Group tag determination method and device, electronic equipment and storage medium

Abstract

The invention discloses a group tag determination method and device, electronic equipment and a storage medium. The method comprises the following steps: determining basic group data corresponding to at least one basic tag and at least one aggregation mode; according to the aggregation mode, aggregating the basic group data corresponding to the basic label to obtain derivative group data; and determining a derivative label according to the basic label and the aggregation mode, and taking the derivative label as a label corresponding to the derivative group data. According to the technical scheme, by dynamically generating and managing the labels corresponding to the group data, an all-dimensional and multi-dimensional group label system can be constructed, data support is provided for operation and management of all rows, and the service capacity of different business requirements is improved.

Description

Technical Field

[0001] This invention relates to the field of big data processing technology, and in particular to a method, apparatus, electronic device, and storage medium for determining group labels. Background Technology

[0002] In the era of big data, customer tags (such as age, gender, and transaction identifiers) are widely used in scenarios such as precision marketing, business management, and customer profiling. Traditional customer tag query methods are usually based on simple drag-and-drop combinations on a page. However, with the growth of both users and data, single query methods suffer from insufficient flexibility and poor scalability.

[0003] In current business operations, customer segmentation and analysis are crucial for improving marketing efficiency and optimizing customer experience. However, traditional customer analysis methods often rely on fixed rules or models, making it difficult to flexibly address the needs of different business scenarios. Existing technologies exhibit relatively static customer tag generation and management, failing to adapt quickly to real-time data or dynamic demands, resulting in insufficient flexibility and accuracy in customer segment analysis. Summary of the Invention

[0004] This invention provides a method, apparatus, electronic device, and storage medium for determining group tags, which can dynamically generate and manage tags corresponding to group data, provide data support for business management in various industries, and improve service capabilities for different business needs.

[0005] In a first aspect, embodiments of the present invention provide a method for determining group labels, the method comprising:

[0006] Identify at least one basic tag corresponding to basic group data, and at least one aggregation method;

[0007] Based on the aggregation method, the basic group data corresponding to the basic tags are aggregated to obtain the derived group data;

[0008] Derived labels are determined based on the base labels and aggregation methods, and these labels are used as the corresponding labels for the derived group data.

[0009] Secondly, embodiments of the present invention also provide a group tag determination device, the device comprising:

[0010] The first data determination module is used to determine the basic group data corresponding to at least one basic label, and at least one aggregation method;

[0011] The second data determination module is used to aggregate the basic group data corresponding to the basic tags according to the aggregation method to obtain the derived group data;

[0012] The label determination module is used to determine derived labels based on the base labels and aggregation methods, which serve as the labels for the derived group data.

[0013] Thirdly, embodiments of the present invention also provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the group tag determination method as described in any of the embodiments of the present invention.

[0014] Fourthly, embodiments of the present invention also provide a storage medium for storing computer-executable instructions, which, when executed by a computer processor, are used to perform the group tag determination method as described in any of the embodiments of the present invention.

[0015] The technical solution of this invention, by dynamically generating and managing tags corresponding to group data, can realize the construction of a comprehensive and multi-dimensional group tag system, providing data support for the operation and management of banks at all levels and improving the service capabilities for different business needs.

[0016] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a flowchart of a group label determination method provided in Embodiment 1 of the present invention;

[0019] Figure 2 This is a flowchart of a group label determination method provided in Embodiment 2 of the present invention;

[0020] Figure 3 This is a schematic diagram of a group tag determination device provided in Embodiment 3 of the present invention;

[0021] Figure 4 This is a schematic diagram of the structure of an electronic device provided in Embodiment 4 of the present invention. Detailed Implementation

[0022] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0023] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or device that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or devices. In the embodiments of this application, certain software, components, models, and other existing industry solutions may be mentioned. These should be considered exemplary, intended only to illustrate the feasibility of implementing the technical solutions of this application, and do not imply that the applicant has already used or necessarily used such solutions.

