Task execution reward issuing method and device, terminal equipment and medium

Abstract

The embodiment of the invention provides a task execution reward issuing method and device, terminal equipment and a medium, and belongs to the technical field of data processing. The method comprises the steps of obtaining initial user information corresponding to a target user, performing information recommendation on the target user according to the initial user information, and obtaining first task information corresponding to the target user; obtaining an associated user corresponding to the target user from a database according to the initial user information; obtaining second task information corresponding to the associated user, and determining third task information corresponding to the target user according to the initial user information and the second task information; fusing the first task information and the third task information to obtain target task information corresponding to the target user; obtaining a task execution result of the target user on the target task information, and determining a reward issuing state corresponding to the target user according to the task execution result; and performing reward issuing on the target user according to the reward issuing state to obtain a target reward result corresponding to the target user.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a method, device, terminal equipment and medium for issuing rewards for task execution. Background Art

[0002] In order to improve user stickiness and effectively increase user participation in current applications or application web pages, and thus achieve the goal of rapid promotion, related technologies usually use task reward mechanisms to motivate users. The setting of these fixed rewards helps to increase user participation interest and fun in the short term, but this method is usually relatively simple and rigid, and it is difficult to effectively stimulate users' long-term interest and continuous participation. Due to the limitations of the traditional reward model, it often cannot meet the diverse needs and personalized interests of users, which leads to a gradual decline in user participation and difficulty in forming sustained activity. Summary of the invention

[0003] The main purpose of the embodiments of the present invention is to provide a method, apparatus, terminal device and medium for issuing rewards for task execution, aiming to solve the problem in related technologies that due to the limitations of traditional reward models, it is often unable to meet the diverse needs and personalized interests of users, which leads to a gradual decline in user participation and difficulty in forming sustained activity.

[0004] In a first aspect, an embodiment of the present invention provides a method for issuing a reward for task execution, comprising:

[0005] Obtaining initial user information corresponding to a target user, and recommending information to the target user based on the initial user information, to obtain first task information corresponding to the target user;

[0006] Obtaining associated users corresponding to the target user from a database according to the initial user information;

[0007] Obtaining the second task information corresponding to the associated user, and determining the third task information corresponding to the target user according to the initial user information and the second task information;

[0008] Merging the first task information and the third task information to obtain target task information corresponding to the target user;

[0009] Obtaining the task execution result of the target user for the target task information, and determining the reward issuance status corresponding to the target user according to the task execution result;

[0010] A reward is issued to the target user according to the reward issuance status to obtain a target reward result corresponding to the target user.

[0011] In a second aspect, an embodiment of the present invention provides a reward distribution device for task execution, comprising:

[0012] An information recommendation module, used to obtain initial user information corresponding to a target user, and recommend information to the target user based on the initial user information to obtain first task information corresponding to the target user;

[0013] A user association module, used to obtain an associated user corresponding to the target user from a database according to the initial user information;

[0014] A task determination module, used to obtain the second task information corresponding to the associated user, and determine the third task information corresponding to the target user according to the initial user information and the second task information;

[0015] A task fusion module, used to fuse the first task information and the third task information to obtain target task information corresponding to the target user;

[0016] A status determination module is used to obtain the task execution result of the target user on the target task information, and determine the reward issuance status corresponding to the target user according to the task execution result;

[0017] The reward determination module is used to issue rewards to the target user according to the reward issuance status and obtain the target reward result corresponding to the target user.

[0018] In a third aspect, an embodiment of the present invention further provides a terminal device, comprising a processor, a memory, a computer program stored in the memory and executable by the processor, and a data bus for realizing connection and communication between the processor and the memory, wherein when the computer program is executed by the processor, the steps of a method for issuing rewards for executing any task provided in the specification of the present invention are implemented.

[0019] In a fourth aspect, an embodiment of the present invention further provides a storage medium for computer-readable storage, characterized in that the storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the steps of a reward distribution method for executing any task provided in the specification of the present invention.

[0020] The embodiment of the present invention provides a method, device, terminal device and medium for issuing rewards for task execution. The method first obtains the initial user information corresponding to the target user, and recommends the first task content based on the initial user information, so as to ensure that the recommended task is more in line with the user's interests and needs, and then searches the associated user associated with the target user from the database according to the initial user information of the target user, and obtains the second task information of the associated user, and then uses the second task information to match the initial user information of the target user, determines the third task information of the target user, and then merges the first task information with the third task information to generate the target task information of the target user. After the target user executes the task according to the target task information, the task execution result corresponding to the target user is obtained, and the reward issuance status is determined according to the task execution result. Subsequently, the target user is rewarded according to the reward issuance status, and the target reward result of the target user is obtained. This method not only improves the user experience through accurate task matching and personalized recommendation, but also increases the user's enthusiasm and participation through an effective incentive mechanism, and also solves the problem that due to the limitations of the traditional reward model in the related technology, it often cannot meet the diverse needs and personalized interests of users, which leads to a gradual decline in user participation and difficulty in forming a sustained activity. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0022] Figure 1 A flowchart of a method for issuing rewards for task execution provided by an embodiment of the present invention;

[0023] Figure 2 for Figure 1 A flowchart of sub-step S101 of the method for issuing rewards for task execution in FIG.

