Data processing method, data processing apparatus, medium and electronic device

Abstract

The application provides a data processing method, a data processing device, a computer readable storage medium and an electronic device, and relates to the technical field of computers. The method comprises the following steps: determining a first activity type vector corresponding to each delivery unit based on travel data; wherein each element in the first activity type vector is used to represent the probability that the corresponding delivery unit belongs to each activity type; obtaining a second activity type vector corresponding to a to-be-delivered object; wherein each element in the second activity type vector is used to represent the probability that the to-be-delivered object belongs to each activity type; and selecting a target delivery unit corresponding to the to-be-delivered object based on the similarity between the first activity type vector and the second activity type vector corresponding to each delivery unit. In this way, the target delivery unit can be selected for the to-be-delivered object according to the travel data and the probability that the to-be-delivered object belongs to each activity type, which is beneficial to improving the conversion rate of the to-be-delivered object.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and in particular to a data processing method, a data processing device, a computer-readable storage medium, and an electronic device. Background Art

[0002] During social events, placing multimedia content in specific locations can help improve its conversion rate. Typically, locations are chosen based on foot traffic. However, different multimedia content may target different audiences, and placing multimedia content in locations chosen based on foot traffic may result in low conversion rates.

[0003] It should be noted that the information disclosed in the above background technology section is only used to enhance the understanding of the background of this application, and therefore may include information that does not constitute an existing solution known to ordinary technicians in this field. Summary of the Invention

[0004] The purpose of this application is to provide a data processing method, a data processing device, a computer-readable storage medium and an electronic device, which can select target delivery units for the objects to be delivered in a targeted manner based on travel data and the probability that the objects to be delivered belong to each activity type, which is conducive to improving the conversion rate of the objects to be delivered.

[0005] Other features and advantages of the present application will become apparent from the following detailed description, or may be learned in part by practice of the present application.

[0006] According to one aspect of the present application, a data processing method is provided, the method comprising:

[0007] Determining a first activity type vector corresponding to each delivery unit based on the travel data; wherein each element in the first activity type vector is used to represent the probability that the corresponding delivery unit belongs to each activity type;

[0008] Obtaining a second activity type vector corresponding to the object to be delivered; wherein each element in the second activity type vector is used to represent the probability that the object to be delivered belongs to each activity type;

[0009] Based on the similarity between the first activity type vector and the second activity type vector corresponding to each delivery unit, a target delivery unit corresponding to the to-be-delivered object is selected.

[0010] In an exemplary embodiment of the present application, delivery units corresponding to the same location correspond to different time periods, and delivery units corresponding to the same time period correspond to different locations.

[0011] In an exemplary embodiment of the present application, before determining the first activity type vector corresponding to each delivery unit based on the travel data, the method further includes:

[0012] The travel data corresponding to each delivery unit is filtered according to the time period and location corresponding to each delivery unit.

[0013] In an exemplary embodiment of the present application, determining the first activity type vector corresponding to each delivery unit based on travel data includes:

[0014] Acquiring destination data corresponding to each travel data based on the activity range corresponding to each travel data;

[0015] The type of activity that generates corresponding travel data based on destination data;

[0016] Establishing the association relationship between the activity type and the corresponding travel data to obtain an association relationship set;

[0017] A first activity type vector corresponding to each delivery unit is generated based on the association relationship set.

[0018] In an exemplary embodiment of the present application, obtaining destination data corresponding to each travel data based on the activity range corresponding to each travel data includes:

[0019] Based on the activity range corresponding to each travel data, at least one of satellite positioning data, platform check-in data, and base station cell data within each activity range is obtained as destination data.

[0020] In an exemplary embodiment of the present application, generating a first activity type vector corresponding to each delivery unit based on the association relationship set includes:

[0021] Determine the amount of travel data belonging to each activity type of the corresponding delivery unit based on the association relationship set;

[0022] Based on the ratio between the multiple travel data volumes corresponding to each delivery unit and the total travel data volume corresponding to each delivery unit, a first activity type vector corresponding to each delivery unit is generated.

[0023] In an exemplary embodiment of the present application, obtaining a second activity type vector corresponding to the object to be delivered includes:

[0024] Obtain keyword information of the target object;

[0025] A second activity type vector corresponding to the object to be delivered is generated according to the keyword information.

[0026] In an exemplary embodiment of the present application, selecting a target delivery unit corresponding to a to-be-delivered object based on the similarity between the first activity type vector and the second activity type vector corresponding to each delivery unit includes:

[0027] Determine the similarity between the first activity type vector and the second activity type vector corresponding to each delivery unit, and obtain a similarity set corresponding to each delivery unit;

[0028] The similarity sets corresponding to each delivery unit are fused within the set to obtain the target similarity corresponding to each delivery unit;

[0029] The target delivery unit corresponding to the object to be delivered is selected according to the target similarity.

[0030] In an exemplary embodiment of the present application, selecting a target delivery unit corresponding to the target to be delivered according to target similarity includes:

[0031] The target similarity is input into the pre-built delivery location selection model to determine the target delivery unit corresponding to the object to be delivered through the delivery location selection model.

[0032] In an exemplary embodiment of the present application, after selecting a target delivery unit corresponding to the delivery object based on the similarity between the first activity type vector and the second activity type vector corresponding to each delivery unit, the method further includes:

[0033] Display the objects to be delivered at the specific location and device corresponding to the target delivery unit and within a specific time period.

