Intelligent-agent-driven independent station full-link marketing management method and system

Abstract

The application relates to the technical field of electronic commerce data processing, and discloses an agent-driven independent station full-link marketing management method and system, which comprises the following steps: acquiring public domain delivery features and generating a tracking token, encapsulating the tracking token as a cross-domain attribution load to write a directional link; a gateway analyzes the load, matches a visual module with a commodity sequence to assemble a page topological structure, collects a front-end active interaction duration to generate an interaction feature vector; aligning data with the tracking token, calculating a conversion intention score according to an unpaid session; calculating a conversion difference rate according to a batch average score, and calculating and updating a bid to write a flow control bidding queue when the difference rate exceeds a silence threshold, so that the application realizes dynamic alignment of cross-domain intention and a page topological structure, eliminates cold start intention fracture, overcomes bid control oscillation caused by long-period payment time lag, and guarantees smooth convergence of marketing resource scheduling.

Description

Technical Field

[0001] This invention relates to an intelligent agent-driven, end-to-end marketing management method and system for independent websites, belonging to the field of e-commerce data processing technology. Background Technology

[0002] Cross-platform marketing resource allocation typically involves three stages: public domain advertising data transmission, independent website page display, and backend transaction attribution. In a conventional architecture, the advertising platform sends a targeted link with basic parameters to the web client via a data interface. After the user enters the independent website through this link, the independent website server outputs a product display page based on the inbound network request. After the user completes payment, the independent website asynchronously sends the transaction conversion data back to the advertising platform via a data interface for subsequent marketing resource allocation and placement adjustments. However, with the continuous strengthening of terminal security mechanisms and increased restrictions on cross-system data tracking, this segmented processing method is gradually becoming less effective. Several issues have been exposed. The semantics of ad creatives and audience characteristics formed on the public domain side are easily lost during cross-domain redirection. Independent website gateways have difficulty obtaining relevant information before users enter the site. The server can only call a general static template to complete the initial page display, which can easily cause the page topology to be mismatched with the user's prior intent. At the same time, there is usually a long time interval between when a user enters the independent website, browses products and finally completes payment. If bids are adjusted based on a small number of completed orders that are returned late, there is a lack of effective feedback during the period before the payment results are returned, which can easily cause unstable bid adjustments and affect the reasonable allocation of marketing resources.

[0003] In practical applications, although existing technologies have attempted to achieve cross-platform redirect tracking and offline attribution by injecting basic tag parameters into network links, bottlenecks still exist in the deep utilization of cross-domain data and real-time dynamic control. For example, Chinese invention patent application CN113918797A discloses a method for tagging and tracking network address redirection behavior. This method injects static parameters such as channel and activity identifiers into advertising links and records and reports redirection information on the target site via cookies to perform effect attribution. However, this type of solution only stays at the surface-level transmission of discrete text parameters and post-event statistics, and cannot achieve cross-platform data attribution. Domain communication carries and parses high-dimensional creative semantic features, which makes it impossible for independent website front-ends to dynamically construct page topology based on users' prior intentions. To compensate for the slow return of transaction conversion data, simply increasing the frequency of bidding commands will not shorten the time required for transaction conversion itself. On the contrary, it may exceed the call frequency limit of the advertising platform's open interface, causing interface call failure and out-of-order overwriting between different bidding commands. In addition, if user interaction time is directly counted according to the actual timestamp, the time when the browser tab is switched to the background or in a suspended state may also be included, which will cause deviations in the time statistics and decay calculation of interaction behavior.

[0004] Therefore, the technical problem to be solved by this invention is how to address the difficulty in matching public domain advertising intentions with the topology of independent website pages in existing technologies, and how to obtain indicators that can reflect the conversion status in advance from active interactive behaviors on the front end of the page without relying on long-term delayed order return data, so as to adjust bids and reduce API concurrent call conflicts and repeated bid fluctuations. Summary of the Invention

[0005] To address the problems in the background art, the technical solution of the present invention is as follows: A method for full-link marketing management of independent websites driven by intelligent agents, comprising the following steps:

[0006] Step S1: The delivery control unit obtains the first feature vector of the public domain delivery event and generates a tracking token. The first feature vector and the tracking token are encapsulated into a cross-domain attribution payload and written into the directed jump link.

[0007] Step S2: The independent site gateway parses the cross-domain attribution payload and extracts the tracking token and the first feature vector; the site orchestration unit matches the visual module and product sequence in the component pool according to the first feature vector, assembles the page topology structure and outputs it; the site data acquisition engine accumulates the active interaction duration during the page front-end activation period, records the behavioral time sequence data including active interaction duration and add-to-cart actions, and generates an interaction feature vector.

[0008] Step S3: The attribution arbitration unit aligns the public domain campaign events, interaction feature vectors, and order records with the tracking token as the primary key, and calculates the conversion intention score based on the interaction feature vectors of unpaid sessions combined with a preset decay constant.

[0009] In step S4, the collaborative scheduling unit calculates the conversion difference rate based on the average conversion intention score of the sampled sessions within the marketing batch and the preset benchmark value; when the conversion difference rate exceeds the silent threshold, the delivery control unit calculates and updates the bid based on the price adjustment coefficient, writes the updated bid into the traffic limiting bidding queue, and issues the latest timestamp bid instruction when there is an available token.

[0010] Preferably, in step S2, the process by which the site orchestration unit matches visual modules and product sequences in the component pool based on the first feature vector is as follows: extract the semantic feature vectors of each candidate component in the component pool, calculate the cosine similarity between the semantic feature vector of each candidate component and the first feature vector; select candidate components with a cosine similarity greater than a preset threshold of 0.75 as visual modules and product sequences, and assemble the page topology structure; when there are no candidate components with a cosine similarity greater than 0.75 in the component pool, the preset general layout components are called in a fallback manner to assemble the page topology structure.

[0011] Preferably, in step S2, the process of the site data acquisition engine recording behavioral time-series data and generating interaction feature vectors includes the following sub-steps: Step S21, after the initial page topology rendering is completed on the client page, the site data acquisition engine registers the focus activation event and focus suspension event of the browser window; Step S22, while the window is in the focus activation state, the timer value is accumulated with a preset sampling period of 1 second as the step size to generate the active interaction duration; Step S23, when a focus suspension event is received, the timer value is frozen until a focus activation event is received again, and the timer is resumed to accumulate; Step S24, the system clock time of the last received active heartbeat signal is used as the reference occurrence time of the corresponding behavioral event.

[0012] Preferably, in step S3, the attribution arbitration unit calculates the conversion intention score. The mathematical model is as follows: ,in, The total number of preset behavioral event categories; For the current session The actual measured value of the behavioral event; and The first The preset lower limit and preset upper limit of the benchmark for similar behavioral events; For the preset first The weight coefficients of each type of behavioral event, and the sum of the weight coefficients of all types is always equal to 1; For the first The time difference between the baseline occurrence time of the behavioral event and the current calculation time; This is the preset attenuation constant.

[0013] Preferably, the behavioral event categories include page dwell time, product browsing depth, add-to-cart actions, and checkout events; a preset decay constant is used. The value ranges from 1800s to 7200s.

[0014] Preferably, in step S4, the conversion difference rate is calculated as follows: the arithmetic mean of the conversion intention scores of all sampled sessions in the current marketing batch is calculated and the difference is compared with the preset benchmark value, and the ratio of the difference to the preset benchmark value is determined as the conversion difference rate.

[0015] Preferably, in step S4, the rules for calculating the updated bid based on the price adjustment coefficient when the conversion difference rate exceeds the silent threshold are as follows: when the conversion difference rate is greater than the silent threshold, the product of the upward adjustment step size ratio, the current promotion plan's base bid, and the difference rate constrained by the maximum single adjustment ratio limit is determined as the positive updated bid; when the conversion difference rate is less than the negative number of the silent threshold, the product of the downward adjustment reduction ratio, the base bid, and the difference rate constrained by the negative cutoff is determined as the negative updated bid; when the absolute value of the conversion difference rate is not greater than the silent threshold, the base bid is not adjusted; wherein, the downward adjustment reduction ratio is strictly greater than the upward adjustment step size ratio.

