Data processing method and device, electronic equipment and storage medium

Abstract

The invention provides a data processing method and device, electronic equipment and a storage medium, relates to the technical field of databases, and can avoid the problem of resource waste caused by inaccurate object evaluation. According to the specific technical scheme, an object set in a database is obtained; the object set comprises a plurality of target objects, and the target objects refer to objects capable of interacting with the server; for each target object in the object set, obtaining a plurality of first indexes of the target object and obtaining a plurality of second indexes of the target object; according to the plurality of first indexes, calculating the activity degree corresponding to the target object; calculating a participation degree corresponding to the target object according to the plurality of first indexes and the plurality of second indexes; and based on the activeness and the participation degree, determining a first target object corresponding to the server in the object set. By implementing the scheme, the resource utilization rate of the server can be improved.

Description

Technical Field

[0001] This application relates to the field of database technology, and in particular to a data processing method, apparatus, electronic device, and storage medium. Background Technology

[0002] Enterprise servers typically need to interact with a large number of objects (such as customers), which can access various resources provided by the server through the network. In order to optimize operational efficiency, enterprises need to identify high-value objects from among these objects, such as active objects and objects with high stickiness to the enterprise, in order to make precise resource allocation and operational strategy formulation.

[0003] Existing object filtering methods typically use a single dimension for filtering, such as filtering objects based on historical cost data or access count.

[0004] However, this filtering method easily filters out inactive or low-value objects with reduced stickiness to the enterprise. These low-value objects not only occupy storage space and continuously generate junk data in the database, affecting overall processing efficiency, but also lead to reduced resource utilization on the server side. Summary of the Invention

[0005] This application provides a data processing method, apparatus, electronic device, and storage medium that can improve the resource utilization of the server.

[0006] To achieve the above objectives, the embodiments of this application adopt the following technical solutions: Firstly, a data processing method is provided. This method first obtains a collection of objects from a database, which includes multiple target objects. These target objects are objects capable of interacting with the server. For example, in an application scenario, the target object could be a user account; in a system management scenario, the target object could be a device or terminal connected to the system.

[0007] For each target object in the object set, this method obtains multiple first indicators and multiple second indicators for the target object. The first indicators indicate the frequency of interaction between the target object and the server, reflecting how frequently the target object accesses the server; the second indicators indicate the depth of interaction between the target object and the server, reflecting the depth and level of engagement in the interaction. Specifically, the server includes first-type resources and second-type resources. The multiple first indicators include server interaction frequency, the first access frequency of the first-type resources, and the second access frequency of the second-type resources. The multiple second indicators include server interaction intensity, the interaction completion rate of the first-type resources, and the resource cost value of the second-type resources.

[0008] Subsequently, the method calculates the activity level of the target object based on multiple primary indicators. Activity level reflects the frequency of interaction between the target object and the server; by comprehensively calculating multiple primary indicators, the activity level of the target object can be fully assessed.

[0009] Furthermore, this method calculates the engagement level of the target object based on multiple primary and secondary indicators. Engagement level considers not only the frequency of interaction between the target object and the server but also the depth of interaction, providing a more comprehensive portrayal of the interaction relationship between the target object and the server. By simultaneously considering both frequency and depth, the engagement index can identify target objects that not only frequently access the server but also deeply utilize server resources.

[0010] Finally, this method identifies the primary target object for the server within the object set based on activity and engagement. By combining these two metrics, it is possible to filter out the core target object with the highest value to the server from the object set.

[0011] This solution, by constructing a multi-dimensional indicator system, evaluates target objects from two aspects: interaction frequency and interaction depth, enabling more accurate identification of high-value target objects on the server side. These primary target objects are typically those with frequent and in-depth interactions with the server. Compared to the single-dimensional screening methods in existing technologies, the method of this application embodiment can more accurately identify high-value objects, improve the accuracy of object screening, reduce the number of low-value objects, and thus improve the resource utilization rate of the server side.

[0012] In one possible implementation of the first aspect, the activity level of the target object is calculated based on multiple first indicators, including: weighting the server interaction frequency, the first access frequency, and the access frequency of the second type of resources according to the indicator weights corresponding to each first indicator, to obtain the activity level of the target object.

[0013] In this implementation, by assigning corresponding weights to different primary indicators, the varying degrees of importance of each indicator in the activity calculation can be reflected. For example, server-side interaction frequency may reflect overall activity levels more accurately than the access frequency of a single resource type, and therefore can be given a higher weight. This weighted calculation method improves the rationality of calculating multiple frequency indicators, preventing important indicators from being overlooked, and thus ensuring that the calculated activity level more accurately reflects the true activity level of the target object.

[0014] In another possible implementation of the first aspect, the participation degree corresponding to the target object is calculated based on multiple first indicators and multiple second indicators, including: calculating a first score of participation degree based on the server-side interaction frequency and the server-side interaction intensity; calculating a second score of participation degree based on the first access frequency and the interaction completion degree of the first type of resource; calculating a third score of participation degree based on the second access frequency and the resource cost value of the second type of resource; and using the sum of the first score, the second score, and the third score as the participation degree corresponding to the target object.

[0015] In this implementation, engagement is determined through a layered calculation approach. First, at the server-side overall level, a first score is calculated based on interaction frequency and intensity, reflecting the overall engagement of the target user with the server. Then, for the first type of resource, a second score is calculated based on access frequency and interaction completion rate, reflecting the target user's depth of use of that resource. Further, for the second type of resource, a third score is calculated based on access frequency and resource cost, reflecting the target user's level of investment in that resource. Finally, the three scores are summed to obtain the overall engagement score.

[0016] This hierarchical calculation method can comprehensively evaluate the participation of target objects from the perspective of different resource types, so that the participation index includes both frequency and depth dimensions, thus improving the accuracy of the determined participation level.

[0017] In another possible implementation of the first aspect, obtaining multiple first indicators of the target object includes: obtaining multiple first indicator data of the target object for each time period within the target time period; wherein, the multiple first indicator data corresponds one-to-one with multiple first indicators, the first indicator data corresponding to the server interaction frequency is used to indicate whether the target object interacts with the server within the corresponding time period, the first indicator data corresponding to the first access frequency is used to indicate whether the target object accesses a first type of resource within the corresponding time period, and the first indicator data corresponding to the second access frequency is used to indicate whether the target object accesses a second type of resource within the corresponding time period; calculating the frequency value of the target object within the target time period based on the first indicator data of the target object for multiple time periods within the target time period and the period weight corresponding to each time period; and calculating the first indicator of the target object based on the frequency value of the target object within the target time period and the number of periods included in the target time period.

[0018] This approach, by dividing the target time period into multiple time cycles and recording the interaction behavior within each cycle, can capture the temporal distribution characteristics of the interaction between the target object and the server. For example, the target time period could be the past 100 days, and the time cycle could be daily, allowing for the recording of whether the target object exhibits corresponding interaction behavior each day. By assigning cycle weights to different time cycles, the influence of data from time cycles closer to the current time on the current evaluation can be reflected. Compared to simple counting methods, this approach more accurately reflects the target object's interaction behavior, improving the reliability of the primary indicator.

