Method, system and electronic equipment for analyzing elevator advertising playback effect data

Abstract

The present disclosure provides an analysis method, system, and electronic device for elevator advertisement playback effect data, relating to the field of data analysis, and particularly to the field of advertisement data analysis. The method comprises: obtaining elevator behavior data of each of multiple groups of people taking an elevator, each group of people being composed of virtual users; generating a crowd flow time series of elevator passengers based on the behavior data, wherein the crowd flow time series includes multiple sets of data, each set of data including the time of requesting an elevator ride, information about the virtual user corresponding to the time, information about the virtual user's departure floor, and information about the virtual user's arrival floor; obtaining elevator operating parameters, and determining the time period of each virtual user taking the elevator based on the elevator operating parameters and the crowd flow time series; obtaining the playback time period of each advertisement among multiple advertisements; and determining playback effect data of each advertisement based on the advertisement playback time period and the time period of each virtual user taking the elevator.

Description

Technical Field

[0001] The present disclosure relates to the field of data processing technology, and in particular to a method, system, and electronic device for analyzing elevator advertisement playback effect data in the field of advertisement data analysis. Background Art

[0002] Elevators are an essential means of transportation in people's lives. Elevators are typically small, confined spaces, making elevator riding a monotonous and tedious experience for most people. Consequently, users tend to focus more on viewing advertisements, leading to the maximum effectiveness of elevator advertising. Therefore, elevator advertising, as a new offline advertising medium, is highly sought after by advertisers. Analyzing the effectiveness of these advertisements is crucial for effective advertising. Summary of the Invention

[0003] The present disclosure provides a method, system and electronic equipment for analyzing elevator advertisement playback effect data.

[0004] According to one aspect of the present disclosure, a method for analyzing elevator advertisement playback effect data is provided, comprising:

[0005] Obtaining behavior data of each of multiple groups of people taking an elevator, wherein the behavior data includes the type of group, information of virtual users constituting the group, and time information and direction information of the group taking the elevator;

[0006] generating a time series of people taking the elevator based on the behavior data, wherein the time series includes multiple sets of data, each set of data including the time of requesting to take the elevator, information of the virtual user corresponding to the time, information of the virtual user's departure floor, and information of the virtual user's arrival floor;

[0007] Obtaining elevator operating parameters, and determining a time period for each virtual user to take the elevator based on the elevator operating parameters and the passenger flow time series;

[0008] Get the playing time period of each advertisement among multiple advertisements;

[0009] The playing effect data of each advertisement is determined according to the playing time period of the advertisement and the time period during which each virtual user takes the elevator.

[0010] According to another aspect of the present disclosure, a system for analyzing the effect of elevator advertisement playback is provided, comprising: a crowd flow simulator, an elevator simulator, a playback simulator, and an analysis module, wherein:

[0011] The crowd flow simulator is used to obtain behavior data of each of multiple groups of people taking an elevator, the behavior data including the type of group, information about the virtual users that make up the group, and information about the time and direction of the group taking the elevator; and generate a time series of people taking the elevator based on the behavior data, the time series including multiple sets of data, each set of data including the time of requesting to take the elevator, information about the virtual user corresponding to the time, information about the virtual user's departure floor, and information about the virtual user's arrival floor.

[0012] The elevator simulator is used to obtain elevator operating parameters and determine the time period for each virtual user to take the elevator based on the elevator operating parameters and the passenger flow time series;

[0013] The playback simulator is used to obtain the playback time period of each advertisement among the multiple advertisements;

[0014] The analysis module is used to determine the playback effect data of each advertisement according to the playback time period of the advertisement and the time period during which each virtual user takes the elevator.

[0015] According to another aspect of the present disclosure, there is provided an electronic device, comprising:

[0016] at least one processor; and

[0017] a memory communicatively connected to the at least one processor; wherein,

[0018] The memory stores instructions that can be executed by the at least one processor. The instructions are executed by the at least one processor to enable the at least one processor to perform the method described in the present disclosure.

[0019] According to another aspect of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to cause the computer to execute the method according to the present disclosure.

