Methods, apparatus, computer-readable storage media, and electronic devices for updating tags

Abstract

This invention discloses a method, apparatus, computer-readable storage medium, and electronic device for updating tags. Relating to the field of artificial intelligence, the method includes: obtaining tags corresponding to each object in a set to be filtered, resulting in multiple tags, wherein the set to be filtered consists of multiple objects, and the multiple tags characterize the object features possessed by each object in the set to be filtered; determining multiple target tags from the multiple tags, and determining the filtering conditions corresponding to the multiple target tags; combining the filtering conditions corresponding to each target tag in the multiple target tags to generate combined tags; and updating the multiple tags corresponding to the set to be filtered based on the combined tags to obtain an updated tag set. This invention solves the technical problem of low tag updating efficiency caused by processing source data to obtain tags in existing technologies.

Description

Technical Field

[0001] This invention relates to the field of artificial intelligence, and more specifically, to a method, apparatus, computer-readable storage medium, and electronic device for updating tags. Background Technology

[0002] In the era of big data, user information permeates the internet. Based on user basic information and other source data, tags representing user characteristics can be generated according to certain rule models. As related systems are continuously developed, the tags or indicators within these systems are becoming increasingly abundant. During the development process, since all tags need to be processed from source data based on rule models, there will be situations such as rule model overlap and rule duplication, which increases the workload of developers and leads to low efficiency in updating tags.

[0003] There is currently no effective solution to the above problems. Summary of the Invention

[0004] This invention provides a method, apparatus, computer-readable storage medium, and electronic device for updating tags, to at least solve the technical problem of low tag updating efficiency caused by processing source data to obtain tags in the prior art.

[0005] According to one aspect of the present invention, a method for updating tags is provided, comprising: obtaining tags corresponding to each object in a set to be filtered to obtain multiple tags, wherein the set to be filtered consists of multiple objects, and the multiple tags characterize the object features of each object in the set to be filtered; determining multiple target tags from the multiple tags, and determining the filtering conditions corresponding to the multiple target tags; combining the filtering conditions corresponding to each target tag in the multiple target tags to generate a combined tag; and updating the multiple tags corresponding to the set to be filtered based on the combined tags to obtain an updated tag set.

[0006] Optionally, the method for updating tags further includes: after combining the filtering conditions corresponding to each target tag among multiple target tags to generate a combined tag, obtaining the update frequency of the combined tag, wherein the update frequency includes at least: a first update frequency and a second update frequency, wherein the first update frequency represents filtering multiple objects once based on the combined tag, and the second update frequency represents filtering multiple objects multiple times based on the combined tag.

[0007] Optionally, the method for updating tags may also include: maintaining the current tag set unchanged when the update frequency of the combined tags is the first update frequency; and updating multiple tags corresponding to the set to be filtered based on the combined tags when the update frequency of the combined tags is the second update frequency, to obtain the updated tag set.

[0008] Optionally, the method for updating tags may further include: after obtaining the update frequency of the combined tags, determining the priority of the combined tags based on the update frequency of the combined tags; and determining the filtering order for filtering multiple objects based on the combined tags based on the priority, wherein the filtering order represents the filtering order between the combined tags and other tags to be filtered.

[0009] Optionally, the method for updating tags further includes: determining the priority of the combined tag as first priority when the update frequency of the combined tag is a first update frequency; determining the priority of the combined tag as first priority when the update frequency of the combined tag is a second update frequency and the generation time of the combined tag is within a preset time range; and determining the priority of the combined tag as second priority when the update frequency of the combined tag is a second update frequency and the generation time of the combined tag is outside the preset time range, wherein the first priority is higher than the second priority.

[0010] Optionally, the method for updating tags also includes: after combining the filtering conditions corresponding to each target tag among multiple target tags to generate a combined tag, determining the location information of each target tag corresponding to the filtering conditions in the combined tag, wherein the location information represents the storage location of the data corresponding to each target tag; filtering multiple objects based on the combined tags and location information to obtain a set of target objects.

[0011] Optionally, the method for updating tags also includes: after filtering multiple objects based on combined tags and location information to obtain a target object set, obtaining a first tag corresponding to the target object set, wherein the first tag is a tag in the tag set that is different from multiple target tags and combined tags; and statistically analyzing the object data of multiple objects contained in the target object set based on the first tag to obtain the data distribution corresponding to the target object set.

[0012] Optionally, the method for updating tags also includes: after filtering multiple objects based on combined tags and location information to obtain a target object set, determining the set characteristics corresponding to the target object set; when the set characteristics indicate that the target object set is an abnormal set, obtaining the transaction information of each object in the target object set; determining at least one abnormal object from the multiple objects contained in the target object set based on the transaction information; and monitoring the transaction behavior of at least one abnormal object.

[0013] Optionally, the method for updating the tag also includes: after statistically analyzing the object data of multiple objects contained in the target object set based on the first tag to obtain the data distribution corresponding to the target object set, determining at least one target push object from the multiple objects contained in the target object set based on the data distribution; and pushing the recommended product to the at least one target push object.