[0034] According to one aspect of the present application, there is provided an object delivery device, comprising:

[0035] A first data determination unit is configured to determine a first activity type vector corresponding to each delivery unit based on the travel data; wherein each element in the first activity type vector is used to represent the probability that the corresponding delivery unit belongs to each activity type;

[0036] A second data determination unit is configured to obtain a second activity type vector corresponding to the target to be delivered; wherein each element in the second activity type vector is used to represent the probability that the target to be delivered belongs to each activity type;

[0037] The delivery unit selection unit is configured to select a target delivery unit corresponding to the object to be delivered based on the similarity between the first activity type vector and the second activity type vector corresponding to each delivery unit.

[0038] In an exemplary embodiment of the present application, delivery units corresponding to the same location correspond to different time periods, and delivery units corresponding to the same time period correspond to different locations.

[0039] In an exemplary embodiment of the present application, the apparatus further includes:

[0040] The data acquisition unit is configured to filter the travel data corresponding to each delivery unit according to the time period and location corresponding to each delivery unit before the first data determination unit determines the first activity type vector corresponding to each delivery unit based on the travel data.

[0041] In an exemplary embodiment of the present application, the first data determining unit determines the first activity type vector corresponding to each delivery unit based on the travel data, including:

[0042] Acquiring destination data corresponding to each travel data based on the activity range corresponding to each travel data;

[0043] The type of activity that generates corresponding travel data based on destination data;

[0044] Establishing the association relationship between the activity type and the corresponding travel data to obtain an association relationship set;

[0045] A first activity type vector corresponding to each delivery unit is generated based on the association relationship set.

[0046] In an exemplary embodiment of the present application, the first data determining unit acquires the destination data corresponding to each travel data based on the activity range corresponding to each travel data, including:

[0047] Based on the activity range corresponding to each travel data, at least one of satellite positioning data, platform check-in data, and base station cell data within each activity range is obtained as destination data.

[0048] In an exemplary embodiment of the present application, the first data determination unit generates a first activity type vector corresponding to each delivery unit based on the association relationship set, including:

[0049] Determine the amount of travel data belonging to each activity type of the corresponding delivery unit based on the association relationship set;

[0050] Based on the ratio between the multiple travel data volumes corresponding to each delivery unit and the total travel data volume corresponding to each delivery unit, a first activity type vector corresponding to each delivery unit is generated.

[0051] In an exemplary embodiment of the present application, the second data determination unit obtains the second activity type vector corresponding to the to-be-delivered object, including:

[0052] Obtain keyword information of the target object;

[0053] A second activity type vector corresponding to the object to be delivered is generated according to the keyword information.

[0054] In an exemplary embodiment of the present application, the delivery unit selection unit selects a target delivery unit corresponding to the delivery object based on the similarity between the first activity type vector and the second activity type vector corresponding to each delivery unit, including:

[0055] Determine the similarity between the first activity type vector and the second activity type vector corresponding to each delivery unit, and obtain a similarity set corresponding to each delivery unit;

[0056] The similarity sets corresponding to each delivery unit are fused within the set to obtain the target similarity corresponding to each delivery unit;

[0057] The target delivery unit corresponding to the object to be delivered is selected according to the target similarity.

[0058] In an exemplary embodiment of the present application, the delivery unit selection unit selects a target delivery unit corresponding to the target delivery object according to the target similarity, including:

[0059] The target similarity is input into the pre-built delivery location selection model to determine the target delivery unit corresponding to the object to be delivered through the delivery location selection model.

[0060] In an exemplary embodiment of the present application, the apparatus further includes:

[0061] The object delivery unit is configured to display the object to be delivered at a specific location device and within a specific time period corresponding to the target delivery unit after the delivery unit selection unit selects a target delivery unit corresponding to the object to be delivered based on the similarity between the first activity type vector and the second activity type vector corresponding to each delivery unit.

[0062] According to one aspect of the present application, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, any one of the above methods is implemented.

[0063] According to one aspect of the present application, an electronic device is provided, comprising: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to perform any one of the above methods by executing the executable instructions.

[0064] The exemplary embodiments of the present application may have some or all of the following beneficial effects:

[0065] In the data processing method provided in an example embodiment of the present application, the first activity type vector corresponding to each delivery unit can be determined based on travel data; wherein, each element in the first activity type vector is used to characterize the probability that the corresponding delivery unit belongs to each activity type; the second activity type vector corresponding to the object to be delivered is obtained; wherein, each element in the second activity type vector is used to characterize the probability that the object to be delivered belongs to each activity type; based on the similarity between the first activity type vector and the second activity type vector corresponding to each delivery unit, the target delivery unit corresponding to the object to be delivered is selected. In this way, the target delivery unit can be selected for the object to be delivered in a targeted manner based on the travel data and the probability that the object to be delivered belongs to each activity type, which is conducive to improving the conversion rate of the object to be delivered. In addition, the targeted selection of the target delivery unit for the object to be delivered can avoid invalid delivery and waste of resources.

[0066] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] The accompanying drawings are incorporated into and constitute a part of the specification, illustrate embodiments consistent with the present application, and together with the specification, are used to explain the principles of the present application. Obviously, the drawings described below are only some embodiments of the present application, and those skilled in the art can derive other drawings based on these drawings without inventive effort.

