CN112085465A

CN112085465A - Data processing method, device and storage medium

Info

Publication number: CN112085465A
Application number: CN202010867653.5A
Authority: CN
Inventors: 许迅腾
Original assignee: Tencent Technology Shenzhen Co Ltd
Current assignee: Tencent Technology Shenzhen Co Ltd
Priority date: 2020-08-26
Filing date: 2020-08-26
Publication date: 2020-12-15

Abstract

The invention provides a data processing method, a device and a storage medium, wherein the method comprises the following steps: determining opening and closing time data of the target area in the target time period based on real-time passenger flow data of the target area in the target time period and target historical passenger flow data of the target area, wherein the target historical passenger flow data is historical peak passenger flow data of the target area in preset time before the target time period; acquiring positioning time data of a user in a target area and positioning data outside the target area in a target time period; respectively determining the initial entering time and the last leaving time of the user in the target time period based on any one of the opening and closing time data and the outside-area positioning data and the positioning time data; based on the initial time of entry and the last time of exit, a length of dwell time of the user within the target area within the target time period is determined. The method and the device can reduce the error of calculation of the stay time of the user in the attention area caused by sparse or unstable positioning of the user.

Description

Data processing method, device and storage medium

Technical Field

The invention belongs to the technical field of computers, and particularly relates to a data processing method, a data processing device and a storage medium.

Background

The prior art generally uses the time period of the first anchor point and the last anchor point occurring in the area concerned by the service as the time period for the user to stay in the area. For example, if the user a has positioning data in AOI at 10:00, 11:11, and 13:30, it is determined that the user a stays in the area for 10: 00-13: 30, and the staying time is 3.5 hours.

The above common approach relies on an important premise assumption: the user needs to trigger the positioning of the mobile phone periodically or frequently to ensure that the result is accurate. However, as modern society has become more and more aware of privacy protection, many manufacturers are increasingly restricting the initiation of unnecessary location authority applications and actual location operations. This makes the positioning data of the user more and more sparse. For example, the user a is at home 9 am, arrives at the attention area at 10 am, and leaves until staying at 18 am, but the positioning data seen in the background is only 8:00 at home, 11:00 and 14:00 at the attention area due to the limitation of the system on the number of times of positioning initiation, and the position information cannot be acquired due to the limitation of the system on unnecessary positioning operation in other cases. According to the existing calculation method, the obtained retention time is 11: 00-14: 00 three hours, which is obviously less than eight hours of the actual 10: 00-18: 00.

Disclosure of Invention

In order to reduce errors in calculation of the stay time of a user in a concerned area caused by sparse or unstable positioning and improve the accuracy of determination of the stay time of the user under the condition of sparse or unstable positioning, the invention provides a data processing method, a data processing device and a storage medium.

In one aspect, the present invention provides a data processing method, where the method includes:

determining opening and closing time data of a target area in a target time period based on real-time passenger flow data of the target area in the target time period and target historical passenger flow data of the target area; the target historical passenger flow data is historical peak passenger flow data of the target area in preset time before the target time period;

acquiring positioning time data of a user in the target area and positioning data outside the target area in the target time period;

respectively determining initial entering time of the user entering the target area and last leaving time of the user leaving the current area in the target time period based on any one of the opening and closing time data and the off-area positioning data and the positioning time data;

determining a dwell time of the user within the target area within the target time period based on the initial time of entry and the last time of exit.

In another aspect, an embodiment of the present invention provides a data processing apparatus, where the apparatus includes:

the switching time data determining module is used for determining switching time data of a target area in a target time period based on real-time passenger flow data of the target area in the target time period and target historical passenger flow data of the target area; the target historical passenger flow data is historical peak passenger flow data of the target area in preset time before the target time period;

the positioning data acquisition module is used for acquiring positioning time data of the user in the target area and positioning data outside the target area in the target time period;

an entering and leaving time determining module, configured to determine, based on the positioning time data and at least one of the opening and closing time data and the outside-area positioning data, an initial entering time when the user enters the target area and a last leaving time when the user leaves the current area within the target time period, respectively;

and the stay time length determining module is used for determining the stay time length of the user in the target area in the target time period based on the initial entering time and the last leaving time.

In another aspect, the present invention provides an electronic device for data processing, which includes a processor and a memory, where at least one instruction or at least one program is stored in the memory, and the at least one instruction or the at least one program is loaded by the processor and executed to implement the data processing method as described above.

In another aspect, the present invention provides a computer-readable storage medium, in which at least one instruction or at least one program is stored, and the at least one instruction or the at least one program is loaded and executed by a processor to implement the data processing method as described above.

According to the data processing method, the data processing device and the storage medium, provided by the embodiment of the invention, the opening and closing time of the target area in the target time period can be automatically mined according to historical passenger flow data, and the stay time of a user in the target area in the target time period is determined by combining the positioning data in the target area and the positioning data outside the target area. Because the method for determining the stay time of the user in the target area in the target time period does not need the continuous positioning data of the user (namely, the user does not need to trigger the positioning of the terminal periodically or frequently), the sparseness and the instability of the positioning data can be considered, the error of determining the stay time of the user with sparse positioning data is reduced, and the accuracy of determining the stay time of the user in the target area is improved. In some scenarios, a high-accuracy determination method of the stay time of the user can be used to determine whether certain marketing activities in the target area can be followed by a significant increase in the stay time of the client in the target area (generally, the longer the stay time, the greater the consumption probability), thereby proving which marketing activities are effective, which can be continued, which are not effective, and which need to be improved or cancelled.

Drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions and advantages of the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.

Fig. 1 is a schematic diagram of an implementation environment of a data processing method according to an embodiment of the present invention.

Fig. 2 is a schematic flowchart of a data processing method according to an embodiment of the present invention.

Fig. 3 is a flowchart illustrating a process of determining an initial entry time of a user into a target area within a target time period based on the out-of-area positioning data and the positioning time data according to the embodiment of the present invention.

Fig. 4 is a schematic flowchart of a process for determining an initial entry time of a user into a target area within a target time period based on opening and closing time data and positioning time data according to an embodiment of the present invention.

Fig. 5 is a schematic diagram of calculating an initial time of entry and a last time of exit according to an embodiment of the present invention.

Fig. 6 is another schematic diagram of calculating an initial time of entry and a last time of exit according to an embodiment of the present invention.

Fig. 7 is another schematic diagram for calculating the initial entry time and the last exit time according to the embodiment of the present invention.

Fig. 8 is a flowchart illustrating a process of determining a last time when a user leaves a current area within a target time period based on the out-of-area positioning data and the positioning time data according to an embodiment of the present invention.

Fig. 9 is a flowchart illustrating a process of determining a last time when a user leaves a current area within a target time period based on opening/closing time data and positioning time data according to an embodiment of the present invention.

Fig. 10 is a flowchart illustrating a process of determining a temporary leaving time when a user temporarily leaves a target area and a temporary entering time when the user temporarily enters the target area according to an embodiment of the present invention.

Fig. 11 is a schematic flowchart of calculating a temporary departure time and a temporary entry time according to an embodiment of the present invention.