[0016] Preferably, the silent threshold ranges from 0.05 to 0.12; the maximum single adjustment ratio ranges from 0.20 to 0.35; the upward adjustment step size ratio ranges from 0.40 to 0.80; and the downward adjustment reduction ratio ranges from 0.60 to 1.10.

[0017] Preferably, in step S4, the process of the delivery control unit writing the updated bid into the rate-limiting bidding queue and issuing a bidding instruction includes the following sub-steps: Step S41, the delivery control unit encapsulates the updated bid into a bid data packet carrying the current timestamp and audience plan identifier, and writes it into the rate-limiting bidding queue; Step S42, the token bucket rate limiter injects tokens into the token bucket at a fixed rate according to the preset call frequency quota of the advertising platform's open data interface; Step S43, within a unit statistical time window, when multiple bid data packets are generated for the same audience plan identifier, the rate-limiting bidding queue performs overlay merging, removes old data packets, and retains... The latest bid data packet with the timestamp is retained; in step S44, when there are available tokens in the token bucket, the bid data packet at the head of the queue is dequeued and a request instruction containing the latest updated bid is sent to the advertising platform. In step S4, the process of the collaborative scheduling unit to obtain the sampling sessions in the current marketing batch is as follows: set a sliding time window, extract all independent sessions that generate inbound network requests and have a session duration of more than 5s within the current sliding time window, and use them as the effective sampling sample set; after removing abnormal samples whose active interaction duration exceeds the preset outlier threshold in the effective sampling sample set, calculate the arithmetic mean of the conversion intention scores of the remaining sampling sessions.

[0018] An agent-driven end-to-end marketing management system for independent websites, used to implement an agent-driven end-to-end marketing management method for independent websites, including:

[0019] The delivery control module is used to obtain the first feature vector of the public domain delivery event and generate a tracking token. It encapsulates the first feature vector and the tracking token into a cross-domain attribution payload and writes it into the targeted jump link. When the conversion difference rate exceeds the silent threshold, it calculates and updates the bid based on the price adjustment coefficient, writes the updated bid into the traffic limiting bidding queue, and issues the latest timestamp bidding instruction when there is an available token.

[0020] The access gateway module is used to parse the cross-domain attribution payload and extract the tracking token and the first feature vector; the site orchestration module is used to match the visual module and the product sequence in the component pool based on the first feature vector and assemble the page topology structure for output.

[0021] The site data acquisition module is used to accumulate active interaction duration during the page's front-end activation period, record behavioral time-series data including active interaction duration and add-to-cart actions, and generate interaction feature vectors.

[0022] The attribution arbitration module is used to align public domain campaign events, interaction feature vectors, and order records with the tracking token as the primary key, and calculate the conversion intention score based on the interaction feature vector of unpaid sessions combined with a preset decay constant; the collaborative scheduling module is used to calculate the conversion difference rate based on the average conversion intention score of sampled sessions within the marketing batch and a preset benchmark value.

[0023] Compared with the prior art, the beneficial effects of the present invention are:

[0024] 1. In the intelligent agent-driven end-to-end marketing management of independent websites, the first feature vector of public domain advertising events is transmitted to the independent website along with the targeted redirect link by tracking tokens. Based on the first feature vector, visual modules and product sequences are matched, so that the user intent on the public domain side can be carried over to the page display stage of the independent website. This reduces the information loss caused by cross-domain redirection and the problem of mismatch between page topology and user's prior intent. At the same time, when matching components are lacking, general layout components are used to avoid the independent website having to rely on fixed general templates to complete the page transition. This helps to reduce the first-screen bounce rate and reduce the bandwidth and computing resource consumption caused by invalid page display.

[0025] 2. By tracking active interaction duration during the on-page activation period and combining it with active heartbeat signals to determine the timing of behavioral events, deviations caused by continuous timing even after the browser tab enters the background or is suspended can be reduced. Based on this, by aligning public domain campaign events, interaction feature vectors, and order records with tracking tokens, and calculating conversion intention scores based on unpaid sessions, user conversion tendencies can be reflected in advance before the final payment results are returned. This reduces the feedback gap caused by relying solely on lagging order data for bid adjustments, lowers the risk of excessive bid adjustments and repeated fluctuations, and makes marketing resource allocation more stable.

[0026] 3. By calculating the conversion difference rate based on the average conversion intent score of sampled sessions within a marketing batch, and using a silent threshold to control whether to adjust bids, the baseline bid can be frequently modified when traffic quality fluctuates slightly. When conversions deteriorate, a larger reduction percentage and a limit on the magnitude of a single adjustment are adopted to help reduce ineffective budget consumption in a timely manner. When conversions are good, bids can be increased more steadily, thereby maintaining the responsiveness of bid adjustments while reducing frequent fluctuations and improving the stability of marketing resource allocation. Attached Figure Description

[0027] Figure 1 This is a flowchart of the cross-domain attribution and rate limiting bidding scheduling method of the present invention;

[0028] Figure 2 This is an architecture diagram of the full-link marketing collaborative interaction system of the present invention.

[0029] The objectives, features, and advantages of this invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0030] The technical solutions of the embodiments of this application will be clearly described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of this application are within the scope of protection of this application.

[0031] A smart agent-driven end-to-end marketing management method for independent websites includes the following steps:

[0032] Step S1: The delivery control unit obtains the first feature vector of the public domain delivery event and generates a tracking token. The first feature vector and the tracking token are encapsulated into a cross-domain attribution payload and written into the directed jump link.

[0033] Step S2: The independent site gateway parses the cross-domain attribution payload and extracts the tracking token and the first feature vector; the site orchestration unit matches the visual module and product sequence in the component pool according to the first feature vector, assembles the page topology structure and outputs it; the site data acquisition engine accumulates the active interaction duration during the page front-end activation period, records the behavioral time sequence data including active interaction duration and add-to-cart actions, and generates an interaction feature vector.

[0034] Step S3: The attribution arbitration unit aligns the public domain campaign events, interaction feature vectors, and order records with the tracking token as the primary key, and calculates the conversion intention score based on the interaction feature vectors of unpaid sessions combined with a preset decay constant.

[0035] In step S4, the collaborative scheduling unit calculates the conversion difference rate based on the average conversion intention score of the sampled sessions within the marketing batch and the preset benchmark value; when the conversion difference rate exceeds the silent threshold, the delivery control unit calculates and updates the bid based on the price adjustment coefficient, writes the updated bid into the traffic limiting bidding queue, and issues the latest timestamp bid instruction when there is an available token.

[0036] Preferably, in step S2, the process by which the site orchestration unit matches visual modules and product sequences in the component pool based on the first feature vector is as follows: extract the semantic feature vectors of each candidate component in the component pool, calculate the cosine similarity between the semantic feature vector of each candidate component and the first feature vector; select candidate components with a cosine similarity greater than a preset threshold of 0.75 as visual modules and product sequences, and assemble the page topology structure; when there are no candidate components with a cosine similarity greater than 0.75 in the component pool, the preset general layout components are called in a fallback manner to assemble the page topology structure.

[0037] Preferably, in step S2, the process of the site data acquisition engine recording behavioral time-series data and generating interaction feature vectors includes the following sub-steps: Step S21, after the initial page topology rendering is completed on the client page, the site data acquisition engine registers the focus activation event and focus suspension event of the browser window; Step S22, while the window is in the focus activation state, the timer value is accumulated with a preset sampling period of 1 second as the step size to generate the active interaction duration; Step S23, when a focus suspension event is received, the timer value is frozen until a focus activation event is received again, and the timer is resumed to accumulate; Step S24, the system clock time at which the last active heartbeat signal was received is used as the reference occurrence time of the corresponding behavioral event.

[0038] Preferably, in step S3, the attribution arbitration unit calculates the conversion intention score. The mathematical model is as follows: ,in, The total number of preset behavioral event categories; For the current session The actual measured value of the behavioral event; and The first The preset lower limit and preset upper limit of the benchmark for similar behavioral events; For the preset first The weight coefficients of each type of behavioral event, and the sum of the weight coefficients of all types is always equal to 1; For the first The time difference between the baseline occurrence time of the behavioral event and the current calculation time; This is the preset attenuation constant.