[0024] The acquisition, transmission, storage, use, and processing of data in this application all comply with the relevant provisions of national laws and regulations.

[0025] Example 1

[0026] Figure 1 The flowchart of a group label determination method provided in Embodiment 1 of the present invention is applicable to the determination of group labels. The method can be executed by a group label determination device, which can be implemented in hardware and / or software and can be configured in a server.

[0027] like Figure 1 As shown, the method for determining the group label includes:

[0028] S110. Determine the basic group data corresponding to at least one basic label, and at least one aggregation method.

[0029] In this embodiment, the basic tags are atomic-level tags formed by directly extracting or simply processing raw data. The raw data can be user order time, purchased product information, payment amount, etc., in an e-commerce scenario. The basic tags can be composed of at least one of the following: purchase amount = 500 yuan, number of purchases = 3, purchase interval = 7 days. The basic tags can be determined from a pre-set tag library. The basic group data are objects or groups that conform to the basic tags.

[0030] Specifically, basic tags are determined from a pre-set tag library. Based on the specific information of each basic tag, objects or groups that match the basic tags are identified from the original database and used as the basic group data corresponding to the current basic tag.

[0031] It should be noted that if the basic label includes multiple sub-labels, the basic group data of the basic label is determined by the intersection of the group data corresponding to each sub-label.

[0032] For example, if the basic tags are "purchase amount = 500 yuan & purchase count = 3 times", then the group corresponding to "purchase amount = 500 yuan" and the group corresponding to "purchase count = 3 times" are determined from the original database (such as the order table). The basic group data corresponding to the basic tags is determined by the union of the two groups. In addition, if there is a statistical time range, such as the last 7 days or the last 30 days, then the basic group data is further filtered to find the group data that fits the statistical time range.

[0033] In this embodiment, the aggregation method is used to process, combine, and summarize the basic tags according to certain rules to form higher-level tags. The aggregation method in this embodiment includes at least one of the following: summation operation, average operation, maximum value operation, minimum value operation, AND operation, and OR operation.

[0034] As an optional but not limited implementation, before determining the basic group data corresponding to at least one basic label, the following is also included:

[0035] The basic tags are divided according to different business attributes and different business scenarios under the same business attribute.

[0036] In this embodiment, business attributes are used to describe business characteristics, including business segments such as corporate, retail, and institutional banking. Business scenarios are used to describe the business objectives under a business attribute. A business attribute can often be divided into multiple business scenarios, including lines such as online finance, customer operations, and risk control. For example, the core business attribute of e-commerce is product transaction, and its business scenarios include user acquisition scenarios, repeat purchase scenarios to increase the purchase frequency of existing users, and product operation scenarios to optimize product structure and improve inventory turnover.

[0037] Due to different business objectives, basic tags are divided according to different business attributes or different business scenarios under the same business attribute. This ensures that the basic tags can not only cover business attributes but also adapt to the refined needs of different business scenarios, realizing a segmented and line-based tag definition mode. This can effectively avoid management chaos when the tag data volume increases rapidly.

[0038] S120. According to the aggregation method, the basic group data corresponding to the basic tags are aggregated to obtain the derived group data.

[0039] In this embodiment, the aggregation method is used to aggregate the basic group data corresponding to the basic label. The aggregation method includes at least one of the following: summation operation, average operation, maximum value operation, minimum value operation, AND operation, and OR operation.

[0040] In this embodiment, the summation operation is to sum the basic group data corresponding to the basic tag, such as determining the total consumption in the past 30 days = the sum of single consumption in the past 30 days; the averaging operation is to calculate the average value of the basic group data corresponding to the basic tag, such as the average order amount = total consumption / number of purchases; the maximum value operation is to determine the maximum value of the basic group data corresponding to the basic tag; the minimum value operation is to determine the minimum value of the basic group data corresponding to the basic tag; the AND operation is to find the intersection of the basic group data corresponding to the basic tag; and the OR operation is to find the union of the basic group data corresponding to the basic tag.

