Image crowd grading management method and system, computer device and storage medium

Abstract

The application discloses a portrait crowd grading control method and system, a computer device and a storage medium. The method comprises the following steps: based on a preset portrait crowd classification task, determining at least one basic label and / or business label, and constructing at least one label combination; based on the label combination, selecting a target crowd; based on the data source dimension of the corresponding label of the selected target crowd, classifying the target crowd to obtain a target crowd type; and based on the mapping relationship between the preset crowd type and the task level, determining the task level corresponding to the target crowd type. In the application, the whole process integration and dynamic task grading from label development to risk response are improved, the processing efficiency is improved, the risk prevention and control ability is strengthened by means of real-time monitoring, grading early warning and precise response measures, the resource utilization efficiency is optimized by differentiating the allocation of resources according to the task characteristics and dynamically adjusting, the human error is reduced, the business continuity is ensured, and the business reliability and user experience are significantly improved.

Description

Technical Field

[0001] This application relates to the field of data management technology, and in particular to a method, system, computer equipment, and storage medium for hierarchical management of profiled populations. Background Technology

[0002] In the current field of online user profiling and management, user segmentation, classification, and risk mitigation technologies have become crucial for supporting business operations and enhancing user experience. While commonly used user management technologies can achieve basic user screening and classification, they suffer from significant shortcomings in automation, accuracy, stability, and end-to-end collaboration, severely hindering improvements in operational efficiency and service quality.

[0003] In existing technologies, tag development largely relies on manual configuration and maintenance, which is not only time-consuming and labor-intensive but also prone to inaccurate tags due to human error. Furthermore, tag management lacks a systematic framework, resulting in disorganized tag classification, poor reusability, and difficulty in quickly adapting to diverse business audience selection needs, significantly increasing operational and time costs. Existing audience selection methods are often limited to simple single-dimensional tag overlay, lacking in-depth integration and comprehensive analysis of multi-source data. This fails to accurately characterize the core features of the audience, leading to a low match between the selected target audience and actual business needs, hindering the implementation of refined operational strategies. Regarding task classification, existing technologies mostly rely on fixed rules or manual judgment, lacking scientific basis and dynamic adjustment mechanisms. This results in high-priority tasks potentially not being processed promptly, while low-priority tasks consume significant resources. Existing technologies lack intelligent resource scheduling mechanisms, failing to dynamically adjust resource allocation based on task priority and real-time resource usage. This leads to inefficiency for high-priority tasks due to resource shortages and wasteful resource consumption by low-priority tasks, limiting overall business processing capacity. Meanwhile, the various stages operate independently without a closed-loop management system, resulting in problems such as resource waste, low efficiency, and poor data consistency, which seriously affect the quality of business operations and growth needs. Summary of the Invention

[0004] Therefore, it is necessary to provide a method, system, computer equipment, and storage medium for hierarchical management of the profiled population to address the above-mentioned technical problems, thereby resolving at least one of the problems existing in the prior art.

[0005] Firstly, the embodiments of this application provide a method for hierarchical management of user profiles, including: Based on the pre-defined user profile classification task, determine at least one basic tag and / or business tag, and construct at least one tag combination; Based on the aforementioned tag combinations, the target audience is selected; Based on the data source dimensions of the tags corresponding to the selected target audience, the target audience is classified to obtain the target audience type; Based on the preset mapping relationship between population type and task level, the task level corresponding to the target population type is determined.

[0006] In one possible implementation, after determining the task level corresponding to the target population type, the method further includes: Determine whether the preset warning trigger conditions are met; If the preset warning triggering conditions are met, determine the population risk warning level and response strategy corresponding to the target population; Perform the corresponding actions according to the risk warning level and response strategy for the population.

[0007] In one possible implementation, determining whether the preset warning triggering condition is currently met includes: Obtain the actual time consumed by the preset profile population classification task; Determine whether the actual time consumed is greater than the preset business execution time; If the actual time consumed is greater than the preset business execution time, then the preset warning triggering condition is determined to be met.

[0008] In one possible implementation, determining the population risk warning level and response strategy corresponding to the target population includes: The difference between the actual time consumed and the preset business execution time is calculated as the delay time; Based on the delay time and the task level, the graded early warning information and automatic response plan are generated. The graded early warning information includes the target population name, target population ID and population risk warning level. The automatic response plan includes response strategies corresponding to the warning level. The tiered early warning information and automatic response plan are integrated into an early warning card and sent to the corresponding user.