Fig. 12 is a logic diagram for removing a temporary away period according to an embodiment of the present invention.

Fig. 13 is an alternative structure diagram of the blockchain system according to the embodiment of the present invention.

Fig. 14 is an alternative schematic diagram of a block structure according to an embodiment of the present invention.

Fig. 15 is a comparison graph of the determination result of the stay time of the user in a theme park by using the method provided by the embodiment of the present invention and the method in the prior art.

FIG. 16 is a comparison graph of the results of determining the length of time a user stays in a gym using the method provided by embodiments of the present invention and methods of the prior art.

Fig. 17 is a schematic structural diagram of a data processing apparatus according to an embodiment of the present invention.

Fig. 18 is a schematic structural diagram of a server according to an embodiment of the present invention.

Detailed Description

With the research and development of Artificial Intelligence (AI), AI has been developed and applied in various fields. AI is an integrated technique of computer science that attempts to understand the essence of intelligence and produces a new intelligent machine that can react in a manner similar to human intelligence.

The artificial intelligence technology is a comprehensive subject and relates to the field of extensive technology, namely the technology of a hardware level and the technology of a software level. The artificial intelligence infrastructure generally includes technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technologies, operation/interaction systems, mechatronics, and the like. The artificial intelligence software technology mainly comprises a computer vision technology, a voice processing technology, a natural language processing technology, machine learning/deep learning, automatic driving and the like.

Specifically, the scheme provided by the embodiment of the invention relates to an automatic driving technology. The automatic driving technology generally includes technologies of environment perception, behavior decision, path planning, motion control and the like. Accordingly, the environmental perception includes perception sensors, positioning, high-precision maps, speed perception, and the like.

In particular, the scheme provided by the embodiment of the invention relates to a high-precision map and positioning technology in environment perception.

In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or server that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.

Fig. 1 is a schematic diagram of an implementation environment of a data processing method according to an embodiment of the present invention. As shown in fig. 1, the implementation environment may include at least a terminal 01 and a server 02, and the terminal 01 and the server 02 may be directly or indirectly connected through wired or wireless communication, and the present invention is not limited herein.

Specifically, the terminal 01 may be, but is not limited to, a smart phone, a tablet computer, a notebook computer, a desktop computer, a smart television, a smart watch, and the like.

Specifically, the server 02 may be an independent physical server, a server cluster or a distributed system formed by a plurality of physical servers, or a cloud server providing basic cloud computing services such as a cloud service, a cloud database, cloud computing, a cloud function, cloud storage, a network service, cloud communication, a middleware service, a domain name service, a security service, a CDN, a big data and artificial intelligence platform, and the like.

It should be noted that fig. 1 is only an example.

Fig. 2 is a schematic flowchart of a data processing method according to an embodiment of the present invention. The method may be used in the implementation environment of fig. 1. The present specification provides method steps as described in the examples or flowcharts, but may include more or fewer steps based on routine or non-inventive labor. The order of steps recited in the embodiments is merely one manner of performing the steps in a multitude of orders and does not represent the only order of execution. In practice, the system or server product may be implemented in a sequential or parallel manner (e.g., parallel processor or multi-threaded environment) according to the embodiments or methods shown in the figures. Specifically, as shown in fig. 2, the method may include:

s101, determining opening and closing time data of a target area in a target time period based on real-time passenger flow data of the target area in the target time period and target historical passenger flow data of the target area; the target historical passenger flow data is historical peak passenger flow data of the target area in a preset time before the target time period.

In this embodiment of the present invention, the opening/closing time data may include an opening time (denoted as startHour) and a closing time (denoted as endHour), and then S101 may include:

and acquiring target real-time passenger flow data of which the passenger flow data is greater than a preset passenger flow threshold value, wherein the preset passenger flow threshold value is a preset percentage of target historical passenger flow data.

And taking the starting time of the time period corresponding to the target real-time passenger flow data as the opening time.

And taking the last time of the time period corresponding to the target real-time passenger flow data as closing time.

The target Area may be an Area Of Interest (AOI), which refers to an Area-like geographic entity in the map data. In the embodiment of the present invention, the AOI may specifically be an area concerned by a service.

In the embodiment of the present invention, the target area includes but is not limited to: areas associated with cultural travel products (including but not limited to tourist attractions, theme parks, Olympic centers, museums, etc.), areas associated with commercial plots (including but not limited to malls, building centers, etc.).

The target time period in the embodiment of the present invention may be set according to specific attributes of the target area, for example, may be set every hour, every day, and the like. The setting mode of the target time period is not limited.

The preset time in the embodiment of the present invention may also be determined according to the specific attribute of the target area, and may also be set with reference to a target time period, where the target time period is assumed to be every day, the corresponding preset time may be several days, and the target time period is assumed to be every hour, the corresponding preset time may be several hours, and the like.

The preset passenger flow threshold value in the embodiment of the invention can be set according to the actual application scene, and the setting mode of the preset passenger flow threshold value is not limited by the invention.

Hereinafter, S101 will be described with the target time zone as "daily":

acquiring historical passenger flow data of a target area within a preset time before a certain day in advance, determining target historical passenger flow data (namely historical peak passenger flow data with the maximum passenger flow) from the historical passenger flow data, counting the real-time passenger flow of the certain day if the historical peak passenger flow data is 1 ten thousand and the preset percentage of the preset peak passenger flow data is 90%, determining a time period corresponding to the real-time passenger flow data with the passenger flow larger than 9000 (namely the target real-time passenger flow data), taking the starting time of the time period as the open time and the last time of the time period as the close time, for example, taking the time period corresponding to the real-time passenger flow data with the passenger flow larger than 9000 as 9:30-17:00, and taking 9:30 as the open time of the certain day, the turn-off time for that day is 17: 00. Thereby, the open time data and the closed time data of the target area in each day can be obtained.

Since each target area generally has corresponding actual open time and actual close time in real application (for example, if the actual open time period of disneyland is 10:00-21:00, the actual open time is 10:00, and the actual close time is 21:00), the method does not adopt the actual open time and the actual close time of each target area, but automatically excavates the open time and the close time of each target area according to historical passenger flow data, and has the advantages that: because the actual opening time and the actual closing time of each target area are different, the actual opening time and the actual closing time of one target area cannot be suitable for the other target area, namely, the universality of the actual opening time and the actual closing time is not good, but in the embodiment of the invention, the opening time and the closing time of each target area are automatically mined according to historical passenger flow data, so that the universality is good, and the method can be suitable for various types of target areas; moreover, the actual opening time and the actual closing time of the target area cannot be both obtained, and if the actual opening time and the actual closing time cannot be obtained, the user staying time in the target area cannot be counted; in addition, although the target area has the actual open time and the actual closed time, in practical application, the actual open time and the actual closed time may be changed correspondingly under the influence of some factors (for example, severe weather, some target areas are adjusted to be open for half a day or open only at night, or under the influence of some epidemic situations, some target areas are adjusted not to be open or open only in a fixed time period, and the like), in this case, if the actual open time and the actual closed time are continuously used, the accuracy of determining the user staying time length is inevitably reduced, but in the embodiment of the present invention, the open time and the closed time of each target area are automatically mined according to the historical passenger flow data, so that the defect that the accuracy of determining the user staying time length is low due to the adjustment of the actual open time and the actual closed time can be effectively avoided, thereby improving the accuracy of the user dwell time determination.