[0019] In another possible implementation of the first aspect, multiple second indicators of the target object are obtained, including: obtaining the server interaction intensity based on the number of interactions between the target object and the server in each time period within the target time period; calculating the interaction completion rate of the first type of resource based on the sum of the second indicator data of the target object in each time period within the target time period and the number of time periods included in the target time period, wherein the second indicator data is used to indicate the completion progress of the interaction task of the target object in the corresponding time period; and determining the resource overhead value of the second type of resource based on the consumption data generated by the target object in each time period within the target time period when acquiring the second type of resource.

[0020] This scheme employs different calculation methods to obtain different types of secondary indicators. For server-side interaction intensity, the density of interactions is quantified by counting the number of interactions; more interactions indicate more frequent and in-depth interaction between the target object and the server. For the interaction completion rate of the first type of resource, the depth of interaction with the first type of resource is reflected by averaging the task completion progress over various time periods. For the resource cost value of the second type of resource, the depth of interaction with the target object on that second resource is quantified by statistically analyzing consumption data. This method of using different calculation methods for different indicator characteristics can more accurately characterize the depth of interaction between the target object and the server across different dimensions, improving the accuracy and comprehensiveness of the secondary indicators.

[0021] In another possible implementation of the first aspect, determining the first target object corresponding to the server in the object set based on activity and engagement includes: determining the object with the highest overall result of activity and engagement from the object set as the first target object corresponding to the server.

[0022] In this implementation, by comprehensively evaluating activity and engagement, target objects with both high access frequency and deep interaction depth can be screened out, thereby improving the accuracy of target objects.

[0023] Secondly, a data processing apparatus is provided. This apparatus includes an object acquisition module, an indicator acquisition module, a first calculation module, a second calculation module, and an object filtering module.

[0024] The object retrieval module is used to retrieve a collection of objects from the database. The object collection includes multiple target objects, which are objects that can interact with the server.

[0025] The metric acquisition module is used to acquire multiple first metrics and multiple second metrics for each target object in the object set. The first metrics indicate the frequency of interaction between the target object and the server, and the second metrics indicate the depth of interaction between the target object and the server. The server includes first-type resources and second-type resources. The multiple first metrics include server interaction frequency, first access frequency of first-type resources, and second access frequency of second-type resources. The multiple second metrics include server interaction intensity, interaction completion rate of first-type resources, and resource cost value of second-type resources.

[0026] The first calculation module is used to calculate the activity level of the target object based on multiple primary indicators.

[0027] The second calculation module is used to calculate the participation rate of the target object based on multiple first indicators and multiple second indicators.

[0028] The object filtering module is used to determine the first target object corresponding to the server in the object set based on activity and participation.

[0029] Thirdly, an electronic device is provided, the method comprising: a memory and at least one processor. The memory is communicatively connected to the processor. The memory is used to store computer program code, the computer program code including computer instructions. When the processor executes the computer instructions, it causes the electronic device to perform the method of the first aspect and any possible implementation thereof.

[0030] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer instructions. When these computer instructions are executed by a processor, they are used to implement the method as described in the first aspect and any possible implementation thereof.

[0031] Fifthly, embodiments of this application provide a computer program product that, when running on a computer or executed by the computer's processor, implements the method described in the first aspect and any possible design thereof. The computer may be an electronic device as described in the second aspect and any possible implementation thereof.

[0032] Understandably, the beneficial effects achieved by the data processing apparatus of the second aspect, the electronic device of the third aspect, the computer-readable storage medium of the fourth aspect, and the computer program product of the fifth aspect provided above can be referred to as the beneficial effects of the first aspect and any possible implementation thereof, which will not be repeated here. Attached Figure Description

[0033] Figure 1 This is a schematic diagram illustrating an application scenario involved in the data processing method provided in the embodiments of this application; Figure 2 This is a schematic diagram illustrating another application scenario involved in the data processing method provided in the embodiments of this application; Figure 3 A schematic flowchart of a data processing method provided in an embodiment of this application; Figure 4 Another flowchart illustrating the data processing method provided in this application embodiment; Figure 5 This is a schematic diagram of the structure of a data processing device provided in an embodiment of this application; Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0034] Hereinafter, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this embodiment, unless otherwise stated, "a plurality of" means two or more.

[0035] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0036] The collection, storage, use, processing, transmission, provision, and disclosure of information such as the first indicator and the second indicator involved in the technical solutions provided in this application comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0037] It should be noted that in the embodiments of this application, certain software, components, models and other existing solutions in the industry may be mentioned. These should be regarded as exemplary and are only intended to illustrate the feasibility of implementing the technical solution of this application. However, it does not mean that the applicant has used or necessarily used the solution.

[0038] In today's internet landscape, particularly in internet marketing, businesses or service providers (hereinafter referred to as the server) frequently need to interact with customer groups (hereinafter referred to as the target audience) to promote resources or services. Please refer to [link / reference]. Figure 1 , Figure 1 This is a schematic diagram of an application scenario involving the data processing method provided in the embodiments of this application. In this scenario, a server 101 can interact with multiple clients 102 within a client group.

[0039] As an example, in a common marketing scenario such as "group buying," it's necessary to identify high-value individuals within the customer base and designate them as organizers or group leaders within that customer group. These organizers can then assist the server in subsequent content promotion, achieving marketing goals at a lower cost.

[0040] Currently, the following methods can be used to identify high-value individuals within a customer group. The first method involves identifying high-value individuals through manual offline outreach; that is, the server can receive input of specific individuals and identify them as organizers of a customer group. The second method involves the service sending invitations to each or some users within the customer group, inviting them to become organizers of that group. Upon receiving confirmation of the invitation, the server identifies the customer corresponding to the confirmation as the organizer of the customer group. The third method involves the server identifying high-value individuals within a customer group using single-dimensional data, such as the customer's time or financial expenditure on the server.

[0041] However, neither the offline promotion in the first method nor the online promotion in the second method, nor the filtering based on a single dimension of data, can achieve accurate evaluation of each customer. The organizers identified by the server play a relatively minor role and are considered low-value objects. These low-value objects not only occupy storage space and continuously generate junk data in the database, affecting overall processing efficiency, but also increase the resource waste rate of the server.

[0042] For example, consider N customer groups, each of which may include K objects. The server identifies an organizer for each customer group, resulting in N organizers. If each organizer is a high-value object capable of fulfilling the resource output tasks provided by the server, such as increasing product sales or increasing the number of participants in activities, then N×K represents the total actual resource output. If M organizers are low-value objects unable to fulfill the resource output tasks provided by the server, then (NM)×K represents the total actual resource output. Clearly, a decrease in the total actual resource output, which characterizes the effectiveness of resource output, indicates increased resource waste on the server, meaning that server resources are not being fully utilized. Here, N, M, and K are positive integers, with M less than or equal to N.