[0009] In one possible implementation, generating the tiered early warning information and automatic response plan based on the delay time and the task level includes: If the task level is a core task and the delay time is greater than the first preset time, then the population risk warning level is a level one warning, and the first response strategy corresponding to the level one warning is obtained. If the task level is a secondary task and the delay time is greater than the second preset time, then the population risk warning level is a level two warning, and the second response strategy corresponding to the level two warning is obtained. If the task level is an edge task and the delay time is greater than a third preset time, then the population risk warning level is a level three warning, and a third response strategy corresponding to the level three warning is obtained.

[0010] In one possible implementation, before determining at least one basic tag and / or business tag based on a preset user profile classification task, and constructing at least one tag combination, the method further includes: Develop tags based on user historical information data tables; Based on the user's historical information data table type, the tag types of the developed tags are determined, including basic tags and business tags.

[0011] In one possible implementation, the step of determining at least one basic tag and / or business tag based on a preset user profile classification task, and constructing at least one tag combination, includes: Based on the pre-defined user profile classification task, select at least one basic tag and / or business tag; Configure corresponding audience filtering conditions for the selected basic tags and / or business tags; Configure the logical relationships between the aforementioned population screening conditions; Based on the aforementioned logical relationship, the population screening conditions are combined to obtain the tag combination.

[0012] Secondly, a user profile-based hierarchical management system is provided, including: The tag determination unit is used to determine at least one basic tag and / or business tag based on a preset user profile classification task, and to construct at least one tag combination; The target audience selection unit is used to select the target audience based on the tag combination; The target audience type determination unit is used to classify the target audience based on the data source dimension of the corresponding tags of the selected target audience to obtain the target audience type; The task level determination unit is used to determine the task level corresponding to the target population type based on a preset mapping relationship between population types and task levels.

[0013] Thirdly, a computer device is provided, including a memory, a processor, and computer-readable instructions stored in the memory and executable on the processor, wherein the processor, when executing the computer-readable instructions, implements the steps of the profile-based population hierarchical management method described above.

[0014] Fourthly, a readable storage medium is provided, which stores computer-readable instructions that, when executed by a processor, implement the steps of the profiled population hierarchical management method described above.

[0015] The aforementioned method, system, computer equipment, and storage medium for hierarchical management of user profiles include the following steps: based on a preset user profile classification task, determining at least one basic tag and / or business tag, and constructing at least one tag combination; based on the tag combination, selecting target users; based on the data source dimension of the tags corresponding to the selected target users, classifying the target users to obtain target user types; and based on a preset mapping relationship between user types and task levels, determining the task level corresponding to the target user type. In this embodiment, by automating the entire process of tag development, audience selection, classification, task grading, and risk response, manual intervention is significantly reduced and processing time is shortened. Combined with a scientific dynamic task grading mechanism that prioritizes critical tasks, overall processing efficiency is significantly improved. Real-time monitoring of task status, tiered early warning, automatic resource matching and scheduling, and data fallback measures effectively prevent potential risks and ensure stable processing. Differentiated resource allocation based on task complexity, priority, and risk level, with dynamic adjustments to resource configuration, avoids resource waste or insufficiency, improving resource utilization efficiency and flexibility. Intelligent management throughout the entire process reduces human error, improves processing accuracy and stability, and the automated risk response mechanism ensures business continuity, thereby enhancing business reliability and user experience. Attached Figure Description

[0016] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the description of the embodiments of this application will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a flowchart illustrating a method for hierarchical control of portrait-based populations in one embodiment of this application; Figure 2 This is a schematic diagram of a portrait-based population hierarchical management system according to one embodiment of this application; Figure 3 This is a schematic diagram of a computer device according to an embodiment of this application. Detailed Implementation

[0018] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0019] In one embodiment, such as Figure 1 As shown, a method for hierarchical management of user profiles is provided, including the following steps: In step S110, based on the preset profile audience classification task, at least one basic tag and / or business tag is determined, and at least one tag combination is constructed; Optionally, the specific objectives of the user profiling task are preset (such as selecting "high-value paying users," "potential conversion users," or other specific business-required user groups). First, from the established tagging system, at least one basic tag and / or business tag suitable for the task is accurately selected. Then, for each selected tag, specific user filtering conditions are configured in conjunction with the task objectives (such as "purchase frequency > 5 times / month," "membership level ≥ 5," "region = first-tier city," etc., with clear judgment criteria). Next, according to the business filtering logic, the relationship between the filtering conditions corresponding to each tag is defined (such as logical operators such as "AND" and "OR" to achieve accurate matching of multi-dimensional conditions). Finally, based on the configured logical relationships, all tags and their corresponding filtering conditions are integrated to form at least one tag combination that can accurately target the target user group.

