Intelligent ticket pre-sale method and device

Abstract

The present invention discloses an intelligent ticket pre-sale method and device, and relates to the field of big data technology. A specific implementation method includes obtaining the flow of people allowed in a preset time period at each entrance, determining a list of entrances with surplus tickets according to the number of reserved tickets corresponding to each entrance, obtaining the location information of the entrances with surplus tickets, and calculating the distance from each hot spot area; identifying tourists through an object recognition device corresponding to the hot spot area, obtaining a list of tourist browsing data information in the hot spot area, calling a wandering judgment component to obtain wandering tourists from the list of tourist browsing data information, and determining the effective number of people in the hot spot area; calling a utility model based on the distance between the entrance with surplus tickets and each hot spot area and the effective number of people in each hot spot area, calculating the congestion condition of the entrance to all hot spot areas, determining a non-congested entrance, and adjusting the number of remaining tickets at the entrance. The implementation method of the present invention can solve the problems of low efficiency and poor real-time performance of passenger flow control and ticket pre-sale in existing scenic spots.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence technology, in particular to image recognition, and proposes an intelligent ticket pre-sale method and device. Background Art

[0002] At present, the scenic spot passenger flow control and ticketing system focuses on conducting tourist thermal analysis and crowd distribution judgment through conventional methods to conduct ticket pre-sales. For example: for a fixed gathering building or scenic spot, the total number of people entering the scenic spot and the number of people distributed in each scenic spot are used to draw a trend curve of the number of people changing and a thermal value image of the distribution of tourists in the scenic spot. It is possible to predict the full time point of the scenic spot before the tourists are full, so as to end ticket sales in advance.

[0003] In the process of implementing the present invention, the inventors found that there are at least the following problems in the prior art:

[0004] The existing scenic spot passenger flow control and ticket pre-sale all conduct thermal and distribution analysis on fixed clustered buildings or scenic spots, which leads to the problem of overly single analysis dimension, low efficiency and poor real-time performance of scenic spot passenger flow control and ticket pre-sale. Summary of the invention

[0005] In view of this, an embodiment of the present invention provides an intelligent ticket pre-sale method and device, which can solve the problems of low efficiency and poor real-time performance of passenger flow control and ticket pre-sale in existing scenic spots.

[0006] To achieve the above-mentioned purpose, according to one aspect of an embodiment of the present invention, a smart ticket pre-sale method is provided, including obtaining the flow of people allowed at each entrance within a preset time period, so as to determine a list of entrances with remaining tickets according to the number of reserved tickets currently corresponding to each entrance, and then obtaining the location information of the entrances with remaining tickets to calculate the distance from each hot spot area; identifying tourists through object recognition devices corresponding to the hot spot areas, obtaining a list of tourist browsing data information in the hot spot areas, and then calling a wandering judgment component to obtain wandering tourists from the list of tourist browsing data information to determine the effective number of people in the hot spot areas; calling a preset utility model, based on the distance between the entrances with remaining tickets and each hot spot area and the effective number of people in each hot spot area, calculating the congestion condition of the entrance to all hot spot areas, and then determining a non-congested entrance from the list of entrances with remaining tickets to adjust the number of remaining tickets for each entrance in the list of entrances with remaining tickets.

[0007] Optionally, after calculating the congestion conditions of the entrance to all hotspot areas, the following steps are performed:

[0008] According to the congestion conditions of the entrances, the entrances in the list of entrances with remaining tickets are sorted in ascending order, and then based on the preset number of congested entrance limits, the target entrances are marked to prohibit ticket sales.

[0009] Optionally, after adjusting the number of remaining tickets of each entrance in the entrance list with remaining tickets, it includes:

[0010] Receiving an entry request, obtaining target attribute information of the visitor to be entered in the entry request, so as to determine whether the preset entry conditions are met;

[0011] Determine whether the entry is marked, and if so, deny the entry request; if not, determine whether the current entry reaches a preset current limiting warning threshold, and if so, deny the entry request; if not, grant entry in response to the entry request.

[0012] Optionally, calling the wandering determination component to obtain wandering visitors from the visitor browsing data information list includes:

[0013] Calling the wandering determination component to monitor the object recognition devices in the object recognition device list corresponding to the hot spot area, whose number of repeated object captures within a preset maximum time interval is greater than or equal to a preset number threshold;

[0014] When the number of the object recognition devices is greater than or equal to a preset number threshold, the tourist information of the captured object is extracted from the tourist browsing data information list as a wandering tourist.

[0015] Optionally, determining the effective number of people in the hot spot area includes:

[0016] The wandering factor corresponding to the hot spot area is obtained, and based on wandering tourists and non-wandering tourists in the tourist browsing data information list, the effective number of people in the hot spot area is calculated through a preset effective model.

[0017] Optionally, the congestion conditions of the entrance to all hot spots are calculated, including:

[0018] The distance weighting factor corresponding to the distance from the entrance to each hot spot area is obtained to calculate the congestion condition from the entrance to each hot spot area based on the effective number of people in each hot spot area, and then obtain the congestion condition from the entrance to all hot spots areas.

[0019] Optionally, before identifying a visitor by an object identification device corresponding to the hotspot area, the following steps are included:

[0020] Deploy multiple object recognition devices to N discrete points and obtain location information of each object recognition device;

[0021] Clustering or mean processing is performed on the location information of the object recognition device to obtain a hotspot area, and then a mapping relationship between the hotspot area and the object recognition device is generated.

[0022] Optionally, it also includes:

[0023] Visitors are identified through object recognition devices corresponding to hot spots to obtain tourist behavior data and emotional data, and then the target discriminant model is pulled from the discriminant model pool to obtain the corresponding discrimination results and send an alarm.