[0014] According to another aspect of the present invention, an apparatus for updating tags is also provided, comprising: an acquisition module, configured to acquire tags corresponding to each object in a set to be filtered, thereby obtaining multiple tags, wherein the set to be filtered consists of multiple objects, and the multiple tags characterize the object features possessed by each object in the set to be filtered; a determination module, configured to determine multiple target tags from the multiple tags and determine the filtering conditions corresponding to the multiple target tags; a combination module, configured to combine the filtering conditions corresponding to each target tag in the multiple target tags to generate a combined tag; and a processing module, configured to update the multiple tags corresponding to the set to be filtered based on the combined tags, thereby obtaining an updated tag set.

[0015] According to another aspect of the present invention, a computer-readable storage medium is also provided, wherein a computer program is stored in the computer-readable storage medium, and the computer program is configured to execute the above-described method for updating tags when it is run.

[0016] According to another aspect of the present invention, an electronic device is also provided, the electronic device including one or more processors; a memory for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement a method for running the programs, wherein the programs are configured to execute the above-described method for updating tags during runtime.

[0017] According to another aspect of the present invention, a computer program product is also provided, including a computer program / instructions, characterized in that the computer program / instructions, when executed by a processor, implement the above-described method for updating tags.

[0018] In this embodiment of the invention, a method of updating tags based on existing multiple tags is adopted. First, the tags corresponding to each object in the set to be filtered are obtained, resulting in multiple tags. Then, multiple target tags are determined from these tags, and the filtering conditions corresponding to these target tags are determined. Next, the filtering conditions corresponding to each target tag are combined to generate a combined tag. Based on this combined tag, the multiple tags corresponding to the set to be filtered are updated, resulting in an updated tag set. The set to be filtered consists of multiple objects, and the multiple tags represent the object characteristics of each object in the set to be filtered.

[0019] In the aforementioned process, when new tags need to be constructed, combined tags are generated by combining the filtering conditions corresponding to each of the multiple target tags. This means that new tags are generated using the filtering conditions corresponding to tags already processed from the source data. This effectively avoids repetitive processing of the source data by staff, thereby significantly improving the efficiency of tag updates. Furthermore, by combining filtering conditions to generate new tags, this application allows for direct retrieval of the target customer set when encountering business needs to filter specific target customers. This further enhances work efficiency and enables rapid response to business requirements.

[0020] Therefore, the solution provided in this application achieves the goal of updating labels based on multiple existing labels, thereby improving the efficiency of label updating and solving the problem of low label updating efficiency caused by processing source data to obtain labels in the prior art. Attached Figure Description

[0021] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings:

[0022] Figure 1 This is a schematic diagram of an optional processing system according to an embodiment of the present invention;

[0023] Figure 2 This is a schematic diagram of an optional tag updating method according to an embodiment of the present invention;

[0024] Figure 3 This is a schematic diagram illustrating an optional method for determining the filtering order of combined tags according to an embodiment of the present invention;

[0025] Figure 4 This is a schematic diagram illustrating the working process of an optional operating device according to an embodiment of the present invention;

[0026] Figure 5 This is a flowchart illustrating the working process of an optional processing system according to an embodiment of the present invention;

[0027] Figure 6 This is a schematic diagram of an optional tag updating device according to an embodiment of the present invention;

[0028] Figure 7 This is a schematic diagram of an optional electronic device according to an embodiment of the present invention. Detailed Implementation

[0029] 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.

[0030] 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 a non-exclusive inclusion; for example, a process, method, system, product, or apparatus 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 apparatus.

[0031] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for display, data used for analysis, etc.) involved in this disclosure are all information and data authorized by the user or fully authorized by all parties.

[0032] Example 1

[0033] According to an embodiment of the present invention, an embodiment of a method for updating a tag is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0034] In an alternative embodiment, a processing system is used to perform the method provided in this application. Figure 1 This is a schematic diagram of an optional processing system according to an embodiment of the present invention, such as... Figure 1 As shown, the processing system may include a definition device, a control device, an operation device, a monitoring device, and a query and download device. The definition device includes a filtering definition device and an analysis definition device. The control device is connected to the filtering definition device, the analysis definition device, and the operation device, respectively. The monitoring device is connected to the control device, the analysis definition device, and the operation device, respectively. The query and download device is connected to the operation device.

[0035] Figure 2 This is a schematic diagram of an optional tag updating method according to an embodiment of the present invention, such as... Figure 2 As shown, the method includes the following steps:

[0036] Step S101: Obtain the label corresponding to each object in the set to be filtered, and obtain multiple labels. The set to be filtered consists of multiple objects, and the multiple labels represent the object characteristics of each object in the set to be filtered.

[0037] In step S101, the tags corresponding to each object in the filter set can be obtained through the filtering definition device. These tags are all tags in the customer profiling system. The customer profiling system can be used to perform statistical analysis on various characteristics and behaviors of customer groups to solve problems in real-world scenarios. The tags corresponding to each object can be stored in a database, server, or other storage devices. In this embodiment, the tags are stored in the HIVE database.

