A multi-end cooperative e-commerce incentive task closed-loop processing system

Abstract

The application discloses a multi-terminal cooperative e-commerce incentive task closed-loop processing system and relates to the technical field of electronic commerce. The system uniformly schedules e-commerce incentive tasks through task classification and a rule engine, cooperatively completes cross-terminal behavior consistency verification, dynamic page path adaptation, user behavior authenticity determination and terminal adaptive message pushing, and forms a complete closed-loop processing flow from task initiation, cross-terminal verification to state notification. The application solves the problems of the existing e-commerce incentive task system, such as cross-terminal cooperation difficulty, poor format adaptability and rigid pushing mode, and improves the task completion rate and operation and maintenance efficiency.

Description

Technical Field

[0001] This invention relates to the field of e-commerce technology, specifically to a multi-terminal collaborative e-commerce incentive task closed-loop processing system, used to realize collaborative processing and closed-loop verification of incentive tasks for users across different terminals. Background Technology

[0002] With the widespread adoption of mobile internet, e-commerce platforms typically employ user incentive systems (such as task systems, points systems, and membership benefits) to enhance user activity and conversion rates. In existing technologies, e-commerce platform incentive task systems usually support users in completing basic tasks such as browsing, registration, and reviews on a single terminal (such as an app or mini-program), and guide users to complete subsequent operations through push notifications or page redirects.

[0003] However, with the diversification of user access terminals (such as apps, mini-programs, H5 pages, and official accounts), incentive tasks are increasingly requiring cross-terminal collaborative completion. For example, users may need to follow an official account within a WeChat mini-program or share it to their Moments within an app. These types of tasks require the system to verify user behavior across terminals and achieve multi-terminal linkage confirmation. Existing technologies have explored data transmission optimization and cross-platform data interoperability for multi-terminal collaborative scenarios, but when applied to e-commerce incentive task systems, the following shortcomings still exist: The system only supports a limited number of task types. Existing e-commerce incentive task systems typically support basic tasks such as browsing and registration, but lack effective support for complex tasks (such as multi-platform collaborative tasks, including following official accounts, sharing for assistance, and message subscriptions), thus failing to meet the diverse user incentive needs of e-commerce platforms. Cross-platform collaboration is difficult. When users complete incentive tasks, they often need to perform repeated operations on multiple devices. The system cannot automatically identify and verify the user's behavior on different devices, resulting in low efficiency of task closure and fragmented user experience. Poor business adaptability: The target page or execution rules required for the same task often differ in different business scenarios (such as different business formats or different activities). However, the existing system cannot be dynamically adjusted through hard-coded configuration, resulting in poor scalability and high maintenance costs. Inflexible message push methods: Push channels require manual configuration and cannot automatically select the appropriate method to push based on the user's current terminal type (such as App, Mini Program, H5), resulting in low message reach and constrained operational efficiency; Limited sharing channels: It only supports a single platform (such as WeChat) and lacks the ability to capture and verify sharing behavior from other channels such as Moments and groups, making it impossible to form a closed loop of multi-channel sharing assistance. Summary of the Invention

[0004] To address the aforementioned issues, the purpose of this invention is to provide a multi-terminal collaborative e-commerce incentive task closed-loop processing system. This system can support the unified processing of complex tasks involving multi-terminal collaboration, achieve cross-terminal user behavior linkage confirmation and closed-loop verification, dynamically adapt to page routing rules for different business scenarios, automatically select message push methods based on terminal type, and support the capture and verification of behaviors that facilitate sharing across multiple channels.