[0024] Figure 3 A schematic diagram of a scenario for implementing the method for issuing rewards for task execution provided in this embodiment;

[0025] Figure 4 A schematic diagram of the module structure of a task execution reward distribution device provided by an embodiment of the present invention;

[0026] Figure 5 A schematic block diagram of the structure of a terminal device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0027] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0028] The flowcharts shown in the accompanying drawings are only examples and do not necessarily include all the contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may also be decomposed, combined or partially merged, so the actual execution order may change according to actual conditions.

[0029] It should be understood that the terms used in this specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the specification of the present invention and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0030] The embodiment of the present invention provides a method, apparatus, terminal device and medium for issuing rewards for task execution. The method for issuing rewards for task execution can be applied to a terminal device, which can be an electronic device such as a tablet computer, a laptop computer, a desktop computer, a personal digital assistant and a wearable device. The terminal device can be a server or a server cluster.

[0031] Some embodiments of the present invention are described in detail below in conjunction with the accompanying drawings. In the absence of conflict, the following embodiments and features in the embodiments can be combined with each other.

[0032] Please refer to Figure 1 , Figure 1 A flowchart of a method for issuing rewards for task execution provided in an embodiment of the present invention.

[0033] like Figure 1 As shown, the reward distribution method for task execution includes steps S101 to S106.

[0034] Step S101: obtaining initial user information corresponding to a target user, and recommending information to the target user based on the initial user information to obtain first task information corresponding to the target user.

[0035] Exemplarily, the initial user information can be collected from various data sources (such as user registration information, historical behavior data, user interest tags, etc.). The initial user information usually includes but is not limited to the target user's basic information (such as age, gender, location), interests, hobbies, browsing and purchase history, etc.

[0036] For example, in the financial field, when it is necessary to introduce or promote products to target users, in order to reduce user resistance, rewards can be obtained by completing tasks, which can increase user interest while promoting products. Therefore, when recommending information to target users, it is necessary to recommend content that they are interested in, thereby increasing the target user's interest. For example, meaningful features can be extracted from the target user's initial user information. For example, if the target user's interests include health insurance and accident insurance, then the features may include the labels "health insurance" or "accident insurance".

[0037] Exemplarily, a recommendation algorithm is applied to generate information that matches the target user's characteristics. Common recommendation algorithms include content-based recommendation, collaborative filtering, hybrid recommendation, etc. The selection of a suitable algorithm depends on specific needs and data characteristics. Thus, the interest characteristics of the target user (such as favorite topics, keywords) are used for recommendation, and then the first task information is determined based on the output of the recommendation algorithm. The first task information may include a description, requirements, rewards, etc. of the task.

[0038] In one embodiment, the information is recommended to the target user according to the initial user information to obtain the first task information corresponding to the target user. Specifically, referring to Figure 2 , step S101 includes: sub-step S1011 to sub-step S1014.

[0039] Sub-step S1011: construct a user portrait corresponding to the target user by analyzing the initial user information.

[0040] Exemplarily, initial user information related to the target user is collected from different sources (such as user registration forms, behavior logs, social media activities, questionnaires, etc.). The initial user information may include basic personal information (such as age, gender, geographic location), interests and hobbies, purchase behavior, browsing history, etc. The initial user information is then processed for missing values, outliers, and duplicate data to ensure data quality, so that the cleaned initial user information is more reliable and can improve the accuracy of the user portrait.

[0041] Exemplarily, key features are extracted from the initial user information. These key features may include the target user's basic information, behavior patterns, areas of interest, etc. For example, the content topics that the target user is interested in are extracted from the browsing history, or the product categories that the user prefers are extracted from the purchase history. Based on the extracted key features, a user profile is established to better reflect the personalized characteristics of the target user.

[0042] For example, key features include basic information such as age, gender, and geographic location, interests and hobbies such as the target user's areas of interest and topics of concern, and behavioral patterns such as the target user's purchasing behavior, browsing history, and interaction frequency. Relevant indicators are then defined for each key feature to quantify the target user's behavior and preferences, so that relevant key features are combined together and machine learning algorithms are applied to predict and describe user characteristics, thereby obtaining a user portrait corresponding to the target user.

[0043] Sub-step S1012: determine the information to be recommended, and calculate a first matching degree between the information to be recommended and the user portrait, and then determine a first recommendation score of the information to be recommended to the target user according to the first matching degree.

[0044] Exemplarily, the information to be recommended is screened out from the available information library or content pool, and the information to be recommended is the information that needs to be promoted or recommended to users for the current project. The information to be recommended is preprocessed to ensure that its format is consistent and the content is complete. For example, irrelevant information is cleaned up and the content format is standardized. The key features of the information to be recommended are extracted. These features may include the subject, category, keywords, etc. of the information. For example, if the information to be recommended is a product, the type, function, price range and other features of the product are extracted, and then the corresponding features are extracted from the user portrait, which reflect the user's interests, preferences and needs. For example, the user's areas of interest, past purchase records, browsing history, etc.

