Intelligent hosting method for whole process of e-commerce operation

Abstract

The invention belongs to the field of artificial intelligence, and particularly relates to an intelligent hosting method for the whole process of e-commerce operation. The method comprises the following steps: establishing a global unified user identity identification system; user behavior event streams from an application program, an applet and a webpage end are collected in real time and processed in a standardized mode, and the user behavior event streams comprise page browsing, commodity clicking, shopping cart adding, staying duration and rolling depth; and performing timestamp alignment and intention semantic fusion on multi-channel user behavior events based on the global unified user identity identification system to construct a user intention evolution sequence. According to the method, a global unified user identity identification system is constructed, so that the problem of cross-channel user identity splitting is thoroughly solved, and seamless collection of behaviors in anonymous and real-name states is realized; according to the method, an intention semantic enhancement mechanism is introduced, the original behavior event is converted into an enhancement event rich in commodity knowledge and context semantics, and the depth and accuracy of user intention characterization are remarkably improved.

Description

Technical Field

[0001] This invention belongs to the field of artificial intelligence, specifically involving an intelligent management method for the entire e-commerce operation process. Background Technology

[0002] In existing technologies, e-commerce platforms typically deploy their own user behavior collection and recommendation logic in separate applications, mini-programs, and web pages. The lack of a unified identity identification system and behavior sequence alignment mechanism among these channels means that when users switch between different devices, their key behavioral information such as browsing history, clicks, adding to cart, and searching cannot be effectively inherited and integrated. The recommendation model is forced to reinitialize the user profile with isolated contexts, which seriously weakens the relevance and timeliness of the recommendation results.

[0003] Traditional solutions often rely on device fingerprints or temporary session identifiers for coarse-grained association, which makes it difficult to cope with complex user interaction scenarios across devices, applications, and platforms. Furthermore, they lack the ability to dynamically model user intent evolution paths, resulting in a disconnect between recommendation strategies and users' actual needs. Summary of the Invention

[0004] This invention provides an intelligent management method for the entire e-commerce operation process, aiming to solve the technical problem that the fragmentation of cross-channel user behavior data leads to the inability of personalized recommendation systems to consistently identify user intent, resulting in a decline in conversion rates.

[0005] To overcome the aforementioned shortcomings, this invention constructs an end-to-end, cross-channel user intent continuity modeling and intelligent hosting system. Based on a globally unique user identity verification mechanism, this system utilizes standardized access, temporal alignment, and semantic enhancement of multi-source heterogeneous behavioral events to form a unified user intent evolution map covering all touchpoints, all time periods, and all behaviors. This map then drives the generation and execution of dynamically adaptive recommendation strategies, achieving a fundamental shift from fragmented data to coherent intent, and from static profiling to dynamic evolution.

[0006] As one embodiment of the present invention, the intelligent management method for the entire e-commerce operation process includes the following steps: First, establishing a unified user identity identification system across the entire domain; second, collecting and standardizing user behavior event streams from applications, mini-programs, and web pages in real time; third, aligning timestamps and fusing semantic intent of multi-channel behavior events based on the unified identity identification to construct a user intent evolution sequence; then, using a deep temporal neural network to dynamically model the intent evolution sequence to generate a user intent state vector at the current moment; finally, based on the user intent state vector, calling the recommendation strategy engine to generate a product or content recommendation list that highly matches the current intent, and distributing it in real time through the target channel.

[0007] Furthermore, the establishment of a unified user identity system across the entire domain specifically includes: when a user first accesses any front-end channel, if the user is already logged in, the global user ID assigned by the platform is directly used as the primary identifier; if the user is not logged in, a temporary anonymous identifier is generated by combining the device feature hash value, network environment fingerprint, coarse-grained geographical location encoding, and the first access timestamp; when the user completes the login operation on any subsequent channel, the system automatically binds and maps the temporary anonymous identifier to the global user ID, and backtracks and merges all collected behavioral events under the temporary identifier to the corresponding global user ID name, thereby achieving seamless identity unification.

[0008] Furthermore, the real-time collection and standardized processing of user behavior event streams specifically includes: deploying lightweight behavior tracking proxy modules on the application, mini-program, and web pages respectively. The tracking proxy modules listen for user interaction events, including page browsing, product clicks, adding to cart, initiating a search, submitting an order, dwell time, and scrolling depth. All events are structurally encapsulated according to a predefined unified event model, which includes event type, event occurrence time, trigger channel identifier, unique code of the target object, interaction intensity quantification value, and contextual environment parameters. The encapsulated events are transmitted to the central event processing center in real time through an encrypted message queue.