[0041] In this embodiment, basic group data corresponding to basic tags can be aggregated based on logical relationships and aggregation methods; basic group data corresponding to basic tags can be aggregated based on statistical calculations and aggregation methods; and basic group data corresponding to basic tags can be aggregated based on behavioral sequences and aggregation methods.

[0042] Specifically, based on logical relationships and aggregation methods, the basic group data corresponding to the basic labels are aggregated. Multiple basic group data are combined through logical relationships such as "AND", "OR", and "NOT" and according to the aggregation methods of AND and / or OR operations. Groups with cross-features or common features are focused to obtain derived group data.

[0043] For example, derived group data can be obtained by performing AND and / or OR operations on the basic group data of basic tag A and basic tag B. It should be noted that the basic group data corresponding to basic tags A and B can only be aggregated to obtain derived group data if the tag dimensions of basic tags A and B are compatible. For example, dimensions such as "gender" are compatible with dimensions like "age" and "consumption behavior".

[0044] Under the e-commerce business attributes, if the basic label 1 is "gender = female", the corresponding basic group data 1 is "80,000 people", the basic label 2 is "age = 25-35 years old", the corresponding basic group data 2 is "60,000 people", and the basic label 3 is "monthly purchase frequency > 3 times", the corresponding basic group data 3 is "30,000 people". According to the aggregation method of AND operation, the basic group data corresponding to the three basic labels are aggregated to obtain the derived group data of "15,000 people" of young female high-frequency consumers.

[0045] Specifically, based on statistical calculations and aggregation methods, the basic group data corresponding to the basic labels are aggregated. This involves performing statistical operations on the numerical data of the basic group data through aggregation methods such as summation and averaging to extract derived group data with quantitative features.

[0046] In e-commerce business scenarios, aggregation methods can be used to calculate total spending and average spending, such as by statistically analyzing spending amount and frequency of activity, to obtain derived group data. For example, if basic tag 1 is "spending amount of 500 yuan", corresponding to basic group data 1 of "80,000 people", basic tag 2 is "spending amount of 1200 yuan", corresponding to basic group data 2 of "60,000 people", and basic tag 3 is "spending amount of 3000 yuan", corresponding to basic group data 3 of "30,000 people", by aggregating the basic group data corresponding to the three basic tags using the average aggregation method, we can obtain derived group data of 170,000 people with an "average spending amount of 1188 yuan".

[0047] Specifically, based on behavioral sequences and aggregation methods, the basic group data corresponding to the basic tags are aggregated. According to the behavioral order or process, multiple behavioral basic tags and corresponding basic group data are associated to determine the derived group data with consistent behavior.

[0048] For example, basic tag 1 is "viewing a video," corresponding to basic group data 1 of "80,000 people"; basic tag 2 is "adding the same product to the cart as in the video," corresponding to basic group data 2 of "60,000 people"; and basic tag 3 is "unpaid product," corresponding to basic group data 3 of "30,000 people." Basic tags 1, 2, and 3 constitute the process of browsing and adding products to the cart but not paying. By aggregating the basic group data corresponding to the above three basic tags, we can obtain the data of a churned group of "20,000 people" who added products to their cart but did not pay. Furthermore, we can analyze the reasons for non-payment based on the derived group data.

[0049] In this embodiment, derivative group data is obtained by multi-dimensional aggregation of the basic group data corresponding to the basic tags. Compared with the basic group data, the derivative group data can deeply describe the business problems in the business attributes. Based on the derivative group data and the business scenarios in the business attributes, the corresponding business problems can be understood or analyzed more clearly, and business decisions can be made.

[0050] As an optional but not limited implementation, based on the aggregation method, the basic group data corresponding to the basic tags is aggregated to obtain derived group data, including:

[0051] According to the aggregation method, the basic group data corresponding to at least two basic labels are aggregated to obtain derived group data;

[0052] The business attributes of each basic tag may be the same or different.