[0045] Exemplarily, the features of the information to be recommended are compared with the features of the user profile. The matching degree can be calculated by a variety of methods, such as feature-based similarity calculation, such as using a similarity measurement method (such as cosine similarity, Jaccard coefficient, Euclidean distance, etc.) to evaluate the matching degree between the information to be recommended and the user profile, thereby obtaining a first matching degree. The similarity measurement can quantify the similarity between the two.

[0046] Exemplarily, according to the result of the first matching degree, the first recommendation score of the information to be recommended is calculated. The first recommendation score may be the first matching degree directly used as the first recommendation score, or weights may be assigned to different features, and a comprehensive score may be calculated according to the first matching degree and the corresponding weights. For example, when a user has a strong preference for a certain feature, a higher weight may be assigned to the feature, so as to obtain the first recommendation score by comprehensively considering the first matching degree and the corresponding weight.

[0047] Sub-step S1013: screening the information to be recommended according to the first recommendation score to obtain the target recommended information.

[0048] Exemplarily, the first recommendation scores of all the information to be recommended are aggregated and normalized to facilitate subsequent sorting and comparison. For example, the scores are mapped to a range of 0 to 1 to eliminate the dimensionality effect of the first recommendation scores.

[0049] Exemplarily, a score threshold is set to determine which information to be recommended meets the recommendation criteria. For example, a minimum score threshold can be set, and only information to be recommended with a score higher than the threshold will be included in the recommendation range. The recommended information is then screened according to the set score threshold, and the screening process involves retaining those information to be recommended whose first recommendation scores are higher than the score threshold, and discarding those information to be recommended whose first recommendation scores are lower than the score threshold. The screened recommended information is then sorted according to the first recommendation score to obtain the target recommended information. Generally, information with a higher first recommendation score is ranked higher. This can help ensure that the most relevant information is displayed first.

[0050] Sub-step S1014: generating the first task information corresponding to the target user according to the target recommendation information.

[0051] Exemplarily, the target recommendation information is analyzed to obtain the core content corresponding to the target recommendation information, and a corresponding task template is designed based on the core content. The task template should contain the fields and formats required to generate the task information, such as the task description, etc., and then fill the core content of the target recommendation information into the task template. Ensure that the relevant data in the target recommendation information can be accurately converted into the first task information. For example, if the target recommendation information is a product, the generated first task information may include "view product details", "answer product questions" or "write product reviews".

[0052] Specifically, by analyzing the initial user information to build a user profile, the user's interests, preferences, and needs can be understood more accurately. This makes the recommended information and task information provided to the user more personalized, thereby improving the user experience. By calculating the matching degree between the information to be recommended and the user profile, and determining the recommendation score based on the matching degree, it can ensure that the information recommended to the user is more accurate. This accuracy not only improves user satisfaction, but also increases the possibility of users interacting with the recommended content. By filtering the recommended information according to the recommendation score, the content that best meets the user's needs can be quickly and effectively selected from a large amount of information. This not only saves users' time, but also improves the efficiency of information utilization.

[0053] Step S102: Obtain associated users corresponding to the target user from a database according to the initial user information.

[0054] Exemplarily, a similarity measurement method is selected to calculate the similarity between different users. For example, cosine similarity, Euclidean distance or other similarity measurement methods are used to calculate the similarity based on the initial user information of the initial user and other user information of other users. When the similarity is greater than a preset value, the other user is determined to be an associated user corresponding to the target user. Alternatively, the social network data contained in the initial user information can be used to query associated users who have a common connection with the initial user through a graph database or a social network analysis tool.

[0055] Step S103: obtaining the second task information corresponding to the associated user, and determining the third task information corresponding to the target user according to the initial user information and the second task information.

[0056] Exemplarily, the second task information corresponding to the associated user is obtained in the same manner as the target user obtains the first task information, which will not be described in detail herein.

[0057] Exemplarily, the recommended information corresponding to the second task information is obtained, and then the matching degree between the recommended information and the initial user information is calculated, so that the second task information is screened according to the matching degree to obtain the third task information corresponding to the target user.

[0058] In some embodiments, determining the third task information corresponding to the target user based on the initial user information and the second task information includes: determining a second matching degree between the second task information and the target user based on the user portrait; determining a second recommendation score of the second task information for the target user based on the second matching degree; and screening the second task information based on the second recommendation score to obtain the third task information.

[0059] Exemplarily, a similarity measurement method (such as cosine similarity, Euclidean distance, etc.) is used to evaluate the matching degree between the third task information and the user portrait. For example, the matching degree of the features of the second task information and the user portrait features of the target user is calculated to obtain the second matching degree between the second task information and the target user.

[0060] Exemplarily, the second recommendation score is calculated based on the second matching degree. The second recommendation score may be the direct second matching degree, or a comprehensive score formed by weighting or normalizing the second matching degree.