[0009] Furthermore, the process of aligning timestamps and fusing semantic intent of multi-channel behavioral events based on a unified identity identifier specifically includes: after receiving event streams from various channels, the central event processing center groups the events according to the user identity identifiers carried in the events; for all events under the same user identifier, they are strictly sorted according to the event occurrence time to form an original behavioral time sequence chain; subsequently, an intent semantic enhancement operation is performed on each event in the original behavioral time sequence chain, the intent semantic enhancement operation including: querying the product knowledge graph based on the unique code of the target object to obtain the product category level, core attribute tags, associated scenario tags, and historical conversion rate indicators; injecting the above semantic information into the original event to generate an enhanced behavioral event rich in contextual semantics; and finally outputting a user intent evolution sequence composed of enhanced behavioral events.

[0010] Furthermore, the dynamic modeling of the user intent evolution sequence using a deep temporal neural network specifically includes: inputting the user intent evolution sequence into an encoder composed of multiple layers of gated recurrent units; the encoder processes each enhanced behavioral event step by step, where each event is represented as a high-dimensional dense vector, which is obtained by linear projection after concatenating event type embedding, product semantic embedding, interaction intensity value, and environmental parameter embedding; the encoder outputs a hidden state at each time step, and performs weighted aggregation of historical hidden states through an attention mechanism to generate the user intent state vector at the current moment; the user intent state vector fully represents the user's focus of interest, decision tendency, and potential demand intensity in the current interaction context.

[0011] Furthermore, the step of generating a recommendation list based on the user intent state vector by calling the recommendation strategy engine specifically includes: the recommendation strategy engine maintains multiple candidate recommendation strategy templates, each template corresponding to a typical user intent pattern, including exploratory, price comparison, repeat purchase, and impulsive purchase; the engine first calculates the cosine similarity between the current user intent state vector and the center vector of each strategy template, and selects the strategy template with the highest similarity as the current activation strategy; subsequently, the activation strategy calls the underlying product recall pool, which is jointly constructed by three layers of mechanisms: collaborative filtering, vector nearest neighbor search, and rule filtering; the recall results are finely ranked and scored according to the user intent state vector, the scoring function is a weighted linear combination, and the weights are dynamically adjusted by the online learning module based on real-time conversion feedback; finally, the sorted product list is output as the recommendation result.

[0012] Furthermore, the method also includes a closed-loop optimization mechanism for recommendation results: the system continuously monitors the user's subsequent behavior in response to the recommendation results, including clicks, adding to cart, placing orders, and abandonment; these feedback behaviors are used as reinforcement signals to update the fine-ranking weight parameters in the online learning module; at the same time, the feedback behaviors are also injected into the user intent evolution sequence, triggering the encoder to make real-time corrections to the user intent state vector, ensuring that the recommendation strategy remains synchronized with the user's latest intent.

[0013] Furthermore, the construction and maintenance of the product knowledge graph specifically includes: periodically extracting all product information from the product master data system; performing natural language processing on product titles, descriptions, and attribute fields to extract entities and relationships; constructing a hierarchical ontology structure based on the category system, defining parent-child relationships, sibling mutually exclusive relationships, and cross-category association relationships between category nodes; associating each product node with its historical behavioral statistical features, including exposure click-through rate, add-to-cart conversion rate, and return rate; the graph supports real-time incremental updates, automatically triggering local subgraph reconstruction when new products are listed or attributes change.

[0014] Furthermore, the central event processing center adopts a distributed stream processing architecture, whose core components include an event parser, an identity normalizer, a semantic enhancer, and a time-series buffer. The event parser is responsible for verifying and deserializing the original events. The identity normalizer queries the identity mapping table and replaces the temporary identifier with the global user ID. The semantic enhancer calls the product knowledge graph interface to complete the semantic injection of events. The time-series buffer maintains a queue of behavioral events within a sliding time window for each user. The window length is set to 72 hours, and events exceeding the window are automatically removed to control the consumption of computing resources and focus on recent intentions.