[0038] Specifically, the filter set can consist of customer objects from business scenarios such as marketing, risk control, and promotion. The tags corresponding to these customer objects can be obtained from source data such as basic information, marketing information, financial information, and transaction information. These tags can be categorized into five data types: numeric, character, dictionary, flag, and date. These tags can specifically include gender, age, spending, and balance, and can be further subdivided, such as age tags into teenagers, middle-aged, and elderly tags, and spending tags into annual spending, monthly spending, daily necessities spending, and entertainment spending tags. These categorized tags are then grouped into the same major category (e.g., age, spending) or the same data table. Furthermore, each tag has corresponding tag data; for example, the gender tag corresponds to the tag data "male" or "female," and the age, spending, and balance tags correspond to specific numbers. It is important to emphasize that the data type and label data of the obtained tags are not fixed and can be divided or selected according to the actual situation. For example, gender tags can be divided into categories belonging to the dictionary, with label data as male or female; they can also be divided into categories belonging to the flags, with label data as 1 (male) or 0 (female), etc.

[0039] It should be noted that by obtaining the label corresponding to each object in the set to be filtered, multiple labels are obtained, so that the combined labels, i.e. the new labels, can be determined based on these labels, thereby ensuring that this application can be implemented normally.

[0040] Step S102: Determine multiple target labels from multiple labels and determine the filtering conditions corresponding to the multiple target labels.

[0041] In step S102, the filtering definition device obtains user input or preset selection instructions to determine multiple target labels from multiple labels. For example, from the aforementioned gender, age, consumption, and balance labels, gender and consumption are determined as target labels.

[0042] Furthermore, after the target labels are determined, the filtering definition device obtains user-inputted or preset condition information to obtain the filtering conditions corresponding to each target label. Based on the different data types of each label, the filtering types corresponding to each data type are also different. The filtering types include at least = (equal to), <> (not equal to), >= (greater than or equal to), > (greater than), <= (less than or equal to), < (less than), in (multiple value matching), not_in (excluding multiple values), between_and (range), and like (fuzzy matching). The correspondence between each data type and the filtering type is shown in the table below:

[0043] Table 1. Comparison of Data Types and Filtering Types

[0044] Data types Filter types numerical values =,<>,>=,>,<=,<,in,not_in,between_and character like dictionary =,<>,in,not_in logo ＝,<> date =,<>,>=,>,<=,<,in,not_in,between_and

[0045] Optionally, if gender is defined as a dictionary type and the tag data is male or female, the corresponding filter conditions can be "gender = male", "gender = female", etc. If consumption is defined as a numeric type and the tag data is a specific number, the corresponding filter conditions can be "consumption between 1000 and 20000" or "consumption > 5000", etc.

[0046] It should be noted that by identifying multiple target labels from multiple labels and determining the corresponding filtering conditions for these target labels, a flexible combination of existing labels is achieved, thereby increasing the selection range of new labels and thus improving the applicability of this application.

[0047] Step S103: Combine the filtering conditions corresponding to each target label among the multiple target labels to generate a combined label.

[0048] In step S103, the filtering definition device combines the filtering conditions corresponding to each target label among multiple target labels, that is, expresses each filtering condition as an "AND" relationship to generate a combined label. For example, if the filtering condition for the gender label is "gender = male" and the filtering condition for the consumption label is "consumption > 5000", then the combined label generated by combining these two labels can be any of "gender = male, consumption > 5000", "gender = male and consumption > 5000", or "gender equals male, consumption greater than 5000", or other forms, such as "male with consumption higher than 5000", etc. The specific form depends on the preset combined label generation logic inside the filtering definition device in actual situation.

[0049] It should be noted that by combining the filtering conditions corresponding to each of the multiple target tags to generate combined tags—that is, generating new tags using the filtering conditions corresponding to tags already processed from the source data—reducing repetitive processing of the source data by staff can be effectively avoided, thereby improving the efficiency of updating tags. Furthermore, generating combined tags by combining filtering conditions, rather than by combining target tags, allows the present application to directly locate the set of target customers using the new tags when facing actual business needs, thus improving work efficiency.

[0050] Step S104: Update the multiple tags corresponding to the set to be filtered based on the combined tags to obtain the updated tag set.

[0051] In step S104, the combined tags can be recorded in the data table or other record file in the HIVE database by the running device to update the tag set.

[0052] It should be noted that by updating multiple tags corresponding to the set to be filtered based on combined tags, the tag system is expanded and enriched, thereby enabling this application to be applicable to more business needs, that is, to respond to business needs more quickly.

[0053] Based on the scheme defined in steps S101 to S104 above, it can be understood that in this embodiment of the invention, the method of updating tags based on existing multiple tags is adopted. First, the tags corresponding to each object in the set to be filtered are obtained, resulting in multiple tags. Then, multiple target tags are determined from these multiple tags, and the filtering conditions corresponding to these target tags are determined. Next, the filtering conditions corresponding to each target tag are combined to generate a combined tag. Then, the multiple tags corresponding to the set to be filtered are updated based on the combined tags, resulting in an updated tag set. Here, the set to be filtered consists of multiple objects, and the multiple tags characterize the object features possessed by each object in the set to be filtered.

[0054] It is noteworthy that in the above process, when new tags need to be constructed, combined tags are generated by combining the filtering conditions corresponding to each of the multiple target tags. This means that new tags are generated using the filtering conditions corresponding to tags already processed from the source data. This effectively avoids repetitive processing of the source data by staff, thereby significantly improving the efficiency of tag updates. Furthermore, by combining filtering conditions to generate new tags, this application allows for direct retrieval of the target customer set when encountering business needs to filter relevant target customers. This further enhances work efficiency and enables rapid response to business requirements.