[0061] Exemplarily, a screening criterion is set to determine which second task information meets the condition based on the second recommendation score. For example, the second task information with a score higher than a certain threshold is screened to obtain the third task information. Thus, according to the screening criterion, the task information that meets the condition is screened out from all the second task information, that is, the third task information is obtained.

[0062] Specifically, by filtering task information based on the second matching degree and recommendation score, more personalized task recommendations can be provided to users. This means that target users will receive tasks that are highly consistent with their preferences and needs, thereby significantly improving their experience and satisfaction. At the same time, by expanding the information dimensions of associated users, the target users' information sources can be further enriched, making the recommended content more attractive and relevant.

[0063] Step S104: merge the first task information and the third task information to obtain target task information corresponding to the target user.

[0064] Exemplarily, the first task information and the third task information are merged to obtain target task information corresponding to the target user.

[0065] In some embodiments, the fusion of the first task information and the third task information to obtain the target task information corresponding to the target user includes: performing intersection processing on the first task information and the third task information to obtain initial task information; obtaining the target matching degree corresponding to the initial task information from the first matching degree and the second matching degree; and sorting the initial task information according to the target matching degree to obtain the target task information corresponding to the target user.

[0066] Exemplarily, the first task information and the third task information are subjected to intersection processing to obtain the initial task information, that is, the initial task information exists in both the first task information and the third task information.

[0067] Exemplarily, the first matching degree and the second matching degree are weightedly summed to obtain the target matching degree, so as to evaluate the relevance between the initial task information and the target user according to the target matching degree, that is, the target matching degree is an indicator to measure the degree of matching between the initial task information and the target user's needs.

[0068] For example, the sorting criteria are set according to the target matching degree. Usually, the task information with higher matching degree is placed in front because they are more in line with the needs of the target user, and then the initial task information is sorted in descending order according to the target matching degree, that is, tasks with higher target matching degree are usually displayed first, so as to sort out the target task information according to the sorting result.

[0069] Specifically, by performing intersection processing on the first task information and the third task information, the task information common to both can be obtained, which is more likely to be what the target user really cares about and needs. This intersection processing can filter out irrelevant information, thereby improving the accuracy of the information. By obtaining the target matching degree corresponding to the initial task information from the first matching degree and the second matching degree, the matching degree between the task information and the target user can be comprehensively evaluated. This method reflects the fit between the task information and the user's needs more comprehensively than a single matching degree, and improves the accuracy of the task information.

[0070] Step S105: obtaining the task execution result of the target user for the target task information, and determining the reward issuance status corresponding to the target user according to the task execution result.

[0071] Exemplarily, after obtaining the target task information corresponding to the target user, the target task information is displayed in the user interface corresponding to the target user, and then the target user's task execution result for the target task information in the user interface is received, for example, the task execution result is completed or incomplete, etc.

[0072] Exemplarily, when the task execution result is completed, the reward issuance status corresponding to the target user is a disbursable status; when the task execution result is incomplete, the reward issuance status corresponding to the target user is a non-disbursable status.

[0073] In some embodiments, determining the reward issuance status corresponding to the target user based on the task execution result includes: when the task execution result meets the preset conditions, adjusting the operating state of the target button corresponding to the reward issuance; obtaining the click result of the target user on the target button in the operating state, and determining the reward issuance status corresponding to the target user based on the click result.

[0074] Exemplarily, the preset condition is that all target task information is completed, or when there are completed tasks in the target task information, if the task execution result meets the preset condition, the operating state of the target button corresponding to the reward issuance is adjusted to a clickable state or an operable state.

[0075] Exemplarily, the target user's click result on the target button in the running state is obtained, and the click result is that the target user triggers the target button or the target user does not trigger the target button. When the click result is that the target user triggers the target button, the reward issuance status corresponding to the target user is determined as a state that requires issuance; when the click result is that the target user does not trigger the target button, the reward issuance status corresponding to the target user is determined as a state that does not require issuance.

[0076] For example, if the task execution result does not meet the preset conditions, the running state of the target button corresponding to the reward issuance is an unclickable state or an inoperable state, and the reward issuance state is also an unissuable state.

[0077] Specifically, rewards are directly linked to task completion results, which can motivate users to work harder to complete tasks. This reward mechanism helps to improve users' motivation to complete tasks, thereby promoting the completion of tasks and the achievement of goals. In addition, timely adjustment of reward issuance status and optimization based on user feedback can improve user satisfaction with the system. Users will feel that the system values ​​their behavior, thereby increasing their dependence and loyalty to the platform.

[0078] Step S106: Issue a reward to the target user according to the reward issuance status, and obtain a target reward result corresponding to the target user.

[0079] Exemplarily, when the reward issuance status is the need to issue status, the total amount of rewards corresponding to the target user is determined according to the task execution result, and the reward is issued to the target user according to the total amount of rewards, and then the target reward result corresponding to the target user is obtained according to the target user's collection result of the issued rewards.

[0080] Exemplarily, when the reward issuance status is a status where issuance is not required, the target reward result corresponding to the target user is empty or zero.