[0015] Furthermore, the training process of the gated recurrent unit encoder adopts a combination of self-supervised pre-training and supervised fine-tuning: in the pre-training stage, the user's historical behavior sequence is used as input to predict the behavior type and target object of the next time step, and the loss function is cross-entropy; in the fine-tuning stage, explicit feedback from users on the recommendation results is collected in real recommendation scenarios to construct positive and negative sample pairs, and the encoder parameters are optimized using a contrastive learning objective, so that the intention state vector corresponding to the positive sample is closer to the recommended product vector, and the negative sample is farther away.

[0016] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. By constructing a unified user identity identification system across the entire domain, the problem of fragmented user identities across channels has been completely solved, and seamless aggregation of behaviors under both anonymous and real-name status has been achieved; 2. By introducing an intent semantic enhancement mechanism, the original behavioral events are transformed into enhanced events rich in product knowledge and contextual semantics, which significantly improves the depth and accuracy of user intent representation; 3. By dynamically modeling the evolution sequence of user intent through deep temporal neural networks, it is possible to capture the short-term fluctuations and long-term trends of user interests, avoiding the lag of traditional static profiling. 4. Through intent-driven dynamic strategy selection and closed-loop feedback optimization, we ensure that the recommendation results are always highly consistent with the user's current real needs, effectively improving click-through rate, add-to-cart rate and final conversion rate in cross-channel scenarios; 5. The entire system is based on event-driven and streaming processing, and has high real-time performance, high scalability and strong robustness, making it suitable for large-scale e-commerce operation environments. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of the overall technical solution architecture of the intelligent management method for the entire e-commerce operation process proposed in this invention; Figure 2 This is a schematic diagram of the core principle framework for cross-channel user intent continuity modeling and dynamic recommendation strategy generation in this invention; Figure 3This is a logical flowchart of the construction of a unified user identity identification system and the normalization of behavioral events in this invention; Figure 4 This is a logical flowchart of the standardized collection of multi-channel user behavior events, semantic enhancement, and intent evolution sequence construction in this invention. Figure 5 This is a flowchart illustrating the logical flow of the dynamic modeling of user intent state and activation of recommendation strategy based on deep temporal neural networks in this invention. Figure 6 This is a schematic diagram of the multi-level interaction relationship and data flow between the terminal channel and the central event processing center in this invention. Detailed Implementation

[0018] Please refer to Figures 1 to 6 This invention provides an intelligent management method for the entire e-commerce operation process, aiming to solve the technical problem that the fragmentation of cross-channel user behavior data leads to the inability of personalized recommendation systems to consistently identify user intent, resulting in a decline in conversion rates.

[0019] To overcome the aforementioned shortcomings, this invention constructs an end-to-end, cross-channel user intent continuity modeling and intelligent hosting system. Based on a globally unique user identity verification mechanism, this system utilizes standardized access, temporal alignment, and semantic enhancement of multi-source heterogeneous behavioral events to form a unified user intent evolution map covering all touchpoints, all time periods, and all behaviors. This map then drives the generation and execution of dynamically adaptive recommendation strategies, achieving a fundamental shift from fragmented data to coherent intent, and from static profiling to dynamic evolution.

[0020] As one embodiment of the present invention, the intelligent management method for the entire e-commerce operation process includes the following steps: S1, establish a unified user identity identification system across the entire domain; S2 collects and standardizes user behavior event streams from applications, mini-programs, and web pages in real time; S3, based on a unified identity identifier, performs timestamp alignment and semantic fusion of intent for multi-channel behavioral events to construct a user intent evolution sequence; S4, use a deep temporal neural network to dynamically model the intent evolution sequence and generate the user intent state vector at the current moment; S5. Based on the user intent state vector, the recommendation strategy engine is invoked to generate a list of recommended products or content that highly matches the current intent, and then distributed in real time through the target channel.

[0021] In step S1, the specific operational process for establishing a unified user identity system across the entire domain is as follows: When a user accesses any front-end channel for the first time, the system first determines whether the user is already logged in. If the user is already logged in, the system directly uses the global user ID assigned by the platform as the primary identifier. This global user ID is uniformly assigned by the central user management center and persistently stored in the distributed user database, possessing global uniqueness and long-term stability. If the user is not logged in, the system generates a temporary anonymous identifier through a combination of device feature hash value, network environment fingerprint, coarse-grained geographical location encoding, and the first access timestamp.