[0024] In addition, the present invention also provides an intelligent ticket pre-sale device, including a parameter configuration module, for obtaining the flow of people allowed at each entrance within a preset time period, so as to determine a list of entrances with remaining tickets according to the number of reserved tickets corresponding to each entrance at present, and then obtain the location information of the entrances with remaining tickets, so as to calculate the distance from each hot spot area;

[0025] The face capture and recognition module is used to identify tourists through the object recognition device corresponding to the hot spot area, obtain the list of tourists browsing data information in the hot spot area, and then call the wandering determination component to obtain wandering tourists from the list of tourists browsing data information to determine the effective number of people in the hot spot area;

[0026] The multi-feature fused ticket pre-sale module is used to call the preset utility model, calculate the congestion condition of the entrance to all hot spots based on the distance between the entrance with remaining tickets and each hot spot area and the effective number of people in each hot spot area, and then determine the non-congested entrance from the list of entrances with remaining tickets to adjust the number of remaining tickets for each entrance in the list of entrances with remaining tickets.

[0027] Optionally, after the multi-feature fusion ticket pre-sale module calculates the congestion conditions of the entrance to all hot spots, it includes:

[0028] According to the congestion conditions of the entrances, the entrances in the list of entrances with remaining tickets are sorted in ascending order, and then based on the preset number of congested entrance limits, the target entrances are marked to prohibit ticket sales.

[0029] Optionally, it also includes:

[0030] The ticket collection module is used to receive an entrance entry request, obtain the target attribute information of the tourist to be entered in the entrance entry request, so as to determine whether the preset entry conditions are met; determine whether the entrance is marked, and if so, reject the entrance entry request; if not, determine whether the current entrance reaches the preset current limiting warning threshold value, and if so, reject the entrance entry request, and if not, allow entry in response to the entrance entry request.

[0031] Optionally, the face capture and recognition module calls the wandering determination component to obtain wandering tourists from the tourist browsing data information list, including:

[0032] Calling the wandering determination component to monitor the object recognition devices in the object recognition device list corresponding to the hot spot area, whose number of repeated object captures within a preset maximum time interval is greater than or equal to a preset number threshold;

[0033] When the number of the object recognition devices is greater than or equal to a preset number threshold, the tourist information of the captured object is extracted from the tourist browsing data information list as a wandering tourist.

[0034] Optionally, the face capture and recognition module determines the effective number of people in the hot spot area, including:

[0035] The wandering factor corresponding to the hot spot area is obtained, and based on wandering tourists and non-wandering tourists in the tourist browsing data information list, the effective number of people in the hot spot area is calculated through a preset effective model.

[0036] Optionally, the multi-feature fusion ticket pre-sale module calculates the congestion conditions of the entrance to all hot spots, including:

[0037] The distance weighting factor corresponding to the distance from the entrance to each hot spot area is obtained to calculate the congestion condition from the entrance to each hot spot area based on the effective number of people in each hot spot area, and then obtain the congestion condition from the entrance to all hot spots areas.

[0038] Optionally, it also includes:

[0039] The geographic annotation module is used to deploy multiple object recognition devices to N discrete points to obtain the location information of each object recognition device; cluster or average the location information of the object recognition device to obtain hot spots, and then generate a mapping relationship between the hot spots and the object recognition devices.

[0040] Optionally, it also includes:

[0041] The tourist statistical analysis module is used to identify tourists through the object recognition devices corresponding to the hot spots to obtain tourist behavior data and emotional data, and then pull the target discriminant model from the discriminant model pool to obtain the corresponding discrimination results and send an alarm.

[0042] An embodiment of the above invention has the following advantages or beneficial effects: the present invention can provide health judgment features for access judgment based on the real-time health status of tourists' health codes, perform intelligent crowd statistical distribution of tourists based on face recognition, introduce multi-dimensional indicators such as wandering characteristics in hot spots and distance weighted characteristics in hot spots, and describe the real-time congestion status of scenic spots in a more real-time, dynamic, comprehensive, refined and hierarchical manner; and, using multi-dimensional indicators and influencing factors, construct an entrance congestion status utility function to model and describe the entrance congestion status, and obtain the real-time entrance congestion status as a basis for ticket sales; and, the present invention can dynamically understand the real-time congestion status of scenic spots and the distribution of wandering crowds, and dynamically adjust the remaining tickets at the entrance during the ticket sales period, so that the crowd congestion is alleviated, which not only eliminates the health and safety risks of tourists to the greatest extent, but also alleviates the congestion status of hot spots in scenic spots, and at the same time ensures the economic benefits of scenic spots and the problem of tourist overcrowding through intelligent ticket sales.

[0043] The further effects of the above-mentioned non-conventional optional manner will be described below in conjunction with the specific implementation manner. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] The accompanying drawings are used to better understand the present invention and do not constitute an improper limitation of the present invention.

[0045] Figure 1 is a schematic diagram of the main process of the smart ticket pre-sale method according to the first embodiment of the present invention;

[0046] Figure 2 is a schematic diagram of the deployment distribution of hotspot area object recognition devices according to an embodiment of the present invention;

[0047] Figure 3 is a schematic diagram of the main process of the smart ticket pre-sale method according to the second embodiment of the present invention;

[0048] Figure 4 is a schematic diagram of main modules of an intelligent ticket pre-sale device according to a first embodiment of the present invention;

[0049] Figure 5 is a schematic diagram of main modules of an intelligent ticket pre-sale device according to a second embodiment of the present invention;

[0050] Figure 6 is an exemplary system architecture diagram to which embodiments of the present invention may be applied;

[0051] Figure 7 It is a schematic diagram of the structure of a computer system of a terminal device or a server suitable for implementing an embodiment of the present invention. DETAILED DESCRIPTION

[0052] The following is a description of exemplary embodiments of the present invention in conjunction with the accompanying drawings, including various details of the embodiments of the present invention to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present invention. Similarly, for clarity and conciseness, the description of well-known functions and structures is omitted in the following description.

[0053] Figure 1 FIG. 1 is a schematic diagram of the main process of the smart ticket pre-sale method according to the first embodiment of the present invention. Figure 1 As shown, the smart ticket pre-sale method includes:

[0054] Step S101, obtain the allowed flow of people at each entrance within a preset time period, determine a list of entrances with remaining tickets based on the number of reserved tickets currently corresponding to each entrance, and then obtain the location information of the entrances with remaining tickets to calculate the distance to each hot spot area.