[0081] For example, the target task information includes multiple task information, such as 5 task information, and then according to the task execution results, it is determined that the target user has completed 3 task information, then the reward values ​​corresponding to the 3 task information completed by the target user are summed to obtain the total reward amount. Assuming that the reward distribution form is a form similar to red envelope rain or a form of raindrops falling in the interface, the total reward amount is divided into multiple reward values, and then the reward values ​​are filled into the red envelope rain or raindrops, so that after the reward is distributed to the target user, the reward values ​​corresponding to the target user clicking on the red envelope rain or raindrops are collected, and then the collected reward values ​​are summed to obtain the target reward result corresponding to the target user.

[0082] In some embodiments, the reward is issued to the target user according to the reward issuance status to obtain the target reward result corresponding to the target user, including: when the reward issuance status satisfies a preset status, using a random function to generate an initial reward result corresponding to the target user; setting an initial reward form corresponding to the reward, and randomly splitting the initial reward result and filling it into the initial reward form to obtain the target reward form corresponding to the initial reward result; obtaining the activation result of the target user for the target reward form, and determining the target reward result corresponding to the target user according to the activation result.

[0083] Exemplarily, when the preset state is a state that requires issuance, the total amount of rewards corresponding to the target user is determined according to the task execution result, and the total amount of rewards is split using a random function to obtain the initial reward result corresponding to the target user.

[0084] Exemplarily, an initial reward form corresponding to the reward is set, and the initial reward form may be a red envelope rain form or a raindrop-like form, so that the initial reward result is randomly split and filled into the initial reward form to obtain a target reward form corresponding to the initial reward result.

[0085] Exemplarily, after the target reward form is displayed on the user interface corresponding to the target user, the target user clicks on the target reward form, thereby determining that the activation result corresponding to the target reward form is in an activated state, that is, the target user has collected the target reward form. If the target user does not click on the target reward form, then the activation result corresponding to the target reward form is determined to be in an inactivated state. Then, when the activation result corresponding to the target reward form is in an activated state, the corresponding reward values ​​in the target reward form are summed to obtain the target reward result corresponding to the target user.

[0086] For example, if the initial reward is in the form of raindrops falling in the interface, the total reward amount is obtained, and then the random function is used to randomly split the total reward amount into multiple reward values, and then the reward values ​​are filled into the raindrops. After the reward is distributed to the target user, the activation results corresponding to the target user clicking on the raindrops are collected, and then the reward values ​​of the target reward form corresponding to the activation results are summed to obtain the target reward result corresponding to the target user, that is, the reward value of each raindrop is random, and the target user can get as many rewards as he can collect raindrops. This method greatly improves the enthusiasm of the target user to participate in the activity tasks.

[0087] Specifically, using a random function to generate the initial reward results and randomly splitting them can make each user's reward unique, increasing the fun and surprise of the reward process. This method can increase user participation and enthusiasm, because they cannot predict what specific rewards they will get, thereby improving the target user's participation and satisfaction.

[0088] In some embodiments, after obtaining the target reward result corresponding to the target user, the method also includes: updating the display status of the user product corresponding to the target user according to the target reward result, and then redisplaying the user product according to the display status; receiving the operation result of the target user on the user product, and determining the consumption result corresponding to the target user based on the operation result.

[0089] Exemplarily, when the target reward result is greater than or equal to the selling price of the user product, the display status of the user product corresponding to the target user is determined to be an operable state; when the target reward result is less than the selling price of the user product, the display status of the user product corresponding to the target user is determined to be an inoperable state.

[0090] Exemplarily, the user product is redisplayed according to the display state, and when the target user clicks or touches the user product in the operable state, the operation result of the target user on the user product is received, so as to determine the corresponding consumption result of the target user as purchasing or using the corresponding user product according to the operation result. In addition, according to the operation result of the target user, after determining the corresponding consumption result, the target reward result corresponding to the target user needs to be deducted.

[0091] Specifically, updating the display status of user products and redisplaying the products enables users to instantly see the status of their products after being rewarded, enhancing the interactive experience. Users can clearly understand what rewards they have received and how these rewards affect their product choices, which can enhance user motivation and improve user satisfaction.

[0092] Please refer to Figure 3 , Figure 3 A schematic diagram of a scenario for implementing the reward distribution method for task execution provided in this embodiment, such as Figure 3 As shown, the method includes obtaining first task information according to the initial information of the target user, and finding the associated users associated with the target user from the database, thereby obtaining the second task information of these associated users. The third task information of the target user is determined by using the initial user information and the second task information. Subsequently, the first task information is merged with the third task information to obtain the target task information of the target user. The task execution result of the target user is obtained according to the target task information. If the task execution result meets the preset conditions, the running state of the target button corresponding to the reward issuance is adjusted. Subsequently, the result of the target user clicking the target button in the running state is obtained, and the reward issuance state is determined accordingly. If the reward issuance state meets the preset state, the initial reward result of the target user is generated by a random function. The initial form of the reward is set, and the initial reward result is randomly split and filled into the reward form, thereby obtaining the target reward form. Finally, the final target reward result is determined according to the activation result of the target user on the target reward form. This method not only improves the user experience through precise task matching and personalized recommendations, but also increases user enthusiasm and participation through effective incentive mechanisms. It also solves the problem that due to the limitations of traditional reward models in related technologies, it often cannot meet the diverse needs and personalized interests of users, which leads to a gradual decline in user participation and difficulty in forming sustained activity.