[0043] Based on this, embodiments of this application provide a data processing method. This method can acquire multiple metrics for each object to accurately determine the target objects needed by the server within the object set, thereby improving server resource utilization.

[0044] The data processing method provided in this application can be applied to electronic devices with data processing capabilities. Alternatively, the electronic device may include a personal computer (PC), tablet computer, laptop computer, portable computer (such as a mobile phone), wearable electronic device (such as a smartwatch), augmented reality (AR) / virtual reality (VR) device, in-vehicle computer, etc. The following embodiments do not impose special limitations on the specific form of the electronic device. The execution subject of the data processing method provided in this application can be the aforementioned electronic device or a data processing apparatus, and the data processing method can be integrated into the electronic device or the processor of the electronic device.

[0045] For ease of explanation, the following content of the embodiments of this application will use the server as the execution subject to describe the data processing process. In the embodiments of this application, the server can refer to the terminal device corresponding to the enterprise employee or the server corresponding to the enterprise. There is no limitation on this. It can be any electronic device in the enterprise that can interact with customers.

[0046] The server retrieves a collection of objects from the database. For each target object in the collection, it obtains multiple primary metrics and multiple secondary metrics. The primary metrics indicate the frequency of interaction between the target object and the server, while the secondary metrics indicate the depth of interaction. The server calculates the activity level of the target object based on the primary metrics and the engagement level based on both the primary and secondary metrics. Based on the activity level and engagement level, the server determines the primary target object for the server within the object collection.

[0047] Please refer to Figure 2 , Figure 2 This is a schematic diagram illustrating another application scenario involving the data processing method provided in this application embodiment. The server 201 may include a first type of resource and a second type of resource. Multiple target objects 202 can interact with the server 201 and access either the first type of resource or the second type of resource within the server 201. To accurately identify high-value target objects among the multiple target objects, in this application embodiment, multiple first indicators may include server interaction frequency, a first access frequency of the first type of resource, and a second access frequency of the second type of resource. Multiple second indicators may include server interaction intensity, interaction completion rate of the first type of resource, and resource overhead value of the second type of resource. The first and second indicators will not be described in detail here; please refer to subsequent content.

[0048] This application's embodiments introduce multiple first and second indicators to calculate two object attributes: activity level and engagement level, thereby evaluating object value across multiple dimensions. Through joint screening of activity level and engagement level, high-value objects, such as customers with high activity levels and strong enterprise loyalty, are accurately identified, improving the accuracy of object screening, reducing invalid and junk data in storage space, and increasing server-side resource utilization.

[0049] Please refer to Figure 3 , Figure 3 This is a schematic flowchart of a data processing method provided in an embodiment of this application. The method includes the following steps.

[0050] S301, The server retrieves a collection of objects from the database.

[0051] The database refers to the database that stores server-side data. The object collection refers to the collection of multiple target objects stored in the database. A target object is an object that can interact with the server; for example, it could be a client, user, or terminal device that establishes a connection with the server. In one example, the server could be a company's server or a terminal device used by company employees. In an application scenario, the target object could be a user account. In a system management scenario, the target object could be a device or terminal accessing the system.

[0052] In some embodiments, the object set may include all target objects in the database, or a subset of target objects in the database that meet specific conditions. For example, the server may retrieve target objects with maintained relationships from the database as an object set, where a maintained relationship refers to a specific connection between the target object and the server. As an example, in a corporate marketing scenario, target objects with maintained relationships could refer to customers maintained by company employees, i.e., customers with whom company employees have established contact. Such customers have typically already interacted with the company and are more likely to become high-value objects compared to customers without maintained relationships.

[0053] In some embodiments, the target objects in the object collection can be objects within the same private domain. For example, multiple objects in a group chat.

[0054] After obtaining the object set, the server needs to evaluate each target object in the set to identify the high-value objects required by the server. To accurately evaluate the value of each target object, in this embodiment, the server needs to obtain multi-dimensional indicator data for each target object.

[0055] S302, for each target object in the object set, the server obtains multiple first indicators and multiple second indicators of the target object.

[0056] The first metric indicates the frequency of interaction between the target object and the server. Interaction frequency refers to how often the target object interacts with the server. The higher the value of the first metric, the more frequently the target object interacts with the server. The second metric indicates the depth of interaction between the target object and the server. Interaction depth refers to the extent or closeness of the interaction between the target object and the server. The higher the value of the second metric, the closer and more intimate the interaction between the target object and the server.

[0057] In this embodiment, the server includes a first type of resource and a second type of resource. The first and second types of resources are different types of resources provided by the server. For example, in a business marketing scenario, the first type of resource can be the activity resources of the business organization, such as marketing activities, promotional activities, group-buying activities, etc. The second type of resource can be product resources or commodity resources provided by the business. It should be noted that the specific types of the first and second types of resources can be adjusted according to the actual application scenario, and this embodiment does not impose any restrictions on this.

[0058] Correspondingly, the multiple primary metrics include server-side interaction frequency, the primary access frequency of primary type resources, and the secondary access frequency of secondary type resources. Server-side interaction frequency refers to the frequency with which the target object interacts with the server. Primary access frequency refers to the frequency with which the target object accesses primary type resources. Secondary access frequency refers to the frequency with which the target object accesses secondary type resources.

[0059] Several secondary metrics include server-side interaction intensity, interaction completion rate of the first type of resource, and resource overhead value of the second type of resource. Server-side interaction intensity refers to the intensity or depth of interaction between the target object and the server. Interaction completion rate of the first type of resource refers to the degree to which the target object completes its interaction with the first type of resource. Resource overhead value of the second type of resource refers to the cost incurred by the target object in acquiring the second type of resource.

[0060] It should be noted that the three primary indicators and three secondary indicators described above depict the interaction between the target object and the server from different dimensions. Server interaction frequency and intensity reflect the overall frequency and depth of interaction between the target object and the server. The primary access frequency and the completion rate of the primary resource type reflect the frequency and depth of the target object's access to the primary resource type. The secondary access frequency and the resource cost of the secondary resource type reflect the frequency and depth of the target object's access to the secondary resource type. Through these multi-dimensional indicators, a comprehensive assessment of the interaction between the target object and the server can be achieved.

[0061] In some embodiments, the process of the server obtaining multiple first indicators of the target object may include: the server obtaining multiple first indicator data of the target object for each time period within the target time period; calculating the frequency value of the target object within the target time period based on the first indicator data of the target object for multiple time periods within the target time period and the period weight corresponding to each time period; and calculating the first indicator of the target object based on the frequency value of the target object within the target time period and the number of periods included in the target time period.

[0062] The target time period refers to the time range used to collect statistical data on the interaction of the target object. For example, the target time period can be a period of time counting backwards from the current time, such as the last 100 days, the last 90 days, or the last 30 days. The time period refers to the smallest statistical unit within the target time period. For example, the time period can be a day, a week, etc. The target time period can include multiple time periods. For example, if the target time period is the last 100 days and the time period is one day, then the target time period includes 100 time periods.

