Intelligent global digital right configuration and distribution method based on multi-source data fusion

Abstract

The invention relates to the technical field of big data and artificial intelligence, and particularly discloses a global digital rights intelligent configuration and distribution method based on multi-source data fusion. The method comprises the following steps: firstly, collecting user global multi-source heterogeneous data through a distributed network, extracting multi-modal features by using space-time alignment and a deep neural network, and constructing a user high-dimensional feature vector; then, inputting the feature vectors into a reinforcement learning model, and intelligently matching an optimal right configuration scheme from a right library through a multi-objective optimization function; thirdly, realizing cross-platform synchronous distribution according to the terminal state and the geofence by using a scene awareness routing algorithm; and finally, on the basis of a federated learning mechanism, realizing online iteration of the model by utilizing full-life-cycle feedback data. Through deep interleaving and intelligent decision making of multi-source data, accurate configuration and non-perception of digital rights and interests are achieved, the rights and interests cancel-after-verification rate and the user experience are improved, meanwhile, the user privacy is effectively protected, and the self-evolution ability is achieved.

Description

Technical Field

[0001] This invention belongs to the field of big data and artificial intelligence technology, specifically involving a method for intelligent configuration and distribution of full-domain digital rights based on multi-source data fusion. Background Technology

[0002] With the deepening development of the digital economy, various digital rights (such as electronic vouchers, membership privileges, and virtual assets) have become a core link connecting online traffic and offline consumption. However, existing digital rights distribution schemes still have the following significant shortcomings: The problem of data silos is serious: traditional rights and benefits allocation often relies on a single online consumption record or a simple geographical location trigger, failing to effectively integrate heterogeneous data from multiple dimensions such as social interaction, travel, physiological status, and offline movement, resulting in fragmented user profiles and difficulty in achieving accurate perception of the entire scenario.

[0003] The configuration logic is static and simplistic: the existing benefit combinations are mostly preset fixed patterns, lacking a dynamic balancing mechanism for real-time user needs, merchant costs and long-term value, resulting in low benefit redemption rates and inefficient resource allocation.

[0004] The distribution method is highly disruptive: existing push mechanisms often cover the entire population regardless of the time or the device, ignoring the user's current physical environment and device interaction status, which can easily lead to information redundancy and even cause user resentment.

[0005] Lack of privacy protection and feedback loop: Balancing data mining and user privacy protection is a major challenge in the process of multi-source data fusion; at the same time, the system often lacks real-time feedback and learning capabilities, making it difficult to evolve itself according to changes in user behavior. Summary of the Invention

[0006] To overcome the above-mentioned technical problems, the present invention provides a method for intelligent configuration and distribution of full-domain digital rights based on multi-source data fusion.

[0007] The present invention adopts the following technical solution: A method for intelligent configuration and distribution of full-domain digital rights based on multi-source data fusion includes the following steps: Multi-source heterogeneous data of users across the entire domain is collected through a distributed sensing network, and the multi-source heterogeneous data is aligned in a unified spatiotemporal coordinate system to construct a real-time user status feature matrix. Multimodal features are extracted from the user's real-time state feature matrix using a deep neural network, and dynamic weights are assigned to features of different dimensions using an attention mechanism to generate a high-dimensional feature vector for the user. The user's high-dimensional feature vector is input into a utility evaluation model based on reinforcement learning. By solving a multi-objective optimization function, the optimal digital rights allocation scheme is matched and generated from the rights pool. Based on the user's current terminal device status and geofence information, a scene-aware routing algorithm is used to determine the distribution path, enabling digital rights to be simultaneously delivered across cross-platform terminals. Real-time monitoring of redemption feedback data for digital rights throughout their entire lifecycle, and online iteration and self-evolution of the utility evaluation model using a federated learning mechanism.

[0008] Preferably, the multi-source heterogeneous data includes e-commerce transaction data, social media sentiment data, offline supermarket movement data, and environmental temperature, humidity, and pedestrian flow data obtained through IoT sensors.

[0009] Preferably, the process of constructing the user real-time state feature matrix includes: using spatiotemporal alignment technology to map unstructured behavioral signals onto a unified time axis and geographic grid, and dynamically adjusting the weight ratio of text features and location features according to the user's current movement rate.

[0010] Preferably, the utility evaluation model employs a deep reinforcement learning algorithm to establish a mapping relationship between the rights portfolio and the user's state through simulated distribution experiments. The multi-objective optimization function comprehensively considers the rights redemption rate, merchant operating costs, and the user's long-term lifetime value.