[0020] The basic tags are developed based on the data warehouse base tables, which are standardized data tables in the data warehouse that store core general data of an enterprise. They are used to support the basic data needs of all business lines, with stable data sources and standardized formats. They typically contain global and general data such as basic user information and account information (e.g., account status, type of activated services). They can be used to develop general identifier tags such as region, age, and membership level.

[0021] The business tags are based on self-built tables, which are data tables independently constructed by each business line according to its specific scenarios and needs. This data is highly targeted and closely tied to specific business operations, typically containing customized data specific to that business line, such as user behavior data (e.g., product browsing history and order payment details for an e-commerce business) and business processing data (e.g., wealth management purchase records and loan application information for a financial business). These can be used to develop customized tags such as purchase frequency, service processing records, and preference patterns.

[0022] In step S120, the target population is selected based on the tag combination; Optionally, based on the filtering rules corresponding to the pre-generated tag combination, the system automatically associates the data warehouse base table with the full user data of the business self-built table to perform multi-dimensional condition matching, accurately extracts the user set that simultaneously meets all the filtering conditions of the tag combination from the full user data, and finally forms a target audience that highly matches the preset profile audience classification task objectives, providing a clear audience target for subsequent operations such as audience classification, task grading and risk protection.

[0023] In step S130, the target population is classified based on the data source dimension of the tags corresponding to the selected target population to obtain the target population type; Optionally, the selected target audience can be categorized based on the data source dimension of the tags used to select the target audience (i.e., whether the data corresponding to the tags comes from the data warehouse base table or a business-built table). For example, if the selection of the target audience mainly relies on basic tags derived from the data warehouse base table (such as region, age, etc.), they are classified as system users; if they mainly rely on business tags derived from the business-built table (such as consumption frequency, service processing records, etc.), they are classified as custom users; if they rely on both basic tags derived from the data warehouse base table and business tags derived from the business-built table, they are classified as mixed users. This classification method, which is bound to the data source dimension, clarifies the core characteristic attributes of the target audience and ultimately obtains the corresponding target audience type, providing a classification basis for subsequent matching of the corresponding task level.

[0024] In step S140, the task level corresponding to the target population type is determined based on the preset mapping relationship between population type and task level.

[0025] Optionally, based on the pre-configured "user type and task level mapping table" in the system, the corresponding task level is matched for the identified target user type. This mapping relationship is pre-set based on factors such as the user's business value, data sensitivity, and processing timeliness requirements. For example, "high-value business scenario users" (such as core paying users) are pre-mapped as the highest priority core task (P0) because they are directly related to revenue conversion; "basic attribute users" (such as ordinary registered users) are pre-mapped as secondary tasks (P1) because they have lower real-time requirements; and "peripheral business scenario users" (such as low-frequency interaction users) are mapped as peripheral tasks (P2). Once the target user type is determined, the system automatically queries the mapping table to accurately match the corresponding task level, thereby clarifying the standards for resource allocation, processing priority, and risk response level for tasks of this user category. This provides a clear level basis for subsequent resource scheduling and risk management, ensuring that the processing needs of high-value users are given priority.

[0026] It should be noted that this application achieves precise management and optimized resource allocation for online-selected groups through a dynamic hierarchical protection mechanism. This approach has broad application scenarios and portability. For example, it can be extended to financial risk assessment and early warning scenarios, developing financial customer tags based on multi-source data, selecting high-risk groups and conducting real-time risk monitoring and early warning, and automatically executing risk response strategies such as adjusting credit limits to ensure the security of financial business. It can also be applied to e-commerce precision marketing and customer management, developing tags based on user behavior and preference data and selecting target customer groups, achieving precise marketing push and customer segmentation management through task hierarchical and resource allocation, thereby improving marketing effectiveness and customer satisfaction. It can also be applied to the field of medical and health data management, integrating multi-source information such as patient medical records and examination data to develop health risk tags, identify high-risk groups and monitor changes in health indicators in real time, automatically issue warnings and match medical resources to assist medical decision-making; at the same time, it can be adapted to intelligent traffic flow optimization scenarios, combining traffic flow, road conditions and other data to develop tags, identify congested areas and monitor traffic conditions in real time, and automatically schedule traffic signal control, allocate resources to optimize traffic flow and alleviate congestion. These scenarios have achieved reasonable allocation and precise management of resources through dynamic classification and differentiated protection measures, which fully demonstrates the broad application prospects of the online profiling population classification protection method proposed in this application.