[0055] In some embodiments, it is defined that at the D(r)th entrance of the current scenic spot, the allowed flow of people in the time period t_duration(p) is:

[0056] psnflow(D(r),t_duration(p)), and satisfies:

[0057]

[0058] Among them, config_weight(D(r)) represents the weight value of the total number of tickets sold at the entrance D(r). For example, for a scenic spot with two entrances, the main entrance is 0.8 and the secondary entrance is 0.2.

[0059]

[0060] It is worth noting that for the D(r)th entrance, time period t_duration(p), the number of tickets already booked is ticket_order(D(r),t), and whether there are any remaining tickets is has_tickets(user_id). The database satisfies the following conditions:

[0061]

[0062] As some embodiments, the hotspot area can be located in the following manner: deploy multiple object recognition devices to N discrete points, obtain the location information of each object recognition device, perform clustering or mean processing on the location information of the object recognition device, obtain the location information of the hotspot area, and then generate a mapping relationship between the hotspot area and the object recognition device.

[0063] In the embodiment, each object recognition device (for example, a camera, an infrared sensor, a pressure sensor, a number of network layer wifi accesses, or one or more other methods as an object recognition device) has an identifier. For object recognition device i, its identifier is defined as camera(i). Assuming that N object recognition devices are deployed at 1 to N discrete points, the geographic location of the object recognition device is marked and collected, which is defined as Location(camera(i)), and:

[0064] Location(camera(i))=Location(Lon(camera(i)),Lat(camera(i))

[0065] Wherein, Lon(camera(i)) represents the longitude information of the object recognition device camera(i), and Lat(camera(i) represents the dimensionality information of the object recognition device camera(i).

[0066] By clustering or averaging N object recognition devices, 1 to M hotspot areas (Zone of Interest) are obtained, such as Figure 2 As shown, referred to as zoi, it is generally a building, scenic spot, or place building with high traffic density or concentrated crowds. Zoi is marked so that each object recognition device belongs to a zoi. Assuming that object recognition device i belongs to hot spot area j, it is expressed by the following formula:

[0067] camera(i)∈zoi(j), j is the index of zoi

[0068] Thus, we can get a list of all object recognition devices in hotspot area j:

[0069] camera_list(zoi(j))

[0070] In addition, there are D(r) entrances in the scenic area, defined as Location(D(r)), and

[0071] Location(D(r))=Location(Lon(D(r)),Lat(D(r))

[0072] Among them, Lon(D(r)) represents the longitude information of the rth gate of the scenic area, and Lat(D(r)) represents the latitude information of the rth gate of the scenic area.

[0073] Therefore, the distance from the entrance of D(r) to zoi(j) can be calculated through Location(D(r)) and zoi(j) location information.

[0074] Step S102, identifying tourists through the object recognition device corresponding to the hot spot area, obtaining a list of tourist browsing data information in the hot spot area, and then calling a wandering determination component to obtain wandering tourists from the list of tourist browsing data information to determine the effective number of people in the hot spot area.

[0075] In some embodiments, the wandering determination component is called to obtain wandering tourists from the tourist browsing data information list. The specific implementation process includes:

[0076] The wandering determination component is called to monitor the object recognition device list corresponding to the hot spot area, and the number of object recognition devices that repeatedly capture objects within a preset maximum time interval is greater than or equal to a preset number threshold. When the number of the object recognition devices is greater than or equal to the preset number threshold, the tourist information of the captured object is extracted from the tourist browsing data information list as a wandering tourist.

[0077] For example, in the qth time period [T(q), T(q+1)] of the hotspot area zoi(j), the tourist whose identity is faceid(i) (for example, the tourist's facial data captured by the object recognition device is used as the unique identifier) ​​is captured by the object recognition device list CAMERA_LIST(zoi(J)), and the screening is:

[0078] The maximum time interval for repeated capture by any object recognition device in camera_list(zoi(j)) <= wander_interval (1)

[0079] The number of times the camera is captured by any object recognition device in camera_list(zoi(j)) is greater than or equal to the number of times threshold Repeat (2)

[0080] Repeatedly captured by any object recognition device in camera_list(zoi(j)), the number of different object recognition devices is greater than or equal to S(3)

[0081] If the above three formulas are satisfied at the same time, the tourist faceid(i) in zoi(j) is defined as a wandering tourist.

[0082] Among them, the number of repeated captures is greater than Repeat, which means that tourists switch back and forth and walk back and forth in certain areas in a short period of time, and the maximum time interval of repeated capture is less than or equal to wander_interval, which means that tourists wander and stop for a limited short period of time. The number of different cameras is greater than S, which means that tourists are continuously photographed in multiple scenes with densely deployed object recognition devices and similar geographical locations.

[0083] A further embodiment determines the effective number of people in the hot spot area. The specific implementation process includes: obtaining the wandering factor corresponding to the hot spot area, and calculating the effective number of people in the hot spot area through a preset effective model based on wandering tourists and non-wandering tourists in the tourist browsing data information list.

[0084] For example, in the qth time period t_duration(p) = [T(q), T(q+1)] of the hot spot area zoi(j), deduplication processing can obtain the number of non-wandering people P(zoi(j), t_duration(p)) and wandering people W(zoi(j), t_duration(p)) in the qth time period [T(q), T(q+1)]. Then, the effective number of people in the current hot spot area zoi(j) in the qth time period [T(q), T(q+1)] is defined as psn_effective, and satisfies:

[0085] psn_effective(zoi(j),t_duration(p))

[0086] =(P(zoi(j),t_duration(p))-W(zoi(j),t_duration(p)))+k1

[0087] *W(zoi(j),t_duration(p))

[0088] Among them, k1 is the wandering factor corresponding to the hot spot area. Because the wandering tourists with feedback in a hot spot area will increase the congestion of the entire area and cause tourists to gather, it can be regulated by adjusting the wandering factor.