[0068] Figure 1 A schematic diagram showing an exemplary system architecture of a data processing method and a data processing device to which embodiments of the present application can be applied;

[0069] Figure 2 The following schematically shows a flow chart of a data processing method according to an embodiment of the present application;

[0070] Figure 3 The following schematically shows a flow chart of a data processing method according to another embodiment of the present application;

[0071] Figure 4 Schematically shows a schematic diagram of delivery results according to an embodiment of the present application;

[0072] Figure 5 The following schematically shows a structural block diagram of a data processing device according to an embodiment of the present application;

[0073] Figure 6 A schematic diagram of the structure of a computer system suitable for implementing the electronic device of the embodiment of the present application is shown. DETAILED DESCRIPTION

[0074] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in various forms and should not be construed as limited to the examples set forth herein; on the contrary, these embodiments are provided so that this application will be more comprehensive and complete and the concepts of the example embodiments will be fully conveyed to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, many specific details are provided to provide a full understanding of the embodiments of the present application. However, those skilled in the art will appreciate that the technical solutions of the present application may be practiced while omitting one or more of the specific details, or that other methods, components, devices, steps, etc. may be employed. In other cases, well-known technical solutions are not shown or described in detail to avoid obscuring various aspects of the present application.

[0075] In addition, the accompanying drawings are merely schematic illustrations of the present application and are not necessarily drawn to scale. Identical reference numerals in the figures denote identical or similar parts, and thus repetitive descriptions thereof will be omitted. Some of the blocks shown in the accompanying drawings are functional entities that do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0076] See also Figure 1 , Figure 1 The following is a schematic diagram showing a system architecture of an exemplary application environment in which a data processing method and a data processing device according to an embodiment of the present application can be applied. Figure 1 As shown, the system architecture 100 may include one or more terminal devices 101, 102, 103, a network 104, and a server 105. The network 104 is used to provide a medium for communication links between the terminal devices 101, 102, 103 and the server 105.

[0077] The network 104 may include various connection types, such as wired, wireless communication links or fiber optic cables, etc. The terminal devices 101, 102, and 103 may be devices that provide voice and / or data connectivity to users, handheld devices with wireless connection capabilities, or other processing devices connected to wireless modems. The wireless terminal can communicate with one or more core networks via the RAN. The wireless terminal may be a user equipment (UE), a handheld terminal, a laptop, a subscriber unit, a cellular phone, a smart phone, a wireless data card, a personal digital assistant (PDA) computer, a tablet computer, a wireless modem, a handheld device, a laptop computer, a cordless phone or a wireless local loop (WLL) station, a machine type communication (MTC) terminal, or other device that can access the network. The terminal and the access network device communicate with each other using a certain air interface technology (for example, 3GPP access technology or non-3GPP access technology). It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is merely illustrative. Any number of terminal devices, networks and servers may be provided as needed. For example, the server 105 may be a server cluster consisting of multiple servers.

[0078] The data processing method provided in the embodiment of the present application can be executed by the server 105, and accordingly, the data processing device is generally provided in the server 105. However, it is easy for those skilled in the art to understand that the data processing method provided in the embodiment of the present application can also be executed by the terminal device 101, 102 or 103, and accordingly, the data processing device can also be provided in the terminal device 101, 102 or 103, and this is not particularly limited in this exemplary embodiment. For example, in an exemplary embodiment, the server 105 can determine the first activity type vector corresponding to each delivery unit based on travel data; wherein each element in the first activity type vector is used to characterize the probability that the corresponding delivery unit belongs to each activity type; obtain the second activity type vector corresponding to the object to be delivered; wherein each element in the second activity type vector is used to characterize the probability that the object to be delivered belongs to each activity type; based on the similarity between the first activity type vector and the second activity type vector corresponding to each delivery unit, select the target delivery unit corresponding to the object to be delivered.

[0079] See also Figure 2 , Figure 2 The flowchart of the data processing method according to one embodiment of the present application is schematically shown. Figure 2 As shown, the data processing method may include: steps S210 to S230.

[0080] Step S210: Determine a first activity type vector corresponding to each delivery unit based on the travel data; wherein each element in the first activity type vector is used to represent the probability that the corresponding delivery unit belongs to each activity type.

[0081] Step S220: Obtain a second activity type vector corresponding to the object to be delivered; wherein each element in the second activity type vector is used to represent the probability that the object to be delivered belongs to each activity type.

[0082] Step S230: Based on the similarity between the first activity type vector and the second activity type vector corresponding to each delivery unit, a target delivery unit corresponding to the object to be delivered is selected.

[0083] Implementation Figure 2 The method shown can specifically select target delivery units for the target audience based on travel data and the probability that the target audience belongs to each activity type, which is conducive to improving the conversion rate of the target audience. In addition, the targeted selection of target delivery units for the target audience can avoid ineffective delivery and avoid wasting resources.

[0084] The above steps of this exemplary embodiment are described in more detail below.

[0085] In step S210, a first activity type vector corresponding to each delivery unit is determined based on the travel data; wherein each element in the first activity type vector is used to represent the probability that the corresponding delivery unit belongs to each activity type.

[0086] Specifically, travel data may include: transportation card swiping data, transportation ticket booking data, taxi code scanning data, shared bicycle usage data, etc., which is not limited in the embodiments of this application.

[0087] In addition, this application does not limit the number of activity types. For example, each activity type can be: home, consumption, travel, work, education, entertainment, and others. The first activity type vector can be expressed as Among them. η1,η2,…,η Z These are the elements in the first activity type vector, η1, η2,…, η Z They are used to represent the probability that the corresponding delivery unit (e.g., delivery unit A1 corresponding to the location Renmin Road West Station and the time period 09:00-13:00) belongs to each activity type, and Z is used to represent the number of activity types. For example, the first activity type vector can be expressed as

[0088] As an optional embodiment, the delivery units corresponding to the same location correspond to different time periods, and the delivery units corresponding to the same time period correspond to different locations. This can improve the precision of delivery, reduce invalid delivery, and achieve accurate delivery.