[0053] In this embodiment, basic group data corresponding to basic tags with the same or different business attributes can be aggregated to obtain derived group data.

[0054] When the business attributes of each basic tag are the same, the basic group data corresponding to multiple basic tags with the same business attributes can be aggregated to obtain the content of the derived group data, as illustrated in the above examples.

[0055] When the business attributes of each basic tag differ, the basic group data corresponding to each basic tag is aggregated to obtain derived group data. In practical applications, basic tags from different sectors (business attributes) can be selected as needed, and joint aggregation can be completed through the relationships between business attributes. For example, "Enterprise Customer Number" can be used to associate the basic tags of the personal sector with the basic tags of the corporate sector to complete relevant business queries linking public and private sectors.

[0056] For example, business attribute 1 is social networking, basic tag 1 is "daily interactions (likes, comments) on social platforms > 5 times", and basic group data 1 is a socially active group of 50,000 people. Business attribute 2 is e-commerce, basic tag 2 is "daily spending on e-commerce platforms > 1,000 yuan", and basic group data 2 is a high-spending group of 30,000 people. The social platform and e-commerce platform IDs are linked. By aggregating the basic group data with the same linked ID from the two basic group data sets, we can obtain a socially active and high-spending group of 25,000 people that simultaneously meets both the criteria of "daily interactions on social platforms > 5 times" and "daily spending on e-commerce platforms > 1,000 yuan".

[0057] It should be noted that aggregating basic group data corresponding to multiple basic tags with the same business attributes to obtain derived group data allows for focusing on the deep features of a single business attribute, improving the efficiency of refined business operations. Aggregating basic group data corresponding to multiple basic tags with different business attributes to obtain derived group data allows for the discovery of group relationships between different business attributes, providing insights for business decision-making. Both aggregation methods require a foundation of associative data to ensure the accuracy and operability of the derived group data.

[0058] S130. Determine the derived labels based on the basic labels and aggregation methods, and use them as the labels corresponding to the derived group data.

[0059] In this embodiment, the derived tags are composite tags obtained by aggregating the base tags. The derived tags are generated by performing logical operations on the base tags or behavioral events according to business rules.

[0060] It should be noted that the derived group data is composite group data obtained by aggregating the basic group data corresponding to the basic tags according to the aggregation method. Therefore, the tags corresponding to this derived group data are the composite tags obtained by aggregating the basic tags. By using the derived tags as the tags corresponding to the derived group data, the characteristics of the derived group data can be made more intuitive, the flexibility of customer group analysis can be improved, the needs of different business scenarios can be quickly responded to, and it is beneficial to provide a data foundation and decision-making for precise operations.

[0061] This invention discloses a method for determining group labels. The method includes: determining basic group data corresponding to at least one basic label, and at least one aggregation method; aggregating the basic group data corresponding to the basic labels according to the aggregation method to obtain derived group data; and determining derived labels based on the basic labels and the aggregation method, using these derived labels as the labels corresponding to the derived group data. The technical solution of this invention, by dynamically generating and managing labels corresponding to group data, can realize the construction of a comprehensive, multi-dimensional group label system, providing data support for the operation and management of various levels of banks and improving the service capabilities for different business needs.

[0062] Example 2

[0063] Figure 2 This is a flowchart of a group tag determination method provided in Embodiment 2 of the present invention. This embodiment is applicable to situations involving the determination of group tags. The method can be executed by a group tag determination device, which can be implemented in hardware and / or software and can be configured in a server. The embodiments of the present invention further specify the methods described above.

[0064] like Figure 2 As shown, the method for determining the group label includes:

[0065] S210. Determine the basic group data corresponding to at least one basic label, and at least one aggregation method.

[0066] S220. Determine the time period based on two time tags, or determine the time period based on the time tags and the basic time aggregation method.