[0020] According to another aspect of the present disclosure, a computer program product is provided, comprising a computer program, which implements the method according to the present disclosure when executed by a processor.

[0021] The present disclosure provides an analysis method, system, device and storage medium for elevator advertisement playback effect data, which can determine the playback effect data of each advertisement.

[0022] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present disclosure, nor are they intended to limit the scope of the present disclosure. Other features of the present disclosure will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The accompanying drawings are provided to facilitate a better understanding of the present invention and do not constitute a limitation of the present disclosure.

[0024] Figure 1 This is a flowchart of a method for analyzing elevator advertisement playback effect data provided by an example of the present disclosure;

[0025] Figure 2 This is a schematic diagram of the elevator advertisement playback effect provided by an example of the present disclosure;

[0026] Figure 3 This is a structural diagram of an analysis system for elevator advertisement playback effect data provided by an example of the present disclosure;

[0027] Figure 4 It is a block diagram of an electronic device used to implement the method for analyzing elevator advertisement playback effect data according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0028] The following description of exemplary embodiments of the present disclosure is made in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding. These details should be considered as merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0029] The actual physical environment of offline advertising is very different from the online environment. Offline advertising lacks accurate traffic positioning, so it is difficult to intuitively understand the advertising effect and provide a relatively reliable value assessment for offline advertising positions. In order to solve the above technical problems, the first embodiment of the present disclosure provides an analysis method for elevator advertising effect data, such as Figure 1 As shown, the method includes:

[0030] Step S101, obtaining the behavior data of each type of people taking the elevator among multiple types of people taking the elevator, the behavior data including the type of people, information of virtual users constituting the group of people, time information and direction information of the group of people taking the elevator.

[0031] In the present disclosure, it is possible to simulate permanent users in a building, i.e., virtual users, and based on the travel patterns of the crowd, it is possible to simulate the data of virtual users taking the elevator. In this way, even in a scenario where there is no ability to collect user information, the playback effect of elevator advertisements in that scenario can be obtained based on the simulated data. For example, assuming that a residential building has 10 floors, with 10 permanent users on each floor, for a total of 100 users, and each user is assigned a unique identifier, it is possible to simulate the data of these 100 users taking the elevator based on the travel patterns of the crowd. Unless otherwise specified, the users described in this disclosure are all virtual users, and all user information and elevator riding data involved in this disclosure are simulated information and data, and are not real user information.

[0032] Based on crowd travel patterns, it can be found that some users have similar elevator behaviors. For example, some users only take the elevator twice a day. The first time is usually between 8:00 and 11:00, from the usual floor to the first floor. The second time is usually between 18:00 and 22:00, from the first floor to the usual floor. In this case, users who meet these conditions can be classified into a group, and the group type can be set to "Office Workers Who Leave Early and Return Late" or "Office Worker 1". For another example, some users take the elevator four times a day. The first time is usually between 8:00 and 9:00, from the usual floor to the first floor. The second time is usually between 11:30 and 12:30, from the first floor to the usual floor. The third time is usually between 13:00 and 14:00, from the usual floor to the first floor. The fourth time is usually between 18:00 and 22:00, from the first floor to the usual floor. In this case, users who meet these conditions can be classified into a group, and the group type can be set to "Office Workers Returning Home at Noon" or "Office Worker 2". In the above example, users are classified only based on data such as the number of times they take the elevator, the time they take the elevator, the floors they stay on, the departure floors, and the arrival floors. It is understandable that other data on users' elevator rides can also be used to classify users as needed, and this disclosure does not impose any restrictions on this.

[0033] The above-mentioned behavioral data includes at least the following information:

[0034] Crowd type: for example, "Office Worker 1" or "Office Worker 2" mentioned above;

[0035] Information about the virtual users that make up this group of people: attribute information of the virtual users.

[0036] The time information of this group of people taking the elevator: for example, 8:00 to 9:00, 11:30 to 12:30, etc.

[0037] Direction information: including uplink and downlink.

[0038] The time information is bound to the direction information. For example, the direction information of the office worker 1 between 8:00 and 11:00 must be downward.