[0011] Preferably, the formula for calculating the comprehensive utility evaluation index E of the multi-objective optimization function is as follows: E=R(u,i)+C(u,i)-D(u) Where R(u,i) represents the user's potential interest rating in the rights and benefits combination, C(u,i) represents the contribution of the configuration scheme to the user's lifetime value, D(u) represents the interference factor, and and are coefficients dynamically adjusted according to operational objectives.

[0012] Preferably, the digital rights configuration scheme includes a composite rights chain consisting of electronic coupons, offline priority access rights, virtual asset value-added services, and cross-border co-branded gift packages.

[0013] Preferably, the scene-aware routing algorithm divides the distribution process into three stages: triggering, carrying, and presentation. In the triggering stage, the instruction is activated through a geofence or intent threshold. In the carrying stage, the data format is adaptively adjusted according to the terminal device type. In the presentation stage, voice, visual, or tactile reminders are selected based on the user interaction environment.

[0014] Preferably, the synchronous reach of the cross-platform terminal is achieved through a cross-platform signaling synchronization mechanism, ensuring that digital rights remain consistent across different applications, SMS channels, and IoT guidance terminals, and become globally invalid immediately after being verified through any channel.

[0015] Preferably, the federated learning mechanism processes the raw behavioral data at edge computing nodes and only sends the encrypted model gradient information back to the central server, thereby achieving global optimization of the configuration strategy while protecting user privacy.

[0016] Preferably, the self-evolution process includes: real-time monitoring of the click time, redemption location and subsequent conversion behavior of the benefits; when the redemption rate is lower than a preset threshold, an abnormal diagnosis process is automatically triggered, and the weight parameters in the utility evaluation model are corrected accordingly.

[0017] Compared with the prior art, the beneficial effects of the present invention are: Achieve precise insights across all times and spaces: By deeply integrating multi-source heterogeneous data through spatiotemporal alignment technology, it is possible to construct a comprehensive user profile covering online interests and offline scenarios, enabling the allocation of benefits to have strong scenario-triggered attributes and demand fit.

[0018] Optimize resource allocation efficiency: Introduce a utility evaluation model based on reinforcement learning, and balance user interest, merchant costs and lifetime value through a multi-objective optimization function to improve the redemption rate of digital benefits and the return on marketing investment.

[0019] Providing a seamless interactive experience: The scene-aware routing algorithm can adaptively adjust the distribution path and presentation format according to the user's current terminal status (such as smart wearables, in-vehicle screens, etc.), realizing on-demand and seamless distribution of benefits, effectively reducing interference to users.

[0020] Balancing privacy and security with self-evolution capabilities: Employing a federated learning framework, the system iterates the model while ensuring the original data remains locally. Combined with a feedback loop throughout the entire lifecycle, the system can automatically adjust its strategies based on real-time verification data, ensuring optimal performance in complex and ever-changing market environments. Attached Figure Description

[0021] Figure 1 This is the timing diagram of the interaction logic of the method of the present invention. Detailed Implementation

[0022] In this embodiment, we take a holistic digital ecosystem built on the foundation of a smart city as the application background, and demonstrate in detail how this method can achieve precise allocation and seamless distribution of digital rights through the deep interweaving of multi-source data.

[0023] I. Spatiotemporal sensing and fusion processing of multi-source heterogeneous data The system first establishes a full-time and space-time perception network covering both the physical and digital worlds. The data source layer not only includes traditional structured data such as user consumption records and APP click streams, but also integrates more complex unstructured information, such as emotionally charged images and text posted by users on social media platforms, anonymized visual traffic flow in offline shopping malls, and environmental temperature, humidity, and pedestrian flow data obtained through IoT sensors.

[0024] To handle this diverse data, the system employs a spatiotemporally aligned feature fusion technique. During data preprocessing, all raw signals are mapped onto a unified time axis and geospatial grid. When a user enters a large shopping mall on a rainy afternoon, the system instantly retrieves the user's recent online search keywords, such as waterproof sneakers or indoor children's playgrounds, and combines this with real-time mall congestion data.

[0025] In this process, the system utilizes a deep neural network to construct a multimodal feature extractor, transforming text, images, and behavioral sequences into feature vectors in a high-dimensional space. We define the fused global feature matrix as M, which assigns dynamic weights to data from different sources through an attention mechanism. If the user's current geographic location indicates that they are in a mobile state, the system automatically reduces the weight of lengthy text information and increases the weight of location-related data, thereby ensuring that the subsequently configured benefits have a strong scene-triggered attribute.