[0063] The first indicator data is used to indicate whether the target object exhibits specific interactive behavior within a corresponding time period. Multiple first indicator data points correspond one-to-one with multiple first indicators. Specifically, the first indicator data corresponding to the server-side interaction frequency indicates whether the target object interacts with the server within a corresponding time period. The first indicator data corresponding to the first access frequency indicates whether the target object accesses a first-type resource within a corresponding time period. The first indicator data corresponding to the second access frequency indicates whether the target object accesses a second-type resource within a corresponding time period.

[0064] As an example, the first metric data can be binary data, such as 0 or 1. If the target object exhibits a corresponding interactive behavior within a certain time period, the first metric data for that time period can be set to 1. If the target object does not exhibit a corresponding interactive behavior within a certain time period, the first metric data for that time period can be set to 0.

[0065] For example, regarding the frequency of server-side interactions, if the target object is on the [number]th [time]... If an interaction occurs with the server within a given time period, such as contacting the server online, then the frequency of server-side interaction within that period is [number missing]. The first indicator data corresponding to each time period It can be set to 1. If the target object is at the [missing information], If there is no interaction with the server within a certain time period, then the server interaction frequency is within that period. The first indicator data corresponding to each time period It can be set to 0. Wherein, A positive integer, representing the nth time before the current time. Each time period.

[0066] Similarly, for the first access frequency, if the target object is in the first... If a user accesses a first-type resource within a given time period, such as browsing an activity link sent by the server, then the first access frequency is defined as follows: The first indicator data corresponding to each time period can be set to 1. If the target object is in the [number]th [period]... If the first type of resource is not accessed within a certain time period, then the first access frequency is in the 1st time period. The first indicator data corresponding to each time period can be set to 0. For the second access frequency, if the target object is in the [number]th [period]... If a user accesses a second type of resource within a given time period, such as browsing a product link sent by the server, then the frequency of this second access is in the [number]th [period]. The first indicator data corresponding to each time period can be set to 1. If the target object is in the [number]th [period]... If the second type of resource is not accessed within a certain time period, then the access frequency of the second type of resource is in the 1st time period. The first indicator data corresponding to each time period can be set to 0.

[0067] After obtaining the primary indicator data of the target object for each time period within the target time period, the server can calculate the primary indicator of the target object based on this primary indicator data. Specifically, the server can first calculate the frequency value of the target object within the target time period based on the primary indicator data of the target object for multiple time periods within the target time period and the period weight corresponding to each time period.

[0068] Period weights are used to represent the importance of different time periods. In some embodiments, different time periods can have different period weights. For example, the closer a time period is to the current time, the greater its period weight can be, indicating that the server places more emphasis on the recent interaction behavior of the target object. As an example, suppose the target time period includes 100 time periods, and the period weight is... The period weight corresponding to each time period can be set to... Thus, the period weight corresponding to the first time period is the largest, that is, the period weight of the most recent time period is 99%, and the period weight corresponding to the 100th time period is the smallest, that is, the period weight of the furthest time period is 1%. For ease of explanation, the following content of the embodiments of this application also assumes that the target time period includes 100 time periods.

[0069] The server can perform a weighted summation of the primary indicator data of the target object across multiple time periods within a target time frame, along with the corresponding period weights, to obtain the frequency value. Specifically, for a given primary indicator, the server can multiply the primary indicator data for each time period by the corresponding period weight, and then sum the products of all time periods to obtain the frequency value corresponding to that primary indicator.

[0070] For example, regarding server-side interaction frequency, assuming the target time period includes 100 time cycles, the first... The first indicator data corresponding to each time period is: , No. The period weight corresponding to each time period is: Then the server can multiply the 100 first indicator data corresponding to 100 time periods with the corresponding period weight to obtain the frequency value corresponding to the server interaction frequency.

[0071] After obtaining the frequency value, the server can calculate the first indicator based on the frequency value and the number of time periods included in the target time period. Specifically, the server can divide the frequency value by the number of time periods to obtain the first indicator. For example, if the target time period includes 100 time periods, the server can divide the frequency value by 100 to obtain the first indicator.

[0072] The server can calculate the server interaction frequency using the following formula: ; in, For server-side interaction frequency, The frequency of server-side interaction is on the first The first indicator data corresponding to each time period.

[0073] For the first access frequency, the server can calculate the first access frequency using the following formula: ; in, The first access frequency, The first access frequency is in the The first indicator data corresponding to each time period.

[0074] For the second access frequency, the server can calculate the second access frequency using the following formula: ; in, For the second access frequency, For the second access frequency in the The first indicator data corresponding to each time period.

[0075] In this way, the server can calculate three primary metrics: server interaction frequency, first access frequency, and second access frequency. It should be noted that since the primary metric data is binary data (0 or 1), and the period weight is a positive integer, the primary metric obtained by dividing the frequency value by the number of periods is usually a value between 0 and 1, representing the frequency of the target object's corresponding interactive behavior within the target time period.

[0076] In some embodiments, the process of the server obtaining multiple second indicators of the target object may include: the server obtaining the interaction intensity based on the number of interactions between the target object and the server in each time period within the target time period; the server calculating the interaction completion degree of the first type of resource based on the sum of the second indicator data of the target object in each time period within the target time period and the number of time periods included in the target time period; and the server determining the resource cost value of the second type of resource based on the consumption data generated by the target object in each time period within the target time period when obtaining the second type of resource.

[0077] First, regarding server-side interaction intensity, the server can count the number of interactions between the target object and the server in each time period within the target time frame. The number of interactions refers to the specific number of times the target object interacts with the server. For example, in a corporate marketing scenario, the number of interactions could be the number of times the target object contacts company employees online.

[0078] In some embodiments, the server can calculate the interaction depth of the target object in each time period within a target time period. This interaction depth is obtained by converting the number of interactions between the target object and the server in the corresponding time period. For example, if the number of interactions of the target object in a certain time period is greater than or equal to a threshold, the server can determine the interaction depth of the target object in that time period as a preset fixed value. If the number of interactions of the target object in a certain time period is less than the threshold, the server can divide the number of interactions in that time period by the threshold to obtain the interaction depth of the target object in that time period.

[0079] As an example, the number of attempts threshold can be set to 10. If the target object is in the 10th... Interacted with the server within a certain time period times, when When the value is less than 10, the interaction depth of the target object during the time period can be set to... When n is greater than or equal to 10, the interaction depth of the target object during the time period can be set to 1.

[0080] The server can obtain the maximum value of the interaction depth of the target object across all time periods within the target time period, which can be used as the server-side interaction strength. In one example, if the target time period includes 100 time periods, the server can calculate the server-side interaction strength using the following formula: ; in, For server-side interaction intensity, To obtain the maximum value, For the target object in the first Interaction depth within a time period.

[0081] By taking the maximum value, the maximum intensity of interaction between the target object and the server can be reflected.

[0082] Secondly, regarding the completion rate of the interaction with the first type of resource, the server needs to obtain the second indicator data for each time period within the target time frame. This second indicator data indicates the progress of the interaction tasks performed by the target object within the corresponding time period. An interaction task refers to the task that the target object needs to complete when interacting with the first type of resource.