[0039] Step S102: Generate a time series of people taking the elevator based on the behavior data. The time series includes multiple sets of data. Each set of data includes the time when the elevator is requested, the information of the virtual user corresponding to the time, the information of the virtual user's departure floor, and the information of the virtual user's arrival floor.

[0040] The time when the request to take the elevator is made may be the time when the virtual user presses the up or down button of the elevator.

[0041] The information of the virtual user corresponding to the moment includes a unique user number corresponding to the virtual user, such as user a, user b, and user c.

[0042] The virtual user's departure floor information includes the floor number where the virtual user is when the virtual user triggers the elevator button. The virtual user's arrival floor information can be determined based on the departure floor information. For example, if the departure floor is a permanent floor, the arrival floor is the first floor; if the departure floor is the first floor, the arrival floor is a permanent floor.

[0043] Combine the household information of each virtual user, the information of the virtual user's departure floor, and the information of the virtual user's arrival floor, and record it as event x i , then the flow time series is recorded as: X t ={x1, x2, ... x i}. In one example, x i In the passenger flow time series, the passengers can be sorted according to the time when they request to take the elevator.

[0044] Step S103: obtaining elevator operation parameters, and determining the time period for each virtual user to take the elevator according to the elevator operation parameters and the passenger flow time series.

[0045] Elevator operating parameters include speed, acceleration, maximum load, door opening and closing time, and the maximum number of people entering and exiting the elevator simultaneously. Combining these elevator parameters with the aforementioned time series of passenger flow can determine the time period during which each virtual user rides the elevator. The start time for this time period is the time when the virtual user enters the elevator, and the end time is the time when the virtual user exits the elevator.

[0046] For example, elevator operating parameters include: maximum number of floors 13, floor height 320, maximum speed 150, maximum load 400 kg, maximum acceleration 48, door closing time 2 seconds, door entry and exit time, and maximum number of people entering and exiting simultaneously 2. Using basic single- and multi-elevator elevator scheduling algorithms, combined with the time at which each virtual user presses the up or down button obtained in step S102, the unique user ID corresponding to the virtual user, the virtual user's departure floor information, and the virtual user's arrival floor information, the time period each virtual user spends in the elevator car can be calculated.

[0047] In one example, the operating parameters of an elevator can be displayed through a human-computer interaction interface, and operations such as modification, updating, deletion, and addition of the operating parameters can be performed through the human-computer interaction interface.

[0048] Step S104: Obtain the playing time period of each advertisement among the multiple advertisements.

[0049] The playing time period of an advertisement is one of the advertisement playing parameters. The advertisement playing parameters may further include the number of times the advertisement is played and the order in which the advertisements are played when there are multiple advertisements.

[0050] In one example, the advertisement playing parameters may be displayed through a human-computer interaction interface, and operations such as modification, updating, deletion, and addition of the advertisement playing parameters may be implemented through the human-computer interaction interface.

[0051] Ad playback parameters can be set based on the situation of people taking elevators. For example, by analyzing the records of users taking elevators over a period of time, a normal distribution curve of users' elevator rides can be obtained. Then, during the peak period of the curve, the playback order and playback duration of the ads can be set according to their priority.

[0052] The time period, number of times and order of each advertisement can be adjusted after obtaining the advertisement playback effect data, so as to achieve the optimal configuration of advertisement playback.

[0053] Step S105 , determining the playing effect data of each advertisement according to the playing time period of the advertisement and the time period when each virtual user takes the elevator.

[0054] Through the above process, we can achieve relatively accurate traffic targeting for elevator ads, thereby obtaining ad playback performance data. Based on this data, we can adjust the ad playback strategy, including playback time and order. After multiple rounds of ad placement optimization, we can achieve the optimal allocation of advertising resources.