[0027] This application provides a method for hierarchical management of user profiles, comprising: determining at least one basic tag and / or business tag based on a preset user profile classification task, and constructing at least one tag combination; selecting target users based on the tag combination; classifying the target users based on the data source dimension of the tags corresponding to the selected target users to obtain target user types; and determining the task level corresponding to the target user type based on a preset mapping relationship between user types and task levels. This application achieves fully automated integration of tag development, user selection, classification, task hierarchical management, and risk response, significantly reducing manual intervention and shortening processing time. Combined with a scientific dynamic task hierarchical mechanism, it prioritizes the execution of critical tasks, significantly improving overall processing efficiency. Through real-time monitoring of task status, hierarchical early warning, automatic matching of resource scheduling, and data fallback, it effectively prevents potential risks and ensures stable processing. It allocates resources differently based on task complexity, priority, and risk level, and dynamically adjusts resource configuration to avoid resource waste or insufficiency, improving resource utilization efficiency and flexibility. Intelligent management throughout the entire process reduces human error, improves processing accuracy and stability, and the automated risk response mechanism ensures business continuity, thereby improving business reliability and user experience.

[0028] In one embodiment of this application, after determining the task level corresponding to the target population type, the method further includes: Determine whether the preset warning trigger conditions are met; If the preset warning triggering conditions are met, determine the population risk warning level and response strategy corresponding to the target population; Perform the corresponding actions according to the risk warning level and response strategy for the population.

[0029] Optionally, once the task level corresponding to the target population type is determined, the system will monitor the execution process of the population classification task in real time and further determine whether the current situation meets the preset risk triggering conditions (such as task processing timeout, insufficient resources, etc.). If the condition is met, the system will determine the risk warning level (such as level 1, level 2, level 3) and corresponding response strategies (such as priority resource scheduling, data backup, etc.) corresponding to the target population based on factors such as task level and risk degree. Subsequently, the system will automatically execute the corresponding operations according to the determined warning level and response strategy, thereby achieving timely prevention and control of risks in population classification tasks and ensuring the stability and reliability of task processing.

[0030] In one embodiment of this application, determining whether the preset warning triggering condition is currently met includes: Obtain the actual time consumed by the preset profile population classification task; Determine whether the actual time consumed is greater than the preset business execution time; If the actual time consumed is greater than the preset business execution time, then the preset warning triggering condition is determined to be met.

[0031] Optionally, the system monitors the task execution process in real time, dynamically obtains the actual time consumed from the start of the preset profile group classification task to the current node, and then compares the actual time consumed with the preset business execution time to determine whether the actual time consumed exceeds the preset business execution time. If the actual time consumed is greater than the preset business execution time, it is determined that the preset risk triggering standard is met, thereby accurately identifying potential time risks in the task execution process.

[0032] In one embodiment of this application, determining the population risk warning level and response strategy corresponding to the target population includes: The difference between the actual time consumed and the preset business execution time is calculated as the delay time; Based on the delay time and the task level, the graded early warning information and automatic response plan are generated. The graded early warning information includes the target population name, target population ID and population risk warning level. The automatic response plan includes response strategies corresponding to the warning level. The tiered early warning information and automatic response plan are integrated into an early warning card and sent to the corresponding user.

[0033] Optionally, based on the calculated delay time (the difference between the actual time consumed and the preset business execution time) and the determined task level (such as core task, secondary task, etc.), a hierarchical early warning information is first automatically generated, clearly marking the name of the target group, the unique target group ID, and the matching early warning level (such as early warning level 1, early warning level 2, early warning level 3), allowing relevant personnel to quickly grasp the risk object and severity; and an automatic response plan, which includes targeted response strategies corresponding to the early warning level (such as emergency resource expansion and suspension of low-priority tasks for level 1 early warning, and task priority upgrade for level 2 early warning, etc.). Subsequently, these two parts of information are integrated into a well-structured and complete early warning card, which is sent to the corresponding users (such as operations personnel and technical maintenance personnel) responsible for the task of classifying the target group through preset hierarchical notification channels (such as priority SMS + platform message for level 1 early warning, platform message + email for level 2 early warning, etc.), ensuring that users can obtain risk information in a timely manner and can directly and quickly deal with the situation according to the response strategy in the card, achieving efficient risk response.