[0005] This was achieved through the following technical solutions: A multi-terminal collaborative e-commerce incentive task closed-loop processing system, comprising: The task classification and rule engine receives task requests from user terminals, classifies and schedules e-commerce incentive tasks, issues instructions to other modules according to task type, summarizes processing results and performs logical judgments. Task types include sharing assistance, browsing, following official accounts, and subscription tasks. The multi-terminal collaborative closed-loop module receives cross-terminal verification instructions issued by the task classification and rule engine, verifies the consistency of user behavior on different terminals through the cross-platform data synchronization interface, and returns the verification results to the task classification and rule engine. The business format page routing and distribution module receives page routing instructions from the task classification and rule engine, stores page mapping rules for different business scenarios, dynamically determines and renders the target page path based on the business format tag, and returns the rendering status to the task classification and rule engine. The data verification and anti-fraud module collects user behavior trajectories and verifies the authenticity of tasks based on the behavior verification instructions issued by the task classification and rule engine, and returns the judgment results to the task classification and rule engine. The dynamic message push adaptation module receives push instructions from the task classification and rule engine after logical judgment, and automatically selects the message push method based on the user's current terminal type, including APP terminal, mini-program terminal, and H5 terminal. Optionally, the task classification and rule engine dynamically parses task dependencies, including the execution order between independent tasks and multi-terminal collaborative tasks, as well as task triggering rules; where independent tasks include browsing tasks and rating tasks; multi-terminal collaborative tasks include following official accounts, sharing for assistance, or message subscription. The task classification and rule engine can support flexible configuration of complex multi-terminal collaborative tasks, meeting the diverse incentive scenario needs of e-commerce platforms.

[0006] Optionally, the cross-platform data synchronization interface includes third-party platform authorization interfaces, third-party message push service interfaces, and SMS gateway interfaces.

[0007] Optionally, the business category tag is dynamically generated based on the page path of the business scenario, user identity, and login status. During task execution, the request parameters are matched with the business category tag to retrieve the corresponding page path. This enables rapid access to new business scenarios and flexible expansion of page routing, avoiding the maintenance costs associated with hard-coded paths.

[0008] Optionally, the dynamic message push adaptation module can dynamically adjust the message push method or timing based on the device model, network environment, and user's historical preferences.

[0009] Optionally, the multi-terminal collaborative closed-loop module can perform cross-terminal behavior verification through the authorization interface of a third-party platform, obtain the user's identity identifier on the third-party platform, and bind and verify the identity identifier with the associated operation status on the third-party platform.

[0010] Optionally, the data verification and anti-fraud module utilizes behavioral trajectory analysis algorithms to verify the authenticity of tasks; these algorithms include timestamp continuity detection, device fingerprint consistency verification, and path legitimacy verification. The data verification and anti-fraud module can identify abnormal behavior from multiple dimensions, reducing the proportion of fraudulent tasks and ensuring the fairness of incentive resource allocation.

[0011] Optionally, after the task classification and rule engine determines that a task has been completed, it records the user's task completion trajectory, which includes the task type, completion time, cross-platform verification records, and anti-fraud verification results.

[0012] Optionally, the multi-terminal collaborative closed-loop module obtains the user's associated operation status on third-party social platforms through a polling detection mechanism, and returns the associated operation status to the task classification and rule engine. The task classification and rule engine triggers reward collection after verification.

[0013] The beneficial effects of this invention compared to the prior art are: 1. Improve task completion rate By combining task classification and rule engine with multi-terminal collaborative closed-loop module, the system enables linked confirmation and binding verification of user behavior across different terminals, allowing users to complete complex tasks without repetitive operations across different terminals. This solves the problem of users repeatedly executing tasks on multiple terminals in existing technologies, improving task completion rate and user experience.

[0014] 2) Enhance cross-platform collaboration efficiency The system automatically selects the push method based on the user's current device type, avoiding the tedious operation of manually configuring push channels. At the same time, it supports the capture and verification of sharing and assistance behavior across multiple channels, solving the problem of limited sharing and assistance channels, reducing manual configuration costs, and improving operation and maintenance efficiency.