[0026] II. Construction of the Equity Utility Function and Intelligent Configuration Logic After obtaining a precise user profile across the entire ecosystem, the core configuration engine begins searching and combining benefits from a vast database. This database stores not only simple discount coupons, but also a diverse range of digital assets such as priority access rights, virtual asset value-added services, and cross-industry co-branded gift packages.

[0027] The core logic of the configuration engine lies in solving a multi-objective optimization problem. The system must consider not only the redemption rate of benefits but also the merchant's operating costs and the user's long-term brand loyalty. To this end, the system introduces a utility evaluation model based on deep reinforcement learning. This model learns the complex mapping relationship between benefit combinations and user states through millions of simulated distribution experiments.

[0028] We set a comprehensive utility evaluation index E, and its calculation process is as follows: E=R(u,i)+C(u,i)-D(u) In this formula, R(u,i) represents user u's potential interest score in benefit combination i, calculated through collaborative filtering and semantic association. C(u,i) represents the contribution of this configuration to the user's lifetime value, such as whether it can guide the user to complete their first cross-category purchase. D(u) represents the interference factor, used to measure the degree of user aversion that frequent push notifications may cause. The coefficients and sum are dynamically adjusted according to current operational goals; for example, during promotional seasons, the system will automatically increase the weight of R to pursue short-term conversions.

[0029] The configuration engine generates an optimal benefits package. For example, for a user who has just completed a marathon, the system might not configure a single sports drink voucher, but a complex chain of benefits including privileges such as booking a nearby massage parlor, a subsidy for replacing professional running shoes, and a digital finisher badge.

[0030] III. Intelligent Decision-Making for Dynamic Distribution Paths Across the Entire Domain After the benefits are configured, the distribution system needs to solve the problem of how to select the best path from the complex and diverse reach channels. This method adopts a scenario-aware routing algorithm, which breaks down the benefits distribution process into three stages: triggering, carrying, and presentation.

[0031] During the triggering phase, the system monitors the user's state transitions in real time. When a user enters a specific geofence or their online behavior triggers a preset intent threshold, the distribution command is activated. During the delivery phase, the system adaptively adjusts based on the user's current device. If the user is using a smartwatch, the benefit will be displayed with a minimalist vibration alert and icon; if the user is browsing a car's large screen, the benefit will be translated into voice interaction suggestions.

[0032] To achieve full coverage, the distribution engine has established a cross-platform signaling synchronization mechanism. This means that benefits can be displayed simultaneously on WeChat mini programs, brand-owned apps, SMS, and even smart signage in shopping malls. However, the system will ensure that once a user completes redemption on one channel, the display on other channels will immediately become invalid or be converted into a successful redemption notification, avoiding information redundancy.

[0033] IV. Federated Learning Feedback Loop with Privacy Protection To address privacy concerns in multi-source data fusion, this embodiment introduces a federated learning framework in the feedback phase. The user's original behavioral data remains on the local terminal or edge computing node, with only the encrypted model gradient information being sent back to the central server.

[0034] The system continuously refines its configuration strategy by monitoring the entire lifecycle data of benefits. Once a benefit is issued, the system records the time it is clicked, the location of its redemption, and the user's subsequent actions after redemption. These feedback signals are input into the online learning module, enabling the model to iterate at the second level.

[0035] If data shows that the redemption rate of a certain type of benefit is significantly lower than expected under specific weather or mood tags, the system will automatically trigger an anomaly diagnosis process to analyze whether the benefit is insufficient or the timing of distribution is off. This self-evolving capability allows the system to grasp micro-changes in the entire digital ecosystem more and more accurately over time.

[0036] V. Typical Application Scenarios To understand this method more intuitively, we selected two more specific implementation scenarios.

[0037] Scenario 1: Immediate recovery and achievement incentives for marathon participants This case study describes how the system uses physiological monitoring data and geospatial data to provide precise benefits configurations for users who complete high-intensity exercise.