[0055] Therefore, the solution provided in this application achieves the goal of updating labels based on multiple existing labels, thereby improving the efficiency of label updating and solving the problem of low label updating efficiency caused by processing source data to obtain labels in the prior art.

[0056] In one optional embodiment, each combined tag is further configured with a corresponding update frequency, and the operating device determines whether to update the tag set based on the update frequency of each combined tag. The methods for obtaining the update frequency of the combined tags and for determining whether to update the tag set are detailed below.

[0057] Optionally, the filtering definition device obtains the update frequency of the combined tags by receiving frequency information input manually. The update frequency includes at least: a first update frequency and a second update frequency, wherein the first update frequency represents a single filtering of multiple objects based on the combined tags, and the second update frequency represents multiple filtering of multiple objects based on the combined tags.

[0058] Specifically, in this embodiment, the first update frequency includes a single execution frequency, which indicates that after the combined tag is generated, the running device performs a single screening of multiple objects based on the combined tag. The second update frequency includes a daily execution frequency and a monthly execution frequency. The daily execution frequency indicates that after the combined tag is generated, the running device performs a screening of multiple objects once a day based on the combined tag; the monthly execution frequency indicates that after the combined tag is generated, the running device performs a screening of multiple objects once a month based on the combined tag.

[0059] Furthermore, after the operating device acquires the combined tag, it identifies the update frequency corresponding to the combined tag. If the update frequency of the combined tag is determined to be the first update frequency, the operating device does not record the combined tag in the data table or other record file used to record tag sets in the HIVE database; if the update frequency of the combined tag is determined to be the second update frequency (i.e., either the daily execution frequency or the monthly execution frequency), the operating device records the combined tag in the aforementioned data table or other record file.

[0060] It should be noted that due to the unpredictability of actual situations, staff often cannot construct the desired combination of tags in one go. If the processing system records the combination of tags entered by the staff every time, it will greatly increase the workload. Therefore, by setting an update frequency for each combination of tags and determining whether to update the tag set based on the update frequency of each combination of tags, it is possible to effectively distinguish between the "trial screening" combination tags and the "expected" tags entered by the staff, thereby achieving the effect of updating only the "expected" tags and improving the work efficiency of this application.

[0061] In one optional embodiment, since there is often more than one combined tag in the processing system, after obtaining the update frequency of the combined tags, it is also necessary to determine the filtering order of each combined tag by a control device. The filtering order represents the filtering order between the combined tag and other tags to be filtered.

[0062] Optionally, the control device first determines the priority of the combined tags based on the update frequency of the combined tags, and then determines the filtering order for filtering multiple objects based on the combined tags based on the priority.

[0063] The control device determines the priority of the combined tag in the following ways: When the combined tag update frequency is a first update frequency, the control device determines the combined tag priority as first priority; when the combined tag update frequency is a second update frequency and the combined tag generation time is within a preset time range, the control device determines the combined tag priority as first priority; when the combined tag update frequency is a second update frequency and the combined tag generation time is outside the preset time range, the control device determines the combined tag priority as second priority, wherein the first priority is higher than the second priority. The preset time range can be the current day, the current month, or other time ranges; in this embodiment, the preset time range is the current day.

[0064] Specifically, Figure 3 This is a schematic diagram illustrating an optional method for determining the filtering order of combined tags according to an embodiment of the present invention, such as... Figure 3As shown, the control device contains a list of combined tags. When the control device obtains that the update frequency of a combined tag in the list is the single execution frequency, considering that the timeliness requirement of the combined tag business scenario is high, the control device determines to prioritize running the combined tag, and sorts the combined tags with the same priority in real time according to their generation time, adds them to the running queue, and inserts them before the unexecuted combined tags with the second update frequency and after the combined tags with the single execution frequency that are already in the queue. When the control device obtains that the update frequency of the combined tag is the daily execution frequency, if the generation date of the combined tag (i.e., the date output by the filtering definition device) is the same day (i.e., the first execution), then the combined tag is determined to have the same priority as the combined tag with the update frequency of the single execution frequency. That is, the queuing mechanism is the same as the aforementioned queuing mechanism for combined tags with the single execution frequency. Conversely, if the generation date of this combined tag is not the same day, then this combined tag will be cut off and ordered for execution. That is, it will be queued for execution starting at midnight every day after the day following the generation date, according to the generation time. When the aforementioned combined tag with the update frequency of the single execution frequency is inserted into the queue, the order between it and this combined tag can be changed. When the control device obtains that the update frequency of the combined tag is the monthly execution frequency, if the generation date of the combined tag (i.e., the date output by the filtering definition device) is the same day (i.e., the first execution), then the combined tag is determined to have the same priority as the combined tag with the update frequency of the single execution frequency. That is, the queuing mechanism is the same as the aforementioned queuing mechanism for combined tags with the single execution frequency. Conversely, if the generation date of this combined tag is not the same day, then this combined tag will be cut off and ordered for execution. That is, it will be queued for execution according to the generation time starting from the end of the second and subsequent months. When the aforementioned combined tag with the update frequency of the single execution frequency is inserted into the queue, the order between it and this combined tag can be changed.