[0083] As an example, in a corporate marketing scenario, the first type of resource can be an event resource, and the interactive task can be the task of participating in the event. The task of participating in the event can include multiple stages, such as browsing the event, registering for the event, completing the event, and claiming rewards. Different stages correspond to different completion progresses.

[0084] In some embodiments, the server can set a progress value for each stage. For example, when the target object is in stage 1... When browsing activities within a certain time period, the interaction completion rate is at the [number]th [period]. The second indicator data for each time period can be set to 0.1. When the target object is in the [number]th [period]... If an activity is viewed and registered within a given time period, the interaction completion rate is [percentage missing]. The second metric for each time period can be set to 0.4. This interaction completion rate is calculated when the target object browses and completes the activity within the t-th time period. The second indicator data for each time period can be set to 0.9. When the target object is in the [number]th [period]... If an activity is viewed, completed, and rewards are claimed within a given time period, the interaction completion rate is [percentage missing]. The second indicator data for each time period can be set to 1.0.

[0085] It should be noted that if the target object does not interact with the first type of resource within a certain time period, such as not browsing any activities, the second indicator data for that time period can be set to 0. Conversely, if the target object interacts with different first type of resources within a certain time period, such as browsing multiple activities, then that time period can correspond to multiple second indicator data points, each representing the target object's progress towards different activities.

[0086] The server can sum the second indicator data of the target object across all time periods within the target time period, obtaining the sum of the second indicator data. Then, the server can divide the sum of the second indicator data by the number of time periods included in the target time period to obtain the interaction completion rate of the first type of resource. As an example, the server can calculate the interaction completion rate of this first type of resource using the following formula: ; in, The interaction completion rate of this first type of resource. For the interaction completion rate in the 1st The second indicator data for each time period.

[0087] Additionally, it's important to understand that only the highest second-highest metric data is used for an activity within the target time period. For example, if a target audience is at different stages of the same first-type resource at different time periods—browsing the activity, registering for the activity, or completing the activity—and the target audience's progress value for that first-type resource within the target time period can be the progress value corresponding to completing the activity, i.e., 0.9.

[0088] Finally, for the resource cost value of the second type of resource, the server needs to obtain the consumption data generated by the target object in each time period within the target time period when acquiring the second type of resource. Consumption data refers to the consumption or expenditure generated by the target object in acquiring the second type of resource. As an example, in a business marketing scenario, the second type of resource can be a product resource, and the consumption data can be the amount spent by the target object on purchasing the product.

[0089] In some embodiments, the server can set a transformation function for consumption data for each time period to convert the consumption data into resource overhead data. As an example, the transformation function could be a logarithmic function. For example, if the target object incurs an expense of [amount] for acquiring a second type of resource in the t-th time period... Yuan. In For amounts less than 1 million yuan, the resource consumption data for this time period can be set to... .exist For transactions of 1 million yuan or more, the resource expenditure data for that time period can be set to 1. By using a logarithmic function, the consumption data can be mapped to a reasonable numerical range to serve as the resource expenditure data.

[0090] If the target object is in the... If no overhead is incurred for acquiring the second type of resource within a given time period, then the resource overhead data for that time period can be set to 0. Additionally, the server can also obtain overhead flags indicating whether the target object incurred overhead for acquiring the second type of resource within each time period. If the target object in the... If the target object incurs overhead for acquiring the second type of resource within a certain time period, the overhead flag can be set to 1. If the target object does not incur overhead for acquiring the second type of resource within the t-th time period, the overhead flag can be set to 0.

[0091] The server can calculate the sum of resource overhead data for the target object within the target time period, and divide it by the number of time periods incurred in acquiring the second type of resource to obtain the resource overhead value of the second type of resource. Specifically, the server can calculate the resource overhead value of the second type of resource according to the following formula: ; in, This represents the resource overhead value for the second type of resource. This refers to the resource cost data of the target object in the t-th time period. The cost identification data for the target object in the t-th time period.

[0092] In this way, the average cost for the target object to acquire the second type of resources within the target time period can be reflected.

[0093] It should be noted that if the target object does not incur any expense in acquiring the second type of resource within the target time period, that is, the purchase identifier data for all time periods is 0, then the resource expense value for the second type of resource can be directly set to 0.

[0094] Through the above process, the server can obtain three primary metrics of the target object: server-side interaction frequency, primary access frequency, and secondary access frequency; and three secondary metrics: server-side interaction intensity, interaction completion rate of the first type of resource, and resource overhead value of the second type of resource. These metrics comprehensively characterize the interaction between the target object and the server from different dimensions.

[0095] S303, the server calculates the activity level of the target object based on multiple primary indicators.

[0096] Activity level refers to the degree of activity between the target object and the server. Higher activity level indicates more frequent interaction between the target object and the server. In this embodiment, the server can calculate activity level based on three first indicators: server interaction frequency, first access frequency, and second access frequency.

[0097] In some embodiments, the server can perform a weighted calculation of the server interaction frequency, the first access frequency, and the second access frequency according to the corresponding weights of each first indicator to obtain the activity level of the target object. The indicator weights are used to represent the degree of contribution of different first indicators to the activity level. Different first indicators can have different indicator weights.

[0098] As an example, the server can multiply the server interaction frequency by the weight of the first metric, the first access frequency by the weight of the second metric, and the second access frequency by the weight of the third metric, then add the three products together to obtain the activity level. Specifically, the server can calculate the activity level of the target object using the following formula: ; In this calculation, the first metric corresponding to the server-side interaction frequency has a weight of 1, the second metric corresponding to the first access frequency has a weight of 5, and the third metric corresponding to the second access frequency has a weight of 10. By adjusting the weights of different first metrics, the calculation method for activity can be adjusted according to the needs of the actual application scenario. It should be noted that the specific values ​​of the above metric weights are only examples and can be adjusted based on experience or experimental results in actual applications.

[0099] S304, the server calculates the participation rate of the target object based on multiple primary indicators and multiple secondary indicators.

[0100] Engagement level refers to the degree of participation or stickiness of the target object in its interaction with the server. Higher engagement level indicates a deeper interaction and a closer connection between the target object and the server. In this embodiment, the server can calculate engagement level based on three first indicators and three second indicators.

[0101] In some embodiments, the server can calculate multiple engagement scores separately and then sum these scores to obtain the engagement score. Specifically, the server calculates a first engagement score based on the server interaction frequency and the server interaction intensity; calculates a second engagement score based on a first access frequency and the interaction completion rate of a first type of resource; calculates a third engagement score based on a second access frequency and the resource cost value of a second type of resource; and uses the sum of the first, second, and third scores as the engagement score corresponding to the target object.

[0102] The first score indicates the level of participation of the target object in the overall interaction with the server. The server can obtain the first score by multiplying the server interaction frequency by the server interaction intensity. Since the server interaction frequency reflects how frequently the target object interacts with the server, and the server interaction intensity reflects how deeply the target object interacts with the server, the product of the two can comprehensively reflect the level of participation of the target object in the interaction with the server.