S103, acquiring positioning time data of the user in the target area and positioning data outside the target area in the target time period.

The "user" in the embodiment of the present invention refers to not only one user or a specific user, but all users who enter the target area within the target time period.

In the embodiment of the present invention, in the target time period, the positioning data (at least including positioning time data and positioning position data) of each user in the target area and the positioning data (at least including positioning time data and positioning position data) outside the target area may be obtained in advance, and the positioning data of each user may be acquired by a terminal used by each user.

In a possible embodiment, the users whose positioning points (determined by the positioning positions) in the target area are smaller than the preset positioning threshold may be pre-filtered according to the positioning data of the respective users in the target area in the target time period, so as to filter the users who temporarily pass through the target area or the users whose positioning points are drifting. The preset positioning threshold value can be changed according to the requirements of a service scene, the corresponding preset positioning threshold value can be set to be larger for navigation and other dense data, the corresponding preset positioning threshold value can be set to be smaller for data in social software, and the setting of the preset positioning threshold value is not limited by the invention.

Because the users temporarily passing through the target area or the users with drifting positioning points are filtered, the number of the users needing to calculate the stay time is reduced, thereby reducing the burden of the system for calculating the stay time of the users and reducing the expenditure of system resources; moreover, the number of users needing to calculate the stay time is reduced, so that the efficiency of calculating the stay time of the users can be effectively improved; in addition, the users temporarily passing through the target area or the users with shifted positioning points are filtered, so that the interference of the positioning of the users temporarily passing through the target area or the positioning of the users with shifted positioning points on the determination of the stay time of the users in the target area can be reduced, the error of the determination of the stay time of the users is effectively reduced, and the accuracy of the determination of the stay time of the users is obviously improved.

In a possible embodiment, positioning points with positioning accuracy greater than a preset accuracy threshold may also be pre-filtered according to positioning data of each user in the target area within the target time period, where the positioning accuracy refers to a proximity degree between spatial entity position information (usually coordinates) and a real position thereof. For a certain user, because the positioning points with the positioning precision greater than the preset precision threshold are filtered, the rest positioning points are all the positioning points with higher positioning precision (namely the precision is less than or equal to the preset precision threshold), the positioning points with higher precision are used for determining the stay time of the user, and the accuracy rate of determining the stay time of the user can be obviously improved.

It should be noted that the positioning data used in the embodiment of the present invention does not need to be continuous positioning data (that is, the user does not need to trigger the positioning of the mobile phone periodically or very frequently), that is, the embodiment of the present invention may consider sparseness and instability of the positioning data separately, reduce an error in determining the staying time of the user with sparse positioning data, and increase accuracy in determining the staying time of the user in the target area. Of course, the invention is well suited for continuous positioning data.

And S105, respectively determining the initial entering time of the user entering the target area and the last leaving time of the user leaving the current area in the target time period based on any one of the opening and closing time data and the off-area positioning data and the positioning time data.

In a possible embodiment, the positioning time data includes a first positioning time when the user first locates in the target area, and the positioning data outside the area includes a first out-of-area positioning data, the first out-of-area positioning data includes a first out-of-area positioning time and a first out-of-area positioning position, and when the first out-of-area positioning time is adjacent to the first positioning time, S105 may include:

s1051, determining initial entering time of a user entering a target area in a target time period based on the external locating data and the locating time data.

Specifically, as shown in fig. 3, S1051 may include:

s10511, calculating a first estimated time spent from the position outside the first area to the target area.

S10513, calculating a first time difference value between the first positioning time and the positioning time outside the first area.

And S10515, when the first expected consumed time is larger than the first time difference value, taking the first positioning time as the initial entering time.

S10517, when the first expected consumed time is smaller than or equal to the first time difference value, taking the sum of the first out-of-region positioning time and the first expected consumed time as the initial entry time.

In another possible embodiment, when the out-of-area positioning data does not include the first out-of-area positioning data, S105 may further include:

and S1053, determining the initial entering time of the user entering the target area in the target time period based on the opening and closing time data and the positioning time data.

Specifically, as shown in fig. 4, S1053 may include:

s10531, when the first positioning time is later than the opening time, taking the average value of the first positioning time and the opening time as the initial entering time.

S10533, when the first positioning time is earlier than or equal to the opening time, the first positioning time is used as the initial entering time.

Hereinafter, S1051 and S1053 will be described in detail, taking the target time period as "daily":

the time when a user first appears in the target Area (AOI) is the first location time (denoted as T _1st), then the initial entry time (denoted as T _ in) of the user into the AOI on a certain day can be calculated through fig. 5-7, where 1 in fig. 5-7 represents the location of the user in the target area, 0 represents the location of the user outside the current area, and the horizontal axis is the time line:

1) regarding S1051:

as shown in fig. 5, if the user has an anchor point before T _1st, since there may be a plurality of anchor points, the anchor point outside the last AOI before T _1st may be used as the first out-of-area anchor point, and the first out-of-area anchor point (denoted as L01) and the first out-of-area anchor time (denoted as T01) of the first out-of-area anchor point are obtained, and since the first out-of-area anchor point is the anchor point outside the last AOI before T _1st, the first out-of-area anchor time (denoted as T01) should be adjacent to the first anchor time (denoted as T _1 st).

Calculating the first expected elapsed time T _ travel1 from L01 to AOI, then:

when T _ travel1 > T _1 st-T01, T _ in is T _1 st.

When T _ travel is less than or equal to T _1 st-T01, T _ in is T01+ T _ travel 1.

When T _ travel1 cannot be calculated, T _ in is (T01+ T _1st) ÷ 2.

It should be noted that the first expected elapsed time T _ travel1 may be calculated as follows: the moving speed of the user at T01 is obtained, the distance from L01 to AOI is calculated, and the quotient of the distance and the speed is taken as the T _ travel 1. The distance from the L01 to the AOI may be a straight line distance from the L01 to the AOI inlet, or a distance from the L01 to the optimal path at the AOI inlet, etc. When the distance from L01 to AOI or the moving speed of the user cannot be obtained, T _ travel1 cannot be calculated, and T _ in can be calculated as (T01+ T _1st) ÷ 2.

2) Regarding S1053:

as shown in fig. 6, if the user has no anchor point before T _1st, that is, the out-of-area positioning data does not include the first out-of-area positioning data, and T _1st > starthome, T _ in is (starthome + T _1st) ÷ 2.

As shown in fig. 7, if the user has no anchor point before T _1st, that is, the out-of-area positioning data does not include the first out-of-area positioning data, and T _1st is less than or equal to starthome, T _ in is T _1 st.