[0055] In the above-described method for analyzing elevator advertisement playback effect data, the user can obtain the behavioral data of each of the multiple groups of people (groups composed of virtual users) riding the elevator, generate a time series of elevator traffic based on the behavioral data, obtain elevator operating parameters, determine the time period of each virtual user riding the elevator based on the elevator operating parameters and the time series of traffic, obtain the playback time period of each of the multiple advertisements, and determine the playback effect data of each advertisement based on the playback time period of the advertisement and the time period of each virtual user riding the elevator. Thus, the playback effect data of each advertisement can be obtained.

[0056] In one example of the present disclosure, in step S101, the above-mentioned behavior data of each of the multiple groups of people riding an elevator is obtained, including: pre-setting the behavior data of virtual users riding an elevator, classifying the virtual users into groups based on the behavior data, and generating a corresponding crowd flow configuration for each group of people, wherein the crowd flow configuration is used to record the behavior data of each group of people riding an elevator. For example, the behavior data of each virtual user riding an elevator is simulated, including information about the virtual user riding the elevator, the time the virtual user rode the elevator, the departure and arrival floors of the virtual user's current elevator ride, and other information. Based on the behavior data, the virtual users can be classified and a crowd flow configuration containing the behavior data of each group of people riding an elevator is generated.

[0057] This approach can simulate elevator usage. Even for elevator scenarios where user behavior data collection is not available, subsequent ad playback performance data can be obtained, allowing for optimization of ad playback performance.

[0058] It should be noted that in the technical solution disclosed herein, the user personal information involved is the information of virtual users. These users do not exist in reality but are virtual. This is because the implementation of the technical solution disclosed herein does not care about the real identity of the user. It is only necessary to simulate the behavioral data of these virtual users taking the elevator based on the number of people that a building can accommodate and the travel patterns of people in real scenarios, and then implement the solution disclosed herein to determine the playback effect of elevator advertisements. Therefore, the acquisition, storage, simulation and application of the virtual user personal information involved in the technical solution disclosed herein are in compliance with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0059] It should be noted that the virtual user information in the above behavioral data includes the virtual user ID and the virtual user's permanent floor. The virtual user ID is used to track the virtual user's data, and the virtual user's permanent floor can be used as a basis for analyzing the virtual user's departure and arrival floors.

[0060] Assuming that the traffic volume in a traffic configuration accounts for 60%, the total number of people is 100, and the total number of floors is 10, then these 60 people can be allocated to each floor according to the uniform distribution or normal distribution principle, and each of the 60 virtual users can be assigned a unique virtual user ID.

[0061] Direction information: including uplink and downlink.

[0062] The time information and direction information of a group of people taking the elevator are used to characterize the time periods when this group of people take the elevator up and down. The time information is bound to the direction information. For example, the direction information of the office worker 1 mentioned above must be down between 8 o'clock and 11 o'clock.

[0063] In one example, a crowd flow configuration for office workers is provided, which includes the following information: the highest floor of a residential building is 10, the total number of people in the building is 100, and the rules within the building. The rule name of the building rules is "Office Workers", which means that this configuration is for the "Office Workers" crowd type; office workers need to meet the following rules: the number of elevator uses in a day is 2, the first time the elevator is used is from 8:00 to 10:00, and the movement direction is 1 (indicating the direction information is downward); the second time the elevator is used is from 18:00 to 22:00, and the movement direction is 2 (indicating the direction information is upward).

[0064] In an example, the crowd flow configuration can be displayed through a human-computer interaction interface, and operations such as modification, update, deletion, and addition of the crowd flow configuration can be implemented through the human-computer interaction interface.

[0065] In another example of the present disclosure, in the above step S102, generating a time series of people taking the elevator based on the behavior data includes:

[0066] Based on the time period when the group of people take the elevator upward, each virtual user in the group of people is distributed within the upward time period, the time when each virtual user requests to take the elevator upward is obtained, and the departure and arrival floors of each virtual user when going upward are determined based on the virtual user's permanent resident floor. The virtual user identifier, the time of the upward trip, and the departure and arrival floors when going upward are combined into a data group;

[0067] Based on the time period when this group of people takes the elevator down, each virtual user in this group of people is distributed within the down time period, and the time when each virtual user requests to take the elevator down is obtained. The departure and arrival floors of each user going down are determined based on the virtual user's permanent resident floor. The virtual user identifier, the time of the down trip, and the departure and arrival floors of the down trip are combined into a data group. All data groups are arranged according to the corresponding time to obtain the crowd flow time series.