[0089] Specifically, the delivery unit is limited by at least a location (such as a bus stop, subway station, airport, etc.) and a time period (such as 8:00-9:00, 9:00-10:00, ...). Optionally, the delivery unit can also be limited by information such as altitude, longitude and latitude, which is not limited in this application.

[0090] For example, each delivery unit can be represented as the following table:

[0091] Delivery unit Place Time period A1 Renmin Road West Station 09：00～13：00 A2 Renmin Road West Station 13：00～20：00 A3 Renmin Road West Station 20：00～09：00 B1 Youyi Road East Station 09：00～13：00 B2 Youyi Road East Station 13：00～20：00 B3 Youyi Road East Station 20：00～09：00 …… …… ……

[0092] As an optional embodiment, before determining the first activity type vector corresponding to each delivery unit based on the travel data, the method further includes filtering the travel data corresponding to each delivery unit based on the time period and location corresponding to each delivery unit. This allows for accurate filtering of travel data, allowing for subsequent targeted delivery based on accurate travel data.

[0093] Specifically, the travel data corresponding to each delivery unit can be one or more, and this embodiment of the application is not limited thereto. For example, if the time period corresponding to delivery unit B1 is 09:00-13:00, and the location corresponding to delivery unit B1 is Youyi Road East Station, then the travel data related to 09:00-13:00 and Youyi Road East Station can be used as the travel data related to delivery unit B1.

[0094] As an optional embodiment, determining the first activity type vector corresponding to each delivery unit based on travel data includes: obtaining destination data corresponding to each travel data based on the activity range corresponding to each travel data; generating the activity type of the corresponding travel data based on the destination data; establishing an association relationship between the activity type and the corresponding travel data to obtain an association relationship set; and generating the first activity type vector corresponding to each delivery unit based on the association relationship set. This allows for the calculation of the first activity type vector representation without requiring user privacy data. While protecting user privacy, the association relationship between the activity type and the corresponding travel data, as well as the first activity type vector generated based on the association relationship set, can be obtained. This facilitates the selection of appropriate delivery units for the delivery targets based on the first activity type vector, thereby achieving precise delivery.

[0095] Specifically, the activity range corresponding to each travel data can be determined based on preset parameters (such as 500m). Before obtaining the destination data corresponding to each travel data based on the activity range corresponding to each travel data, the above method may also include: taking the starting point / end point corresponding to the travel data as the center of the circle and the preset parameters as the radius, and determining the circular range corresponding to each travel data as the activity range of the travel data. For example, the travel data can be expressed as: getting on the bus at Youyi Road East Station at 9:10 and getting off at Xizhimen Station at 9:30, then the 500m range with Youyi Road East Station and / or Xizhimen Station as the center of the circle can be used as the corresponding activity range. The user may enter the Municipal Library after getting off at Xizhimen Station, so the Municipal Library can be used as the destination of the travel data.

[0096] Generating the activity type of the corresponding travel data according to the destination data includes: determining the type (eg, consumption) to which the destination data (eg, XX shopping mall) belongs; and generating the type as the activity type of the corresponding travel data.

[0097] In addition, an association relationship is established between the activity type and the corresponding travel data to obtain an association relationship set, including: using the activity type as the key and the corresponding travel data as the value for corresponding storage (i.e., KV storage) to realize the construction of the association relationship between the activity type and the corresponding travel data. Based on this, an association relationship set can be obtained, and the association relationship set can include one or more association relationships, and different travel data can correspond to the same activity type.

[0098] As an optional embodiment, destination data corresponding to each travel data item is obtained based on the activity range corresponding to each travel data item, including obtaining at least one of satellite positioning data, platform check-in data, and base station cell data within each activity range as the destination data. This allows for accurate acquisition of destination data, allowing destination data to be obtained from one or more aspects, thereby increasing the probability of obtaining accurate destination data.

[0099] Specifically, satellite positioning data can be represented by coordinates, platform check-in data can be represented by location names, and base station cell data can be represented by cell identifiers.

[0100] The method includes obtaining at least one of satellite positioning data, platform check-in data, and base station cell data within each activity range as destination data based on the activity range corresponding to each travel data, and generating destination data based on the satellite positioning data, platform check-in data, and base station cell data. It should be noted that the platform check-in data may be location information included in a user's dynamic posting on a social platform, such as XX Community, XX Apartment, XX Shopping Mall, XX Supermarket, XX Convenience Store, XX Restaurant, XX Company, XX Publishing House, XX Railway Station, XX University, XX Technical College, XX Primary School, XX Middle School, XX Park, XX Museum, XX Gymnasium, XX Zoo, XX Church, and XX Nursing Home.

[0101] As an optional embodiment, generating a first activity type vector corresponding to each delivery unit based on the association relationship set includes: determining the amount of travel data belonging to each activity type for the corresponding delivery unit based on the association relationship set; and generating the first activity type vector corresponding to each delivery unit based on the ratio between the multiple travel data amounts corresponding to each delivery unit and the total travel data amount corresponding to each delivery unit. This can improve the representation accuracy of the first activity type vector and facilitate subsequent precise delivery.

[0102] Specifically, based on the ratio between multiple travel data volumes corresponding to each delivery unit and the total travel data volume corresponding to each delivery unit, a first activity type vector corresponding to each delivery unit is generated, including: generating a ratio between multiple travel data volumes corresponding to each delivery unit and the total travel data volume corresponding to each delivery unit, obtaining a ratio set corresponding to each delivery unit, and determining the ratio set as the first activity type vector corresponding to the delivery unit.