As can be seen from the above calculation strategy, the positioning data used for calculating T _ in the embodiment of the present invention is not continuous positioning data, for example, the positioning data used in S1051 is only the first out-of-area positioning data, the positioning data used in S1053 is only the first positioning time, that is, in the embodiment of the present invention, it is not necessary that the positioning data of the AOI of the user is continuous data when calculating T _ in, that is, discontinuous, sparse, and unstable positioning data generated by the user can be used, through various calculation strategies, and calculates T _ in by combining the opening time and the closing time determined according to the historical passenger flow volume data, therefore, errors of T _ in calculation caused by sparseness and instability of positioning data points are reduced, the calculation accuracy of T _ in is improved, and the accuracy of determining the stay time of a subsequent user in the AOI is further improved.

In another possible embodiment, the positioning time data includes a last positioning time of a last occurrence of positioning of the user in the target area, the positioning data outside the area includes second area outside positioning data, the second area outside positioning data includes a second area outside positioning time and a second area outside positioning position, and when the second area outside positioning time is adjacent to the last positioning time, S105 may further include:

and S1055, determining the last time of leaving of the user from the current area in the target time period based on the external positioning data and the positioning time data.

Specifically, as shown in fig. 8, S1055 may include:

s10551, calculating a second estimated time for the target area to reach the position outside the second area.

S10553, calculating a second time difference value between the positioning time outside the second area and the last positioning time.

And S10555, when the second expected consumed time is larger than the second time difference value, taking the last positioning time as the last leaving time.

And S10557, when the second expected consumed time is less than or equal to the second time difference, taking the difference between the second outside-area positioning time and the second expected consumed time as the last leaving time.

In another possible embodiment, when the out-of-area positioning data does not include the second out-of-area positioning data, S105 may further include:

and S1057, determining the last leaving time of the user leaving the current area in the target time period based on the opening and closing time data and the positioning time data.

Specifically, as shown in fig. 9, S1057 may include:

s10571, when the last positioning time is earlier than the closing time, taking the average value of the last positioning time and the closing time as the last leaving time.

And S10573, when the last positioning time is later than or equal to the closing time, taking the last positioning time as the last leaving time.

Hereinafter, S1055 and S1057 will be described in detail, taking the target time period as "daily":

the last time a user appears in the AOI is the last positioning time (denoted as T _ last), then the last departure time (denoted as T _ out) of the user from the target area on the day may include the following cases through fig. 5-7:

1) regarding S1055:

as shown in fig. 5, if there are a plurality of positioning points after T _ last, the positioning point outside the first AOI after T _ last may be used as the second out-of-area positioning data, and the second out-of-area positioning position (denoted as L02) and the second out-of-area positioning time (denoted as T02) of the second out-of-area positioning data are obtained, and since the second out-of-area positioning data is the positioning point outside the first AOI after T _ last, the second out-of-area positioning time (denoted as T02) should be adjacent to the first last positioning time (denoted as T _ last).

The second expected elapsed time to compute AOI to L02 is T _ travel2, then:

when T _ travel2> T02-T _ last, T _ out equals T _ last.

When T _ travel2 is less than or equal to T02-T _ last, T _ out is T02-T _ travel.

When T _ travel2 cannot be calculated, T _ out is (T02+ T _ last) ÷ 2.

It should be noted that the second expected elapsed time T _ travel2 may be calculated as follows: the moving speed of the user at T02 is obtained, the distance from AOI to L02 is calculated, and the quotient of the distance and the speed is taken as the T _ travel 1. The distance from the AOI to the L02 can be a straight line distance from the AOI outlet to the L02, a distance from the AOI outlet to the optimal path of the L02 and the like. When the distance from AOI to L02 or the moving speed of the user cannot be obtained, T _ travel2 cannot be calculated, and T _ out can be calculated as (T02+ T _ last) ÷ 2.

2) Regarding S1057:

continuing with fig. 6, if the user has no anchor point after T _ last, that is, the out-of-area positioning data does not include the second out-of-area positioning data, and T _ last is greater than or equal to starthome, T _ out is (endhome + T _ last) ÷ 2.

As shown in fig. 7, if the user has no anchor point after T _ last, that is, the out-of-area positioning data does not include the second out-of-area positioning data, and T _ last < starthome, T _ out is T _ last.

As can be seen from the above calculation strategy, the positioning data used for calculating T _ out in the embodiment of the present invention is not continuous positioning data, for example, the positioning data used in S1055 is only the positioning data outside the second area, the positioning data used in S1057 is only the last positioning time, that is, in the embodiment of the present invention, it is not necessary that the positioning data of the AOI is continuous data when calculating T _ out, that is, discontinuous, sparse, and unstable positioning data generated by the user can be used, through various calculation strategies, and calculates T out in conjunction with the open time and the close time determined from the historical passenger flow volume data, therefore, errors of T _ out calculation caused by sparseness and instability of positioning data points are reduced, the calculation accuracy of T _ out is improved, and the accuracy of determining the stay time of a subsequent user in the AOI is further improved.

And S107, determining the stay time of the user in the target area in the target time period based on the initial entering time and the last leaving time.

In one possible embodiment, if the user does not temporarily leave the target area within the target time period, the difference between the last leaving time and the last entering time is taken as the time period the user stays in the target area.

Because T _ in and T _ out do not need the positioning data of the user in the AOI to be continuous data during calculation, namely discontinuous, sparse and unstable positioning data generated by the user can be used for calculating T _ in and T _ out, which is equivalent to calculating the stay time of the user in the AOI by using the discontinuous, sparse and unstable positioning data generated by the user, thereby reducing the error of calculation of the stay time of the user in the AOI caused by the sparseness and instability of the positioning data points and improving the accuracy of determination of the stay time of the user in the AOI.

In another possible embodiment, if the user temporarily leaves the target area within the target time period and temporarily enters the target area after leaving for a period of time, the temporary leaving time for the user to temporarily leave the target area and the temporary entering time for the user to temporarily enter the target area need to be calculated, so as to calculate the temporary leaving period for the user to temporarily leave the target area, specifically before S107, the method may further include:

s106, determining the temporary leaving time when the user temporarily leaves the target area and the temporary entering time when the user temporarily enters the target area.

Specifically, as shown in fig. 10, S106 may include:

s10601, acquiring third area external positioning data of the user outside the target area between the initial entering time and the last leaving time, wherein the third area external positioning data comprises a plurality of third area external positioning positions and a plurality of third area external positioning times.

S10603, when the number of the plurality of the outer positioning positions of the third area is greater than a preset positioning threshold and the total duration of the outer positioning time of the plurality of the third area is greater than a preset duration threshold, sequentially sorting the plurality of the outer positioning positions of the third area according to corresponding positioning time to obtain an outer positioning data sequence.

S10605, acquiring first area positioning data and second area positioning data from positioning data of the user between the initial entering time and the last leaving time, wherein the first area positioning data are adjacent to the first-ordered area outside positioning data in the area outside positioning data sequence, and the last-ordered area outside positioning data in the second area outside positioning data sequence are adjacent.

S10607, determining the temporary leaving time of the user for temporarily leaving the target area based on the first-ranked external location data and the first area location data.