[0022] The device feature hash value is calculated by standardizing hardware and software attributes such as device model, operating system version, screen resolution, and browser kernel type. The network environment fingerprint includes public IP address ranges, carrier identifiers, and network types (e.g., 4G, 5G, Wi-Fi). The coarse-grained geographic location encoding maps latitude and longitude coordinates to city-level administrative division codes. The first access timestamp is accurate to the millisecond level. The data from these four dimensions are concatenated and input into a secure hash algorithm, outputting a fixed-length hexadecimal string as a temporary anonymous identifier. This temporary anonymous identifier remains valid throughout the user session and is reported with each behavioral event.

[0023] When a user logs in through any subsequent channel, the system automatically binds and maps the temporary anonymous identifier to the global user ID, and retrospectively merges all collected behavioral events under the temporary identifier to the corresponding global user ID. This process is executed by the identity normalizer in the central event processing center, ensuring seamless identity normalization and avoiding any interruption perceived by the user.

[0024] In step S2, the specific operation flow for real-time collection and standardized processing of user behavior event streams is as follows: Lightweight behavior tracking proxy modules are deployed on the application, mini-program, and web page. These proxy modules are embedded into the front-end code in a non-intrusive manner to monitor user interaction events, including page views, product clicks, adding items to the cart, initiating searches, submitting orders, dwell time, and scroll depth. All events are structured and encapsulated according to a predefined unified event model.

[0025] The unified event model includes the following fields: event type, event occurrence time, trigger channel identifier, unique target object code, interaction intensity quantification value, and context environment parameters. The event type is represented by an enumeration value, such as "PAGE_VIEW", "ITEM_CLICK", "ADD_TO_CART", etc.; the event occurrence time is a timestamp in ISO8601 format; the trigger channel identifier is a preset channel code, such as "APP_ANDROID", "MINI_PROGRAM_WECHAT", "WEB_DESKTOP"; the unique target object code is a globally unique identifier for a product, page, or search keyword; the interaction intensity quantification value is dynamically assigned based on the event type, for example, page dwell time is in seconds, scroll depth is expressed as a percentage, and click events are uniformly assigned a value of 1; the context environment parameters include auxiliary information such as the current page URL, the source of the previous page, device orientation, battery status, and network latency. The encapsulated event is transmitted to the central event processing center in real time via an encrypted message queue. The message queue adopts a Kafka-based distributed architecture, supports high-throughput and low-latency message delivery, and has built-in message verification and retransmission mechanisms to ensure that events are not lost or duplicated.

[0026] In step S3, the specific operation process for timestamp alignment and semantic fusion of multi-channel behavioral events based on a unified identity identifier is as follows: After receiving event streams from various channels, the central event processing center groups the events according to the user identity identifiers carried in the events. All events under the same user identifier are strictly sorted according to their occurrence time to form an original behavioral timeline chain. Subsequently, semantic enhancement operations are performed on each event in the original behavioral timeline chain. The semantic enhancement operations include: querying the product knowledge graph based on the unique code of the target object to obtain the product's category level, core attribute tags, associated scenario tags, and historical conversion rate indicators.

[0027] The product category hierarchy is represented by a three-level tree structure, such as "Home Appliances > Kitchen Appliances > Rice Cookers"; core attribute tags include structured fields such as brand, price range, energy efficiency rating, and material; related scenario tags are pre-labeled by operations personnel or automatically generated through user behavior clustering, such as "Newlywed Purchases," "Back to School Season," and "Holiday Gifts"; historical conversion rate metrics include the product's exposure click-through rate, add-to-cart conversion rate, and order conversion rate over the past 30 days. After injecting the above semantic information into the original events, enhanced behavioral events rich in contextual semantics are generated. Each enhanced behavioral event contains original fields and newly added semantic fields, forming a complete unit of intent expression. The final output is a user intent evolution sequence composed of enhanced behavioral events, arranged in chronological order, fully recording the user's interest migration path and decision evolution process across all channels.

[0028] In step S4, the specific operation process for dynamically modeling the user intent evolution sequence using a deep temporal neural network is as follows: The user intent evolution sequence is input into an encoder composed of multiple layers of gated recurrent units. The encoder contains three layers of gated recurrent units, each with 512 hidden units. Each enhanced behavioral event is represented as a high-dimensional dense vector, obtained by concatenating event type embeddings, product semantic embeddings, interaction intensity values, and environmental parameter embeddings, followed by linear projection. Event type embeddings are obtained by searching a pre-trained event type vocabulary, with a dimension of 64; product semantic embeddings are obtained by weighted averaging hierarchical embeddings of category level, core attribute labels, and associated scene labels, with a dimension of 256; interaction intensity values ​​are normalized and directly used as scalar inputs; environmental parameter embeddings map contextual parameters to a 128-dimensional vector using a multilayer perceptron.