[0103] For example, the amount of travel data corresponding to the activity type "stay at home" in delivery unit B1 is 10, the amount of travel data corresponding to the activity type "consumption" is 20, the amount of travel data corresponding to the activity type "travel" is 50, the amount of travel data corresponding to the activity type "work" is 10, the amount of travel data corresponding to the activity type "education" is 30, the amount of travel data corresponding to the activity type "entertainment" is 10, and the amount of travel data corresponding to the activity type "other" is 10. Then, the amount of travel data corresponding to delivery unit B1 is Then, the first activity type vector corresponding to the delivery unit B1 can be calculated

[0104]

[0105] In step S220, a second activity type vector corresponding to the object to be delivered is obtained; wherein each element in the second activity type vector is used to represent the probability that the object to be delivered belongs to each activity type.

[0106] Specifically, the object to be delivered can be a multimedia resource, such as a picture resource, a video resource, etc., which is not limited in the embodiment of the present application. The second activity type vector can be expressed as in. These are the elements in the second activity type vector mentioned above, They are used to represent the probability that the target belongs to each activity type, and Z is used to represent the number of activity types. For example, the second activity type vector can be expressed as

[0107] As an optional embodiment, obtaining a second activity type vector corresponding to a target for delivery includes: obtaining keyword information for the target; and generating a second activity type vector corresponding to the target based on the keyword information. This can enhance the second activity type vector's ability to represent the target, facilitating subsequent targeted delivery based on the second activity type vector.

[0108] Specifically, the keyword information of the object to be delivered may include one or more keywords, and the keywords may be used to represent the subject, content, etc. of the object to be delivered, which is not limited in the embodiments of the present application.

[0109] In step S230 , based on the similarity between the first activity type vector and the second activity type vector corresponding to each delivery unit, a target delivery unit corresponding to the object to be delivered is selected.

[0110] Specifically, the similarity between the first activity type vector and the second activity type vector corresponding to each delivery unit can be expressed as

[0111] As an optional embodiment, based on the similarity between the first activity type vector and the second activity type vector corresponding to each delivery unit, selecting a target delivery unit corresponding to the target delivery object includes: determining the similarity between the first activity type vector and the second activity type vector corresponding to each delivery unit to obtain a similarity set corresponding to each delivery unit; performing an intra-set fusion on the similarity sets corresponding to each delivery unit to obtain a target similarity corresponding to each delivery unit; and selecting a target delivery unit corresponding to the target delivery object based on the target similarity. This enables delivery unit selection based on the first activity type vector and the second activity type vector, facilitates precise delivery to the target delivery object, and improves the conversion rate of the target delivery object.

[0112] Specifically, determining the similarity between the first activity type vector and the second activity type vector corresponding to each delivery unit to obtain the similarity set corresponding to each delivery unit includes: for each delivery unit, the following steps can be performed: Substituting the first activity type vector and the second activity type vector corresponding to the delivery unit into the expression To obtain the similarity set corresponding to the delivery unit.

[0113] In addition, the similarity sets corresponding to each delivery unit are fused within the set to obtain the target similarity corresponding to each delivery unit, including: based on the expression The similarity sets corresponding to each delivery unit are fused within the set to obtain the target similarity δ corresponding to each delivery unit kt .

[0114] As an optional embodiment, selecting a target delivery unit corresponding to the target to be delivered based on target similarity includes: inputting the target similarity into a pre-built delivery location selection model to determine the target delivery unit corresponding to the target to be delivered using the delivery location selection model. This allows for automatic selection of target delivery units based on the pre-built delivery location selection model, thereby improving the automation level of delivery unit selection.

[0115] Specifically, the algorithm corresponding to the site selection model can be expressed as: The constraints corresponding to the site selection model can be expressed as: Among them, k is used to represent the kth location, K is used to represent the total number of locations, t is used to represent the tth time period, T is used to represent the total number of time periods, p is used to represent the number of objects to be delivered, and x kt Used to represent the selection state of the delivery unit (e.g., unselected state, selected state, etc.), δ kt As the target similarity, it is used here to characterize the weight of k in time period t.

[0116] As an optional embodiment, after selecting a target delivery unit corresponding to the target delivery object based on the similarity between the first activity type vector and the second activity type vector corresponding to each delivery unit, the method further includes: displaying the target delivery object at a specific location device and within a specific time period corresponding to the target delivery unit. This can achieve precise delivery of the target delivery object and help improve the conversion rate of the target delivery object.

[0117] Specifically, the target delivery unit may correspond to one or more specific location devices, the specific location device may be a device with a display function, and the delivery unit corresponding to the specific location device may be one or more, which is not limited in the embodiment of the present application.

[0118] See also Figure 3 , Figure 3 The flowchart of the data processing method according to another embodiment of the present application is schematically shown. Figure 3 As shown, the data processing method may include: steps S310 to S390.

[0119] Step S310: filtering the travel data corresponding to each delivery unit according to the time period and location corresponding to each delivery unit.

[0120] Step S320: Based on the activity range corresponding to each travel data, at least one of satellite positioning data, platform check-in data, and base station cell data within each activity range is obtained as destination data.

[0121] Step S330: Generate activity types of corresponding travel data according to destination data, establish associations between activity types and corresponding travel data, obtain an association set, and determine the amount of travel data belonging to each activity type of the corresponding delivery unit based on the association set.