[0103] The second score indicates the target object's level of participation in the first type of resource. The server can multiply the first access frequency by the interaction completion rate of the first type of resource to obtain the second score. Since the first access frequency reflects how frequently the target object accesses the first type of resource, and the interaction completion rate of the first type of resource reflects the target object's level of completion of the first type of resource, the product of the two can comprehensively reflect the target object's level of participation in the first type of resource.

[0104] The third score reflects the target object's level of participation in the second type of resource. The server can multiply the second access frequency by the resource cost of the second type of resource to obtain the third score. Since the second access frequency reflects how frequently the target object accesses the second type of resource, and the resource cost of the second type of resource reflects the cost incurred by the target object in acquiring the second type of resource, their product can comprehensively reflect the target object's level of participation in the second type of resource.

[0105] In some embodiments, the server can multiply the first score, second score, and third score by their respective weighting coefficients, and then sum them to obtain the participation score. Specifically, the server can calculate the participation score using the following formula: ; The first score is The corresponding weight coefficient is 1, and the second score is The corresponding weight coefficient is 5, and the third score is The corresponding weighting coefficient is 10. It should be noted that the specific values ​​of the weighting coefficients mentioned above are merely examples and can be adjusted based on experience or experimental results in practical applications. By adjusting the weighting coefficients for different scores, the calculation method for participation can be tailored to the needs of the specific application scenario.

[0106] Through the above calculation process, the server can obtain the target object's activity level and engagement level. Activity level reflects the frequency of interaction between the target object and the server, while engagement level reflects the depth of interaction. These two metrics comprehensively evaluate the value of the target object from different perspectives.

[0107] S305, the server determines the first target object corresponding to the server in the object set based on activity and participation.

[0108] The first target object refers to a high-value object identified by the server from the object set. In this embodiment, the server can determine the first target object based on its activity and engagement. Target objects with high activity and engagement are typically those that interact frequently and deeply with the server; such objects are more valuable.

[0109] In some embodiments, the server can determine the object with the highest overall activity and engagement score from the object set, and use it as the server's first target object. The overall score refers to the comprehensive evaluation result calculated based on activity and engagement. The server can calculate the overall score in various ways.

[0110] As an example, the server can add up activity and engagement to obtain a comprehensive result. The server can calculate the comprehensive result for each target object in the object set, and then select the target object with the largest comprehensive result as the first target object.

[0111] As another example, the server can multiply activity and engagement by their respective overall weights, and then sum them to obtain the overall result. By adjusting the overall weights, the contribution of activity and engagement to the overall result can be controlled.

[0112] As another example, the server can multiply activity level and engagement level to obtain a combined result. This approach requires the target audience to have both high activity level and high engagement level to achieve a high combined result.

[0113] In some embodiments, the server can set a comprehensive threshold. The server can identify target objects whose comprehensive results are greater than the comprehensive threshold as the first target objects, thereby avoiding misjudging target objects with excessively low comprehensive results as high-value objects.

[0114] In some embodiments, the server can determine multiple first target objects. For example, the server can sort all target objects in the object set according to the synthesis results from largest to smallest, and select the top i target objects as the first target objects, where i is a positive integer. As an example, the server can select the top 10, top 20, or top 50 target objects with the highest synthesis results as the first target objects.

[0115] Through the data processing method described above, the server can accurately identify high-value primary target objects from the object set. These primary target objects are typically those that interact frequently and deeply with the server. Compared to existing technologies that use random promotion or single-dimensional filtering, the method in this application embodiment can more accurately identify high-value objects, improve the accuracy of object filtering, reduce the number of low-value objects, and thus improve the resource utilization of the server.

[0116] In some embodiments, after identifying the first target object, the server can send an invitation message to the first target object, inviting it to become a specific role. For example, in a group-buying marketing scenario, the server can send an invitation message to the first target object, inviting it to become a group leader.

[0117] In some embodiments, the server can also generate a recommendation list of the first target object. The recommendation list may include information such as the first target object's identifier, overall results, activity level, and engagement level. The server can provide this recommendation list to company employees for their reference. In response to employee actions, the server can determine whether to send an invitation message to the corresponding first target object.

[0118] Please refer to Figure 4 , Figure 4 This is another schematic flowchart illustrating the data processing method provided in an embodiment of this application. The method may include the following steps.

[0119] S401, the server receives the interaction request from the target object.

[0120] An interaction request refers to an interaction request sent by a target object to a server through a terminal device. Interaction requests can include various types, such as query requests, access requests, purchase requests, and contact requests. For example, a target object can access a webpage, application, or mini-program provided by the server through its terminal device to initiate access to either type one or type two resources. The target object can also contact the server through instant messaging tools, email, or other means.

[0121] S402, the server processes interaction requests and records interaction data.

[0122] After receiving an interaction request, the server can process it accordingly based on the type of request. For example, if the interaction request is for accessing a first-type resource, the server can send the content of the first-type resource to the target device. If the interaction request is for purchasing a second-type resource, the server can handle the purchase process, such as generating an order and processing payment.

[0123] Simultaneously, the server can record the interaction data for this interaction and store the interaction data in the database. Interaction data can include information such as interaction time, interaction type, interaction content, and target object identifier. For example, the server can record information such as the target object accessing an activity link, purchasing a product, or contacting the server at a certain time.

[0124] S403, the server determines whether the calculation trigger condition has been met.

[0125] The calculation trigger condition refers to the condition that triggers the server to calculate activity and engagement. The server can set various calculation trigger conditions.

[0126] In some embodiments, the calculation trigger condition can be a time-based condition. For example, the server can set a specific time each day, week, or month as the calculation trigger time. When the calculation trigger time is reached, the server can perform calculations on activity and engagement.

[0127] In some embodiments, the calculation trigger condition can be an event condition. For example, the server can be configured to trigger a calculation when a calculation request is received. The calculation request could be a request sent by an employee via a terminal device, indicating a need to obtain the latest recommended list of the first target object.

[0128] In some embodiments, the calculation trigger condition can be a data condition. For example, the server can set a trigger condition when the amount of interactive data in the database exceeds a threshold.

[0129] If the server determines that the calculation trigger condition has been met, it executes S404. If the server determines that the calculation trigger condition has not been met, it returns to execute S401 and continues to receive and process interaction requests from the target object.

[0130] S404, the server performs the calculation of activity and engagement.

[0131] The server can calculate the activity and participation of each target object in the object set according to the above embodiments. The specific calculation process can be found in the descriptions of S302 to S304 above.

[0132] S405, the server determines the first target object.

[0133] The server can calculate a comprehensive result based on the activity and engagement of the target object, and determine the first target object based on the comprehensive result. The specific determination process can be found in the description of S305 above.

[0134] S406, the server sends an invitation message to the first target object.

[0135] An invitation message is used to invite a primary target audience to become a specific role or participate in a specific activity. In a group-buying marketing scenario, an invitation message might be an invitation to the primary target audience to become a group leader. The invitation message can include the invitation content, role description, benefits description, and operation instructions.