S10609, determining the temporary entering time of the user for entering the target area temporarily based on the sorted last outside-area positioning data and the second outside-area positioning data.

Hereinafter, S10601 to S10609 are explained in detail:

in the period of T _ In and T _ Out, if the user has a preset number of positioning points outside the AOI and the duration is greater than the preset duration threshold (i.e., the number of the positioning positions outside the third areas is greater than the preset positioning threshold and the total duration of the positioning time outside the third areas is greater than the preset duration threshold In S10603), the corresponding stay time outside the area, that is, the temporary leaving time period formed by the temporary leaving time (denoted as Temp _ Out) and the temporary entering time (Temp _ In), needs to be subtracted.

The schematic diagram of the calculation principle of Temp _ Out and Temp _ In may be shown In fig. 11, where 1 In fig. 11 represents that the user is located In the target area, 0 represents that the user is located outside the current area, the horizontal axis is a time line, and the calculation manner of Temp _ Out may refer to T _ Out In S1055, and specifically may be:

sequencing a plurality of third area outside positioning positions according to corresponding positioning time to obtain an outside positioning data sequence, acquiring first area inside positioning data (the adjacent positioning data can refer to the adjacent positioning time of the corresponding positioning time) adjacent to the outside positioning data at the top of sequencing from the positioning data between T _ in and T _ Out, wherein the first area inside positioning data is equivalent to the positioning data appearing in AOI at the last time, the "outside positioning data at the top of sequencing" is equivalent to the positioning point outside the first AOI after the "first area inside positioning data", and under the condition, the positioning time corresponding to the outside positioning data at the top of sequencing "and the midpoint of the positioning time corresponding to the" first area inside positioning data "can be used as the Temp _ Out.

As shown In fig. 11, the calculation method of Temp _ In may refer to T _ In S1051, and specifically may be:

sequencing a plurality of third area external positioning positions according to corresponding positioning time to obtain an external positioning data sequence, acquiring the external positioning data sequence from the positioning data between T _ In and T _ out, sequencing second area internal positioning data (the adjacent can refer to the adjacent of corresponding positioning time) adjacent to the final external positioning data, wherein the final external positioning data is equivalent to a first external positioning point after the second area internal positioning data, and the positioning time corresponding to the final external positioning data and the midpoint of the positioning time corresponding to the second area internal positioning data can be used as the Temp _ In under the condition.

Wherein the difference between Temp _ In and Temp _ Out constitutes a temporary departure period.

It should be noted that, a user may have multiple temporary leaving periods within a target time period (for example, within one day), and the calculation method of each temporary leaving period may refer to the above method, and after determining the multiple temporary leaving periods, the multiple temporary leaving periods are added to obtain the total temporary leaving time (Σ (Temp _ In-Temp _ Out)) of the user within the target time period.

It can be seen from the above calculation strategies that the positioning data used for calculating Temp _ In and Temp _ Out In this embodiment is not continuous positioning data (only the first area positioning data, the second area positioning data, the first-ordered Out-of-area positioning data, and the last-ordered Out-of-area positioning data need to be used), that is, discontinuous, sparse, and unstable positioning data generated by the user can be used, and through various calculation strategies, Temp _ In and Temp _ Out are calculated, thereby reducing errors In the calculation of Temp _ In and Temp _ Out caused by the sparseness and instability of the positioning data points, improving the calculation accuracy of Temp _ In and Temp _ Out, and further improving the accuracy of determining the stay time of the subsequent user In the AOI.

Accordingly, S107 may include:

a third time difference between the last departure time and the initial entry time is calculated.

A fourth time difference between the temporary entry time and the temporary exit time is calculated.

And taking the difference value between the third time difference value and the fourth time difference value as the stay time length.

In this embodiment of the present invention, if the user has only one temporary leaving period, the fourth time difference In S10703 is a difference between the temporary entering time of the temporary leaving period and the corresponding temporary leaving time, and if there are multiple temporary leaving periods, the fourth difference In S10703 is equal to Σ (Temp _ In-Temp _ Out) In S106, that is, the calculation formula of the staying duration may be: t _ Out-T _ In-sigma (Temp _ In-Temp _ Out), a corresponding logic block diagram to remove the temporary away period may be as shown In fig. 12.

In this embodiment, the corresponding out-of-area temporary stopping time is subtracted, so that the accuracy of determining the user stopping time can be further improved.

By the method provided by the embodiment of the invention, the stay time of each user in the target area within the target time can be determined. In order to improve the application value of the stay time, after the stay time of each user in the target area is obtained, the user records with the stay time being less than the preset time threshold can be removed, so that errors caused by drift of the passing users and the positioning points are further removed.

According to the embodiment of the invention, the stay time of each user in each target area can be accurately determined through the method.

In one possible embodiment, at least one of the opening and closing time data in S101, the opening time in S10103, the closing time in S10105, the out-of-area positioning data in S103, the initial and last entry and exit times in S105, the dwell time in S107 may be stored in the blockchain system. Referring To fig. 13, fig. 13 is an optional structural diagram of the blockchain system according To the embodiment of the present invention, a point-To-point (P2P, Peer To Peer) network is formed among a plurality of nodes, and a P2P Protocol is an application layer Protocol operating on a Transmission Control Protocol (TCP). In the blockchain system, any machine such as a server and a terminal can be added to become a node, and the node comprises a hardware layer, a middle layer, an operating system layer and an application layer.

Referring to the functions of each node in the blockchain system shown in fig. 13, the functions involved include:

1) routing, a basic function that a node has, is used to support communication between nodes.

Besides the routing function, the node may also have the following functions:

2) the application is used for being deployed in a block chain, realizing specific services according to actual service requirements, recording data related to the realization functions to form recording data, carrying a digital signature in the recording data to represent a source of task data, and sending the recording data to other nodes in the block chain system, so that the other nodes add the recording data to a temporary block when the source and integrity of the recording data are verified successfully.

3) And the Block chain comprises a series of blocks (blocks) which are mutually connected according to the generated chronological order, new blocks cannot be removed once being added into the Block chain, and recorded data submitted by nodes in the Block chain system are recorded in the blocks.

Referring to fig. 14, fig. 14 is an optional schematic diagram of a Block Structure (Block Structure) according to an embodiment of the present invention, where each Block includes a hash value of a transaction record stored in the Block (hash value of the Block) and a hash value of a previous Block, and the blocks are connected by the hash values to form a Block chain. The block may include information such as a time stamp at the time of block generation. A Blockchain (Blockchain), which is essentially a decentralized database, is a string of data blocks, each of which is associated using cryptography.

The data processing method provided by the embodiment of the invention has the following beneficial effects:

1) the method can automatically mine the opening and closing time of the target area in the target time period according to historical passenger flow data, and determine the stay time of the user in the target area in the target time period by combining the positioning data in the target area and the positioning data outside the target area. Because the method for determining the stay time of the user in the target area in the target time period does not need the continuous positioning data of the user (namely, the user does not need to trigger the positioning of the terminal periodically or frequently), the sparseness and the instability of the positioning data can be considered, the error of determining the stay time of the user with sparse positioning data is reduced, and the accuracy of determining the stay time of the user in the target area is improved.