[0039] Preferably, the behavioral event categories include page dwell time, product browsing depth, add-to-cart actions, and checkout events; a preset decay constant is used. The value range is from 1800s to 7200s.

[0040] Preferably, in step S4, the conversion difference rate is calculated as follows: the arithmetic mean of the conversion intention scores of all sampled sessions in the current marketing batch is calculated and the difference is compared with the preset benchmark value, and the ratio of the difference to the preset benchmark value is determined as the conversion difference rate.

[0041] Preferably, in step S4, the rules for calculating the updated bid based on the price adjustment coefficient when the conversion difference rate exceeds the silent threshold are as follows: when the conversion difference rate is greater than the silent threshold, the product of the upward adjustment step size ratio, the current promotion plan's base bid, and the difference rate constrained by the maximum single adjustment ratio limit is determined as the positive updated bid; when the conversion difference rate is less than the negative number of the silent threshold, the product of the downward adjustment reduction ratio, the base bid, and the difference rate constrained by the negative cutoff is determined as the negative updated bid; when the absolute value of the conversion difference rate is not greater than the silent threshold, the base bid is not adjusted; wherein, the downward adjustment reduction ratio is strictly greater than the upward adjustment step size ratio.

[0042] Preferably, the silent threshold ranges from 0.05 to 0.12; the maximum single adjustment ratio ranges from 0.20 to 0.35; the upward adjustment step size ratio ranges from 0.40 to 0.80; and the downward adjustment reduction ratio ranges from 0.60 to 1.10.

[0043] Preferably, in step S4, the process of the delivery control unit writing the updated bid into the rate-limiting bidding queue and issuing a bidding instruction includes the following sub-steps: Step S41, the delivery control unit encapsulates the updated bid into a bid data packet carrying the current timestamp and audience plan identifier, and writes it into the rate-limiting bidding queue; Step S42, the token bucket rate limiter injects tokens into the token bucket at a fixed rate according to the preset call frequency quota of the advertising platform's open data interface; Step S43, within a unit statistical time window, when multiple bid data packets are generated for the same audience plan identifier, the rate-limiting bidding queue performs overlay merging, removes old data packets, and retains... The latest bid data packet with the timestamp is retained; in step S44, when there are available tokens in the token bucket, the bid data packet at the head of the queue is dequeued and a request instruction containing the latest updated bid is sent to the advertising platform. In step S4, the process of the collaborative scheduling unit to obtain the sampling sessions in the current marketing batch is as follows: set a sliding time window, extract all independent sessions that generate inbound network requests and have a session duration of more than 5s within the current sliding time window, and use them as the effective sampling sample set; after removing abnormal samples whose active interaction duration exceeds the preset outlier threshold in the effective sampling sample set, calculate the arithmetic mean of the conversion intention scores of the remaining sampling sessions.

[0044] An agent-driven end-to-end marketing management system for independent websites, used to implement an agent-driven end-to-end marketing management method for independent websites, including:

[0045] The delivery control module is used to obtain the first feature vector of the public domain delivery event and generate a tracking token. It encapsulates the first feature vector and the tracking token into a cross-domain attribution payload and writes it into the targeted jump link. When the conversion difference rate exceeds the silent threshold, it calculates and updates the bid based on the price adjustment coefficient, writes the updated bid into the traffic limiting bidding queue, and issues the latest timestamp bidding instruction when there is an available token.

[0046] The access gateway module is used to parse the cross-domain attribution payload and extract the tracking token and the first feature vector; the site orchestration module is used to match the visual module and the product sequence in the component pool based on the first feature vector and assemble the page topology structure for output.

[0047] The site data acquisition module is used to accumulate active interaction duration during the page's front-end activation period, record behavioral time-series data including active interaction duration and add-to-cart actions, and generate interaction feature vectors.

[0048] The attribution arbitration module is used to align public domain campaign events, interaction feature vectors, and order records with the tracking token as the primary key, and calculate the conversion intention score based on the interaction feature vector of unpaid sessions combined with a preset decay constant; the collaborative scheduling module is used to calculate the conversion difference rate based on the average conversion intention score of sampled sessions within the marketing batch and a preset benchmark value.

[0049] Example 1: In the collaborative scheduling scenario of a distributed service cluster of a cross-border e-commerce independent website and a multi-channel public domain advertising platform, the system faces the challenges of high latency, weak correlation, and heterogeneous multi-source characteristics in cross-domain data links caused by the limited privacy tracking of distributed network clients. There is a semantic topological break between the pre-audience intent of inbound traffic on the public domain advertising side and the landing page of the independent website. At the same time, there is a physical time lag of several hours to several days from when a user clicks on an ad to enter the website, browses products, and finally submits an order and completes gateway payment. If the bidding parameters are adjusted directly based on sparse and delayed order feedback data, control blind spots and bidding divergence oscillations will occur during the long feedback window period. In addition, the client browser's multi-tab switching and background suspension introduce false time decay deviations, and the public domain advertising platform's open interface has strict unit time call frequency quota limits, making it easy for cross-system marketing resource scheduling to experience interface circuit breakers and out-of-order instruction overwriting.

[0050] In this embodiment, the delivery control unit, site orchestration unit, site data acquisition engine, attribution arbitration unit, and collaborative scheduling unit automatically execute feature transfer, page orchestration, conversion intention calculation, and bid adjustment based on public domain delivery events, page interaction status, and marketing batch statistics. This forms an agent-driven, end-to-end marketing management process for independent websites. Addressing the aforementioned issues of cross-domain data fragmentation and traffic dissipation, the delivery control unit monitors public domain delivery events in real time through the advertising platform's open data interface, extracts a first feature vector containing core category code, visual color scheme encoding, and promotional selling point text vectors, and obtains the benchmark bid set for the current promotion plan. The delivery control unit, relying on a preset hash distribution algorithm, generates a globally unique tracking token TK-20260831-8942 for each ad click event. The first feature vector and the tracking token are then lightweighted, serialized, and Base64 encoded for compression, encapsulated into a cross-domain attribution payload, and written into the parameter field of the redirect link. This generates a landing page redirect link containing the tracking token and payload encoding, enabling direct transmission of public domain pre-emptive intent to the independent website gateway. During the encapsulation of the cross-domain attribution payload, to prevent the Uniform Resource Locator (URL) parameter from exceeding the 8KB physical length limit set by the web browser and reverse proxy gateway, the first feature vector uses a low-bit-width fixed-point quantization compression method, converting the multi-dimensional floating-point array into a compact binary byte stream, and packaging it with a 32-byte fixed-length tracking token into a structure. The overall compressed payload size is strictly controlled within 512 bytes, and then converted into a secure string using standard encoding before being written into the parameter field of the redirect link. This ensures high-fidelity transmission of public domain intent features while avoiding the potential for network transmission interruption caused by excessively long parameters.

[0051] When a network client triggers a redirect link to initiate an inbound network request to the independent website, the independent website gateway intercepts and parses the cross-domain attribution payload in the request, extracting the tracking token TK-20260831-8942 and the first feature vector. The site orchestration unit extracts the semantic feature vectors of each candidate component in the independent website's pre-set page component pool, calculates the cosine similarity between each semantic feature vector and the creative semantic encoding in the first feature vector, and filters out visual modules and product sequences with a cosine similarity greater than a preset threshold of 0.75. The visual modules include the main visual banner module and promotional copy components, and dynamically constructs a page topology structure that is strictly aligned with the semantics of the delivered creative to output rendering to the client. If there are no candidate components in the component pool that meet the threshold condition, the pre-set general layout components are called in a fallback manner. After the client page completes the initial page topology rendering, the site data acquisition engine registers the focus activation event and focus suspension event of the browser window on the client page. During the window's focus activation state, the timer value is accumulated with a sampling period of 1 second to generate the active interaction duration. When a focus suspension event is received, the timer value is frozen until the window is reconnected. Upon receiving a focus activation event, the accumulation resumes, and the system clock time of the last received active heartbeat signal is used as the baseline occurrence time of the corresponding behavioral event, thereby filtering out false time delay noise introduced by background static states. The site data acquisition engine records behavioral time-series data including active interaction duration, product browsing depth, product add-to-cart actions, and checkout events. Among these, active interaction duration is used as the page dwell time, and the page scrolling progress of the product display container is used as the product browsing depth, thereby generating an interaction feature vector. During the assembly of the page topology structure, the site arrangement unit, according to the preset tree-like layout skeleton rules, injects the main visual banner module from the visual module into the top-level container node of the first screen, attaches the promotional copy component to the floating announcement node below the main visual banner, and attaches the product sequence to the product display container node in a grid flow layout. Each component node is relatively positioned and adaptively spaced through the cascading style sheet grid layout system, generating a complete document object model tree structure containing viewport parameters and rendering order at each level. This document object model tree structure is then used as the page topology structure, converted into a hypertext markup language stream, and pushed to the client for rendering.