[0015] 3) Achieve flexible adaptation of business formats By using the business page routing and distribution module, the corresponding page path is dynamically retrieved and rendered during task execution, solving the problem of not being able to dynamically adjust the target page for the same task in different scenarios and requiring hard-coded configuration. This mechanism supports rapid expansion to new business scenarios, reducing the path configuration cycle from several days to just a few minutes.

[0016] 4) Strengthen anti-cheating capabilities By using data verification and anti-fraud modules, abnormal behavior can be identified from multiple dimensions, effectively reducing the proportion of fake tasks and ensuring the fairness of incentive resource allocation.

[0017] In summary, the technical solution of this invention, through the collaborative work of task classification and rule engine, multi-terminal collaborative closed-loop module, dynamic message push adaptation module, business page routing and distribution module, and data verification and anti-fraud module, achieves efficient collaboration, flexible configuration, and reliable verification of e-commerce incentive task system in multi-terminal scenarios, and has outstanding substantive features and significant progress. Attached Figure Description

[0018] Figure 1 This is an architecture diagram of a multi-terminal collaborative e-commerce incentive task closed-loop processing system according to the present invention. Figure 2 This is a flowchart of the task engine for a multi-terminal collaborative e-commerce incentive task closed-loop processing system according to the present invention. Detailed Implementation

[0019] The technical solutions in the embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0020] like Figure 1 As shown, this invention provides a multi-terminal collaborative e-commerce incentive task closed-loop processing system, including a task classification and rule engine 101, a multi-terminal collaborative closed-loop module 102, a business format page routing and distribution module 103, a data verification and anti-fraud module 104, and a dynamic message push adaptation module 105. Users initiate task requests through terminal devices (including Apps, mini-programs, and H5 pages), the system processes the task according to its type, and finally returns a task completion confirmation to the user. Specific steps include: First, the task classification and rule engine 101 receives task requests from user terminals, classifies e-commerce incentive tasks, and defines task completion conditions. Task types include sharing assistance, browsing, following official accounts, and subscription tasks. The rule engine supports dynamic configuration, allowing operators to set effective time, participation restrictions, and reward distribution rules for different task types. It can also configure exclusive judgment conditions and trigger rules for independent tasks and multi-terminal collaborative tasks, which take effect in real time after configuration.

[0021] Furthermore, the task classification and rule engine 101 dynamically parses task dependencies through the rule engine. Task dependencies include the execution order between independent tasks and multi-terminal collaborative tasks, as well as triggering rules. Among them, independent tasks include browsing tasks and evaluation tasks; multi-terminal collaborative tasks include following public accounts, sharing help, or message subscriptions.

[0022] The triggering rules include task triggering methods and completion judgment rules. Specifically, the task triggering methods for independent tasks are divided into two types: active triggering and passive triggering. Taking browsing tasks as an example, the active triggering method for browsing tasks includes: the user actively clicks the task entry, the task classification and rule engine 101 sends a page routing instruction to the business format page routing distribution module 103, and the business format page routing distribution module 103 retrieves the corresponding page path according to the business format tag and completes the jump to the target page; the passive triggering method is that when the user browses the target page, the data verification and anti-fraud module 104 detects that the business format tag of the current page matches the tasks already claimed by the user, and automatically triggers task counting. The completion judgment rules can be configured to include page dwell time reaching a preset threshold, completion of at least one valid interaction, legal jump path, and a limit on the number of times the same device or user can complete the task within a day, etc.

[0023] The triggering method for multi-terminal collaborative tasks varies depending on the terminal type: On the mini-program side, after the user clicks the follow button, the embedded H5 page triggers the third-party platform authorization process, and the multi-terminal collaborative closed-loop module 102 obtains the user's identity identifier on the third-party platform; on the app side, after the user clicks follow, they are redirected to the third-party platform SDK, and the task classification and rule engine 101 records the operation trajectory. The completion judgment rules include: the user completes authorization and binds to the platform account, the third-party platform interface detects that the user has followed, the follow behavior occurs within the task validity period, and the same identity identifier is counted only once; unfollowing is considered invalid.