[0065] It should be noted that by determining the filtering order of each combination of tags, the operation of this application can be made more realistic, that is, to respond more quickly to urgent business needs, thereby improving the applicability of this application.

[0066] In one optional embodiment, after the control device determines the filtering order of each combination tag, the operating device obtains the first combination tag in the queue in real time and filters multiple objects in the set to be filtered based on the combination tag.

[0067] Optionally, first determine the position information of each target label corresponding to the filtering conditions in the combined label, and then filter multiple objects based on the combined label and position information to obtain a set of target objects.

[0068] Location information represents the storage location of the data corresponding to each target tag. The following is a detailed explanation of the location information. In this embodiment, the tags in the customer profiling system are stored in multiple data tables in the HIVE database, based on the tag classification. Each data table stores at least one tag, its tag data, and the corresponding customer. For example, all gender tags are stored in data table A, and all consumption tags and balance tags are stored in data table B, etc. Therefore, a field relationship mapping table representing the field location of the tag data corresponding to each target tag is pre-set in the HIVE database or other storage devices to achieve pre-setting of location information, thereby facilitating querying by the operating device during operation.

[0069] Specifically, the running device generates Spark job programs based on the combined tags and then runs them. Figure 4 This is a schematic diagram illustrating the operation process of an optional operating device according to an embodiment of the present invention, such as... Figure 4 As shown, the working process of the running device through the Spark job program is as follows: First, obtain the combined tags, determine the target tags and corresponding filtering conditions based on the combined tags, such as including "tag 1" and "tag 2", with the filtering condition "tag 1 = value 1" and the filtering condition "tag 2 > value 2". Then, determine the data table where each target tag belongs (i.e., the preliminary classification in the figure). After that, define the query conditions of the job running device based on the data table. For example, if tag 1 and tag 2 are in the same data table, there is only one query condition, which is "tag 1 = value 1 and tag 2 > value 2". If tag 1 If tag 1 and tag 2 are not in the same data table, then there are two query conditions: "tag 1 = value 1" (condition 1) and "tag 2 > value 2" (condition 2). Then, the running device obtains the location information of each target tag and combines the location information, target tag and corresponding filtering conditions to construct an SQL statement for querying data corresponding to the query conditions (e.g., Select customer number, customer name from table 1 where field 1 = value 1) to filter out multiple result sets such as result set 1 and result set 2 from multiple data tables. Taking the intersection of the aforementioned multiple result sets yields the final result, i.e., the target object set.

[0070] It should be noted that, on the one hand, by filtering multiple objects in the set to be filtered based on this combined label, multiple objects with this combined label can be obtained. This is equivalent to determining the label data (yes / no) of each object in the set to be filtered corresponding to this combined label, thereby achieving the purpose of updating the label. On the other hand, by filtering multiple objects in the set to be filtered based on this combined label, staff can help identify target customer groups through the combined labels, thus facilitating statistical analysis and other work based on these target customer groups.

[0071] In one optional embodiment, after the running device obtains the target object set, the analysis and definition device acquires a first tag corresponding to the target object set. Then, the control device and the running device perform statistical analysis on the object data of multiple objects included in the target object set based on the first tag to obtain the data distribution corresponding to the target object set. Here, the first tag is a tag in the tag set that differs from multiple target tags and combined tags.

[0072] Optionally, if the multiple target tags corresponding to the combined tag are gender and consumption, the first tag can be age, balance, or other tags different from the combined tag or the multiple target tags corresponding to the combined tag. In this embodiment, the analysis and definition device first obtains the result information of the combined tag having been executed, and then customizes a statistical task based on the first tag input by the user (such as age) and statistical requirements (such as the distribution of the target object set between 10 and 20 years old and 50 and 60 years old). After that, the statistical request corresponding to the statistical task is sent to the running device through the control device, so that the running device performs statistics on the object data of multiple objects contained in the target object set based on the first tag and statistical requirements to obtain the corresponding data distribution.

[0073] In an optional embodiment, staff can use the aforementioned processing system to screen the target customer group. The following is a detailed description of the aforementioned screening process.

[0074] Figure 5 This is a flowchart illustrating the working process of an optional processing system according to an embodiment of the present invention, such as... Figure 4 , Figure 5As shown, the filtering definition device first receives the target tags and filtering conditions input by the staff to determine the combined tags. After the combined tags are determined, the filtering definition device sets the update frequency (single execution frequency, daily execution frequency, monthly execution frequency) for the combined tags, and then submits a query request to the control device. Subsequently, the control device determines the execution order of the combined tags according to the submission time and update frequency, updates the combined tag status in real time, and assigns the combined tags to the running device. When the running device receives a query request for the combined tags, it parses the combined tags to generate a Spark batch job executable program, and then runs the Spark batch job executable program. During the execution, the running status is sent to the monitoring device. When the execution is successful, the running device writes the obtained target object set (results) into a data table in the HIVE database for staff to view or download via the query and download device. During this process, the monitoring device receives and stores the running status. After successful completion, staff can view or download the filtering results, i.e., the filtered target customer group, through the query and download device. Furthermore, the analysis and definition device can obtain information about the completed screening results through the monitoring device, then accept information such as the first tag input by the staff to customize statistical tasks, and finally submit a statistical request to the control device. This allows the control device to determine the execution order of the statistical tasks and send them to the operating device for execution to generate statistical results. The monitoring device is used to monitor the status of the combined tags in the control device, the operating status and result writing status in the operating device, and also to obtain the screening results. The query and download device is used to query or download the screening results or statistical results. For results with fewer than 30,000 entries, it provides report query and data file download functions; for results with more than 30,000 entries, it only provides data file download functionality.