[0052] During the user session lifecycle, when an order record is generated, the site server stores the tracking token corresponding to the session along with the order record. The attribution arbitration unit then uses the tracking token as the primary key, aligning public domain campaign events, interaction feature vectors, and order records. For unpaid sessions, the attribution arbitration unit reads the behavioral time-series data from the interaction feature vector and calculates the conversion intention score by combining it with a preset decay constant. Its calculation model is as follows: ,in, The total number of preset behavioral event categories is set in this working condition. ; For the current session The actual measured value of the behavioral event; and The first The preset lower limit and preset upper limit of the benchmark for similar behavioral events; For the pre-calibrated first The weight coefficients of class-based behavioral events satisfy the following conditions: ; For the first The time difference between the baseline occurrence time of the type of behavioral event and the current arbitration calculation time; The preset attenuation constant ranges from 1800s to 7200s, and is calibrated to 3600s for this operating condition. In specific session sampling, the configuration and measured values ​​of each sub-parameter are as follows: Page dwell time. benchmark interval to Weight Time difference Substitute into the calculation to obtain the item value Product browsing depth item benchmark interval to Weight Time difference Substitute into the calculation to obtain the item value Adding items to cart Second, benchmark interval Next Next, weight Time difference Substitute into the calculation to obtain the item value Checkout event item Second, benchmark interval Next Next, weight Time difference Substitute into the calculation to obtain the item value The conversion intention score for a single session is obtained by summing up the sub-items of each behavior. .

[0053] The collaborative scheduling unit sets a sliding time window, extracts inbound independent sessions with a duration greater than 5 seconds as the effective sampling sample set, removes abnormal samples whose active interaction duration exceeds a preset outlier threshold, and accumulates 100 effective sessions to calculate the average conversion intention score. Combined with preset benchmark values Calculate the conversion difference rate The system has preset price adjustment coefficients and control parameters, including a silent threshold. Maximum adjustment ratio limit per transaction Adjust step size ratio and the reduction percentage and satisfy The asymmetric dynamic damping constraint, due to the current conversion difference rate satisfying The ad delivery control unit determines the positive update bid by multiplying the step size increase ratio, the current campaign's base bid, and the conversion difference rate under the constraint of the maximum single adjustment ratio limit. Calculate and round to the smallest unit of currency to determine the positive update bid. Simultaneously, the positive update bid and the benchmark bid are algebraically summed to obtain the target bid. The delivery control unit encapsulates the positive update bid into a bid data packet carrying the current timestamp and audience campaign identifier, and writes it to the rate-limiting bidding queue. The token bucket injects tokens at a fixed rate based on the ad platform's data interface call frequency quota. When multiple bid data packets are generated within a unit statistical time window, the rate-limiting bidding queue performs the latest timestamp overwrite and merge to eliminate redundant calls, and dequeues and issues request instructions when there are available tokens in the token bucket. If the conversion difference rate is less than the negative of the silent threshold, i.e. The delivery control unit determines the negative update bid by multiplying the reduction percentage, the benchmark bid, and the conversion difference rate under the negative cutoff constraint. Calculate, and with Boundary truncation protection is implemented; when the absolute value of the conversion difference rate is not greater than the silent threshold, i.e. When, output The system does not adjust the baseline bid, thus avoiding frequent invalid requests and control oscillations. After calculating a negative update bid with negative numerical attributes under negative deviation conditions, the delivery control unit algebraically adds the negative update bid to the positive baseline bid set in the current promotion plan to obtain a target bid with a value lower than the baseline bid but greater than zero. At the same time, the system sets a minimum protection price of 0.05 USD. When the calculated result after adjustment is lower than this protection price, it is forcibly truncated and taken as the protection price to ensure that the target bid remains an executable positive bid value.

[0054] By establishing an intent alignment channel based on cross-domain attribution load between public domain advertising and independent website delivery, the system calculates conversion intent scores with time decay characteristics using active heartbeat signals and high-frequency interactive behaviors. Combined with an asymmetric damping adaptive adjustment mechanism and a traffic-limiting bidding queue, the system can extract early conversion status in real time before payment, reducing intent loss and cross-system price fluctuations, and maintaining the stability of marketing resource allocation under interface call quota constraints.

[0055] Example 2: The current test platform uses a distributed independent website cloud server cluster with 8 computing nodes, and connects to the sandbox testing system of the public domain advertising platform through a standardized application interface. The test data is taken from 12,000 access session data streams generated by the independent website through public domain advertising over 30 consecutive natural days. The data input also includes random out-of-focus background suspension events caused by terminal multi-task switching and dynamic network transmission jitter of 50ms to 300ms. The call frequency threshold of the advertising platform's open interface is set to 10 times per second to test the operation under multi-source heterogeneous cross-domain communication, long-delay conversion feedback, and interface call quota constraints.

[0056] The test parameters follow the parameter settings from the aforementioned conversion intention score and bid adjustment process, with a preset decay constant. The value range is from 1800s to 7200s, with a baseline of 3600s. For independent website users, the half-confidence time interval from initial screen interaction to initiating checkout is concentrated between 30 and 120 minutes, corresponding to 1800s to 7200s. Below 1800 seconds, the time weight of earlier add-to-cart actions decays too quickly; above 7200 seconds, the time difference between recent and earlier actions weakens, and the silent threshold is reached. Take 0.08 as the upper limit of the maximum adjustment ratio in a single transaction. Take 0.25 and increase the step size ratio. Take 0.5 and adjust the reduction percentage. Take 0.8, and satisfy In addition, the silent threshold ranges from 0.05 to 0.12, the maximum single adjustment ratio ranges from 0.20 to 0.35, the upward adjustment step size ratio ranges from 0.40 to 0.80, and the downward adjustment reduction ratio ranges from 0.60 to 1.10.

[0057] During the trial operation phase, each test group input simulated inbound traffic batches with the same distribution. Each test batch contained 100 sampling sessions. The base bids were set to 1.25 USD and 2 USD respectively according to the audience plan. The sample groups of this invention sequentially performed cross-domain attribution payload encapsulation, visual module and product sequence matching based on a 0.75 cosine similarity threshold, active interaction duration collection based on a 1-second sampling period, conversion intention score calculation, conversion difference rate determination, and traffic-limiting bidding queue scheduling. Partially missing control group 1 canceled the dynamic matching of cross-domain attribution payload and page topology structure, and used a static general landing page instead; partially missing control group 2 canceled window focus monitoring and active heartbeat signal accumulation, and used the system absolute physical timestamp difference to calculate attenuation; partially missing control group 3 canceled the feedforward calculation of conversion intention score, and only adjusted the price based on long-latency single-transaction return data; partially missing control group 4 canceled the traffic-limiting bidding queue, and directly called the advertising platform interface synchronously after calculating the updated bid; the out-of-range control group 1 reversed the upward adjustment step size ratio and the downward adjustment reduction ratio, and set and The out-of-range control group 2 set the silence threshold to... The out-of-range control group 3 set the preset attenuation constant to... The upper limit of the maximum adjustment ratio for a single instance was set for the out-of-range control group 4. During the experiment, the average conversion intention score, conversion difference rate, updated bid, first-screen bounce rate, front-end interaction measurement deviation, interface call blocking rate, number of price adjustment overshoot oscillations, and the full-cycle marketing ROI achievement rate were recorded for each group.