[0034] In one embodiment of this application, generating the graded early warning information and automatic response plan based on the delay time and the task level includes: If the task level is a core task and the delay time is greater than the first preset time, then the population risk warning level is a level one warning, and the first response strategy corresponding to the level one warning is obtained. If the task level is a secondary task and the delay time is greater than the second preset time, then the population risk warning level is a level two warning, and the second response strategy corresponding to the level two warning is obtained. If the task level is an edge task and the delay time is greater than a third preset time, then the population risk warning level is a level three warning, and a third response strategy corresponding to the level three warning is obtained.

[0035] Optionally, for the highest priority core tasks, a strict first preset time threshold (e.g., 10 minutes) can be set. If the actual delay exceeds this threshold, the highest level one alert (three-tier notification: Lark robot, telephone, and SMS notification to the responsible person) will be immediately triggered, and the first response strategy matching the first alert will be automatically invoked (usually a powerful measure such as emergency resource expansion, task priority scheduling, and ensuring the timely completion of the task). For secondary tasks with lower priority, a relatively lenient second preset time threshold (e.g., 30 minutes) can be set. When the delay exceeds this threshold, a second level alert will be triggered. The system sends early warnings (two-tier notification: Lark robot and telephone notification to the responsible person), simultaneously activating the corresponding second response strategy (such as allocating idle resources to ensure task output; if output cannot be achieved within 10 minutes, automatically using T+2 data (such as the most recent day's data from the database) as a fallback). For the lowest priority edge tasks, a more lenient third preset time threshold (such as 60 minutes) is set. Only when the delay exceeds this threshold is a third-tier early warning triggered (one-tier notification: Lark robot notifies the responsible person), and the corresponding third response strategy is executed (directly adopting the fallback solution, in which case the system automatically uses T+2 data as a fallback). This method of sending different levels of early warning information to technical personnel according to task level ensures efficient information delivery. Based on the matched strategy, it automatically executes resource scheduling or data fallback operations without manual intervention. This ensures that the risks of core tasks are dealt with in the most timely manner, while avoiding the excessive occupation of emergency resources by low-priority tasks, achieving precise and rational risk early warning and response.

[0036] For example, when creating a user group calculation task, the business side can set a deadline for task output in advance (e.g., "complete the daily user group calculation before 8:00 AM every day"). After the deadline arrives, it can check whether the task has generated complete user group data results (such as user group list, tag details, etc.). If a complete result is generated before the deadline, it is judged as "on-time output"; if the result is not generated by the deadline or is incomplete, it is judged as "not on-time output". At this time, the timeout duration can be calculated, and the warning level and response measures can be determined in combination with the task level. For example, suppose the business side sets the output deadline for the "Mid-Autumn Festival and National Day promotion core user group" calculation task to "6:00 AM every day": Risk trigger: The deadline of 6:00 AM has arrived, the system detects that the user group data has not been generated, judges it as "not on-time output", and triggers a risk warning. Audience and Task Level Matching: This audience only uses basic data warehouse tags (such as "This audience's spending over 5000 RMB in the past 3 months" and "Membership level V5 and above" are both business tags), belonging to the "System Audience," corresponding to task level P0 (core task). If the delay exceeds the first preset time (e.g., 10 minutes), a Level 1 alert is triggered. At this point, a "three-tier notification" can be initiated: sending an alert card via Lark robot, automatically dialing the technical lead's number, and sending an SMS message containing the audience name: "Mid-Autumn Festival and National Day Promotion Core User Audience"; Audience ID: 10241013001; Alert Level: Level 1. The system automatically executes "full resource scheduling," suspending the computing resources of all current P1 / P2 tasks, prioritizing allocation to this P0 task, and initiating server expansion to ensure audience calculation is completed within 30 minutes.

[0037] It should be noted that a knowledge base for population risk classification, early warning, and protection plans can be built. This knowledge base stores the risk classification standards, early warning rules, and corresponding protection measures for various population groups. Once a task level is obtained, the corresponding early warning level and protection plan can be determined by querying this knowledge base. It is worth noting that this knowledge base establishes a real-time feedback and update mechanism. That is, the system dynamically adjusts and supplements the strategy content in the knowledge base based on the effectiveness of risk management in actual business operations, newly emerging risk types, and response experience, ensuring that the stored risk classification and early warning standards and protection plans can continuously adapt to business changes and provide timely and accurate reference for risk handling.