[0038] 1. Multi-source data perception and state recognition On the day of the race, the system integrated real-time data from Ms. Wang's smart wearable device. When the sensors detected a sustained drop in Ms. Wang's heart rate from a high level, and her GPS track stopped moving for more than ten minutes in the race finish line area, the data fusion engine determined that she had successfully completed the race. At this point, the system automatically retrieved Ms. Wang's historical data on social media platforms, identifying that she had repeatedly searched for keywords such as myofascial release and low-glucose electrolyte replenishment. Through spatiotemporal alignment technology, the system aligned Ms. Wang's current fatigue state with the distribution of businesses near the finish line, generating a high-dimensional state vector.

[0039] 2. Intelligent solution of equity utility function Based on Ms. Wang's current physiological state vector, the core configuration engine begins searching the rights and benefits database. At this point, the utility function E=R(u,i)+C(u,i)-D(u) comes into play. Considering that Ms. Wang is currently in a state of physical exhaustion, the system adjusts the weight representing her potential interest score to the highest level. After calculation, instead of selecting a regular sports brand discount coupon, the system configures a composite benefit called a "Complete Energy Pack," which includes: a 30-minute plantar fascia massage privilege at a massage parlor near the finish line, a customized electrolyte drink available from a vending machine, and a limited-time digital collectible badge printed with her completion time.

[0040] 3. Seamless distribution with scene awareness The distribution system detected that Ms. Wang's phone battery was low and that she was walking, so it abandoned the heavy-touch approach of video calls. Instead, the system chose to push a short benefit card via vibration alerts from her smartwatch and displayed walking navigation to the massage parlor through a permanent entry on the phone's negative one screen. When Ms. Wang approached the vending machine, the system automatically popped up the redemption interface via Bluetooth near-field communication technology, achieving deep interaction between the benefit and the physical environment.

[0041] 4. Feedback loop and strategy evolution After Ms. Wang redeemed her massage privileges, the system recorded her stay duration and subsequent feedback. This feedback data was fed back into a federated learning framework, where the model discovered that for users who completed a full marathon, immediate physical recovery benefits were more effective at conversion than long-term product discounts. This finding was immediately used to adjust the configuration weights for this group, enabling the system to provide more targeted services in the next race.

[0042] Scenario 2: Cross-domain workplace collaboration scenario for Mr. Lin, a technology professional. This case study demonstrates how the system can provide high-value users with precise cross-industry benefits by analyzing their online interests and offline career paths.

[0043] 1. Multi-source data perception and state recognition Mr. Lin is a software engineer living in a high-tech industrial park in the city. The system, through a multi-source data fusion engine, integrated his online behavior over the past week: he frequently browsed technical reviews of foldable phones on professional forums and added two flagship models to his shopping cart on e-commerce platforms. Meanwhile, offline location data showed that Mr. Lin spent more than ten hours a day working in the industrial park. The system, through a knowledge graph, discovered that there was a flagship experience store of the brand located right below Mr. Lin's office building, and that the store was currently hosting a developer salon.

[0044] 2. Dynamic generation of equity portfolios The configuration engine identified Mr. Lin's strong desire to try new technologies and his high-net-worth professional attributes. To maximize his lifetime value, the system moved beyond a simple hardware promotion logic and tailored a set of workplace collaboration benefits for him. This benefit package not only included priority offline experience with a foldable phone that Mr. Lin was interested in and exclusive gifts with the purchase, but also an overtime allowance voucher for a chain coffee shop within the tech park, as well as a one-month premium membership to cloud collaboration software. This configuration aimed to drive the conversion of high-value hardware through essential workplace benefits.

[0045] 3. Seamless distribution with scene awareness The distribution system detected a decrease in Mr. Lin's computer activity at 3 PM on Friday, indicating he was taking a short break. The distribution engine avoided Mr. Lin's peak work hours, instead sending the benefits package via a notification on his frequently used professional social networking app. When Mr. Lin left the office building, a geofence triggered a secondary notification, and his phone screen automatically displayed directions to the coffee shop and mobile phone experience store, informing him that there was no queue at the current store. This timing significantly reduced any sense of disruption.

[0046] 4. Feedback loop and strategy evolution After picking up his coffee, Mr. Lin went into the experience store to try out the phone and expressed his intention to buy it. The system captured this complete conversion path through data tracking and analyzed that essential workplace benefits have a significant traffic-driving effect on high-value technology products. Based on this feedback, the system automatically updated the configuration template for the tech park population, prioritizing this type of cross-industry collaboration strategy. Through this continuous closed-loop learning, the system achieves accurate prediction and intelligent response to the needs of specific user groups.

[0047] Although embodiments of the present invention have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and variations can be made to the above embodiments without departing from the principles and spirit of the present invention, the scope of which is defined by the claims and their equivalents.