[0089] It is worth noting that when a tourist passes through 1 to N discrete camera monitoring points, the face is recognized and captured; the labeled object recognition device geographic information is obtained through object recognition device information association, and then a piece of data information under the regional zoi_id is added, for example:

[0090] [zoi_id,faceid(i),Lon,Lat,timestamp,camera_id]

[0091] Among them, zoi_id represents the identification id of the hot spot area (such as a scenic spot, building, etc.) where the current camera is located; faceid(i) identifies the identification id of the face information library of the current user; Lon represents longitude information; Lat represents latitude information; timestamp represents the captured timestamp; camera_id represents the current camera id.

[0092] By obtaining data for a certain time period T (for example, one day) and arranging the timestamp of the same zoi_id in ascending order, a list of tourist tour data information for a region zoi_id can be obtained.

[0093] As other embodiments, tourists are identified through object recognition devices corresponding to hot spots to obtain tourist behavior data and emotional data, and then the target discrimination model is pulled from the discrimination model pool (for example, the behavior discrimination model CNN, the emotion discrimination model SVM, etc.) to obtain the corresponding discrimination result and send an alarm. It can be seen that the present invention can realize more abundant behavior and emotion analysis other than human face, and perform analysis and statistical mining such as discrimination of dangerous behaviors in scenic spots and areas prone to falls in scenic spots.

[0094] Step S103, calling a preset utility model, based on the distance between the entrance with remaining tickets and each hot spot area and the effective number of people in each hot spot area, calculates the congestion condition of the entrance to all hot spots, and then determines the non-congested entrance from the list of entrances with remaining tickets to adjust the number of remaining tickets for each entrance in the list of entrances with remaining tickets.

[0095] In some embodiments, after calculating the congestion conditions of the entrances to all hot spots, the entrances in the list of entrances with remaining tickets can be arranged in ascending order according to the congestion conditions of the entrances, and then based on the preset congestion entrance limit number, the target entrances can be marked to prohibit ticket sales access. For example: the list of entrances arranged in ascending order according to the congestion conditions of the entrances is defined as enterlist_asc(t_duration(p)), and forbidden_num is defined to represent the congestion entrance limit number. For example, forbidden_num=2 means that ticket purchase access is not allowed at the two most congested and second most congested entrances in the above ascending order, and so on. The forbidden_num most congested entrances can also be excluded from the enterlist_asc(t_duration(p)) list, and the non-congested entrance list is defined as: enterlist_asc_uncrowded(t_duration(p)).

[0096] In a further embodiment, after adjusting the number of remaining tickets for each entrance in the list of entrances with remaining tickets, an entrance entry request can be received, and the target attribute information of the tourist to be entered in the entrance entry request can be obtained to determine whether the preset entry conditions are met. Then, it is determined whether the entrance is marked, and if so, the entrance entry request is rejected; if not, it is determined whether the current entrance reaches the preset current limiting warning threshold value, and if so, the entrance entry request is rejected, and if not, entry is allowed in response to the entrance entry request. For example: define the following formula:

[0097] vistor_permits(user_id)=health_state*has_tickets(user_id)>0 (4)

[0098] ticket_order(D(r),t)≤psnflow(t_duration(p))*enter_th (5)

[0099] D(r)∈enterlist_asc_uncrowded (6)

[0100] Among them, formula (4) indicates that the current tourist meets the conditions of health and surplus tickets. Formula (5) indicates that the current entrance has not reached the flow limit warning threshold value in the sub-time period. Formula (6) indicates that the current entrance belongs to the non-congested entrance list.

[0101] In a preferred embodiment, if the entrance satisfies the condition ! Formula (4), where ! represents a logical negation operation, it means that the preliminary judgment condition for visitor admission is not met, and ticket purchase and entry into the park are not allowed.

[0102] If the current tourist at the entrance D(r) satisfies formula (4) and formula (5), that is, under the premise of meeting the preliminary judgment conditions for tourist admission, enter_th represents the warning threshold value for the current entrance to limit the flow, for example, the value is 0.9, then tourists can directly purchase tickets from this gate to enter the scenic area (without judging whether the current entrance is in the list of non-congested entrances).

[0103] If the current tourist at entrance D(r) satisfies formula (4) &&! formula (5) && formula (6), where && represents "condition and", then under the premise of meeting the preliminary judgment conditions for tourist admission, that is, when the current warning threshold value requiring flow restriction is about to be reached, and the current entrance is not one of the most congested forbidden_num entrances, the tourist can directly purchase a ticket from this gate to enter the scenic area.

[0104] If the current tourist at the entrance D(r) satisfies formula (4) &&! formula (5) &&! formula (6), then under the premise of satisfying the preliminary judgment condition for tourist access (i.e. satisfying formula (4)), when the current warning threshold value requiring flow restriction is about to be reached, and the current entrance is also one of the most congested forbidden_num entrances, the tourist cannot purchase tickets from this entrance for admission, but can consider purchasing tickets from other entrances for admission. At this time, the ticket software system will kindly remind and recommend other entrances for admission, and provide detailed remaining ticket information and entry guides for other entrances for admission. Thus, the present invention realizes early warning and control, and conveniently guides tourists to avoid the current congested entrance and enter the scenic spot from the non-crowded channel entrance.

[0105] It is worth noting that enterlist_asc_uncrowded(t_duration(p)) is strongly correlated with the current sub-time period t_duration(p). Therefore, with the dynamic control of the ticket system, there is a possibility that the congested entrance will become an unobstructed, non-congested entrance again at some time in the future. Therefore, the present invention dynamically adjusts the remaining tickets at the entrance during the ticket sales period to alleviate the congestion of the crowd.

[0106] As other embodiments, the congestion conditions from the entrance to all hot spots are calculated. The specific implementation process includes obtaining the distance weighting factor corresponding to the distance from the entrance to each hot spot area, so as to calculate the congestion conditions from the entrance to each hot spot area based on the effective number of people in each hot spot area, and then obtain the congestion conditions from the entrance to all hot spots areas.