[0093] See also Figure 4 , Figure 4 A reward distribution device 200 for task execution provided in an embodiment of the present application includes an information recommendation module 201, a user association module 202, a task determination module 203, a task fusion module 204, a state determination module 205, and a reward determination module 206, wherein the information recommendation module 201 is used to obtain initial user information corresponding to a target user, and recommend information to the target user based on the initial user information to obtain first task information corresponding to the target user; the user association module 202 is used to obtain associated users corresponding to the target user from a database based on the initial user information; the task determination module 203 is used to obtain associated users corresponding to the target user from a database based on the initial user information; and the task determination module 203 is used to obtain associated users corresponding to the target user from a database based on the initial user information. Used to obtain the second task information corresponding to the associated user, and determine the third task information corresponding to the target user based on the initial user information and the second task information; a task fusion module 204 is used to fuse the first task information and the third task information to obtain the target task information corresponding to the target user; a status determination module 205 is used to obtain the task execution result of the target user for the target task information, and determine the reward issuance status corresponding to the target user based on the task execution result; a reward determination module 206 is used to issue a reward to the target user based on the reward issuance status to obtain the target reward result corresponding to the target user.

[0094] In some implementations, the information recommendation module 201, in the process of recommending information to the target user according to the initial user information and obtaining the first task information corresponding to the target user, performs:

[0095] Constructing a user profile corresponding to the target user by analyzing the initial user information;

[0096] Determine the information to be recommended, and calculate a first matching degree between the information to be recommended and the user portrait, and then determine a first recommendation score of the information to be recommended to the target user according to the first matching degree;

[0097] Filtering the information to be recommended according to the first recommendation score to obtain the target recommended information;

[0098] The first task information corresponding to the target user is generated according to the target recommendation information.

[0099] In some implementations, during the process of determining the third task information corresponding to the target user according to the initial user information and the second task information, the task determination module 203 performs:

[0100] Determining a second matching degree between the second task information and the target user according to the user portrait;

[0101] determining a second recommendation score of the second task information for the target user according to the second matching degree;

[0102] The second task information is screened according to the second recommendation score to obtain the third task information.

[0103] In some implementations, the task fusion module 204 performs, during the process of fusing the first task information and the third task information to obtain the target task information corresponding to the target user:

[0104] Performing intersection processing on the first task information and the third task information to obtain initial task information;

[0105] Obtaining a target matching degree corresponding to the initial task information from the first matching degree and the second matching degree;

[0106] The initial task information is sorted according to the target matching degree to obtain the target task information corresponding to the target user.

[0107] In some implementations, the state determination module 205, in the process of determining the reward issuance state corresponding to the target user according to the task execution result, performs:

[0108] When the task execution result meets the preset conditions, the operation state of the target button corresponding to the reward issuance is adjusted;

[0109] Obtain the target user's click result on the target button in the running state, and determine the reward issuance status corresponding to the target user according to the click result.

[0110] In some implementations, the reward determination module 206 performs, during the process of issuing a reward to the target user according to the reward issuance status and obtaining a target reward result corresponding to the target user:

[0111] When the reward issuance status meets the preset status, a random function is used to generate an initial reward result corresponding to the target user;

[0112] Setting an initial reward form corresponding to a reward, and randomly splitting the initial reward result and filling it into the initial reward form to obtain a target reward form corresponding to the initial reward result;

[0113] The target user's activation result for the target reward form is obtained, and the target reward result corresponding to the target user is determined according to the activation result.

[0114] In some implementations, after obtaining the target reward result corresponding to the target user, the reward determination module 206 further executes:

[0115] Update the display status of the user product corresponding to the target user according to the target reward result, and then redisplay the user product according to the display status;

[0116] Receive the target user's operation result on the user product, and determine the consumption result corresponding to the target user according to the operation result.

[0117] In some implementations, the task execution reward distribution device 200 may be applied to a terminal device.

[0118] It should be noted that technical personnel in the relevant field can clearly understand that, for the convenience and conciseness of description, the specific working process of the reward distribution device 200 for task execution described above can refer to the corresponding process in the aforementioned task execution reward distribution method embodiment, and will not be repeated here.

[0119] See also Figure 5 , Figure 5 A schematic block diagram of the structure of a terminal device provided in an embodiment of the present invention.

[0120] like Figure 5 As shown, the terminal device 300 includes a processor 301 and a memory 302, and the processor 301 and the memory 302 are connected via a bus 303, such as an I2C (Inter-integrated Circuit) bus.

[0121] Specifically, the processor 301 is used to provide computing and control capabilities to support the operation of the entire terminal device. The processor 301 can be a central processing unit (CPU), and the processor 301 can also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. Among them, the general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc.