[0058] Under standard positive deviation conditions, sample group 1 of this invention summarizes 100 sampled sessions to obtain the average conversion intention score. The conversion difference rate is calculated based on the preset benchmark value. The value exceeded the silent threshold. Based on the constraints of the step-up ratio, the benchmark bid, and the maximum single adjustment ratio, the updated bid was calculated to be 0.13 USD. The corresponding first-screen bounce rate was 18.2%, the interface call blocking rate was 0%, the number of price adjustment overshoot oscillations was 0, and the full-cycle marketing ROI achievement rate was 114.5%.

[0059] Under high conversion intention conditions, sample group 2 of this invention also collected 100 sessions, and the average conversion intention score increased to [percentage missing]. Thus, the conversion difference rate is obtained. After being constrained by the maximum single adjustment ratio limit, the updated bid was 0.16 USD. The first-screen bounce rate for this group was 16.5%, the API call blocking rate was 0%, the number of price adjustment overshoot oscillations was 0, and the overall marketing ROI achievement rate was 121.8%.

[0060] Under negative and poor flow conditions, the average conversion intention score was obtained from 100 sampled sessions of sample group 3 of this invention. The conversion difference rate is formed relative to the preset benchmark value. The ad placement control unit calculates and updates the bid according to the reduction ratio and negative truncation constraint, resulting in -0.4 USD; the corresponding first-screen bounce rate is 21.4%, the interface call blocking rate is 0%, the number of price adjustment overshoot oscillations is 0, and the full-cycle marketing ROI achievement rate is 108.2%.

[0061] Under dead-zone steady-state noise conditions, the average conversion intention score was calculated for sample group 4 of 100 sampled sessions in this invention. Conversion Difference Rate Since its absolute value did not exceed the silent threshold, the updated bid remained at 0 USD; the first-screen bounce rate for this group was 18.6%, the API call blocking rate was 0%, the number of price adjustment overshoot oscillations was 0, and the full-cycle marketing ROI achievement rate was 102.4%.

[0062] In the partially missing control group 1, after canceling cross-domain attribution loads and dynamic page topology matching, the average conversion intention score was obtained across 100 sampled sessions. Conversion Difference Rate Based on this, the updated bid was -0.25 USD. Since the inbound page no longer matches the visual module and product sequence based on the first feature vector, the first-screen bounce rate of this group rose to 68.4%; the interface call blocking rate was 0%, the number of price adjustment overshoot oscillations was 2, and the full-cycle marketing ROI achievement rate was 62.3%.

[0063] In the partially missing control group 2, after canceling window focus monitoring and active heartbeat signal accumulation, the physical time during the background suspension period was still included in the behavioral event time difference. After calculating 100 sampled sessions, the average conversion intention score was... Conversion difference rate The updated bid was -0.25 USD; its first-screen bounce rate was 19.1%, the API call blocking rate was 0%, the number of price adjustment overshoot oscillations was 3, and the full-cycle marketing ROI achievement rate was 74.6%.

[0064] After the feedforward calculation of conversion intention score was cancelled in the partially missing control group 3, the 100 sampled sessions no longer generated corresponding average conversion intention score and conversion difference rate, and both data were recorded as –. This group adjusted prices based on long-latency order feedback data, and the updated bids varied between +0.45 USD and -0.5 USD; the first-screen bounce rate was 18.8%, the interface call blocking rate was 0%, the number of price adjustment overshoot oscillations reached 14, and the full-cycle marketing ROI achievement rate was 58.7%.

[0065] The partially missing control group 4 retained the conversion intention calculation and bid adjustment process, but removed the traffic-limiting bidding queue and directly sent updated bids to the advertising platform interface. Its 100 sampled sessions yielded an average conversion intention score. Conversion Difference Rate The updated bid was 0.13 USD, the first-screen bounce rate was 18.3%; under the constraint of 10 API call frequency per second, the API call blocking rate of this group reached 24.6%, the number of price adjustment overshoot oscillations was 1, and the full-cycle marketing ROI achievement rate was 81.2%.

[0066] The out-of-range control group 1 set the step size increase ratio to... The reduction percentage is set to The average conversion intention score across 100 sampled sessions was Conversion difference rate Under these circumstances, the updated bid increased to 0.23 USD; the first-screen bounce rate was 18.4%, the API call blocking rate was 0%, the number of price adjustment overshoot oscillations increased to 8, and the full-cycle marketing ROI achievement rate was 71.5%.

[0067] The out-of-range control group 2 reduced the silent threshold to The average conversion intention score was obtained from 100 sampled sessions. Conversion Difference Rate At that time, the deviation had exceeded the silent threshold, resulting in an updated bid of 0.03 USD; the corresponding first-screen bounce rate was 18.5%, the interface call blocking rate was 0%, the number of price adjustment overshoot oscillations reached 11, and the full-cycle marketing ROI achievement rate was 88.3%.

[0068] The out-of-range control group 3 shortened the preset attenuation constant to Under the same sampling rules, the time decay magnitude corresponding to early behaviors increases, and the average conversion intention score of 100 sampled sessions decreases to The conversion difference rate is calculated from the preset benchmark value. After negative truncation, the updated bid was -0.4 USD; the first-screen bounce rate was 18.7%, the API call blocking rate was 0%, the number of price adjustment overshoot oscillations was 4, and the full-cycle marketing ROI achievement rate was 69.1%.

[0069] The upper limit of the maximum single adjustment ratio was increased to the control group 4, which is outside the range. The average conversion intention score across 100 sampled sessions was Conversion difference rate At that time, the updated bid reached -0.8 USD; the first-screen bounce rate for this group was 20.9%, the API call blocking rate was 0%, the number of price adjustment overshoot oscillations was 9, and the full-cycle marketing ROI achievement rate was 66.4%.

[0070] In the component matching threshold test, the cosine similarity between the semantic feature vector of the candidate component and the first feature vector is calculated. When the threshold is set to 0.75, the semantic matching accuracy between page components and advertising creatives is 94.2% and the recall rate is 88.6%. When the threshold is higher than 0.75, the number of visual modules and product sequences that meet the conditions decreases and the page topology uses general layout components more frequently. When the threshold is lower than 0.75, more candidate components with low similarity will be included.

[0071] Based on the results of each group's operation, the cross-domain attribution load and the matching of page topology structure directly correspond to the first screen reception results. The timing method of the active heartbeat signal affects the statistical results of the conversion intention score. The feedforward calculation of the conversion intention score affects the number of price adjustments during the long-term payment feedback period. The rate-limited bidding queue directly affects the interface call blocking situation. When the control parameters exceed the predetermined range, the number of price adjustment overshoot oscillations increases accordingly. The silent threshold of 0.05 to 0.12 is determined based on the standard deviation confidence interval of the batch conversion intention score sampling. The upper limit of the single maximum adjustment ratio of 0.20 to 0.35 is set based on the bounded input stability boundary of the bidding system. The upward adjustment step size ratio of 0.40 to 0.80 and the downward adjustment reduction ratio of 0.60 to 1.10 maintain the relationship that the downward adjustment reduction ratio is greater than the upward adjustment step size ratio, so that the downward adjustment magnitude when there is a negative deviation is greater than the upward adjustment magnitude when there is a positive deviation, and maintain the stability of the bidding adjustment feedback process.