[0122] Step S340: Based on the ratio between the multiple travel data volumes corresponding to each delivery unit and the total travel data volume corresponding to each delivery unit, generate a first activity type vector corresponding to each delivery unit; wherein each element in the first activity type vector is used to represent the probability that the corresponding delivery unit belongs to each activity type.

[0123] Step S350: Obtain keyword information of the target to be delivered, and generate a second activity type vector corresponding to the target to be delivered based on the keyword information; wherein each element in the second activity type vector is used to represent the probability that the target to be delivered belongs to each activity type.

[0124] Step S360: Determine the similarity between the first activity type vector and the second activity type vector corresponding to each delivery unit, and obtain a similarity set corresponding to each delivery unit.

[0125] Step S370: performing intra-set fusion on the similarity sets corresponding to each delivery unit to obtain the target similarity corresponding to each delivery unit.

[0126] Step S380: input the target similarity into the pre-built delivery location selection model to determine the target delivery unit corresponding to the object to be delivered through the delivery location selection model.

[0127] Step S390: Display the object to be delivered at the specific location device and within the specific time period corresponding to the target delivery unit.

[0128] It should be noted that steps S310 to S390 are Figure 2 The steps shown correspond to their embodiments. For the specific implementation of steps S310 to S390, please refer to Figure 2The steps and embodiments shown are not described in detail here.

[0129] It can be seen that implementation Figure 3 The method shown can specifically select target delivery units for the target audience based on travel data and the probability that the target audience belongs to each activity type, which is conducive to improving the conversion rate of the target audience. In addition, the targeted selection of target delivery units for the target audience can avoid ineffective delivery and avoid wasting resources.

[0130] See also Figure 4 , Figure 4 The schematic diagram of the delivery result according to one embodiment of the present application is shown schematically. Figure 4 As shown, if educational advertisements are to be placed, according to Figure 3 The steps and embodiments shown can achieve accurate delivery to the target object, and the delivery result can be expressed as Figure 4 , including a delivery diagram 410 corresponding to the time period 08:00-12:00, a delivery diagram 420 corresponding to the time period 14:00-18:00, and a delivery diagram 430 corresponding to the time period 18:00-24:00.

[0131] See also Figure 5 , Figure 5 The data processing device 500 is schematically shown as a block diagram of the structure of a data processing device according to an embodiment of the present application. Figure 2 The method shown corresponds to Figure 5 As shown, the data processing device 500 includes:

[0132] A first data determination unit 501 is configured to determine a first activity type vector corresponding to each delivery unit based on travel data; wherein each element in the first activity type vector is used to represent the probability that the corresponding delivery unit belongs to each activity type;

[0133] The second data determination unit 502 is configured to obtain a second activity type vector corresponding to the target object; wherein each element in the second activity type vector is used to represent the probability that the target object belongs to each activity type;

[0134] The delivery unit selection unit 503 is configured to select a target delivery unit corresponding to the object to be delivered based on the similarity between the first activity type vector and the second activity type vector corresponding to each delivery unit.

[0135] It can be seen that implementation Figure 5The device shown can select target delivery units for the target recipients based on travel data and the probability that the target recipients belong to each activity type, which is conducive to improving the conversion rate of the target recipients. In addition, the targeted selection of target delivery units for the target recipients can avoid ineffective delivery and avoid wasting resources.

[0136] In an exemplary embodiment of the present application, delivery units corresponding to the same location correspond to different time periods, and delivery units corresponding to the same time period correspond to different locations.

[0137] It can be seen that the implementation of this optional embodiment can improve the precision of delivery, reduce invalid delivery, and achieve accurate delivery.

[0138] In an exemplary embodiment of the present application, the apparatus further includes:

[0139] The data acquisition unit is configured to filter the travel data corresponding to each delivery unit according to the time period and location corresponding to each delivery unit before the first data determination unit 501 determines the first activity type vector corresponding to each delivery unit based on the travel data.

[0140] It can be seen that the implementation of this optional embodiment can achieve accurate screening of travel data and can subsequently achieve accurate delivery based on accurate travel data.

[0141] In an exemplary embodiment of the present application, the first data determining unit 501 determines the first activity type vector corresponding to each delivery unit based on the travel data, including:

[0142] Acquiring destination data corresponding to each travel data based on the activity range corresponding to each travel data;

[0143] The type of activity that generates corresponding travel data based on destination data;

[0144] Establishing the association relationship between the activity type and the corresponding travel data to obtain an association relationship set;

[0145] A first activity type vector corresponding to each delivery unit is generated based on the association relationship set.

[0146] It can be seen that the implementation of this optional embodiment can realize the calculation of the first activity type vector representation without the need for user privacy data. Under the premise of protecting user privacy, the association relationship between the activity type and the corresponding travel data and the first activity type vector generated based on the association relationship set can be obtained, which is conducive to selecting suitable delivery units for the objects to be delivered according to the first activity type vector, thereby achieving precise delivery.

[0147] In an exemplary embodiment of the present application, the first data determining unit 501 obtains the destination data corresponding to each travel data based on the activity range corresponding to each travel data, including:

[0148] Based on the activity range corresponding to each travel data, at least one of satellite positioning data, platform check-in data, and base station cell data within each activity range is obtained as destination data.

[0149] It can be seen that by implementing this optional embodiment, accurate acquisition of destination data can be achieved, and destination data can be obtained from one or more aspects, thereby increasing the probability of acquiring accurate destination data.