[0068] After obtaining the crowd flow configuration for a certain group of people, each virtual user in that group can be distributed within the corresponding uplink or downlink time period, thereby obtaining the time at which each virtual user requests an elevator ride. When distributing virtual users within the corresponding time periods, the number of people on each floor that meets the crowd flow configuration can be counted based on the virtual user's regular floor. Then, a corresponding time for each virtual user to request an elevator ride can be assigned according to the rules of uniform or normal distribution. When determining the virtual user's departure and arrival floors, if the elevator is for a residential building, then when traveling up, the departure floor is the first floor, and the arrival floor is the virtual user's regular floor; when traveling down, the departure floor is the virtual user's regular floor, and the arrival floor is the first floor. This method can sort the virtual user's elevator behavior data by time, generating a crowd flow time series, which facilitates the analysis of subsequent playback effect data.

[0069] In another example of the present disclosure, the playback effect data includes the duration and number of times the advertisement was viewed. Accordingly, the playback effect data of each advertisement is determined based on the advertisement playback time period and the time period during which each user took the elevator, including:

[0070] The ad viewing duration is calculated by adding up the time periods when all virtual users' elevator rides overlap with the ad playback time periods. For example, if Ad A plays from 6:00 to 6:10, and User A's elevator ride time is from 6:09:50 to 6:10:10, then the overlap between Ad A and User A is 10 seconds, meaning Ad A was viewed for 10 seconds.

[0071] Determining the playback effect data of each advertisement according to the playback time period of the advertisement and the time period during which each virtual user takes the elevator, including:

[0072] The number of virtual users whose elevator riding time period overlaps with the advertisement playing time period is determined as the number of times the advertisement is viewed.

[0073] For example, user A took the elevator twice between 6:00 AM and 11:00 PM, from 8:10:23 to 8:10:55 and from 4:12:33 to 4:13:16 PM. User B took the elevator from 12:12:11 to 12:13:23 and from 7:31:59 to 7:32:49 PM. User C took the elevator from 6:39:53 to 6:41:55 and from 10:44:17 to 11:13:59 PM. Assume that, based on the ad playback time series, ad A was viewed 3 times, ad B was viewed 4 times, and ad C was viewed 5 times.

[0074] Based on the number of times each ad was viewed, the distribution of ad views can also be calculated. For example, if user A took an elevator twice between 6:00 AM and 11:00 PM, from 8:10:23 to 8:10:55 and from 4:12:33 to 4:13:16 PM, respectively, these elevator rides overlapped with Ad A's playback time only once. Therefore, user A can be considered to have viewed Ad A once between 6:00 AM and 11:00 PM. Similarly, the number of times each user viewed Ad A between 6:00 AM and 11:00 PM can be calculated. Users can then be categorized by the number of times they viewed Ad A and the proportion of each category relative to the total number of viewers can be calculated to determine the distribution of Ad A's views. For example, 2 people watched Ad A once, 3 people watched Ad A twice, and 1 person watched Ad A thrice, so the total number of people is 6. Then the distribution of the number of times Ad A was watched is: once 1 / 3 = 33%, twice 1 / 2 = 50%, and three times 1 / 6 = 16%.

[0075] like Figure 2 The following is a schematic diagram of elevator advertising effectiveness data provided in an example of the present disclosure. In this diagram, the ad playback timeline and the pedestrian flow time series are integrated. Advertisements A, B, and C are sorted by playback time and order, displaying the playback status of the three ads from 6:00 AM to 11:00 PM in a timeline format, including the playback time and number of times each ad was played. User A took the elevator twice between 6:00 AM and 11:00 PM, from 8:10:23 AM to 8:10:55 AM and from 4:12:33 PM to 4:13:16 PM, respectively. User B took the elevator from 12:12:11 PM to 12:13:23 PM and from 7:31:59 PM to 7:32:49 PM, respectively. User C took the elevator from 6:39:53 AM to 6:41:55 AM and from 10:44:17 PM to 11:13:59 PM, respectively. Based on the playback timeline of the three ads and the behavioral data of users a, b, and c, the total viewing time and viewing frequency distribution of each ad are obtained.