[0107] For example, the utility function of the crowding condition from the entrance D(r) to the hot spot zoi(j) is defined as crowd_effecvice(D(r),zoi(j),t_duration(p)), and satisfies:

[0108]

[0109] Among them, the distance weighting factor distance_weight(D(r),zoi(j)) is achieved by introducing the negative correlation between the distance between the entrance and the hot spot area zoi(j)). The farther the hot spot area is from the entrance, the smaller the impact on the current entrance congestion.

[0110] For D(r), the corresponding entrance congestion utility functions of all the above hot spots zoi(1)-zoi(j) are summed up and accumulated, and finally the overall entrance congestion utility function crowd_effecvice(D(r),t_duration(p)) is obtained, and it satisfies

[0111]

[0112] Among them, the physical meaning of the entrance congestion utility function crowd_effecvice(D(r), t_duration(p)) is: it can be used to measure whether the current entrance D(r) is a crowded entrance. The more effective people there are and the closer the scenic spot is to the current entrance, it means that the current entrance D(r) will have a large passenger flow now or at some point in the future, which requires early warning and control. Of course, the utility function can be used as one of the indicators for measuring crowd distribution statistics or other scenic spot congestion.

[0113] It is worth noting that for the rth entrance D(r) of the scenic spot, the distance to the j hot spots can be expressed as distance(D(r),zoi(j)), and the maximum value is selected from the r*j distance values: max_distance_d2zoi, so

[0114]

[0115] Define the distance weighted feature distance_weight(D(r),zoi(j)) of the hotspot area of ​​entrance D(r) and satisfy

[0116]

[0117] It can be seen that when distance(D(r),zoi(j)) is small, for example, near 0, distance_weight is large and close to 1+k_dist; when distance(D(r),zoi(j)) is large, for example, close to max_distance_d2zoi, distance_weight reaches the minimum value 1+k_dist / e. The present invention introduces max_distance_d2zoi, normalizes the value of distance(D(r),zoi(j)), and uses the normalized distance to process the distance weighting factor, so that the distance_weight(D(r),zoi(j)) weight is dynamically scaled within a limited controllable range, thereby further achieving the effect of regulating the congestion situation.

[0118] Figure 3 FIG. 4 is a schematic diagram of the main flow of the smart ticket pre-sale method according to the second embodiment of the present invention. Figure 3 As shown, the implementation process of the smart ticket pre-sale method may also include:

[0119] Step S301, start the ticket pre-sale core service.

[0120] Step S302, determine whether it is the first time to open, if so, proceed to step S303 and then step S304, if not, directly proceed to step S304.

[0121] Step S303, start and initialize all parameters (such as Figure 4 Each module in or Figure 5). Specifically, parameter configuration can be performed through a preset interface (for example, it can be implemented through a parameter configuration module), for example: configure the mapping relationship between zoi and object recognition devices, for example, associate 100 object recognition devices with 10 zoi information, there are 100 object recognition device locations, the 1st to 10th cameras camera(1) to camera(10) belong to zoi(1), the 10th to 20th cameras camera(10) to camera(20) belong to zoi(2), and so on. Configure the threshold of the number of repeated captures, for example, Repeat=10. Configure the maximum time interval threshold of repeated captures, for example, wander_interval=30min. Configure the threshold of the number of repeated captures of different object recognition devices, for example, S=3. Configure the number of entrances D(r), for example, R=4. Configure the visitor capacity, for example, vistor_capacity=2000. Configure sub-time periods, for example, P = 10, the sub-intervals are [8:00-9:00], ... [17:00-18:00], and the uniform distribution weight config_weight is 1 / R. Configure the wandering factor, for example, k1 = 1.5, k_dist = 3. Configure the number of hotspot areas, for example, j = 10.

[0122] Step S304, obtain the D(r)th entrance, time period t_duration(p), and allowed passenger flow psnflow(D(r), t_duration(p)).

[0123] Assume that there are D(r) entrances to the scenic spot, and the value range of r is 1, 2, …R. The capacity of regular tourists that the scenic spot can accommodate is vistor_capacity. The ticket sales time period is T, for example, [T_start, T_start+T] is a ticket sales time period. The ticket sales time period T is divided to obtain [T(1)~T(2)], [T(p), T(p+1)]…[T(P), T(P+1)], a total of P sub-time periods; the p-th time period is represented by t_duration(p), and it is obvious that t_duration(p)=[T(p), T(p+1)]; generally, we use one hour on the hour, for example, [8:00-9:00], …[17:00-18:00] to represent an independent ticket sales time period.

[0124] Preferably, the current weather is represented by the weather variable, and the values ​​can be: sunny, rainy, snowy or other, etc. Weather has positive and negative effects on ticket sales. Define factor_weather(weather) to represent the influencing factor. For example, define the influencing factor of sunny days as 1 and rainy days as 0.7, indicating the negative effect of rainy weather on the ticket sales of scenic spots, and more tickets can be pre-sold on sunny days. In addition, health_state is used to represent the current health status of tourists. For example, combined with factors such as colds and epidemics, the health status of tourists on that day can be obtained by obtaining the epidemic health code in real time, with 1 representing health and 0 representing unhealthy. You can carry the tourist identity (such as ID card ID, mobile phone number ID) to call the third-party publicly queryable health code Internet application web interface to feedback health data in real time.

[0125] Step S305, the tourists who have entered the scenic area frequently move around multiple locations and are captured and photographed by an object recognition device (such as a camera).

[0126] Step S306, for the current zoi_id, the current time subset T_duration(q), traverse and capture the information records of the tourist list faceid(i), and the wandering tourists and non-wandering tourists can be obtained by the following screening conditions:

[0127] The maximum time interval for repeated capture by any object recognition device in camera_list(zoi(j)) <= wander_interval (1)

[0128] The number of times the camera is captured by any object recognition device in camera_list(zoi(j)) is greater than or equal to the number of times threshold Repeat (2)

[0129] Repeatedly captured by any object recognition device in camera_list(zoi(j)), the number of different object recognition devices is greater than or equal to S (3)

[0130] Step S307, after all zoi_ids are traversed, the effective number of people in the hotspot area psn_effective(zoi(j), t_duration(p)) is calculated for the current zoi_id and the current time subset T_duration(q).