[0150] In an exemplary embodiment of the present application, the first data determination unit 501 generates a first activity type vector corresponding to each delivery unit based on the association relationship set, including:

[0151] Determine the amount of travel data belonging to each activity type of the corresponding delivery unit based on the association relationship set;

[0152] Based on the ratio between the multiple travel data volumes corresponding to each delivery unit and the total travel data volume corresponding to each delivery unit, a first activity type vector corresponding to each delivery unit is generated.

[0153] It can be seen that implementing this optional embodiment can improve the representation accuracy of the first activity type vector, which helps to achieve subsequent precise delivery.

[0154] In an exemplary embodiment of the present application, the second data determining unit 502 obtains the second activity type vector corresponding to the to-be-delivered object, including:

[0155] Obtain keyword information of the target object;

[0156] A second activity type vector corresponding to the object to be delivered is generated according to the keyword information.

[0157] It can be seen that implementing this optional embodiment can enhance the characterization capability of the second activity type vector for the target object, and facilitate subsequent precise delivery based on the second activity type vector.

[0158] In an exemplary embodiment of the present application, the delivery unit selection unit 503 selects a target delivery unit corresponding to the delivery object based on the similarity between the first activity type vector and the second activity type vector corresponding to each delivery unit, including:

[0159] Determine the similarity between the first activity type vector and the second activity type vector corresponding to each delivery unit, and obtain a similarity set corresponding to each delivery unit;

[0160] The similarity sets corresponding to each delivery unit are fused within the set to obtain the target similarity corresponding to each delivery unit;

[0161] The target delivery unit corresponding to the object to be delivered is selected according to the target similarity.

[0162] It can be seen that the implementation of this optional embodiment can realize the selection of delivery units based on the first activity type vector and the second activity type vector, which helps to achieve accurate delivery to the target object and improve the conversion rate of the target object.

[0163] In an exemplary embodiment of the present application, the delivery unit selection unit 503 selects a target delivery unit corresponding to the target delivery object according to the target similarity, including:

[0164] The target similarity is input into the pre-built delivery location selection model to determine the target delivery unit corresponding to the object to be delivered through the delivery location selection model.

[0165] It can be seen that by implementing this optional embodiment, the target delivery unit can be automatically selected based on the pre-built delivery site selection model, thereby improving the degree of automation of delivery unit selection.

[0166] In an exemplary embodiment of the present application, the apparatus further includes:

[0167] The object delivery unit is used to display the object to be delivered at a specific location device and within a specific time period corresponding to the target delivery unit after the delivery unit selection unit 503 selects the target delivery unit corresponding to the object to be delivered based on the similarity between the first activity type vector and the second activity type vector corresponding to each delivery unit.

[0168] It can be seen that the implementation of this optional embodiment can achieve accurate delivery to the target objects, which helps to improve the conversion rate of the target objects.

[0169] It should be noted that, although several modules or units of the device for action execution are mentioned in the above detailed description, this division is not mandatory. In fact, according to the embodiment of the application, the features and functions of two or more modules or units described above can be concretized in one module or unit. On the contrary, the features and functions of one module or unit described above can be further divided into multiple modules or units to be concretized.

[0170] Since the various functional modules of the data processing device of the example embodiment of the present application correspond to the steps of the example embodiment of the above-mentioned data processing method, for details not disclosed in the embodiment of the device of the present application, please refer to the embodiment of the above-mentioned data processing method of the present application.

[0171] See also Figure 6 , Figure 6 A schematic diagram of the structure of a computer system suitable for implementing an electronic device according to an embodiment of the present application is shown.

[0172] It should be noted that Figure 6 The computer system 600 of the electronic 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 application.

[0173] like Figure 6 As shown, computer system 600 includes a central processing unit (CPU) 601, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 602 or a program loaded from a storage unit 608 into a random access memory (RAM) 603. Various programs and data required for system operation are also stored in RAM 603. CPU 601, ROM 602, and RAM 603 are connected to each other via a bus 604. An input / output (I / O) interface 605 is also connected to bus 604.

[0174] The following components are connected to the I / O interface 605: an input section 606 including a keyboard, a mouse, and the like; an output section 607 including devices such as a cathode ray tube (CRT), a liquid crystal display (LCD), and a speaker; a storage section 608 including a hard disk; and a communication section 609 including a network interface card such as a LAN card or a modem. The communication section 609 performs communication processing via a network such as the Internet. A drive 610 is also connected to the I / O interface 605 as needed. Removable media 611, such as a magnetic disk, an optical disk, a magneto-optical disk, or a semiconductor memory, is installed in the drive 610 as needed, so that computer programs read therefrom can be installed into the storage section 608 as needed.

[0175] In particular, according to an embodiment of the present application, the process described with reference to the flowchart above can be implemented as a computer software program. For example, an embodiment of the present application includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via the communication section 609, and / or installed from a removable medium 611. When the computer program is executed by the central processing unit (CPU) 601, the various functions defined in the method and apparatus of the present application are executed.

[0176] As another aspect, the present application further provides a computer-readable medium, which may be included in the electronic device described in the above embodiments, or may exist independently without being incorporated into the electronic device. The computer-readable medium carries one or more programs, and when the one or more programs are executed by the electronic device, the electronic device implements the method described in the above embodiments.

[0177] It should be noted that the computer-readable medium shown in this application can be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. The computer-readable storage medium can 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 can 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 this application, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, device, or device. In this application, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, which carries computer-readable program code. This propagated data signal can take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport a program for use by or in conjunction with an instruction execution system, apparatus, or device. Program code embodied on a computer-readable medium may be transmitted using any suitable medium, including but not limited to wireless, wireline, optical fiber cable, RF, or any suitable combination thereof.