[0024] Next, the task classification and rule engine 101 determines the task type and, based on the task type, sends corresponding execution instructions in parallel to downstream modules (multi-terminal collaborative closed-loop module 102, business format page routing and distribution module 103, and data verification and anti-fraud module 104), and receives the processing results returned by each module. If it is an independent task (such as browsing or rating), the execution instructions are sent directly to the business format page routing and distribution module 103 and the data verification and anti-fraud module 104; if it is a multi-terminal collaborative task (such as following a public account, sharing to help, or subscribing to messages), the cross-terminal verification instruction is sent to the multi-terminal collaborative closed-loop module 102, the page routing instruction is sent to the business format page routing and distribution module 103, and the behavior verification instruction is sent to the data verification and anti-fraud module 104.

[0025] After each downstream module performs its corresponding processing, it returns the results to the Task Classification and Rules Engine 101. Specifically, the multi-terminal collaboration closed-loop module 102 returns the cross-terminal behavior verification result, the business page routing and distribution module 103 returns the page rendering completion status, and the data verification and anti-fraud module 104 returns the behavior authenticity judgment result. The Task Classification and Rules Engine 101 summarizes the results returned by the three modules and performs logical judgment. The task is considered complete only if cross-terminal verification is successful, page rendering is complete, and the behavior is judged as normal; otherwise, the task is considered to have failed or terminated. After determining that the task is complete, the Task Classification and Rules Engine 101 records the user's task completion trajectory, including the task type, completion time, cross-terminal verification record, and anti-fraud verification result. Based on the judgment result, the Task Classification and Rules Engine 101 performs the following processing: If the judgment is successful, it triggers reward distribution (either the user actively claims it or the system automatically distributes it), synchronizes the task completion data, and can automatically continue to the next task; if the judgment fails, it resets the task status, guides the user to re-execute the task or perform an abnormal retry, and for behaviors judged as fraudulent, it records the abnormality and triggers an alarm.

[0026] Subsequently, the task classification and rule engine 101 issues a push instruction to the dynamic message push adaptation module 105 based on the judgment result. Finally, the dynamic message push adaptation module 105 executes the push according to the user terminal type, sending the user a task completion notification and incentive distribution information or a task failure prompt, thus forming a complete closed-loop processing flow. The task classification and rule engine 101 can support flexible configuration of complex tasks involving multi-terminal collaboration, meeting the diverse incentive scenario needs of e-commerce platforms.

[0027] The multi-terminal collaborative closed-loop module 102 receives cross-terminal verification instructions from the task classification and rule engine 101. It obtains the associated operation status of the third-party platform when the user initiates the task request through a cross-platform data synchronization interface and a polling detection mechanism, and binds and verifies this status with the operation on the terminal that initiated the task request. This confirms the completion of the cross-terminal behavior and returns the verification result to the task classification and rule engine 101. This achieves linked confirmation of user behavior across different terminals, avoiding repetitive user operations and improving the efficiency of the task closed loop and the consistency of the user experience. The cross-platform data synchronization interface includes third-party platform authorization interfaces (such as the WeChat Open Platform interface), third-party message push service interfaces, and SMS gateway interfaces.