[0072] Example 3: The current delivery control unit is deployed on a network node containing a load balancer and an application service cluster. It monitors public domain delivery events in real time and captures audience interaction data streams through the advertising platform's open data interface. For each public domain delivery event, the delivery control unit extracts the first feature vector and obtains the baseline bid set for the current promotion plan. Subsequently, a salted hash algorithm is used to calculate the hash value of the ad click identifier, timestamp, and audience plan identifier, generating a globally unique tracking token TK-20260831-8942. The first feature vector and the tracking token are serialized as key-value pairs according to a preset binary structure and Base64 encoded and compressed. This is then encapsulated into a cross-domain attribution payload and written into the Uniform Resource Locator (URL) parameter field of the redirect link on the independent website's landing page. The tracking token and cross-domain attribution payload are simultaneously written to a distributed in-memory database. The preset effective lifespan is 72 hours. After the client triggers the redirect link, the independent website... Based on this, the system receives the first feature vector and tracking token transmitted from the public domain advertising platform. In the process of generating the first feature vector, the core category code is first mapped to a 32-dimensional one-hot encoding, and the main color of the advertising image is converted into a 3-dimensional visual main color encoding. At the same time, a pre-trained bidirectional word embedding network is used to semantically encode the promotional selling point text, resulting in a 64-dimensional promotional selling point text vector. Then, the 32-dimensional one-hot encoding, the 3-dimensional visual main color encoding, and the 64-dimensional promotional selling point text vector are linearly concatenated along the channel dimension and normalized to the unit norm, forming the first feature vector of a 99-dimensional dense floating-point array.

[0073] After the client's network request reaches the independent website gateway via the public network, the independent website gateway intercepts the inbound data packet, calls the decoding module to parse the cross-domain attribution payload in the Uniform Resource Locator (URL) parameters, extracts the tracking token TK-20260831-8942 and the first feature vector, and the site orchestration unit extracts the semantic feature vectors of each candidate component from the independent website's pre-built component pool and calculates their cosine similarity with the first feature vector; the candidate components include the main visual banner module and promotional copy component in the visual module, as well as the recommended product sequence. The semantic feature vectors of each candidate component are obtained in advance through offline annotation and feature extraction. Among them, the main visual banner... The image module extracts the background main color tone and visual focus area features, the promotional copy component extracts the semantic embedding vectors of the title text and promotional key phrases, and the recommended product sequence extracts the category tags and selling point feature vectors. All component features are mapped to the same 99-dimensional space as the first feature vector and normalized to the unit norm. The site layout unit selects candidate components with a cosine similarity greater than 0.75 as visual modules and product sequences, assembles the page topology structure, and outputs the rendering stream to the client browser. When there are no candidate components with a cosine similarity greater than 0.75 in the component pool, the pre-built general layout components are called to assemble the page topology structure.

[0074] After the client-side page completes the initial page topology rendering and mounts the running script, the site data acquisition engine registers the browser window's focus activation event, focus loss and suspension event, and page visibility state change event. While the browser window is in a focus-active and foreground visible state, the timer value is accumulated at a fixed sampling period of 1 second to generate the active interaction duration. When the window is detected to be out of focus, the tab is switched to the background and suspended, or no effective mouse movement or touch action is detected for 30 consecutive seconds, the accumulation of the timer value is paused until the foreground focus activation and interaction events are received again, and then the accumulation resumes. The site data acquisition engine uses the system clock time of the last received active heartbeat signal as the baseline occurrence time of the corresponding behavior event and records the behavior time sequence data. Among them, the page dwell time is recorded by the active interaction duration, the product browsing depth is recorded by the percentage of the page scroll depth of the product display area, and the frequency of add-to-cart actions and checkout events are collected at the same time to generate an interaction feature vector and send it back to the server.

[0075] The attribution arbitration unit uses the tracking token as the primary key, aligns public domain delivery events, interaction feature vectors, and order records in the distributed stream computing engine, and calculates conversion intention scores for sessions that have generated interactions but have not yet completed payment: In this embodiment, the total number of behavioral event categories The first type of behavioral event is the page dwell time, and its actual measurement value is Preset lower limit of the benchmark Preset baseline upper limit Weighting coefficient The corresponding time difference is The second type of behavioral event is the depth of product browsing, and its actual measurement value is... Preset lower limit of the benchmark Preset baseline upper limit Weighting coefficient The corresponding time difference is The third type of behavioral event is the "add to cart" action, and its actual measurement value is... Preset lower limit of the benchmark Next, preset baseline upper limit Second, weighting coefficient The corresponding time difference is The fourth type of behavioral event is the checkout event, and its actual measurement value is... Preset lower limit of the benchmark Next, preset baseline upper limit Second, weighting coefficient The corresponding time difference is Each weight coefficient satisfies When the actual measured value of any behavioral event exceeds the corresponding preset upper limit, it is truncated according to that preset upper limit; preset attenuation constant. This yields the conversion intention score for a single conversation. .

[0076] The collaborative scheduling unit sets a sliding time window, extracts independent sessions that generate inbound network requests and have a session duration of more than 5 seconds within the current sliding time window, and uses them as the effective sampling sample set; then it removes abnormal samples whose active interaction duration exceeds the preset outlier threshold of 3600 seconds, and calculates the arithmetic mean of the conversion intention scores of the remaining sampled sessions. The preset baseline value is Conversion difference rate according to Calculate the system configuration silent threshold. Maximum adjustment ratio limit per transaction Adjust step size ratio and the percentage reduction ,in ,when At that time, the product of the upward adjustment step size ratio, the benchmark bid, and the conversion difference rate under the constraint of the maximum single adjustment ratio limit is determined as the positive update bid, i.e. ;when At that time, the product of the reduction percentage, the benchmark bid, and the conversion difference rate under the negative truncation constraint is determined as the negative update bid, i.e. ;when At that time, the benchmark bid is not adjusted, and it is ordered that... When it is necessary to form the target bid for actual execution on the advertising platform, according to Once the target bid is determined, the delivery control unit will update the bid. The bid data packet, encapsulated with the current timestamp and audience campaign identifier, is written to the rate-limiting bidding queue. The rate-limiting bidding queue adopts an asynchronous scheduling method and is configured with a token bucket rate limiter. Tokens are injected at a fixed rate of 10 tokens per second according to the call frequency quota of the advertising platform's open data interface. When multiple bid data packets are generated for the same audience campaign identifier within a unit statistical time window, the rate-limiting bidding queue overwrites and merges the old data packets, retaining only the bid data packet with the latest timestamp. When there are available tokens in the token bucket, the data packet at the head of the queue is dequeued and the latest bid instruction is sent to the advertising platform's open interface.

[0077] When a third-party advertising platform interface times out or the data link is momentarily interrupted, the rate-limiting bidding queue initiates retry and circuit breaker protection. After three consecutive failed interface calls, dequeueing is suspended, and retrying is performed exponentially with an initial backoff base of 1 second and a maximum backoff waiting time of 16 seconds. After the circuit breaker enters the open state, bidding data packets are suspended from dequeueing. After a 30-second cooldown period, it enters a half-open probe state and issues a single probe bidding command. If the probe is successful, normal dequeueing resumes; if the probe fails, it re-enters the open state. During the circuit breaker period, the rate-limiting bidding queue continues to perform timestamp overwrite and merging on new bidding data packets for the same audience plan, so that the latest timestamp bidding data packet is retained when dequeueing resumes. When the independent website gateway experiences cross-domain attribution payload parsing anomalies or a missing tracking token, it switches to basic session mode and outputs the default page topology to maintain continuous rendering of the client page.

[0078] Example 4: This example combines Figures 1 to 2 This paper describes the intelligent agent-driven, end-to-end marketing management method and system for independent websites, such as... Figure 1 As shown, in step S1, the delivery control unit obtains the first feature vector of the public domain delivery event and generates a tracking token. The first feature vector and the tracking token are encapsulated into a cross-domain attribution payload and written into the directed jump link. In step S2, the independent site gateway parses the cross-domain attribution payload and extracts the tracking token and the first feature vector. The site orchestration unit matches visual modules and product sequences in the component pool based on the first feature vector, assembles the page topology structure, and outputs it. The site data acquisition engine accumulates the active interaction duration during the page front-end activation period, records the behavioral time-series data including active interaction duration and add-to-cart actions, and generates an interaction feature vector. In step S3, the attribution arbitration unit aligns the public domain delivery event, interaction feature vector, and order record with the tracking token as the primary key. It calculates the conversion intention score based on the interaction feature vector of the unpaid session combined with a preset decay constant. In step S4, the collaborative scheduling unit calculates the conversion difference rate based on the average conversion intention score of the sampled sessions within the marketing batch and a preset benchmark value. When the conversion difference rate exceeds the silent threshold, the delivery control unit calculates and updates the bid based on the price adjustment coefficient, writes the updated bid into the traffic-limiting bidding queue, and issues the latest timestamp bid instruction when there is an available token.