[0131] Step S308, calculating the distance (D(r), zoi(j)) from D(r) to the j hotspot areas.

[0132] Step S309, by calculating the hotspot area distance weighted feature distance_weight(D(r), zoi(j)) of the entrance D(r), calculate the crowding utility function crowd_effecvice(D(r), zoi(j), t_duration(p)) from the entrance D(r) to the hotspot area zoi(j).

[0133] Step S310, calculate the crowding utility function crowd_effecvice(D(r), t_duration(p)) of the entrance D(r) to all hot spots in the scenic area, and obtain the non-congested entrance list enterlist_asc_uncrowded.

[0134] Step S311, receiving a ticket purchase request from a visitor at the D(r) entrance, and determining whether the visitor has preliminary admission qualifications. Specifically, it can be determined based on vistor_permits(user_id) whether the visitor has preliminary admission qualifications.

[0135] vistor_permits(user_id)=health_state*has_tickets(user_id)

[0136] It can be seen that when health_state does not meet the epidemic prevention requirements or for other reasons, it does not have the initial qualifications for admission.

[0137] Step S312, determining that the entrance exists in the non-congested entrance list, obtaining the remaining tickets corresponding to the entrance to determine whether the tourist is allowed to enter.

[0138] Figure 4 Schematic diagram of the main modules of the intelligent ticket pre-sale device according to the first embodiment of the present invention. Figure 4As shown, the intelligent ticket pre-sale device includes a parameter configuration module 401, a face capture and recognition module 402, and a multi-feature fusion ticket pre-sale module 403. Among them, the parameter configuration module 401 obtains the flow of people allowed in each entrance during the preset time period, so as to determine the list of entrances with surplus tickets according to the number of reserved tickets corresponding to each entrance at present, and then obtain the location information of the entrances with surplus tickets to calculate the distance from each hot spot area. The face capture and recognition module 402 identifies tourists through the object recognition device corresponding to the hot spot area, obtains the list of tourists browsing data information in the hot spot area, and then calls the wandering determination component to obtain wandering tourists from the list of tourists browsing data information to determine the effective number of people in the hot spot area. The multi-feature fusion ticket pre-sale module 403 calls the preset utility model, and based on the distance between the entrance with surplus tickets and each hot spot area and the effective number of people in each hot spot area, calculates the congestion condition of the entrance to all hot spots, and then determines the non-congested entrance from the list of entrances with surplus tickets to adjust the number of surplus tickets of each entrance in the list of entrances with surplus tickets.

[0139] In some embodiments, after the multi-feature fusion ticket pre-sale module 403 calculates the congestion conditions of the entrance to all hot spots, it includes:

[0140] According to the congestion conditions of the entrances, the entrances in the list of entrances with remaining tickets are sorted in ascending order, and then based on the preset number of congested entrance limits, the target entrances are marked to prohibit ticket sales.

[0141] In some embodiments, the face capture and recognition module 402 calls the wandering determination component to obtain wandering tourists from the tourist browsing data information list, including:

[0142] Calling the wandering determination component to monitor the object recognition devices in the object recognition device list corresponding to the hot spot area, whose number of repeated object captures within a preset maximum time interval is greater than or equal to a preset number threshold;

[0143] When the number of the object recognition devices is greater than or equal to a preset number threshold, the tourist information of the captured object is extracted from the tourist browsing data information list as a wandering tourist.

[0144] In some embodiments, the face capture and recognition module 402 determines the effective number of people in the hotspot area, including:

[0145] The wandering factor corresponding to the hot spot area is obtained, and based on wandering tourists and non-wandering tourists in the tourist browsing data information list, the effective number of people in the hot spot area is calculated through a preset effective model.

[0146] In some embodiments, the multi-feature fusion ticket pre-sale module 403 calculates the congestion conditions of the entrance to all hot spots, including:

[0147] The distance weighting factor corresponding to the distance from the entrance to each hot spot area is obtained to calculate the congestion condition from the entrance to each hot spot area based on the effective number of people in each hot spot area, and then obtain the congestion condition from the entrance to all hot spots areas.

[0148] As other embodiments, Figure 5 As shown, the intelligent ticket pre-sale device may also include a ticket pre-sale core service module 404, a visitor face data storage module 405, a geographic annotation module 406, a visitor statistical analysis module 407 and a ticket collection module 408.

[0149] The ticket pre-sale core service module 404 starts and initializes other modules.

[0150] The visitor face data storage module 405 registers and stores the visitor's unique identification (such as ID number, mobile phone number, etc.), name, face image and other attribute information. For example, the face capture and recognition module 402 can compare and calculate the features of the face image data captured by the object recognition device with the face data pre-stored in the visitor face data storage module 405 (such as using open source software such as FaceNet, various face recognition algorithms implemented on GitHub, etc.), and generate face recognition information after matching.

[0151] The geographic annotation module 406 can deploy multiple object recognition devices to N discrete points to obtain the location information of each object recognition device; perform clustering or mean processing on the location information of the object recognition device to obtain the hotspot area, and then generate a mapping relationship between the hotspot area and the object recognition device. It can be seen that the geographic annotation module 406 can record the latitude and longitude information of the object recognition device in advance for the deployed object recognition device, so as to facilitate the subsequent retrieval and calculation of trajectory data.

[0152] The tourist statistics analysis module 407 can identify tourists through object recognition devices corresponding to hot spots to obtain tourist behavior data and emotional data, and then pull the target discriminant model from the discriminant model pool to obtain corresponding discrimination results and send an alarm.

[0153] The ticket collection module 408 can receive an entrance entry request, obtain the target attribute information of the tourist to be entered in the entrance entry request, so as to determine whether the preset entry conditions are met; determine whether the entrance is marked, and if so, reject the entrance entry request; if not, determine whether the current entrance reaches the preset current limiting warning threshold value, and if so, reject the entrance entry request, and if not, allow entry in response to the entrance entry request.