[0028] The cross-platform data synchronization interface enables the sharing of user identity and behavior data across different platforms, achieving consistency verification of cross-platform behavior. Specifically, the multi-terminal collaboration closed-loop module 102 divides e-commerce incentive tasks into independent tasks and multi-terminal collaborative tasks. For multi-terminal collaborative tasks, the multi-terminal collaboration closed-loop module 102 obtains the user's identity identifier or behavior status on different terminals through the cross-platform data synchronization interface, and binds and verifies the identity identifier with the associated operation status on the third-party platform to confirm the completion status of the cross-terminal behavior. For example, when a user clicks the "Follow Official Account" button on the mini-program page, the embedded H5 page triggers the WeChat OAuth2.0 authorization process, and the multi-terminal collaboration closed-loop module 102 obtains the user's official account openId through the WeChat Open Platform interface. The task classification and rule engine 101 establishes the association between the openId and the user's identity after the user logs into the mini-program and stores it in the user table. During binding verification, it directly calls the association relationship in the user table. Once the multi-terminal collaborative closed-loop module 102 obtains the openId, the task classification and rule engine 101 queries the user table for the user identity corresponding to the openId to complete the identity binding verification. Subsequently, the multi-terminal collaborative closed-loop module 102 polls the interface provided by WeChat to check whether the user has followed the official account. Each polling interval is a preset time (e.g., 1 second) until the follow status is obtained or a timeout occurs. If the user is detected as having followed, the follow task is confirmed as complete; if the user has not followed after the timeout, the task is considered to have failed. The multi-terminal collaborative closed-loop module 102 returns the verification result (success or failure) to the task classification and rule engine 101. For example, after a user shares to WeChat Moments via the app, the multi-terminal collaborative closed-loop module 102 checks whether the sharing behavior is complete via the WeChat API and returns the detection result (success or failure) to the task classification and rule engine 101.

[0029] The business format page routing and distribution module 103 stores page mapping rules for different business scenarios and dynamically adjusts and renders the corresponding page paths during task execution. Specifically, the module stores these rules in a business format tag database. The page mapping rules are configured and stored using a key-value pair format, and each rule includes at least a page path, a business type identifier, and a business format tag identifier. During task execution, the corresponding page path is dynamically retrieved and rendered, supporting dynamic switching of target pages under different business scenarios and avoiding the maintenance costs associated with hard-coded paths. The page path refers to the URL or route address of the target business page to which the user is redirected after completing the task. The business format tags in the database are dynamically generated based on the page path of the business scenario, the user's identity identifier, and the login status. During task execution, the request parameters are matched with the business format tags to retrieve the corresponding page path, enabling rapid access to new business scenarios and flexible expansion of page routing.

[0030] The data verification and anti-fraud module 104 is used to verify the authenticity of the task and prevent the task from being falsely completed. Specifically, the data verification and anti-fraud module 104 uses a behavior trajectory analysis algorithm to determine the authenticity of the task; the behavior trajectory analysis algorithm includes timestamp continuity detection, device fingerprint consistency verification, and path legality verification.

[0031] Timestamp continuity detection is used to determine whether task execution time conforms to normal behavior patterns. The criteria include: the total task completion time is not less than a preset minimum duration threshold; the number of task triggers by the same device or account within a unit of time does not exceed a set frequency limit; and in multi-terminal collaborative tasks, the time interval between operations on different terminals does not exceed a preset effective duration. Device fingerprint consistency verification is used to verify whether multi-terminal operations belong to the same real user. Device fingerprints are generated using a multi-parameter combination and irreversible encryption algorithm: hardware parameters, system parameters, network parameters, and software parameters of the device are collected, standardized, and then concatenated in a preset order. An irreversible encryption algorithm is used to generate a unique device identifier.

[0032] Path validity verification is used to check whether the user's redirection path conforms to the business logic set for the task. This includes basic validity rules, task-specific validity rules, and illegal path interception rules. Basic validity rules require that the redirection path includes the task trigger page and that the redirection path is continuous and does not skip core steps. Task-specific validity rules configure corresponding valid paths for different task types. Illegal path interception rules are used to intercept redirections from external illegal links, redirections from scripts or plug-ins, and abnormal redirections without page loading records.

[0033] If any dimension fails the verification, the task is deemed to have been falsely completed, the task is terminated, and the anomaly is recorded. The data verification and anti-fraud module can identify abnormal behavior from multiple dimensions, reduce the proportion of false tasks, and ensure the fairness of incentive resource allocation.