[0136] The server can send invitation messages to the primary target audience in various ways. For example, the server can send invitation messages via instant messaging tools, SMS, email, application push notifications, etc. The server can also display the invitation message on the page when the primary target audience visits the server.

[0137] S407, The server receives the response information of the first target object to the invitation information.

[0138] After receiving the invitation, the first target can choose to accept or decline the invitation. The first target can send a response message to the server through its terminal device. The response message indicates the first target's confirmation of the invitation, such as accepting or declining the invitation.

[0139] If the response message indicates that the first target object should accept the invitation, the server can assign the corresponding role or permissions to that first target object. For example, in a group-buying marketing scenario, the server can set the first target object as the group leader and provide it with the functions and benefits required by the group leader, such as creating group-buying activities, inviting group members, and viewing the group-buying progress.

[0140] If the response indicates that the first target object has declined or failed to respond to the invitation, the server can mark that first target object as having not accepted the invitation. The server can then choose to send invitation messages to other first target objects, or resend the invitation message to the first target object at a later time.

[0141] Through the above process, the server can record the interaction data of the target object in real time, calculate the activity and participation level periodically or as needed, determine the first target object, and send invitation information to the first target object, thus realizing the automated processing of object identification and invitation.

[0142] In some embodiments, the server can also incorporate the target object's social influence into the data processing method described above. Social influence refers to the target object's ability to influence others within a social network. In some application scenarios, a target object with high social influence can motivate more other objects to participate in activities, thus possessing greater value.

[0143] The server can obtain social network data of the target, such as the number of friends, followers, and interaction frequency. The server can then calculate the target's social influence based on this data. When identifying the primary target, the server can combine social influence with activity and engagement to determine a comprehensive result.

[0144] In some embodiments, the server can also use different indicator weights or calculation methods for different types of target objects. For example, the server can classify target objects into different types based on their attributes, such as age, gender, region, and consumption level. Different types of target objects may exhibit differences in activity and engagement, thus different indicator weights or calculation methods can be used.

[0145] As an example, for a target audience of younger people, accessing the first type of resource might be more important; therefore, the server could increase the weight of the first access frequency. For a target audience with high overhead, the overhead of acquiring the second type of resource might be more important; therefore, the server could increase the weight of the resource overhead value of the second type of resource. By using different calculation methods for different types of target audiences, the accuracy of identifying the first target audience can be improved.

[0146] Figure 5This is a schematic diagram of the structure of a data processing device provided in an embodiment of this application. Figure 5 As shown, the data processing device includes: an object acquisition module 501, an indicator acquisition module 502, a first calculation module 503, a second calculation module 504, and an object filtering module 505.

[0147] The object acquisition module 501 is used to acquire a collection of objects from the database. The object collection includes multiple target objects, which are objects that can interact with the server. The indicator acquisition module 502 is used to acquire multiple first indicators and multiple second indicators for each target object in the object set. The first indicators are used to indicate the frequency of interaction between the target object and the server, and the second indicators are used to indicate the depth of interaction between the target object and the server. The server includes first type resources and second type resources. The multiple first indicators include the server interaction frequency, the first access frequency of the first type resources and the second access frequency of the second type resources. The multiple second indicators include the server interaction intensity, the interaction completion degree of the first type resources and the resource cost value of the second type resources.

[0148] The first calculation module 503 is used to calculate the activity level of the target object based on multiple first indicators.

[0149] The second calculation module 504 is used to calculate the participation degree of the target object based on multiple first indicators and multiple second indicators.

[0150] The object filtering module 505 is used to determine the first target object corresponding to the server in the object set based on activity and participation.

[0151] In other embodiments, the first calculation module 503 is further configured to perform weighted calculations on the server interaction frequency, the first access frequency, and the access frequency of the second type of resources according to the index weights corresponding to each first index, so as to obtain the activity level corresponding to the target object.

[0152] In other embodiments, the second calculation module 504 is further configured to calculate a first score of participation based on the server-side interaction frequency and the server-side interaction intensity; calculate a second score of participation based on the first access frequency and the interaction completion degree of the first type of resource; calculate a third score of participation based on the second access frequency and the resource overhead value of the second type of resource; and use the sum of the first score, the second score, and the third score as the participation degree corresponding to the target object.

[0153] In other embodiments, the aforementioned indicator acquisition module 502 is further configured to acquire multiple first indicator data of the target object for each time period within the target time period; wherein, the multiple first indicator data corresponds one-to-one with multiple first indicators, the first indicator data corresponding to the server interaction frequency is used to indicate whether the target object interacts with the server within the corresponding time period, the first indicator data corresponding to the first access frequency is used to indicate whether the target object accesses a first type of resource within the corresponding time period, and the first indicator data corresponding to the second access frequency is used to indicate whether the target object accesses a second type of resource within the corresponding time period; based on the first indicator data of the target object for multiple time periods within the target time period and the period weight corresponding to each time period, the frequency value of the target object within the target time period is calculated; based on the frequency value of the target object within the target time period and the number of periods included in the target time period, the first indicator of the target object is calculated.

[0154] In other embodiments, the aforementioned indicator acquisition module 502 is further configured to: obtain the server interaction intensity based on the number of interactions between the target object and the server in each time period within the target time period; calculate the interaction completion rate of the first type of resource based on the sum of the second indicator data of the target object in each time period within the target time period and the number of time periods included in the target time period; the second indicator data is used to indicate the completion progress of the interaction task of the target object in the corresponding time period; and determine the resource overhead value of the second type of resource based on the consumption data generated by the target object in each time period within the target time period when acquiring the second type of resource.

[0155] In other embodiments, the object filtering module 505 is further configured to determine the object with the highest overall result of activity and participation from the object set as the first target object corresponding to the server.

[0156] The data processing apparatus provided in this application embodiment can execute the methods shown in the above method embodiments. Its implementation principle and beneficial effects can be referred to the relevant descriptions in the method embodiments, and will not be repeated here.

[0157] Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Figure 6 As shown, the electronic device includes: a memory 601, a transceiver 602, and at least one processor 603.

[0158] Transceiver 602 is used to interact with other devices to send and receive data.

[0159] The memory 601 stores computer program code, which includes computer instructions. These computer instructions run in the described electronic device to implement the method shown in the above-described method embodiments. For example, the memory may include high-speed random access memory (RAM), and may also include non-volatile memory (NVM), such as at least one disk storage device, and may also be a USB flash drive, portable hard drive, read-only memory, disk, or optical disc, etc.

[0160] Processor 603 can be a general-purpose processor, including a Central Processing Unit (CPU), a network processor (NP), etc.; it can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. Processor 603 can also be other general-purpose processors. The general-purpose processor can be a microprocessor or any conventional processor.

[0161] The memory 601, transceiver 602, and processor 603 are communicatively connected. For example, the memory 601 and transceiver 602 can be connected to the processor 603 via a system bus and communicate with each other. The system bus can be a peripheral component interconnect (PCI) bus, an extended industry standard architecture (EISA) bus, an industry standard architecture (ISA) bus, etc. The system bus can be divided into address bus, data bus, control bus, etc. For ease of representation, only one thick line is used in the figure, but this does not mean that there is only one bus or one type of bus.