[0067] In this embodiment, the time tag is a tag with a specific data date. For example, time tag A = 20250730 and time tag B = 20250630. The time period is determined based on the two time tags, and the time period is the time range between July 30, 2025 and June 30, 2025.

[0068] The basic time aggregation methods include comparison with yesterday, comparison with last week, comparison with last month, etc., which represent the time difference between the previous time point and the next time point. If the time tag is 20241231 and the basic time aggregation method is "compared with last month", then the calculation is performed based on the time tag A with the date 20241231 and the time tag B with the date 20241130.

[0069] It should be noted that if there is no predetermined time tag, and the basic time aggregation method is "compared to last month", then the latest date is the default time tag A, and the calculation is performed between time tag A and the data date of the previous month.

[0070] Furthermore, if the time period is determined based on the time tag and the basic time aggregation method, the data date of time tag B can be calculated based on the time interval and aggregated according to time dimensions such as specific time period, dynamic time period (nearly N days / months / quarters / years), this month / quarter / year, as well as specific time period and dynamic time period.

[0071] In practical applications, if "Dynamic Time Period" is selected, a specific number can be specified at the "N years / months / days" field. If "Specific Time Period" is selected, a date selection box will pop up for selection. In this embodiment, the method of determining the time period is not specifically limited and can be applied flexibly.

[0072] S230. According to the aggregation method, the basic group data corresponding to the basic tags within the time period are aggregated to obtain the derived group data.

[0073] In this embodiment, the time period is used to filter basic group data with basic tags that are not within the target period. By combining the aggregation method with the time period, basic group data that simultaneously meet multiple basic tags within the time period is limited, so that the aggregation result is closer to the business scenario. The derived group data, because it has the time period dimension superimposed, can directly serve short-term operational decisions.

[0074] S240. Determine the derived labels based on the basic labels and aggregation methods, and use them as the labels corresponding to the derived group data.

[0075] As an optional but not limited implementation, the method further includes:

[0076] Identify temporary group data and determine temporary labels as labels for the temporary group data.

[0077] In this embodiment, temporary group data refers to group data formed in short-term, sudden, or specific temporary scenarios, such as time-limited events, emergency incidents, and holiday marketing. Temporary group data is characterized by its high timeliness and short lifespan. Temporary tags are short-term tags that describe the core characteristics of temporary group data.

[0078] Specifically, temporary group data can be identified through behavioral databases within temporary scenarios. For example, during a limited-time flash sale event on a retail platform, individuals who browse flash sale items more than 5 times, add more than 3 flash sale items to their cart but do not place an order, and log in more than 2 times during the event can be identified through this behavioral data.

[0079] Furthermore, based on the core characteristics of temporary group data ("attention but no conversion"), combined with the scenario attribute ("limited time"), the temporary tag can be defined as "payment by units that show high interest in aerial photography during the limited-time event". At the same time, this temporary tag is only valid on the day of the limited-time event and for 24 hours after its end.

[0080] As an optional but not limited implementation, the method also includes, but is not limited to, the following steps:

[0081] Identify target group data;

[0082] Perform marketing operations on the target group data;

[0083] Based on the evaluation index values ​​of at least one dimension before marketing operations were performed on the target group data, and the evaluation index values ​​of at least one dimension after marketing operations were performed on the target group data, determine the comparison value of the evaluation index of at least one dimension.

[0084] The marketing operations are evaluated based on the comparison values ​​of the aforementioned evaluation indicators.

[0085] In this embodiment, the target group data can be at least one of basic group data, derived group data, and temporary group data. The evaluation indicator comparison value is determined by comparing the evaluation indicator value before performing marketing operations on the target group data with the evaluation indicator value after performing marketing operations on the target group data.