[0076] By counting the viewing time and number of times an ad is viewed, it can be intuitively reflected whether the playback effect of the ad has met expectations.

[0077] Through the above scheme, the traffic of each advertisement (i.e., the viewing time and the number of times) can be estimated, and the playback effect of each advertisement can be intuitively displayed, which can serve as a basis for optimizing the advertising playback.

[0078] In order to implement the above analysis method, an example of the present disclosure provides an analysis system for elevator advertisement playing effect data, such as Figure 3 As shown, the system includes:

[0079] The crowd flow simulator 10 is configured to obtain behavior data of each of multiple groups of people taking an elevator, the behavior data including the type of group, information about the virtual users that make up the group, and information about the time and direction of the group taking the elevator; and to generate a time series of people taking the elevator based on the behavior data, the time series including multiple sets of data, each set of data including the time of requesting an elevator ride, information about the virtual user corresponding to the time, information about the virtual user's departure floor, and information about the virtual user's arrival floor.

[0080] The elevator simulator 20 is used to obtain elevator operating parameters and determine the time period for each virtual user to take the elevator based on the elevator operating parameters and the passenger flow time series;

[0081] A playback simulator 30, for obtaining a playback time period of each of the multiple advertisements;

[0082] The analysis module 40 is used to determine the playback effect data of each advertisement according to the playback time period of the advertisement and the time period when each virtual user takes the elevator.

[0083] In one embodiment, the crowd flow simulator 10 is also used to pre-set the behavior data of virtual users taking the elevator, classify the virtual users into groups according to the behavior data, and generate the crowd flow configuration corresponding to each group of people, and the crowd flow configuration is used to record the behavior data of this group of people taking the elevator.

[0084] In one possible implementation, the user information constituting this group of people includes virtual user identifiers and virtual user permanent floors; the time information and direction information of this group of people taking the elevator represent the time periods when this group of people take the elevator up and down.

[0085] In one embodiment, the crowd flow simulator 10 is further configured to distribute each virtual user in the group of people within the ascending time period according to the time period when the group of people takes the elevator upward, obtain the time when each virtual user requests to take the elevator upward, and determine the departure and arrival floors of each virtual user according to the virtual user's permanent resident floor, and form a data group with the virtual user identifier, the time of the ascending, and the departure and arrival floors of the ascending.

[0086] Based on the time period when this group of people takes the elevator down, each virtual user in this group of people is distributed within the down time period. The time when each virtual user requests to take the elevator down is obtained. The departure and arrival floors of each virtual user are determined based on the virtual user's permanent resident floor. The user ID, the time of the down trip, and the departure and arrival floors are combined into a data group.

[0087] Arrange all data groups according to the corresponding time to obtain the pedestrian flow time series.

[0088] The analysis module 40 is further configured to accumulate the time periods when all virtual users take the elevator and the advertisements are played, thereby obtaining the viewing time of the advertisements.

[0089] The analysis module 40 is further configured to determine the number of users whose elevator riding time period coincides with the advertisement playing time period as the number of times the advertisement has been viewed.

[0090] It should be noted that in the technical solution disclosed herein, the user personal information involved is the information of virtual users. These users do not exist in reality but are virtual. This is because the implementation of the technical solution disclosed herein does not care about the real identity of the user. It is only necessary to simulate the behavioral data of these virtual users taking the elevator based on the number of people that a building can accommodate and the travel patterns of people in real scenarios, and then implement the solution disclosed herein to determine the playback effect of elevator advertisements. Therefore, the acquisition, storage, simulation and application of the virtual user personal information involved in the technical solution disclosed herein are in compliance with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0091] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium, and a computer program product.