[0122] Specifically, the memory 302 may be a Flash chip, a read-only memory (ROM) disk, an optical disk, a USB flash drive, or a mobile hard disk.

[0123] Those skilled in the art will understand that Figure 5 The structure shown in the figure is only a block diagram of a partial structure related to the embodiment of the present invention, and does not constitute a limitation on the terminal device to which the embodiment of the present invention is applied. The specific server may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0124] The processor is used to run a computer program stored in the memory, and implement any one of the task execution reward distribution methods provided in the embodiments of the present invention when executing the computer program.

[0125] In one embodiment, the processor is used to run a computer program stored in the memory, and implements the following steps when executing the computer program:

[0126] Obtaining initial user information corresponding to a target user, and recommending information to the target user based on the initial user information, to obtain first task information corresponding to the target user;

[0127] Obtaining associated users corresponding to the target user from a database according to the initial user information;

[0128] Obtaining the second task information corresponding to the associated user, and determining the third task information corresponding to the target user according to the initial user information and the second task information;

[0129] Merging the first task information and the third task information to obtain target task information corresponding to the target user;

[0130] Obtaining the task execution result of the target user for the target task information, and determining the reward issuance status corresponding to the target user according to the task execution result;

[0131] A reward is issued to the target user according to the reward issuance status to obtain a target reward result corresponding to the target user.

[0132] In some implementations, the processor 301, in the process of recommending information to the target user according to the initial user information and obtaining the first task information corresponding to the target user, executes:

[0133] Constructing a user profile corresponding to the target user by analyzing the initial user information;

[0134] Determine the information to be recommended, and calculate a first matching degree between the information to be recommended and the user portrait, and then determine a first recommendation score of the information to be recommended to the target user according to the first matching degree;

[0135] Filtering the information to be recommended according to the first recommendation score to obtain the target recommended information;

[0136] The first task information corresponding to the target user is generated according to the target recommendation information.

[0137] In some implementations, during the process of determining the third task information corresponding to the target user according to the initial user information and the second task information, the processor 301 executes:

[0138] Determining a second matching degree between the second task information and the target user according to the user portrait;

[0139] determining a second recommendation score of the second task information for the target user according to the second matching degree;

[0140] The second task information is screened according to the second recommendation score to obtain the third task information.

[0141] In some implementations, the processor 301, in the process of fusing the first task information and the third task information to obtain the target task information corresponding to the target user, executes:

[0142] Performing intersection processing on the first task information and the third task information to obtain initial task information;

[0143] Obtaining a target matching degree corresponding to the initial task information from the first matching degree and the second matching degree;

[0144] The initial task information is sorted according to the target matching degree to obtain the target task information corresponding to the target user.

[0145] In some implementations, the processor 301, during the process of determining the reward issuance status corresponding to the target user according to the task execution result, executes:

[0146] When the task execution result meets the preset conditions, the operation state of the target button corresponding to the reward issuance is adjusted;

[0147] Obtain the target user's click result on the target button in the running state, and determine the reward issuance status corresponding to the target user according to the click result.

[0148] In some implementations, the processor 301, in the process of issuing a reward to the target user according to the reward issuance status and obtaining a target reward result corresponding to the target user, executes:

[0149] When the reward issuance status meets the preset status, a random function is used to generate an initial reward result corresponding to the target user;

[0150] Setting an initial reward form corresponding to a reward, and randomly splitting the initial reward result and filling it into the initial reward form to obtain a target reward form corresponding to the initial reward result;

[0151] The target user's activation result for the target reward form is obtained, and the target reward result corresponding to the target user is determined according to the activation result.

[0152] In some implementations, after obtaining the target reward result corresponding to the target user, the processor 301 further executes:

[0153] Update the display status of the user product corresponding to the target user according to the target reward result, and then redisplay the user product according to the display status;

[0154] Receive the target user's operation result on the user product, and determine the consumption result corresponding to the target user according to the operation result.

[0155] It should be noted that technical personnel in the relevant field can clearly understand that, for the convenience and conciseness of description, the specific working process of the terminal device described above can refer to the corresponding process in the embodiment of the reward distribution method for the aforementioned task execution, and will not be repeated here.

[0156] An embodiment of the present invention also provides a storage medium for computer-readable storage, wherein the storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the steps of a reward distribution method for executing any task provided in the description of the embodiment of the present invention.

[0157] The storage medium may be an internal storage unit of the terminal device in the aforementioned embodiment, such as a hard disk or memory of the terminal device. The storage medium may also be an external storage device of the terminal device, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the terminal device.