[0162] Optionally, the memory 601 can be either standalone or integrated with the processor 603. When the memory 601 is set up independently, it is connected to the processor 603 via a system bus.

[0163] This application also provides a chip for executing instructions, which is used to execute the data processing method described in the above embodiments.

[0164] This application also provides a computer-readable storage medium storing computer instructions. When these computer instructions are executed by a processor, they are used to implement the technical solution of the data processing method described in the above embodiments. Specifically, when the computer instructions are executed by a processor, the electronic device can perform the technical solution of the time processing method described in the above embodiments.

[0165] This application also provides a computer program product, which includes a computer program stored in a computer-readable storage medium. At least one processor can read the computer program from the computer-readable storage medium, and when the at least one processor executes the computer program, it can implement the technical solution of the data processing method in the above embodiments.

[0166] The aforementioned computer-readable storage media can be implemented from any type of volatile or non-volatile storage device or a combination thereof, such as Static Random-Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The computer-readable storage media can be any available medium accessible to a general-purpose or special-purpose computer.

[0167] An exemplary computer-readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the storage medium. Of course, the computer-readable storage medium can also be a component of the processor. The processor and the computer-readable storage medium can reside in an application-specific integrated circuit (ASIC). Alternatively, the processor and the computer-readable storage medium can exist as discrete components in an electronic control unit or main control device; this application does not limit this.

[0168] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or modules, and may be electrical, mechanical, or other forms.

[0169] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to implement the solution of this embodiment according to actual needs.

[0170] Furthermore, the functional modules in the various embodiments of this application can be integrated into one processing unit, or each module can exist physically separately, or two or more modules can be integrated into one unit. The unit composed of the above modules can be implemented in hardware or in the form of hardware plus software functional units.

[0171] The integrated modules described above, implemented as software functional modules, can be stored in a computer-readable storage medium. These software functional modules, stored in a storage medium, include several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute some steps of the methods of the various embodiments of this application.

[0172] It should be understood that the steps of the method disclosed in the embodiments of this application can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules in the processor.

[0173] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0174] Finally, it should be noted that 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 or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

Claims

1. A data processing method, characterized in that, include: Retrieve a collection of objects from the database; the collection of objects includes multiple target objects, which are objects that can interact with the server. For each target object in the object set, multiple first indicators and multiple second indicators of the target object are obtained; the first indicators are used to indicate the frequency of interaction between the target object and the server, and the second indicators are used to indicate the depth of interaction between the target object and the server; wherein, the server includes a first type of resource and a second type of resource, the multiple first indicators include server interaction frequency, a first access frequency of the first type of resource and a second access frequency of the second type of resource, and the multiple second indicators include server interaction intensity, interaction completion degree of the first type of resource, and resource overhead value of the second type of resource; The activity level of the target object is calculated based on the multiple first indicators. Calculate the participation degree of the target object based on the plurality of first indicators and the plurality of second indicators; Based on the activity level and the participation level, the first target object corresponding to the server in the object set is determined.

2. The method according to claim 1, characterized in that, The step of calculating the activity level of the target object based on the plurality of first indicators includes: Based on the weights of each of the first indicators, the server interaction frequency, the first access frequency, and the access frequency of the second type of resource are weighted and calculated to obtain the activity level of the target object.

3. The method according to claim 1, characterized in that, The step of calculating the participation degree of the target object based on the plurality of first indicators and the plurality of second indicators includes: Calculate the first score of the participation degree based on the server-side interaction frequency and the server-side interaction intensity; The second score of the participation degree is calculated based on the first access frequency and the interaction completion degree of the first type of resource; The third score of the participation is calculated based on the second access frequency and the resource overhead value of the second type of resource; The sum of the first score, the second score, and the third score is taken as the participation degree corresponding to the target object.

4. The method according to any one of claims 1-3, characterized in that, The acquisition of multiple first indicators of the target object includes: The system acquires multiple first indicator data for the target object in each time period within a target time period; wherein, the multiple first indicator data corresponds one-to-one with the plurality of first indicators, the first indicator data corresponding to the server interaction frequency is used to indicate whether the target object interacts with the server in the corresponding time period, the first indicator data corresponding to the first access frequency is used to indicate whether the target object accesses the first type of resource in the corresponding time period, and the first indicator data corresponding to the second access frequency is used to indicate whether the target object accesses the second type of resource in the corresponding time period; Based on the first indicator data of the target object in multiple time periods within the target time period and the period weight corresponding to each time period, the frequency value of the target object within the target time period is calculated. The first index of the target object is calculated based on the frequency value of the target object within the target time period and the number of time periods included in the target time period.

5. The method according to any one of claims 1-3, characterized in that, The acquisition of multiple second indicators of the target object includes: The interaction intensity of the server is obtained based on the number of interactions between the target object and the server in each time period within the target time period. The interaction completion rate of the first type of resource is calculated based on the sum of the second indicator data of the target object in each time period within the target time period, and the number of time periods included in the target time period; the second indicator data is used to indicate the completion progress of the interaction task of the target object within the corresponding time period. Based on the consumption data generated by the second type of resource obtained by the target object in each time period within the target time period, the resource cost value of the second type of resource is determined.

6. The method according to any one of claims 1-3, characterized in that, The step of determining the first target object corresponding to the server in the object set based on the activity level and the participation level includes: From the set of objects, the object with the highest combined result of activity and participation is determined as the first target object corresponding to the server.

7. A data processing apparatus, characterized in that, include: An object acquisition module is used to acquire a collection of objects from a database; the object collection includes multiple target objects, and the target objects refer to objects that can interact with the server. The metric acquisition module is used to acquire multiple first metrics and multiple second metrics for each target object in the object set; the first metrics are used to indicate the frequency of interaction between the target object and the server, and the second metrics are used to indicate the depth of interaction between the target object and the server; wherein, the server includes a first type of resource and a second type of resource, the multiple first metrics include server interaction frequency, a first access frequency of the first type of resource and a second access frequency of the second type of resource, and the multiple second metrics include server interaction intensity, interaction completion degree of the first type of resource, and resource overhead value of the second type of resource; The first calculation module is used to calculate the activity level of the target object based on the plurality of first indicators; The second calculation module is used to calculate the participation degree of the target object based on the plurality of first indicators and the plurality of second indicators; The object filtering module is used to determine the first target object corresponding to the server in the object set based on the activity level and the participation level.

8. An electronic device, characterized in that, include: A memory and at least one processor; the memory is communicatively connected to the processor; the memory is used to store computer program code, the computer program code including computer instructions; when the processor executes the computer instructions, the electronic device performs the method as described in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that, when executed by a processor, are used to implement the method as described in any one of claims 1-6.

10. A computer program product, characterized in that, When the computer program product is run on a computer / executed by the computer's processor, it implements the method as described in any one of claims 1-6.

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