[0029] The concatenated vector has a dimension of 449, which is compressed to 512 dimensions through a linear projection layer and used as input to the gated recurrent unit. The encoder outputs the hidden state at each time step and aggregates the historical hidden states using a weighted attention mechanism. The attention weights are calculated by the dot product of the current hidden state and the historical hidden states using the Softmax function. Finally, a user intent state vector with a dimension of 512 is generated for the current moment. This vector fully represents the user's focus of interest, decision-making tendency, and intensity of potential needs in the current interaction context.

[0030] In step S5, the specific operation process of generating a recommendation list based on the user intent state vector by calling the recommendation strategy engine is as follows: The recommendation strategy engine maintains four candidate recommendation strategy templates, corresponding to four typical user intent patterns: exploratory, price comparison, repeat purchase, and impulsive. Each strategy template is represented by a central vector, which is obtained offline by clustering a large number of user intent state vectors. The engine first calculates the cosine similarity between the current user intent state vector and the central vectors of each strategy template, and selects the strategy template with the highest similarity as the current activation strategy. Subsequently, the activation strategy calls the underlying product recall pool. The recall pool is constructed by a three-layer mechanism of collaborative filtering, vector nearest neighbor search, and rule filtering.

[0031] The collaborative filtering layer calculates user similarity based on the user-product interaction matrix, recalling products favored by similar users. The vector nearest neighbor search layer performs an approximate nearest neighbor search on the user intent state vector and the product embedding vector, recalling semantically similar products. The rule filtering layer excludes products that do not comply with business rules, such as those with insufficient inventory, prohibited sales areas, or user blacklists. The results from these three layers are then combined and finely ranked and scored based on the user intent state vector. The scoring function is a weighted linear combination, in the following form: in, For goods The estimated click-through rate The cosine similarity between the product embedding and the user intent state vector. As a decay factor for product launch time, Price range matching degree. Weight. , , , The online learning module dynamically adjusts its recommendations based on real-time conversion feedback. It employs the FTRL optimization algorithm, using positive actions such as clicks, adding to cart, and placing orders as positive samples, and exposure without interaction as negative samples, continuously updating the weight parameters. The final output is a sorted list of products as the recommendation result, which is then delivered to the user's terminal in real-time via the target channel's rendering interface.

[0032] The method also includes a closed-loop optimization mechanism for recommendation performance: the system continuously monitors subsequent user actions related to the recommendation results, including clicks, adding to cart, placing orders, and abandonment. These feedback behaviors are marked as reinforcement signals and used to update the fine-grained ranking weight parameters in the online learning module. Simultaneously, feedback behaviors are also injected into the user intent evolution sequence, triggering the encoder to instantly correct the user intent state vector. For example, if a user clicks on a recommended product but does not add it to cart, the system treats this event as weak positive feedback, slightly increasing the attention weight of related categories; if the user places an order directly, it serves as strong positive feedback, significantly enhancing the semantic representation of the product and its associated context. This mechanism ensures that the recommendation strategy remains synchronized with the user's latest intent, avoiding recommendation failure due to intent drift.

[0033] The construction and maintenance of the product knowledge graph specifically includes: periodically extracting all product information from the product master data system, with an extraction frequency of once every 24 hours. Natural language processing is performed on product titles, descriptions, and attribute fields to extract entities and relationships. Entities include brands, models, functional keywords, etc.; relationships include "belongs to category," "compatible accessories," "alternative models," etc. A hierarchical ontology structure is constructed based on the category system, defining parent-child relationships, sibling mutual exclusion relationships, and cross-category association relationships between category nodes. Each product node is associated with its historical behavioral statistical characteristics, including exposure click-through rate, add-to-cart conversion rate, and return rate over the past 7, 30, and 90 days. The graph supports real-time incremental updates; when a new product is listed or its attributes change, local subgraph reconstruction is automatically triggered. The reconstruction process uses graph neural networks for embedding propagation to ensure semantic consistency between new nodes and existing nodes.