2) According to the embodiment of the invention, the method for automatically excavating the opening time and closing time of each target area is adopted according to historical passenger flow data, the universality is better, the method can be suitable for various types of target areas, and the defect of poor universality caused by using the actual opening time and the actual closing time of the target area is avoided; furthermore, in the embodiment of the invention, the open time and the close time of each target area are automatically excavated according to the historical passenger flow data, so that the defect that the stay time of the user in the target area cannot be counted because the actual open time and the actual close time cannot be obtained can be avoided, and the accuracy of determining the stay time of the user is improved; in addition, according to the method and the device for automatically excavating the opening time and the closing time of each target area, disclosed by the embodiment of the invention, the defect of low accuracy rate of determining the stay time of the user due to adjustment of the actual opening time and the actual closing time can be effectively avoided, so that the accuracy of determining the stay time of the user is improved.

3) The users whose positioning points (determined by the positioning positions) in the target area are smaller than a preset positioning threshold value can be pre-filtered according to the positioning data of each user in the target area in the target time period so as to filter the users temporarily passing through the target area or the users whose positioning points drift, thereby reducing the burden of the system for calculating the stay time of the users and reducing the system resource overhead; moreover, the number of users needing to calculate the stay time is reduced, so that the efficiency of calculating the stay time of the users can be effectively improved; in addition, the users temporarily passing through the target area or the users with shifted positioning points are filtered, so that the interference of the positioning of the users temporarily passing through the target area or the positioning of the users with shifted positioning points on the determination of the stay time of the users in the target area can be reduced, the error of the determination of the stay time of the users is effectively reduced, and the accuracy of the determination of the stay time of the users is obviously improved.

4) And pre-filtering positioning points with positioning precision greater than a preset precision threshold according to the positioning data of each user in the target area in the target time period. For a certain user, because the positioning points with the positioning precision greater than the preset precision threshold are filtered, the rest positioning points are all the positioning points with higher positioning precision (namely the precision is less than or equal to the preset precision threshold), the positioning points with higher precision are used for determining the stay time of the user, and the accuracy rate of determining the stay time of the user can be obviously improved.

5) The positioning data used for calculating the T _ in and the T _ out in the embodiment of the invention is not continuous positioning data, namely in the embodiment of the invention, when the T _ in and the T _ out are calculated, the positioning data of the AOI by a user is not required to be continuous data, namely, the discontinuous, sparse and unstable positioning data generated by the user can be used, and the T _ in and the T _ out are calculated by various calculation strategies and combining the opening time and the closing time determined according to historical passenger flow data, so that the T _ in calculation error caused by the sparseness and instability of the positioning data points is reduced, the calculation accuracy of the T _ in and the T _ out is improved, and the accuracy of determining the stay time of the user in the AOI is further improved.

6) For a user outside the temporary departure AOI, Temp _ In and Temp _ Out of the user can be calculated to obtain a temporary departure time period, and the temporary departure time period is subtracted on the basis of T _ In and T _ ou, so that the accuracy rate of determining the stay time of the user can be further improved.

The following describes beneficial effects produced by the data processing method provided by the embodiment of the present invention with specific application data:

the data processing method provided by the embodiment of the invention can be used for scenes such as travel products, commercial plot analysis and the like which need to count the user stay time and the percentage of users with different stay time to the total amount of the users. For example, a store may count the past time, the percentage of the time spent in the flow, and the daily changes to the past time, to determine whether certain marketing campaigns significantly increase the length of the time spent in the store (generally, the longer the residence time, the more likely the consumer will be), thereby proving which marketing campaigns are effective, which can be continued, and which are less effective, and which need to be improved or cancelled.

Fig. 15 is a comparison graph of the results of determining the stay time of the users in the theme park by using the method provided by the embodiment of the present invention and the method in the prior art, wherein the abscissa is time scale (default to 1 hour per scale), for example, 3 represents the stay time of 2-3 hours, and the ordinate is the ratio of "the number of users staying at a certain time in the stay time" to "the number of all users staying in the target time period". As can be seen from fig. 15, the calculation results using the data processing method provided by the embodiment of the present invention are: the dwell time of most users in the theme park is 6-10 hours, which coincides with the actual situation of the theme park open time of 10:00-21:00, and the calculation results using the prior art method are: the residence time of most users is concentrated under 3 hours and over 12 hours, which is not consistent with the actual situation of the open time of 10:00-21: 00.

Fig. 16 is a comparison graph of the results of determining the stay time of users in a gym having 5-6 hours of activity in a target time period using the method provided by the embodiment of the present invention and the method of the prior art, wherein the abscissa is time-graded (default to 1 hour per grade), e.g. 3 represents the stay time of 2-3 hours, and the ordinate is the ratio of the number of users staying at a certain time to the number of all users staying in the target time period. As can be seen from fig. 16, the calculation results using the data processing method provided by the embodiment of the present invention are: the majority of users stay in the gym for 5-6 hours, which coincides with the activity time of the gym, and the calculation using the prior art method is: most users have dwell times centered under 2 hours and over 10 hours, which is much different from the activity hours of the gym.

As shown in fig. 17, an embodiment of the present invention further provides a data processing apparatus, which may include at least:

the open-close time data determining module 201 may be configured to determine open-close time data of the target area in the target time period based on real-time passenger flow data of the target area in the target time period and target historical passenger flow data of the target area; the target historical passenger flow data is historical peak passenger flow data of the target area in a preset time before the target time period.

Specifically, the open/close time data includes an open time and a close time, and the open/close time data determining module 201 may include:

the target real-time passenger flow data acquisition unit can be used for acquiring target real-time passenger flow data of which the passenger flow data is greater than a preset passenger flow threshold value, wherein the preset passenger flow threshold value is a preset percentage of target historical passenger flow data.

The open time determining unit may be configured to use a start time of a time period corresponding to the target real-time passenger flow data as the open time.

The closing time determining unit may be configured to use a last time of a time period corresponding to the target real-time passenger flow data as the closing time.

The positioning data acquiring module 203 may be configured to acquire positioning time data of the user in the target area and positioning data outside the target area in the target time period.

The entering and leaving time determining module 205 may be configured to determine an initial entering time when the user enters the target area and a last leaving time when the user leaves the current area within the target time period, respectively, based on the positioning time data and at least one of the opening and closing time data and the outside-area positioning data.

In a possible embodiment, the positioning time data includes a first positioning time when the user first locates within the target area, and the out-of-area positioning data includes a first out-of-area positioning data, the first out-of-area positioning data includes a first out-of-area positioning time and a first out-of-area positioning position, and when the first out-of-area positioning time is adjacent to the first positioning time, the entry-exit-time determining module 205 may be configured to: based on the out-of-area positioning data and the positioning time data, an initial entry time for the user to enter the target area within the target time period is determined.

Accordingly, the entry-departure-time determination module 205 may include:

the first estimated elapsed time calculation unit may be configured to calculate a first estimated elapsed time from the location outside the first area to the target area.