[0092] Figure 4 A schematic block diagram of an example electronic device 800 that can be used to implement embodiments of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are provided as examples only and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0093] like Figure 4 As shown, the device 800 includes a computing unit 801, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 802 or a computer program loaded from a storage unit 808 into a random access memory (RAM) 803. Various programs and data required for the operation of the device 800 can also be stored in the RAM 803. The computing unit 801, the ROM 802, and the RAM 803 are connected to each other via a bus 804. An input / output (I / O) interface 805 is also connected to the bus 804.

[0094] Various components in device 800 are connected to I / O interface 805, including an input unit 806, such as a keyboard, mouse, etc.; an output unit 807, such as various types of displays, speakers, etc.; a storage unit 808, such as a magnetic disk, optical disk, etc.; and a communication unit 809, such as a network card, modem, wireless communication transceiver, etc. The communication unit 809 allows device 800 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0095] The computing unit 801 can be various general-purpose and / or specialized processing components with processing and computing capabilities. Some examples of the computing unit 801 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units that run machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The computing unit 801 performs the various methods and processes described above, such as the method for analyzing elevator advertisement playback effect data. For example, in some embodiments, the method for analyzing elevator advertisement playback effect data can be implemented as a computer software program that is tangibly contained in a machine-readable medium, such as the storage unit 808. In some embodiments, part or all of the computer program can be loaded and / or installed on the device 800 via the ROM 802 and / or the communication unit 809. When the computer program is loaded into the RAM 803 and executed by the computing unit 801, one or more steps of the method for analyzing elevator advertisement playback effect data described above can be performed. Alternatively, in other embodiments, the computing unit 801 may be configured to execute the method for analyzing elevator advertisement playback effect data in any other appropriate manner (eg, by means of firmware).

[0096] Various embodiments of the systems and techniques described herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), system-on-chip systems (SOCs), programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system that includes at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0097] The program code for implementing the method of the present disclosure can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device so that when the program code is executed by the processor or controller, the functions / operations specified in the flow chart and / or block diagram are implemented. The program code can be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0098] In the context of the present disclosure, a machine-readable medium can be a tangible medium that can contain or store a program for use by or in conjunction with an instruction execution system, device or equipment. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium can include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0099] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0100] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), and the Internet.

[0101] A computer system may include a client and a server. The client and server are generally remote from each other and typically interact through a communication network. The client-server relationship arises through computer programs running on the respective computers and having a client-server relationship with each other. The server may be a cloud server, a server in a distributed system, or a server integrated with a blockchain.

[0102] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in this disclosure can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved. This is not a limitation herein.

[0103] The above specific embodiments do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure shall be included within the scope of protection of this disclosure.

Claims

1. A method for analyzing elevator advertising playback effect data, comprising: According to the travel patterns of the crowd, the behavioral data of each group of people taking the elevator is obtained, wherein the behavioral data includes the type of crowd, information about the virtual users constituting the crowd, and the time and direction information of the crowd taking the elevator; generating a time series of people taking the elevator based on the behavior data, wherein the time series includes multiple sets of data, each set of data including the time of requesting to take the elevator, information of the virtual user corresponding to the time, information of the virtual user's departure floor, and information of the virtual user's arrival floor; Obtaining elevator operating parameters, and determining a time period for each virtual user to take the elevator based on the elevator operating parameters and the passenger flow time series; Get the playing time period of each advertisement among multiple advertisements; Determining playback effect data of each advertisement according to the playback time period of the advertisement and the time period during which each virtual user takes the elevator; The information of the virtual users constituting this group of people includes virtual user identifiers and virtual user resident floors; the time information and direction information of the elevator rides by this group of people represent the time periods when this group of people take the elevator up and down; The method of generating a time series of people flow in an elevator based on the behavior data includes: distributing each virtual user in the group of people within the ascending time period according to the time period when the group of people takes the elevator upward, obtaining the time when each virtual user requests to take the elevator upward, determining the departure floor and arrival floor of each virtual user according to the virtual user's permanent resident floor, and forming a data group with the virtual user identifier, the time of ascending, and the departure floor and arrival floor of ascending; distributing each virtual user in the group of people within the descending time period according to the time period when the group of people takes the elevator downward, obtaining the time when each virtual user requests to take the elevator downward, determining the departure floor and arrival floor of each virtual user according to the virtual user's permanent resident floor, and forming a data group with the user identifier, the time of descending, and the departure floor and arrival floor of descending; and arranging all data groups according to corresponding time periods to obtain the time series of people flow.