[0086] Furthermore, in addition to the methods for determining the target group mentioned above, group data can also be queried by writing processing logic based on target tags, or by filtering and searching for target tags and dragging them into the filter box to set customer group filtering conditions, thereby obtaining target group data. The target tags can be at least one of basic tags, derived tags, and temporary tags.

[0087] Furthermore, marketing operations are performed on the target group data, such as sending coupons, promotional text messages, or exclusive benefits. By comparing the evaluation index values ​​before and after the operation, the comparison value of the evaluation index is calculated, thereby judging the effectiveness of the marketing operation.

[0088] Specifically, the evaluation indicator comparison value can be the ratio of the difference between the evaluation indicator value after the operation and the evaluation indicator value before the operation to the evaluation indicator value before the operation. The evaluation indicator comparison value can represent the success rate of the marketing operation, making it easier to accurately determine the actual target group data and marketing effect.

[0089] In this embodiment, marketing operations are performed on the target group data, and the comparison values ​​of evaluation indicators can be used to optimize marketing strategies (such as adjusting the target audience, budget, and format), thereby achieving data-driven decision-making, meeting the needs of different business scenarios, and providing service capabilities to meet different business requirements.

[0090] This invention discloses a method for determining group labels. The method includes: determining basic group data corresponding to at least one basic label, and at least one aggregation method; aggregating the basic group data corresponding to the basic labels according to the aggregation method to obtain derived group data; and determining derived labels based on the basic labels and the aggregation method, using these derived labels as the labels corresponding to the derived group data. The technical solution of this invention, by dynamically generating and managing labels corresponding to group data, can realize the construction of a comprehensive, multi-dimensional group label system, providing data support for the operation and management of various levels of banks and improving the service capabilities for different business needs.

[0091] Example 3

[0092] Figure 3 This is a schematic diagram of a group tag determination device provided in Embodiment 3 of the present invention.

[0093] like Figure 3 As shown, the group tagging device includes:

[0094] The first data determination module is used to determine the basic group data corresponding to at least one basic label, and at least one aggregation method;

[0095] The second data determination module is used to aggregate the basic group data corresponding to the basic tags according to the aggregation method to obtain the derived group data;

[0096] The label determination module is used to determine derived labels based on the base labels and aggregation methods, which serve as the labels for the derived group data.

[0097] Based on the above embodiments, optionally, before determining the basic group data corresponding to at least one basic label, the method further includes:

[0098] The basic tags are divided according to different business attributes and different business scenarios under the same business attribute.

[0099] Based on the above embodiments, optionally, the aggregation method includes at least one of the following: summation operation, average operation, maximum value operation, minimum value operation, AND operation, and OR operation.

[0100] Based on the above embodiments, optionally, according to the aggregation method, the basic group data corresponding to the basic tags are aggregated to obtain derived group data, including:

[0101] The time period can be determined based on two time tags, or based on the time tags and the basic time aggregation method.

[0102] Based on the aggregation method, the basic group data corresponding to the basic tags within the time period are aggregated to obtain the derived group data.

[0103] Based on the above embodiments, optionally, according to the aggregation method, the basic group data corresponding to the basic tags are aggregated to obtain derived group data, which further includes:

[0104] According to the aggregation method, the basic group data corresponding to at least two basic labels are aggregated to obtain derived group data;

[0105] The business attributes of each basic tag may be the same or different.

[0106] Optionally, based on the above embodiments, the method further includes:

[0107] Identify temporary group data and determine temporary labels as labels for the temporary group data.

[0108] Optionally, based on the above embodiments, the method further includes:

[0109] Determine the target group data, which includes at least one of the following: basic group data, derived group data, and temporary group data;

[0110] Perform marketing operations on the target group data;

[0111] Based on the evaluation index values ​​of at least one dimension before marketing operations were performed on the target group data, and the evaluation index values ​​of at least one dimension after marketing operations were performed on the target group data, determine the comparison value of the evaluation index of at least one dimension.

[0112] The marketing operations are evaluated based on the comparison values ​​of the aforementioned evaluation indicators.