Claims

1. A method for intelligent allocation and distribution of full-domain digital rights based on multi-source data fusion, characterized in that, The method includes the following steps: Multi-source heterogeneous data of users across the entire domain is collected through a distributed sensing network, and the multi-source heterogeneous data is aligned in a unified spatiotemporal coordinate system to construct a real-time user status feature matrix. Multimodal features are extracted from the user's real-time state feature matrix using a deep neural network, and dynamic weights are assigned to features of different dimensions using an attention mechanism to generate a high-dimensional feature vector for the user. The user's high-dimensional feature vector is input into a utility evaluation model based on reinforcement learning. By solving a multi-objective optimization function, the optimal digital rights allocation scheme is matched and generated from the rights pool. Based on the user's current terminal device status and geofence information, a scene-aware routing algorithm is used to determine the distribution path, enabling digital rights to be simultaneously delivered across cross-platform terminals. Real-time monitoring of redemption feedback data for digital rights throughout their entire lifecycle, and online iteration and self-evolution of the utility evaluation model using a federated learning mechanism.

2. The method for intelligent allocation and distribution of full-domain digital rights based on multi-source data fusion according to claim 1, characterized in that, The multi-source heterogeneous data includes e-commerce transaction data, social media sentiment data, offline supermarket movement data, and environmental temperature, humidity, and pedestrian flow data obtained through IoT sensors.

3. The method for intelligent allocation and distribution of full-domain digital rights based on multi-source data fusion according to claim 1, characterized in that, The process of constructing the user real-time state feature matrix includes: using spatiotemporal alignment technology to map unstructured behavioral signals onto a unified time axis and geographic grid, and dynamically adjusting the weight ratio of text features and location features according to the user's current movement rate.

4. The method for intelligent allocation and distribution of full-domain digital rights based on multi-source data fusion according to claim 1, characterized in that, The utility evaluation model employs a deep reinforcement learning algorithm to establish a mapping relationship between the benefit portfolio and the user's state through simulated distribution experiments. The multi-objective optimization function comprehensively considers the benefit redemption rate, merchant operating costs, and the user's long-term lifetime value.

5. The method for intelligent allocation and distribution of full-domain digital rights based on multi-source data fusion according to claim 4, characterized in that, The formula for calculating the comprehensive utility evaluation index E of the multi-objective optimization function is as follows: E=R(u,i)+C(u,i)-D(u) Where R(u,i) represents user u's potential interest score in benefit combination i, C(u,i) represents the contribution of the configuration scheme to the user's lifetime value, D(u) represents the interference factor, and and are coefficients dynamically adjusted according to operational objectives.

6. The method for intelligent allocation and distribution of full-domain digital rights based on multi-source data fusion according to claim 1, characterized in that, The digital rights configuration scheme includes a complex rights chain consisting of electronic coupons, offline priority access rights, virtual asset value-added services, and cross-border co-branded gift packages.

7. The method for intelligent allocation and distribution of full-domain digital rights based on multi-source data fusion according to claim 1, characterized in that, The scene-aware routing algorithm divides the distribution process into three stages: triggering, carrying, and presentation. In the triggering stage, the instruction is activated through a geofence or intent threshold. In the carrying stage, the data format is adaptively adjusted according to the terminal device type. In the presentation stage, voice, visual, or tactile reminders are selected based on the user interaction environment.

8. The method for intelligent allocation and distribution of full-domain digital rights based on multi-source data fusion according to claim 1, characterized in that, The synchronous reach of the cross-platform terminals is achieved through a cross-platform signaling synchronization mechanism, ensuring that digital rights remain consistent across different applications, SMS channels, and IoT guidance terminals, and become globally invalid immediately after being verified through any channel.

9. The method for intelligent allocation and distribution of full-domain digital rights based on multi-source data fusion according to claim 1, characterized in that, The federated learning mechanism processes raw behavioral data at edge computing nodes and only sends the encrypted model gradient information back to the central server, thereby achieving global optimization of configuration strategies while protecting user privacy.

10. The method for intelligent allocation and distribution of full-domain digital rights based on multi-source data fusion according to claim 1, characterized in that, The self-evolution process includes: real-time monitoring of the click time, redemption location and subsequent conversion behavior of benefits; when the redemption rate is lower than a preset threshold, an abnormal diagnosis process is automatically triggered, and the weight parameters in the utility evaluation model are corrected accordingly.

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