[0038] In one embodiment of this application, before determining at least one basic tag and / or business tag based on a preset profile-based audience classification task, and constructing at least one tag combination, the method further includes: Develop tags based on user historical information data tables; Based on the user's historical information data table type, the tag types of the developed tags are determined, including basic tags and business tags.

[0039] Optionally, using a user history information data table (covering various historical data such as user basic attributes and business behaviors) as the data foundation, key information is extracted and processed to develop corresponding tags. Then, based on the type of these user history information data tables (such as data warehouse basic tables or business self-built tables), the developed tags are classified. If the tag originates from the data warehouse basic table, it is classified as a basic tag; if it originates from the business self-built table, it is classified as a business tag.

[0040] In one embodiment of this application, the step of determining at least one basic tag and / or business tag based on a preset profile-based audience classification task, and constructing at least one tag combination, includes: Based on the pre-defined user profile classification task, select at least one basic tag and / or business tag; Configure corresponding audience filtering conditions for the selected basic tags and / or business tags; Configure the logical relationships between the aforementioned population screening conditions; Based on the aforementioned logical relationship, the population screening conditions are combined to obtain the tag combination.

[0041] Optionally, based on the specific objectives of the classification task (such as selecting groups with specific characteristics), at least one suitable basic tag (such as region, age, etc.) and / or business tag (such as consumption frequency, service preference, etc.) is selected from the developed tags. Then, corresponding audience filtering conditions are set for each selected tag (such as specific judgment criteria such as "greater than / less than / equal to / contains a certain value or range"). Next, the relationships between these filtering conditions are defined according to business logic requirements (such as logical operators such as "AND" and "OR" to achieve accurate matching of multiple conditions). Finally, the filtering conditions corresponding to all tags are integrated according to the set logical relationships to form a tag combination that can accurately lock the target audience, providing clear filtering rules for subsequent audience selection.

[0042] For example, taking the selection of "high-value repeat purchase users" as an example: Three tags are selected from the tag library: the basic data warehouse tag "V5 or above member user" and the business-built tags "Number of orders placed in the past 6 months" and "Cumulative spending in the past 6 months". Then, filter conditions are configured for the selected tags, such as setting the condition for the tag "Number of orders placed in the past 6 months" to "greater than or equal to 3 times"; setting the condition for the tag "Whether or not a V5 or above member user" to "Yes"; and setting the condition for the tag "Cumulative spending in the past 6 months" to "greater than or equal to 2000 yuan". Then, the logical operator "AND" is selected, meaning that all three conditions must be met simultaneously. The system then filters out users from the entire user base who "have placed ≥3 orders in the past 6 months, are V5 or above members, and have a cumulative spending of ≥2000 yuan", ultimately generating a list of "high-value repeat purchase users" and completing the selection process.

[0043] In this embodiment, core competitive advantages are built through a fully automated closed-loop system, scientific task grading and resource optimization, real-time risk monitoring and early warning, and flexible dynamic resource scheduling. This forms four core protection points: a fully automated system, task grading and resource allocation strategies, a real-time risk monitoring and early warning system, and dynamic resource scheduling strategies. Full-process automation significantly reduces manual intervention and shortens processing time, while scientific task grading further improves overall processing efficiency. Real-time risk monitoring and strategy knowledge base-supported tiered early warning and precise responses enhance risk control capabilities and ensure stable processing. Differentiated resource allocation and dynamic scheduling avoid resource waste or shortages, improving resource utilization efficiency and adaptability. Intelligent management of the entire process reduces human error, ensuring business continuity and reliability. Ultimately, this comprehensively improves the efficiency, stability, and user experience of business processing, providing strong support for intelligent business monitoring and operation in the logistics industry and other fields.

[0044] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0045] In one embodiment, a profile-based population hierarchical management system is provided, which corresponds one-to-one with the profile-based population hierarchical management method described in the above embodiments. For example... Figure 2 As shown, the profiling-based audience hierarchical management system includes a tag determination unit 10, a target audience selection unit 20, a target audience type determination unit 30, and a task level determination unit 40. Detailed descriptions of each functional module are as follows: The tag determination unit 10 is used to determine at least one basic tag and / or business tag based on a preset profile audience classification task, and to construct at least one tag combination; The target audience selection unit 20 is used to select the target audience based on the tag combination; The target audience type determination unit 30 is used to classify the target audience based on the data source dimension of the corresponding tags of the selected target audience to obtain the target audience type; The task level determination unit 40 is used to determine the task level corresponding to the target population type based on a preset mapping relationship between population types and task levels.