[0113] This invention discloses a group label determination device, comprising: a first data determination module, used to determine basic group data corresponding to at least one basic label, and at least one aggregation method; a second data determination module, used to aggregate the basic group data corresponding to the basic labels according to the aggregation method to obtain derived group data; and a label determination module, used to determine derived labels according to the basic labels and the aggregation method, as labels corresponding to the derived group data. The technical solution of this invention, by dynamically generating and managing labels corresponding to group data, can realize the construction of a comprehensive, multi-dimensional group label system, providing data support for the operation and management of various levels of banks and improving the service capabilities for different business needs.

[0114] The group label determination device provided in the embodiments of the present invention can execute the group label determination method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the method execution.

[0115] Example 4

[0116] Figure 4 A schematic diagram of an electronic device 10 that can be used to implement embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0117] like Figure 4As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 may also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0118] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0119] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as the group label determination method.

[0120] In some embodiments, the group tag determination method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the group tag determination method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the group tag determination method by any other suitable means (e.g., by means of firmware).

[0121] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0122] Computer programs used to implement the methods of the present invention can be written in any combination of one or more programming languages. These computer programs can be provided to the processor of a general-purpose computer, a special-purpose computer, or other programmable group tag determination device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The computer programs can be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0123] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0124] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0125] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0126] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through a communication network. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0127] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0128] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for determining group labels, characterized in that, include: Identify at least one basic tag corresponding to basic group data, and at least one aggregation method; Based on the aggregation method, the basic group data corresponding to the basic tags are aggregated to obtain the derived group data; Derived labels are determined based on the base labels and aggregation methods, and these labels are used as the corresponding labels for the derived group data.

2. The method according to claim 1, characterized in that, Before determining the basic group data corresponding to at least one basic label, the following steps are also included: The basic tags are divided according to different business attributes and different business scenarios under the same business attribute.

3. The method according to claim 1, characterized in that, The aggregation method includes at least one of the following: summation, average, maximum value, minimum value, AND, and OR operations.

4. The method according to claim 1, characterized in that, Based on the aggregation method, the basic group data corresponding to the basic tags are aggregated to obtain derived group data, including: The time period can be determined based on two time tags, or based on the time tags and the basic time aggregation method. Based on the aggregation method, the basic group data corresponding to the basic tags within the time period are aggregated to obtain the derived group data.

5. The method according to claim 1, characterized in that, According to the aggregation method, the basic group data corresponding to the basic tags are aggregated to obtain the derived group data, which also includes: According to the aggregation method, the basic group data corresponding to at least two basic labels are aggregated to obtain derived group data; The business attributes of each basic tag may be the same or different.

6. The method according to claim 1, characterized in that, The method further includes: Identify temporary group data and determine temporary labels as labels for the temporary group data.

7. The method according to any one of claims 1-6, characterized in that, The method further includes: Determine the target group data, which includes at least one of the following: basic group data, derived group data, and temporary group data; Perform marketing operations on the target group data; Based on the evaluation index values ​​of at least one dimension before marketing operations were performed on the target group data, and the evaluation index values ​​of at least one dimension after marketing operations were performed on the target group data, determine the comparison value of the evaluation index of at least one dimension. The marketing operations are evaluated based on the comparison values ​​of the aforementioned evaluation indicators.

8. A group tagging device, characterized in that, include: The first data determination module is used to determine the basic group data corresponding to at least one basic label, and at least one aggregation method; The second data determination module is used to aggregate the basic group data corresponding to the basic tags according to the aggregation method to obtain the derived group data; The label determination module is used to determine derived labels based on the base labels and aggregation methods, which serve as the labels for the derived group data.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the group label determination method as described in any one of claims 1-7.

10. A storage medium for storing computer-executable instructions, characterized in that, The computer-executable instructions, when executed by a computer processor, are used to perform the group label determination method as described in any one of claims 1-7.

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