[0034] The central event processing center adopts a distributed stream processing architecture, with core components including an event parser, an identity normalizer, a semantic enhancer, and a time-series buffer. The event parser is responsible for verifying and deserializing raw events, verifying message signatures, field integrity, and timestamp validity. The identity normalizer queries an identity mapping table, replacing temporary identifiers with global user IDs. This mapping table is stored in a Redis cluster and supports millisecond-level queries. The semantic enhancer calls a product knowledge graph interface to inject event semantics; the interface response timeout threshold is set to 50 milliseconds. The time-series buffer maintains a queue of behavioral events within a sliding time window for each user, with a window length of 72 hours. Events exceeding the window are automatically removed to control computational resource consumption and focus on recent intents. The buffer uses a circular queue structure, supporting efficient enqueue and dequeue operations.

[0035] The training process of the gated recurrent unit encoder adopts a combination of self-supervised pre-training and supervised fine-tuning: In the pre-training stage, the user's historical behavior sequence is used as input to predict the behavior type and target object in the next time step, and the loss function is cross-entropy. The training data comes from anonymized behavior logs from the past 6 months, containing a total of 1 billion events. In the fine-tuning stage, explicit user feedback on the recommendation results is collected in a real recommendation scenario to construct positive and negative sample pairs. Positive samples are products that the user clicked or ordered, and negative samples are products that were exposed but not interacted with. Contrastive learning is used to optimize the encoder parameters so that the distance between the intent state vector corresponding to the positive sample and the recommended product vector is closer, and the distance between the negative sample and the recommended product vector is farther. The contrastive loss function is defined as: in, For the user intent state vector, For positive sample product vectors, For negative sample product vectors, For cosine similarity, The temperature coefficient is set to 0.1. Through this training mechanism, the encoder can learn an intent representation space that is highly aligned with the user's actual preferences.

[0036] In summary, this invention completely solves the problem of fragmented user identities across channels by constructing a unified user identity system, achieving seamless aggregation of behaviors under both anonymous and real-name status. By introducing an intent semantic enhancement mechanism, original behavioral events are transformed into enhanced events rich in product knowledge and contextual semantics, significantly improving the depth and accuracy of user intent representation. Through dynamic modeling of the user intent evolution sequence using a deep temporal neural network, it can capture short-term fluctuations and long-term trends in user interests, avoiding the lag of traditional static profiling. Through intent-driven dynamic strategy selection and closed-loop feedback optimization, it ensures that the recommendation results are always highly consistent with the user's current real needs, effectively improving click-through rate, add-to-cart rate, and final conversion rate in cross-channel scenarios. The entire system, with event-driven and streaming processing as its core, possesses high real-time performance, high scalability, and strong robustness, making it suitable for large-scale e-commerce operation environments.

Claims

1. A fully intelligent managed e-commerce operation method, characterized by: include: Establish a unified user identity identification system across the entire domain; Real-time collection and standardized processing of user behavior event streams from applications, mini-programs and web pages, including page browsing, product clicks, adding to cart, initiating search, submitting orders, dwell time and scrolling depth; Based on the aforementioned unified user identity identification system, timestamp alignment and semantic fusion of user behavior events from multiple channels are performed to construct a user intent evolution sequence. The user intent evolution sequence is dynamically modeled using a deep temporal neural network to generate the user intent state vector at the current moment; Based on the user intent state vector, the recommendation strategy engine is invoked to generate a list of recommended products or content that highly matches the current intent, and then distributed in real time through the target channel.

2. The intelligent management method for the entire e-commerce operation process according to claim 1, characterized in that, The establishment of a unified user identity identification system across the entire domain includes: When a user visits any front-end channel for the first time, if the user is already logged in, the global user ID assigned by the platform will be used as the primary identifier; if the user is not logged in, a temporary anonymous identifier will be generated by combining the device feature hash value, network environment fingerprint, coarse-grained geolocation code and the first access timestamp. When a user completes a login operation through any subsequent channel, the temporary anonymous identifier is bound and mapped to the global user ID, and all behavioral events collected under the temporary anonymous identifier are retrospectively merged under the corresponding global user ID.

3. The intelligent management method for the entire e-commerce operation process according to claim 2, characterized in that, The real-time collection and standardized processing of user behavior event streams includes: A lightweight behavior tracking proxy module is deployed on the application, mini-program, and web page respectively, and the tracking proxy module listens for user interaction events; All events are encapsulated in a structured manner according to a predefined unified event model, which includes event type, event occurrence time, trigger channel identifier, unique code of target object, interaction intensity quantification value, and context environment parameters. The encapsulated events are transmitted to the central event processing center in real time via an encrypted message queue.