[0079] like Figure 2As shown, the multi-channel public domain advertising platform generates public domain advertising events, interacts with the application service cluster through open data interfaces, and sends a redirect link to the client browser of the network client. The client browser responds to the redirect link and sends an inbound network request to the independent site gateway of the reverse proxy gateway. The reverse proxy gateway is connected to the application service cluster. The independent site gateway receives the inbound network request and transmits the cross-domain attribution load to the rate limiting bidding queue of the load balancer. The load balancer establishes a communication connection with the application service cluster. The application service cluster is configured with a site orchestration unit and a collaborative scheduling unit. The application service cluster writes the tracking token and order record into the distributed memory database to store the order record, and transmits the alignment interaction feature vector to the distributed stream computing engine. The distributed stream computing engine is configured with an attribution arbitration unit to receive the alignment interaction feature vector and perform calculation processing.

[0080] Example 5: During system deployment and parameter benchmark calibration, the collaborative scheduling unit and the attribution arbitration unit extract the time series of attributed samples from the first 30 natural days in the historical session database. They then statistically analyze the distribution quantiles of four types of behavioral events: page dwell time, product browsing depth, add-to-cart actions, and checkout events. The system uses the 5th and 95th percentiles of the historical order samples to determine the preset lower and upper limits of each behavioral event, respectively. The benchmark interval for page dwell time is defined as follows: to The baseline range for product browsing depth is calibrated as follows: to The baseline range for adding items to the cart is set as follows: Next The baseline interval for each checkout event is defined as follows: Next The attribution arbitration unit assigns weight coefficients based on the historical information gain contribution of each behavioral item to the final payment conversion, and determines them as follows: , , and ,satisfy The normalization constraint, combined with the session temporal evolution span, will be used to set the preset decay constant. Set to 3600 seconds, the attribution arbitration unit used a decision tree model to perform feature importance fitting analysis on attributed order conversion sessions over the past 30 calendar days. The results showed that the relative information gain ratios of four types of behavior on order conversion were 0.15, 0.15, 0.35, and 0.35, respectively. Simultaneously, the average conversion intention score generated by the benchmark traffic batches that achieved ROI balance under the same model was statistically analyzed, and the median was used to determine the preset benchmark value. This serves as the benchmark for calculating the conversion difference rate.

[0081] During online service operation and dynamic adjustment, the independent website gateway verifies the Uniform Resource Locator (URL) parameters in inbound network requests. When cross-domain attribution payload parsing anomalies, hash signature verification inconsistencies, or tracking tokens exceeding the preset 72-hour lifespan are detected, the independent website gateway switches to a degraded path and outputs pre-set general layout components to maintain page rendering continuity. During the client-side front-end interaction data collection process, the site data collection engine accumulates the active interaction duration at a sampling period of 1 second. When the window loses focus, the tab is transferred to the background and suspended, or the inactivity exceeds 30 seconds, the timer is paused. The system clock time of the last received active heartbeat signal is used as the baseline occurrence time of the corresponding behavioral event, and the time difference between this baseline occurrence time and the current calculation time is used as the baseline occurrence time. The collaborative scheduling unit sets a sliding time window to extract independent sessions with a duration greater than 5 seconds. It then uses 3600 seconds as a preset outlier threshold to remove abnormal samples whose active interaction duration exceeds this threshold. From the remaining sessions, it obtains 100 sampled sessions and calculates the average conversion intention score. Combined with preset benchmark values Calculate conversion difference rate The deployment control unit is at the silent threshold. Maximum adjustment ratio limit per transaction Adjust step size ratio With the reduction percentage Calculate updated bids under constraints and based on Forming a target bid The delivery control unit will update the bid. The bid data packet is encapsulated as carrying the current timestamp and audience plan identifier, and written to the rate-limited bidding queue configured with a token generation rate of 10 tokens per second. Within a unit statistical time window, when multiple bid data packets are generated for the same audience plan identifier, the rate-limited bidding queue performs overwrite and merging, removes the old data packets and retains the bid data packet with the latest timestamp, and dequeues and distributes it when there are available tokens in the token bucket. When the third-party advertising platform interface times out 3 times in a row, the rate-limited bidding queue starts exponential backoff retry and circuit breaker protection to ensure that the bid data packets under network jitter and interface call frequency constraints are still scheduled according to the latest timestamp.

[0082] Example 6: During the operation of high-concurrency promotional nodes, cross-domain network transmission experiences dynamic jitter. The site data acquisition engine and attribution arbitration unit perform online dynamic calibration and abnormal slice filtering for distributed multi-terminal data drift. The independent site gateway parses the cross-domain attribution payload in the inbound network request and extracts the tracking token TK-20260831-8942 and the first feature vector. The site orchestration unit calculates the cosine similarity between the semantic feature vector of each candidate component in the component pool and the first feature vector, selects candidate components with a cosine similarity greater than 0.75 as visual modules and product sequences, assembles the page topology structure, and displays it on the client page. The system incorporates a window focus listener and a periodic heartbeat detector. While the window is in a focused, active interaction duration is accumulated at a fixed sampling period of 1 second. When a window loses focus and suspends, or the page switches to the background, the timing is paused. The system clock time of the last received active heartbeat signal is used as the baseline occurrence time of the corresponding behavioral event. The collaborative scheduling unit extracts inbound sessions within a sliding time window, removing instantaneous outbound sessions with a duration of 5 seconds or less, and abnormal samples with active interaction durations exceeding a preset outlier threshold of 3600 seconds. For the remaining valid sampled sessions, the attribution arbitration unit combines weighted coefficients... , , and and preset attenuation constant The page dwell time, product browsing depth, add-to-cart actions, and checkout events are calculated separately, and the scores of each item are added together to obtain the single-session conversion intention score. .

[0083] To address the proactive frequency reduction of timers implemented by browsers when tabs are suspended in the background, the site data acquisition engine simultaneously introduces a high-precision monotonic clock from the system for bidirectional calibration within the page runtime environment. Each time a user's mouse movement or scrolling interaction event is received, the absolute nanosecond timestamp of the high-precision monotonic clock is read and compared with the difference of the accumulated timer. When the time difference between the two exceeds 1 second and the page is out of focus, the system time of the last valid interaction event is used as the freezing benchmark, so that the clock drift caused by the frequency reduction or suspension of the background timer is not included in the active interaction duration.

[0084] When the quality of public domain traffic declines significantly and forms a negative deviation, the collaborative scheduling unit collects 100 valid sessions within the current marketing batch and calculates the average conversion intention score. The preset baseline value is The conversion difference rate is: Silent threshold ,at this time Entering negative bid adjustment, the base bid for the current promotion plan. Lowering the percentage reduction Maximum adjustment ratio in a single instance Since the absolute value of the conversion difference rate exceeds the upper limit of the maximum adjustment ratio in a single instance, it is determined according to the negative truncation constraint: Therefore, the negative update bid is calculated: The negative update bid is algebraically summed with the baseline bid to obtain the target bid actually executed by the advertising platform: The delivery control unit will update the bid negatively. The bid data packet, encapsulated as a data packet carrying the current timestamp and audience program identifier, is written to a rate-limited bidding queue configured with a token generation rate of 10 tokens per second. When the advertising platform's open data interface call quota is exhausted or network transmission is blocked, the rate-limited bidding queue overwrites and merges multiple bid data packets corresponding to the same audience program identifier within a unit statistical time window, discarding old data packets and retaining the data packet with the latest timestamp. When there are available tokens in the token bucket, the latest bid data packet at the head of the queue is dequeued and a request instruction is sent to the advertising platform. When the interface call times out three times in a row, the rate-limited bidding queue initiates exponential backoff retry and circuit breaker protection.