[0075] In one alternative embodiment, after the processing system filters multiple objects based on combined tags and location information to obtain a set of target objects, the obtained set of target objects can be used for risk control scenarios.

[0076] Optionally, a third-party server can determine the set characteristics corresponding to the target object set. Then, when the set characteristics indicate that the target object set is an anomalous set, the transaction information of each object in the target object set is obtained. Next, based on the transaction information, the third-party server identifies at least one anomalous object from the multiple objects contained in the target object set and monitors the transaction behavior of this at least one anomalous object. It should be noted that other electronic devices with data processing capabilities can also be used to implement the above method instead of a third-party server.

[0077] Specifically, the third-party server determines the set characteristics of the target object set based on the combined tags corresponding to the target object set. These set characteristics can characterize the economic strength, consumption preferences, etc., of each object in the target object set. When the obtained set characteristics contain negative factors, such as "frequent small loans" or "poor repayment ability," it indicates that the target object set corresponding to these characteristics is an abnormal set, meaning users who may be involved in abnormal transactions. Subsequently, the third-party server obtains and analyzes the transaction information of each object in the abnormal set to determine if any anomalies occur in the transaction information. When it finds that an object's transaction information exhibits phenomena such as "frequent short-term transactions" or "sudden increase in consumption," the third-party server identifies that object as an abnormal object and monitors the transaction behavior of at least one abnormal object.

[0078] It should be noted that by determining the set characteristics corresponding to the target object set and finding abnormal objects based on the set characteristics, and monitoring the abnormal objects, this application can issue warnings or prevent abnormal transactions by abnormal objects as soon as possible, thereby reducing risks and minimizing economic losses.

[0079] In one optional embodiment, after the processing system performs statistics on the object data of multiple objects contained in the target object set based on the first label to obtain the data distribution of the target object set, it can promote business based on the data distribution, or provide the data distribution to various applications for system function development.

[0080] Optionally, a third-party server can determine at least one target push object from multiple objects included in the target object set based on data distribution, and then push the recommended product to at least one target push object. It should be noted that other electronic devices with data processing capabilities can also be used to implement the above method instead of a third-party server.

[0081] Specifically, if the target audience is a set of individuals filtered based on the combined tags "gender = female, monthly clothing spending > 5000", and the data distribution shows the age distribution of this target audience across the age groups of "18-27 years old", "28-37 years old", "38-47 years old", and "48-57 years old", with "18-27 years old" accounting for 40%, "28-37 years old" for 30%, "38-47 years old" for 10%, and "48-57 years old" for 20%, then the third-party server can identify the age groups with the highest distribution as the target audience and recommend relevant clothing or shopping apps to these target audiences. Furthermore, the third-party server can also identify the age groups of "28-37 years old" and "48-57 years old" with moderate distributions as target audiences and recommend discounted products to these target audiences to stimulate consumption. Furthermore, third-party servers can identify the age group with the smallest distribution as the target audience for push notifications, and then recommend "flash sale" products or coupons to these target audiences to increase user interest. It's also important to emphasize that third-party servers can identify multiple types of target audiences at once and push corresponding products to different types of target audiences.

[0082] Optionally, the third-party server can also share the above data distribution information with backend developers, so that backend developers can determine the target audience of the relevant business based on the data distribution information, and then develop the corresponding system functions or software based on the preferences or habits of the target audience.

[0083] It should be noted that by identifying target audiences based on data distribution and pushing recommended products to them, it is possible to effectively expand customer base in marketing, promotion and other business scenarios. At the same time, it can also facilitate the development of new products, new systems and new businesses in the future, thereby promoting consumption and improving economic efficiency.

[0084] As can be seen from the above, the method provided in this application has two advantages: First, the combined label, as a combination of multiple labels and different condition values ​​based on the customer profile system, is itself equivalent to a composite label, which can serve as a supplement to the customer profile system and enrich the indicator label system of the customer profile; second, this application provides individuals and downstream applications with a way to quickly and flexibly customize customer group screening operations based on customer profile indicators. The result data can serve various business managers in customer acquisition work in business scenarios such as marketing, risk control, and promotion, and can also be provided to various applications for system function development.

[0085] In the aforementioned process, when new tags need to be constructed, combined tags are generated by combining the filtering conditions corresponding to each of the multiple target tags. This means that new tags are generated using the filtering conditions corresponding to tags already processed from the source data. This effectively avoids repetitive processing of the source data by staff, thereby significantly improving the efficiency of tag updates. Furthermore, by combining filtering conditions to generate new tags, this application allows for direct retrieval of the target customer set when encountering business needs to filter specific target customers. This further enhances work efficiency and enables rapid response to business requirements.