4. The intelligent management method for the entire e-commerce operation process according to claim 3, characterized in that, The method of aligning timestamps and fusing semantic intent of multi-channel user behavior events based on a unified user identity system across the entire domain to construct a user intent evolution sequence includes: Group the event stream according to the user identity identifier carried in the event; All events under the same user ID are strictly sorted according to the time of occurrence to form the original behavioral sequence chain; Perform intent semantic enhancement operations on each event in the original behavior time sequence chain to generate enhanced behavior events. The intent semantic enhancement operations include querying the product knowledge graph based on the unique code of the target object, obtaining the product category level, core attribute tags, associated scenario tags and historical conversion rate indicators, and injecting the above semantic information into the original event. Output a sequence of user intent evolution consisting of enhanced behavioral events.

5. The intelligent management method for the entire e-commerce operation process according to claim 4, characterized in that, The step of dynamically modeling the user intent evolution sequence using a deep temporal neural network to generate the user intent state vector at the current moment includes: The user intent evolution sequence is input into an encoder composed of multiple layers of gated loop units stacked together; Each enhanced behavioral event is represented as a high-dimensional dense vector, which is obtained by concatenating event type embedding, product semantic embedding, interaction intensity value and environmental parameter embedding and then linearly projecting. The encoder outputs the hidden state at each time step and uses an attention mechanism to weighted aggregate the historical hidden states to generate the user intent state vector at the current moment.

6. The intelligent management method for the entire e-commerce operation process according to claim 5, characterized in that, The event type embedding is obtained by looking up a pre-trained event type vocabulary; The product semantic embedding is obtained by weighted averaging after hierarchical embedding of category level, core attribute tags, and associated scenario tags; the interaction intensity value is normalized and used as a scalar input; The environmental parameter embedding uses a multilayer perceptron to map contextual environmental parameters to a fixed-dimensional vector.

7. The intelligent management method for the entire e-commerce operation process according to claim 6, characterized in that, The process of generating a recommendation list by invoking the recommendation strategy engine based on the user intent state vector includes: Maintain multiple candidate recommendation strategy templates, each template corresponding to a typical user intent pattern, including exploratory, price comparison, repeat purchase, and impulsive. Calculate the cosine similarity between the current user intent state vector and the center vector of each policy template, and select the policy template with the highest similarity as the current activation policy; The activation strategy invokes the underlying product recall pool, which is constructed by a three-layer mechanism of collaborative filtering, vector nearest neighbor search, and rule filtering. The recall results are finely ranked and scored based on the user intent state vector. The scoring function is a weighted linear combination, and the weights are dynamically adjusted by the online learning module based on real-time conversion feedback. Output the sorted list of products as the recommendation result.

8. The intelligent management method for the entire e-commerce operation process according to claim 7, characterized in that, The method also includes a closed-loop optimization mechanism for recommendation performance: Continuously monitor users' subsequent behavior in response to recommendations, including clicks, adding to cart, placing orders, and abandonment. The subsequent behavior is used as a reinforcement signal to update the fine-ranking weight parameters in the online learning module; The subsequent actions are injected into the user intent evolution sequence, triggering the encoder to make immediate corrections to the user intent state vector.

9. The intelligent management method for the entire e-commerce operation process according to claim 8, characterized in that, The construction and maintenance of the product knowledge graph includes: Regularly extract full product information from the product master data system; Natural language processing is used to extract entities and relationships from product titles, descriptions, and attribute fields. A hierarchical ontology structure is constructed based on the category system, defining the parent-child relationship, sibling mutual exclusion relationship, and cross-category association relationship between category nodes; Associate each product node with its historical behavioral statistics, including exposure click-through rate, add-to-cart conversion rate, and return rate; It supports real-time incremental updates, and automatically triggers local sub-image reconstruction when new products are listed or attributes are changed.

10. The intelligent management method for the entire e-commerce operation process according to claim 9, characterized in that, The central event processing center adopts a distributed stream processing architecture, whose core components include an event parser, an identity normalizer, a semantic enhancer, and a time-series buffer. The event parser is used to verify and deserialize the original events; The identity normalizer is used to query the identity mapping table and replace the temporary identifier with the global user ID. The semantic enhancer is used to call the product knowledge graph interface to complete event semantic injection; The timing buffer maintains a queue of behavioral events within a sliding time window for each user. The window length is set to 72 hours, and events exceeding the window are automatically removed.

Images