from the time when the passenger starts waiting to the time when the passenger completes the project according to the number of waiting passengers; The end time determination module determines the service end time when the passenger receives all the service items according to the service duration of each service item and the service items to be completed by the passenger.

16. The elevator demand prediction system according to claim 15, characterized in that: The end time determination module uses the actual project end time of the service project provided by the first system as the service end time of the first service.

Images