[0154] It should be noted that the smart ticket pre-sale method and the smart ticket pre-sale device of the present invention have a corresponding relationship in terms of specific implementation contents, so the repeated contents will not be described again.

[0155] Figure 6 An exemplary system architecture 600 is shown to which the smart ticket pre-sale method or smart ticket pre-sale device according to the embodiment of the present invention can be applied.

[0156] like Figure 6 As shown, system architecture 600 may include terminal devices 601, 602, 603, network 604 and server 605. Network 604 is used to provide a medium for communication links between terminal devices 601, 602, 603 and server 605. Network 604 may include various connection types, such as wired, wireless communication links or optical fiber cables, etc.

[0157] Visitors can use terminal devices 601, 602, 603 to interact with server 605 through network 604 to receive or send messages, etc. Various communication client applications can be installed on terminal devices 601, 602, 603, such as shopping applications, web browser applications, search applications, instant messaging tools, email clients, social platform software, etc. (only for example).

[0158] The terminal devices 601, 602, 603 may be various electronic devices having a smart ticket pre-sale screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop computers, desktop computers, and the like.

[0159] The server 605 may be a server that provides various services, such as a backend management server (only an example) that provides support for shopping websites browsed by visitors using the terminal devices 601, 602, and 603. The backend management server may analyze and process the received data such as product information query requests, and feed back the processing results (such as target push information, product information - only an example) to the terminal device.

[0160] It should be noted that the smart ticket pre-sale method provided in the embodiment of the present invention is generally executed by the server 605 , and accordingly, the computing device is generally arranged in the server 605 .

[0161] It should be understood that Figure 6 The number of terminal devices, networks and servers in the embodiment is only for illustration. Any number of terminal devices, networks and servers may be provided according to implementation requirements.

[0162] Reference below Figure 7, which shows a schematic diagram of the structure of a computer system 700 of a terminal device suitable for implementing an embodiment of the present invention. Figure 7 The terminal device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0163] like Figure 7 As shown, the computer system 700 includes a central processing unit (CPU) 701, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 702 or a program loaded from a storage part 708 into a random access memory (RAM) 703. In the RAM 703, various programs and data required for the operation of the computer system 700 are also stored. The CPU 701, the ROM 702, and the RAM 703 are connected to each other via a bus 704. An input / output (I / O) interface 705 is also connected to the bus 704.

[0164] The following components are connected to the I / O interface 705: an input section 706 including a keyboard, a mouse, etc.; an output section 707 including a cathode ray tube (CRT), a liquid crystal smart ticket pre-sale device (LCD), etc., and a speaker, etc.; a storage section 708 including a hard disk, etc.; and a communication section 709 including a network interface card such as a LAN card, a modem, etc. The communication section 709 performs communication processing via a network such as the Internet. A drive 710 is also connected to the I / O interface 705 as needed. A removable medium 711, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 710 as needed, so that a computer program read therefrom is installed into the storage section 708 as needed.

[0165] In particular, according to the embodiments disclosed in the present invention, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present invention include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 709, and / or installed from the removable medium 711. When the computer program is executed by the central processing unit (CPU) 701, the above-mentioned functions defined in the system of the present invention are executed.

[0166] It should be noted that the computer-readable medium shown in the present invention may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, 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 above. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or device. In the present invention, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable program code. This propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer-readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, optical cable, RF, etc., or any suitable combination of the above.

[0167] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present invention. In this regard, each box in the flow chart or block diagram can represent a module, a program segment, or a part of a code, and the above-mentioned module, program segment, or a part of a code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flow chart, and the combination of the boxes in the block diagram or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0168] The modules involved in the embodiments of the present invention may be implemented by software or hardware. The modules described may also be set in a processor, for example, it may be described as: a processor including a parameter configuration module, a face capture and recognition module, and a multi-feature fusion ticket pre-sale module. The names of these modules do not, in some cases, constitute limitations on the modules themselves.

[0169] As another aspect, the present invention also provides a computer-readable medium, which may be included in the device described in the above embodiment; or it may exist independently and not be assembled into the device. The above computer-readable medium carries one or more programs, and when the above one or more programs are executed by a device, the device includes obtaining the flow of people allowed in a preset time period at each entrance, so as to determine the list of entrances with remaining tickets according to the number of reserved tickets corresponding to each entrance, and then obtain the location information of the entrances with remaining tickets to calculate the distance from each hot spot area; perform tourist identification through the object identification device corresponding to the hot spot area, obtain the list of tourist browsing data information of the hot spot area, and then call the wandering determination component to obtain wandering tourists from the list of tourist browsing data information to determine the effective number of people in the hot spot area; call the preset utility model, based on the distance between the entrance with remaining tickets and each hot spot area and the effective number of people in each hot spot area, calculate the congestion condition of the entrance to all hot spots, and then determine the non-congested entrance from the list of entrances with remaining tickets to adjust the number of remaining tickets for each entrance in the list of entrances with remaining tickets.

[0170] The technical solution according to the embodiment of the present invention can solve the problems of low efficiency and poor real-time performance of passenger flow control and ticket pre-sale in existing scenic spots.

[0171] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may occur depending on design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A smart ticket pre-sale method, characterized in that: include: Obtain the flow of people allowed at each entrance during a preset time period, determine the list of entrances with remaining tickets based on the number of reserved tickets currently corresponding to each entrance, and then obtain the location information of the entrances with remaining tickets to calculate the distance to each hotspot area; Identify tourists through the object recognition device corresponding to the hot spot area, obtain the tourist browsing data information list of the hot spot area, and then call the wandering determination component to obtain wandering tourists from the tourist browsing data information list to determine the effective number of people in the hot spot area; Calling a preset utility model, based on the distance between the entrance with surplus tickets and each hot spot area and the effective number of people in each hot spot area, calculates the congestion status of the entrance to all hot spots, and then determines a non-congested entrance from the list of entrances with surplus tickets to adjust the number of surplus tickets for each entrance in the list of entrances with surplus tickets; Determining the effective number of people in the hot spot area includes: obtaining the wandering factor corresponding to the hot spot area, and calculating the effective number of people in the hot spot area through a preset effective model based on wandering tourists and non-wandering tourists in the tourist browsing data information list.