[0034] The dynamic message push adaptation module 105 automatically selects the message push method based on the user's current terminal type, avoiding the tedious manual configuration of push channels and improving operational efficiency. Specifically, the dynamic message push adaptation module 105 automatically selects the push method based on the terminal type, which includes APP terminals, mini-program terminals, and H5 terminals. For example, for APP terminals, JPush is prioritized; for mini-program terminals, WeChat customer service messages are used; and for H5 terminals, SMS or email pushes are used. In addition, the dynamic message push adaptation module 105 also dynamically adjusts the push method or push timing based on device model, network environment, and user's historical preferences. This further improves the accuracy of message delivery and optimizes the user's receiving experience. For different types of tasks, the task classification and rule engine 101 executes corresponding processes, such as... Figure 2 As shown: For sharing assistance tasks, users initiate sharing via an app, H5 page, or WeChat mini-program. The task classification and rule engine 101 calls the corresponding platform's sharing interface, carrying the task identifier and sharer identifier in the shared link. The multi-platform collaborative closed-loop module 102 detects whether the sharing behavior is completed. After a friend enters the assistance page through the shared link, the multi-platform collaborative closed-loop module 102 confirms the task status based on the task identifier and sharer identifier in the link. If completed, the reward is claimed; otherwise, the task is exited.

[0035] For browsing tasks, users complete the browsing behavior through an H5 page. The business format page routing and distribution module 103 renders the corresponding page according to the business format tag. The data verification and anti-fraud module 104 captures the page entry and exit timestamps through page lifecycle events and calculates the actual dwell time. The page entry timestamp is recorded when the user navigates to the target page, and the page exit timestamp is recorded when the user actively exits or the page loses focus. The dwell time is calculated by subtracting the entry timestamp from the exit timestamp. The anti-fraud module simultaneously collects page interaction behaviors (such as swiping and clicking). Dwell time without effective interaction is not included in the statistics. It also verifies whether the continuity of timestamps deviates from the task trigger and navigation timestamps within a preset range. If the dwell time meets the preset task conditions and passes the anti-fraud verification, the reward is claimed; otherwise, the task is terminated.

[0036] For tasks involving following a WeChat Official Account, the task classification and rules engine 101 first determines whether the user has already followed the account. If not, it triggers the WeChat OAuth 2.0 authorization process via an embedded H5 page. The multi-terminal collaborative closed-loop module 102 obtains the user's Official Account openId and guides the user to complete the follow process and verify their follow status. Once verified, the task classification and rules engine 101 triggers reward collection.

[0037] For subscription tasks, the task classification and rule engine 101 initiates a subscription invitation based on the user's terminal type (APP or H5). The dynamic message push adaptation module 105 selects the push method based on the terminal type and receives the callback result of the subscription operation. The multi-terminal collaboration closed-loop module 102 obtains the subscription status and binds it with the user's operation on the terminal that initiated the task request to verify the completion of the subscription behavior. The data verification and anti-fraud module 104 verifies the authenticity of the subscription behavior. If completed, the reward is triggered; otherwise, the task is terminated.

[0038] Throughout the execution of various tasks, the task classification and rule engine 101 acts as the scheduling center, receiving processing results from the multi-terminal collaborative closed-loop module 102, the business page routing and distribution module 103, and the data verification and anti-fraud module 104. After all necessary conditions are met, it issues a final push instruction to the dynamic message push adaptation module 105. The dynamic message push adaptation module 105 executes the push according to the user terminal type, sending task completion notifications and incentive distribution information to the user, thus forming a complete closed-loop processing flow from task initiation, cross-terminal verification, page routing, behavior verification to message push.

[0039] In summary, this invention, through the organic combination of multi-terminal collaborative closed-loop design, dynamic message push adaptation, business page routing and distribution, and multi-dimensional anti-fraud modules, achieves efficient collaboration, flexible configuration, and reliable verification of the e-commerce incentive task system in multi-terminal scenarios, demonstrating significant progress.