[0086] Therefore, the solution provided in this application achieves the goal of updating labels based on multiple existing labels, thereby improving the efficiency of label updating and solving the problem of low label updating efficiency caused by processing source data to obtain labels in the prior art.

[0087] Example 2

[0088] According to an embodiment of the present invention, an embodiment of a device for updating tags is provided, wherein, Figure 6 This is a schematic diagram of an optional tag updating device according to an embodiment of the present invention, such as... Figure 6 As shown, the device includes:

[0089] The acquisition module 601 is used to acquire the label corresponding to each object in the set to be filtered, and obtain multiple labels. The set to be filtered consists of multiple objects, and the multiple labels represent the object characteristics of each object in the set to be filtered.

[0090] The determination module 602 is used to determine multiple target tags from multiple tags and to determine the filtering conditions corresponding to the multiple target tags;

[0091] The combination module 603 is used to combine the filtering conditions corresponding to each target label among multiple target labels to generate a combined label;

[0092] The processing module 604 is used to update multiple tags corresponding to the set to be filtered based on the combined tags, so as to obtain the updated tag set.

[0093] It should be noted that the above-mentioned acquisition module 601, determination module 602, combination module 603 and processing module 604 correspond to steps S101 to S104 in the above embodiments. The four modules and the corresponding steps implement the same examples and application scenarios, but are not limited to the content disclosed in the above embodiment 1.

[0094] Optionally, the device for updating tags further includes: a first sub-acquisition module, used to acquire the update frequency of the combined tags, the update frequency including at least: a first update frequency and a second update frequency, wherein the first update frequency represents a single screening of multiple objects based on the combined tags, and the second update frequency represents multiple screenings of multiple objects based on the combined tags.

[0095] Optionally, the processing module 604 includes: a first sub-processing module, used to maintain the current tag set unchanged when the update frequency of the combined tags is a first update frequency; and a second sub-processing module, used to update multiple tags corresponding to the set to be filtered based on the combined tags when the update frequency of the combined tags is a second update frequency, to obtain an updated tag set.

[0096] Optionally, the tag updating device further includes: a first sub-determining module for determining the priority of the combined tags based on the update frequency of the combined tags; and a second sub-determining module for determining the filtering order of multiple objects based on the combined tags based on the priority, wherein the filtering order represents the filtering order between the combined tags and other tags to be filtered.

[0097] Optionally, the first sub-determining module includes: a third sub-determining module, used to determine the priority of the combined tag as the first priority when the update frequency of the combined tag is the first update frequency; a fourth sub-determining module, used to determine the priority of the combined tag as the first priority when the update frequency of the combined tag is the second update frequency and the generation time of the combined tag is within a preset time range; and a fifth sub-determining module, used to determine the priority of the combined tag as the second priority when the update frequency of the combined tag is the second update frequency and the generation time of the combined tag is outside the preset time range, wherein the first priority is higher than the second priority.

[0098] Optionally, the tag updating device further includes: a sixth sub-determination module, used to determine the location information of each target tag corresponding to the filtering conditions in the combined tags, wherein the location information represents the storage location of the data corresponding to each target tag; and a filtering module, used to filter multiple objects based on the combined tags and location information to obtain a set of target objects.

[0099] Optionally, the device for updating tags further includes: a second sub-acquisition module, used to acquire a first tag corresponding to the target object set, wherein the first tag is a tag in the tag set that is different from multiple target tags and combined tags; and a statistics module, used to perform statistics on the object data of multiple objects contained in the target object set based on the first tag, to obtain the data distribution corresponding to the target object set.

[0100] Optionally, the device for updating the tag further includes: a seventh sub-determination module, used to determine the set characteristics corresponding to the target object set; a third sub-acquisition module, used to acquire the transaction information of each object in the target object set when the set characteristics indicate that the target object set is an abnormal set; an eighth sub-determination module, used to determine at least one abnormal object from the multiple objects contained in the target object set based on the transaction information; and a monitoring module, used to monitor the transaction behavior of at least one abnormal object.

[0101] Optionally, the device for updating the label further includes: an eighth sub-determination module, used to determine at least one target push object from multiple objects contained in the target object set based on data distribution; and a push module, used to push the recommended product to at least one target push object.

[0102] Example 3

[0103] According to another aspect of the present invention, a computer-readable storage medium is also provided, wherein a computer program is stored in the computer-readable storage medium, wherein the computer program is configured to execute the above-described method for updating tags when running.

[0104] Example 4

[0105] According to another aspect of the present invention, an electronic device is also provided, wherein, Figure 7 This is a schematic diagram of an optional electronic device according to an embodiment of the present invention, such as... Figure 7 As shown, the electronic device includes one or more processors; and a memory for storing one or more programs, which, when executed by one or more processors, cause the one or more processors to implement a method for running the programs, wherein the programs are configured to execute the aforementioned method for updating tags during runtime.

[0106] Example 5

[0107] According to another aspect of the present invention, a computer program product is also provided, including a computer program / instructions, which, when executed by a processor, implement the above-described method for updating tags.

[0108] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0109] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0110] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual couplings, direct couplings, or communication connections may be through some interfaces; indirect couplings or communication connections between units or modules may be electrical or other forms.