[0046] In one embodiment of this application, the system further includes: a risk warning unit, used for: Determine whether the preset warning trigger conditions are met; If the preset warning triggering conditions are met, determine the population risk warning level and response strategy corresponding to the target population; Perform the corresponding actions according to the risk warning level and response strategy for the population.

[0047] In one embodiment of this application, the risk warning unit is further configured to: Obtain the actual time consumed by the preset profile population classification task; Determine whether the actual time consumed is greater than the preset business execution time; If the actual time consumed is greater than the preset business execution time, then the preset warning triggering condition is determined to be met.

[0048] In one embodiment of this application, the risk warning unit is further configured to: The difference between the actual time consumed and the preset business execution time is calculated as the delay time; Based on the delay time and the task level, the graded early warning information and automatic response plan are generated. The graded early warning information includes the target population name, target population ID and population risk warning level. The automatic response plan includes response strategies corresponding to the warning level. The tiered early warning information and automatic response plan are integrated into an early warning card and sent to the corresponding user.

[0049] In one embodiment of this application, the risk warning unit is further configured to: If the task level is a core task and the delay time is greater than the first preset time, then the population risk warning level is a level one warning, and the first response strategy corresponding to the level one warning is obtained. If the task level is a secondary task and the delay time is greater than the second preset time, then the population risk warning level is a level two warning, and the second response strategy corresponding to the level two warning is obtained. If the task level is an edge task and the delay time is greater than a third preset time, then the population risk warning level is a level three warning, and a third response strategy corresponding to the level three warning is obtained.

[0050] In one embodiment of this application, the system further includes a tag development unit, used for: Develop tags based on user historical information data tables; Based on the user's historical information data table type, the tag types of the developed tags are determined, including basic tags and business tags.

[0051] In one embodiment of this application, the tag determining unit 10 is further configured to: Based on the pre-defined user profile classification task, select at least one basic tag and / or business tag; Configure corresponding audience filtering conditions for the selected basic tags and / or business tags; Configure the logical relationships between the aforementioned population screening conditions; Based on the aforementioned logical relationship, the population screening conditions are combined to obtain the tag combination.

[0052] In this embodiment, core competitive advantages are built through a fully automated closed-loop system, scientific task grading and resource optimization, real-time risk monitoring and early warning, and flexible dynamic resource scheduling. This forms four core protection points: a fully automated system, task grading and resource allocation strategies, a real-time risk monitoring and early warning system, and dynamic resource scheduling strategies. Full-process automation significantly reduces manual intervention and shortens processing time, while scientific task grading further improves overall processing efficiency. Real-time risk monitoring and strategy knowledge base-supported tiered early warning and precise responses enhance risk control capabilities and ensure stable processing. Differentiated resource allocation and dynamic scheduling avoid resource waste or shortages, improving resource utilization efficiency and adaptability. Intelligent management of the entire process reduces human error, ensuring business continuity and reliability. Ultimately, this comprehensively improves the efficiency, stability, and user experience of business processing, providing strong support for intelligent business monitoring and operation in the logistics industry and other fields.

[0053] Specific limitations regarding the profiling-based population tiered management system can be found in the limitations of the profiling-based population tiered management method described above, and will not be repeated here. Each module in the aforementioned profiling-based population tiered management system can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0054] In one embodiment, a computer device is provided, which may be a terminal device, and its internal structure diagram may be as follows: Figure 3 As shown, the computer device includes a processor, memory, and network interface connected via a system bus. The processor provides computing and control capabilities. The memory includes a readable storage medium storing computer-readable instructions. The network interface communicates with external terminals via a network connection. When executed by the processor, the computer-readable instructions implement a population profiling and hierarchical management method. The readable storage medium provided in this embodiment includes both non-volatile and volatile readable storage media.

[0055] In this embodiment of the application, a computer device is provided, including a memory, a processor, and computer-readable instructions stored in the memory and executable on the processor. When the processor executes the computer-readable instructions, it implements the steps of the above-described population grading and management method. In this embodiment of the application, a readable storage medium is provided, which stores computer-readable instructions. When the computer-readable instructions are executed by a processor, they implement the steps of the above-described population classification and control method.