[0085] The timestamp overlay merging of the rate-limited bidding queue is only performed within the millisecond window of dequeue waiting. It is used to clear overly redundant instructions that have accumulated due to network congestion or interface speed limitations. Each updated bid written to the queue is generated based on the conversion difference rate of the current marketing batch and after being constrained by the maximum adjustment ratio limit for a single transaction. Therefore, the overlay merging only changes the retention order of data packets to be sent under the same audience plan identifier, and does not change the calculation result of the updated bid or its adjustment boundary. The bid adjustment in consecutive marketing batches still operates according to the established damping relationship and maintains smooth convergence.

[0086] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0087] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for managing the entire marketing chain of an independent website driven by an intelligent agent, characterized in that, Includes the following steps: Step S1: The delivery control unit obtains the first feature vector of the public domain delivery event and generates a tracking token. The first feature vector and the tracking token are encapsulated into a cross-domain attribution payload and written into the directed jump link. Step S2: The independent site gateway parses the cross-domain attribution payload and extracts the tracking token and the first feature vector; Site The arrangement unit matches visual modules and product sequences in the component pool based on the first feature vector, and assembles the page topology structure for output. The site data collection engine accumulates active interaction duration during the page's front-end activation period, records behavioral time-series data including active interaction duration and add-to-cart actions, and generates interaction feature vectors. Step S3: The attribution arbitration unit aligns the public domain campaign events, interaction feature vectors, and order records with the tracking token as the primary key, and calculates the conversion intention score based on the interaction feature vectors of unpaid sessions combined with a preset decay constant. In step S4, the collaborative scheduling unit calculates the conversion difference rate based on the average conversion intention score of the sampled sessions within the marketing batch and the preset benchmark value; when the conversion difference rate exceeds the silent threshold, the delivery control unit calculates and updates the bid based on the price adjustment coefficient, writes the updated bid into the traffic limiting bidding queue, and issues the latest timestamp bid instruction when there is an available token.

2. The intelligent agent-driven independent website full-link marketing management method according to claim 1, characterized in that, In step S2, the process by which the site orchestration unit matches visual modules and product sequences in the component pool based on the first feature vector is as follows: extract the semantic feature vectors of each candidate component in the component pool, calculate the cosine similarity between the semantic feature vector of each candidate component and the first feature vector; select candidate components with a cosine similarity greater than a preset threshold of 0.75 as visual modules and product sequences, and assemble the page topology structure; when there are no candidate components with a cosine similarity greater than 0.75 in the component pool, the preset general layout components are called in a fallback manner to assemble the page topology structure.

3. The agent-driven, independent website end-to-end marketing management method according to claim 1, characterized in that, In step S2, the process of the site data acquisition engine recording behavioral time-series data and generating interactive feature vectors includes the following sub-steps: Step S21, after the initial page topology rendering is completed on the client page, the site data acquisition engine registers the focus activation event and focus suspension event of the browser window. Step S22: While the window is in the focus active state, the timer value is accumulated with a preset sampling period of 1 second as the step size to generate the active interaction duration; Step S23: When a defocus suspension event is received, the timer value is frozen until a focus activation event is received again, at which point the accumulated timer is resumed. Step S24: Use the system clock time of the last received active heartbeat signal as the reference occurrence time of the corresponding behavioral event.

4. The agent-driven, independent website end-to-end marketing management method according to claim 1, characterized in that, In step S3, the attribution arbitration unit calculates the conversion intention score. The mathematical model is as follows: ,in, The total number of preset behavioral event categories; For the current session The actual measured value of the behavioral event; and The first The preset lower limit and preset upper limit of the benchmark for similar behavioral events; For the preset first The weight coefficients of each type of behavioral event, and the sum of the weight coefficients of all types is always equal to 1; For the first The time difference between the baseline occurrence time of the behavioral event and the current calculation time; This is the preset attenuation constant.

5. The agent-driven, independent website end-to-end marketing management method according to claim 4, characterized in that, Behavioral event categories include page dwell time, product browsing depth, add-to-cart actions, and checkout events; preset decay constant. The value ranges from 1800s to 7200s.

6. The agent-driven, independent website end-to-end marketing management method according to claim 1, characterized in that, In step S4, the conversion difference rate is calculated as follows: the arithmetic mean of the conversion intention scores of all sampled sessions in the current marketing batch is calculated and the difference is compared with the preset benchmark value. The ratio of the difference to the preset benchmark value is determined as the conversion difference rate.

7. The agent-driven, independent website end-to-end marketing management method according to claim 6, characterized in that, In step S4, the rules for calculating the updated bid based on the price adjustment coefficient when the conversion difference rate exceeds the silent threshold are as follows: When the conversion difference rate is greater than the silent threshold, the product of the upward adjustment step size ratio, the current promotion plan's base bid, and the difference rate under the constraint of the maximum single adjustment ratio is determined as the positive updated bid; when the conversion difference rate is less than the negative number of the silent threshold, the product of the downward adjustment reduction ratio, the base bid, and the difference rate under the negative truncation constraint is determined as the negative updated bid; when the absolute value of the conversion difference rate is not greater than the silent threshold, the base bid is not adjusted; wherein, the downward adjustment reduction ratio is strictly greater than the upward adjustment step size ratio.

8. The agent-driven, end-to-end marketing management method for independent websites according to claim 7, characterized in that, The silent threshold ranges from 0.05 to 0.12; the maximum single adjustment ratio ranges from 0.20 to 0.35; the upward adjustment step size ratio ranges from 0.40 to 0.80; and the downward adjustment reduction ratio ranges from 0.60 to 1.

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9. The agent-driven, independent website full-link marketing management method according to claim 1, characterized in that, In step S4, the process of the delivery control unit writing the updated bid into the rate-limited bidding queue and issuing a bid instruction includes the following sub-steps: Step S41, the delivery control unit encapsulates the updated bid into a bid data packet carrying the current timestamp and audience plan identifier, and writes it into the rate-limited bidding queue; Step S42, the token bucket rate limiter injects tokens into the token bucket at a fixed rate according to the preset call frequency quota of the advertising platform's open data interface. Step S43: Within the unit statistical time window, when multiple bid data packets are generated for the same audience plan identifier, the rate limiting bidding queue performs coverage merging, removes old data packets, and retains the bid data packet with the latest timestamp; Step S44: When there are available tokens in the token bucket, the bid data packet at the head of the queue is dequeued, and a request instruction containing the latest updated bid is sent to the advertising platform. In step S4, the process of the collaborative scheduling unit obtaining the sampled sessions within the current marketing batch is as follows: Set a sliding time window, extract all independent sessions that generate inbound network requests within the current sliding time window and have a session duration of more than 5 seconds, as the effective sampled sample set; After removing abnormal samples in the effective sampled sample set whose active interaction duration exceeds the preset outlier threshold, calculate the arithmetic mean of the conversion intention score of the remaining sampled sessions.

10. An agent-driven independent website full-link marketing management system, used to implement the agent-driven independent website full-link marketing management method of claim 1, characterized in that, include: The delivery control module is used to obtain the first feature vector of the public domain delivery event and generate a tracking token. It encapsulates the first feature vector and the tracking token into a cross-domain attribution payload and writes it into the targeted jump link. When the conversion difference rate exceeds the silent threshold, it calculates and updates the bid based on the price adjustment coefficient, writes the updated bid into the traffic limiting bidding queue, and issues the latest timestamp bidding instruction when there is an available token. The access gateway module is used to parse cross-domain attribution payloads and extract tracking tokens and the first feature vector. The site orchestration module is used to match visual modules and product sequences in the component pool based on the first feature vector, and assemble the page topology structure for output. The site data acquisition module is used to accumulate active interaction duration during the page's front-end activation period, record behavioral time-series data including active interaction duration and add-to-cart actions, and generate interaction feature vectors. The attribution arbitration module is used to align public domain campaign events, interaction feature vectors, and order records with the tracking token as the primary key, and calculate the conversion intention score based on the interaction feature vector of the unpaid session combined with a preset decay constant. The collaborative scheduling module is used to calculate the conversion difference rate based on the average conversion intention score of the sampled sessions within the marketing batch and the preset benchmark value.

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