2. The method according to claim 1, characterized in that After calculating the congestion conditions of the entrance to all hot spots, it includes: According to the congestion conditions of the entrances, the entrances in the list of entrances with remaining tickets are sorted in ascending order, and then based on the preset number of congested entrance limits, the target entrances are marked to prohibit ticket sales.

3. The method according to claim 2, characterized in that After adjusting the number of remaining tickets for each entrance in the list of entrances with remaining tickets, including: Receiving an entry request, obtaining target attribute information of the visitor to be entered in the entry request, so as to determine whether the preset entry conditions are met; Determine whether the entry is marked, and if so, deny the entry request; if not, determine whether the current entry reaches a preset current limiting warning threshold, and if so, deny the entry request; if not, grant entry in response to the entry request.

4. The method according to claim 1, characterized in that: Call the wandering determination component to get wandering visitors from the visitor browsing data information list, including: Calling the wandering determination component to monitor the object recognition devices in the object recognition device list corresponding to the hot spot area, whose number of repeated object captures within a preset maximum time interval is greater than or equal to a preset number threshold; When the number of the object recognition devices is greater than or equal to a preset number threshold, the tourist information of the captured object is extracted from the tourist browsing data information list as a wandering tourist.

5. The method according to claim 1, characterized in that The congestion conditions of the entrance to all hot spots are calculated, including: The distance weighting factor corresponding to the distance from the entrance to each hot spot area is obtained to calculate the congestion condition from the entrance to each hot spot area based on the effective number of people in each hot spot area, and then obtain the congestion condition from the entrance to all hot spots areas.

6. The method according to claim 1, characterized in that Before identifying a visitor through an object recognition device corresponding to a hotspot area, the following steps are performed: Deploy multiple object recognition devices to N discrete points and obtain location information of each object recognition device; Clustering or mean processing is performed on the location information of the object recognition device to obtain a hotspot area, and then a mapping relationship between the hotspot area and the object recognition device is generated.

7. The method according to any one of claims 1 to 6, characterized in that: Also includes: Visitors are identified through object recognition devices corresponding to hot spots to obtain tourist behavior data and emotional data, and then the target discriminant model is pulled from the discriminant model pool to obtain the corresponding discrimination results and send an alarm.

8. An intelligent ticket pre-sale device, characterized in that: include: The parameter configuration module is used to obtain the flow of people allowed at each entrance within a preset time period, so as to determine the list of entrances with remaining tickets according to the number of reserved tickets corresponding to each entrance, and then obtain the location information of the entrances with remaining tickets to calculate the distance from each hot spot area; The face capture and recognition module is used to identify tourists through the object recognition device corresponding to the hot spot area, obtain the list of tourists browsing data information in the hot spot area, and then call the wandering determination component to obtain wandering tourists from the list of tourists browsing data information to determine the effective number of people in the hot spot area; The multi-feature fusion ticket pre-sale module is used to call the preset utility model, calculate the congestion status of the entrance to all hot spots based on the distance between the entrance with remaining tickets and each hot spot area and the effective number of people in each hot spot area, and then determine the non-congested entrance from the list of entrances with remaining tickets to adjust the number of remaining tickets for each entrance in the list of entrances with remaining tickets; The face capture and recognition module is specifically used to determine the effective number of people in the hot spot area, including: obtaining the wandering factor corresponding to the hot spot area, and calculating the effective number of people in the hot spot area through a preset effective model based on wandering tourists and non-wandering tourists in the tourist browsing data information list.

9. The device according to claim 8, characterized in that After the multi-feature fusion ticket pre-sale module calculates the congestion conditions of all hot spots from the entrance, it includes: According to the congestion conditions of the entrances, the entrances in the list of entrances with remaining tickets are sorted in ascending order, and then based on the preset number of congested entrance limits, the target entrances are marked to prohibit ticket sales.

10. The device according to claim 9, characterized in that Also includes: The ticket collection module is used to receive an entry request and obtain the target attribute information of the tourist to be entered in the entry request to determine whether the preset entry conditions are met; Determine whether the entry is marked, and if so, deny the entry request; if not, determine whether the current entry reaches a preset current limiting warning threshold, and if so, deny the entry request; if not, grant entry in response to the entry request.

11. The device according to claim 8, characterized in that The face capture and recognition module calls the wandering determination component to obtain wandering tourists from the list of tourist browsing data information, including: Calling the wandering determination component to monitor the object recognition devices in the object recognition device list corresponding to the hot spot area, whose number of repeated object captures within a preset maximum time interval is greater than or equal to a preset number threshold; When the number of the object recognition devices is greater than or equal to a preset number threshold, the tourist information of the captured object is extracted from the tourist browsing data information list as a wandering tourist.

12. The device according to claim 8, characterized in that The multi-feature fusion ticket pre-sale module calculates the congestion conditions of all hot spots from the entrance, including: The distance weighting factor corresponding to the distance from the entrance to each hot spot area is obtained to calculate the congestion condition from the entrance to each hot spot area based on the effective number of people in each hot spot area, and then obtain the congestion condition from the entrance to all hot spots areas.

13. The device according to claim 8, characterized in that Also includes: A geographic annotation module is used to deploy multiple object recognition devices to N discrete points and obtain the location information of each object recognition device; Clustering or mean processing is performed on the location information of the object recognition device to obtain a hotspot area, and then a mapping relationship between the hotspot area and the object recognition device is generated.

14. The device according to any one of claims 8 to 13, characterized in that: Also includes: The tourist statistical analysis module is used to identify tourists through the object recognition devices corresponding to the hot spots to obtain tourist behavior data and emotional data, and then pull the target discriminant model from the discriminant model pool to obtain the corresponding discrimination results and send an alarm.

15. An electronic device, characterized in that: include: one or more processors; a storage device for storing one or more programs, When the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 7.

16. A computer readable medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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