[0040] The above embodiments are merely illustrative of the technical concept of the present invention and should not be construed as limiting the scope of protection of the present invention. Any modifications made to the technical solutions based on the technical concept proposed in this invention shall fall within the scope of protection of this invention.

Claims

1. A multi-terminal collaborative e-commerce incentive task closed-loop processing system, characterized in that, include: The task classification and rule engine receives task requests from user terminals, classifies and schedules e-commerce incentive tasks, issues instructions to other modules according to task type, summarizes processing results and performs logical judgments. Task types include sharing assistance, browsing, following official accounts, and subscription tasks. The multi-terminal collaborative closed-loop module receives cross-terminal verification instructions issued by the task classification and rule engine, verifies the consistency of user behavior on different terminals through the cross-platform data synchronization interface, and returns the verification results to the task classification and rule engine. The business format page routing and distribution module receives page routing instructions from the task classification and rule engine, stores page mapping rules for different business scenarios, dynamically determines and renders the target page path based on the business format tag, and returns the rendering status to the task classification and rule engine. The data verification and anti-fraud module collects user behavior trajectories and verifies the authenticity of tasks based on the behavior verification instructions issued by the task classification and rule engine, and returns the judgment results to the task classification and rule engine. The dynamic message push adaptation module receives push instructions issued by the task classification and rule engine after logical judgment, and automatically selects the message push method according to the user's current terminal type, including APP terminal, mini program terminal and H5 terminal.

2. The multi-terminal collaborative e-commerce incentive task closed-loop processing system according to claim 1, characterized in that, The task classification and rule engine dynamically parses task dependencies, including the execution order between independent tasks and multi-terminal collaborative tasks, as well as task triggering rules. Independent tasks include browsing tasks and rating tasks; multi-terminal collaborative tasks include following public accounts, sharing to help, or subscribing to messages.

3. The multi-terminal collaborative e-commerce incentive task closed-loop processing system according to claim 1, characterized in that, Cross-platform data synchronization interfaces include third-party platform authorization interfaces, third-party message push service interfaces, and SMS gateway interfaces.

4. The multi-terminal collaborative e-commerce incentive task closed-loop processing system according to claim 1, characterized in that, Business type tags are dynamically generated based on the page path of the business scenario, user identity identifier, and login status. When the task is executed, the request parameters are matched with the business type tags and the corresponding page path is retrieved.

5. The multi-terminal collaborative e-commerce incentive task closed-loop processing system according to claim 1, characterized in that, The dynamic message push adaptation module dynamically adjusts the message push method or timing based on device model, network environment, and user's historical preferences.

6. The multi-terminal collaborative e-commerce incentive task closed-loop processing system according to claim 1, characterized in that, The multi-terminal collaborative closed-loop module realizes cross-terminal behavior verification through the authorization interface of the third-party platform, obtains the user's identity identifier on the third-party platform, and binds and verifies the identity identifier with the associated operation status on the third-party platform.

7. The multi-terminal collaborative e-commerce incentive task closed-loop processing system according to claim 1, characterized in that, The data verification and anti-fraud module uses behavioral trajectory analysis algorithms to verify the authenticity of tasks; these algorithms include timestamp continuity detection, device fingerprint consistency verification, and path legality verification.

8. The multi-terminal collaborative e-commerce incentive task closed-loop processing system according to claim 1, characterized in that, After a task is completed, the task classification and rule engine records the user's task completion trajectory, which includes the task type, completion time, cross-platform verification records, and anti-fraud verification results.

9. A multi-terminal collaborative e-commerce incentive task closed-loop processing system according to claim 1, characterized in that, The multi-terminal collaborative closed-loop module obtains the user's associated operation status on the third-party platform through a polling detection mechanism, and returns the associated operation status to the task classification and rule engine. After the task classification and rule engine verifies the associated operation status, it triggers the reward claim.

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