The first time difference value calculating unit may be configured to calculate a first time difference value between the first time location and the time of location outside the first area.

The first initial entry time determining unit may be configured to use the first positioning time as the initial entry time when the first expected elapsed time is greater than the first time difference.

The second initial entry time determining unit may be configured to use a sum of the first out-of-region localization time and the first expected elapsed time as the initial entry time when the first expected elapsed time is less than or equal to the first time difference value.

In one possible embodiment, when the out-of-area location data does not include the first out-of-area location data, then the entry-departure-time determination module 205 may be configured to: and determining the initial entering time of the user entering the target area in the target time period based on the opening and closing time data and the positioning time data.

Accordingly, the entry-departure-time determination module 205 may include:

the third initial entry time determining unit may be configured to use an average value of the first positioning time and the opening time as the initial entry time when the first positioning time is later than the opening time.

The fourth initial entry time determining unit may be configured to take the first positioning time as the initial entry time when the first positioning time is earlier than or equal to the open time.

In a possible embodiment, the positioning time data includes a last positioning time of a last occurrence of positioning of the user in the target area, the positioning data outside the target area includes second out-of-area positioning data, the second out-of-area positioning data includes a second out-of-area positioning time and a second out-of-area positioning position, and when the second out-of-area positioning time is adjacent to the last positioning time, the entering leaving time determining module 205 may be configured to: and positioning data and positioning time data outside the area, and determining the last time when the user leaves the current area in the target time period.

Accordingly, the entry-departure-time determination module 205 may include:

the second estimated elapsed time calculation unit may be configured to calculate a second estimated elapsed time of the target area to the position outside the second area.

The second time difference calculation unit may be configured to calculate a second time difference between the time of the second out-of-area positioning and the time of the last positioning.

The first last time-of-departure determination unit may be configured to take the last positioning time as the last time-of-departure when the second expected elapsed time is greater than the second time difference.

The second last time-of-departure determining unit may be configured to determine, as the last time-of-departure, a difference between the outside-second-region localization time and the second expected elapsed time when the second expected elapsed time is less than or equal to the second time difference.

In another possible embodiment, when the out-of-region location data does not include the second out-of-region location data, then the entry-departure-time determination module 205 may be configured to: and determining the last leaving time of the user leaving the current area in the target time period based on the opening and closing time data and the positioning time data.

Accordingly, the entry-departure-time determination module 205 may include:

the third last departure time determining unit may be configured to take an average of the last location time and the closing time as the last departure time when the last location time is earlier than the closing time.

A fourth last departure time determination unit that may be configured to take the last location time as the last departure time when the last location time is later than or equal to the closing time.

In one possible embodiment, the apparatus may further include: a temporary exit and entry time determination module, which may include:

the third area external positioning data acquisition unit can be used for acquiring the third area external positioning data of the user outside the target area between the initial entering time and the last leaving time, and the third area external positioning data comprises a plurality of third area external positioning positions and a plurality of third area external positioning times.

The external positioning data sequence determining unit can be used for sequencing the external positioning positions of the third areas according to corresponding positioning time when the number of the external positioning positions of the third areas is greater than a preset positioning threshold and the total duration of the external positioning time of the third areas is greater than a preset duration threshold, so as to obtain the external positioning data sequence.

The first regional positioning data are adjacent to the first ranked out-of-region positioning data in the out-of-region positioning data sequence, and the last ranked out-of-region positioning data in the second regional positioning data sequence are adjacent to each other.

The temporary leaving time determining unit may be configured to determine a temporary leaving time at which the user temporarily leaves the target area based on the first-ranked out-of-area positioning data and the first-area positioning data.

The temporary entry time determining unit may be configured to determine a temporary entry time for the user to temporarily enter the target area based on the sorted last out-of-area positioning data and the second out-of-area positioning data.

The stay duration determination module 207 may be configured to determine a stay duration of the user in the target area within the target time period based on the initial entry time and the last exit time.

Specifically, the stay time period determination module 207 may include:

the third time difference calculation unit may be configured to calculate a third time difference between the last departure time and the initial arrival time.

The fourth time difference calculation unit may be configured to calculate a fourth time difference between the temporary entry time and the temporary exit time.

The staying length determination unit may be configured to determine a difference between the third time difference and the fourth time difference as the staying length.

It should be noted that the embodiments of the present invention provide embodiments of apparatuses based on the same inventive concept as the embodiments of the method described above.

The embodiment of the present invention further provides an electronic device for data processing, where the electronic device includes a processor and a memory, where the memory stores at least one instruction or at least one program, and the at least one instruction or the at least one program is loaded and executed by the processor to implement the data processing method provided in the foregoing method embodiment.

Embodiments of the present invention also provide a computer-readable storage medium, which may be disposed in a terminal to store at least one instruction or at least one program for implementing a data processing method in the method embodiments, where the at least one instruction or the at least one program is loaded and executed by a processor to implement the data processing method provided in the method embodiments.

Alternatively, in the present specification embodiment, the storage medium may be located at least one network server among a plurality of network servers of a computer network. Optionally, in this embodiment, the storage medium may include, but is not limited to: various media capable of storing program codes, such as a usb disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic disk, or an optical disk.

The memory of the embodiments of the present disclosure may be used to store software programs and modules, and the processor may execute various functional applications and data processing by operating the software programs and modules stored in the memory. The memory can mainly comprise a program storage area and a data storage area, wherein the program storage area can store an operating system, application programs needed by functions and the like; the storage data area may store data created according to use of the device, and the like. Further, the memory may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid state storage device. Accordingly, the memory may also include a memory controller to provide the processor access to the memory.

Embodiments of the present invention also provide a computer program product or computer program comprising computer instructions stored in a computer readable storage medium. The processor of the computer device reads the computer instructions from the computer readable storage medium, and the processor executes the computer instructions to enable the computer device to execute the data processing method provided by the method embodiment.

The data processing method provided by the embodiment of the invention can be executed in a terminal, a computer terminal, a server or a similar arithmetic device. Taking the example of the operation on a server, fig. 18 is a hardware structure block diagram of the server of the data processing method according to the embodiment of the present invention. As shown in fig. 18, the server 300 may have a relatively large difference due to different configurations or performances, and may include one or more Central Processing Units (CPUs) 310 (the processors 310 may include but are not limited to a Processing device such as a microprocessor MCU or a programmable logic device FPGA), a memory 330 for storing data, and one or more storage media 320 (e.g., one or more mass storage devices) for storing applications 323 or data 322. Memory 330 and storage medium 320 may be, among other things, transient or persistent storage. The program stored in the storage medium 320 may include one or more modules, each of which may include a series of instruction operations for the server. Still further, the central processor 310 may be configured to communicate with the storage medium 320 to execute a series of instruction operations in the storage medium 320 on the server 300. The server 300 may also include one or more powersA source 360, one or more wired or wireless network interfaces 350, one or more input-output interfaces 340, and/or one or more operating systems 321, such as a Windows Server^TM，Mac OS X^TM，Unix^TM，Linux^TM，FreeBSD^TMAnd so on.