2. The analysis method according to claim 1, wherein The step of obtaining the elevator behavior data of each of the multiple groups of people taking the elevator includes: Behavioral data of virtual users taking the elevator is preset, the virtual users are classified into groups according to the behavioral data, and a crowd flow configuration corresponding to each group of people is generated, and the crowd flow configuration is used to record the behavioral data of the group of people taking the elevator.

3. The analysis method according to claim 1, wherein The playback effect data includes the viewing duration and the number of times the advertisement is viewed.

4. The analysis method according to claim 3, wherein Determining the playback effect data of each advertisement according to the playback time period of the advertisement and the time period during which each virtual user takes the elevator, including: The time periods when all virtual users take the elevator and the advertisements are played are accumulated to obtain the viewing time of the advertisements.

5. The analysis method according to claim 3, wherein Determining the playback effect data of each advertisement according to the playback time period of the advertisement and the time period during which each virtual user takes the elevator, including: The number of virtual users whose elevator riding time period overlaps with the advertisement playing time period is determined as the number of times the advertisement is viewed.

6. A system for analyzing elevator advertising playback effect data, comprising: Crowd flow simulator, elevator simulator, playback simulator and analysis module, including: The crowd flow simulator is used to obtain, based on crowd travel patterns, the behavior data of each of multiple groups of people taking an elevator, the behavior data including the type of group, information about the virtual users that make up that group, and the time and direction of that group's elevator rides; and to generate a time series of people taking the elevator based on the behavior data, the time series including multiple sets of data, each set including the time of requesting an elevator ride, information about the virtual user corresponding to that time, information about the virtual user's departure floor, and information about the virtual user's arrival floor. The elevator simulator is used to obtain elevator operating parameters and determine the time period for each virtual user to take the elevator based on the elevator operating parameters and the passenger flow time series; The playback simulator is used to obtain the playback time period of each advertisement among the multiple advertisements; The analysis module is configured to determine the playback effect data of each advertisement based on the playback time period of the advertisement and the time period during which each virtual user takes the elevator; The information of the virtual users constituting this group of people includes virtual user identifiers and virtual user resident floors; the time information and direction information of the elevator rides by this group of people represent the time periods when this group of people take the elevator up and down; The crowd flow simulator is further used to: distribute each virtual user in this type of crowd within the ascending time period according to the time period when this type of crowd takes the elevator upward, obtain the time when each virtual user requests to take the elevator upward, determine the departure floor and arrival floor of each virtual user according to the virtual user's permanent resident floor, and form a data group with the virtual user identifier, the time of ascending, and the departure floor and arrival floor of ascending; distribute each virtual user in this type of crowd within the descending time period according to the time period when this type of crowd takes the elevator downward, obtain the time when each virtual user requests to take the elevator downward, determine the departure floor and arrival floor of each virtual user according to the virtual user's permanent resident floor, and form a data group with the virtual user identifier, the time of descending, and the departure floor and arrival floor of descending; and arrange all data groups according to the corresponding time periods to obtain the crowd flow time series.

7. The system for analyzing elevator advertisement playing effect data according to claim 6, wherein: The crowd flow simulator is further used to pre-set the behavior data of virtual users taking the elevator, classify the virtual users into groups according to the behavior data, and generate a crowd flow configuration corresponding to each group of people. The crowd flow configuration is used to record the behavior data of this group of people taking the elevator.

8. An electronic device comprising: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 5.

9. A non-transitory computer-readable storage medium storing computer instructions, wherein: The computer instructions are used to cause the computer to execute the method according to any one of claims 1 to 5.

10. A computer program product comprising a computer program, which, when executed by a processor, implements the method according to any one of claims 1 to 5.

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