[0056] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by instructing related hardware with computer-readable instructions. These computer-readable instructions can be stored in a non-volatile readable storage medium or a volatile readable storage medium. When executed, these computer-readable instructions can include the processes of the embodiments of the methods described above. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0057] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0058] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method for hierarchical management of a user profile, characterized in that, The method includes: Based on the pre-defined user profile classification task, determine at least one basic tag and / or business tag, and construct at least one tag combination; Based on the aforementioned tag combinations, the target audience is selected; Based on the data source dimensions of the tags corresponding to the selected target audience, the target audience is classified to obtain the target audience type; Based on the preset mapping relationship between population type and task level, the task level corresponding to the target population type is determined.

2. The method for hierarchical management of the portrait population as described in claim 1, characterized in that, After determining the task level corresponding to the target population type, the process further includes: Determine whether the preset warning trigger conditions are met; If the preset warning triggering conditions are met, determine the population risk warning level and response strategy corresponding to the target population; Perform the corresponding actions according to the risk warning level and response strategy for the population.

3. The method for hierarchical management of the portrait population as described in claim 2, characterized in that, The determination of whether the preset warning triggering conditions are met includes: Obtain the actual time consumed by the preset profile population classification task; Determine whether the actual time consumed is greater than the preset business execution time; If the actual time consumed is greater than the preset business execution time, then the preset warning triggering condition is determined to be met.

4. The method for hierarchical management of the portrait population as described in claim 3, characterized in that, The determination of the population risk warning level and response strategy corresponding to the target population includes: The difference between the actual time consumed and the preset business execution time is calculated as the delay time; Based on the delay time and the task level, the graded early warning information and automatic response plan are generated. The graded early warning information includes the target population name, target population ID and population risk warning level. The automatic response plan includes response strategies corresponding to the warning level. The tiered early warning information and automatic response plan are integrated into an early warning card and sent to the corresponding user.

5. The method for hierarchical management of the portrait population as described in claim 4, characterized in that, The generation of the tiered early warning information and automatic response plan based on the delay time and the task level includes: If the task level is a core task and the delay time is greater than the first preset time, then the population risk warning level is a level one warning, and the first response strategy corresponding to the level one warning is obtained. If the task level is a secondary task and the delay time is greater than the second preset time, then the population risk warning level is a level two warning, and the second response strategy corresponding to the level two warning is obtained. If the task level is an edge task and the delay time is greater than a third preset time, then the population risk warning level is a level three warning, and a third response strategy corresponding to the level three warning is obtained.

6. The method for hierarchical management of portrait-based populations as described in claim 1, characterized in that, Before determining at least one basic tag and / or business tag and constructing at least one tag combination based on the preset user profile classification task, the method further includes: Develop tags based on user historical information data tables; Based on the user's historical information data table type, the tag types of the developed tags are determined, including basic tags and business tags.

7. The method for hierarchical management of portrait-based populations as described in claim 1 or 6, characterized in that, The task of classifying the population based on a preset profile involves determining at least one basic tag and / or a business tag, and constructing at least one tag combination, including: Based on the pre-defined user profile classification task, select at least one basic tag and / or business tag; Configure corresponding audience filtering conditions for the selected basic tags and / or business tags; Configure the logical relationships between the aforementioned population screening conditions; Based on the aforementioned logical relationship, the population screening conditions are combined to obtain the tag combination.

8. A profile-based population hierarchical management system, characterized in that, The system includes: The tag determination unit is used to determine at least one basic tag and / or business tag based on a preset user profile classification task, and to construct at least one tag combination; The target audience selection unit is used to select the target audience based on the tag combination; The target audience type determination unit is used to classify the target audience based on the data source dimension of the corresponding tags of the selected target audience to obtain the target audience type; The task level determination unit is used to determine the task level corresponding to the target population type based on a preset mapping relationship between population types and task levels.

9. A computer device comprising a memory, a processor, and computer-readable instructions stored in the memory and executable on the processor, characterized in that, When the processor executes the computer-readable instructions, it implements the steps of the portrait-based population hierarchical management method as described in any one of claims 1 to 7.

10. A readable storage medium storing computer-readable instructions, characterized in that, When the computer-readable instructions are executed by the processor, they implement the steps of the portrait-based population hierarchical management method as described in any one of claims 1 to 7.

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