[0178] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present application. In this regard, each box in the flowchart or block diagram can represent a module, program segment, or a part of code, and the above-mentioned module, program segment, or a part of 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 an order different from that 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 flowchart, and the combination of the boxes in the block diagram or flowchart, can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0179] The units involved in the embodiments described in this application may be implemented by software or hardware, and the units described may also be set in a processor. In some cases, the names of these units do not constitute limitations on the units themselves.

[0180] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present application that follow the general principles of the present application and include common knowledge or customary techniques in the art not disclosed herein. The description and examples are to be considered as exemplary only, and the true scope and spirit of the present application are indicated by the preceding claims.

Claims

1. A data processing method, characterized in that: include: Determining a first activity type vector corresponding to each delivery unit based on travel data; wherein each element in the first activity type vector represents the probability that the corresponding delivery unit belongs to each activity type; the travel data includes at least one of transportation card swiping data, transportation ticket booking data, taxi code scanning data, and shared bicycle usage data; each delivery unit is defined by the location and time period of the delivery unit; Obtaining a second activity type vector corresponding to the object to be delivered; wherein each element in the second activity type vector is used to represent the probability that the object to be delivered belongs to each activity type; selecting a target delivery unit corresponding to the object to be delivered based on the similarity between the first activity type vector and the second activity type vector corresponding to each delivery unit; Among them, the first activity type vector is determined in the following manner: obtaining the destination data corresponding to the travel data based on the activity range corresponding to the travel data; generating the activity type of the corresponding travel data according to the destination data; establishing an association relationship between the activity type and the corresponding travel data to obtain an association relationship set; and generating the first activity type vector corresponding to each delivery unit based on the association relationship set.

2. The method according to claim 1, characterized in that The delivery units corresponding to the same location correspond to different time periods, and the delivery units corresponding to the same time period correspond to different locations.

3. The method according to claim 1, characterized in that Before determining the first activity type vector corresponding to each delivery unit based on the travel data, the method further includes: The travel data corresponding to each delivery unit is filtered according to the time period and location corresponding to each delivery unit.

4. The method according to claim 1, wherein Acquiring destination data corresponding to the travel data based on the activity range corresponding to the travel data includes: Based on the activity range corresponding to the travel data, at least one of satellite positioning data, platform check-in data, and base station cell data within each activity range is obtained as destination data.

5. The method according to claim 1, wherein Generating a first activity type vector corresponding to each delivery unit based on the association relationship set includes: Determining the amount of travel data belonging to each activity type of the corresponding delivery unit based on the association relationship set; Based on the ratio between the multiple travel data volumes corresponding to the respective delivery units and the total travel data volume corresponding to the respective delivery units, a first activity type vector corresponding to the respective delivery units is generated.

6. The method according to claim 1, characterized in that Obtaining the second activity type vector corresponding to the object to be delivered includes: Obtain keyword information of the object to be delivered; A second activity type vector corresponding to the to-be-delivered object is generated according to the keyword information.

7. The method according to claim 1, characterized in that Selecting a target delivery unit corresponding to the to-be-delivered object based on the similarity between the first activity type vector and the second activity type vector corresponding to each delivery unit includes: Determine the similarity between the first activity type vector and the second activity type vector corresponding to each delivery unit to obtain a similarity set corresponding to each delivery unit; Performing intra-set fusion on the similarity sets corresponding to the respective delivery units to obtain target similarities corresponding to the respective delivery units; A target delivery unit corresponding to the to-be-delivered object is selected according to the target similarity.

8. The method according to claim 7, characterized in that Selecting a target delivery unit corresponding to the to-be-delivered object according to the target similarity includes: The target similarity is input into a pre-built delivery location selection model to determine a target delivery unit corresponding to the object to be delivered through the delivery location selection model.

9. The method according to any one of claims 1 to 8, characterized in that After selecting a target delivery unit corresponding to the to-be-delivered object based on the similarity between the first activity type vector and the second activity type vector corresponding to each delivery unit, the method further includes: The object to be delivered is displayed at a specific location device and within a specific time period corresponding to the target delivery unit.

10. An object delivery device, characterized in that: include: a first data determination unit configured to determine a first activity type vector corresponding to each delivery unit based on travel data; wherein each element in the first activity type vector represents a probability that the corresponding delivery unit belongs to each activity type; the travel data includes at least one of transportation card swiping data, transportation ticket booking data, taxi code scanning data, and shared bicycle usage data; and each delivery unit is defined by the location and time period of the delivery unit; A second data determination unit is configured to obtain a second activity type vector corresponding to the object to be delivered; wherein each element in the second activity type vector is used to represent the probability that the object to be delivered belongs to each activity type; a delivery unit selection unit, configured to select a target delivery unit corresponding to the to-be-delivered object based on the similarity between the first activity type vector and the second activity type vector corresponding to each delivery unit; Among them, the first activity type vector is determined in the following manner: obtaining the destination data corresponding to the travel data based on the activity range corresponding to the travel data; generating the activity type of the corresponding travel data according to the destination data; establishing an association relationship between the activity type and the corresponding travel data to obtain an association relationship set; and generating the first activity type vector corresponding to each delivery unit based on the association relationship set.

11. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 9 is implemented.

12. An electronic device, characterized in that: include: processor; as well as a memory for storing executable instructions of the processor; The processor is configured to perform the method according to any one of claims 1 to 9 by executing the executable instructions.

Images