[0111] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0112] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0113] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0114] The above are merely preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for updating tags, characterized in that, include: Obtain the label corresponding to each object in the set to be filtered, and obtain multiple labels. The set to be filtered consists of multiple objects, and the multiple labels represent the object characteristics of each object in the set to be filtered. From the plurality of tags, determine a plurality of target tags, and determine the filtering conditions corresponding to the plurality of target tags; The filtering conditions corresponding to each of the multiple target tags are combined to generate a combined tag; The update frequency of the combined tags is obtained, and the update frequency includes at least: a first update frequency and a second update frequency, wherein the first update frequency represents a single filtering of the multiple objects based on the combined tags, and the second update frequency represents multiple filtering of the multiple objects based on the combined tags; Based on the update frequency of the combined tags, the priority of the combined tags is determined; based on the priority, the filtering order for filtering the multiple objects based on the combined tags is determined, wherein the filtering order represents the filtering order between the combined tags and other tags to be filtered; Determine the location information of each target tag corresponding to the filtering conditions in the combined tags, wherein the location information represents the storage location of the data corresponding to each target tag; filter the multiple objects based on the combined tags and the location information to obtain a target object set; Based on the combined tags, the multiple tags corresponding to the set to be filtered are updated to obtain the updated tag set.

2. The method for updating tags according to claim 1, characterized in that, Based on the combined tags, multiple tags corresponding to the set to be filtered are updated to obtain an updated tag set, including: If the update frequency of the combined tags is the first update frequency, the current tag set remains unchanged; When the update frequency of the combined tags is the second update frequency, the multiple tags corresponding to the set to be filtered are updated based on the combined tags to obtain the updated tag set.

3. The method for updating tags according to claim 1, characterized in that, Determining the priority of the combined tags based on their update frequency includes: If the update frequency of the combined tag is the first update frequency, the priority of the combined tag is determined to be the first priority; If the update frequency of the combined tag is the second update frequency and the generation time of the combined tag is within a preset time range, the priority of the combined tag is determined to be the first priority; If the update frequency of the combined tag is the second update frequency and the generation time of the combined tag is outside the preset time range, the priority of the combined tag is determined to be the second priority, wherein the first priority is higher than the second priority.

4. The method for updating tags according to claim 1, characterized in that, After filtering the multiple objects based on the combined tags and the location information to obtain a target object set, the method includes: Obtain a first tag corresponding to the target object set, wherein the first tag is a tag in the tag set that is different from the plurality of target tags and the combined tag; Based on the first label, the object data of multiple objects contained in the target object set are statistically analyzed to obtain the data distribution corresponding to the target object set.

5. The method for updating tags according to claim 1, characterized in that, After filtering the multiple objects based on the combined tags and the location information to obtain a target object set, the method includes: Determine the set characteristics corresponding to the target object set; When the set features characterize the target object set as an abnormal set, the transaction information of each object in the target object set is obtained; Based on the transaction information, at least one abnormal object is identified from the multiple objects contained in the target object set; The transaction behavior of the at least one abnormal object is monitored.

6. The method for updating tags according to claim 4, characterized in that, After statistically analyzing the object data of multiple objects contained in the target object set based on the first label to obtain the data distribution corresponding to the target object set, the method includes: Based on the data distribution, at least one target push object is determined from the multiple objects contained in the target object set; The recommended product is pushed to the at least one target push object.

7. A device for updating tags, characterized in that, include: The acquisition module is used to acquire the label corresponding to each object in the set to be filtered, and obtain multiple labels. The set to be filtered consists of multiple objects, and the multiple labels represent the object characteristics of each object in the set to be filtered. The determining module is used to determine multiple target tags from the multiple tags and to determine the filtering conditions corresponding to the multiple target tags; The combination module is used to combine the filtering conditions corresponding to each of the multiple target tags to generate a combined tag; The processing module is used to update multiple tags corresponding to the set to be filtered based on the combined tags, so as to obtain an updated tag set; The first sub-acquisition module is used to acquire the update frequency of the combined tags. The update frequency includes at least a first update frequency and a second update frequency, wherein the first update frequency represents a single screening of the multiple objects based on the combined tags, and the second update frequency represents multiple screenings of the multiple objects based on the combined tags. The first sub-determination module is used to determine the priority of the combined tags based on the update frequency of the combined tags; the second sub-determination module is used to determine the filtering order of the multiple objects based on the combined tags based on the priority, wherein the filtering order represents the filtering order between the combined tags and other tags to be filtered; The sixth sub-determination module is used to determine the location information of each target tag corresponding to the filtering conditions in the combined tags, wherein the location information represents the storage location of the data corresponding to each target tag; the filtering module is used to filter the multiple objects based on the combined tags and the location information to obtain a set of target objects.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, wherein the computer program is configured to perform the method for updating tags as described in any one of claims 1 to 6 when executed.

9. An electronic device, characterized in that, The electronic device includes one or more processors; A memory for storing one or more programs, which, when executed by one or more processors, cause the one or more processors to perform operations on the programs, wherein the programs are configured to execute the method of updating tags as described in any one of claims 1 to 6.

10. A computer program product comprising a computer program / instructions, characterized in that, When the computer program / instructions are executed by a processor, they implement the method for updating tags as described in any one of claims 1 to 6.

Images