The input output interface 340 may be used to receive or transmit data via a network. Specific examples of the network described above may include a wireless network provided by a communication provider of the server 300. In one example, the input/output Interface 340 includes a Network adapter (NIC) that can be connected to other Network devices through a base station to communicate with the internet. In one example, the input/output interface 340 may be a Radio Frequency (RF) module, which is used for communicating with the internet in a wireless manner.

It will be understood by those skilled in the art that the structure shown in fig. 18 is merely an illustration and is not intended to limit the structure of the electronic device. For example, the server 300 may also include more or fewer components than shown in FIG. 18, or have a different configuration than shown in FIG. 18.

It should be noted that: the precedence order of the above embodiments of the present invention is only for description, and does not represent the merits of the embodiments. And specific embodiments thereof have been described above. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.

The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, as for the device and server embodiments, since they are substantially similar to the method embodiments, the description is simple, and the relevant points can be referred to the partial description of the method embodiments.

It will be understood by those skilled in the art that all or part of the steps of implementing the above embodiments may be implemented by hardware, or may be implemented by a program instructing relevant hardware, and the program may be stored in a computer-readable storage medium, and the above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disk, etc.

The present invention is not limited to the above preferred embodiments, and any modifications, equivalent replacements, improvements, etc. within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method of data processing, the method comprising:

2. The method of claim 1, wherein the open/close time data comprises open time and close time, and the determining the open/close time data of the target area in the target time period based on real-time traffic data of the target area in the target time period and target historical traffic data of the target area comprises:

acquiring target real-time passenger flow data of which the passenger flow data are greater than a preset passenger flow threshold value, wherein the preset passenger flow threshold value is a preset percentage of the target historical passenger flow data;

taking the starting time of the time period corresponding to the target real-time passenger flow data as the opening time;

and taking the last time of the time period corresponding to the target real-time passenger flow data as the closing time.

3. The method according to claim 2, wherein the positioning time data includes a first positioning time when the user first appears to be positioned in the target area, and the positioning data outside the target area includes a first out-of-area positioning data, the first out-of-area positioning data includes a first out-of-area positioning time and a first out-of-area positioning position, and when the first out-of-area positioning time is adjacent to the first positioning time, the determining an initial entering time when the user enters the target area and a last leaving time when the user leaves the current area in the target time period based on the on-off time data and the out-of-area positioning data, and the positioning time data respectively includes: determining an initial entry time for the user to enter the target area within the target time period based on the out-of-area positioning data and the positioning time data:

specifically, the determining the initial entry time of the user into the target area in the target time period based on the data about the location outside the target area and the data about the location time includes:

calculating a first expected elapsed time from the first out-of-region localized position to the target region;

calculating a first time difference value between the first time location time and the first area outside location time;

when the first expected consumed time is greater than the first time difference value, taking the first positioning time as the initial entering time;

and when the first expected consumed time is less than or equal to the first time difference value, taking the sum of the first out-of-region positioning time and the first expected consumed time as the initial entry time.

4. The method according to claim 3, wherein when the out-of-area positioning data does not include first out-of-area positioning data, the determining an initial entering time of the user into the target area and a last leaving time of the user leaving the current area within the target time period based on the on-off time data and the out-of-area positioning data, and the positioning time data respectively comprises: determining initial entering time of the user entering the target area in the target time period based on the opening and closing time data and the positioning time data;

specifically, the determining the initial entering time of the user into the target area in the target time period based on the opening and closing time data and the positioning time data includes:

when the first positioning time is later than the opening time, taking the average value of the first positioning time and the opening time as the initial entering time;

when the first positioning time is earlier than or equal to the open time, taking the first positioning time as the initial entry time.

5. The method according to claim 2, wherein the positioning time data includes a last positioning time when the user last appears to be positioned in the target area, the positioning data outside the area includes second out-of-area positioning data, the second out-of-area positioning data includes second out-of-area positioning time and a second out-of-area positioning position, and when the second out-of-area positioning time is adjacent to the last positioning time, the determining, based on the opening/closing time data and the out-of-area positioning data, an initial entering time when the user enters the target area and a last leaving time when the user leaves the current area within the target time period respectively includes: determining the last time the user leaves the current area within the target time period based on the out-of-area positioning data and the positioning time data;

specifically, the determining the last time when the user leaves the current area in the target time period based on the location data outside the area and the location time data includes:

calculating a second expected elapsed time from the target region to a location outside the second region;

calculating a second time difference between the second out-of-area positioning time and the last positioning time;

when the second expected elapsed time is greater than the second time difference, taking the last positioning time as the last leaving time;

and when the second expected consumed time is less than or equal to the second time difference, taking the difference between the second outside-area positioning time and the second expected consumed time as the last leaving time.

6. The method according to claim 5, wherein when the out-of-area positioning data does not include a second out-of-area positioning data, the determining an initial entering time of the user into the target area and a last leaving time of the user leaving the current area within the target time period based on the on-off time data and the out-of-area positioning data, and the positioning time data respectively comprises: determining the last leaving time of the user leaving the current area in the target time period based on the opening and closing time data and the positioning time data;

specifically, the determining the last time when the user leaves the current area in the target time period based on the opening/closing time data and the positioning time data includes:

when the last positioning time is earlier than the closing time, taking the average value of the last positioning time and the closing time as the last leaving time;

when the last positioning time is later than or equal to the closing time, taking the last positioning time as the last leaving time.

7. The method of claim 1, further comprising:

acquiring third area external positioning data of the user outside the target area between the initial entering time and the last leaving time, wherein the third area external positioning data comprises a plurality of third area external positioning positions and a plurality of third area external positioning times;

when the number of the plurality of third area external positioning positions is larger than a preset positioning threshold value and the total duration of the plurality of third area external positioning time is larger than a preset duration threshold value, sequentially sequencing the plurality of third area external positioning positions according to corresponding positioning time to obtain an external positioning data sequence;

acquiring first regional positioning data and second regional positioning data from positioning data of the user between the initial entering time and the last leaving time, wherein the first regional positioning data is adjacent to the first-ranked out-of-region positioning data in the sequence of the out-of-region positioning data, and the second regional positioning data is adjacent to the last-ranked out-of-region positioning data in the sequence of the out-of-region positioning data;

determining a temporary departure time for the user to temporarily depart from the target area based on the top-ranked out-of-area positioning data and the first in-area positioning data;

and determining a temporary entering time for the user to temporarily enter the target area based on the finally sorted out-of-area positioning data and the second out-of-area positioning data.

8. The method of claim 7, wherein determining a length of time a user remains within the target area for a target time period based on the initial time of entry and the last time of exit comprises:

calculating a third time difference between the last departure time and the initial entry time;

calculating a fourth time difference between the temporary entry time and the temporary exit time;

and taking the difference between the third time difference and the fourth time difference as the stay time length.

9. A data processing apparatus, characterized in that the apparatus comprises:

10. A computer-readable storage medium, in which at least one instruction or at least one program is stored, which is loaded and executed by a processor to implement the data processing method according to any one of claims 1 to 8.