[0158] It will be appreciated by those skilled in the art that all or some of the steps, systems, and functional modules / units in the methods disclosed above may be implemented as software, firmware, hardware, and appropriate combinations thereof. In a hardware embodiment, the division between the functional modules / units mentioned in the above description does not necessarily correspond to the division of physical components; for example, a physical component may have multiple functions, or a function or step may be performed by several physical components in cooperation. Some or all physical components may be implemented as software executed by a processor, such as a central processing unit, a digital signal processor, or a microprocessor, or implemented as hardware, or implemented as an integrated circuit, such as an application-specific integrated circuit. Such software may be distributed on a computer-readable medium, which may include a computer storage medium (or non-transitory medium) and a communication medium (or temporary medium). As known to those skilled in the art, the term computer storage medium includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data). Computer storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tapes, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store the desired information and can be accessed by a computer. In addition, it is well known to those of ordinary skill in the art that communication media typically contain computer-readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism, and may include any information delivery media.

[0159] It should be understood that the term "and / or" used in the present specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, including these combinations. It should be noted that, in this article, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or system including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or system. In the absence of further restrictions, an element defined by the sentence "including a..." does not exclude the presence of other identical elements in the process, method, article or system including the element.

[0160] The serial numbers of the embodiments of the present invention are only for description and do not represent the advantages and disadvantages of the embodiments. The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed by the present invention, and these modifications or replacements should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A method for issuing rewards for task execution, characterized in that: The method comprises: Obtaining initial user information corresponding to a target user, and recommending information to the target user based on the initial user information, to obtain first task information corresponding to the target user; Obtaining associated users corresponding to the target user from a database according to the initial user information; Obtaining the second task information corresponding to the associated user, and determining the third task information corresponding to the target user according to the initial user information and the second task information; Merging the first task information and the third task information to obtain target task information corresponding to the target user; Obtaining the task execution result of the target user for the target task information, and determining the reward issuance status corresponding to the target user according to the task execution result; A reward is issued to the target user according to the reward issuance status to obtain a target reward result corresponding to the target user.

2. The method according to claim 1, characterized in that: The recommending information to the target user according to the initial user information to obtain first task information corresponding to the target user includes: Constructing a user profile corresponding to the target user by analyzing the initial user information; Determine the information to be recommended, and calculate a first matching degree between the information to be recommended and the user portrait, and then determine a first recommendation score of the information to be recommended to the target user according to the first matching degree; Filtering the information to be recommended according to the first recommendation score to obtain the target recommended information; The first task information corresponding to the target user is generated according to the target recommendation information.

3. The method according to claim 2, characterized in that The determining the third task information corresponding to the target user according to the initial user information and the second task information includes: Determining a second matching degree between the second task information and the target user according to the user portrait; determining a second recommendation score of the second task information for the target user according to the second matching degree; The second task information is screened according to the second recommendation score to obtain the third task information.

4. The method according to claim 3, characterized in that The fusing the first task information and the third task information to obtain target task information corresponding to the target user includes: Performing intersection processing on the first task information and the third task information to obtain initial task information; Obtaining a target matching degree corresponding to the initial task information from the first matching degree and the second matching degree; The initial task information is sorted according to the target matching degree to obtain the target task information corresponding to the target user.

5. The method according to claim 1, characterized in that: Determining the reward issuance status corresponding to the target user according to the task execution result includes: When the task execution result meets the preset conditions, the operation state of the target button corresponding to the reward issuance is adjusted; Obtain the target user's click result on the target button in the running state, and determine the reward issuance status corresponding to the target user according to the click result.

6. The method according to claim 1, characterized in that The issuing of a reward to the target user according to the reward issuing status to obtain a target reward result corresponding to the target user includes: When the reward issuance status meets the preset status, a random function is used to generate an initial reward result corresponding to the target user; Setting an initial reward form corresponding to a reward, and randomly splitting the initial reward result and filling it into the initial reward form to obtain a target reward form corresponding to the initial reward result; The target user's activation result for the target reward form is obtained, and the target reward result corresponding to the target user is determined according to the activation result.

7. The method according to any one of claims 1 to 6, characterized in that: After obtaining the target reward result corresponding to the target user, the method further includes: Update the display status of the user product corresponding to the target user according to the target reward result, and then redisplay the user product according to the display status; Receive the target user's operation result on the user product, and determine the consumption result corresponding to the target user according to the operation result.

8. A task execution reward distribution device, characterized in that: include: An information recommendation module, used to obtain initial user information corresponding to a target user, and recommend information to the target user based on the initial user information to obtain first task information corresponding to the target user; A user association module, used to obtain an associated user corresponding to the target user from a database according to the initial user information; A task determination module, used to obtain the second task information corresponding to the associated user, and determine the third task information corresponding to the target user according to the initial user information and the second task information; A task fusion module, used to fuse the first task information and the third task information to obtain target task information corresponding to the target user; A status determination module is used to obtain the task execution result of the target user on the target task information, and determine the reward issuance status corresponding to the target user according to the task execution result; The reward determination module is used to issue rewards to the target user according to the reward issuance status and obtain the target reward result corresponding to the target user.

9. A terminal device, characterized in that: The terminal device includes a processor and a memory; The memory is used to store computer programs; The processor is used to execute the computer program and implement the reward issuance method for task execution as described in any one of claims 1 to 7 when executing the computer program.

10. A computer storage medium for computer storage, characterized in that: The computer storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the steps of the reward distribution method for task execution described in any one of claims 1 to 7.

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